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PRO'V^
Regulatory Impact Analysis for the Proposed
Reconsideration of the National Ambient Air
Quality Standards for Particulate Matter
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EPA-452/P-22-001
December 2022
Regulatory Impact Analysis for the Proposed Reconsideration of the National Ambient Air
Quality Standards for Particulate Matter
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Research Triangle Park, NC
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CONTACT INFORMATION
This document has been prepared by staff from the Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency. Questions related to this document
should be addressed to U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, C439-02, Research Triangle Park, North Carolina 27711 (email:
oaqpseconomics@epa.gov). Please submit comments on this document to the following
docket for the regulatory impact analysis: EPA-HQ-OAR-2019-0587. The docket for the
notice of proposed rulemaking is EPA-HQ-OAR-2015-0072.
ACKNOWLEDGEMENTS
In addition to EPA staff from the Office of Air Quality Planning and Standards, personnel
from the Office of Policy's National Center for Environmental Economics contributed data
and analysis to this document.
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TABLE OF CONTENTS
LIST OF TABLES x
LIST OF FIGURES xvi
EXECUTIVE SUMMARY ES-1
Overview of the Proposal ES-1
Overview of the Regulatory Impact Analysis ES-2
ES.l Design of the Regulatory Impact Analysis ES-3
ES.1.1 Establishing the Analytical Baseline ES-5
ES.l.2 Estimating PM2.5 Emissions Reductions Needed for Annual and 24-hour Alternative
Standard Levels Analyzed ES-7
ES.l.3 Control Strategies and PM2.5 Emissions Reductions ES-10
ES.1.4 Estimates of PM2.5 Emissions Reductions Still Needed after Applying Control
Technologies and Measures ES-11
ES.l.5 Engineering Costs ES-13
ES.l.6 Human Health Benefits ES-14
ES.l.7 Welfare Benefits of Meeting the Primary and Secondary Standards ES-19
ES.l.8 Environmental Justice ES-20
ES.2 Qualitative Assessment of the Remaining Air Quality Challenges ES-22
ES.3 Results of Benefit-Cost Analysis ES-24
ES.4 References ES-29
CHAPTER 1: OVERVIEW AND BACKGROUND 1-1
Overview of the Proposal 1-1
Overview of the Regulatory Impact Analysis 1-1
1.1 Background 1-3
1.1.1 National Ambient Air Quality Standards 1-4
1.1.2 Role of Executive Orders in the Regulatory Impact Analysis 1-5
1.1.3 Nature of the Analysis 1-5
1.2 The Need for National Ambient Air Quality Standards 1-6
1.3 Design of the Regulatory Impact Analysis 1-7
1.3.1 Establishing the Baseline for Evaluation of Proposed and Alternative Standards... 1-8
1.3.2 Cost Analysis Approach 1-9
1.3.3 Benefits Analysis Approach 1-10
1.3.4 Welfare Benefits of Meeting the Primary and Secondary Standards 1-10
1.4 Organization of the Regulatory Impact Analysis 1-10
1.5 References 1-12
CHAPTER 2: AIR QUALITY MODELING AND METHODS 2-1
Overview 2-1
2.1 PM2.5 Characteristics 2-2
2.1.1 PM2.5 Size and Composition 2-2
2.1.2 PM2.5 Regional Characteristics 2-5
2.1.3 PM2.5 Trends 2-7
2.2 Modeling PM2.5 in the Future 2-12
2.2.1 Air Quality Modeling Platform 2-13
2.2.1.1 Model Configuration 2-13
2.2.1.2 Emission Inventory 2-15
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2.2.1.3 Model Evaluation 2-17
2.2.2 Future-Year PM2.5 Design Values 2-18
2.3 Calculating Emission Reductions for Meeting the Existing and Alternative Standard Levels
2-20
2.3.1 Developing Air Quality Ratios 2-20
2.3.2 Emission Reductions to Meet 12/35 2-24
2.3.3 Emission Reductions to Meet Alternative Standards 2-26
2.3.4 Limitations of Using Air Quality Ratios 2-28
2.4 Description of Air Quality Challenges in Select Areas 2-29
2.4.1 Delaware County, PA 2-29
2.4.2 Border Areas 2-31
2.4.2.1 Imperial County, CA 2-31
2.4.2.2 Cameron and Hidalgo County, TX 2-33
2.4.3 Small Mountain Valleys in the West 2-35
2.4.4 California Areas 2-39
2.4.4.1 San Joaquin Valley, CA 2-39
2.4.4.2 South Coast Air Basin, CA 2-43
2.4.4.3 San Luis Obispo and Napa, CA 2-47
2.5 Calculating PM2.5 Concentration Fields for Standard Combinations 2-49
2.5.1 Creating the PM2.5 Concentration Field for 2032 2-49
2.5.2 Creating Spatial Fields Corresponding to Meeting Standards 2-51
2.6 References 2-53
APPENDIX 2A: ADDITIONAL AIR QUALITY MODELING INFORMATION 2A-1
Overview 2A-1
2A.1 2016 CMAQ Modeling 2A-3
2A.1.1 Model Configuration 2A-3
2A.1.2 Model Performance Evaluation 2A-5
2A.2 Projecting PM2.5 DVs to 2032 2A-20
2A.2.1 Monitoring Data for PM2.5 Projections 2A-21
2A.2.2 Future-Year PM2.5 Design Values 2A-39
2A.3 Developing Air Quality Ratios and Estimating Emission Reductions 2A-45
2A.3.1 Developing Air Quality Ratios for Primary PM2.5 Emissions 2A-46
2A.3.2 Developing Air Quality Ratios for NOx in Southern California 2A-50
2A.3.3 Developing Air Quality Ratios for NOx in SJV, CA 2A-52
2A.3.4 Applying Air Quality Ratios to Estimate Emission Reductions 2A-54
2A.3.4.1 Emission Reductions Needed to Meet 12/35 2A-55
2A.3.4.2 Emission Reductions Needed to Meet 10/35, 9/35, 8/35, and 10/30 2A-59
2A.4 Calculating PM2.5 Concentration Fields for Standard Combinations 2A-64
2A.4.1 Creating the PM2.5 Concentration Field for 2032 2A-64
2A.4.2 Creating Spatial Fields Corresponding to Meeting Standards 2A-66
2A.5 Calculating DV Impacts for Further EGU Emission Reductions 2A-68
2A.5.1 Estimating the Influence of Additional Primary PM2.5 EGU Reductions 2A-68
2A.5.2 Estimating the Regional Influence of Additional SO2 and NOx EGU Emission
Reductions 2A-69
2A.5.3 Estimating the Local Influence of Additional SO2 and NOx EGU Emission Reductions
2A-72
2A.6 References 2A-75
CHAPTER 3: CONTROL STRATEGIES AND PM2.5 EMISSIONS REDUCTIONS 3-1
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Overview 3-1
3.1 Preparing the 12/35 |a,g/m3 Analytical Baseline 3-4
3.2 Illustrative Control Strategies and PM2.5 Emissions Reductions from the Analytical
Baseline 3-5
3.2.1 Estimating PM2.5 Emissions Reductions Needed for Annual and 24-hour Alternative
Standard Levels Analyzed 3-6
3.2.2 Applying Control Technologies and Measures 3-10
3.2.3 Estimates of PM2.5 Emissions Reductions Resulting from Applying Control
Technologies and Measures 3-15
3.2.4 Potential Influence of EGU Emissions Reductions from Planned Retirements 3-22
3.2.5 Estimates of PM2.5 Emissions Reductions Still Needed after Applying Control
Technologies and Measures 3-24
3.2.6 Qualitative Assessment of the Remaining Air Quality Challenges and Emissions
Reductions Potentially Still Needed 3-31
3.2.6.1 Delaware County, Pennsylvania (Northeast) 3-32
3.2.6.2 Border Areas (Southeast, California) 3-34
3.2.6.3 Small Mountain Valleys (West) 3-37
3.2.6.4 California Areas 3-39
3.3 Limitations and Uncertainties 3-43
3.4 References 3-46
APPENDIX 3A: CONTROL STRATEGIES AND PM2.5 EMISSIONS REDUCTIONS 3A-1
Overview 3A-1
3A.1 Types of Control Measures 3A-1
3A.1.1 PM Control Measures for Non-EGU Point Sources 3A-1
3A.1.2 PM Control Measures for Non-point (Area) Sources 3A-2
3A.2 EGU Trends Reflected in EPA's Integrated Planning Model (IPM) v6 Platform, Summer
2021 Reference Case Projections 3A-3
3A.3 Applying Control Technologies and Measures 3A-4
CHAPTER 4: ENGINEERING COST ANALYSIS AND QUALITATIVE DISCUSSION OF
SOCIAL COSTS 4-1
Overview 4-1
4.1 Estimating Engineering Costs 4-2
4.1.1 Methods, Tools, and Data 4-3
4.1.2 Cost Estimates for the Control Strategies 4-4
4.2 Limitations and Uncertainties in Engineering Cost Estimates 4-11
4.3 Social Costs 4-13
4.4 References 4-17
APPENDIX 4A: ENGINEERING COST ANALYSIS 4A-1
Overview 4A-1
4A.1 Estimated Costs by County for Alternative Standard Levels 4A-1
CHAPTER 5: BENEFITS ANALYSIS APPROACH AND RESULTS 5-1
Overview 5-1
5.1 Updated Methodology Presented in the RIA 5-5
5.2 Human Health Benefits Analysis Methods 5-6
5.2.1 Selecting Air Pollution Health Endpoints to Quantify 5-7
5.2.2 Calculating Counts of Air Pollution Effects Using the Health Impact Function 5-9
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5.2.3 Calculating the Economic Valuation of Health Impacts 5-11
5.3 Benefits Analysis Data Inputs 5-11
5.3.1 Demographic Data 5-12
5.3.2 Baseline Incidence and Prevalence Estimates 5-13
5.3.3 Effect Coefficients 5-16
5.3.3.1 PM2.5 Premature Mortality Effect Coefficients for Adults 5-17
5.3.4 Unquantified Human Health Benefits 5-19
5.3.5 Unquantified Welfare Benefits 5-22
5.3.5.1 Visibility Impairment Benefits 5-24
5.3.6 Climate Effects of PM2.5 5-25
5.3.6.1 Climate Effects of Carbonaceous Particles 5-26
5.3.6.2 Climate Effects: Summary and Conclusions 5-27
5.3.7 Economic Valuation Estimates 5-28
5.4 Characterizing Uncertainty 5-28
5.4.1 Monte Carlo Assessment 5-29
5.4.2 Sources of Uncertainty Treated Qualitatively 5-30
5.5 Benefits Results 5-31
5.5.1 Benefits of the Applied Control Strategies for the Alternative Combinations of
Primary PM2.5 Standard Levels 5-31
5.6 Discussion 5-38
5.7 References 5-41
APPENDIX 5A: BENEFITS OF THE PROPOSED AND ALTERNATIVE STANDARD LEVELS
5A-1
Overview 5A-1
5A.1 Benefits of the Proposed and More Stringent Alternative Standard Levels of Primary PM2.5
Standards 5 A-2
5A.2 References 5A-8
CHAPTER 6: ENVIRONMENTAL JUSTICE 6-1
Introduction 6-1
6.1 Analyzing EJ Impacts in This Proposal 6-3
6.2 EJ Analysis of Exposures Under Current Standard and Alternative Standard Levels 6-6
6.2.1 Total Exposure 6-7
6.2.1.1 National 6-7
6.2.1.2 Regional 6-12
6.2.2 Exposure Changes 6-15
6.2.2.1 National 6-15
6.2.2.2 Regional 6-17
6.2.3 Proportional Changes in Exposure 6-20
6.2.3.1 National 6-21
6.2.3.2 Regional 6-22
6.3 EJ Analysis of Health Effects under Current Standards and Alternative Standard Levels
6-23
6.3.1 Total Mortality Rates 6-25
6.3.1.1 National 6-25
6.3.1.2 Regional 6-26
6.3.2 Mortality Rate Changes 6-27
6.3.2.1 National 6-28
6.3.2.2 Regional 6-28
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6.3.3 Proportional Changes in Mortality Rates 6-30
6.3.3.1 National 6-31
6.3.3.2 Regional 6-31
6.4 EJ Case Study of Exposure and Health Effects in Impacted Areas 6-32
6.4.1 Exposures 6-34
6.4.2 Mortality Rates 6-37
6.5 Summary 6-39
6.6 Environmental Justice Appendix 6-44
6.6.1 Input Information 6-44
6.6.1.1 EJ Exposure Analysis Input Data 6-44
6.6.1.2 EJ Health Effects Analysis Input Data 6-44
6.6.2 EJ Analysis of Total Exposures Associated with Meeting the Standards 6-47
6.6.2.1 National 6-47
6.6.2.2 Regional 6-49
6.6.3 EJ Analysis of Exposure Changes Associated with Meeting the Standards 6-53
6.6.3.1 National 6-53
6.6.3.2 Regional 6-55
6.6.4 Proportionality of Exposure Changes Associated with Meeting the Standards 6-58
6.6.4.1 National 6-58
6.6.4.2 Regional 6-59
6.6.5 EJ Analysis of Total Mortality Rates Associated with Meeting the Standards 6-61
6.6.5.1 National 6-61
6.6.5.2 Regional 6-62
6.6.6 EJ Analysis of Mortality Rate Change Associated with Meeting the Standards 6-64
6.6.6.1 National 6-64
6.6.6.2 Regional 6-65
6.6.7 Proportionality of Mortality Rate Changes Associated with Meeting the Standards
6-67
6.6.7.1 National 6-67
6.6.7.2 Regional 6-68
6.7 References 6-70
CHAPTER 7: LABOR IMPACTS 7-1
Overview 7-1
7.1 Labor Impacts 7-1
7.2 References 7-6
CHAPTER 8: COMPARISON OF BENEFITS AND COSTS 8-1
Overview 8-1
8.1 Results 8-2
8.2 Limitations of Present Value Estimates 8-10
8.3 References 8-13
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LIST OF TABLES
Table ES-1 Summary of PM2.5 Emissions Reductions Needed by Area in 2032 to Meet Current
Primary Annual and 24-hour Standards of 12/35 |a,g/m3 (tons/year) ES-7
Table ES-2 By Area, Summary of PM2.5 Emissions Reductions Needed, InTons/Year and as
Percent of Total Reduction Needed Nationwide, for Alternative Primary Standard
Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 ES-9
Table ES-3 Summary of PM2.5 Estimated Emissions Reductions from CoST by Area for the
Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3,
and 8/35 |a,g/m3 in 2032 (tons/year) ES-11
Table ES-4 Summary of PM2.5 Emissions Reductions Still Needed by Area for the Alternative
Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3, and 8/35
|a,g/m3 in 2032 (tons/year) ES-12
Table ES-5 By Area, Summary of Annualized Control Costs for Alternative Primary Standard
Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 for 2032
(millions of 2017$) ES-14
Table ES-6 Estimated Avoided PM-Related Premature Mortalities and Illnesses of the Control
Strategies for the Alternative Primary PM2.5 Standard Levels for 2032 (95%
Confidence Interval) ES-17
Table ES-7 Estimated Monetized Benefits of the Control Strategies for Alternative Primary PM2.5
Standard Levels in 2032, Incremental to Attainmentof 12/35 |ig/m3 (billions of
2017$) ES-18
Table ES-8 Estimated Monetized Benefits by Region of the Control Strategies for the Alternative
Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 |ig/m3
(billions of 2017$) ES-19
Table ES-9 Summary of Counties by Bin that Still Need Emissions Reductions for Proposed
Alternative Primary Standard Levels oflO/35 |a,g/m3 and9/35 |a,g/m3 ES-23
Table ES-10 Estimated Monetized Benefits, Costs, and Net Benefits of the Control Strategies
Applied Toward the Primary Alternative Standard Levels of 10/35 |a,g/m3,10/30
|a,g/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 for the U.S. (millions of 2017$)....ES-26
Table ES-11 Summary of Present Values and Equivalent Annualized Values for Estimated
Monetized Compliance Costs, Benefits, and Net Benefits of the Control Strategies
Applied Toward the Proposed Primary Alternative Standard Level of 10/35 |a,g/m3
(millions of 2017$, 2032-2051, discounted to 2022 using 3 and 7 percent discount
rates) ES-27
Table ES-12 Summary of Present Values and Equivalent Annualized Values for Estimated
Monetized Compliance Costs, Benefits, and Net Benefits of the Control Strategies
Applied Toward the Proposed Primary Alternative Standard Level of 9/3 5 |a,g/m3
(millions of 2017$, 2032-2051, discounted to 2022 using 3 and 7 percent discount
rates) ES-2 8
Table 2-1 Annual and 24-Hour Air Quality Ratios for Primary PM2.5 Emissions 2-23
Table 2-2 Information on Areas with Challenging Residential Wood Combustion Issues 2-39
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Table 2A-1 Definition of Statistics Used in the CMAQ Model Performance Evaluation 2A-8
Table 2A-2 CMAQ Performance Statistics for PM2.5 at AQS Sites in 2016 2A-10
Table 2A-3 CMAQ Performance Statistics for PM2.5 Sulfate at CSN and IMPROVE Sites in 2016
2A-12
Table 2A-4 CMAQ Performance Statistics for PM2.5 Nitrate at CSN and IMPROVE Sites in 2016
2A-14
Table 2A-5 CMAQ Performance Statistics for PM2.5 EC at CSN and IMPROVE Sites in 2016
2A-16
Table 2A-6 CMAQ Performance Statistics for PM2.5 OC at CSN and IMPROVE Sites in 2016
2A-18
Table 2A-7 Wildfire Episodes and Counties Where Data Were Screened for Exclusion if PM2.5
Concentrations Exceeded the Extreme Value Threshold of 61 [ig nr3 2A-23
Table 2A-8 PM2.5 DVs for 2032 Projection and 12/35 Analytical Baseline for the Highest DVs in
the County for Counties with Annual 2032 DVs Greater 8 jug nr3 or 24-hour 2032
DVs Greater than 30 jug nr3 2A-42
Table 2A-9 Annual and 24-Hour Air Quality Ratios for Primary PM2.5 Emissions 2A-48
Table 2A-10 County Groups for Calculating Air Quality Ratios for NOx Emission Changes in
Southern California 2A-51
Table 2A-11 2032 PM2.5 DVs and NOx-adjusted PM2.5 DVs for the Highest Annual and 24-Hour DV
Monitors in South Coast Counties 2 A-52
Table 2A-12 2032 PM2.5 DVs and NOx-adjusted PM2.5 DVs for the Highest Annual and 24-Hour DV
Monitors in SJV Counties 2A-54
Table 2A-13 Summary of Primary PM2.5 Emissions Reductions by County Needed to Meet the
Existing Standards (12/35) for Counties with 2032 Annual DVs greater than 8 jug m-
3 or 24-Hour DVs Greater than 30 jug nr3 2A-56
Table 2A-14 Primary PM2.5 Emission Reductions Needed to Meet the Alternative Standard Levels
of 10/35,10/30, 9/35, and 8/35 Relative to the 12/35 Analytical Baseline 2A-60
Table 2A-15 Primary PM2.5 Emission Reductions from EGUs Expected beyond 2032 Modeling
Case and Estimated Impact on DVs for Counties Exceeding Alternative Standards in
the 2032 Case 2A-69
Table 2A-16 SO2 and NOx Emission Reductions from EGUs Expected Beyond 2032 Modeling Case
by County 2A-70
Table 2A-17 2032 PM2.5 DVs and Estimated Influence of Emission Reductions from EGUs in
Franklin and Jefferson, MO, and Randolph, IL on DVs in Nearby Counties 2A-73
Table 2A-18 2032 PM2.5 DVs and Estimated Influence of Emission Reductions from EGUs in
Clermont and Hamilton, OH on DVs in Nearby Counties 2A-74
Table 3-1 Summary of PM2.5 Emissions Reductions Needed by Area in 2032 to Meet Current
Primary Annual and 24-hour Standards of 12/35 |a,g/m3 (tons/year) 3-5
Table 3-2 By Area, Summary of PM2.5 Emissions Reductions Needed, in Tons/Year and as
Percent of Total Reductions Needed Nationwide, for Alternative Primary Standard
Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 3-8
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Table 3-3 By Inventory Sector, Control Measures Applied in Analyses of the Current Standards
and the Alternative Primary Standard Levels 3-14
Table 3-4 Summary of PM2.5 Estimated Emissions Reductions from CoST by Area for the
Alternative Primary Standard Levels of 10/35 ng/m3,10/30 ng/m3, 9/35 ng/m3,
and 8/35 |a,g/m3 in 2032 (tons/year) 3-15
Table 3-5 Summary of PM2.5 Emissions and Estimated Emissions Reductions from CoST by
Inventory Sector for Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30
Hg/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 (tons/year) 3-17
Table 3-6 Summary of Estimated Emissions Reductions from CoST by Inventory Sector and
Control Technology for Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30
Hg/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 (tons/year) 3-19
Table 3-7 Summary of Estimated PM2.5 Emissions Reductions from CoST by Inventory Source
Classification Code Sectors for Alternative Primary Standard Levels of 10/35 |a,g/m3,
10/30 |a,g/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 (tons/year) 3-21
Table 3-8 Summary of PM2.5 Emissions Reductions Still Needed by Area for the Alternative
Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3, and 8/35
|a,g/m3 in 2032 (tons/year) 3-26
Table 3-9 Summary of PM2.5 Emissions Reductions Still Needed by Area and by County for the
Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3,
and 8/35 |a,g/m3 in 2032 (tons/year) 3-27
Table 3-10 Summary of Counties by Bin that Still Need Emissions Reductions for Proposed
Alternative Primary Standard Levels of 10/35 |a,g/m3 and 9/35 |a,g/m3 3-32
Table 3-11 Summary of Estimated PM2.5 Emissions Reductions Needed and Emissions
Reductions Identified by CoST for the West for the Proposed Primary Standard
Level of 9/35 |a,g/m3 in 2032 (tons/year) 3-37
Table 3A-1 By Area and Emissions Inventory Sector, Control Measures Applied in Analyses of
the Current Standards and Alternative Primary Standard Levels 3A-8
Table 3A-2 Summary of PM2.5 Estimated Emissions Reductions from CoST for the Northeast (57
counties) for Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3,
9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 (tons/year) 3A-11
Table 3A-3 Summary of PM2.5 Estimated Emissions Reductions from CoST for the Adjacent
Counties in the Northeast (75 counties) for Alternative Primary Standard Levels of
10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 (tons/year)
3A-13
Table 3A-4 Summary of PM2.5 Estimated Emissions Reductions from CoST for the Southeast (35
counties) for Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3,
9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 (tons/year) 3A-16
Table 3A-5 Summary of PM2.5 Estimated Emissions Reductions from CoST for the Adjacent
Counties in the Southeast (32 counties) for Alternative Primary Standard Levels of
10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 (tons/year)
3A-17
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3 A-6
3 A-7
3 A-8
4-1
4-2
4-3
4-4
4-5
4A-1
4 A-2
4 A-3
4 A-4
4 A-5
4 A-6
Summary of PM2.5 Estimated Emissions Reductions from CoST for the West (36
counties) for Alternative Primary Standard Levels of 10/35 ng/m3,10/30 ng/m3,
9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 (tons/year) 3A-18
Summary of PM2.5 Estimated Emissions Reductions from CoST for California (26
counties) for Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3,
9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 (tons/year) 3A-19
Remaining PM2.5 Emissions and Potential Additional Reduction Opportunities
3A-20
By Area, Summary of Annualized Control Costs for Alternative Primary Standard
Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 for 2032
(millions of 2017$) 4-5
By Emissions Inventory Sector, Summary of Annualized Control Costs for
Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3,
and 8/35 |a,g/m3 for 2032 (millions of 2017$) 4-7
By Area and by Emissions Inventory Sector, Summary of Annualized Control Costs
for Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3,
and 8/35 |a,g/m3 for 2032 (millions of 2017$) 4-7
By Control Technology, Summary of Annualized Control Costs for Alternative
Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3, and 8/35
|a,g/m3 for 2032 (millions of 2017$) 4-9
By Emissions Inventory Sector and Control Technology, Summary of Annualized
Control Costs for Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30
|a,g/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 for 2032 (millions of 2017$) 4-10
Summary of Estimated Annual Control Costs for the Northeast (57 counties) for
Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3,
and 8/35 |a,g/m3 for 2032 (millions of 2017$) 4A-1
Summary of Estimated Annual Control Costs for Adjacent Counties in the Northeast
(75 counties) for Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30
|a,g/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 for 2032 (millions of 2017$) 4A-3
Summary of Estimated Annual Control Costs for the Southeast (35 counties) for
Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3,
and 8/35 |a,g/m3 for 2032 (millions of 2017$) 4A-5
Summary of Estimated Annual Control Costs for Adjacent Counties in the Southeast
(32 counties) for Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30
|a,g/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 for 2032 (millions of 2017$) 4A-6
Summary of Estimated Annual Control Costs for the West (36 counties) for
Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3,
and 8/35 |a,g/m3 for 2032 (millions of 2017$) 4A-7
Summary of Estimated Annual Control Costs for California (26 counties) for
Alternative Primary Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3,
and 8/35 |a,g/m3 for 2032 (millions of 2017$) 4A-8
xiii
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Table 5-1 Estimated Monetized Benefits of the Applied Control Strategies for the Proposed
and Alternative Combinations of Primary PM2.5 Standard Levels in 2032,
Incremental to Attainment of 12/35 (billions of 2017$) 5-5
Table 5-2 Human Health Effects of Pollutants Potentially Affected by Attainment of the
Primary PM2.5 NAAQS 5-9
Table 5-3 Baseline Incidence Rates for Use in Impact Functions 5-14
Table 5-4 Causal Determinations Identified in Integrated Science Assessment for Oxides of
Nitrogen, Oxides of Sulfur, and Particulate Matter — Ecological Criteria 2020b ....5-23
Table 5-5 Estimated Avoided PM-Related Premature Mortalities and Illnesses of the Applied
Control Strategies for the Proposed and More Stringent Alternative Primary PM2.5
Standard Levels for 2032 (95% Confidence Interval) 5-34
Table 5-6 Monetized PM-Related Premature Mortalities and Illnesses of the Applied Control
Strategies for the Proposed and More Stringent Alternative Primary PM2.5 Standard
Levels for 2032 (Millions of 2017$, 3% discount rate; 95% Confidence Interval)
5-35
Table 5-7 Monetized PM-Related Premature Mortalities and Illnesses of the Applied Control
Strategies for the Proposed and More Stringent Alternative Primary PM2.5 Standard
Levels for 2032 (Millions of 2017$, 7% discount rate; 95% Confidence Interval)
5-36
Table 5-8 Estimated Monetized Benefits of the Applied Control Strategies for the Proposed
and More Stringent Alternative Combinations of Primary PM2.5 Standard Levels in
2032, Incremental to Attainment of 12/35 (billions of 2017$) 5-37
Table 5-9 Estimated Monetized Benefits by Region of the Applied Control Strategies for the
Proposed and More Stringent Alternative Combinations of Primary PM2.5 Standard
Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$) 5-38
Table 5 A-1 Estimated Avoided PM-Related Premature Mortalities and Illnesses of Meeting the
Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032
(95% Confidence Interval) 5A-3
Table 5 A-2 Monetized Avoided PM-Related Premature Mortalities and Illnesses of Meeting the
Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032
(Millions of 2017$, 3% discount rate; 95% Confidence Interval) 5A-4
Table 5 A-3 Monetized Avoided PM-Related Premature Mortalities and Illnesses of Meeting the
Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032
(Millions of 2017$, 7% discount rate; 95% Confidence Interval) 5A-5
Table 5A-4 Total Estimated Monetized Benefits of Meeting the Proposed and More Stringent
Alternative Primary Standard Levels in 2032, Incremental to Attainment of 12/35
(billions of 2017$) 5A-6
Table 5 A-5 Total Estimated Monetized Benefits by Region of Meeting the Proposed and More
Stringent Alternative Primary Standard Levels in 2032, Incremental to Attainment
of 12/35 (billions of 2017$) 5A-7
Table 6-1 Populations Included in the PM2.5 Exposure Analysis 6-7
Table 6-2 Hazard Ratios, Beta Coefficients, and Standard Errors (SE) from Di etal., 2017....6-45
xiv
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Table 7-1 Baseline Industry Employment 7-3
Table 7-2 Employmentper $1 Million Output (2017$) by Industry (4-digitNAICS) 7-5
Table 8-1 Estimated Monetized Benefits, Costs, and Net Benefits of the Control Strategies
Applied Toward Primary Alternative Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3,
9/35 |a,g/m3, and 8/35 |a,g/m3 in 2032 for the U.S. (millions of 2017$) 8-3
Table 8-2 Summary of Present Values and Equivalent Annualized Values for Estimated
Monetized Compliance Costs of the Control Strategies Applied Toward the Primary
Alternative Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3 8/35 |a,g/m3
(millions of 2017$, 2032-2051, discounted to 2022, 3 percent discount rate) 8-5
Table 8-3 Summary of Present Values and Equivalent Annualized Values for Estimated
Monetized Compliance Costs of the Control Strategies Applied Toward the Primary
Alternative Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3 8/35 |a,g/m3
(millions of 2017$, 2032-2051, discounted to 2022, 7 percent discount rate) 8-6
Table 8-4 Summary of Present Values and Equivalent Annualized Values for Estimated
Monetized Benefits of the Control Strategies Applied Toward the Primary
Alternative Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3 8/35 |a,g/m3
(millions of 2017$, 2032-2051, discounted to 2022, 3 percent discount rate) 8-7
Table 8-5 Summary of Present Values and Equivalent Annualized Values for Estimated
Monetized Benefits of the Control Strategies Applied Toward the Primary
Alternative Standard Levels of 10/35 |a,g/m3,10/30 |a,g/m3, 9/35 |a,g/m3 8/35 |a,g/m3
(millions of 2017$, 2032-2051, discounted to 2022, 7 percent discount rate) 8-8
Table 8-6 Summary of Present Values and Equivalent Annualized Values for Estimated
Monetized Compliance Costs, Benefits, and Net Benefits of the Control Strategies
Applied Toward the Proposed Primary Alternative Standard Level of 10/35 |a,g/m3
(millions of 2017$, 2032-2051, discounted to 2022 using 3 and 7 percent discount
rates) 8-9
Table 8-7 Summary of Present Values and Equivalent Annualized Values for Estimated
Monetized Compliance Costs, Benefits, and Net Benefits of the Control Strategies
Applied Toward the Proposed Primary Alternative Standard Level of 9/3 5 |a,g/m3
(millions of 2017$, 2032-2051, discounted to 2022 using 3 and 7 percent discount
rates) 8-10
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LIST OF FIGURES
Figure ES-1 Geographic Areas Used in Analysis ES-6
Figure ES-2 Counties Projected to Exceed in Analytical Baseline for Alternative Standard Levels
of 10/35 ug/m3, 9/35 ug/m3, and 8/35 ug/m3 ES-9
Figure ES-3 Counties that Still Need PM2.5 Emissions Reductions for Proposed Alternative
Standard Level ofl0/35ug/m3 ES-12
Figure ES-4 Counties that Still Need PM2.5 Emissions Reductions for Proposed Alternative
Standard Level of 9/35 ug/m3 ES-13
Figure 2-1 Annual Average PM2.5 Concentrations over the U.S. in 2019 Based on the Hybrid
Satellite Modeling Approach ofvan Donkelaar etal. (2021) 2-5
Figure 2-2 Seasonally Weighted Annual Average PM2.5 Concentrations in the U.S. from 2000 to
2019 (406 sites) 2-8
Figure 2-3 National Emission Trends of PM2.5, PM10, and Precursor Gases from 1990 to 2017
2-8
Figure 2-4 Annual Anthropogenic Source Sector Emission Totals (1000 tons per year) for NOx,
S02, and PM2.5 for 2016 and 2032 2-10
Figure 2-5 Gridded PM2.5 Concentrations over Selected Urban Areas Based on the 2032
Modeling Case Described Below with the Enhanced Voronoi Neighbor Averaging
Approach 2-12
Figure 2-6 Map of the Outer 3 6US3 (36 x 36 km Horizontal Resolution) and Inner 12US2 (12 x
12 km Horizontal Resolution) Modeling Domains 2-14
Figure 2-7 Regional Groupings for Calculating Air Quality Ratios 2-22
Figure 2-8 Counties with Projected 2032 PM2.5 DVs that Exceed the 24-Hour (24-hr Only),
Annual (Annual Only) or Both (Both) Existing Standards (12/35 jug nr3) 2-25
Figure 2-9 Counties with PM2.5 DVs that Exceed Alternative Annual (Annual Only), 24-Hour
(24-hr Only), or Both (Both) Standards in the 12/35 Analytical Baseline 2-27
Figure 2-10 Total Primary PM2.5 Emission Reductions Needed to Meet the Alternative Standard
Levels of 10/35,10/30, 9/35, and 8/35 Relative to the 12/35 Analytical Baseline in
the Eastern and Western U.S 2-28
Figure 2-11 Location of the Chester Site in Relation to the Evonik Degussa and PQ Corporation
Facilities 2-30
Figure 2-12 Imperial County and the Nonattainment Area 2-32
Figure 2-13 Nighttime Aerial View of Calexico, CA and Mexicali, MX 2-32
Figure 2-14 Annual Source Sector Emission Totals (1000 tons per year) for PM2.5 for 2016 and
2032 in Imperial County 2-33
Figure 2-15 Location of Mission and Brownsville Monitors in Hidalgo and Cameron County,
respectively, with Annual Wind Patterns from Meteorological Measurements 2-34
Figure 2-16 Annual Source Sector Emission Totals (1000 tons per year) for PM2.5 for 2016 and
2032 in Cameron and Hidalgo County Combined 2-35
xvi
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Figure 2-17 Air Pollution Layer Associated with a Temperature Inversion in Missoula, MT in
November 2018 2-36
Figure 2-18 Plumas County, CA (Grey), Portola Nonattainment Area (Red), and City of Portola
(Purple) 2-37
Figure 2-19 Lincoln County, MT (Grey), Libby Nonattainment Area (Red), and City of Libby
(Purple) 2-37
Figure 2-20 San Joaquin Valley Nonattainment Area and Location of Highest PM2.5 Monitor in
Bakersfield (06-029-0016) 2-40
Figure 2-21 Recent Annual PM2.5 DVs at the Highest SJV Monitor for Design Value Periods (e.g.,
11-13: 2011-2013). Dashed line is the 2012 Annual PM2.5 NAAQS Level (12 jug nr3)
2-41
Figure 2-22 Decrease in the Number of Days SJV Exceeded the 24-hr NAAQS Level (35 jug nr3)
2-41
Figure 2-23 Annual Source Sector PM2.5 Emission Totals in SJV Counties for 2032 Modeling Case
2-43
Figure 2-24 South Coast Air Basin Nonattainment Area and Locations of Highest PM2.5 Monitors
in Los Angeles (06-037-4008), Riverside (06-065-8005), and San Bernardino (06-
071-0027) 2-44
Figure 2-25 Recent Annual PM2.5 DVs at the Highest South Coast Monitor for Design Value
Periods (e.g., 11-13: 2011-2013). Dashed line is the 2012 Annual PM2.5 NAAQS Level
(12 |a,gm-3) 2-45
Figure 2-26 Annual Source Sector PM2.5 Emission Totals in the SoCAB Counties for 2032
Modeling Case 2-46
Figure 2-27 San Luis Obispo County and Location of Highest PM2.5 Monitor in Arroyo Grande
(06-079-2007) 2-47
Figure 2-28 Recent and Projected Annual PM2.5 DVs at the Arroyo Grande Monitor (06-079-
2007) in San Luis Obispo County for DV Periods (e.g., 11-13: 2011-2013; 32-32:
Projected 2032 DV) 2-48
Figure 2-29 Napa County and Location of PM2.5 Monitor (06-055-0003) 2-49
Figure 2-30 PM2.5 Concentration for 2032 based on eVNA Method 2-51
Figure 2-31 PM2.5 Concentration Improvement Associated with Meeting 12/35 Relative to the
2032 case 2-52
Figure 2 A-1 Map of the Outer 3 6US3 (36 x 36 km Horizontal Resolution) and Inner 12US2 (12 x
12 km Horizontal Resolution) Modeling Domains Used for the PM NAAQS RIA... 2A-5
Figure 2A-2 U.S. Climate Regions (Karl and Koss, 1984) Used in the CMAQ Model Performance
Evaluation 2 A-8
Figure 2A-3 Comparison of CMAQ Predictions of PM2.5 and Observations at AQS Sites for County
Highest PM2.5 Monitors with 2032 PM2.5 DVs Greater than 8/30 2A-9
Figure 2A-4 NMB in 2016 CMAQ Predictions of PM2.5 Components at CSN and IMPROVE Sites
2 A-11
xvii
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2 A-5
2 A-6
2 A-7
2 A-8
2A-9
2 A-10
2 A-11
2A-12
2A-13
2 A-14
2 A-15
2 A-16
2A-17
2A-18
2 A-19
2 A-20
2 A-21
2A-22
2A-23
2A-24
NMB in 2016 CMAQ Predictions of PM2.5 Components at CSN and IMPROVE Sites for
Monitors in Counties with 2032 PM2.5 DVs Greater than 8/30 2A-11
Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from the
Camp Fire on 11/10/2018 2A-27
Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from the
North Bay/Wine Country Fires on 10/09/2017 2A-27
Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from
Fires Across the Pacific Northwest/Northern California on 08/29/2017 2A-28
Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from
Fires in Washington and Oregon on 08/09/2018 2A-28
Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from
Fires in Montana on 08/19/2018 2A-29
Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from
Fires in Montana, Washington and Idaho on 08/22/2015 2A-29
Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from the
416/Burro Complex Fires on 06/10/2018 2A-30
Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from the
Butte Fire on 09/11/2015 2A-30
Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from the
Carr/Mendocino/Ferguson Fires on 08/04/2018 2A-31
Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from
Fires in the Appalachians on 11/10/2016 2A-31
Daily PM2.5 (in [ig nr3) from a Subset of Monitors Impacted by the Camp Fire in
November 2018 2A-32
Daily PM2.5 (in [ig nr3) from a Subset of Monitors Impacted by the North Bay/Wine
Country Fires in October 2017 2A-33
Daily PM2.5 (in [ig nr3) from a Subset of Monitors Impacted by Fires in the Pacific
Northwest/Northern California in August-September 2017 2A-34
Daily PM2.5 (in [ig m 3) from a Subset of Monitors Impacted by Fires in Washington
and Oregon in July-August 2018 2A-35
Daily PM2.5 (in [ig m 3) from the Monitors Impacted by Fires and Smoke in Montana
in August 2018 2A-36
Daily PM2.5 (in [ig m 3) from a Subset of Monitors Impacted by Fires in Montana,
Washington and Idaho in August 2015 2A-37
Daily PM2.5 (in [ig m 3) from the Monitor in Plata, CO Impacted by the 416/Burro Fire
Complex in June 2018 2A-37
Daily PM2.5 (in [ig m 3) from the Two monitors Impacted by the Butte Fire in
September 2015 2A-38
Daily PM2.5 (in [ig m 3) from a Subset of Monitors Impacted by the
Carr/Mendocino/Ferguson Fires in August 2018 2A-38
xviii
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Figure 2A-25 Daily PM2.5 (in [ig m3) from a Subset of Monitors Impacted by Fires in the
Appalachians in November 2016 2A-39
Figure 2A-26 Counties with Projected 2032 PM2.5 DVs that Exceed the 24-Hour (24-hr Only),
Annual (Annual Only) or Both the 24-Hour and Annual (Both) Standards for the
Combination of Existing Standards (12/35) 2A-40
Figure 2A-27 Counties with PM2.5 DVs in the 12/35 Analytical Baseline that Exceed the 24-Hour
(24-hr Only), Annual (Annual Only) or Both the 24-Hour and Annual (Both)
Standards for the Combination of Existing Standards 2A-41
Figure 2A-28 Counties with 50% Reduction in Anthropogenic Primary PM2.5 Emissions in 2028
Sensitivity Modeling 2A-47
Figure 2A-29 Regional Groupings for Calculating Air Quality Ratios 2A-48
Figure 2A-30 Counties Used in Estimating the Relative Impact of Emissions in Core and
Neighboring Counties 2A-49
Figure 2A-31 Counties with 50% Reduction in Anthropogenic NOx Emissions in 2028 Sensitivity
Modeling 2A-51
Figure 2A-32 Total Primary PM2.5 Emission Reductions Needed to Meet the Alternative Standard
Levels of 10/35,10/30, 9/35, and 8/35 Relative to the 12/35 Analytical Baseline in
the East and West 2A-60
Figure 2A-33 PM2.5 Concentration for 2032 based on eVNA Method 2A-66
Figure 2A-34 PM2.5 Concentration Improvement Associated with Meeting 12/35 Relative to the
2032 Case 2A-67
Figure 2A-35 PM2.5 Counties with 50% Reductions of SO2 Emissions in the 2028 CMAQ Sensitivity
Simulations (Green) and Eastern States Considered in the EGU Sensitivity Analysis
(Red) 2A-71
Figure 2A-36 Distributions of the Estimated Changes in Annual PM2.5 DVs in the Eastern U.S.
Associated with NOx and SO2 EGU Emission Reductions in the Eastern US Beyond
the 2032 Modeling Case 2A-72
Figure 2A-37 County Group in 2028 Sensitivity Modeling Used in Estimating the Response of DVs
to EGU Emission Changes in Franklin and Jefferson, MO, and Randolph, IL 2A-73
Figure 2A-38 County Group in 2028 Sensitivity Modeling Used in Estimating the Response of DVs
to EGU Emission Changes in Clermont and Hamilton, OH 2A-74
Figure 3-1 Geographic Areas Used in Analysis 3-4
Figure 3-2 Counties Projected to Exceed in Analytical Baseline for Alternative Standard Levels
of 10/35 |a,g/m3, 9/35 |a,g/m3, and 8/35 |a,g/m3 3-8
Figure 3-3 Counties Projected to Exceed in Analytical Baseline for Alternative Standard Levels
of 10/30 |a,g/m3 3-9
Figure 3-4 PM2.5 Emissions Reductions and Costs Per Ton (CPT) in 2032 (tons, 2017$) 3-13
Figure 3-5 Counties that Still Need PM2.5 Emissions Reductions for Proposed Alternative
Standard Level of 10/35 |a,g/m3 3-29
Figure 3-6 Counties that Still Need PM2.5 Emissions Reductions for Proposed Alternative
Standard Level of 9/35 |a,g/m3 3-29
xix
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Figure 3-7 Counties that Still Need PM2.5 Emissions Reductions for More Stringent Alternative
Standard Level of 8/35 |a,g/m3 3-30
Figure 3-8 Counties that Still Need PM2.5 Emissions Reductions for More Stringent Alternative
Standard Level of 10/30 |a,g/m3 3-30
Figure 5-1 Data Inputs and Outputs for the BenMAP-CE Model 5-12
Figure 6-1 Heat Map of National Average Annual PM2.5 Concentrations (|ig/m:i) by
Demographic for Current and Alternative PM NAAQS Levels (10/35,10/30, 9/35,
and 8/35) After Application of Controls 6-9
Figure 6-2 National Distributions of Annual PM2.5 Concentrations by Demographic for Current
and Alternative PM NAAQS Levels After Application of Controls 6-12
Figure 6-3 Heat Map of Regional Average Annual PM2.5 Concentrations (|ig/m:i) by
Demographic for Current (12/35) and Alternative PM NAAQS Levels (10/35,10/30,
9/35, and 8/35) After Application of Controls 6-13
Figure 6-4 Regional Distributions of Annual PM2.5 Concentrations by Demographic for Current
and Alternative PM NAAQS Levels After Application of Controls 6-14
Figure 6-5 Heat Map of National Reductions in Average Annual PM2.5 Concentrations (|ig/m:i)
for Demographic Groups When Moving from Current to Alternative PM NAAQS
Levels After Application of Controls 6-16
Figure 6-6 National Distributions of Annual PM2.5 Concentration Reductions for Demographic
Groups When Moving from Current to Alternative PM NAAQS Levels After
Application of Controls 6-17
Figure 6-7 Heat Map of Regional Reductions in PM2.5 Concentrations ([ig/m3) for Demographic
Groups When Moving from Current to Alternative PM NAAQS Levels After
Application of Controls 6-18
Figure 6-8 Regional Distributions of Total PM2.5 for Demographic Groups When Moving from
Current to Alternative PM NAAQS Levels After Application of Controls 6-19
Figure 6-9 Heat Map of National Percent Reductions in Average Annual PM2.5 Concentrations
(|ig/m3) for Demographic Groups When Moving from Current to Alternative PM
NAAQS Levels After Application of Controls 6-22
Figure 6-10 Heat Map of Regional Percent Reductions in Average Annual PM2.5 Concentrations
(|ig/m3) for Demographic Groups When Moving from Current (12/35) to Alternative
PM NAAQS Level (10/35,10/30, 9/35, and 8/35 After Application of Controls....6-23
Figure 6-11 Heat Map of National Average Annual Total Mortality Rates (per 10 OK) for
Demographic Groups for Current and Alternative PM NAAQS Levels After
Application of Controls 6-26
Figure 6-12 National Distributions of Total Annual Mortality Rates for Demographic Groups for
Current and Alternative PM NAAQS Levels After Application of Controls 6-26
Figure 6-13 Heat Map of Regional Average Annual Total Mortality Rates (per 100K) for
Demographic Groups for Current and Alternative PM NAAQS Levels After
Application of Controls 6-27
Figure 6-14 Regional Distributions of Total Annual Mortality Rates for Demographic Groups for
Current and Alternative PM NAAQS Levels After Application of Controls 6-27
xx
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Figure 6-15 Heat Map of National Average Annual Mortality Rate Reductions (per 100k) for
Demographic Groups When Moving from Current to Alternative PM NAAQS Levels
After Application of Controls 6-28
Figure 6-16 National Distributions of Annual Mortality Rate Reductions for Demographic Groups
When Moving from Current to Alternative PM NAAQS Levels After Application of
Controls 6-28
Figure 6-17 Heat Map of Regional Average Annual Mortality Rate Reductions (per 100k) for
Demographic Groups When Moving from Current and Alternative PM NAAQS Levels
After Application of Controls 6-29
Figure 6-18 Regional Distributions of Annual Mortality Rate Reductions for Demographic
Groups When Moving from Current to Alternative PM NAAQS Levels After
Application of Controls 6-30
Figure 6-19 HeatMap of National Average Percent Mortality Rate Reductions (per lOOkPeople)
for Demographic Groups When Moving from Current to Alternative PM NAAQS
Levels After Application of Controls 6-31
Figure 6-20 HeatMap of Regional Average Percent Mortality Rate Reductions (per 100k) for
Demographic Groups When Moving from Current to Alternative PM NAAQS Levels
After Application of Controls 6-31
Figure 6-21 Map of Areas in which PM2.5 Concentrations Change when Moving from 12/35 to
9/35 After Application of Controls 6-33
Figure 6-22 Heat Map of National Average Annual PM2.5 Concentrations and Concentration
Changes ([ig/m3) by Demographic for 12/35, 9/35, and 12/35-9/35 in the Subset of
Areas that Do and Do Not Experience Changes in Air Quality When Moving from
12/35 to 9/35 6-35
Figure 6-23 Heat Map of Regional Average Annual PM2.5 Concentrations and Concentration
Changes ([ig/m3) by Demographic for 12/35, 9/35, and 12/35-9/35 in the Subset of
Areas that Do and Do Not Experience Changes in Air Quality When Moving from
12/35 to 9/35 6-36
Figure 6-24 Heat Map of National Percent Reductions in Average Annual PM2.5 Concentrations
for Demographic Groups in the Subset of Areas in which PM2.5 Concentrations
Change When Moving from 12/35 to 9/35 6-37
Figure 6-25 HeatMap of National Average Annual Total Mortality Rates and Mortality Rate
Reductions (per 100K) by Demographic for 12/35, 9/35, and 12/35-9/35 in the
Subset of Areas that Do and Do Not Experience Changes in Air Quality when Moving
from 12/35 to 9/35 6-38
Figure 6-26 HeatMap of Regional Average Annual Total Mortality Rates and Mortality Rate
Reductions (per 10OK) by Demographic for 12/35 9/35, and 12/35-9/35, in the
Subset of Areas that Do and Do Not Change When Moving from 12/35-9/35 6-38
Figure 6-27 Heat Map of National and Regional Percent Reductions in Average Annual Total
Mortality Rates (per 100K) by Demographic in the Subset of Areas in which PM2.5
Concentrations Change When Moving from 12/35-9/35 6-39
Figure 6-28 Heat Map of National Average Annual PM2.5 Concentrations (|ig/m:i) Associated
Either with Control Strategies (Controls) or with Meeting the Standards (Standards)
xxi
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by Demographic for Current (12/35) and Alternative PM NAAQS Levels (10/35,
10/30, 9/35, and 8/35) 6-48
Figure 6-29 National Distributions of Annual PM2.5 Concentrations Associated Either with
Control Strategies or with Meeting the Standards by Demographic for Current and
Alternative PM NAAQS Levels 6-49
Figure 6-30 Heat Map of Regional Average Annual PM2.5 Concentrations ([ig/m3) Associated
Either with Control Strategies or with Meeting the Standards by Demographic for
Current and Alternative PM NAAQS Levels 6-51
Figure 6-31 Regional Distributions of Annual PM2.5 Concentrations Associated Either with
Control Strategies or with Meeting the Standards by Demographic for Current and
Alternative PM NAAQS Levels 6-52
Figure 6-32 Heat Map of National Average Annual Reductions in PM2.5 Concentrations (|ig/m:i)
Associated Either with Control Strategies or with Meeting the Standards by
Demographic When Moving from Current to Alternative PM NAAQS Levels 6-53
Figure 6-33 National Distributions of Annual Reductions in PM2.5 Concentrations Associated
Either with Control Strategies or with Meeting the Standards by Demographic When
Moving from Current to Alternative PM NAAQS Levels 6-54
Figure 6-34 Heat Map of National Reductions in Average Annual PM2.5 Concentrations (|ig/m:i)
Associated Either with Control Strategies or with Meeting the Standards by
Demographic When Moving from Current to Alternative PM NAAQS Levels 6-56
Figure 6-35 National Distributions of Reductions in Annual PM2.5 Concentrations Associated
Either with Control Strategies or with Meeting the Standards by Demographic When
Moving from Current to Alternative PM NAAQS Levels 6-57
Figure 6-36 Heat Map of National Percent Reductions in Average Annual PM2.5 Concentrations
(|ig/m:i) Associated Either with Control Strategies or with Meeting the Standards by
Demographic When Moving From Current to Alternative PM NAAQS Levels 6-58
Figure 6-37 Heat Map of Regional Percent Reductions in Average Annual PM2.5 Concentrations
(|ig/m:i) Associated Either with Control Strategies or with Meeting the Standards by
Demographic When Moving From Current to Alternative PM NAAQS Levels 6-60
Figure 6-38 Heat Map of National Average Annual Total Mortality Rates (per 100K People)
Associated Either with Control Strategies or with Meeting the Standards by
Demographic for Current and Alternative PM NAAQS Levels 6-61
Figure 6-39 National Distributions of Total Mortality Rates Associated Either with Control
Strategies or with Meeting the Standards by Demographic for Current and
Alternative PM NAAQS Levels 6-62
Figure 6-40 Heat Map of Regional Average Annual Total Mortality Rates (per 100K People)
Associated Either with Control Strategies or with Meeting the Standards by
Demographic for Current and Alternative PM NAAQS Levels 6-62
Figure 6-41 Regional Distributions of Total Mortality Rates Associated Either with Control
Strategies or with Meeting the Standards by Demographic for Current and
Alternative PM NAAQS Levels 6-63
xxii
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Figure 6-42
Figure 6-43
Figure 6-44
Figure 6-45
Figure 6-46
Figure 6-47
xxiii
Heat Map of National Average Annual Total Mortality Rate Reductions (per 100K
People) Associated Either with Control Strategies or with Meeting the Standards by
Demographic When Moving from Current to Alternative PM NAAQS Levels 6-64
National Distributions of Annual Total Mortality Rate Reductions Associated Either
with Control Strategies or with Meeting the Standards by Demographic When
Moving from Current to Alternative PM NAAQS Levels 6-65
Heat Map of Regional Average Annual Total Mortality Rate Reductions (per 10 OK
People) Associated Either with Control Strategies or with Meeting the Standards by
Demographic When Moving from Current to Alternative PM NAAQS Levels 6-65
Regional Distributions of Average Annual Total Mortality Rate Reductions
Associated Either with Control Strategies or with Meeting the Standards by
Demographic for When Moving from Current to Alternative PM NAAQS Levels....6-66
Heat Map of National Percent Changes in Average Mortality Rate Reductions
Associated Either with Control Strategies or with Meeting the Standards by
Demographic When Moving from Current to Alternative PM NAAQS Levels 6-67
Heat Map of Regional Percent Reductions in Average Mortality Rate Reductions
Associated Either with Control Strategies or with Meeting the Standards by
Demographic When Moving from Current to Alternative PM NAAQS Levels 6-69
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EXECUTIVE SUMMARY
Overview of the Proposal
In setting primary and secondary national ambient air quality standards (NAAQS),
the Environmental Protection Agency's (EPA) responsibility under the law is to establish
standards that protect public health and welfare. The Clean Air Act (CAA) requires the EPA,
for each criteria pollutant, to set standards that protect public health with "an adequate
margin of safety" and public welfare from "any known or anticipated adverse effects." As
interpreted by the Agency and the courts, the CAA requires the EPA to base the decisions
for primary standards on health considerations only; economic factors cannot be
considered. The prohibition against considering cost in the setting of the primary air
quality standards does not mean that costs, benefits, or other economic consequences are
unimportant. The Agency believes that consideration of costs and benefits is an essential
decision-making tool for the efficient implementation of these standards. The impacts of
costs, benefits, and efficiency are considered by the States when they make decisions
regarding what timelines, strategies, and policies are appropriate for their circumstances.
On June 10, 2021, the EPA announced its decision to reconsider the 2020 Particulate
Matter (PM) NAAQS final action. The EPA is reconsidering the December 2020 decision
because the available scientific evidence and technical information indicate that the current
standards may not be adequate to protect public health and welfare, as required by the
CAA. The EPA has concluded that the existing annual primary PM2.5 standard for PM, set at
a level of 12.0 |ig/m3, is not requisite to protect public health with an adequate margin of
safety. The EPA Administrator is proposing to revise the existing standard to provide
increased public health protection. Specifically, the EPA Administrator is proposing to
revise the level of the standard within the range of 9-10 |ig/m3, while soliciting comment
on levels down to 8 |ig/m3 and up to 11 |ig/m3. The primary 24-hour PM2.5 standard
provides protection against exposures to short-term "peak" concentrations of PM2.5 in
ambient air. The EPA Administrator is proposing to retain the primary 24-hour PM2.5
standard at its current level of 35 |ig/m3 and is soliciting comment on revising the level of
the standard to as low as 25 |ig/m3.
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The EPA has also concluded that the existing secondary PM standards are requisite
to protect public welfare from known or anticipated effects and is proposing to retain the
secondary standards for PM. Specifically, for the secondary annual PM2.5 standard, the EPA
Administrator is proposing to retain the existing standard of 15.0 |ig/m3. For the secondary
24-hour PM2.5 standard, the EPA Administrator is proposing to retain the existing standard
of 35 |ig/m3; however, the Administrator is soliciting comment on revising the level of the
standard to as low as 25 |ig/m3. For the secondary 24-hour PM10 standard, the EPA
Administrator is proposing to retain the existing standard of 150 |ig/m3.
Overview of the Regulatory Impact Analysis
Per Executive Orders 12866 and 13563 and the guidelines of the Office of
Management and Budget's (OMB) Circular A-4, in this Regulatory Impact Analysis (RIA) we
are analyzing the proposed annual and current 24-hour alternative standard levels of
10/35 ng/m3 and 9/35 |~ig/m3, as well as the following two more stringent alternative
standard levels: (1) an alternative annual standard level of 8 |~ig/m3 in combination with
the current 24-hour standard (i.e., 8/35 |j,g/m3), and (2) an alternative 24-hour standard
level of 30 |~ig/m3 in combination with the proposed annual standard level of 10 ng/m3 (i.e.,
10/30 |j,g/m3). Because the EPA is proposing that the current secondary PM standards be
retained, we did not evaluate alternative secondary standard levels. The RIA includes the
following chapters: Chapter 2: Emissions, Air Quality Modeling and Methods; Chapter 3:
Control Strategies and PM2.5 Emissions Reductions; Chapter 4: Engineering Cost Analysis
and Social Costs; Chapter 5: Benefits Analysis Approach and Results; Chapter 6:
Environmental Justice Impacts; Chapter 7: Labor Impacts; and Chapter 8: Comparison of
Benefits and Costs.
The RIA presents estimates of the costs and benefits of applying illustrative national
control strategies in 2032 after implementing existing and expected regulations and
assessing emissions reductions to meet the current annual and 24-hour particulate matter
NAAQS (12/35 |j,g/m3). The selection of 2032 as the analysis year in the RIA does not
predict or prejudge attainment dates that will ultimately be assigned to individual areas
under the CAA. The CAA contains a variety of potential attainment dates and flexibility to
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move to later dates, provided that the date is as expeditious as practicable. For the
purposes of this analysis, the EPA assumes that it would likely finalize designations for the
proposed particulate matter NAAQS in late 2024. Furthermore, also for the purposes of this
analysis and depending on the precise timing of the effective date of those designations, the
EPA assumes that nonattainment areas classified as Moderate would likely have to attain in
late 2032. As such, we selected 2032 as the primary year of analysis.
The analyses in this RIA rely on national-level data (emissions inventory and control
measure information) for use in national-level assessments (air quality modeling, control
strategies, environmental justice, and benefits estimation). However, the ambient air
quality issues being analyzed are highly complex and local in nature, and the results of
these national-level assessments therefore contain uncertainty. It is beyond the scope of
this RIA to develop detailed local information for the areas being analyzed, including
populating the local emissions inventory, obtaining local information to increase the
resolution of the air quality modeling, and obtaining local information on emissions
controls, all of which would reduce some of the uncertainty in these national-level
assessments. For example, having more refined data would be ideal for agricultural dust
and burning, prescribed burning, and non-point (area) sources due to their large
contribution to primary PM2.5 emissions and the limited availability of emissions controls.1
ES.l Design of the Regulatory Impact Analysis
The goal of this RIA is to provide estimates of the potential costs and benefits of the
illustrative national control strategies in 2032. Because States are ultimately responsible
for implementing strategies to meet alternative standard levels, this RIA provides insights
and analysis of a limited number of illustrative control strategies that states might adopt to
implement a proposed standard level.
We developed our projected baselines for emissions and air quality for 2032. To
estimate the costs and benefits of the illustrative national control strategies for the
proposed and more stringent annual and 24-hour PM2.5 alternative standard levels, we first
prepared an analytical baseline for 2032 that assumes full compliance with the current
1 Examples of area source emissions include area fugitive dust, residential wood combustion, and commercial
cooking emissions.
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standards of 12/35 |~ig/m3. From that analytical baseline, we estimate PM2.5 emissions
reductions needed to reach the proposed and alternative annual and 24-hour PM2.5
standard levels and then analyze illustrative control strategies that areas might employ.
Because PM2.5 concentrations are most responsive to direct PM emissions
reductions, for the illustrative control strategies we analyze direct, local PM2.5 emissions
reductions by individual counties.2 For the eastern U.S. where counties are relatively small
and terrain is relatively flat, we identified potential PM2.5 emissions reductions within each
county and in adjacent counties within the same state, where needed. As discussed in
Chapter 3, Section 3.2.2, when we applied the emissions reductions from adjacent counties,
we used a |~ig/m3 per ton PM2.5 air quality ratio that was four times less responsive than the
ratio used when applying in-county emissions reductions. Because the counties in the
western U.S. are generally large and the terrain is more complex, we only identified
potential PM2.5 emissions reductions within each county.
We then prepare illustrative control strategies. We apply end-of-pipe control
technologies to non-electric generating unit (non-EGU) stationary sources (e.g., fabric
filters, electrostatic precipitators, venturi scrubbers) and control measures to nonpoint
(area) sources (e.g., installing controls on charbroilers), to residential wood combustion
sources (e.g., converting woodstoves to gas logs), and for area fugitive dust emissions (e.g.,
paving unpaved roads) in analyzing PM2.5 emissions reductions. The estimated PM2.5
emissions reductions from these control applications do not fully account for all the
emissions reductions needed to reach the proposed and more stringent alternative
standard levels in some counties in the northeast, southeast, west, and California. In
Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6, we discuss the remaining air quality
2 As discussed in Chapter 2, Section 2.1.3, the spatial distributions of PM2.5 concentrations in the U.S. are
characterized by an "urban increment" of consistently higher PM2.5 concentrations over urban than
surrounding areas. Monitored concentrations are highest in urban areas and relatively low in rural areas.
Conceptually, PM2.5 concentrations in urban areas can be viewed as the superposition of the urban
increment and the contributions from regional and natural background sources. The decreases in
anthropogenic SO2 and NOx emissions in recent decades have reduced regional background concentrations
and increased the relative importance of the urban increment. The projections of additional large reductions
in SO2 and NOx emissions in the 2032 case further motivate the need for control of local primary PM2.5
sources to address the highest PM2.5 concentrations in urban areas. The 2032 projections include wildfire
emissions at their 2016 levels, but these emissions were not targeted for control.
ES-4
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challenges for areas in the northeast and southeast, as well as in the west and California for
the proposed alternative standard levels of 10/35 |~ig/m3 and 9/35 |j,g/m3; the areas
include a county in Pennsylvania affected by local sources, counties in border areas,
counties in small western mountain valleys, and counties in California's air basins and
districts. The characteristics of the air quality challenges for these areas include features of
local source-to-monitor impacts, cross-border transport, effects of complex terrain in the
west, and identifying wildfire influence on projected PM2.5 DVs that could potentially
qualify for exclusion as atypical, extreme, or unrepresentative events (U.S. EPA, 2019a).
Lastly, we estimate the engineering costs and human health benefits associated with the
illustrative control strategies, as well as assess environmental justice considerations.
Chapter 2, Section 2.1.3, includes discussions of historical and projected emissions
trends for direct PM2.5 and precursor emissions (i.e., SO2, NOx, VOC, and ammonia), as well
as of the "urban increment" of consistently higher PM2.5 concentrations over urban areas.
We did not apply controls to EGUs or mobile sources beyond what is reflected in the
projections between 2016 and 2032. The projections reflect SO2 and NOx emissions
decreases between 2016 and 2032 — over this period (1) NOx emissions are projected to
decrease by 3.8 million tons (40 percent), with the greatest reductions from mobile source
and EGU emissions inventory sectors, and (2) SO2 emissions are projected to decrease by 1
million tons (38 percent), with the greatest reductions from the EGU emissions inventory
sector.
ES.1.1 Establishing the Analytical Baseline
To project air quality to the future, the Community Multiscale Air Quality Modeling
System (CMAQ) model was applied to simulate air quality over the U.S. during 2016 and for
a case with emissions representative of 2032. In the 2032 projections, PM2.5 design values
(DVs) exceeded the current standards for some counties in the west.3 As described in
Chapter 2, Section 2.3.2, we adjusted the PM2.5 DVs for 2032 to account for emissions
reductions needed to attain the current annual and 24-hour PM2.5 standards of 12/35
3 PM2.5 DVs were projected to 2032 using the air quality model results in a relative sense, as recommended by
the EPA modeling guidance, by projecting monitoring data with relative response factors [RRFs] developed
from the 2016 and 2032 CMAQ modeling.
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|~ig/m3 to form the 12/35 |.ig/m3 analytical baseline; it is from this baseline that we estimate
the incremental costs and benefits associated with control strategies for the proposed and
more stringent alternative standard levels relative to the current standards. The analytical
baseline reflects, among other existing regulations, the Revised Cross-State Air Pollution
Rule Update, the Safer Affordable Fuel Efficient (SAFE) Vehicles Final Rule for Model Years
2021-2026, the Standards of Performance for Greenhouse Gas Emissions from New,
Modified, and Reconstructed Stationary Sources: EGUs, and the Mercury and Air Toxics
Standards. For a more complete list of regulations, please see Chapter 2, Section 2.2.1.
We present results throughout the RIA by northeast, southeast, west, and California,
and Figure ES-1 includes a map of the U.S. with these areas identified. Table ES-1 presents a
summary of the PM2.5 emissions reductions needed by area to meet the current standards
to form the 12/35 |J.g/m3 analytical baseline.
Figure ES-1 Geographic Areas Used in Analysis
ES-6
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Table ES-1 Summary of PM2.5 Emissions Reductions Needed by Area in 2032 to
Meet Current Primary Annual and 24-hour Standards of 12/35 (ig/m3
(tons/year)
Area
12/35
Northeast
0
Southeast
0
West
2,298
CA
6,907
Total
9,205
Eighteen counties need PM2.5 emissions reductions to meet the current standards in
2032 - 9 counties in California and 9 counties in the west.4 The counties in California
include several counties in the San Joaquin Valley Air Pollution Control District and the
South Coast Air Quality Management District, as well as Plumas County in Northern
California and Imperial County in southern California. No counties in the northeast or
southeast U.S. need PM2.5 emissions reductions to meet the current annual and 24-hour
standards.
ES.1.2 Estimating PM2.5 Emissions Reductions Needed for Annual and 24-hour
Alternative Standard Levels Analyzed
We apply regional PM2.5 air quality ratios to estimate PM2.5 DVs at air quality
monitor locations and then again to estimate the emissions reductions needed to reach the
proposed and more stringent annual and 24-hour alternative standard levels analyzed. To
develop air quality ratios that relate the change in DV in a county to the change in primary
PM2.5 emissions in that county, we performed air quality sensitivity modeling with
reductions in primary PM2.5 emissions in selected counties. More specifically, we conducted
a 2028 CMAQ sensitivity modeling simulation with 50 percent reductions in primary PM2.5
emissions from anthropogenic sources in counties with annual 2028 DVs greater than 8
Hg/m3. We divided the change in annual and 24-hour PM2.5 DVs in these counties by the
change in emissions in the respective counties to determine the air quality ratio at
individual monitors.
4 The 18 counties require primary PM emissions reductions to meet the current standards of 12/35 |J.g/m3
following application of the NOx emission reductions in San Joaquin Valley and the South Coast to adjust the
2032 DVs. For additional discussion, see Appendix 2A, Section 2A.3.2.
ES-7
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We developed representative air quality ratios for regions of the U.S. from the ratios
at individual monitors as in the 2012 PM2.5 NAAQS review (U.S. EPA, 2012). These regions
are shown in Chapter 2, Figure 2-7, and the air quality ratios for primary PM2.5 emissions
used in estimating the emission reductions needed to just meet the alternative standard
levels analyzed are listed in Chapter 2, Table 2-1. We estimated the emissions reductions
needed to just meet the alternative standard levels analyzed using the primary PM2.5 air
quality ratios in combination with the required incremental change in concentration.
Chapter 2, Section 2.3.1 includes a brief discussion of developing air quality ratios and
estimated emissions reductions needed to just meet the alternative standard levels
analyzed, and Appendix 2A, Section 2A.3 includes more detailed discussions.
Table ES-2 presents a summary of the estimated emissions reductions needed by
area to reach the annual and 24-hour alternative standard levels. For each alternative
standard level, Table ES-2 also includes an area's percent of the total estimated emissions
reductions needed nationwide to reach that alternative standard level in all locations. For
example, for the proposed standard level of 10/35 |~ig/m3, California's 10,128 estimated
tons needed is 81 percent of the total estimated emissions reductions needed nationwide
to meet 10/35 |~ig/m3. See Appendix 2A, Table 2A-14 for the estimated PM2.5 emissions
reductions, from the analytical baseline, needed by county for the alternative standard
levels analyzed. Figure ES-2 shows the counties projected to exceed the annual and 24-
hour alternative standard levels of 10/35 |~ig/m3, 9/35 |~ig/m3, and 8/35 |~ig/m3 in the
analytical baseline. Additional information on the air quality modeling, as well as
information about projected future DVs, DV targets, and air quality ratios is provided in
Chapter 2 and Appendix 2A.
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Table ES-2 By Area, Summary of PM2.5 Emissions Reductions Needed, In
Tons/Year and as Percent of Total Reduction Needed Nationwide, for
Alternative Primary Standard Levels of 10/35 jag/m3,10/30 jig/m3,
9/35 |ug/m3, and 8/35 (ig/m3 in 2032
Area
10/35
10/30
9/35
8/35
Northeast
1,068
1,221
6,996
30,843
Southeast
474
474
4,088
18,028
West
820
7,852
3,078
9,708
CA
10,128
12,230
17,750
28,293
Total
12,490
21,776
31,912
86,872
Area
10/35
10/30
9/35
8/35
Northeast
9%
6%
22%
36%
Southeast
4%
2%
13%
21%
West
7%
36%
10%
11%
CA
81%
56%
56%
33%
¦ Reductions required for 10/35, 9/35, and 8/35
Reductions required for 9/35 and 8/35
¦ Reductions required for 8/35
Figure ES-2 Counties Projected to Exceed in Analytical Baseline for Alternative
Standard Levels of 10/35 |ig/m3,9/35 (ig/m3, and 8/35 |ig/m3
For each alternative standard level, Chapter 2, Section 2,3.3 includes a discussion of
the number of counties that are projected to exceed in 2032, and Figure 2-9 includes maps
of counties projected to exceed along with the number of counties. The following
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summarizes the number of counties, by alternative standard level, in each geographic area
that need PM2.5 emissions reductions from the analytical baseline.
• 10/35 |j,g/m3-- 24 counties need PM2.5 emissions reductions. This includes 4
counties in the northeast, 2 counties in the southeast, 3 counties in the west,
and 15 counties in California.
• 10/30 |j,g/m3-- 47 counties need PM2.5 emissions reductions. This includes 4
counties in the northeast, 2 counties in the southeast, 23 counties in the west,
and 18 counties in California.
• 9/35 ng/m3 -- 51 counties need PM2.5 emissions reductions. This includes 14
counties in the northeast, 8 counties in the southeast, 8 counties in the west,
and 21 counties in California.
• 8/35 ng/m3 --141 counties need PM2.5 emissions reductions. This includes 57
counties in the northeast, 35 counties in the southeast, 24 counties in the
west, and 25 counties in California.
ES.1.3 Control Strategies and PM2.5 Emissions Reductions
We identified control measures using the EPA's Control Strategy Tool (CoST) (U.S.
EPA, 2019b) and the control measures database.5 CoST estimates emissions reductions and
engineering costs associated with control technologies or measures applied to non-electric
generating unit (non-EGU) point, non-point (area), residential wood combustion, and area
fugitive dust sources of air pollutant emissions by matching control measures to emissions
sources by source classification code (SCC). For these control strategy analyses, to
maximize the number of emissions sources we included a lower emissions source size
threshold (5 tons per year) and a higher marginal cost per ton threshold ($160,000/ton)
than reflected in prior NAAQS RIAs (25-50 tpy, $15,000-$20,000/ton). In Chapter 3, Figure
3-4 shows estimated PM2.5 emissions reductions for several emissions source sizes and cost
thresholds up to the $160,000/ton marginal cost threshold. We selected the $160,000/ton
5 More information about CoST and the control measures database can be found at the following link:
https://www.epa.gov/economic-and-cost-analysis-air-pollution-regulations/cost-analysis-modelstools-air-
pollution.
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marginal cost threshold because it is around that cost level that (i) road paving controls get
selected and applied, and (ii) opportunities for additional emissions reductions diminish.
By area, Table ES-3 includes a summary of the estimated emissions reductions from
control applications for the alternative standards analyzed. These emissions reductions
were used to create the PM2.5 spatial surfaces described in Appendix 2A, Section 2A.4.2 for
the human health benefits assessments presented in Chapter 5. See Chapter 3, Tables 3-5
through 3-7 for additional summaries of estimated PM2.5 emissions reductions from CoST.
Table ES-3 Summary of PM2.5 Estimated Emissions Reductions from CoST by Area
for the Alternative Primary Standard Levels of 10/35 |ig/m3,10/30
(ig/m3, 9/35 ng/m3, and 8/35 (ig/m3 in 2032 (tons/year)
PM2.5 Emissions Reductions
Area
10/35
10/30
9/35
8/35
Northeast
1,070
1,222
6,334
19,142
Northeast (Adjacent Counties)
0
0
1,737
15,440
Southeast
475
475
3,040
12,212
Southeast (Adjacent Counties)
0
0
194
4,892
West
224
2,206
947
4,711
CA
1,792
2,481
2,958
4,925
Total
3,561
6,384
15,210
61,321
Note: Totals may not match related tables due to independent rounding. In the northeast and southeast
when we applied the emissions reductions from adjacent counties, we used a ppb/ton PM2.5 air quality
ratio that was four times less responsive than the ratio used when applying in-county emissions
reductions.
ES.1.4 Estimates of PM2.5 Emissions Reductions Still Needed after Applying
Control Technologies and Measures
The estimated PM2.5 emissions reductions from the control strategies do not fully
account for all the emissions reductions needed to reach the proposed and more stringent
alternative standard levels in some counties in the northeast, southeast, west, and
California. By area, Table ES-4 includes a summary of the estimated emissions reductions
still needed after control applications for the alternative standards analyzed. See Chapter 3,
Table 3-9 for an additional summary of estimated emissions reductions still needed. Figure
ES-3 and Figure ES-4 show the counties that still need emissions reductions after control
applications for the proposed alternative standard levels of 10/35 |~ig/m3 and 9/35 |~ig/m3.
Section ES.2 below includes a qualitative discussion of the remaining air quality challenges.
In addition, Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6 provide more detailed
discussions of these air quality challenges.
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Table ES-4
Summary of PM2.5 Emissions Reductions Still Needed by Area for the
Alternative Primary Standard Levels of 10/35 |ig/m3,10/30 |_ig/m3,
9/35 (ig/m3, and 8/35 (ig/m3 in 2032 (tons/year)
Region
10/35
10/30
9/35
8/35
Northeast
0
0
238
6,741
Southeast
0
0
994
4,780
West
595
5,651
2,132
5,023
CA
8,336
9,749
14,793
23,368
Total
8,931
15,400
18,157
39,912
¦ Counties with Sufficient Identified Reductions to Meet 10/35
¦ Counties Still Needing Reductions to Meet 10/35
Figure ES-3 Counties that Still Need PM2.5 Emissions Reductions for Proposed
Alternative Standard Level of 10/35 |ig/m3
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¦ Counties with Sufficient Identified Reductions to Meet 9/35
¦ Counties Still Needing Reductions to Meet 9/35
Figure ES-4 Counties that Still Need PM2.5 Emissions Reductions for Proposed
Alternative Standard Level of 9/35 (ig/m3
ES.1.5 Engineering Costs
The EPA also used CoST and the control measures database to estimate engineering
control costs. We estimated costs for non-EGU point, non-point (area), residential wood
combustion, and area fugitive dust sources of air pollutant emissions. CoST calculates
engineering costs using one of two different methods: (1) an equation that incorporates
key operating unit information, such as unit design capacity or stack flow rate, or (2) an
average annualized cost-per-ton factor multiplied by the total tons of reduction of a
pollutant. The engineering cost analysis uses the equivalent uniform annual costs (EUAC)
method, in which annualized costs are calculated based on the equipment life for the
control measure and the interest rate incorporated into a capital recoveiy factor.
Annualized costs represent an equal stream of yearly cos ts over the period the control
technology is expected to operate. The cost estimates reflect the engineering costs
annualized using a 7 percent interest rate.
By area, Table ES-5 includes a summary of estimated control costs from control
applications for the alternative standard levels analyzed. See Chapter 4, Tables 4-2 through
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4-5 for additional summaries of estimated control costs associated with the control
strategies.
Table ES-5 By Area, Summary of Annualized Control Costs for Alternative Primary
Standard Levels of 10/35 ng/m3,10/30 ng/m3, 9/35 ng/m3, and 8/35
(j,g/m3 for 2032 (millions of 2017$)
Area
10/35
10/30
9/35
8/35
Northeast
$7.3
$12.8
$183.5
$560.2
Northeast (Adjacent Counties)
$0
$0
$22.3
$539.7
Southeast
$4.1
$4.1
$50.4
$250.6
Southeast (Adjacent Counties)
$0
$0
$18.2
$186.5
West
$19.0
$150.0
$34.2
$121.8
CA
$64.1
$90.4
$84.7
$162.9
Total
$94.5
$257.2
$393.3
$1,821.7
For the proposed alternative standard level of 10/35 ng/m3, the majority of the
estimated costs are incurred in California because 15 of the 24 counties that need
emissions reductions are located in California. Looking at the more stringent alternative
standard level of 10/30 ng/m3, in the west an additional 20 counties need emissions
reductions and estimated costs increase significantly; estimated costs for the proposed
alternative standard level of 9/35 |~ig/m3 are higher than for 10/35 |~ig/m3 but lower than
for 10/30 ng/m3 in this area. For alternative standard levels of 9/35 |~ig/m3 and 8/35
Hg/m3, more controls are available to apply in the northeast and the southeast as compared
to California and the west. Therefore, the estimated costs for the northeast and southeast
are higher for 9/35 ng/m3 and 8/35 ng/m3.
In the northeast and southeast when we applied the emissions reductions from
adjacent counties, we applied a ratio of 4:1. That is, four tons of PM2.5 emissions reductions
would be required from an adjacent county to reduce one ton of emissions reduction
needed in a given county. Application of this ratio contributes to the higher estimated cost
estimates for alternative standard levels of 9/35 ng/m3 and 8/35 ng/m3.
ES.1.6 Human Health Benefits
We estimate the quantity and economic value of air pollution-related effects using a
"damage-function." This approach quantifies counts of air pollution-attributable cases of
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adverse health outcomes and assigns dollar values to those counts, while assuming that
each outcome is independent of one another. We construct this damage function 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."
We use the environmental Benefits Mapping and Analysis Program—Community
Edition (BenMAP-CE) software program to quantify counts of premature deaths and
illnesses attributable to photochemical modeled changes in annual mean PM2.5 for the year
2032 using a health impact function (Sacks et al., 2018). A health impact function combines
information regarding: the concentration-response relationship between air quality
changes and the risk of a given adverse outcome; the population exposed to the air quality
change; the baseline rate of death or disease in that population; and the air pollution
concentration to which the population is exposed.
After quantifying the change in adverse health impacts, the final step is to estimate
the economic value of these avoided impacts. The appropriate economic value for a change
in a health effect depends on whether the health effect is viewed ex ante (before the effect
has occurred) or ex post (after the effect has occurred). Reductions in ambient
concentrations of air pollution generally lower the risk of future adverse health effects by a
small amount for a large population. The appropriate economic measure is therefore ex
ante willingness-to-pay (WTP) for changes in risk. However, epidemiological studies
generally provide estimates of the relative risks of a particular health effect avoided due to
a reduction in air pollution. A convenient way to use this data in a consistent framework is
to convert probabilities to units of avoided statistical incidences. This measure is calculated
by dividing individual WTP for a risk reduction by the related observed change in risk.
Applying the impact and valuation functions to the estimated changes in PM2.5 yields
estimates of the changes in physical damages (e.g., premature mortalities, cases of hospital
admissions and emergency department visits) and the associated monetary values for
those changes. Table ES-6 presents the estimated avoided incidences of PM-related
illnesses and premature mortality resulting from emissions reductions associated with the
application of the illustrative control strategies for each of the alternative standard levels in
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2032. Table ES-7 and Table ES-8 present a summary of the monetized benefits associated
with emissions reductions from the application of the illustrative control strategies for
each of the alternative standard levels, both nationally and by region, thereby allowing the
comparison of cost and benefits of the application of the illustrative controls. As mentioned
above and discussed in Chapter 3, Section 3.2.5, the estimated PM2.5 emissions reductions
from control applications do not fully account for all the emissions reductions needed to
reach the proposed and more stringent alternative standard levels in some counties in the
northeast, southeast, west, and California. In Chapter 2, Section 2.4 and Chapter 3, Section
3.2.6, we discuss the remaining air quality challenges for areas in the northeast and
southeast, as well as in the west and California for the proposed alternative standard levels
of 10/35 ng/m3 and 9/35 |~ig/m3. In Appendix 5A a set of tables summarizes the benefits
associated with identifying all of the emissions reductions needed to reach the proposed
and more stringent alternative standard levels. For Table ES-7 and Table ES-8, the
monetized value of unquantified effects is represented by adding an unknown "B" to the
aggregate total. This B represents both uncertainty and a bias in this analysis, as it reflects
health and welfare benefits that we are unable to quantify. Note that not all known PM
health effects could be quantified or monetized.
ES-16
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Table ES-6 Estimated Avoided PM-Related Premature Mortalities and Illnesses of
the Control Strategies for the Alternative Primary PM2.5 Standard
Levels for 2032 (95% Confidence Interval)
Avoided Mortality3
10/35 |j.g/m3
10/30 (j.g/m3
9/35 |J.g/m3
8/35 |J.g/m3
Pope III et al., 2019 (adult
mortality ages 18-99
years]
1,700
(1,200 to 2,100)
1,900
(1,400 to 2,400)
4,200
(3,000 to 5,300)
9,200
(6,600 to 12,000)
Wu et al., 2020 (adult
mortality ages 65-99
years]
810
(710 to 900)
920
(810 to 1,000)
2,000
(1,800 to 2,200)
4,400
(3,900 to 4,900)
Woodruff et al., 2008
1.6
1.8
4.7
11
(infant mortality")
(-0.99 to 4.0)
(-1.1 to 4.6)
(-3.0 to 12)
(-6.9 to 28)
Avoided Morbidity
10/35 ng/m3
10/30 (j.g/m3
9/35 |J.g/m3
8/35 |J.g/m3
Hospital admissions—
140
150
310
660
cardiovascular (age > 18)
(100 to 170)
(110 to 190)
(230 to 400)
(480 to 840)
Hospital admissions—
93
100
210
460
respiratory
(31 to 150)
(35 to 170)
(74 to 350)
(160 to 740)
ED visits-cardiovascular
260
290
630
1,400
(-100 to 610)
(-110 to 670)
(-240 to 1,500)
(-530 to 3,200)
ED visits—respiratory
490
530
1,200
2,700
(95 to 1,000)
(100 to 1,100)
(240 to 2,600)
(540 to 5,700)
Acute Myocardial
29
32
67
143
Infarction
(5.9 to 17)
(19 to 45)
(39 to 94)
(83 to 200)
Cardiac arrest
15
16
34
72
(-5.9 to 33)
(-6.6 to 37)
(-14 to 76)
(-29 to 160)
Hospital admissions-
360
390
850
1,900
Alzheimer's Disease
(270 to 440)
(300 to 480)
(640 to 1,000)
(1,500 to 2,400)
Hospital admissions-
48
54
120
270
Parkinson's Disease
(25 to 70)
(28 to 79)
(63 to 180)
(140 to 390)
Stroke
55
61
130
270
(14 to 94)
(16 to 110)
(33 to 220)
(71 to 470)
Lung cancer
65
73
150
320
(20 to 110)
(22 to 120)
(46 to 250)
(99 to 530)
Hay Fever/Rhinitis
15,000
16,000
35,000
75,000
(3,500 to 25,000)
(4,000 to 28,000)
(8,500 to 60,000)
(18,000 to 130,000)
Asthma Onset
2,200
2,500
5,400
11,000
(2,100 to 2,300)
(2,400 to 2,600)
(5,100 to 5,600)
(11,000 to 12,000)
Asthma symptoms -
310,000
350,000
740,000
1,600,000
Albuterol use
(-150,000 to
(-170,000 to
(-360,000 to
(-780,000 to
750,000)
850,000)
1,800,000)
3,900,000)
Lost work days
110,000
130,000
270,000
580,000
(97,000 to
(110,000 to
(230,000 to
(490,000 to
130,000)
150,000)
310,000)
660,000)
Minor restricted-activity
680,000
750,000
1,600,000
3,400,000
days
(550,000 to
(610,000 to
(1,300,000 to
(2,700,000 to
800,000)
890,000)
1,900,000)
4,000,000)
Note: Values rounded to two significant figures.
a Reported here are two alternative estimates of the number of premature deaths among adults due to long-
term exposure to PM2.5. These values should not be added to one another.
ES-17
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Table ES-7 Estimated Monetized Benefits of the Control Strategies for Alternative
Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of
12/35 ^ig/m3 (billions of 2017$)
ft r ^g/m3 Annual & 10 jig/m3 Annual & 9 jig/m3 Annual & 8 jig/m3 Annual &
Benefits Estimate 3g tlg/m3 24-hour 30 ng/m3 24-hour 35 ng/m3 24-hour 35 ng/m3 24-hour
Economic value of avoided PIVh.s-related morbidities and premature deaths using PM2.5 mortality
estimate from Pope III et al., 2019
3% discount $17 + B $20+ B $43 + B $95 + B
rate
7% discount $16+ B $18 + B $39 + B $86 + B
rate
Economic value of avoided PIVh.s-related morbidities and premature deaths using PM2.5 mortality
estimate from Wu et al., 2020
3% discount $8.5 + B $9.6 + B $21 + B $46 + B
rate
7% discount $7 6 + B 6 + B $19 + B $41 + B
rate
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized
benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not all
possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified
health and welfare benefits.
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Table ES-8 Estimated Monetized Benefits by Region of the Control Strategies for
the Alternative Primary PM2.5 Standard Levels in 2032, Incremental to
Attainment of 12/35 ^ig/m3 (billions of 2017$)
Benefits
Region
10 jig/m3
Annual &
10 jig/m3
Annual &
9 Jig/m3
Annual &
8 Jig/m3
Annual &
Estimate
35 Jig/m3 24-
hour
30 Jig/m3 24-
hour
35 Jig/m3 24-
hour
35 Jig/m3 24-
hour
Economic value of avoided PM2.5
-related morbidities and premature deaths using PM2.5 mortality estimate
from Pope III et al., 2019
3%
discount
rate
California
$13+ B
$14+ B
$17+ B
$23+ B
Northeast
$2.3+ B
$2.6+ B
$15+ B
$40+ B
Southeast
$1.8 + B
$1.8 + B
$8.8 + B
$22+ B
West
$0.018+ B
$1.1+ B
$2.2 + B
$11+ B
7%
discount
rate
California
$12 + B
$13+ B
$16+ B
$21+ B
Northeast
$2 + B
$2.3+ B
$13+ B
$36+ B
Southeast
$1.6+ B
$1.6+ B
$7.9 + B
$20+ B
West
$0.016+ B
$1 + B
$2 + B
$9.5 + B
Economic value of avoided PM2.5
-related morbidities and premature deaths using PM2.5 mortality estimate
from Wu et al., 2020
30/0
discount
rate
California
$6.5 + B
$6.9 + B
$8.4 + B
$11+ B
Northeast
$1.1+ B
$1.3+ B
$7.3 + B
$19+ B
Southeast
$0.84+ B
$0.84+ B
$4.1 + B
$10+ B
West
$0.0092 + B
$0.56 + B
$1.1+ B
$5.1+ B
7%
discount
rate
California
$5.8 + B
$6.2 + B
$7.5 + B
$10+ B
Northeast
$1 + B
$1.2 + B
$6.6+ B
$17+ B
Southeast
$0.75+ B
$0.75+ B
$3.6+ B
$9.2 + B
West
$0.0082 + B
$0.5 + B
$0.97+ B
$4.6 + B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized
benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not possible to
quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and
welfare benefits.
ES.1.7 Welfare Benefits of Meeting the Primary and Secondary Standards
Even though the primary standards are designed to protect against adverse effects
to human health, the emissions reductions would have welfare benefits in addition to the
direct health benefits. The term welfare benefits covers both environmental and societal
benefits of reducing pollution. Welfare benefits of the primary PM standard include
reduced vegetation effects resulting from PM exposure, reduced ecological effects from
particulate matter deposition and from nitrogen emissions, reduced climate effects, and
changes in visibility. This RIA does not assess welfare effects quantitatively; this is
discussed further in Chapter 5.
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ES.1.8 Environmental Justice
Environmental justice (EJ) concerns for each rulemaking are unique and should be
considered on a case-by-case basis, and EPA's EJ Technical Guidance6 states that "[t]he
analysis of potential EJ concerns for regulatory actions should address three questions:
1. Are there potential EJ concerns associated with environmental stressors
affected by the regulatory action for population groups of concern in the
baseline?
2. Are there potential EJ concerns associated with environmental stressors
affected by the regulatory action for population groups of concern for the
regulatory option(s) under consideration?
3. For the regulatory option(s) under consideration, are potential EJ concerns
created or mitigated compared to the baseline?"
To address these questions, EPA developed an analytical approach that considers
the purpose and specifics of the proposed rulemaking, as well as the nature of known and
potential exposures and impacts. For the proposal, we quantitatively evaluate the potential
for disparities in PM2.5 exposures and mortality effects across different demographic
populations under illustrative control strategies associated with implementation of the
current standard (12/35 ng/m3* or baseline) and potential alternative PM2.5 standard levels
(10/35 mg/m3,10/30 |Lxg /m3, 9/35 |Lxg /m3, and 8/35 |Lxg /m3) at the national and regional
levels. Specifically, we provide information on totals, changes, and proportional changes in
1) exposures, in terms of annual average PM2.5 concentrations and 2) premature mortality,
in terms of rates per 100,000 individuals across and within various demographic
populations. Each type of analysis has strengths and weaknesses, but when taken together,
can respond to the above three questions from EPA's Environmental Justice (EJ) Technical
Guidance.
Beginning with the first question, under the 12/35 |~ig/m3 analytical baseline, some
populations are predicted to experience disproportionately higher annual PM2.5 exposures
6 U.S. Environmental Protection Agency (EPA), 2015. Guidance on Considering Environmental Justice During
the Development of Regulatory Actions.
ES-20
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nationally than the reference (overall) population, both in terms of aggregated average
exposure and across the distribution of air quality. Specifically, Hispanics, Asians, Blacks,
and those less educated (no high school) have higher national annual exposures, on
average and across the distributions, than both the overall reference population or other
populations (e.g., non-Hispanic, White, and more educated). In particular, the Hispanic
population is estimated to experience the highest exposures, both on average and across
PM2.5 concentration distributions, of all demographic groups analyzed. These
disproportionalities are also observed at the regional level, though to different extents.
In response to the second question, while a lower standard level would be predicted
to reduce PM2.5 exposures and mortality rates across all demographic groups, disparities
seen in the baseline are also reflected in the standard levels under consideration. However,
as to the third question, for most populations assessed, PM2.5 exposure disparities are
mitigated in the illustrative air quality scenarios reflecting control strategies (10/35 |~ig/m3,
10/30 ng/m3, 9/35 |j,g/m3, and 8/35 |j,g/m3) as compared to the baseline (12/35 |j,g/m3),
and more so as the alternative standard levels become more stringent. At the national
scale, Hispanics, Asians, and those less educated are estimated to see greater proportional
reductions in PM2.5 concentrations than reference populations under all alternative
standard levels evaluated, with proportional reductions increasing as the alternative
standard levels decrease. However, exposures in the Black population are estimated to
proportionally decrease on par with exposures in reference population. Considering the
four geographic regions (northeast, southeast, west, and California), proportionally greater
reductions in PM2.5 concentrations experienced by Asian, Hispanic, and less educated
populations are most notable in the southeast and California, whereas PM2.5 concentration
reductions among Black populations tend to be proportionally larger than among the
reference population in California, the west, and the northeast, especially under the
proposed alternative standard level of 9/35 |~ig/m3 and the more stringent alternative
standard level of 8/35 |~ig/m3. In Section 6.6.2.2 we provide some insight into exposures in
areas with remaining air quality challenges (i.e., without sufficient emissions control
strategies to reach alternative standard levels).
ES-21
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In terms of health effects, some populations are also predicted to experience
disproportionately higher rates of premature mortality than the reference population
under the baseline scenario (question 1). Black populations are estimated to have the
highest national and regional PIVh.s-attributable mortality rates, both on average and across
population distributions. Differential PM2.5 exposures for this population in some parts of
the country, which may contribute to higher magnitude concentration-response
relationships between exposure and premature mortality, as well as other underlying
health factors that may increase susceptibility to adverse outcomes among Black
populations. Health disparities associated with the baseline scenario are also predicted for
the proposed and more stringent standard levels (question 2), although as the alternative
standard levels become increasingly stringent, differences in mortality rates across
demographic groups decline, particularly for the proposed and more stringent alternative
standard levels evaluated (9/35 |j,g/m3and 8/35 |j,g/m3) (question 3).
ES.2 Qualitative Assessment of the Remaining Air Quality Challenges
For the proposed alternative standard levels of 10/35 |~ig/m3 and 9/35 |j,g/m3,the
analysis indicates that some areas in the northeast and southeast, as well as in the west and
California may still need emissions reductions (Figure ES-3 and Figure ES-4). As discussed
in Chapters 2 and 3, the remaining air quality challenges for the proposed alternative
standard levels can be grouped into the following "bins": Delaware County, Pennsylvania,
border areas, small mountain valleys, and California areas. By bin, Table ES-9 below
summarizes the counties that may need additional emissions reductions for the proposed
alternative standard levels.
ES-22
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Table ES-9 Summary of Counties by Bin that Still Need Emissions Reductions for
Proposed Alternative Primary Standard Levels of 10/35 (ig/m3 and
9/35 jig/m3
Counties3 for
Additional Counties3 for
Bin
Area
10/35 mg/m3
9/35 mg/m3
Delaware County,
Pennsylvania
Northeast
--
Delaware County, PA
Border Areas
Southeast
--
Cameron County, TX
Hidalgo County, TX
California
Imperial County, CA
—
Small Mountain Valleys
West
Plumas County, CA
Lemhi County, ID
Shoshone County, ID
Lincoln County, MT
Benewah County, ID
California Areas
Fresno County, CA (SJVAPCD)
Kern County, CA (SJVAPCD)
Kings County, CA (SJVAPCD)
Los Angeles County, CA (SCAQMD)
Madera County, CA (SJVAPCD)
Merced County, CA (SJVAPCD)
Riverside County, CA (SCAQMD)
San Bernardino County, CA (SCAQMD)
Stanislaus County, CA (SJVAPCD)
Tulare County, CA (SJVAPCD)
Napa County, CA (BAAQMD)
San Joaquin County, CA (SJVAPCD)
San Luis Obispo County, CA
Note: For California counties that are part of multi-county air districts, the relevant district is indicated in parentheses;
BAAQMD = Bay Area Air Quality Management District, SCAQMD = South Coast Air Quality Management District, and
SJVAPCD= San Joaquin Valley Air Pollution Control District.
a The following counties have no identified PM2.5 emissions reductions because available controls were applied for the
current standard of 12/35 |ig/m3 and additional controls were notavailable: Imperial, Kern, Kings, Lemhi, Plumas,
Riverside, San Bernardino, Shoshone, and Tulare.
The characteristics of the air quality challenges for these areas include features of
local source-to-monitor impacts, cross-border transport, effects of complex terrain in the
west and California, and identifying wildfire influence on projected PM2.5 DVs that could
potentially qualify for exclusion as atypical, extreme, or unrepresentative events (U.S. EPA,
2019a). For bin-specific detailed discussions of these air quality challenges, see Chapter 2,
Section 2.4. Further, for each bin for discussions of the estimated PM2.5 emissions
reductions needed, the control strategy analyses and controls applied, the estimated PM2.5
emissions reductions still needed after the application of controls, and the bin-specific air
quality challenges, see Chapter 3, Section 3.2.6.
For Delaware County, Pennsylvania, a more detailed local analysis of the local
source emissions reductions impacts is needed. For the border areas that may be
influenced by cross-border emissions, more detailed analyses of international transport
ES-23
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emissions are needed to assess the relevance of Section 179B of the Clean Air Act. For the
small mountain valleys in the west that are influenced by the temperature inversions,
residential wood combustion, and wildfire smoke additional detailed analyses that reflect
local PM2.5 response factors, emissions inventory information, and control measure
information are needed. In addition, more detailed analyses are needed to characterize the
influence of wildfires on PM2.5 concentrations and the potential for some wildfires to
qualify for exclusion as atypical, extreme, or unrepresentative events.
Lastly, the air quality in the SJVAPCD and SCAQMD is influenced by complex terrain
and meteorological conditions that are best characterized with a high-resolution air quality
modeling platform developed for the specific conditions of the air basins. Specific, local
information on control measures to reduce emissions from agricultural dust and burning,
prescribed burning, and many of the non-point (area) emissions sources (e.g., commercial
and residential cooking) is needed given the magnitude of emissions from these sources in
these areas. Further, more detailed analyses are needed to characterize the influence of
wildfires on PM2.5 concentrations and the potential for some wildfires to qualify for
exclusion as atypical, extreme, or unrepresentative events.
ES.3 Results of Benefit-Cost Analysis
As discussed above and in Chapter 3, Section 3.2.5, the estimated PM2.5 emissions
reductions from control applications do not fully account for all the emissions reductions
needed to reach the proposed and more stringent alternative standard levels in some
counties in the northeast, southeast, west, and California. In Chapter 2, Section 2.4 and
Chapter 3, Section 3.2.6, we discuss the remaining air quality challenges for areas in the
northeast and southeast, as well as in the west and California for the proposed alternative
standard levels of 10/35 |~ig/m3 and 9/35 |~ig/m3. The EPA calculates the monetized net
benefits of the proposed alternative standard levels by subtracting the estimated
monetized compliance costs from the estimated monetized benefits in 2032. These
estimates do not fully account for all of the emissions reductions needed to reach the
proposed and more stringent alternative standard levels. In 2032, the monetized net
benefits of the proposed alternative standard level of 10/35 |~ig/m3 are approximately $8.4
billion and $17 billion using a 3 percent real discount rate for the benefits estimates, and
ES-24
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the monetized net benefits of the proposed alternative standard level of 9/35 |~ig/m3 are
approximately $20 billion and $43 billion using a 3 percent real discount rate for the
benefits estimates (in 2017$). The benefits are associated with two point estimates from
two different epidemiologic studies discussed in more detail in Chapter 5, Section 5.3.3.
Table ES-10 presents a summary of these impacts for the proposed alternative standard
levels and the more stringent alternative standard levels for 2032.
ES-25
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Table ES-10 Estimated Monetized Benefits, Costs, and Net Benefits of the Control
Strategies Applied Toward the Primary Alternative Standard Levels of
10/35 |ig/m3,10/30 |ig/m3, 9/35 (j,g/m3, and 8/35 |ig/m3 in 2032 for
the U.S. (millions of 2017$)
10/35
10/30
9/35
8/35
Benefits3
$8,500 and $17,000
$9,600 and $20,000
$21,000 and $43,000
$46,000 and $95,000
Costsb
$95
$260
$390
$1,800
Net Benefits
$8,400 and $17,000
$9,300 and $19,000
$20,000 and $43,000
$44,000 and $93,000
Notes: Rows may not appear to add correctly due to rounding. We focus results to provide a snapshot of costs
and benefits in 2032, using the best available information to approximate social costs and social benefits
recognizing uncertainties and limitations in those estimates.
a We assume that there is a cessation lag between the change in PM exposures and the total realization of
changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related
to PM2.5 exposures occur in a distributed fashion over the 20 years following exposure, which affects the
valuation of mortality benefits at different discount rates. Similarly, we assume there is a cessation lag between
the change in PM exposures and both the development and diagnosis of lung cancer. The benefits are associated
with two point estimates from two different epidemiologic studies, and we present the benefits calculated at a
real discount rate of 3 percent. The benefits exclude additional health and welfare benefits that could not be
quantified (see Chapter 5, Sections 5.3.4 and 5.3.5).
b The costs are annualized using a 7 percent interest rate.
As part of fulfilling analytical guidance with respect to E.0.12866, the EPA presents
estimates of the present value (PV) of the monetized benefits and costs over the twenty-
year period 2032 to 2051. To calculate the present value of the social net benefits of the
proposed alternative standard levels, annual benefits and costs are discounted to 2022 at 3
percent and 7 percent discount rates as directed by OMB's Circular A-4. The EPA also
presents the equivalent annualized value (EAV), which represents a flow of constant annual
values that, had they occurred in each year from 2032 to 2051, would yield a sum
equivalent to the PV. The EAV represents the value of a typical cost or benefit for each year
of the analysis, in contrast to the 2032-specific estimates.
For the twenty-year period of 2032 to 2051, for the proposed alternative standard
level of 10/35 |j,g/m3the PV of the net benefits, in 2017$ and discounted to 2022, is $200
billion when using a 3 percent discount rate and $90 billion when using a 7 percent
discount rate. The EAV is $13 billion per year when using a 3 percent discount rate and
$8.5 billion when using a 7 percent discount rate. For the twenty-year period of 2032 to
2051, for the proposed alternative standard level of 9/35 |j,g/m3the PV of the net benefits,
in 2017$ and discounted to 2022, is $490 billion when using a 3 percent discount rate and
$220 billion when using a 7 percent discount rate. The EAV is $33 billion per year when
ES-26
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using a 3 percent discount rate and $21 billion when using a 7 percent discount rate. The
comparison of benefits and costs in PV and EAV terms for the proposed alternative
standard levels can be found in Table ES-11 and Table ES-12. Estimates in the tables are
presented as rounded values.
Table ES-11 Summary of Present Values and Equivalent Annualized Values for
Estimated Monetized Compliance Costs, Benefits, and Net Benefits of
the Control Strategies Applied Toward the Proposed Primary
Alternative Standard Level of 10/35 ng/m3 (millions of 2017$, 2032-
2051, discounted to 2022 using 3 and 7 percent discount rates)
Benefits3 Costsb Net Benefits
Year
3%
7%
3%
7%
3%
7%
2032
$13,000
$8,000
$70
$48
$13,000
$7,900
2033
$13,000
$7,500
$68
$45
$13,000
$7,400
2034
$12,000
$7,000
$66
$42
$12,000
$6,900
2035
$12,000
$6,500
$64
$39
$12,000
$6,500
2036
$12,000
$6,100
$63
$37
$11,000
$6,100
2037
$11,000
$5,700
$61
$34
$11,000
$5,700
2038
$11,000
$5,300
$59
$32
$11,000
$5,300
2039
$11,000
$5,000
$57
$30
$10,000
$4,900
2040
$10,000
$4,600
$56
$28
$10,000
$4,600
2041
$9,900
$4,300
$54
$26
$9,900
$4,300
2042
$9,700
$4,100
$52
$24
$9,600
$4,000
2043
$9,400
$3,800
$51
$23
$9,300
$3,800
2044
$9,100
$3,500
$49
$21
$9,100
$3,500
2045
$8,800
$3,300
$48
$20
$8,800
$3,300
2046
$8,600
$3,100
$47
$19
$8,500
$3,100
2047
$8,300
$2,900
$45
$17
$8,300
$2,900
2048
$8,100
$2,700
$44
$16
$8,000
$2,700
2049
$7,900
$2,500
$43
$15
$7,800
$2,500
2050
$7,600
$2,400
$41
$14
$7,600
$2,300
2051
$7,400
$2,200
$40
$13
$7,400
$2,200
Present Value
$200,000
$91,000
$1,100
$540
$200,000
$90,000
Equivalent
Annualized Value
$13,000
$8,500
$72
$51
$13,000
$8,500
Notes: Rows may not appear to add correctly due to rounding. The annualized present value of costs and
benefits are calculated over a 20-year period from 2032 to 2051.
a The benefits values use the larger of the two avoided premature deaths estimates presented in Chapter 5,
Table 5-7, and are discounted at a rate of 3 percent over the SAB-recommended 20-year segmented lag. The
benefits exclude additional health and welfare benefits that could not be quantified (see Chapter 5, Sections
5.3.4 and 5.3.5).
b The costs are annualized using a 7 percent interest rate.
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Table ES-12 Summary of Present Values and Equivalent Annualized Values for
Estimated Monetized Compliance Costs, Benefits, and Net Benefits of
the Control Strategies Applied Toward the Proposed Primary
Alternative Standard Level of 9/35 (ig/m3 (millions of 2017$, 2032-
2051, discounted to 2022 using 3 and 7 percent discount rates)
Benefits3 Costsb Net Benefits
Year
3%
7%
3%
7%
3%
7%
2032
$32,000
$20,000
$290
$200
$32,000
$20,000
2033
$31,000
$18,000
$280
$190
$31,000
$18,000
2034
$30,000
$17,000
$280
$170
$30,000
$17,000
2035
$29,000
$16,000
$270
$160
$29,000
$16,000
2036
$29,000
$15,000
$260
$150
$28,000
$15,000
2037
$28,000
$14,000
$250
$140
$27,000
$14,000
2038
$27,000
$13,000
$250
$130
$27,000
$13,000
2039
$26,000
$12,000
$240
$120
$26,000
$12,000
2040
$25,000
$11,000
$230
$120
$25,000
$11,000
2041
$25,000
$11,000
$220
$110
$24,000
$11,000
2042
$24,000
$10,000
$220
$100
$24,000
$9,900
2043
$23,000
$9,400
$210
$95
$23,000
$9,300
2044
$23,000
$8,800
$210
$89
$22,000
$8,700
2045
$22,000
$8,200
$200
$83
$22,000
$8,100
2046
$21,000
$7,700
$190
$78
$21,000
$7,600
2047
$21,000
$7,200
$190
$72
$20,000
$7,100
2048
$20,000
$6,700
$180
$68
$20,000
$6,600
2049
$19,000
$6,300
$180
$63
$19,000
$6,200
2050
$19,000
$5,800
$170
$59
$19,000
$5,800
2051
$18,000
$5,500
$170
$55
$18,000
$5,400
Present Value
$490,000
$220,000
$4,500
$2,300
$490,000
$220,000
Equivalent
Annualized Value
$33,000
$21,000
$300
$210
$33,000
$21,000
Notes: Rows may not appear to add correctly due to rounding. The annualized present value of costs and
benefits are calculated over a 20-year period from 2032 to 2051.
•' The benefits values use the larger of the two avoided premature deaths estimates presented in Chapter 5,
Table 5-7, and are discounted at a rate of 3 percent over the SAB-recommended 20-year segmented lag. The
benefits exclude additional health and welfare benefits that could not be quantified (see Chapter 5, Sections
5.3.4 and 5.3.5).
b The costs are annualized using a 7 percent interest rate.
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ES.4 References
Pope III, CA, Lefler, JS, Ezzati, M, Higbee, JD, Marshall, JD, Kim, S-Y, Bechle, M, Gilliat, KS,
Vernon, SE and Robinson, AL (2019). Mortality risk and fine particulate air pollution in
a large, representative cohort of US adults. Environmental health perspectives 127(7):
077007.
Sacks, JD, Lloyd, JM, Zhu, Y, Anderton, J, Jang, CJ, Hubbell, B and Fann, N (2018). The
Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-
CE): A tool to estimate the health and economic benefits of reducing air pollution.
Environmental Modelling Software 104: 118-129
U.S. EPA (2012). Regulatory Impact Analysis for the Final Revisions to the National
Ambient Air Quality Standards for Particulate Matter, U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-
452/R-12-005. Available at: https://www.epa.gov/sites/default/files/2020-
07/documents/naaqs-pm_ria_final_2012-12.pdf.
U.S. EPA (2019a). Additional Methods, Determinations, and Analyses to Modify Air Quality
Data Beyond Exceptional Events. U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards, Research Triangle Park, NC, EPA-457/B-19-002.
Available: https://www.epa.gov/sites/default/files/2019-
04/documents/clarification_memo_on_data_modification_methods.pdf
U.S. EPA (2019b). CoST v3.7 User's Guide. Office of Air Quality Planning and Standards,
Research Triangle Park, NC. November 2019. Available at <
https://www.cmascenter.org/help/documentation. cfm?model=cost&version=3.7>.
Woodruff, TJ, Darrow, LA and Parker, JD (2008). Air pollution and postneonatal infant
mortality in the United States, 1999-2002. Environmental Health Perspectives 116(1):
110-115.
Wu, X, Braun, D, Schwartz, J, Kioumourtzoglou, M and Dominici, F (2020). Evaluating the
impact of long-term exposure to fine particulate matter on mortality among the elderly.
Science advances 6(29): eaba5692.
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CHAPTER 1: OVERVIEW AND BACKGROUND
Overview of the Proposal
On June 10, 2021, the Environmental Protection Agency (EPA) announced its
decision to reconsider the 2020 Particulate Matter (PM) National Ambient Air Quality
Standards (NAAQS) final action. In this reconsideration, the EPA has concluded that the
existing annual primary PM2.5 standard for PM, set at a level of 12.0 |ig/m3, is not requisite
to protect public health with an adequate margin of safety. The EPA Administrator is
proposing to revise the level of the standard within the range of 9-10 |ig/m3, while
soliciting comment on levels down to 8 |ig/m3 and up to 11 |ig/m3. The primary 24-hour
PM2.5 standard provides protection against exposures to short-term "peak" concentrations
of PM2.5 in ambient air. The EPA Administrator is proposing to retain primary 24-hour
PM2.5 standard at its current level of 35 |ig/m3 and is soliciting comment on revising the
level of the standard to as low as 25 |ig/m3.
The EPA has also concluded that the existing secondary PM standards are requisite
to protect public welfare from known or anticipated effects and is proposing to retain the
secondary standards for PM. Specifically, for the secondary annual PM2.5 standard, the EPA
Administrator is proposing to retain the existing standard of 15.0 |ig/m3. For the secondary
24-hour PM2.5 standard, the EPA Administrator is proposing to retain the existing standard
of 35 |ig/m3; however, the Administrator is soliciting comment on revising the level of the
standard to as low as 25 |ig/m3. For the secondary 24-hour PM10 standard, the EPA
Administrator is proposing to retain the existing standard of 150 |ig/m3. The docket for the
proposed rulemaking is EPA-HQ-OAR-2015-0072.
Overview of the Regulatory Impact Analysis
This chapter summarizes the purpose and background of this Regulatory Impact
Analysis (RIA). In this RIA, we are analyzing the proposed annual and current 24-hour
alternative standard levels of 10/35 |~ig/m3 and 9/35 |~ig/m3, as well as the following two
more stringent alternative standard levels: (1) an alternative annual standard level of 8
Hg/m3 in combination with the current 24-hour standard (i.e., 8/35 |j,g/m3), and (2) an
alternative 24-hour standard level of 30 |~ig/m3 in combination with the proposed annual
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standard level of 10 |~ig/m3 (i-e., 10/30 |j,g/m3). The RIA presents estimated costs and
benefits of the control strategies analyzed for the proposed and more stringent alternative
standard levels. According to the Clean Air Act ("the Act"), the Environmental Protection
Agency (EPA) must use health-based criteria in setting the NAAQS and cannot consider
estimates of compliance cost.
The analyses in this RIA rely on national-level data (emissions inventory and control
measure information) for use in national-level assessments (air quality modeling, control
strategies, environmental justice, and benefits estimation). However, the ambient air
quality issues being analyzed are highly complex and local in nature, and the results of
these national-level assessments therefore contain uncertainty. It is beyond the scope of
this RIA to develop detailed local information for the areas being analyzed, including
populating the local emissions inventory, obtaining local information to increase the
resolution of the air quality modeling, and obtaining local information on emissions
controls, all of which would reduce some of the uncertainty in these national-level
assessments. For example, having more refined data would be ideal for agricultural dust
and burning, prescribed burning, and non-point (area) sources due to their large
contribution to primary PM2.5 emissions and the limited availability of emissions controls.1
To maximize the number of emissions sources included and controls analyzed in the
analyses, we included a lower emissions source size threshold (5 tons per year) and a
higher marginal cost per ton threshold ($160,000/ton) than reflected in prior NAAQS RIAs
(25-50 tpy, $15,000-$20,000/ton). As discussed in Chapter 2, Section 2.1.3, given historical
and projected trends in NOx and SO2 emissions reductions (reducing background PM
concentrations and increasing the importance of urban PM concentrations), we analyze
direct PM emissions reductions because our modeling indicates that these reductions will
be the most effective at reducing PM concentrations in counties projected to exceed the
proposed standard levels. The spatial distributions of PM2.5 concentrations in the U.S. are
characterized by an "urban increment" of consistently higher PM2.5 concentrations over
urban than surrounding areas. Monitored concentrations are highest in urban areas and
1 Examples of area source emissions include area fugitive dust, residential wood combustion, and commercial
cooking emissions.
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relatively low in rural areas. Conceptually, PM2.5 concentrations in urban areas can be
viewed as the superposition of the urban increment and the contributions from regional
and natural background sources. The decreases in anthropogenic SO2 and NOx emissions in
recent decades have reduced regional background concentrations and increased the
relative importance of the urban increment. The projections of additional large reductions
in SO2 and NOx emissions in the 2032 case further motivate the need for control of local
primary PM2.5 sources to address the highest PM2.5 concentrations in urban areas. Lastly,
Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6 discuss the remaining air quality
challenges for areas in the northeast and southeast, as well as in the west and California for
the proposed alternative standard levels of 10/35 |~ig/m3 and 9/35 |j,g/m3; the areas
include a county in Pennsylvania affected by local sources, border areas, counties in small
western mountain valleys, and counties in California's air basins and districts. The
characteristics of the air quality challenges for these areas include features of local source-
to-monitor impacts, cross-border transport, effects of complex terrain in the west, and
identifying wildfire influence on projected PM2.5 DVs that could potentially qualify for
exclusion as atypical, extreme, or unrepresentative events (U.S. EPA, 2019).
The remainder of this chapter provides a brief background on the NAAQS, the need
for the NAAQS, and an overview of structure of this RIA. The EPA prepared this RIA both to
provide the public with information on the benefits and costs of meeting a revised PM2.5
NAAQS and to meet the requirements of Executive Orders 12866 and 13563.
1.1 Background
In setting primary ambient air quality standards, the EPA's responsibility under the
law is to establish standards that protect public health, regardless of the costs of
implementing those standards. As interpreted by the Agency and the courts, the CAA
requires the EPA to create standards based on health considerations only. The prohibition
against the consideration of cost in the setting of the primary air quality standards,
however, does not mean that costs or other economic consequences are unimportant or
should be ignored. The Agency believes that consideration of costs and benefits is essential
to making efficient, cost-effective decisions for implementing these standards. The impact
of cost and efficiency is considered by states during the implementation process, as they
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decide what timelines, strategies, and policies are appropriate for their circumstances. This
RIA is not part of the standard setting and is intended to inform the public about the
potential costs and benefits that may result when new standards are implemented.
1.1.1 National Ambient Air Quality Standards
Sections 108 and 109 of the CAA govern the establishment and revision of the
NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify pollutants that
"may reasonably be anticipated to endanger public health or welfare" and to issue air
quality criteria for them. These air quality criteria are intended to "accurately reflect the
latest scientific knowledge useful in indicating the kind and extent of all identifiable effects
on public health or welfare which may be expected from the presence of [a] pollutant in the
ambient air." PM is one of six pollutants for which the EPA has developed air quality
criteria.
Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate
"primary" and "secondary" NAAQS for pollutants identified under section 108. Section
109(b)(1) defines a primary standard as an ambient air quality standard "the attainment
and maintenance of which in the judgment of the Administrator, based on [the] criteria and
allowing an adequate margin of safety, [is] requisite to protect the public health." A
secondary standard, as defined in section 109(b)(2), must "specify a level of air quality the
attainment and maintenance of which in the judgment of the Administrator, based on [the]
criteria, is requisite to protect the public welfare from any known or anticipated adverse
effects associated with the presence of [the] pollutant in the ambient air." Welfare effects as
defined in section 302(h) [42 U.S.C. 7602(h)] include but are not limited to "effects on soils,
water, crops, vegetation, manmade materials, animals, wildlife, weather, visibility and
climate, damage to and deterioration of property, and hazards to transportation, as well as
effects on economic values and on personal comfort and well-being."
Section 109(d) of the CAA directs the Administrator to review existing criteria and
standards at 5-year intervals. When warranted by such review, the Administrator is to
retain or revise the NAAQS. After promulgation or revision of the NAAQS, the standards are
implemented by the states.
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1.1.2 Role of Executive Orders in the Regulatory Impact Analysis
While this RIA is separate from the NAAQS decision-making process, several
statutes and executive orders still apply to any public documentation. The analyses
required by these statutes and executive orders are presented in the proposed rule
preamble, and below we briefly discuss requirements of Orders 12866 and 13563 and the
guidelines of the Office of Management and Budget (OMB) Circular A-4 (U.S. 0MB, 2003).
In accordance with Executive Orders 12866 and 13563 and the guidelines of OMB
Circular A-4, the RIA presents the estimated benefits and costs associated with control
strategies for a range of annual and 24-hour PM2.5 alternative standard levels. The
estimated benefits and costs associated with emissions controls are incremental to a
baseline of attaining the current standards (annual and 24-hour PM2.5 standards of 12/35
Hg/m3 in ambient air). OMB Circular A-4 requires analysis of one potential alternative
standard level more stringent than the proposed standard and one less stringent than the
proposed standard. The Agency is proposing to revise the current annual PM2.5 standards
to a level within the range of 9-10 |~ig/m3 and is soliciting comment on an alternative
annual standard level down to 8 |~ig/m3 and a level up to 11 ng/m3. The Agency is also
proposing to retain the current 24-hour standard of 35 ng/m3 and is soliciting comment on
an alternative 24-hour standard level of 25 ng/m3. In this RIA, we are analyzing the
proposed annual and current 24-hour alternative standard levels of 10/35 ng/m3 and 9/35
Hg/m3, as well as the following two more stringent alternative standard levels: (1) an
alternative annual standard level of 8 ng/m3 in combination with the current 24-hour
standard (i.e., 8/35 |j,g/m3), and (2) an alternative 24-hour standard level of 30 ng/m3 in
combination with the proposed annual standard level of 10 ng/m3 (i.e., 10/30 |j,g/m3).
1.1.3 Nature of the Analysis
The control strategies presented in this RIA are an illustration of one possible set of
control strategies states might choose to implement in response to the proposed standards.
States—not the EPA—will implement the proposed NAAQS and will ultimately determine
appropriate emissions control strategies and measures. State Implementation Plans (SIPs)
will likely vary from the EPA's estimates provided in this analysis due to differences in the
data and assumptions that states use to develop these plans. Because states are ultimately
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responsible for implementing strategies to meet the proposed standards, the control
strategies in this RIA are considered hypothetical. The hypothetical strategies were
constructed with the understanding that there are inherent uncertainties in estimating and
projecting emissions and applying control measures to specific emissions or emissions
sources. Additional important uncertainties and limitations are documented in the relevant
chapters of the RIA.
The EPA's national program rules require technology application or emissions limits
for a specific set of sources or source groups. In contrast, a NAAQS establishes a standard
level and requires states to identify and secure emissions reductions to meet the standard
level from any set of sources or source groups. To avoid double counting the impacts of
NAAQS and other national program rules, the EPA includes previously promulgated federal
regulations and enforcement actions in its baseline for this analysis (See Section 1.3.1
below for additional discussion of the baseline). The benefits and costs of the proposed
standards will not be realized until specific control measures are mandated by SIPs or
other federal regulations.
1.2 The Need for National Ambient Air Quality Standards
OMB Circular A-4 indicates that one of the reasons a regulation such as the NAAQS
may be issued is to address a market failure. The major types of market failure include
externality, market power, and inadequate or asymmetric information. Correcting market
failures is one reason for regulation, but it is not the only reason. Other possible
justifications include improving the function of government, removing distributional
unfairness, or promoting privacy and personal freedom.
Environmental problems are classic examples of externalities -- uncompensated
benefits or costs imposed on another party as a result of one's actions. For example, the
smoke from a factory may adversely affect the health of local residents and soil the
property in nearby neighborhoods. If bargaining was costless and all property rights were
well defined, people would eliminate externalities through bargaining without the need for
government regulation.
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From an economics perspective, setting an air quality standard is a straightforward
remedy to address an externality in which firms emit pollutants, resulting in health and
environmental problems without compensation for those incurring the problems. Setting a
standard with an adequate margin of safety attempts to place the cost of control on those
who emit the pollutants and lessens the impact on those who suffer the health and
environmental problems from higher levels of pollution. For additional discussion on the
PM2.5 air quality problem, see Chapter 2 of the Policy Assessment for the Reconsideration
of the National Ambient Air Quality Standards for Particulate Matter (U.S. EPA, 2022a).
1.3 Design of the Regulatory Impact Analysis
The RIA presents the estimates of costs and benefits of applying hypothetical
national control strategies for the proposed and more stringent alternative annual and 24-
hour standard levels of 10/35 |~ig/m3,10/30 |~ig/m3, 9/35 |~ig/m3, and 8/35 |~ig/m3,
incremental to attaining the current PM2.5 standards and implementing existing and
expected regulations. We assume that potential nonattainment areas everywhere in the
U.S. will be designated such that they are required to attain the proposed standards by
2032.
The selection of 2032 as the analysis year in the RIA does not predict or prejudge
attainment dates that will ultimately be assigned to individual areas under the CAA. The
CAA contains a variety of potential attainment dates and flexibility to move to later dates,
provided that the date is as expeditious as practicable. For the purposes of this analysis, the
EPA assumes that it would likely finalize designations for the proposed particulate matter
NAAQS in late 2024. Furthermore, also for the purposes of this analysis and depending on
the precise timing of the effective date of those designations, the EPA assumes that
nonattainment areas classified as Moderate would likely have to attain in late 2032. As
such, we selected 2032 as the primary year of analysis. States with areas classified as
Moderate and higher are required to develop attainment demonstration plans for those
nonattainment areas.
The EPA recognizes that areas designated nonattainment for the proposed PM2.5
NAAQS and classified as Moderate will likely incur some costs prior to the 2032 analysis
year. States with nonattainment areas designated as Moderate are required by the CAA to
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develop SIPs demonstrating attainment by no later than the assigned attainment date. The
CAA also requires these states to address Reasonably Available Control Technologies
(RACT) for sources in the Moderate nonattainment area, which would lead to additional
point source controls in an area beyond existing federal emissions control measures.
Additionally, the CAA requires some Moderate areas with larger populations to implement
basic vehicle inspection and maintenance in the area. Should these federal programs and
CAA required programs prove inadequate for the area to attain the proposed standards by
the attainment date, the state would need to identify additional emissions control
measures in its SIP to meet attainment requirements.
1.3.1 Establishing the Baseline for Evaluation of Proposed and Alternative
Standards
To develop and evaluate control strategies, it is important to estimate PM2.5 levels in
the future after attaining the current standards of 12/35 |~ig/m3, taking into account
projections of future air quality reflecting on-the-books Federal regulations, enforcement
actions, state regulations, population and where possible, economic growth. Establishing
this baseline for the analysis then allows us to estimate the incremental costs and benefits
associated with the alternative standard levels. For the purposes of this analysis and
depending on the precise timing of the effective date of designations, the EPA assumes that
areas will be designated such that they are required to reach attainment by 2032, and we
developed our projected baselines for emissions and air quality for 2032.2
Attaining the current standards of 12/35 |~ig/m3 reflects emissions reductions (i)
already achieved as a result of national regulations, (ii) expected prior to 2032 from
recently promulgated national regulations (i.e., reductions that were not realized before
promulgation of the previous standard but are expected prior to attainment of the current
PM2.5 standards), and (iii) from additional controls that the EPA estimates need to be
included to reach the current standard. Additional emissions reductions achieved as a
result of state and local agency regulations and voluntary programs are reflected to the
2 Because of the complex nature of air quality in California, we adjusted baseline air quality in 2032 to reflect
mobile source NOx emissions reductions for California that would occur between 2032 and 2035. These
emissions reductions are the result of mobile source regulations expected to be fully implemented by 2035.
California provided the mobile source inventory data for 2035.
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extent that they are represented in emissions inventory information submitted to the EPA
by state and local agencies.
We took two steps to develop the baseline for this analysis. First, national PM2.5
concentrations were projected to the analysis year (2032) based on forecasts of population
and where possible, economic growth and the application of emissions controls resulting
from national rules promulgated prior to this analysis, as well as state programs and
enforcement actions. Second, we estimated additional emissions reductions needed to
meet the current standards of 12/35 |~ig/m3. Below is a list of some of the national rules
reflected in the baseline. For a more complete list, please see Chapter 2, Section 2.2.1 (Air
Quality Modeling Platform) and the technical support document (TSD) for the 2016v2
emissions modeling platform titled Preparation of Emissions Inventories for the 2016v2
North American Emissions Modeling Platform (U.S. EPA, 2022b). If the national rules
reflected in the baseline result in changes in PM2.5 concentrations or actual emissions
reductions that are lower or higher than those estimated, the costs and benefits estimated
in this RIA would be higher or lower, respectively.
• Revised Cross-State Air Pollution Rule Update (RCU), (U.S. EPA, 2021)
• The Standards of Performance for Greenhouse Gas Emissions from New, Modified,
and Reconstructed Stationary Sources: EGUs (U.S. EPA, 2015)
• Mercury and Air Toxics Standards (U.S. EPA, 2011)
• Safer Affordable Fuel Efficient (SAFE) Vehicles Final Rule for Model Years 2021-
2026 (U.S. EPA, U.S. DOT, 2020)
• Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium-
and Heavy-Duty Engines and Vehicles - Phase 2 (U.S. EPA, U.S. DOT, 2016)
• Tier 3 Motor Vehicle Emission and Fuel Standards (U.S. EPA, 2014)
We did not conduct this analysis incremental to controls applied as part of previous
NAAQS analyses because the data and modeling on which these previous analyses were
based are now considered outdated and are not compatible with this PM2.5 NAAQS analysis.
1.3.2 Cost Analysis Approach
The EPA estimated the costs of applying hypothetical national control strategies.
Where available, we apply end-of-pipe controls to achieve emissions reductions and
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present the costs associated with these PM2.5 emissions reductions. These cost estimates
reflect only engineering costs, which generally include the costs of purchasing, installing,
and operating the referenced control technologies. The technologies and control strategies
selected for analysis illustrate one way in which nonattainment areas could reduce
emissions. As mentioned above, the air quality issues being analyzed are highly complex
and local in nature, and the results of these national-level assessments contain uncertainty.
The EPA anticipates that state and local governments will consider programs that are best
suited for local conditions.
1.3.3 Benefits Analysis Approach
The EPA estimated the number and economic value of the avoided PM2.5-
attributable premature deaths and illnesses associated with the control strategies analyzed
for the proposed alternative standard levels. We quantified an array of mortality and
morbidity effects using the BenMAP-CE tool (U.S. EPA 2018), which has been used in recent
RIAs. As compared to the 2012 PM NAAQS RIA (U.S. EPA, 2012), the Agency applied
concentration-response relationships from newer epidemiologic studies, assessed a wider
array of human health endpoints and updated other economic and demographic input
parameters. Each of these updates is fully described in Chapter 5, the benefits analysis
approach and results chapter. Unquantified health benefits, welfare benefits, and climate
benefits are also discussed in Chapter 5.
1.3.4 Welfare Benefits of Meeting the Primary and Secondary Standards
Even though the primary standards are designed to protect against adverse effects
to human health, the emissions reductions would have welfare benefits in addition to the
direct health benefits. The term welfare benefits covers both environmental and societal
benefits of reducing pollution. Welfare benefits of the primary PM standard include
reduced vegetation effects resulting from PM exposure, reduced ecological effects from
particulate matter deposition and from nitrogen emissions, reduced climate effects, and
changes in visibility. This RIA does not assess welfare effects quantitatively; this is
discussed further in Chapter 5.
1.4 Organization of the Regulatory Impact Analysis
This RIA is organized into the following remaining chapters:
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Chapter 2: Air Quality Modeling and Methods. The data, tools, and methods
used for the air quality modeling are described in this chapter, as well as the
post-processing techniques used to produce a number of air quality metrics
for input into the analysis of benefits and costs.
Chapter 3: Control Strategies and PM2.5Emissions Reductions. The chapter
presents the hypothetical control strategies and estimated emissions
reductions in 2032 after applying the control strategies.
Chapter 4: Engineering Cost Analysis and Qualitative Discussion of Social Costs.
The chapter summarizes the methods, tools, and data used to estimate the
engineering costs of the alternative standard levels analyzed. The chapter
also provides a qualitative discussion of social costs.
Chapter 5: Benefits Analysis Approach and Results. The chapter quantifies the
estimated health-related benefits of the PM-related air quality improvements
associated with the control strategies for the proposed and alternative
standard levels analyzed. The chapter also presents qualitative discussions of
welfare benefits and climate benefits.
Chapter 6: Environmental Justice. This chapter includes an assessment of
environmental justice impacts associated with the control strategies for the
proposed and alternative standard levels analyzed.
Chapter 7: Labor Impacts. This chapter provides a qualitative discussion of
potential labor impacts.
Chapter 8: Comparison of Benefits and Costs. The chapter compares estimates
of the benefits with costs and summarizes the net benefits of the proposed
and alternative standard levels analyzed.
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1.5 References
U.S. EPA (2011). Regulatory Impact Analysis for the Final Mercury and Air Toxics
Standards. Research Triangle Park, NC. U.S. Environmental Protection Agency, Office of
Air Quality Planning and Standards, Health and Environmental Impact Division. U.S.
EPA. EPA-452/R-11-011. December 2011. Available at:
https://www.epa.gOv/sites/default/files/2020-07/documents/utilities_ria_final-
mats_2011-12.pdf.
U.S. EPA (2012). Regulatory Impact Analysis for the Final Revisions to the National
Ambient Air Quality Standards for Particulate Matter. Research Triangle Park, NC. U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, Health
and Environmental Impact Division. U.S. EPA. EPA-452/R-12-005. December 2012.
Available at: https://www3.epa.gov/ttnecasl/regdata/RIAs/finalria.pdf.
U.S. EPA (2014). Control of Air Pollution from Motor Vehicles: Tier 3 Motor Vehicle
Emission and Fuel Standards Final Rule Regulatory Impact Analysis. U.S. Environmental
Protection Agency, Office Transportation and Air Quality. EPA-420/R-14-005. March
2014. Available at:
https://nepis.epa.gov/Exe/ZyPDF.cgi/P100ISWM.PDF?Dockey=P100ISWM.PDF.
U.S. EPA (2015). Regulatory Impact Analysis for the Final Standards of Performance for
Greenhouse Gas Emissions from New, Modified, and Reconstructed Stationary Sources:
Electric Utility Generating Units. Research Triangle Park, NC. U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards, Health and
Environmental Impact Division. U.S. EPA. EPA-452/R-15-005. August 2015.
https://www.epa.gov/sites/default/files/2020-07/documents/utilities_ria_final-nsps-
egus_2015-08.pdf.
U.S. EPA., U.S. DOT (2016). Greenhouse Gas Emissions and Fuel Efficiency Standards for
Medium- and Heavy-Duty Engines and Vehicles - Phase 2 Regulatory Impact Analysis.
U.S. Environmental Protection Agency, Office Transportation and Air Quality. U.S.
Department of Transportation, National Highway Traffic Safety Administration. EPA-
420/R-16-900. August 2016. Available at:
https://nepis.epa.gov/Exe/ZyPDF.cgi/P100P7NS.PDF?Dockey=P100P7NS.PDF
U.S. EPA (2018). Environmental Benefits Mapping and Analysis Program - Community
Edition User's Manual. Office of Air Quality Planning and Standards. Research Triangle
Park, NC. U.S. EPA. Available at: https://www.epa.gov/sites/production/files/2015-
04/documents/benmap-ce_user_manual_march_2015.pdf.
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U.S. EPA (2019). Additional Methods, Determinations, and Analyses to Modify Air Quality
Data Beyond Exceptional Events. U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards, Research Triangle Park, NC, EPA-457/B-19-002.
Available: https://www.epa.gov/sites/default/files/2019-
04/documents/clarification_memo_on_data_modification_methods.pdf
U.S. EPA., U.S. DOT (2020). Final Regulatory Impact Analysis, The Safer Affordable Fuel-
Efficient (SAFE) Vehicles Rule for Model Year 2021 - 2026 Passenger Cars and Light
Trucks. U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, Health and Environmental Impact Division. U.S. Department of
Transportation, National Highway Traffic Safety Administration. March 2020. Available
at:
https://www.nhtsa.gov/sites/nhtsa.gov/files/documents/final_safe_fria_web_version_
200701.pdf
U.S. EPA (2021). Regulatory Impact Analysis for the Final Revisions Revised Cross-State Air
Pollution Rule (CSAPR) Update for the 2008 Ozone NAAQS. Research Triangle Park, NC.
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards,
Health and Environmental Impact Division. U.S. EPA. EPA-452/R-21-002. March 2021.
Available at: https://www.epa.gov/sites/default/files/2021-
03/documents/revised_csapr_update_ria_final.pdf.
U.S. EPA (2022a). Policy Assessment for the Reconsideration of the National Ambient Air
Quality Standards for Particulate Matter. Office of Air Quality Planning and Standards,
Health and Environmental Impacts Division. Research Triangle Park, NC. U.S. EPA. EPA-
452/R-22-004. May 2022. Available at: https://www.epa.gov/naaqs/particulate-
matter-pm-standards-policy-assessments-current-review-O.
U.S. EPA (2022b). Technical Support Document (TSD): Preparation of Emissions
Inventories for the 2016v2 North American Emissions Modeling Platform. Research
Triangle Park, NC. Office of Air Quality Planning and Standards, Air Quality Assessment
Division. U.S. EPA. EPA-452/B-22-001. February 2022. Available at:
https://www.epa.gov/system/files/documents/2022-
02/2016v2_emismod_tsd_february2022.pdf.
U.S. 0MB (2003). Circular A-4, September 17, 2003, Available at:
https: //www. whitehouse.gov/wp-
content/uploads/legacy_drupal_files / omb / circulars/A4/a-4.pdf.
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CHAPTER 2: AIR QUALITY MODELING AND METHODS
Overview
To evaluate the incremental costs and benefits of meeting the alternative PM2.5
standard levels relative to meeting the existing standards, models were used to predict
PM2.5 concentrations and emissions associated with the standard levels. Air quality was
simulated using a 2016-based modeling platform with the Community Multiscale Air
Quality (CMAQ) model. The modeling platform paired a 2016 CMAQ simulation with a
corresponding CMAQ simulation with emissions representative of 2032 that reflect effects
of finalized rules and other factors.
Air quality ratios, which relate a change in PM2.5 design values (DVs) to a change in
emissions, were used to estimate the emission reductions needed to meet the existing and
alternative NAAQS in areas projected to exceed the standards in 2032. These emission
estimates are used in identifying controls and associated costs of meeting the alternative
standard levels relative to meeting the existing standards. A PM2.5 concentration field was
developed using the 2032 CMAQ modeling and was adjusted according to the required
change in PM2.5 concentrations to create PM2.5 fields associated with meeting standard
levels. These PM2.5 concentration fields are used in calculating the health benefits
associated with meeting the standard levels.
The overall steps in the process are as follows:
Step 1. Project annual and 24-hour PM2.5 DVs to 2032 using a CMAQ simulation for 2016
and a corresponding CMAQ simulation with emissions representative of 2032
that reflects effects of finalized rules and other factors.
Step 2. Develop air quality ratios that relate a change in PM2.5 DV to a change in
emissions for use in estimating the emissions reductions needed to just meet the
existing and alternative NAAQS. The air quality ratios are developed using CMAQ
sensitivity modeling with reductions in anthropogenic emissions in select
counties.
Step 3. Using the air quality ratios from Step 2, estimate the emission reductions beyond
the 2032 modeling case that are needed to meet the existing standards and
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adjust PM2.5 DVs accordingly. The resulting PM2.5 DVs define the 12/35 analytical
baseline that is used as the reference case in estimating the incremental costs
and benefits of meeting alternative standard levels relative to existing standards.
Note that emission reductions applied to meet the existing standards do not
contribute to incremental costs and benefits in the Regulatory Impact Analysis
(RIA).
Step 4. Using the air quality ratios from Step 2, estimate the primary PM2.5 emission
reductions needed to meet the alternative standard levels beyond the 12/35
analytical baseline. These emission reduction estimates are used in developing
controls to meet the alternative standard levels.
Step 5. Develop a gridded national PM2.5 concentration field associated with the 2032
case by fusing the 2032 CMAQ modeling with projected monitor concentrations.
Adjust the 2032 concentration field according to the changes in PM2.5 DVs
needed to meet standard levels to create PM2.5 fields associated with each
standard level. These PM2.5 concentration fields are used in calculating the
health benefits associated with meeting alternative standard levels.
In the remainder of this chapter, contextual information on PM2.5 and its
characteristics in the U.S. is first provided in Section 2.1. The projection of air quality from
2016 to 2032 is then described in Section 2.2. In Section 2.3, the development of air quality
ratios and their application to estimating emission reductions is described. In Section 2.4,
the air quality challenges in select areas are described in terms of highly local influences on
PM2.5 concentrations. Finally, the development of the PM2.5 concentration fields associated
with meeting the existing and alternative standards is described in Section 2.5.
2.1 PM2.5 Characteristics
2.1.1 PM2.5 Size and Composition
As described in the Integrated Science Assessment (US EPA, 2019a) and Policy
Assessment (US EPA, 2022a), PM (particulate matter) refers to the mass concentration of
suspended particles in the atmosphere. Atmospheric particles range in size from less than
1 nanometer (10-9 meter) to over 100 micrometers (|im, or 10"6 meter) in diameter. For
reference, a typical strand of human hair is 70 [im in diameter and a grain of salt is about
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100 [im. Atmospheric particles are often classified into size ranges associated with the
three distinct modes evident in measured ambient particle size distributions. The size
ranges include ultrafine particles (<0.1 [im), accumulation mode or fine particles (0.1 to ~3
[im), and coarse particles (>1 [im). For regulatory purposes, fine particles are measured as
PM2.5, which refers to the total mass concentration of particles with aerodynamic diameter
less than 2.5 [im.
PM is made up of many different chemical components. The major components
include carbonaceous matter (elemental and organic carbon) and inorganic species such as
sulfate, nitrate, ammonium, and crustal species. PM includes solid and liquid particles as
well as multiphase particles (e.g., particles with a solid core surrounded by an inorganic
aqueous solution with an organic coating). The phase state and composition of an
atmospheric particle can vary with atmospheric conditions. For example, the aqueous
phase of a particle may effloresce (i.e., crystallize) when the atmospheric relative humidity
falls below a threshold. Similarly, as gas-phase concentrations and meteorological
conditions (e.g., temperature and relative humidity) change, chemical species can condense
and evaporate from particles to maintain or approach equilibrium with their gas-phase
counterparts (Seinfeld and Pandis, 2016).
PM can be directly emitted into the atmosphere or formed in the atmosphere
through chemical and physical processes. PM that is directly emitted into the atmosphere
by sources is referred to as primary PM. Elemental carbon and crustal species are examples
of primary PM components. PM that is formed in situ through atmospheric processes is
referred to as secondary PM. Secondary PM is formed through pathways including new
particle nucleation, condensation and reactive uptake of gas-phase species, and cloud and
fog evaporation (Seinfeld and Pandis, 2016). Nucleation of new particles occurs when
molecular clusters formed from gas-phase species grow into stable particles. Condensation
of atmospheric gases onto preexisting particles occurs when gas-phase concentrations
exceed the equilibrium vapor concentrations of the particle constituents. PM formation
from cloud and fog processes occurs when semi- and non-volatile chemical species formed
via aqueous chemistry in cloud and fog remain suspended in ambient particles following
cloud/fog evaporation.
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Gaseous SO2 emissions lead to PM2.5 formation following SO2 oxidation to sulfuric
acid in the gas and aqueous phases (Seinfeld and Pandis, 2016). Sulfuric acid is essentially
non-volatile under atmospheric conditions and leads to PM2.5 sulfate formation by
contributing to new particle formation, condensation onto preexisting particles, and
remaining in particles following cloud/fog evaporation. Enhanced particle acidity due to
PM2.5 sulfate formation reduces the equilibrium vapor concentration of ammonia (the
primary atmospheric base) and promotes condensation of ammonia onto particles, thereby
forming PM2.5 ammonium. PM2.5 sulfate and associated water and acidity also influence
chemical pathways for the formation of secondary organic aerosol (SOA).
Gaseous NOx emissions lead to PM2.5 formation following NOx oxidation to nitric
acid, which is semi-volatile under atmospheric conditions (Seinfeld and Pandis, 2016).
Condensation of nitric acid onto particles tends to be favorable under cool, humid
conditions with abundant ammonia, and results in PM2.5 nitrate formation. Due to effects of
nitric acid on particle acidity, ammonia often co-condenses with nitric acid to yield PM2.5
ammonium. NOx emissions also influence secondary PM concentrations by modulating
many atmospheric oxidation processes and by contributing to the production of organic
nitrates. Monoterpene nitrates and isoprene nitrates are examples of PM2.5 species that can
be formed from products of anthropogenic NOx emissions and biogenic volatile organic
compound (VOC) emissions. SOA formation occurs following the oxidation ofVOC
emissions in the atmosphere. SOA formation is an active area of research and involves
myriad species and reactions occurring in the gas, particle, and aqueous phases. Gaseous
ammonia emissions can influence PM concentrations by affecting cloud and aerosol acidity
in addition to condensing on particles to form PM2.5 ammonium.
The emission sources of primary PM2.5 and the gaseous precursors of PM2.5 have
recently been summarized in the PM NAAQS Policy Assessment (USEPA, 2022a). EGUs
make up the largest emissions source sector for SO2. The largest NOx emissions sectors
include mobile sources (on-road and non-road) and EGUs. Ammonia emissions are greatest
from the agricultural sector (fertilizer and livestock waste) and from fires. VOC emissions
are largest from mobile sources, industrial processes, fires, and biogenic sources. Primary
PM2.5 emissions are largest from fires, fugitive dust (paved/unpaved road dust and
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construction dust), and area sources (e.g., residential wood combustion). Fires are an
important source of particulate organic matter. Note that some PM2.5 components (e.g.,
elemental carbon and crustal species) occur due to direct emissions alone while other PM2.5
components (e.g., organic carbon and sulfate) occur due to a combination of direct
emissions and secondary formation in the atmosphere.
2.1.2 PM2.5 Regional Characteristics
PM2.5 concentrations vary in magnitude and composition over the U.S. with distinct
regional and seasonal features. The characteristics of PM2.5 concentrations in the U.S. have
recently been summarized in the Integrated Science Assessment (USEPA, 2019a), and the
spatial distribution of PM2.5 over the U.S. is shown in Figure 2-1 based on a hybrid satellite
modeling method (van Donkelaar et al., 2021). In the Eastern U.S., organic carbon and
sulfate have the highest contribution to total PM2.5 concentrations in most locations. In the
Upper Midwest and Ohio Valley, nitrate can also be an important contributor to PM2.5, due
to the cool, humid conditions in winter and influence of ammonia that promotes
ammonium nitrate formation. In the Southeastern U.S., organic carbon concentrations are
relatively high due to the abundance of biogenic VOC emissions that contribute to SOA
formation following oxidation in the presence of anthropogenic emissions. Areas of
relatively high PM2.5 concentrations within the Eastern U.S. are associated with urban
centers.
Longitude
Figure 2-1 Annual Average PM2.5 Concentrations over the U.S. in 2019 Based on
the Hybrid Satellite Modeling Approach of van Donkelaar et al. (2021)
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The Western U.S. is characterized by some of the lowest and highest PM2.5
concentrations in the country, with relatively sharp spatial gradients in PM2.5 compared to
the east. The complex terrain of the Western U.S. has an important influence on air
pollution processes as does the relative abundance of wildfires (and prescribed burning).
In the Northwest, meteorological temperature inversions often occur in small mountain
valleys in winter and trap pollution emissions in a shallow atmospheric layer at the surface.
Emissions from home heating with residential wood combustion can build up in the surface
layer and produce episodically high PM2.5 concentrations in winter. Elevated wintertime
PM2.5 in these mountain valleys can approach or sometimes exceed the 24-hour PM2.5
standard, which is based on a 98th percentile form.
In large western air basins (e.g., San Joaquin Valley, CA; South Coast Air Basin, CA;
and Salt Lake Valley, UT), emission sources are more diverse than in the small mountain
valleys and include NOx emissions from urban centers and ammonia from agriculture.
Meteorological conditions are also more complex than in the smaller valleys and can
include a persistent aloft temperature inversion from high-pressure-driven air subsidence
in addition to a near-surface temperature inversion from nighttime radiative cooling. The
near-surface inversion has the effect of concentrating primary PM2.5 emissions near the
ground, whereas the aloft inversion caps the nighttime residual air layer, in which NOx is
converted to nitrate through heterogeneous aerosol chemistry. In the morning, when the
near-surface inversion breaks and the surface mixed layer grows due to surface heating,
the PM2.5 nitrate and ammonium formed overnight in the residual layer are entrained to
the surface. This entrainment has the effect of diluting primary PM2.5 concentrations near
the surface and enhancing surface concentrations of secondary PM2.5. PM2.5 concentrations
in the South Coast Air Basin are also affected by the land-sea breeze circulation and a semi-
permanent high-pressure cell. Due to the large populations, diverse emission sources, and
terrain-driven meteorological features, the San Joaquin Valley and South Coast Air Basin
experience elevated annual-average PM2.5 concentrations as well as short-term PM2.5
enhancements. These characteristics can create challenges for meeting both the annual and
24-hour PM2.5 standards.
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PM2.5 concentrations in the Western U.S. are also strongly influenced by emissions
from wildfires, which are relatively common in summer but increasingly occur year-round.
In the Southwest, dust emission sources are prevalent, and windblown dust makes
substantial contributions to PM2.5 concentrations under dry, windy conditions. Organic
carbon is often the largest PM2.5 contributor in the west due to the influence of combustion
sources such as wildfire and residential wood combustion. Crustal species are also
important contributors in dust-prone areas, and ammonium nitrate is a major PM2.5
component in large air basins during meteorological stagnation periods in fall and winter.
Along the border with Mexico, western areas also experience important cross-border
transport contributions to PM2.5 (e.g., Calexico, CA experiences contributions from the
much the larger city of Mexicali, MX, which is in the same airshed just across the border).
2.1.3 PM2.5 Trends
Over the last several decades, PM2.5 concentrations have decreased on average over
the U.S. (Figure 2-2). As described in the recent PM NAAQS Policy Assessment (USEPA,
2022a), the reductions in PM2.5 concentrations correspond to the reductions in PM2.5
precursor emissions illustrated in Figure 2-3. Among the PM2.5 precursors (i.e., SO2, NOx,
VOC, and ammonia), the largest emission reductions occurred for SO2 and NOx. SO2
emissions decreased by 84% between 2002 and 2017, and NOx emissions decreased by
60%. Reductions in SO2 emissions were relatively large from stationary sources such as
EGUs in the Eastern U.S. NOx emission reductions were driven by reduced emissions from
mobile sources and EGUs. Compared with SO2 and NOx, emissions of primary PM2.5 and
ammonia have been relatively flat in recent decades. The small changes in primary PM2.5
emissions in Figure 2-3 are likely due to changes in emission estimation methods for
source sectors over time. Wildfire emissions are not included in the data for Figure 2-3, but
an upward trend in PM2.5 emissions is evident in estimates generated for National Emission
Inventory years (i.e., 2005, 2008, 2011, 2014, and 2017).1 Studies have also predicted that
climate change presents increased potential for very large fires in the contiguous U.S. in the
future (e.g., Barbero et al., 2015).
1 https://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data
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30,
m 25-
TO
3 20-
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
22222222222222222222
00000000000000000000
00000000001111111111
012345678901 23456789
Figure 2-2 Seasonally Weighted Annual Average PM2.5 Concentrations in the U.S.
from 2000 to 2019 (406 sites)
Note: The white line indicates the mean concentration while the gray shading denotes the
10th and 90th percentile concentrations.
30000
Year
—IMH3 NOx -»-PM2.5 -«-PM10 -»-S02 -«-VOCs
Figure 2-3 National Emission Trends of PM2.5, PM10, and Precursor Gases from
1990 to 2017
Note: Data do not include wildfire emissions.
As described in the PM NAAQS Policy Assessment (USEPA, 2022a), PM2.5 precursor
emission reductions have altered the seasonal variation in PM2.5 concentrations over the
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U.S. Through 2008, the peak in the national average PM2.5 concentration occurred during
summer, largely due to sulfate formation from summertime increases in EGU SO2 emissions
in the Eastern U.S. and wildfires in the West. However, starting in 2009, the summertime
peaks in PM2.5 concentrations have been smaller than those in winter as PM2.5 sulfate
concentrations have decreased (Chan et al., 2018). The decrease in sulfate in the Eastern
U.S. has increased the relative contribution of organic carbon and sources of primary PM2.5,
whose emissions have remained flat as SO2 emissions have decreased. Primary PM2.5
sources in urban centers contribute to the "urban increment" of consistently higher PM2.5
concentrations in urban than surrounding areas (Chan et al., 2018).
To explore how emission trends may persist into the future, models are applied to
project emission inventories accounting for expected future emission changes from
finalized rules and other factors. Air quality models are then used to simulate pollutant
concentrations under conditions of the projected future emissions. For the purposes of the
RIA, model projections from 2016 to 2032 were developed for air quality analyses as
described in section 2.2. As shown in Figure 2-4, the trends in NOx, SO2, and primary PM2.5
emissions from the recent past (Figure 2-3) are projected to continue into the near future.
From 2016 to 2032, anthropogenic NOx emissions are projected to decrease by 3.8 million
tons (40%), with the greatest reductions from mobile-source sectors (nonroad and onroad)
and EGUs. SO2 emissions are projected to decrease by 1 million tons (38%), with the
greatest reductions from the EGU sector. For primary PM2.5, emissions are relatively flat
from 2016 to 2032, with a decrease of 100k tons (3%) mainly due to reductions from
mobile sources and EGUs. Primary PM2.5 emissions from the largest emitting sectors (e.g.,
dust, agricultural and prescribed fires, residential wood combustion, and areas sources)
are essentially constant or slightly increasing (e.g., dust) (Figure 2-4).2 This projected
behavior is consistent with past trends, in which NOx and SO2 emissions declined steadily
while primary PM2.5 emissions were relatively constant (Figure 2-3).
2 Prescribed burning emissions were held constant at 2016 levels in the model projections, although these
emissions could potentially change in the future.
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NOx
3000-
2000-
1000-
CC
a)
>,1500-
C
O
2
AgPrFire NonPt Nonroad Onroad EGU O&G
S02
Other NonlPM RWC
1000-
U)
c 500-
o
U)
52 o-
2016
2032
E
LU
AgPrFire NonPt Nonroad Onroad EGU O&G
PM25
Other NonlPM RWC
Dust AgPrFire NonPt Nonroad Onroad EGU O&G Other NonlPM RWC
Figure 2-4 Annual Anthropogenic Source Sector Emission Totals (1000 tons per
year) for NOx, SO2, and PM2.5 for 2016 and 2032
Note that AgPrFire: agricultural and prescribed fire; Nonpt: non-point area sources; O&G: oil and
gas; Other: airports, commercial marine vehicles, rail, and solvents; NonlPM: remaining non-EGU
point sources; RWC: residential wood combustion.
As mentioned above, spatial distributions of PM2.5 concentrations in the U.S. are
characterized by an "urban increment" of consistently higher PM2.5 concentrations over
urban than surrounding areas. Monitored concentrations are highest in urban areas and
relatively low in rural areas. Conceptually, PM2.5 concentrations in urban areas can be
viewed as the superposition of the urban increment and the contributions from regional
and natural background sources. The decreases in anthropogenic SO2 and NOx emissions in
recent decades have reduced regional background concentrations and increased the
relative importance of the urban increment. The projections of additional large reductions
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in SO2 and NOx emissions in the 2032 case further motivates the need for control of local
primary PM2.5 sources to address the highest PM2.5 concentrations in urban areas.
In Figure 2-5, PM2.5 concentrations are shown over four urban areas in the Eastern
U.S. based on the 2032 modeling case described in Section 2.2. A common feature of these
diverse locations is the relatively high PM2.5 concentrations over the urban area and lower
concentrations just outside of the urban core. PM2.5 concentrations in the urban core of
these Eastern U.S. areas exceed alternative standards levels considered in the RIA, whereas
concentrations surrounding the urban core are below the alternative standard levels. In the
illustrative control strategy analysis of the RIA, the urban exceedances are addressed by
focusing on primary PM2.5 emission controls in the local county. This approach is consistent
with the exceedances being driven by the urban PM2.5 increment, the relatively high
responsiveness of PM2.5 concentrations to primary PM2.5 emission reductions (e.g.,
Appendix 2A.5), and the reductions in regional PM2.5 concentrations from the large SO2 and
NOx emission reductions in recent decades and in the 2032 projection. Patterns may vary
in the Western U.S. where the spatial extent of the PM2.5 increment may be influenced by
complex terrain that defines distinct air basins.
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ug/m3
ug/m3
I
34.00
33.75
o 33.50
T5
=3
33.25
33.00
-87.5 -87.0 -86.5
Longitude
ug/m3
ug/m3
-85.0 -84.5 -84.0
Longitude
Figure 2-5 Gridded PM2.5 Concentrations over Selected Urban Areas Based on the
2032 Modeling Case Described Below with the Enhanced Voronoi
Neighbor Averaging Approach
Atlanta
2.2 Modeling PM2.5 in the Future
To evaluate the incremental costs and benefits of meeting the alternative PM2.5
standard levels proposed in this RIA relative to meeting the existing standards, models
were used to predict PM2.5 concentrations associated with emissions representative of a
2032 future year to inform subsequent analyses. The projections were performed using a
20'16-based modeling platform with the Community Multiscale Air Quality (CMAQ) model
-96.5 -96.0 -95.5 -95.0 -94.5
Longitude
41.5
41.0
-88.5 -88.0 -87.5 -87.0
Longitude
Chicago
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(www.epa.gov/cmaq). The modeling platform paired a 2016 CMAQ simulation with a
corresponding CMAQ simulation based on emissions representative of 2032. The 2032
emission projections account for numerous factors including the effects of finalized rules.
This modeling platform was chosen because it represents the most recent, complete set of
emissions information currently available for national-scale modeling. The approach used
for projecting future-year air quality with the platform is described in this section.
2.2.1 Air Quality Modeling Platform
To project air quality to the future, the CMAQ model was applied to simulate air
quality over the U.S. during 2016 and for a case with emissions representative of 2032.
Other than the differences in emissions inventories for the 2016 and 2032 CMAQ
simulations, all other model inputs specified for the 2016 base year remained unchanged in
the 2032 modeling case. Inputs for CMAQ simulations include files with emissions,
meteorology, and initial and boundary condition data.
2.2.1.1 Model Configuration
CMAQ is a three-dimensional grid-based Eulerian air quality model designed to
estimate the formation and fate of oxidant precursors, primary and secondary PM2.5
concentrations, and deposition over regional spatial scales (e.g., over the contiguous U.S.)
(Appel et al., 2021, Appel et al., 2018, Appel et al., 2017). CMAQ simulates the key processes
(e.g., emissions, transport, chemistry, and deposition) that affect primary (directly emitted)
and secondary (formed by atmospheric processes) PM2.5 using state-of-the-science process
parameterizations and input data for emissions, meteorology, and initial and boundary
conditions. CMAQ's representation of the chemical and physical mechanisms that govern
the formation and fate of air pollution enable simulations of the impacts of emission
controls on PM2.5 concentrations.
CMAQ version 5.3.2 (www.epa.gov/cmaq) was used to simulate air quality for 2016
to provide a reference simulation for the 2032 air quality projection. The geographic
extents of the outer and inner air quality modeling domains are shown in Figure 2-6. The
outer domain covers the 48 contiguous states along with most of Canada and Mexico using
a horizontal resolution of 36 x 36 km. Air quality modeling for the 36-km domain was used
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to provide chemical boundary conditions for the simulation on the nested 12-km domain
used in air quality analyses in the RIA.
Gas-phase chemistry in the CMAQ simulations was based on the Carbon Bond 2006
mechanism (CB6r3) (Emery et al., 2015), and deposition was modeled with the M3DRY
parameterization. Aerosol processes were parameterized with the AER07 module using
ISORROPIAII for inorganic aerosol thermodynamics (Fountoukis and Nenes, 2007) and the
non-volatile treatment for primary organic aerosol (Appel et al., 2017, Simon and Bhave,
2012). Emissions of biogenic compounds were modeled with the Biogenic Emission
Inventory System (BEIS) (Bash et al., 2016). Anthropogenic emissions were based on 2016
version 2 emissions modeling platform (USEPA, 2022b), which included emissions for
2016 and the projected 2032 case. Meteorological data were based on a 2016 simulation
with version 3.8 of the Weather Research Forecasting (WRF) model (Skamarock et al.,
2008). The meteorological fields include hourly-varying horizontal wind components (i.e.,
speed and direction), temperature, moisture, vertical diffusion rates, and rainfall rates for
each grid cell in each vertical layer. Additional details on the model configuration are
available in section 2A.1.1 of Appendix 2A.
Figure 2-6 Map of the Outer 36US3 (36 x 36 km Horizontal Resolution) and Inner
12US2 (12 x 12 km Horizontal Resolution) Modeling Domains
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2.2.1.2 Emission Inventory
The future-year emission inventory is projected from the 2016 version 2 emissions
modeling platform. The projected emission case is labeled 2032, although the emission
projections are based on a combination of projection years3. The development of the 2016
base-year inventory, the projection methodology, and the controls applied to create the
projected inventory are described in detail in the emissions Technical Support Document
(TSD): Preparation of Emissions Inventories for the 2016v2 North American Emissions
Modeling Platform (USEPA, 2022b). The types of sources included in the emission
inventory include stationary point sources such as EGUs and non-EGUs; non-point
emissions sources including those from oil and gas production and distribution,
agriculture, residential wood combustion, fugitive dust, and residential and commercial
heating and cooking; mobile source emissions from onroad and nonroad vehicles, aircraft,
commercial marine vessels, and locomotives; wild, prescribed, and agricultural fires; and
biogenic emissions from vegetation and soils.4
The EGU emissions were developed using the Summer 2021 version of the
Integrated Planning Model (IPM) (USEPA, 2021). The IPM is a multiregional, dynamic,
deterministic linear programming model of the U.S. electric power sector. The EGU
projected inventory represents demand growth, fuel resource availability, generating
technology cost and performance, and other economic factors affecting power sector
behavior. It also reflects environmental rules and regulations, consent decrees and
settlements, plant closures, and newly built units for the calendar year 2030. In this
analysis, the projected EGU emissions include the 2021 Revised Cross-State Air Pollution
Rule Update (RCU), the 2016 Standards of Performance for Greenhouse Gas Emissions
from New, Modified, and Reconstructed Stationary Sources, the Mercury and Air Toxics
3 2032: non-road, onroad, airports, non-EGU point (except for biorefineries / ethanol plants), paved-road
dust, oil and gas (except in WRAP states), residential wood combustion (except held constant at 2016 levels
in CA, OR, and WA), and solvents; 2030: EGUs, US commercial marine vehicles, rail, and livestock; 2028:
most Canada and Mexico emissions; 2016: fertilizer, fires, biogenics, and fugitive dust (other than paved
road)
4 Emissions reductions from the Revised 2023 and Later Model Year Light-Duty Vehicle Greenhouse Gas
Emissions Standards (2021) and the Federal Implementation Plan for Managing Emissions from Oil and
Natural Gas Sources on Indian Country Lands within the Uintah and Ouray Indian Reservation in Utah
(2022) are not reflected in the baseline for this analysis. Given the focus of these rules, any potential impacts
are likely to be small. Updated air quality modeling will be conducted for a final PM NAAQS RIA.
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Rule (MATS) finalized in 2011, and other finalized rules. Full documentation and results of
the Summer 2021 Reference Case for EGUs are available at https://www.epa.gov/power-
sector-modeling/results-using-epas-power-sector-modeling-platform-v6-summer-2021-
reference.
Regulations for non-EGU point sources and non-point sources reflected in the
inventories include:
• New Source Performance Standards (NSPS) for oil and natural gas sources
(2016), process heaters (2013), natural gas turbines (2012), and
reciprocating internal combustion engines;
• NSPS for residential wood combustion (2015);
• Fuel sulfur rules in mid-Atlantic and northeast states (current through
2019);
• NSPS and Emission Guidelines for Commercial and Industrial Solid Waste
Incineration (CISWI) from March 2011;
• NSPS Subpart JA for Standards of Performance for Petroleum Refineries from
June 2008;
• Specific consent decrees; and
• Ozone Transport Commission controls for Portable Fuel Containers,
consumer products, architectural and industrial maintenance coatings, and
various other solvents.
Note that the Boiler MACT is assumed to be fully implemented by 2016 except for North
Carolina, in which it was fully implemented by 2017. Known closures are also implemented
for non-EGU point sources.
Onroad and nonroad mobile source emissions were developed using the Motor
Vehicle Emission Simulator version 3 (MOVES3). The SMOKE-MOVES emissions modeling
framework was used that leverages MOVES-generated emission factors, county and SCC-
specific activity data, and hourly meteorological data. MOVES3 was run in emission rate
mode to create emission factor tables for the 2032 future modeling year for all
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representative counties and fuel months. These emissions represent the effects the Safer
Affordable Fuel Efficient (SAFE) Vehicles Final Rule for Model Years 2021-2026 (March
2020); Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium-
and Heavy-Duty Engines and Vehicles - Phase 2 (October 2016); Tier 3 Vehicle Emission
and Fuel Standards Program (March 2014) and other finalized rules. A full discussion of the
future year base inventory is provided in USEPA (2022b). Nonroad emissions rules related
to nonroad spark-ignition engines, equipment, and vessels from October 2008 are
reflected.
Emissions for commercial marine vessels and locomotive engines reflect the rules
finalized in 2010 and 2008:
• Growth and control from Locomotives and Marine Compression-Ignition
Engines Less than 30 Liters per Cylinder: March 2008
• Category 3 marine diesel engines Clean Air Act and International Maritime
Organization standards: April 2010
• Growth and control from Locomotives and Marine Compression-Ignition
Engines Less than 30 Liters per Cylinder: March 2008
2.2.1.3 Model Evaluation
An operational model performance evaluation for PM2.5 and its speciated
components (e.g., sulfate, nitrate, elemental carbon, and organic carbon) was performed to
estimate the ability of the CMAQ modeling system to replicate the 2016 base year
concentrations. This evaluation includes statistical assessments of model predictions
versus observations from national monitoring networks paired in time and space. Details
on the evaluation methodology and the calculation of performance statistics are provided
in section 2A.1.2 of Appendix 2A. Overall, the performance statistics for PM2.5 and its
components from the CMAQ 2016 simulation are within or close to the ranges found in
other recent applications. These model performance results provide confidence that our
use of the 2016 modeling platform is a scientifically credible approach for assessing PM2.5
concentrations for the purposes of the RIA.
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2.2.2 Future-Year PM2.5 Design Values
To evaluate the incremental costs and benefits associated with meeting alternative
standard levels relative to the existing standard, PM2.5 DVs were first projected to 2032
accounting for emission reductions expected from finalized rules. The air quality and
emission changes associated with meeting the existing and alternative standard levels
were then estimated as described below in Section 2.3. PM2.5 DVs were projected to 2032
using the air quality model results in a relative sense, as recommended by the EPA
modeling guidance (USEPA, 2018), by projecting monitoring data with relative response
factors (RRFs) developed from the 2016 and 2032 CMAQ modeling.
PM2.5 RRFs were calculated as the ratios of modeled PM2.5 species concentrations in
the future year (2032) to the base year (2016) for each PM2.5 component (i.e., sulfate,
nitrate, organic carbon, elemental carbon, crustal material, and ammonium). The 2032
PM2.5 DVs were calculated by applying the species-specific RRFs to ambient PM2.5 species
concentrations from the PM2.5 monitoring network. Observed PM2.5 concentrations were
disaggregated into species concentrations by applying the SANDWICH method (Frank,
2006) and through interpolation of PM2.5 species data from the Chemical Speciation
Network (CSN) and the Interagency Monitoring of Protected Visual Environments
(IMPROVE) monitoring network. The RRF method for projecting PM2.5 DVs was
implemented using EPA's Software for Modeled Attainment Test-Community Edition
(SMAT-CE) version 1.8 (USEPA, 2018, Wang et al., 2015). More details on the PM2.5
projection method using RRFs are provided in the user's guide for the predecessor to the
SMAT-CE software (Abt, 2014).
Ambient PM2.5 measurements from the 2014-2018 period centered on the 2016
CMAQ modeling period were used in projecting PM2.5 DVs. PM2.5 species measurements
from the IMPROVE and CSN networks during 2015-2017 were used to disaggregate the
measured total PM2.5 concentrations into components. In addition to exclusion of EPA-
concurred exceptional events, limited exclusion of wildfire and fireworks influence on
PM2.5 concentrations was applied to the 2014-2018 PM2.5 monitoring data. Monitoring data
were evaluated (i.e., screened) for potential wildfire and fireworks influence because PM2.5
concentrations may be influenced by atypical, extreme, or unrepresentative events such as
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wildfires or fireworks that may be appropriate for exclusion as described in EPA's
memorandum Additional Methods, Determinations, and Analyses to Modify Air Quality Data
Beyond Exceptional Events (USEPA, 2019b). The steps in implementing the limited
screening of major wildfire and fireworks influence on PM2.5 concentrations are as follows.
Step 1. An extreme-concentration cutoff of 61 [ig nr3 was identified based the 99.9th
percentile value from all daily PM2.5 concentrations across all sites in the long-
term AQS observations (2002-2018).
Step 2. Specific states and months where wildfires frequently occur were screened for
instances of monitors exceeding the cutoff concentration. Potential wildfire
periods were identified as those with PM2.5 concentrations above the cutoff
concentration in June-October in CA, WA, OR, MT, ID, and CO.
Step 3. For potential wildfire periods, the presence of visible wildfire smoke was
examined using satellite imagery from NASA's Worldview platform
(https://worldview.earthdata.nasa.gov). Timeseries of PM2.5 concentrations at
individual sites were also examined to confirm that the PM2.5 enhancements are
temporally consistent with wildfire events.
Step 4. For wildfire periods confirmed by the satellite imagery and timeseries analysis,
PM2.5 concentrations above the cutoff concentration of 61 [ig nr3 occurring
during the identified wildfire episode window at impacted sites were excluded.
If the satellite imagery and timeseries analysis did not corroborate the wildfire
event, data from the period were retained.
Step 5. In addition to the screening criteria above, data for the Camp Fire in northern CA
during November 2018 and the Appalachian Fires in NC, TN, and GA during
November 2016 were evaluated for exclusion if concentrations exceeded the
extreme value threshold of 61 |Lxg nr3. These large fire episodes produced
obvious PM2.5 concentration impacts across multiple monitors and were clearly
evident in the satellite imagery.
Step 6. In addition to the limited exclusion of major wildfire influence, data were
evaluated to identify days for potential exclusion due to the influence of isolated
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fireworks events on PM2.5 concentrations. The 99.9th percentile concentration of
61 [ig nr3 was applied as the cutoff value across all sites for New Year's Eve and
the Fourth of July.
The excluded site-day combinations represent a small fraction (0.4%) of the total
site-day combinations for the flagged sites. Since the cutoff value (61 |Lxg nr3) is much
greater than the 24-hour and annual standard levels, wildfire contributions to PM2.5
concentrations above the standard levels likely persists in the data following screening.
Comprehensive identification and exclusion of such wildfire impacts would require
detailed analyses that are beyond the scope of this national assessment. More information
on the wildfire and fireworks screening are provided in section 2A.2.1 of Appendix 2A.
2.3 Calculating Emission Reductions for Meeting the Existing and Alternative
Standard Levels
To estimate the tons of emissions reductions needed to reach attainment of the
existing and proposed alternative standard levels, we calculated air quality ratios based on
how modeled concentrations changed with changes in emissions in CMAQ sensitivity
modeling. Air quality ratios represent an estimate of how the DVs at a monitor would
change in response to emissions reductions and have been used in prior PM NAAQS RIAs
(USEPA, 2012a, USEPA, 2012b). Air quality ratios have units of |Lxg nr3 per 1000 tons of
emissions. The remainder of this section describes the development of air quality ratios
and their application to estimating emission reductions for meeting the existing and
alternative standards.
2.3.1 Developing Air Quality Ratios
In the illustrative control strategy analysis in the RIA, the alternative standard level
exceedances are addressed by focusing on primary PM2.5 emission controls in the local
county. This approach is consistent with the exceedances generally being driven by the
urban PM2.5 increment, the relatively high responsiveness of PM2.5 concentrations to
primary PM2.5 emission reductions (e.g., Appendix 2A.5), and the reductions in regional
PM2.5 concentrations from the large SO2 and NOx emission reductions in recent decades
and in the 2032 projection (section 2.1.3). To develop air quality ratios that relate the
change in DV in a county to the change in primary PM2.5 emissions in that county, CMAQ
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sensitivity modeling was performed with reductions in primary PM2.5 emissions in selected
counties. The modeling was conducted using CMAQ version 5.2.1 for a 2028 modeling case
similar to that of recent regional haze modeling (USEPA, 2019c) due to the availability of
the 2028 (but not 2032) modeling at the time of the work. Since air quality ratios reflect
the sensitivity of air quality to emission changes (rather than absolute concentrations), the
air quality ratios based on the 2028 modeling are suitable for application to our 2032
modeling case.
To develop air quality ratios for primary PM2.5 emissions, we used the following
method:
Step 1. A CMAQ sensitivity simulation was conducted with 50% reductions in primary
PM2.5 emissions from anthropogenic sources in counties with annual 2028 DVs
greater than 8 |Lxg rrr3.
Step 2. The change in annual and 24-hour PM2.5 DVs at monitors in counties where
emission reductions were applied was calculated using projected DVs from the
2028 modeling with the SMAT-CE software.
Step 3. The change in DVs at individual monitors was divided by the change in
emissions in the respective county to determine the air quality ratio (|Lxg nr3 per
1000 tons) for the individual monitors.
Step 4. The responsiveness of air quality at a specific monitor location to primary PM2.5
emission reductions depends on several factors including the specific
meteorology and topography in an area and the nearness of the emissions
source to the monitor. As described in a previous PM NAAQS RIA (USEPA,
2012a), the strong local influence of changes in directly emitted PM2.5 on air
quality produces large variability in air quality ratios that can result in non-
representative values for general application. To address this issue,
representative air quality ratios for regions of the U.S. were developed from the
ratios at individual monitors. The five regions are illustrated in Figure 2-7. The
Northeast region was defined by combining the Upper Midwest, Ohio Valley, and
Northeast U.S. climate regions (Karl and Koss, 1984); the Southeast region was
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defined by combining the Southeast and South U.S. climate regions (Karl and
Koss, 1984); and California was separated into southern and northern regions as
done previously (USEPA, 2012a) due to differences in PM2.5 responsiveness in
those areas. For each region, representative air quality ratios were calculated as
the 75th percentile of air quality ratios for individual monitors within the region.
The 75th percentile was selected to avoid use of extreme values while accounting
for the relatively high responsiveness of the highest-DV monitors that are most
relevant to our application.
45°N-
40°N -
35°N-
30°N -
25°N-
120°W 110°W 100°W
90°W 80°W 70°W
Figure 2-7 Regional Groupings for Calculating Air Quality Ratios
The air quality ratios for primary PM2.5 emissions used in estimating the emission
reductions needed to meet standard levels at monitors in the five regions are shown in
Table 2-1. These data give an estimate of how PM2.5 DVs at a monitor would change if 1000
tons of primary PM2.5 emissions were reduced in the county in which the monitor is
located. Additional details on the development of the air quality ratios are available in
section 2A.3.1 of Appendix 2A.
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Table 2-1
Annual and 24-Hour Air Quality Ratios for Primary PM2.5 Emissions
Region Annual Air Quality Ratio 24-hour Air Quality Ratio
fug m ! per kton) (p.g m ! per kton)
Northeast 1.37 4.33
Southeast 1.22 3.51
West 2.14 8.70
Northern California 3.15 9.97
Southern California 1.18 2.56
The air quality ratios in Table 2-1 relate the change in DV in a county to a change in
emissions in that county. The ratios are developed for local spatial scales because
concentrations are most responsive to changes in local emissions. However, emission
controls may not always be identified in the local county, and emission reductions in
neighboring counties may sometimes be appropriate, such as in the Eastern U.S. where
counties are relatively small and terrain is relatively flat. To apply emission reductions in
the neighboring counties in the Eastern U.S., the responsiveness of annual PM2.5 DVs for
emission reductions within the county was compared to the responsiveness of DVs in the
neighboring counties using the 2028 sensitivity modeling. Annual DVs were estimated to
be 4 times more responsive on average for emission reductions in the county containing
the monitor than for emission reductions in a neighboring county in the Eastern U.S.
Primary PM2.5 emission reductions were not applied in neighboring counties in the
Western U.S. (including California) due to the large size of the counties and the complex
terrain that often isolates the influence of primary PM2.5 emissions to the local air basin.
Additional information related to air quality ratios for neighboring counties is available in
section 2A.3.1 of Appendix 2A.
At monitors in the South Coast Air Basin and San Joaquin Valley (SJV) of California,
PM2.5 DVs exceeded the existing standards in the 2032 modeling case. Air quality
management plans apply reductions in NOx emissions in addition to reductions in primary
PM2.5 emissions to meet the existing NAAQS in these air basins (SCAQMD, 2017, SJVAPCD,
2018). The NOx emission reductions help in meeting the existing standards by reducing
concentrations of PM2.5 ammonium nitrate in the air basins as described in section 2.1.2. In
creating the 12/35 analytical baseline of DVs associated with meeting existing standards,
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we applied 75% reductions in NOx emissions in SJV and South Coast in addition to primary
PM2.5 emission reductions. To apply the NOx emission reductions, air quality ratios for NOx
emissions were developed for South Coast and SJV monitors. Air quality ratios for South
Coast were developed using 2028 sensitivity modeling for NOx emissions similar to the
approach described above for the primary PM2.5 air quality ratios. For SJV, air quality ratios
were developed from sensitivity modeling results presented in the SJV air quality
management plan (SJVAPCD, 2018), which was based on a fine-scale CMAQ modeling
platform. Additional details on the South Coast and SJV air quality ratios for NOx are
available in section 2A.3.2 and 2A.3.3 of Appendix 2A. Note that the NOx emission
reductions were applied in attaining the existing standards and therefore do not contribute
to the incremental costs and benefits of meeting alternative standard levels relative to
meeting the existing standards.
2.3.2 Emission Reductions to Meet 12/35
PM2.5 DVs from the 2032 projection were adjusted using air quality ratios to
correspond with just meeting the existing standard level to create the 12/35 analytical
baseline. The 12/35 analytical baseline is used as the reference case for estimating the
incremental costs and benefits of meeting the alternative standard levels relative to the
existing 12/35 standard combination.
The counties with projected 2032 PM2.5 DVs that exceed the existing standard levels
and require air quality adjustments to meet 12/35 are shown in Figure 2-8. Counties that
exceed only the 24-hour standard are in northern California, Oregon, Washington, Idaho,
Utah, and Montana. Elevated PM2.5 episodically occurs in winter in these areas due to
meteorological temperature inversions that concentrate PM2.5 in shallow layers near the
ground in complex terrain. In California, multiple counties exceed both the annual and 24-
hour standards, and three counties (Los Angeles, San Bernardino, and Imperial) exceed
only the annual standard. Los Angeles and San Bernardino are in the South Coast Air Basin
along with Riverside County, which exceeds both the annual and 24-hour standard.
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12/35
50-
30
Annual Only: 3
24-hr Only: 11
20 -Total: 22 (
-120
-100 -90
Longitude
Both
Annual Only
24-hr Only
Figure 2-8 Counties with Projected 2032 PM2.5 DVs that Exceed the 24-Hour (24-
hr Only), Annual (Annual Only) or Both (Both) Existing Standards
(12/35 |Jg m 3)
To create the PM2.5 DVs for the 12/35 analytical baseline, the reductions in primary
PM2.5 emissions needed to just meet 12/35 at the highest DV monitor by county were
calculated using the air quality ratios in Table 2-1. The emission reductions were calculated
as follows:
A Emission^ = w^e;,std-wTarflet,std x
sta AQratiostd 1 J
where AEmissioristd is the emission reduction required to meet the annual or 24-hour
standard; DVTarget,std is the level of the annual or 24-hour standard to be met; DVModeistd is the
modeled PM2.5 design value for the annual or 24-hour standard at the county highest
monitor; AQratiostd is the air quality ratio for that standard; and the factor of 1000 converts
units from kton to ton.
For example, the highest annual PM2.5 DV in Kern County is 14.54 |j,g nr3 at site 06-
029-0016 after applying the 75% NOx emission reduction to the 2032 DVs in SJV. The
annual air quality ratio for primary PM2.5 emissions in Northern California is 3.15 |j,g nr3
per 1000 tons. Therefore, to meet an annual standard of 12 |j,g nr3, a total of 794 tons of
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primary PM2.5 emissions is needed (i.e., (14.54-12.04)/3.15 x 1000). The highest 24-hour
PM2.5 DV in Kern County is 40.4 |Lxg nr3 at site 06-029-0010 after applying the 75% NOx
emission reduction to the 2032 DVs. The 24-hour air quality ratio for primary PM2.5
emissions in Northern California is 9.97 |Lxg nr3 per 1000 tons. Therefore, to meet a 24-hour
standard of 35 |Lxg nr3, a total of 502 tons of primary PM2.5 emissions would be needed (i.e.,
(40.4-35.4)/9.97 x 1000). To determine the overall emission reductions needed to meet the
combination of annual and 24-hour standards, the maximum needed reduction across
standards is calculated. For the Kern County example, a total 794 tons of primary PM2.5
emission reductions are needed to meet the 12/35 standard combination (i.e., the
maximum of 794 tons and 502 tons).
After the emission reductions needed to meet a standard combination are identified,
the PM2.5 DVs are adjusted to correspond with the emission reductions. The PM2.5 DVs
associated with meeting a standard combination at the highest monitor in a county are
calculated as follows:
DVstd.combo DVinitial AETTlissiOTlg^d combo ^ AQvutio/1000 (2-2)
In the Kern County example, the adjusted annual DV for the 12/35 case is 12.04 ngrrr3 (i.e.,
14.54 - (794 x 3.15 / 1000)) and the adjusted 24-hour DV is 32.5 [ig nr3 (40.4 - (794 x 9.97
/ 1000)).
2.3.3 Emission Reductions to Meet Alternative Standards
PM2.5 DVs in the 12/35 analytical baseline exceed the levels of the alternative
standards in some areas of the country. The emission reductions needed to resolve these
exceedances and the associated air quality improvements contribute to the incremental
costs and benefits of the alternative standard levels.
Exceedances of the alternative standard levels in the 12/35 analytical baseline are
shown by county in Figure 2-9. Since the PM2.5 DVs have been adjusted to meet the 24-hour
standard level of 35 |Lxg nr3 in the analytical baseline, there are no exceedances of the 24-
hour standard for the cases of 10/35, 9/35, and 8/35. For the 10/35 case, six counties in
the east, three in the northwest, and fifteen in California have annual PM2.5 DVs greater
than 10 |Lxg nr3 in the 12/35 analytical baseline. For the 10/30 case, twenty-three counties
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have 24-hr DVs greater than 30 j_ig nr3 with annual DVs less than 10 j^ig nr3, and eleven
counties exceed both the 24-hr and annual standards. For the 9/35 case, twenty-two
counties exceed the annual standard in the Eastern U.S., compared with six for the 10/35
and 10/30 cases. The total number of counties exceeding the standards increases from 51
to 141 when moving from 9/35 to 8/35. Additional information on PM2.5 DVs for the 2032
projection and 12/35 analytical baseline are available in section 2A.2.2 of Appendix 2A.
10/35 10/30
40°N -
35°N -
Both: 0
30°N ¦Annual Only: 51
24-hr Only: 0
25oN .Total: 51 , , ,
120°W 110°W 100°W 90°W
80°W
70°W 120°W 110°W 100°W 90°W 80°W 70°W
45°N
40°N-
35°N
45°N -
30°N
25°N
"Annual Only: 24
24-hr Only: 0
.Total: 24
I Both
Annual Only
24-hr Only
Figure 2-9 Counties with PM2.5 DVs that Exceed Alternative Annual (Annual Only),
24-Hour (24-hr Only), or Both (Both) Standards in the 12/35
Analytical Baseline
The primary PM2.5 emission reductions needed to meet the alternative standard
levels of 10/35,10/30, 9/35, and 8/35 relative to the 12/35 analytical baseline were
calculated using Equation 2-1 and the air quality ratios in the Table 2-1. The emission
reductions needed to meet the standard levels in the Eastern and Western U.S. are shown
in Figure 2-10. These emission estimates are used to inform identification of emission
controls for meeting the standard levels analyzed. Additional information on estimating the
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emission reductions needed to meet alternative standards is available in section 2A.3.4.2 of
Appendix 2A.
50,000-
1^40,000-
~ 30,000-
cn
c
East
¦g 20,000- — M ¦ west
£ 10,000-
0-
¦¦
12/35 10/35 10/30 9/35 8/35
Standard Level
Figure 2-10 Total Primary PM2.5 Emission Reductions Needed to Meet the
Alternative Standard Levels of 10/35,10/30, 9/35, and 8/35 Relative
to the 12/35 Analytical Baseline in the Eastern and Western U.S.
2.3.4 Limitations of Using Air Quality Ratios
There are important limitations to the methodology of calculating and using air
quality ratios to predict the response of air quality to emissions changes. The air quality
ratios are calculated with results from only two CMAQ model runs and assume that the
monitor DVs would decrease with additional reductions in the future similar to how the
CMAQ model runs predicted changes in air quality concentrations. In addition, the model
response to emissions changes is analyzed at the county-level and air quality
concentrations at a monitor are assumed to decrease linearly with emission reductions in a
county. Due to the strong local influence of changes in primary PM2.5 emissions on air
quality, the generalized air quality ratio approach may not capture the specific features of
how the DV at a monitor in a county would respond to changes in specific primary PM2.5
emissions in the county. Ideally, direct modeling would be applied to account for the
location of the source relative to the location of the monitor using a model configuration
designed to capture the local features near the source. Such source-specific, high-resolution
modeling is beyond the scope of this national assessment.
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The exact impact of using the air quality ratio methodology to estimate the emission
reductions needed for attainment and the associated effect on the cost and benefits is
uncertain and may vary from monitor-to-monitor. We do not believe that this methodology
tends towards any general trend or results systematically in either an underestimation or
overestimation of the costs and benefits of attaining the alternative standard levels.
2.4 Description of Air Quality Challenges in Select Areas
Several groups of areas have air quality characteristics that limit our ability to
characterize how standard levels might be met given highly local influences that require
more specific information beyond what is available for this type of national analysis. The
challenging air quality characteristics include features of local source-to-monitor impacts,
cross-border transport, effects of complex terrain in the west, and identifying wildfire
influence on projected PM2.5 DVs that could potentially qualify for exclusion as atypical,
extreme, or unrepresentative events (USEPA, 2019b). In particular, we note that our
analysis is limited in its ability to evaluate potential air quality improvements in border
counties, major California air basins, small western mountain valleys, and an area in
Pennsylvania affected by local sources. As a result, we have treated these areas differently
in the control strategy analysis as described in Chapter 3. In this section, we describe the
nature of the air quality conditions in these areas and the challenges they present for our
national assessment.
2.4.1 Delaware County, PA
PM2.5 concentrations at the Chester monitor (site ID: 42-045-0002) in Delaware
County, Pennsylvania appear to be strongly influenced by one or two nearby facilities. As
described in the PA Department of Environmental Protection (PADEP) 2014 Annual
Ambient Air Monitoring Network Plan (PADEP, 2014), the Chester monitor is located on
the property of Evonik Degussa Corporation (Figure 2-11). The neighboring PQ
Corporation produces sodium silicate and provides it to Evonik Degussa Corporation to
undergo a drying process. Speciation data discussed in the 2014 monitoring plan
demonstrated an anomalously high amount of silicon at the Chester speciation monitor
that suggests PM2.5 concentrations are strongly influenced by local emissions from the PQ
and Evonik facilities. To confirm the source influence, additional PM2.5 monitoring was
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performed at the Marcus Hook site about 2.5 miles from the Chester site. In PADEP's 2018
monitoring plan (PADEP, 2018), the state concluded that local sources are impacting the
Chester monitoring site based on comparison of PM2.5 concentrations from the Chester and
Marcus Hook sites. Our 2032 DV projections are consistent with a local source influence on
the Chester monitor. For instance, the annual 2032 DV at Chester is 9.96 jag nr3 and is 8.61
Hg nr3 at the Marcus Hook site about 2.5 miles away. Given the local nature of the source-
to-monitor influence at the Chester site, controllable emissions likely exist at the facilities
to resolve the air quality issue. However, specifically quantifying the impacts of the near-
monitor controls would require a detailed local analysis beyond the scope of the national
RIA.
Figure 2-11 Location of the Chester Site in Relation to the Evonik Degussa and PQ
Corporation Facilities
Source: PADEP, 2018
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2.4.2 Border Areas
2.4.2.1 Imperial County, CA
As described in the Clean Air Act Section 179B5 Technical Demonstration by the
California Air Resources Board (CARB, 2018b), the Imperial County PM2.5 nonattainment
area is an agricultural community located in the southeast corner of California that shares a
southern border with Mexicali, Mexico. Imperial County includes three PM2.5 monitoring
sites, located in the cities of Calexico (site ID: 06-025-0005), El Centro (site ID: 06-025-
1003), and Brawley (site ID: 06-025-0007) (Figure 2-12). Although these three cities are of
similar size and have similar emission sources, the PM2.5 DV at the Calexico monitor closest
to the U.S.-Mexico border is much greater than the other two monitors. The projected 2032
annual PM2.5 DV is 12.45 |Lxg nr3 in Calexico, 9.13 |Lxg nr3 in Brawley, and 8.02 |Lxg nr3 in El
Centro. The Calexico monitor is in an airshed that includes both Calexico and Mexicali and
is less than one mile from the international border. Previous analysis has demonstrated
that Mexicali emissions have a daily influence on PM2.5 concentrations in Calexico and can
contribute to PM2.5 NAAQS exceedances there (CARB, 2018a, CARB, 2018b).
The city of Mexicali has a population of about 700,000 (CARB, 2018a) and Calexico
has a population of 38,633 (2020 U.S. Census). The nighttime aerial view of Calexico and
Mexicali in Figure 2-13 illustrates the much larger scale of urban activity in Mexicali than
Calexico. Substantially greater emissions have been estimated for Mexicali than Calexico
(i.e., 3.4x greater for NOx, 13.7x greater for combined SO2 and sulfate, and 57% greater for
primary PM2.5, CARB, 2018b). PM2.5 emissions in Imperial County are dominated by dust
with limited contribution from other controllable sectors (Figure 2-14). Considering the
influence of Mexicali emissions on PM2.5 concentrations in Calexico, the limited emissions
available for control in Imperial County, and the relatively lower concentrations predicted
at the two Imperial County monitors away from the border, EPA believes it is reasonable to
assume that a significant portion of the emissions affecting this area cannot be controlled in
5179B refers the section of the Clean Air Act that addresses situations where a nonattainment area would be
able to attain and maintain, or would have attained, the NAAQS but for emissions emanating from outside of
the U.S.
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California. However, a detailed local analysis beyond the scope of the RIA would be needed
to evaluate this possibility.
PM2.5 nonattainment area
Cities
Major Roads
PM2.5 Site
Lakes
Figure 2-12 Imperial County and the Nonattainment Area
Source: CARB, 2018a
Figure 2-13 Nighttime Aerial View of Calexico, CA and Mexicali, MX
Source: CARB, 2018b
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2016
2032
Dust AgPrFire NonPt Nonroad Onroad EGU
Other NonlPM RWC
Figure 2-14 Annual Source Sector Emission Totals (1000 tons per year) for PM2.5
for 2016 and 2032 in Imperial County
Note: Sector names defined in Figure 2-4
2.4.2.2 Cameron and Hidalgo County, TX
The Brownsville monitor in Cameron County, TX (site ID: 48-061-0006) and the
Mission monitor in Hidalgo County, TX (site ID: 48-215-0043) are in the Lower Rio Grande
Valley, which includes the northern portion of the state of Tamaulipas, Mexico. Addressing
the exceedances of the 9/35 standard level at the monitors in Cameron (2032 annual DV:
9.75 |j,g m-3) and Hidalgo (2032 annual DV: 10.30 |j,g m-3) is challenging due to the location
of these areas along the U.S.-Mexico border. The Brownsville monitor is within one mile of
the Mexican metropolitan area of Matamoros (population: 540,000; datamexico.org) and
the Mission monitor is about nine miles from the Mexican metropolitan area of Reynosa
(population: 700,000; datamexico.org). Due to the southeast to northwest wind pattern
(Figure 2-15), emissions from these local metropolitan areas in Mexico might influence
PM2.5 concentrations at the Brownsville and Mission monitors. Studies have also identified
long-range transport of emissions from agricultural burning and wildfire in the
southwestern states of Mexico and Central America as major regional sources that
influence air quality along the U.S.-Mexico border (Karnae and John, 2019, TCEQ, 2015).
Long-range transport of Saharan dust also episodically influences concentrations in this
area based on speciation data, satellite imagery, and wind-flow back trajectories (TCEQ,
2015).
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Dust makes up the largest fraction of primary PM2.5 emissions in Hidalgo and
Cameron County in the 2016 and 2032 modeling cases (Figure 2-16). Paved-road dust
emissions are projected to increase in these counties between 2016 and 2032 due to
projected increases in the vehicle miles travelled. Non-point (area source) emissions are
also projected to increase due to population-based emission projection factors. Increases in
dust and non-point emissions from 2016 to 2032 offset the decreases in primary PM2.5
emissions projected for EGUs and mobile (onroad/nonroad) sources in Cameron and
Hidalgo County (Figure 2-16). A local area analysis would be better suited than the national
RIA to understand the potential growth in dust and area source emissions as well as the
potential contributions of international transport to projected exceedances in this area.
Figure 2-15 Location of Mission and Brownsville Monitors in Hidalgo and Cameron
County, respectively, with Annual Wind Patterns from Meteorological
Measurements
Source: TCEQ, 2015
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Dust AgPrFire NonPt Nonroad Onroad EGU O&G Other NonlPM RWC
Figure 2-16 Annual Source Sector Emission Totals (1000 tons per year) for PM2.5
for 2016 and 2032 in Cameron and Hidalgo County Combined
Note: Sector names defined in Figure 2-4
2.4.3 Small Mountain Valleys in the West
As described in section 2.1.2, meteorological temperature inversions often occur in
small northwestern mountain valleys in winter and trap pollution emissions in a shallow
atmospheric layer at the surface. Primary PM2.5 emissions, particularly from home heating
with residential wood combustion, can build up in the surface layer and produce high PM2.5
concentrations in winter (e.g., Figure 2-17). The mountain valleys are often very small in
size relative to the area of the surrounding county and the scales resolved by
photochemical air quality models. For instance, the Portola nonattainment area for the
2012 PM2.5 NAAQS and the city of Portola are shown within Plumas County, CA in Figure 2-
18. The Libby nonattainment area for the 1997 PM2.5 NAAQS and the city of Libby are
shown within Lincoln County, MT in Figure 2-19.
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Figure 2-17 Air Pollution Layer Associated with a Temperature Inversion in
Missoula, MT in November 2018
Source: Tommy Martino, Missoulian6
6 Missoula health official: Air quality likely to worsen over next few days. David Erickson Missoulian, updated
Jan. 14,2019. Available at — https://missoulian.com/news/local/missoula-health-official-air-quality-likely-
to-worsen-over-next-few-days/article_clf00499-8al0-562 5-8af8-043d7ba02a4d.amp.html,
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39.8-N "l9llia ™
lise *\ yj
$ !k.
. - ....
39.6°n-,-* " 2 i-rff / s. , WiHpiHH
121.6°W 121.4°W 121,2°W 121.0°W 120.8°W 120.6°W 120.4°W 120.2°W
Longitude
Figure 2-18 Plumas County, CA (Grey), Porto la Nonattainment Area (Red), and City
of Portola (Purple)
Source: Map Data ©2022 Google.
49.0-N
48.8°N -
48.6°N
CD
*6
3
"3 48.4°N
48.2°N
48.0°N
116.0°W 115.8-W 115.6-W 115.4°W 115.2°W 115.0°W 114.8°W 114.6°W
Longitude
Figure 2-19 Lincoln County, MT (Grey), Libby Nonattainment Area (Red), and City
of Libby (Purple)
Source: Map Data ©2022 Google.
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Due to the small size of the urban areas within the western mountain valleys, air
quality planning is commonly based on linear rollback methods (rather than air quality
process modeling) for these areas (e.g., LRAPA, 2012, NSAQMD, 2017). The linear rollback
approach relates wood-smoke contribution estimates at the exceeding monitor to the local
(sub-county) wood combustion emission totals to estimate the tons of emission reductions
needed to meet the standard. Due to the high effectiveness of reducing PM2.5 emissions
near monitors under stagnant meteorological conditions, the PM2.5 response factors from
linear rollback methods estimate that relatively small emission reductions can greatly
influence PM2.5 concentrations in the mountain valleys. For instance, based on the linear
rollback analysis in the Portola, CA state implementation plan (NSAQMD, 2017), a
reduction of 100 tons of primary PM2.5 emissions would reduce the annual DV by about 6.6
|Lxg nr3. This responsiveness is about 30x more efficient than photochemical modeling
estimates of PM2.5 responsiveness for county-wide emission reductions under typical
meteorological conditions (i.e., outside of mountain valley stagnation conditions). Our
national RIA analysis did not apply linear rollback-based response factors for the mountain
valleys because emission and control information are available only at the county level, and
therefore controls cannot be targeted to the local communities in our analysis. To address
standard exceedances in the small mountain valleys, a detailed analysis would be necessary
that considers local PM2.5 response factors and applies controls in the local community.
Challenges due to the wood-smoke issues just described occur in five western
counties including Plumas, CA; Lincoln, MT; Shoshone, ID; Lemhi, ID; and Benewah, ID. The
populations of the relevant cities within these counties range from 1,913 to 3,182 (Table 2-
2). In addition to challenges related to residential wood combustion and meteorological
temperature inversions, PM2.5 concentrations in these areas may also be influenced by
wildfire smoke that could potentially qualify as atypical, extreme, or unrepresentative
events. Some wildfire influence likely persists in the projected 2032 PM2.5 DVs despite the
removal of EPA-concurred exceptional events and the wildfire screening described in
section 2.2.2. Sensitivity projections with lower cutoff concentrations and broader
temporal screening of wildfire influence were performed to explore the potential for
wildfire impacts to affect attainment of the standards. The sensitivity projections (Table 2-
2-38
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2) suggest that the elevated concentrations in Benewah County may be driven largely by
wildfires and that annual DVs in Lemhi, Shoshone, and Lincoln could be up to 0.8 to 1 |Lxg nr
3 lower if detailed analyses led to additional data exclusion. However, a detailed local
analysis would be needed to fully characterize the wildfire influence on attainment in these
areas as well as the wood-smoke issues discussed above.
Table 2-2 Information on Areas with Challenging Residential Wood Combustion
Issues
County,
City
Annual 2032 DV
Annual 2032 DV
Annual 2032 DV
State
(Population3)
(|j.gm-3)
Alternative Fire
Alternative Fire
Screening Ib
Screening IIC
fug m-3)
fug m-3)
Plumas, CA
Portola (1,913)
14.52
14.49
14.23
Lincoln, MT
Libby (2,845)
11.08
10.79
10.04
Shoshone, ID
Pinehurst (1,620)
11.04
10.57
10.10
Lemhi, ID
Salmon (3,182)
11.03
10.59
10.21
Benewah, ID
St Maries (2,465)
9.61
8.83
8.58
a Population from Census.gov (https://www.census.gov/programs-surveys/popest/technical-
documentation/research/evaluation-estimates/2020-evaluation-estimates/2010s-cities-and-towns-
total.html]
b Screening based on a cutoff concentration of 2 5 |_ig nr:i (rather than the default value of 61 |_ig nr:i]
c Screening based on a cutoff concentration of 20 |_ig nr:i (rather than the default value of 61 |_ig nr:i] and
inclusion of all days in June-October (rather than the flagged fire periods alone).
2.4.4 Califo rnia Areas
Several areas in California present challenges in the RIA analysis in addition to the
Imperial and Plumas County areas discussed above. The additional areas, described in this
section, are SJV, South Coast Air Basin, and two relatively isolated counties (San Luis
Obispo and Napa).
2.4.4.1 San Joaquin Valley, CA
SJV is a large inter-mountain air basin covering approximately 25,000 square miles
(SJVAPCD, 2018) that makes up the southern portion of California's Central Valley. SJV is
formed by the Sierra Nevada mountains in the east, the coastal mountain ranges in the
west, and the convergence of mountain ranges at the Tehachapi mountains in the south.
The SJV nonattainment area (Figure 2-10) includes eight counties with a combined
population of about 4.3 million. Due to the typical north to south wind pattern (Ying and
2-39
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Kleeman, 2009) and wintertime meteorological inversions, PM2.5 concentrations tend to be
highest in the south near Bakersfield and the convergence of the mountain ranges.
Longitude
Figure 2-20 San Joaquin Valley Nonattainment Area and Location of Highest PM2.5
Monitor in Bakersfield (06-029-0016)
Source: Map Data ©2022 Google.
SJV is currently in nonattainment of the 1997 and 2012 annual PM2.5 NAAQS and the
2006 24-hr PM2.5 NAAQS. The ambient DVs at the highest SJV monitor for the 2009-2011 to
2019-2021 DV periods are shown in Figure 2-21. Discerning progress from the SJV DVs
over this period is complicated by the year-to-year variability in wildfire activity and
meteorological conditions that strongly influence PM2.5 concentrations. However, the
effectiveness of SJV control strategies has previously been demonstrated in terms of
reductions in the annual number of days that exceed the 24-hr standard level of 35 jig nr3
(Figure 2-22; SJVAPCD, 2018). SJV control strategies focus on reducing NOx emissions to
lower ammonium nitrate concentrations and reducing primary PM2.5 emissions to lower
carbonaceous and crustal PM2.5 concentrations (SJVAPCD, 2018). These strategies are
based on decades of modeling research and multiple intensive field measurement
Site ID
• 060290016
121.5°W121,0°W120.5°W120.0°W119.5°W119.0°W118.5°W118.0°W
2-40
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campaigns such as the 1995 Integrated Monitoring Study (IMS), the 2000/2001 California
Regional PM10/PM2.5 Air Quality Study (CRPAQS), the 2010 California Research at the
Nexus of Air Quality and Climate Change (CalNex) study, and the 2013 Deriving
Information on Surface Conditions from Column and Vertically Resolved Observations
Relevant to Air Quality (DISCOVER-AQ) study. The effectiveness of NOx reduction for
control of ammonium nitrate in SJV has also been demonstrated using data from the long-
term ambient monitoring record (Pusede et al., 2016).
25-
co
20-
F
15-
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=L
ID
oJ
10-
CL
5-
0-
SJV
09-11 10-12 11-13 12-14 13-15 14-16 15-17 16-18 17-19 18-20 19-21
Design Value Period
Figure 2-21 Recent Annual PM2.5 DVs at the Highest SJV Monitor for Design Value
Periods (e.g., 11-13: 2011-2013). Dashed line is the 2012 Annual PM2.5
NAAQS Level (12 |ag m 3)
Figure 2-22 Decrease in the Number of Days SJV Exceeded the 24-hr NAAQS Level
(35 ^g m 3)
Source: SJVAPCD, 2018
2-41
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SJV air quality is influenced by emissions from large cities such as Bakersfleld
(population: 400,000) and Fresno (population: 540,000), an extremely productive
agricultural region, dust exacerbated by drought, major goods transport corridors (i.e.,
Interstate-5 and Highway 99), and wildfire. Primary PM2.5 emission totals are shown for
SJV in our 2032 modeling case in Figure 2-23. PM2.5 emissions are largest from agricultural
dust from the production of crops and livestock, agricultural burning, paved and unpaved
road dust, and prescribed burning. Wildfire also contributed 22,000 tons of PM2.5 emissions
to SJV based on 2016 levels.
The highest projected 2032 annual DV in SJV is 16.20 |Lxg nr3 in Bakersfleld (site ID:
06-029-0016). To address standard exceedances in SJV in the RIA, we applied 75% NOx
emissions reductions beyond the 2032 modeling case and pursued emission reductions of
primary PM2.5. However, the RIA is not well suited to identifying the specific measures
needed to meet standards in SJV given the nature and magnitude of the air quality
challenges. Challenges include air quality influenced by complex terrain and meteorological
conditions that would be best characterized with a high-resolution modeling platform
developed for the specific conditions of the valley. Also, specific local information on
measures for reducing emissions from agricultural dust and burning and prescribed
burning would be valuable given the magnitude of those emissions in SJV. Characterizing
the influence of wildfire on PM2.5 concentrations and potential atypical, extreme, or
unrepresentative events in SJV would also benefit from a local analysis. Wildfire screening
is particularly complex in California because different parts of the state have different
wildfire seasonality (e.g., Barbero et al., 2014), and severe wildfire episodes can occur
during periods where anthropogenic PM2.5 concentrations may also be high. Progress
toward meeting the alternative standards in SJV will likely occur as an outgrowth of
existing efforts to meet the 1997, 2006, and 2012 PM2.5 NAAQS.
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Figure 2-23 Annual Source Sector PM2.5 Emission Totals in SJV Counties for 2032
Modeling Case
Note that DustAgProd: Dust from Agricultural Production; AgBurn: Open Agricultural Burning;
DustRoad: Paved and Unpaved Road Dust; NonlPM: Non-EGU Point Sources; Onroad: Onroad
Mobile Sources; ResWoodComb: Residential Wood Combustion; Cooking: Commercial Cooking and
Residential Grilling; Other: Airports, Commercial Marine Vehicles, Rail, Solvents, and Other Non-
Point Area Sources; Nonroad: Nonroad Mobile Sources; WasteBurn: Open Waste Burning;
DustConstruct: Construction Dust; GasComb: Gas Combustion; and DustMineQrry: Dust from
Mining and Quarrying. Wildfire emissions (Not Shown) are 22,000 tons. Point Source Emissions for
NonlPM, EGU, and Oil&Gas Reflect Levels in the Nonattainment Area.
2.4.4.2 South Coast Air Basin, CA
The South Coast Air Basin (SoCAB) is formed by mountain ranges on three sides and
the Pacific Ocean in the west (Figure 2-24). SoCAB includes all or part of four counties (LA,
Riverside, San Bernardino, and Orange) with a combined population of over 17 million and
diverse emission sources associated with the large population, the ports of LA and Long
Beach, wildfire, and transportation of goods. The semi-permanent Pacific high-pressure
system leads to subsidence temperature inversions over SoCAB that can influence air
pollution processes by capping vertical mixing over the basin (Jacobson, 2002, Lu and
Turco, 1995). The sea-breeze circulation transports emissions from coastal ports and Los
Angeles to inland areas such as Riverside and San Bernardino (Lu and Turco, 1995,
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Neuman et a I., 2003, Pilinis et al., 2000). This transport, along with concurrent formation of
secondary PM2.5 and limited ventilation due to terrain blocking and temperature
inversions, causes the highest PM2.5 concentrations to occur downwind of LA in Riverside
and San Bernardino. For instance, the projected 2032 annual DV at the highest site in LA is
12.73 |_igm 3 (site ID: 06-037-4008) and is 14.10 ngnr3 in Riverside (site ID: 06-065-8005)
and 14.96 jj,gnr3 in San Bernardino (site ID: 06-071-0027).
& , z
San Bernardino
BurbanklSifej
j o'r
Los Angeles
18
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.oncLBeach °Anaheim
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21 Temecula
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34.6°N -
34.4°N
0> 34.2 N -
34.0°N -
Santa Monica
118.5 W
O '.ri
Victorville
o
Hesperia
33.8°N -
33.6°N -
33.4°N H
119.0°W
118.0°W 117.5°W
Longitude
117.0°W
116,5°W
Site ID
• 060374008
a 060658005
¦ 060710027
Figure 2-24 South Coast Air Basin Nonattainment Area and Locations of Highest
PM2.5 Monitors in Los Angeles (06-037-4008), Riverside (06-065-
8005), and San Bernardino (06-071-0027)
Source: Map Data ©2022 Google.
PM2.5 DVs in SoCAB exceed the 2012 annual PM2.5 NAAQS and the 2006 24-hr PM2.5
NAAQS. As in SJV, limited progress is evident in the trend of recent annual DVs in SoCAB
(Figure 2-25). However, year-to-year variability in wildfire emissions and meteorology
might mask air quality management progress. The 2016 Air Quality Management Plan
demonstrates the effectiveness of control programs during the 1999 to 2015 period in
which SoCAB experienced significant population growth (SCAQMD, 2017). Emission
control programs for SoCAB focus on reducing NOx emissions to lower ammonium nitrate
2-44
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concentrations and primary PM2.5 emissions to lower carbonaceous PM2.5 concentrations.
Ammonium nitrate tends to be elevated in Riverside and San Bernardino due to the mixing
of NOx emissions from LA with ammonia emissions from dairy facilities near Chino during
transport inland (Neuman et al., 2003, Nowak et al., 2012). The largest primary PM2.5
emission sources in our 2032 modeling are commercial and residential cooking, onroad
mobile sources, and paved and unpaved road dust (Figure 2-26). PM2.5 control strategies in
SoCAB are based on decades of study including intensive measurement and modeling
campaigns such as the 1987 Southern California Air Quality Study (SQAQS) and the 2010
CalNex campaign.
25
20
E
15
CD
3
ID
oJ
10
CL
5
0
SOCAB
09-11 10-12 11-13 12-14 13-15 14-16 15-17 16-18 17-19 18-20 19-21
Design Value Period
Figure 2-25 Recent Annual PM2.5 DVs at the Highest South Coast Monitor for Design
Value Periods (e.g., 11-13: 2011-2013). Dashed line is the 2012
Annual PM2.5 NAAQS Level (12 (j,g m 3)
2-45
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Figure 2-26 Annual Source Sector PM2.5 Emission Totals in the SoCAB Counties for
2032 Modeling Case
Note: See Figure 2-23 for Label Definitions. Wildfire emissions (Not Shown) are 8,000 Tons.
To address standard exceedances in SoCAB in the RIA, we applied 75% NOx
emission reductions beyond the 2032 modeling case and pursued emission reductions of
primary PM2.5. However, the RIA is not well suited to identifying the specific measures
needed to meet standards in SoCAB given the nature and magnitude of the air quality
challenges. Challenges include air quality influenced by complex terrain and meteorological
conditions that would be best characterized with a high-resolution modeling platform
developed for the specific conditions of the air basin. Also, specific local information on
measures for reducing emissions from the major area sources would be valuable given the
magnitude of these emissions in SoCAB. Characterizing the influence of wildfire on PM2.5
concentrations and potential atypical, extreme, or unrepresentative events in SoCAB would
also benefit from a local analysis. Progress toward meeting the alternative standards in
SoCAB will likely occur as an outgrowth of existing efforts to meet the 2006 and 2012 PM2.5
NAAQS.
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2.4.4.3 San Luis Obispo and Napa, CA
The RIA analysis identified challenges in meeting the 9/35 standard at the Arroyo
Grande site (06-079-2007) in San Luis Obispo County. Local sources and wildfires could be
the main contributors to PM2.5 concentrations at this site based on the coastal situation and
surrounding mountains (Figure 2-27). In recent years, the PM2.5 DVs have decreased at the
Arroyo Grande site such that the annual PM2.5 DVs for the 2018-2020 and 2019-2021
periods are 8.0 and 7.7 j_ig nr3, respectively (Figure 2-28). The projected 2032 annual DV
(9.63 [.ig nr3) is based on monitoring from the 2014-2018 period and does not capture the
recent air quality improvements. Based on the ambient data for the two most recent DV
periods, the Arroyo Grande site may not require additional emission reductions to meet
alternative standard levels.
-121.0 -120.5 -120.0 -119.5
Longitude
Figure 2-27 San Luis Obispo County and Location of Highest PM2.5 Monitor in
Arroyo Grande (06-079-2007)
Source: Map Data ©2022 Google.
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11-13 12-14 13-15 14-16 15-17 16-18 17-19 18-20 19-21 32-32
Design Value Period
Figure 2-28 Recent and Projected Annual PM2.5 DVs at the Arroyo Grande Monitor
(06-079-2007) in San Luis Obispo County for DV Periods (e.g., 11-13:
2011-2013; 32-32: Projected 2032 DV)
The RIA analysis also identified challenges in meeting alternative standard levels in
Napa County. The projected 2032 annual DV at the highest-DV site in Napa (06-055-0003)
is 10.09 |j,g m-3. Since the site is located in a valley (Figure 2-29), PM2.5 concentrations may
have relatively large contributions from local emission sources. Contributions from
regional sources in the Bay Area, Central Valley, and wildfire are also possible. For instance,
severe wildfires occurred in Napa during the Wine Country Fires in Fall 2017. A previous
study reported that modeled concentrations of carbonaceous PM2.5 at the Napa site were
underestimated, often by a factor of two to three (BAAQMD, 2009). The analysis suggested
that carbonaceous PM2.5 emissions, possibly from wood burning may have been strongly
underrepresented in the Napa emission inventory. Additional work to develop local
emission inventories and modeling for the area would be needed to identify appropriate
emission reductions in Napa.
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:Guinda'-,
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122.6°W22.50W22.4oW22.30W22.20W22.1 °W
Longitude
Figure 2-29 Napa County and Location of PM2.5 Monitor (06-055-0003)
Source: Map Data ©2022 Google,
2.5 Calculating PM2.5 Concentration Fields for Standard Combinations
National PM2.5 concentration fields corresponding to meeting the existing and
alternative standard levels were developed to inform health benefit calculations. First, a
gridded PM2.5 concentration field for the 2032 CMAQ modeling case was developed using
the enhanced Voronoi Neighbor Average (eVNA) method. Next, the incremental difference
in annual PM2.5 DVs between the 2032 case and cases of meeting standard combinations
was calculated at monitors and interpolated to the spatial grid. The resulting field of
incremental PM2.5 concentration was then subtracted from the 2032 eVNA field to create
the gridded field for the standard combination. The steps in developing the PM2.5
concentration fields are described further below.
2.5.1 Creating the PM2.5 Concentration Field for 2032
The gridded field of annual average PM2.5 concentrations for 2032 was developed
using the eVNA method that combines information from the model and monitors to predict
2-49
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PM2.5 concentrations. The eVNA approach was applied using SMAT-CE, version 1.8, and has
been previously described in EPA's modeling guidance document (USEPA, 2018) and the
user's guide for the predecessor software to SMAT-CE (Abt, 2014). Briefly, the steps in
developing the eVNA PM2.5 concentration field for 2032 are as follows:
Step 1. Quarterly average PM2.5 component concentrations measured during the 2015-
2017 period were interpolated to the spatial grid using inverse distance-
squared-weighting of monitored concentrations that were further weighted by
the ratio of the 2016 CMAQ value in the prediction grid cell to CMAQ value in the
monitor-containing grid cell. The weighting by CMAQ predictions adjusts the
interpolation of monitor data to account for spatial gradients in the CMAQ fields.
This step results in an interpolated spatial field of gradient-adjusted observed
concentrations for each PM2.5 component and each quarter representative of
2016.
Step 2. The 2016 eVNA component concentration in each grid cell is multiplied by the
corresponding ratio (i.e., RRF) of the quarterly-average CMAQ concentration
predictions in 2032 and 2016. This step results in spatial concentration fields for
each PM2.5 component in each quarter of 2032.
Step 3. The 2032 PM2.5 component concentrations are summed to give the total PM2.5
concentration for each quarter in 2032. The quarterly PM2.5 concentrations are
then averaged to create the 2032 PM2.5 concentration field. The resulting PM2.5
concentration field for 2032 is shown in Figure 2-30.
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-120 -110 -100 -90 -80 -70
Longitude
Figure 2-30 PM2.5 Concentration for 2032 based on eVNA Method
2.5.2 Creating Spatial Fields Corresponding to Meeting Standards
To create spatial fields corresponding to meeting standard levels, the 2032
concentration field was adjusted according to the change in PM2.5 concentrations
associated with the difference in annual PM2.5 DVs between the 2032 case and the cases
where standards are met. To implement this adjustment, the following steps were applied:
Step 1. The difference in annual PM2.5 DVs was calculated at the county highest monitor
between the 2032 case and cases of meeting the 12/35,10/30,10/35, 9/35, and
8/35 standard combinations. For the county non-highest monitors, the
difference in PM2.5 DVs was estimated by proportionally adjusting DVs according
to the percent change in PM2.5 DV at the highest monitor.
Step 2. The difference in DVs between the 2032 case and the cases of meeting the
standard combinations were then interpolated to the spatial grid using inverse-
distance-squared VNA interpolation (Abt, 2014, Gold et al., 1997). The
interpolated field was clipped to grid cells within 50 km of monitors whose DVs
changed in meeting the standard level (USEPA, 2012b).
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Step 3. National PM2.5 concentration fields were developed for each standard
combination by subtracting the corresponding spatial field of PM2.5
concentration differences from Step 2 from the 2032 eVNA concentration field.
An example of a spatial field for the incremental change in PM2.5 concentration
between the 2032 case and the case of meeting the existing standard combination, 12/35,
is shown in Figure 2-31. Additional details on the method for developing PM2.5
concentration fields are available in section 2A.4 of Appendix 2A.
-120 -110 -100 -90 -80 -70
Longitude
Figure 2-31 PM2.5 Concentration Improvement Associated with Meeting 12/35
Relative to the 2032 case
ng m 3
I
1
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2.6 References
Abt (2014). User's Guide: Modeled Attainment Test Software. Abt Associates, Prepared for
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APPENDIX 2A: ADDITIONAL AIR QUALITY MODELING INFORMATION
Overview
A 2016-based modeling platform was used to project future-year air quality for
2032 to identify areas that would exceed the existing and potential alternative PM NAAQS
after accounting for expected emission reductions from 'on-the-books' rules. This platform
uses the Community Multiscale Air Quality (CMAQ; www.epa.gov/cmaq) model for air
quality simulation and incorporates the most recent, complete set of base year emissions
information available for national modeling. PM2.5 design values (DVs) were projected to
2032 using relative response factors (RRFs) developed from CMAQ simulations based on
emissions estimated for 2016 and projected to 2032.
Air quality ratios, which relate a change in PM2.5 DVs to a change in emissions, were
used to estimate the emission reductions needed to just meet the existing and alternative
NAAQS in areas projected to exceed the standards in 2032. The emission reduction
estimates are used in identifying controls and associated costs of meeting the standards. To
inform calculations of the health benefits of meeting standards, annual-mean PM2.5
concentration fields corresponding to cases where the existing and alternative NAAQS are
just met were developed. The PM2.5 concentration fields were created by adjusting the
2032 field based on the CMAQ modeling using the incremental change in annual PM2.5 DV
needed to meet the standards.
The overall steps in the air quality analysis are:
1. Project annual and 24-hour PM2.5 DVs to 2032 using a CMAQ simulation for
2016 and a corresponding CMAQ simulation with emissions representative
of 2032.
2. Develop air quality ratios that relate a change in PM2.5 DVs to a change in
emissions for use in estimating the emission reductions needed to just meet
the existing and alternative NAAQS. The air quality ratios are developed
using the change in DVs associated with CMAQ sensitivity modeling where
50% reductions in anthropogenic emissions were applied in targeted
counties relative to previous CMAQ modeling for 2028.
2A-1
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3. Using the air quality ratios from Step 2, estimate the emission reductions
needed to meet the existing standards (12/35) beyond the 2032 modeling
case. For counties in the San Joaquin Valley (SJV) and South Coast Air Basin of
California, 75% reductions in anthropogenic NOx emissions are applied in
addition to reductions in primary PM2.5 emissions in this step.
Concentrations of ammonium nitrate are elevated in SJV and South Coast,
and these areas are pursuing both NOx and primary PM2.5 emission
reductions to meet the existing standards. For other counties, primary PM2.5
emission reductions alone are applied. The resulting PM2.5 DVs define the
12/35 analytical baseline that is used as the reference case in estimating the
incremental costs and benefits of meeting alternative standards relative to
existing standards.
4. Using the air quality ratios from Step 2, estimate the primary PM2.5 emission
reductions needed to meet the alternative standards beyond the 12/35
analytical baseline.
5. Develop a gridded national PM2.5 concentration field associated with the
2032 case by fusing the 2032 CMAQ modeling with projected monitor
concentrations. Adjust the 2032 concentration field according to the changes
in PM2.5 DVs needed to meet standard levels to create national PM2.5
concentration fields associated with meeting the existing and alternative
standard levels.
6. As a sensitivity analysis, estimate the influence on PM2.5 DVs of emission
reductions beyond the 2032 modeling that are expected to occur due to EGU
retirements and other factors became known or on-the-books after the EGU
emissions projections were conducted for the 2032 CMAQ modeling.1
1 The EGU fleet information for the case used in the 2032 CMAQ modeling (i.e., NEEDS v6 Summer 2021
Reference Case) and the case that informed the sensitivity analysis (i.e., NEEDS v6 rev: 1-24-22) are both
available here: https://www.epa.gov/power-sector-modeling/national-electric-energy-data-system-needs-
v6.
2 A-2
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In the remainder of this Appendix, the 2016 air quality model configuration and
simulation are described and evaluated in Section 2A.1. The projection of air quality from
2016 to 2032 is described in Section 2A.2. The development of air quality ratios and their
application to estimating emission reductions is described in Section 2A.3. The
development of the PM2.5 concentration fields is described in Section 2A.4. Finally, the
sensitivity analysis for EGU emission reductions beyond the 2032 CMAQ modeling case is
described in section 2A.6.
2A.1 2016 CMAQ Modeling
CMAQ modeling was performed for 2016 to provide a reference simulation for the
PM2.5 DV projections to 2032 that are described in section 2A.2.
2A.1.1 Model Configuration
CMAQ is a three-dimensional grid-based Eulerian air quality model designed to
estimate the formation and fate of oxidant precursors, primary and secondary PM2.5
concentrations, and deposition over regional spatial scales (e.g., over the contiguous U.S.)
(Appel et al., 2021, Appel et al., 2018, Appel et al., 2017). CMAQ simulates the key processes
(e.g., emissions, transport, chemistry, and deposition) that affect primary (directly emitted)
and secondary (formed by atmospheric processes) PM using state-of-the-science process
parameterizations and input data for emissions, meteorology, and initial and boundary
conditions. CMAQ's representation of the chemical and physical mechanisms that govern
the formation and fate of air pollution enable simulations the impacts of emission controls
on PM2.5 concentrations.
CMAQ version 5.3.2 (doi: 10.5281/zenodo.4081737) was used to simulate air
quality for 2016 to provide a reference simulation for the 2032 air quality projection. The
geographic extents of the outer and inner air quality modeling domains are shown in
Figure 2A-1. The outer domain covers the 48 contiguous states along with most of Canada
and Mexico with a horizontal resolution of 36 x 36 km. Air quality modeling for the 36-km
domain was used to provide chemical boundary conditions for the nested 12-km domain
simulation, which was used in projecting air quality to the future. Both model domains
have 35 vertical layers with a top at about 17.6 km (50 millibars). The chemical boundary
and initial conditions for the 36-km modeling domain were developed with version 3.1.1 of
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the hemispheric CMAQ model (https://www.epa.gov/cmaq/hemispheric-scale-
applications). The simulations included 10 days of model spin-up in December 2015 and
produced hourly pollutant concentrations for each grid cell across each modeling domain.
Gas-phase chemistry in the CMAQ simulations was based on the Carbon Bond 2006
mechanism (CB6r3) (Emery et al., 2015), and deposition was modeled with the M3DRY
parameterization. Aerosol processes were parameterized with the AER07 module using
ISORROPIAII for inorganic aerosol thermodynamics (Fountoukis and Nenes, 2007) and the
non-volatile treatment for primary organic aerosol (Appel et al., 2017, Simon and Bhave,
2012). Emissions used were based on version 2 of the 2016 emissions modeling platform
as described in detail previously (USEPA, 2022). Emissions of anthropogenic precursors for
secondary organic aerosol (SOA) (Murphy et al., 2017) were not added to the simulation
beyond what was captured in the National Emissions Inventory. Emissions of biogenic
compounds were modeled with the Biogenic Emission Inventory System (BEIS) (Bash et al.,
2016). Emissions of sea-spray aerosol (Gantt et al., 2015) were simulated online within
CMAQ using 2016 meteorology.
The 2016 meteorological data were derived from running Version 3.8 of the
Weather Research Forecasting Model (WRF) (Skamarock et al., 2008). The meteorological
outputs from WRF include hourly-varying horizontal wind components (i.e., speed and
direction), temperature, moisture, vertical diffusion rates, and rainfall rates for each grid
cell in each vertical layer. Selected physics options used in the WRF simulations include
Pleim-Xiu land surface model (Pleim et al., 2001, Xiu and Pleim, 2001), Asymmetric
Convective Model version 2 planetary boundary layer scheme (Pleim, 2007), Kain-Fritsch
cumulus parameterization (Kain, 2004) utilizing the moisture-advection trigger (Ma and
Tan, 2009), Morrison double moment microphysics (Morrison et al., 2005, Morrison and
Gettelman, 2008), and RRTMG longwave and shortwave radiation schemes (Iacono et al.,
2008). The meteorological model configuration and evaluation have been described
previously (USEPA, 2019c).
2A-4
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Figure 2A-1 Map of the Outer 36US3 (36 x 36 km Horizontal Resolution) and Inner
12US2 (12 x 12 km Horizontal Resolution) Modeling Domains Used for
the PM NAAQS RIA
2A.1.2 Model Performance Evaluation
CMAQ predictions were evaluated by comparison with observations from U.S.
monitoring networks in 2016. Modeled PM2.5 concentrations were compared with available
observations from U.S. EPA's Air Quality System (AQS) database (www.epa.gov/aqs).
Modeled concentrations of PM2.5 components (nitrate; sulfate; elemental carbon; EC; and
organic carbon, OC) were compared with observations from the Chemical Speciation
Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE)
network (USEPA, 2019d). CSN sites tend to be in relatively urban areas and IMPROVE sites
in relatively rural areas. Model predictions were paired with observations in space and
time by averaging predictions to the observation sampling period and matching
predictions with monitors in a model grid cell. Regional performance statistics were
summarized according to the U.S. climate regions defined in Figure 2A-2. The absolute and
normalized bias and error statistics and Pearson correlation coefficient used in evaluating
model performance are defined in Table 2A-1. As described below, performance statistics
for this application are generally within the range of model performance statistics reported
in previous applications (Kelly etal., 2019, Simon etal., 2012) and suggest that the
simulations are suitable for use in our application.
2 A-5
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In Figure 2A-3, PM2.5 model performance is shown for the AQS sites having the
highest PM2.5 DVs in the county for counties with projected annual PM2.5 DVs greater than 8
|Lxg nr3 or 24-hour DVs greater than 30 |Lxg nr3. For regions in the eastern U.S., normalized
mean biases (NMBs) are within 15% and Pearson correlation coefficients are 0.58 or
greater for all regions, except for the South (r=0.37). In western regions, the model is
generally biased low compared with observations, with NMBs ranging from -16% in the
Northwest to -30% in the Southwest. Underpredictions in western regions could be related
to challenges in representing the influence of complex terrain in the 12-km modeling,
challenges in simulating wildfire impacts, and underestimates of windblown dust influence.
PM2.5 performance statistics by region and season across all sites are provided in Table 2A-
2. For the annual period, NMB is within 13% in eastern regions and correlation coefficients
are 0.55 or greater in four of the five regions. In the western regions, NMB ranged from
8.0% in the Northwest to -25.9% in the Northern Rockies and Plains and correlation
coefficients ranged from 0.10 to 0.43.
Model performance statistics for PM2.5 sulfate by region and season for sites in the
CSN and IMPROVE networks are provided in Table 2A-3. The annual NMBs in sulfate
predictions are within ±16% for all regions except the Northwest (NMB: 64%) at CSN sites
and within ±23% for all regions except the Northwest (NMB: 41%) at IMPROVE sites.
Overpredictions of PM2.5 species concentrations in the Northwest have been previously
attributed to challenges in simulating the atmospheric mixing height near the Puget Sound
and at coastal sites and in simulating wildfire influence on concentrations (Kelly et al.,
2019). Concentrations are relatively low in the Northwest compared with the eastern U.S.,
and mean biases (MBs) in sulfate predictions are <0.25 |Lxg nr3 for both networks in the
Northwest. Correlation coefficients over the annual period for sulfate predictions and
observations were greater than 0.56 in six of the nine regions for CSN sites and seven of the
nine regions at IMPROVE sites. Spatially, sulfate predictions tend to be biased slightly low
in the southern and eastern parts of the domain and biased slightly high toward the
Northwestern part of the domain (Figure 2A-4 and 2A-5).
Model performance statistics for PM2.5 nitrate by region and season for sites in the
CSN and IMPROVE networks are provided in Table 2A-4. In five of the nine regions, the
2 A-6
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annual NMB in nitrate predictions is within ±18% at CSN sites and within ±30% at
IMPROVE sites. Nitrate predictions are biased low in the West at CSN (NMB: -48.8%) and
IMPROVE (NMB: -24.8%) sites. Underpredictions of nitrate during meteorological
inversion episodes in western mountain basins have been identified in the past due to
challenges in resolving the influence of complex terrain and chemical and meteorological
coupling in 12-km modeling (Baker et al., 2011, Kelly et al., 2019). Outside of the
Northwest, correlation coefficients for the annual period ranged from 0.59 to 0.79 at CSN
sites and 0.52 to 0.75 at IMPROVE sites. Spatially, nitrate predictions tend to be biased high
in the eastern US and low in western U.S. (Figure 2A-4 and 2A-5).
Model performance statistics for PM2.5 OC by region and season for sites in the CSN
and IMPROVE networks are provided in Table 2A-5. The annual NMB in OC predictions is
within ±30% for six of the nine regions at CSN sites and seven of the nine regions at
IMPROVE sites. PM2.5 OC predictions are biased high (positive NMB) in eight of the nine
regions at CSN sites and five of the nine regions at IMPROVE sites. Correlation coefficients
over the annual period for OC predictions and observations were greater than 0.5 in five of
the nine regions for CSN sites and four of the nine regions at IMPROVE sites. Spatially, OC
predictions tend to be biased high in the eastern U.S. and low in the western U.S., although
spatial variability exists (Figure 2A-4 and 2A-5). Modeling of the emissions, volatility and
atmospheric chemistry related to organic aerosol formation is an active area of research
(USEPA, 2019d).
Model performance statistics for PM2.5 EC by region and season for sites in the CSN
and IMPROVE networks are provided in Table 2A-6. The annual NMB in EC predictions is
within ±17% at CSN sites and within ±25% at IMPROVE sites for all regions except the
Northwest. As mentioned above, overpredictions of PM2.5 species concentrations in the
Northwest may be associated with challenges in modeling the mixing height near the coast,
wildfire influence, and other factors. Correlation coefficients for the EC predictions and
observations over the annual period were greater than 0.5 in seven of the nine regions for
CSN and IMPROVE sites. Spatially, EC predictions tend to be biased slightly low through
much of the US with predictions biased high along the coast of the Northeast and
Northwest (Figure 2A-4 and 2A-5).
2 A-7
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Northeast
Northern Rockies & Plains
Northwest
Ohio Valley
South
Southeast
Southwest
Upper Midwest
West
25 "Goople (Mao data ©2018 Gooale. INEGI J Mpvirn
-120 -100
Longitude
CD
~o
3
40-
Figure 2A-2 U.S. Climate Regions (Karl and Koss, 1984) Used in the CMAQ Model
Performance Evaluation
Table 2A-1
Statistic
Definition of Statistics Used in the CMAQ Model Performance
Evaluation
Description
MB (|-Lg m :i] = - O0
RMSE C^igm:3) = VSf=ift - 0,y/n
NMB (%) = X 100
Li Oi
NME r%] =ItlP,!fil X 100
r = -
nioi
Z"=i(Pi-P)(Oj-Q)
Mean bias (MB] is defined as the average difference between
predicted (P] and observed (0) concentrations for the total
number of samples (n)
Root mean-squared error (RMSE]
The normalized mean bias (NMB) is defined as the sum of the
difference between predictions and observations divided by
the sum of observed values
Normalized mean error (NME] is defined as the sum of the
absolute value of the difference between predictions and
observations divided by the sum of observed values
Pearson correlation coefficient
Sl.CPi-P^ISl.COi-o)
2A-8
-------
Northeast
Ohio Valley
Upper Midwest
NMB: 2 %
MB: 0.17.
RMSE: 4.69
r: 0,67
" * S:'.
NMB: 15%
MB: 1.39
RMSE: 5.08
r: 0.68
r '
• IS" . . .
NMB: 3 %
MB: 0.26
RMSE: 3.71
r: 0,74
Southeast
South
Southwest
NMB: 3 %
MB: 0.29
* RMSE: 4.48
r: 0,65
NMB: 5 %
MB: 0.47
RMSE: 6.02
r: 0,37
V
"* *
NMB: -30 %.
MB: -2.64,'
RMSE: 7.4
r: 0,29
Morthern Rockies & Plains
Northwest
West
NMB: -24 % '
MB: -1.85,
RMSE: 7.07
r: 0,21
NMB:-16%
MB: -1.34, '
RMSE: 10.33
* r:.af4
•W,.
NMB:-17%,
MB:-1.83,'
RMSE: 6.39
• . r: 0.53
. 4 • • v'
0 25 50 75 0 25 50 75 0 25 50 75
Observed (|ig m~3)
Figure 2A-3 Comparison of CMAQ Predictions of PM2.5 and Observations at AQS
Sites for County Highest PM2.5 Monitors with 2032 PM2.5 DVs Greater
than 8/30
2A-9
-------
Table 2A-2 CMAQ Performance Statistics for PM2.5 at AQS Sites in 2016
Region
Season
N
Avg.
Obs.
(M.gm-3)
Avg.
Mod.
C^gm-3)
MB
(M.gm-3)
NMB
(%)
RMSE
(^gm-3)
NME
(%)
r
Northeast
Winter
13305
8.32
10.16
1.84
22.1
5.95
47.6
0.65
Spring
13491
6.86
7.41
0.56
8.1
3.85
39.0
0.69
Summer
13636
7.20
6.61
-0.58
-8.1
3.45
35.3
0.56
Fall
13413
6.72
7.94
1.22
18.1
4.90
46.8
0.65
Annual
53845
7.27
8.02
0.75
10.3
4.63
42.3
0.64
Southeast
Winter
10996
7.37
8.47
1.09
14.8
5.26
41.8
0.48
Spring
11218
8.05
7.60
-0.45
-5.6
3.47
29.9
0.60
Summer
11501
8.01
6.59
-1.43
-17.8
3.55
32.6
0.51
Fall
11454
8.88
8.69
-0.19
-2.2
5.28
32.9
0.63
Annual
45169
8.09
7.83
-0.26
-3.2
4.47
34.0
0.55
Ohio Valley
Winter
10729
8.47
10.61
2.15
25.4
5.50
44.8
0.57
Spring
10739
7.76
8.42
0.66
8.6
4.65
39.3
0.47
Summer
10753
8.54
8.36
-0.18
-2.0
3.93
32.7
0.49
Fall
10761
8.92
10.48
1.56
17.5
5.67
40.2
0.62
Annual
42982
8.42
9.47
1.05
12.5
4.99
39.3
0.55
Upper Midwest
Winter
6638
8.19
9.62
1.43
17.4
5.07
42.6
0.65
Spring
6556
7.04
7.53
0.48
6.9
7.98
44.2
0.30
Summer
6253
6.02
6.00
-0.02
-0.4
3.35
39.9
0.52
Fall
6863
6.42
7.65
1.23
19.2
4.29
45.2
0.65
Annual
26310
6.93
7.73
0.80
11.5
5.46
43.1
0.52
South
Winter
7935
6.93
8.35
1.42
20.4
4.77
46.1
0.53
Spring
8266
8.00
7.21
-0.79
-9.9
4.23
35.6
0.51
Summer
7974
9.02
6.20
-2.81
-31.2
5.39
45.6
0.35
Fall
7951
7.96
8.40
0.44
5.5
4.52
37.0
0.57
Annual
32126
7.98
7.53
-0.44
-5.6
4.74
41.0
0.43
Winter
5373
8.12
6.59
-1.53
-18.9
8.45
58.1
0.39
Southwest
Spring
5447
4.77
5.16
0.39
8.3
4.07
52.7
0.32
Summer
5548
6.35
3.99
-2.36
-37.2
4.63
49.6
0.28
Fall
5574
5.58
5.08
-0.50
-9.0
4.35
49.5
0.42
Annual
21942
6.20
5.19
-1.00
-16.2
5.64
52.9
0.37
N. Rockies &
Winter
5006
5.80
3.60
-2.21
-38.0
6.77
62.9
0.29
Plains
Spring
5238
5.22
4.04
-1.17
-22.5
15.61
61.9
0.11
Summer
5267
6.55
4.32
-2.23
-34.0
34.38
66.1
0.09
Fall
5065
4.58
4.46
-0.12
-2.6
6.55
63.8
0.22
Annual
20576
5.54
4.11
-1.43
-25.9
19.66
63.8
0.10
Northwest
Winter
9961
7.90
6.18
-1.72
-21.8
8.67
70.3
0.24
Spring
10059
4.23
5.42
1.19
28.1
4.88
64.3
0.45
Summer
9884
4.72
6.37
1.65
34.9
8.29
69.0
0.44
Fall
9864
5.63
6.32
0.69
12.2
6.67
69.0
0.39
Annual
39768
5.62
6.07
0.45
8.0
7.28
68.5
0.32
West
Winter
10915
9.47
7.53
-1.94
-20.5
6.83
47.0
0.61
Spring
11124
6.79
5.99
-0.79
-11.7
3.80
39.3
0.57
Summer
11516
9.24
7.46
-1.78
-19.3
9.73
43.8
0.25
Fall
11097
8.64
7.15
-1.50
-17.3
6.24
43.8
0.47
Annual
44652
8.54
7.03
-1.50
-17.6
7.01
43.8
0.43
2 A-10
-------
50-
45-
40-
35-
30-
® 25-
rs
aj
50-
45-
40-
35-
30-
25-
PM25_EC
PM25 OC
A
s-aI hm a
l~
\A A A
~ tot
V\
-120
-100
-80
RM25_N03
£4
u i«4 OA
jA-A—
K O
i~r$5r
. i /v^
~~v-> ¦
?OA
120
100
80
60
40
20
0
-20
-40
-60
-80
100
-120
network
• CSN
* IMPROVE
Figure 2A-4 NMB in 2016 CMAQ Predictions of PM2.5 Components at CSN and
IMPROVE Sites
"D
^5
PM25 EC
PM25 N03
-120 -100 -80 -120 -100 -80
Longitude
Figure 2A-5 NMB in 2016 CMAQ Predictions of PM2.5 Components at CSN and
IMPROVE Sites for Monitors in Counties with 2032 PM2.5 DVs Greater
than 8/30
> 120
100
80
60
40
20
0
-20
-40
-60
-80
-100
-120
network
• CSN
PM25_OC
PM25„S04
2A-11
-------
Table 2A-3 CMAQ Performance Statistics for PM2.5 Sulfate at CSN and IMPROVE
Sites in 2016
Region Network
Season
N
Avg.
Obs.
km3)
Avg.
Mod.
fug m1)
MB
(ugm')
NMB
(%)
RMSE
(M-gm3)
NME
(%)
r
Northeast CSN
Winter
696
1.04
1.06
0.02
1.6
0.87
43.0
0.29
Spring
741
0.92
1.02
0.10
10.8
0.51
38.4
0.59
Summer
736
1.16
0.98
-0.18
-15.5
0.51
28.7
0.79
Fall
705
0.88
0.96
0.09
9.8
0.55
36.7
0.70
Annual
2878
1.00
1.01
0.00
0.5
0.62
36.3
0.57
IMPROVE
Winter
349
0.72
0.67
-0.05
-7.3
0.29
30.3
0.70
Spring
387
0.73
0.72
-0.01
-1.6
0.29
26.8
0.76
Summer
396
0.70
0.62
-0.08
-11.9
0.34
32.2
0.83
Fall
375
0.58
0.57
-0.01
-1.9
0.26
30.4
0.84
Annual
1507
0.68
0.65
-0.04
-5.8
0.30
29.9
0.80
Southeast CSN
Winter
456
0.93
1.02
0.09
9.5
0.44
35.4
0.64
Spring
490
1.11
1.08
-0.03
-2.8
0.48
30.9
0.56
Summer
463
1.11
0.89
-0.22
-19.6
0.49
32.0
0.54
Fall
447
0.95
0.97
0.02
1.9
0.36
25.7
0.72
Annual
1856
1.03
0.99
-0.04
-3.5
0.45
31.0
0.60
IMPROVE
Winter
342
0.95
0.85
-0.10
-10.6
0.44
35.0
0.61
Spring
379
1.24
0.97
-0.27
-21.6
0.61
30.6
0.45
Summer
394
1.21
0.77
-0.44
-36.3
0.61
41.0
0.58
Fall
366
1.04
0.86
-0.17
-16.8
0.39
26.9
0.72
Annual
1481
1.12
0.86
-0.25
-22.6
0.53
33.6
0.56
Ohio Valley CSN
Winter
510
1.34
1.14
-0.20
-15.0
0.81
35.3
0.52
Spring
526
1.19
1.21
0.02
1.7
0.61
34.2
0.43
Summer
515
1.65
1.50
-0.15
-9.0
0.86
30.5
0.67
Fall
499
1.23
1.22
-0.01
-0.9
0.62
31.0
0.67
Annual
2050
1.35
1.27
-0.09
-6.3
0.74
32.7
0.61
IMPROVE
Winter
192
1.07
0.89
-0.18
-16.7
0.49
30.9
0.69
Spring
213
1.16
0.95
-0.22
-18.6
0.53
28.9
0.56
Summer
211
1.48
1.12
-0.36
-24.5
0.67
33.9
0.73
Fall
202
1.27
1.04
-0.23
-17.8
0.50
28.1
0.80
Annual
818
1.25
1.00
-0.25
-19.8
0.55
30.6
0.72
Upper Midwest CSN
Winter
278
1.03
1.10
0.07
7.0
0.53
33.8
0.73
Spring
292
0.93
1.15
0.22
24.1
0.47
39.5
0.71
Summer
275
1.04
1.08
0.04
3.5
0.47
33.4
0.82
Fall
280
0.76
1.00
0.24
31.7
0.56
48.3
0.77
Annual
1125
0.94
1.08
0.14
15.4
0.51
38.1
0.75
IMPROVE
Winter
194
0.76
0.71
-0.05
-6.9
0.30
27.6
0.83
Spring
208
0.76
0.75
-0.01
-1.4
0.32
30.3
0.71
Summer
210
0.68
0.59
-0.10
-14.3
0.32
31.0
0.88
Fall
210
0.63
0.61
-0.01
-1.8
0.34
35.3
0.79
Annual
822
0.71
0.66
-0.04
-6.1
0.32
30.9
0.81
South CSN
Winter
258
0.99
1.12
0.12
12.5
0.63
39.9
0.60
Spring
273
1.16
1.08
-0.08
-7.3
0.84
38.6
0.63
Summer
264
1.49
1.07
-0.42
-28.3
0.81
39.9
0.46
Fall
257
1.31
1.24
-0.07
-5.3
0.61
32.0
0.67
Annual
1052
1.24
1.12
-0.11
-9.2
0.73
37.5
0.56
IMPROVE
Winter
212
0.75
0.78
0.03
4.2
0.38
34.2
0.73
Spring
242
0.97
0.80
-0.17
-17.1
0.65
38.5
0.60
Summer
221
1.42
0.77
-0.65
-45.5
0.91
48.4
0.55
Fall
234
1.10
0.89
-0.21
-19.1
0.51
33.1
0.73
Annual
909
1.06
0.81
-0.25
-23.4
0.64
39.6
0.59
Southwest CSN
Winter
189
0.50
0.51
0.01
1.9
0.72
74.3
0.19
Spring
195
0.40
0.67
0.27
68.7
0.36
77.6
0.38
Summer
192
0.70
0.49
-0.22
-30.7
0.45
45.2
0.13
Fall
200
0.50
0.52
0.02
3.0
0.28
45.1
0.35
2A-12
-------
Region Network
Season
N
Avg.
Obs.
(M-gm3)
Avg
Mod.
tam3)
MB
(M-g m')
NMB
(%)
RMSE
(M-gm3)
NME
(%)
r
Annual
776
0.53
0.55
0.02
4.1
0.48
58.1
0.13
IMPROVE
Winter
829
0.25
0.39
0.14
57.0
0.37
83.1
0.36
Spring
909
0.39
0.62
0.23
60.3
0.35
70.8
0.47
Summer
900
0.65
0.40
-0.25
-38.3
0.48
49.3
0.32
Fall
877
0.47
0.40
-0.07
-14.7
0.30
45.4
0.38
Annual
3515
0.44
0.45
0.01
3.1
0.38
57.7
0.28
N. Rockies & CSN
Winter
141
0.51
0.62
0.11
21.0
0.46
54.4
0.70
Plains
Spring
145
0.54
0.65
0.11
20.5
0.32
45.7
0.68
Summer
135
0.54
0.55
0.01
1.5
0.29
37.5
0.79
Fall
139
0.47
0.55
0.08
17.5
0.30
43.8
0.80
Annual
560
0.51
0.59
0.08
15.2
0.35
45.4
0.73
IMPROVE
Winter
542
0.32
0.42
0.10
31.3
0.29
66.1
0.73
Spring
573
0.38
0.50
0.13
34.0
0.25
53.3
0.67
Summer
603
0.36
0.40
0.04
10.0
0.21
42.2
0.54
Fall
574
0.34
0.40
0.06
17.7
0.26
48.8
0.70
Annual
2292
0.35
0.43
0.08
22.9
0.25
52.0
0.67
Northwest CSN
Winter
129
0.30
0.57
0.27
92.1
0.51
119.4
0.21
Spring
135
0.38
0.73
0.36
93.6
0.47
97.7
0.64
Summer
135
0.50
0.60
0.10
20.8
0.34
53.8
0.44
Fall
134
0.34
0.59
0.24
70.7
0.42
93.6
0.39
Annual
533
0.38
0.62
0.24
64.0
0.44
86.3
0.38
IMPROVE
Winter
405
0.15
0.26
0.11
75.6
0.20
97.5
0.65
Spring
474
0.30
0.49
0.19
61.2
0.29
69.4
0.73
Summer
488
0.35
0.39
0.04
11.8
0.23
48.9
0.45
Fall
471
0.24
0.33
0.09
38.0
0.24
71.2
0.62
Annual
1838
0.27
0.37
0.11
40.5
0.24
66.2
0.62
West CSN
Winter
246
0.46
0.61
0.15
33.1
0.44
70.5
0.35
Spring
257
0.76
0.80
0.04
5.1
0.53
49.5
0.44
Summer
258
1.31
0.74
-0.58
-43.9
1.25
51.1
0.35
Fall
235
0.77
0.65
-0.12
-16.1
0.52
47.2
0.49
Annual
996
0.83
0.70
-0.13
-15.8
0.77
52.5
0.37
IMPROVE
Winter
510
0.22
0.38
0.16
74.2
0.33
104.3
0.36
Spring
549
0.51
0.61
0.10
20.5
0.36
53.8
0.39
Summer
548
0.74
0.52
-0.22
-30.2
0.49
47.6
0.36
Fall
527
0.47
0.44
-0.03
-6.0
0.30
47.1
0.45
Annual
2134
0.49
0.49
0.00
0.3
0.38
55.2
0.39
2A-13
-------
Table 2A-4 CMAQ Performance Statistics for PM2.5 Nitrate at CSN and IMPROVE
Sites in 2016
Region Network
Season
N
Avg.
Obs.
km3)
Avg.
Mod.
km3)
MB
(ug m!)
NMB
(%)
RMSE
(ugm')
NME
(%)
r
Northeast CSN
Winter
696
1.72
2.44
0.72
42.1
1.55
61.7
0.74
Spring
741
0.86
0.84
-0.01
-1.7
0.72
55.6
0.77
Summer
736
0.32
0.21
-0.12
-36.2
0.30
62.2
0.52
Fall
705
0.64
0.72
0.08
12.0
0.65
61.0
0.66
Annual
2878
0.88
1.04
0.16
18.3
0.92
60.1
0.79
IMPROVE
Winter
349
0.49
1.05
0.56
113.3
0.99
127.3
0.66
Spring
387
0.32
0.32
0.00
1.3
0.40
65.7
0.61
Summer
396
0.15
0.20
0.05
31.1
0.26
91.3
0.40
Fall
375
0.22
0.30
0.07
32.1
0.35
82.5
0.57
Annual
1507
0.29
0.45
0.16
55.2
0.56
96.6
0.62
Southeast CSN
Winter
456
0.68
1.33
0.66
97.0
1.15
112.4
0.64
Spring
490
0.37
0.37
-0.01
-1.5
0.41
63.1
0.60
Summer
463
0.20
0.22
0.02
9.2
0.21
70.8
0.35
Fall
450
0.33
0.43
0.11
32.8
0.46
76.6
0.62
Annual
1859
0.39
0.58
0.19
48.4
0.66
87.6
0.68
IMPROVE
Winter
342
0.49
0.63
0.14
27.5
0.50
70.9
0.60
Spring
379
0.34
0.26
-0.08
-24.1
0.29
56.2
0.42
Summer
394
0.19
0.19
0.01
4.1
0.16
59.5
0.44
Fall
366
0.29
0.28
-0.01
-3.7
0.29
61.9
0.61
Annual
1481
0.32
0.33
0.01
3.0
0.33
63.2
0.61
Ohio Valley CSN
Winter
510
2.41
2.37
-0.04
-1.8
1.63
43.5
0.59
Spring
526
0.91
0.87
-0.03
-3.5
1.04
64.7
0.44
Summer
515
0.37
0.37
0.00
1.0
0.48
78.3
0.27
Fall
499
0.82
0.84
0.02
2.7
0.81
58.4
0.59
Annual
2050
1.13
1.11
-0.01
-1.1
1.08
53.4
0.69
IMPROVE
Winter
192
1.31
1.07
-0.24
-18.5
1.15
55.2
0.54
Spring
213
0.53
0.32
-0.21
-39.2
0.65
59.4
0.60
Summer
211
0.19
0.18
-0.01
-5.5
0.21
68.0
0.32
Fall
202
0.50
0.37
-0.13
-26.1
0.60
62.4
0.55
Annual
818
0.62
0.47
-0.15
-23.5
0.72
58.6
0.64
Upper Midwest CSN
Winter
278
2.76
2.72
-0.04
-1.5
1.54
37.6
0.75
Spring
292
1.15
1.14
-0.01
-1.1
1.27
58.0
0.52
Summer
275
0.35
0.36
0.01
3.8
0.55
85.8
0.27
Fall
280
0.81
0.82
0.01
1.1
0.87
55.9
0.65
Annual
1125
1.27
1.26
-0.01
-0.6
1.12
48.6
0.77
IMPROVE
Winter
194
1.44
1.11
-0.34
-23.3
1.32
49.9
0.66
Spring
208
0.58
0.35
-0.22
-38.3
0.78
58.6
0.67
Summer
210
0.12
0.14
0.02
20.5
0.16
75.5
0.55
Fall
210
0.33
0.27
-0.06
-18.1
0.50
65.4
0.52
Annual
822
0.60
0.46
-0.14
-24.0
0.80
55.5
0.72
South CSN
Winter
258
0.97
1.02
0.06
5.8
0.85
53.7
0.63
Spring
273
0.35
0.30
-0.06
-16.1
0.36
61.9
0.52
Summer
264
0.25
0.25
0.00
0.4
0.26
68.5
0.31
Fall
257
0.35
0.37
0.02
6.3
0.38
69.0
0.46
Annual
1052
0.48
0.48
0.00
1.0
0.51
60.0
0.68
IMPROVE
Winter
212
0.81
0.65
-0.16
-19.3
0.73
54.1
0.63
Spring
242
0.34
0.24
-0.10
-30.4
0.37
57.1
0.59
Summer
221
0.21
0.14
-0.07
-35.1
0.17
57.5
0.42
Fall
234
0.24
0.18
-0.06
-24.7
0.26
50.3
0.53
Annual
909
0.39
0.30
-0.10
-24.8
0.43
54.6
0.68
Southwest CSN
Winter
189
2.86
1.00
-1.86
-65.2
4.52
73.3
0.51
Spring
195
0.48
0.30
-0.18
-37.9
0.54
55.2
0.66
Summer
192
0.26
0.13
-0.13
-50.0
0.32
84.4
-0.09
Fall
200
0.59
0.36
-0.23
-38.6
0.91
76.4
0.57
2A-14
-------
Region Network
Season
N
Avg.
Obs.
(M-gm3)
Avg.
Mod.
(M-gm3)
MB
(Hgm3)
NMB
(%)
RMSE
(Hgm3)
NME
(%)
r
Annual
776
1.03
0.44
-0.59
-57.1
2.30
72.3
0.59
IMPROVE
Winter
829
0.28
0.16
-0.12
-42.7
0.51
71.6
0.55
Spring
909
0.17
0.14
-0.03
-19.5
0.15
56.3
0.27
Summer
900
0.15
0.07
-0.09
-56.0
0.14
60.1
0.55
Fall
877
0.12
0.09
-0.04
-29.2
0.13
59.3
0.44
Annual
3515
0.18
0.11
-0.07
-37.5
0.28
63.2
0.52
N. Rockies & CSN
Winter
141
1.19
1.02
-0.17
-14.3
1.23
54.6
0.61
Plains
Spring
145
0.50
0.36
-0.14
-28.3
0.53
53.5
0.78
Summer
135
0.17
0.12
-0.05
-27.3
0.20
64.6
0.48
Fall
139
0.31
0.34
0.02
7.1
0.45
70.3
0.58
Annual
560
0.55
0.46
-0.08
-15.5
0.72
57.3
0.68
IMPROVE
Winter
542
0.39
0.24
-0.15
-37.8
0.57
68.6
0.64
Spring
573
0.16
0.13
-0.04
-23.4
0.22
67.4
0.33
Summer
603
0.08
0.05
-0.03
-42.2
0.08
58.2
0.34
Fall
574
0.11
0.11
0.00
2.0
0.17
76.6
0.45
Annual
2292
0.18
0.13
-0.05
-29.1
0.31
68.3
0.63
Northwest CSN
Winter
129
1.33
0.98
-0.35
-26.4
2.25
82.2
0.33
Spring
135
0.38
1.09
0.70
183.8
2.08
211.6
0.56
Summer
135
0.25
1.26
1.01
396.6
1.83
406.9
0.42
Fall
134
0.51
0.95
0.44
86.7
1.21
142.4
0.19
Annual
533
0.61
1.07
0.46
75.0
1.88
149.4
0.17
IMPROVE
Winter
405
0.33
0.23
-0.10
-30.6
0.78
91.5
0.32
Spring
474
0.15
0.26
0.11
75.0
0.77
113.7
0.56
Summer
488
0.14
0.30
0.16
111.7
0.80
168.2
0.39
Fall
471
0.17
0.23
0.07
39.5
0.52
108.8
0.39
Annual
1838
0.19
0.26
0.07
34.1
0.73
114.8
0.27
West CSN
Winter
246
3.02
1.41
-1.61
-53.4
3.59
61.7
0.66
Spring
257
1.25
0.70
-0.55
-43.9
1.51
53.2
0.76
Summer
258
1.16
0.74
-0.42
-36.3
1.18
51.3
0.64
Fall
235
1.73
0.81
-0.92
-53.4
2.57
69.2
0.54
Annual
996
1.78
0.91
-0.87
-48.8
2.39
60.2
0.67
IMPROVE
Winter
510
0.50
0.36
-0.14
-28.2
0.94
60.4
0.80
Spring
549
0.41
0.34
-0.07
-17.3
0.36
47.9
0.81
Summer
548
0.34
0.28
-0.06
-17.5
0.38
55.1
0.45
Fall
527
0.44
0.28
-0.15
-34.6
0.86
61.8
0.75
Annual
2134
0.42
0.32
-0.10
-24.8
0.68
56.5
0.75
2A-15
-------
Table 2A-5 CMAQ Performance Statistics for PM2.5 EC at CSN and IMPROVE Sites in
2016
Region Network
Season
N
Avg
Obs.
(.US m1)
Avg.
Mod.
(.Lig m1)
MB
(M-gm3)
NMB
(%)
RMSE
(M-gm3)
NME
(%)
r
Northeast CSN
Winter
672
0.67
0.78
0.11
16.5
0.68
53.7
0.60
Spring
736
0.58
0.55
-0.03
-4.8
0.44
43.2
0.58
Summer
725
0.58
0.50
-0.08
-13.5
0.35
39.6
0.58
Fall
725
0.62
0.70
0.07
11.9
0.56
47.9
0.58
Annual
2858
0.61
0.63
0.02
2.9
0.52
46.3
0.58
IMPROVE
Winter
373
0.19
0.28
0.09
46.4
0.17
62.3
0.83
Spring
422
0.15
0.18
0.03
16.9
0.10
43.4
0.85
Summer
423
0.16
0.17
0.01
4.6
0.09
39.1
0.82
Fall
406
0.19
0.21
0.02
11.3
0.15
41.6
0.82
Annual
1624
0.17
0.21
0.03
19.7
0.13
46.6
0.82
Southeast CSN
Winter
392
0.59
0.58
-0.01
-1.3
0.36
41.6
0.60
Spring
424
0.55
0.43
-0.13
-23.3
0.34
40.3
0.63
Summer
388
0.43
0.41
-0.02
-5.0
0.30
47.9
0.41
Fall
374
0.63
0.55
-0.08
-12.6
0.41
41.4
0.63
Annual
1578
0.55
0.49
-0.06
-11.1
0.35
42.4
0.59
IMPROVE
Winter
376
0.28
0.26
-0.02
-5.9
0.34
48.5
0.52
Spring
416
0.32
0.21
-0.11
-34.4
0.66
50.0
0.29
Summer
425
0.23
0.17
-0.06
-24.2
0.25
45.4
0.60
Fall
395
0.36
0.26
-0.10
-28.0
0.26
38.3
0.85
Annual
1612
0.30
0.23
-0.07
-24.2
0.42
45.2
0.51
Ohio Valley CSN
Winter
498
0.49
0.57
0.07
15.1
0.33
45.0
0.64
Spring
548
0.54
0.45
-0.09
-16.3
0.31
38.4
0.59
Summer
523
0.61
0.47
-0.15
-24.0
0.35
39.6
0.44
Fall
523
0.71
0.62
-0.09
-12.7
0.41
36.1
0.63
Annual
2092
0.59
0.52
-0.06
-11.0
0.35
39.3
0.59
IMPROVE
Winter
192
0.21
0.22
0.02
7.8
0.17
44.4
0.52
Spring
213
0.22
0.17
-0.04
-19.4
0.16
40.5
0.36
Summer
211
0.19
0.14
-0.05
-26.7
0.08
33.0
0.70
Fall
202
0.31
0.24
-0.07
-23.6
0.18
34.1
0.68
Annual
818
0.23
0.19
-0.04
-16.6
0.15
37.6
0.56
Upper Midwest CSN
Winter
278
0.34
0.54
0.21
60.4
0.45
78.7
0.55
Spring
285
0.46
0.47
0.01
2.4
0.39
49.1
0.52
Summer
278
0.41
0.40
-0.01
-2.7
0.26
45.0
0.45
Fall
279
0.47
0.52
0.06
12.3
0.32
47.6
0.69
Annual
1120
0.42
0.48
0.07
15.6
0.36
53.7
0.53
IMPROVE
Winter
220
0.15
0.20
0.06
40.8
0.14
56.1
0.79
Spring
239
0.19
0.17
-0.03
-14.1
0.23
44.1
0.54
Summer
237
0.18
0.14
-0.04
-21.5
0.12
42.0
0.81
Fall
240
0.20
0.19
-0.02
-8.4
0.14
42.0
0.78
Annual
936
0.18
0.17
-0.01
-3.9
0.16
45.2
0.66
South CSN
Winter
226
0.63
0.58
-0.05
-7.6
0.35
39.7
0.60
Spring
251
0.47
0.39
-0.08
-16.6
0.27
37.9
0.54
Summer
208
0.43
0.41
-0.02
-4.9
0.30
51.2
0.36
Fall
194
0.60
0.60
-0.01
-0.9
0.37
44.3
0.49
Annual
879
0.53
0.49
-0.04
-7.7
0.32
42.6
0.55
IMPROVE
Winter
212
0.15
0.14
-0.00
-2.1
0.11
43.2
0.68
Spring
242
0.16
0.15
-0.02
-10.1
0.23
53.6
0.61
Summer
219
0.11
0.08
-0.03
-24.9
0.07
44.6
0.63
Fall
234
0.17
0.12
-0.06
-32.0
0.10
40.6
0.70
Annual
907
0.15
0.12
-0.03
-17.5
0.14
45.7
0.63
Southwest CSN
Winter
180
0.80
0.83
0.04
4.6
0.47
44.0
0.55
Spring
194
0.30
0.44
0.14
48.7
0.27
64.3
0.66
Summer
179
0.32
0.38
0.06
17.8
0.22
47.1
0.42
Fall
187
0.54
0.63
0.09
15.8
0.36
49.0
0.62
2A-16
-------
Region Network
Season
N
Avg
Obs.
tam3)
Avg.
Mod.
tom1)
MB
(M-gm3)
NMB
(%)
RMSE
(M-gm3)
NME
(%)
r
Annual
740
0.49
0.57
0.08
16.9
0.34
49.1
0.66
IMPROVE
Winter
829
0.15
0.12
-0.02
-15.1
0.17
47.3
0.86
Spring
909
0.07
0.09
0.02
22.7
0.11
66.0
0.67
Summer
894
0.10
0.09
-0.01
-11.8
0.09
51.2
0.63
Fall
882
0.12
0.10
-0.02
-15.3
0.13
49.4
0.78
Annual
3514
0.11
0.10
-0.01
-7.8
0.13
52.0
0.79
N. Rockies & CSN
Winter
124
0.27
0.25
-0.02
-8.1
0.52
92.1
0.09
Plains
Spring
145
0.20
0.17
-0.03
-16.8
0.20
55.1
0.45
Summer
161
0.22
0.18
-0.04
-18.2
0.16
43.2
0.45
Fall
146
0.24
0.20
-0.05
-18.5
0.37
66.0
0.16
Annual
576
0.23
0.20
-0.04
-15.5
0.33
64.1
0.20
IMPROVE
Winter
540
0.05
0.06
0.01
18.5
0.08
78.6
0.37
Spring
573
0.07
0.07
-0.01
-6.9
0.21
77.5
0.49
Summer
601
0.10
0.14
0.03
32.5
0.42
82.1
0.27
Fall
574
0.09
0.08
-0.01
-14.4
0.14
61.3
0.27
Annual
2288
0.08
0.09
0.01
8.1
0.25
74.6
0.33
Northwest CSN
Winter
132
0.76
1.05
0.29
38.4
1.07
83.4
0.42
Spring
135
0.46
1.04
0.58
126.0
1.29
146.3
0.61
Summer
129
0.41
1.11
0.70
171.2
1.19
175.7
0.49
Fall
130
0.59
1.21
0.62
105.5
1.23
131.3
0.44
Annual
526
0.56
1.10
0.55
98.7
1.20
126.0
0.45
IMPROVE
Winter
425
0.08
0.13
0.04
51.8
0.32
104.5
0.79
Spring
482
0.08
0.18
0.10
121.8
0.53
158.1
0.72
Summer
488
0.15
0.29
0.14
90.2
0.66
153.2
0.35
Fall
471
0.12
0.25
0.13
104.2
0.56
150.3
0.69
Annual
1866
0.11
0.21
0.10
93.3
0.54
144.8
0.49
West CSN
Winter
241
1.05
0.93
-0.12
-11.4
0.56
36.9
0.61
Spring
253
0.41
0.50
0.09
22.1
0.27
45.0
0.76
Summer
247
0.43
0.53
0.09
21.6
0.23
38.1
0.75
Fall
235
0.67
0.76
0.09
13.9
0.37
39.8
0.67
Annual
976
0.64
0.68
0.04
6.3
0.38
39.2
0.72
IMPROVE
Winter
510
0.12
0.11
-0.01
-6.2
0.16
57.6
0.84
Spring
546
0.08
0.09
0.02
20.5
0.09
65.8
0.74
Summer
556
0.19
0.18
-0.01
-3.1
0.51
63.9
0.44
Fall
527
0.15
0.16
0.01
6.4
0.20
60.4
0.68
Annual
2139
0.13
0.14
0.00
2.4
0.29
61.9
0.55
2A-17
-------
Table 2A-6 CMAQ Performance Statistics for PM2.5 OC at CSN and IMPROVE Sites in
2016
Region Network
Season
N
Avg
Obs.
(.US m1)
Avg.
Mod.
(.Lig m1)
MB
(M-gm3)
NMB
(%)
RMSE
(M-gm3)
NME
(%)
r
Northeast CSN
Winter
672
1.81
3.25
1.45
80.2
2.61
91.5
0.67
Spring
736
1.56
2.24
0.68
43.5
1.51
58.8
0.64
Summer
725
1.93
2.02
0.09
4.5
0.91
33.8
0.67
Fall
725
1.84
2.67
0.84
45.7
1.76
60.6
0.69
Annual
2858
1.78
2.53
0.75
42.1
1.78
60.2
0.65
IMPROVE
Winter
373
0.75
1.64
0.89
119.3
1.32
122.4
0.81
Spring
422
0.74
1.10
0.36
48.0
0.76
64.5
0.76
Summer
423
1.19
1.19
0.00
0.0
0.70
37.3
0.67
Fall
406
0.93
1.33
0.40
43.6
1.18
63.8
0.66
Annual
1624
0.91
1.31
0.40
44.0
1.01
66.0
0.66
Southeast CSN
Winter
392
2.03
2.97
0.94
46.3
1.99
61.8
0.66
Spring
424
2.02
2.45
0.43
21.1
1.19
42.7
0.76
Summer
388
1.92
2.33
0.42
21.8
1.01
38.5
0.72
Fall
374
2.78
3.06
0.28
10.1
2.67
45.3
0.58
Annual
1578
2.18
2.70
0.52
23.8
1.82
47.0
0.59
IMPROVE
Winter
376
1.21
1.78
0.57
46.9
3.66
82.1
0.21
Spring
416
4.04
1.71
-2.33
-57.6
40.44
81.9
0.15
Summer
425
1.56
1.42
-0.14
-9.1
2.62
42.9
0.23
Fall
395
2.03
1.99
-0.05
-2.4
2.27
45.6
0.58
Annual
1612
2.23
1.72
-0.52
-23.2
20.69
66.6
0.10
Ohio Valley CSN
Winter
498
1.61
2.59
0.98
61.1
1.76
72.6
0.62
Spring
548
1.61
1.95
0.34
20.9
1.23
47.8
0.58
Summer
523
1.88
1.91
0.03
1.5
0.85
32.8
0.56
Fall
523
2.47
2.76
0.29
11.7
1.87
39.5
0.70
Annual
2092
1.89
2.30
0.40
21.2
1.48
46.4
0.64
IMPROVE
Winter
192
0.96
1.64
0.68
70.4
2.66
96.2
0.29
Spring
213
1.12
1.49
0.37
33.0
3.23
66.4
0.20
Summer
211
1.33
1.28
-0.05
-4.0
0.59
33.3
0.71
Fall
202
1.84
2.02
0.18
9.5
2.05
50.4
0.60
Annual
818
1.32
1.60
0.29
21.7
2.34
57.4
0.34
Upper Midwest CSN
Winter
278
1.18
2.73
1.55
132.2
2.54
134.8
0.55
Spring
285
1.56
2.22
0.66
42.1
2.08
72.2
0.38
Summer
278
1.64
1.79
0.15
8.8
0.94
38.4
0.49
Fall
279
1.58
2.21
0.64
40.4
1.41
55.5
0.74
Annual
1120
1.49
2.24
0.75
50.2
1.85
70.8
0.44
IMPROVE
Winter
220
0.60
1.25
0.65
107.6
1.11
111.6
0.70
Spring
239
0.90
1.09
0.19
20.5
1.58
70.9
0.34
Summer
237
1.18
0.92
-0.26
-21.8
0.58
38.2
0.54
Fall
240
0.90
1.02
0.12
13.6
0.72
46.4
0.69
Annual
936
0.90
1.07
0.17
18.4
1.07
60.2
0.42
South CSN
Winter
226
2.19
2.86
0.67
30.5
2.08
64.4
0.55
Spring
251
1.57
1.90
0.33
21.2
1.15
52.5
0.55
Summer
208
1.68
2.07
0.39
23.3
1.45
56.0
0.55
Fall
194
2.31
3.08
0.77
33.6
2.62
59.5
0.58
Annual
879
1.92
2.45
0.53
27.6
1.87
58.6
0.57
IMPROVE
Winter
212
0.74
1.10
0.36
48.7
1.25
69.2
0.63
Spring
242
1.01
1.04
0.02
2.4
1.76
61.7
0.51
Summer
219
1.09
0.88
-0.21
-19.4
0.67
46.2
0.73
Fall
234
1.11
0.97
-0.14
-12.3
0.63
40.2
0.73
Annual
907
0.99
1.00
0.00
0.4
1.19
52.7
0.52
Southwest CSN
Winter
180
2.17
3.03
0.86
39.5
2.53
74.8
0.33
Spring
194
0.92
1.49
0.57
62.2
1.27
83.2
0.37
Summer
179
1.47
1.38
-0.09
-6.1
0.84
37.9
0.43
Fall
187
1.53
2.02
0.49
32.1
1.40
67.9
0.47
2A-18
-------
Region Network
Season
N
Avg
Obs.
tam3)
Avg.
Mod.
tom1)
MB
(M-gm3)
NMB
(%)
RMSE
(M-gm3)
NME
(%)
r
Annual
740
1.51
1.97
0.46
30.5
1.63
65.7
0.43
IMPROVE
Winter
829
0.52
0.50
-0.02
-4.3
0.80
48.1
0.77
Spring
909
0.42
0.46
0.04
8.5
0.65
55.6
0.45
Summer
894
0.86
0.62
-0.24
-27.6
0.76
50.4
0.53
Fall
882
0.60
0.50
-0.10
-16.2
0.55
46.5
0.66
Annual
3514
0.60
0.52
-0.08
-13.4
0.70
49.9
0.62
N. Rockies & CSN
Winter
124
1.05
1.10
0.05
4.5
1.93
95.2
0.12
Plains
Spring
145
0.87
0.75
-0.12
-13.4
0.76
54.9
0.48
Summer
161
1.45
0.87
-0.58
-40.0
1.05
47.2
0.49
Fall
146
1.01
0.77
-0.24
-23.6
1.04
52.0
0.23
Annual
576
1.11
0.87
-0.24
-21.9
1.24
59.6
0.25
IMPROVE
Winter
540
0.30
0.34
0.05
15.5
0.42
70.0
0.36
Spring
573
0.59
0.47
-0.12
-20.9
1.33
63.6
0.55
Summer
601
1.22
1.03
-0.20
-16.0
3.17
58.2
0.25
Fall
574
0.63
0.49
-0.14
-22.5
1.05
60.6
0.23
Annual
2288
0.70
0.59
-0.11
-15.3
1.84
61.1
0.34
Northwest CSN
Winter
132
2.54
4.33
1.80
70.9
4.33
98.1
0.45
Spring
135
1.43
3.62
2.19
152.9
4.59
160.7
0.58
Summer
129
1.55
3.83
2.28
147.0
3.58
153.7
0.48
Fall
130
1.99
3.90
1.92
96.5
3.53
116.0
0.48
Annual
526
1.88
3.92
2.05
109.1
4.04
126.3
0.45
IMPROVE
Winter
425
0.36
0.61
0.25
69.1
1.54
128.6
0.57
Spring
482
0.54
0.82
0.28
51.8
1.81
93.3
0.53
Summer
488
1.32
1.57
0.24
18.5
2.94
83.3
0.47
Fall
471
0.77
1.28
0.51
66.8
2.43
116.9
0.55
Annual
1866
0.76
1.08
0.32
42.3
2.26
98.6
0.44
West CSN
Winter
241
3.61
4.10
0.49
13.6
2.89
48.9
0.58
Spring
253
1.52
1.85
0.32
21.3
1.10
44.5
0.64
Summer
247
2.40
2.23
-0.17
-7.2
1.44
36.0
0.51
Fall
235
2.77
3.10
0.33
11.9
1.96
43.7
0.62
Annual
976
2.56
2.80
0.24
9.4
1.96
43.8
0.63
IMPROVE
Winter
510
0.59
0.52
-0.07
-12.0
0.60
50.5
0.87
Spring
546
0.62
0.51
-0.11
-17.5
0.48
46.7
0.61
Summer
556
1.72
1.39
-0.33
-19.2
2.77
52.4
0.43
Fall
527
1.07
1.00
-0.07
-6.7
1.22
50.0
0.64
Annual
2139
1.01
0.86
-0.15
-14.7
1.58
50.6
0.54
2A-19
-------
2A.2 Projecting PM2.5 DVs to 2032
PM2.5 DVs were projected to 2032 using air quality modeling to inform estimates of
the emission reductions needed to meet standards beyond the reductions expected to
occur due to finalized rules. The projections were performed by pairing the 2016 CMAQ
simulation with a corresponding CMAQ simulation based on emissions representative of
2032. The 2032 emissions case accounts for factors including emission reductions between
2016 and 2032 from 'on-the-books' rules and has been described in detail previously
(USEPA, 2022). Other than differences in the emissions inputs, all aspects of the 2032
CMAQ modeling were specified identical to the 2016 modeling. These aspects include the
meteorology, boundary conditions, the 12-km modeling domain, and the model
configuration.
To predict the influence of the emission reductions between 2016 and 2032 on
PM2.5 DVs, PM2.5 relative response factors (RRFs) were calculated using the CMAQ results to
project monitoring data to 2032. RRFs are the ratios of modeled PM2.5 species
concentrations in the future year (2032) to the base year (2016). RRFs are used in
projecting air quality to help mitigate the influence of systematic biases in model
predictions (e.g., systematic biases in the 2016 and 2032 modeling may partially cancel in
the ratio) (Cohan and Chen, 2014, NRC, 2004, USEPA, 2018). RRFs are calculated for each
PM2.5 component (i.e., sulfate, nitrate, organic carbon, elemental carbon, crustal material,
and ammonium). The annual and 24-hour PM2.5 DVs for the future year are calculated by
applying the species-specific RRFs to ambient PM2.5 concentrations from the PM2.5
monitoring network, which are disaggregated into species concentrations by applying the
SANDWICH method (Frank, 2006) and through interpolation of PM2.5 species data from the
CSN and IMPROVE monitoring networks. Details on the PM2.5 projection method using
RRFs are provided in the user's guide for the predecessor to the SMAT-CE software (Abt,
2014). The RRF method for calculating future-year PM2.5 annual and 24-hour PM2.5 DVs
was implemented here using the Software for Modeled Attainment Test-Community
Edition (SMAT-CE) version 1.8 (USEPA, 2018, Wang etal., 2015).
2 A-20
-------
2A.2.1 Monitoring Data for PM2.5 Projections
PM2.5 DVs were projected using ambient PM2.5 measurements from the 2014-2018
period centered on the 2016 CMAQ modeling period. PM2.5 species measurements from the
IMPROVE and CSN networks during 2015-2017 were used to disaggregate the measured
total PM2.5 concentrations into components for the RRF calculations. As in the 2012 PM2.5
NAAQS RIA (USEPA, 2012a), limited exclusion of wildfire and fireworks influence on PM2.5
concentrations was applied to the 2014-2018 PM2.5 monitoring data in addition to
exclusion of EPA-concurred exceptional events. Monitoring data were evaluated (i.e.,
screened) for potential wildfire and fireworks influence because PM2.5 concentrations may
be influenced by atypical, extreme, or unrepresentative events such as wildfires or
fireworks that may be appropriate for exclusion as described in EPA's memorandum
Additional Methods, Determinations, and Analyses to Modify Air Quality Data Beyond
Exceptional Events (USEPA, 2019a). Due to the challenges in identifying wildfire influence
on monitored concentrations, only limited screening of major wildfire influence is possible
here, and wildfire impacts on concentrations likely persists in the screened data.
The steps in implementing the limited screening of major wildfire and fireworks
influence on PM2.5 concentrations are as follows:
1. An extreme value cutoff of 61 [ig nr3 was identified based the 99.9th
percentile value from all daily PM2.5 concentrations across all sites in the
long-term AQS observations (2002-2018).
2. Specific states and months were screened for instances of monitors
exceeding the extreme value cutoff to identify potential periods of interest.
States included for screening were CA, WA, OR, MT, ID, and CO. These states
were selected due to the prevalence of wildfire in the western U.S. and the
potential for NAAQS exceedances in these areas. States in the southwest were
not included in part due to challenges in distinguishing wildfire influence
from dust events. Months included were June-October (while November can
be a high fire month for parts of the western U.S., it becomes more difficult to
distinguish wildfire PM2.5 from residential wood smoke and other
anthropogenic sources during the late fall).
2 A-21
-------
3. For periods flagged under the previous step, the presence of visible wildfire
smoke was corroborated using satellite imagery from NASA's Worldview
platform (https://worldview.earthdata.nasa.gov) for the dates and
geographic location identified. If corroboration with satellite imagery was
not possible, the episode was not included. Timeseries for individual sites
flagged were also examined to confirm PM2.5 enhancements temporally
consistent with the wildfire events identified (Figures 2A-16 to 2A-25).
4. For wildfire periods confirmed with satellite imagery, all concentrations
above the extreme value cutoff of 61 [ig nr3 occurring during the identified
wildfire episode window at impacted sites were removed.
5. In addition to the evaluation criteria above, data corresponding to the Camp
Fire (northern CA during November 2018) and the Appalachian Fires (NC,
TN, GA during November 2016) were evaluated for exclusion if
concentrations exceeded the extreme value threshold of 61 |Lxg nr3. These
large fire episodes show obvious impacts across multiple monitors and were
clearly documented with satellite imagery (Figures 2A-6 to and 2A-15).
6. In addition to the limited exclusion of major wildfire influence, data were
evaluated to identify days for potential exclusion due to the influence of
isolated fireworks events on PM2.5 concentrations. The 99.9th percentile value
of 61 [ig nr3 was applied as the cutoff across all sites for New Year's Eve and
the Fourth of July.
A full list of episodes and counties that were evaluated for potential exclusion of
monitor data due to influence from wildfire and fireworks is shown in Table 2A-7. Example
satellite imagery and timeseries of PM2.5 at impacted monitors for each episode are shown
in Figures 2A-6to 2A-14. The flagged day-site combinations represent 0.4% (767/200,201)
of all possible day-site combinations for those sites. Not all days flagged for potential fire
influence were excluded only those above the 61 [ig nr3 threshold.
2A-22
-------
Table 2A-7 Wildfire Episodes and Counties Where Data Were Screened for
Exclusion if PM2.5 Concentrations Exceeded the Extreme Value
Threshold of 61 ng m3
Episode
Dates
Impacted County
State
Camp Fire
Nov. 8-20, 2018
Alameda
CA
Stanislaus
CA
San Joaquin
CA
Sonoma
CA
Butte
CA
Contra Costa
CA
Colusa
CA
Fresno
CA
Mendocino
CA
Sacramento
CA
Napa
CA
Solano
CA
Placer
CA
San Francisco
CA
Marin
CA
Yolo
CA
T ehama
CA
Lake
CA
Santa Clara
CA
Santa Cruz
CA
Nevada
CA
Kings
CA
Merced
CA
San Mateo
CA
Madera
CA
Monterey
CA
North Bay/Wine Country Fires
Oct. 8-20, 2017
Napa
CA
San Joaquin
CA
Mendocino
CA
Solano
CA
Contra Costa
CA
Lake
CA
Sonoma
CA
Marin
CA
Alameda
CA
Nevada
CA
Mendocino
CA
Pacific Northwest/northern CA Fires of 2017
Aug. 1- Sept. 13, 2017
Benewah
ID
Lemhi
ID
Shoshone
ID
2A-23
-------
Episode
Dates
Impacted County State
Washington/Oregon Fires of 2018
Ada ID
Canyon ID
Bannock ID
Jackson OR
Lane OR
Josephine OR
Crook OR
Klamath OR
Harney OR
Flathead MT
Silver Bow MT
Lewis and Clark MT
Powder River MT
Fergus MT
Missoula MT
Ravalli MT
Lincoln MT
Yakima WA
Spokane WA
Clark WA
King WA
Pierce WA
Okanogan WA
Snohomish WA
Shasta CA
T ehama CA
Sonoma CA
Siskiyou CA
July 14-Aug. 25,2018 Spokane WA
Okanogan WA
Skagit WA
Whatcom WA
Snohomish WA
Kitsap WA
King WA
Clark WA
Pierce WA
Yakima WA
Jackson OR
Lake OR
Harney OR
Klamath OR
Josephine OR
Lane OR
2A-24
-------
Episode
Dates
Impacted County State
Siskiyou
CA
Montana Fires of 2018
Aug. 13-Aug. 28, 2018
Lincoln
MT
Missoula
MT
Lewis and Clark
MT
Yellowstone
MT
Fergus
MT
Flathead
MT
Shoshone
ID
Montana/Washington/Idaho Fires of 2015
Aug. 15-Aug. 30, 2015
Missoula
MT
Ravalli
MT
Lincoln
MT
Missoula
MT
Flathead
MT
Lewis and Clark
MT
Powder River
MT
Silver Bow
MT
Fergus
MT
Lemhi
ID
Shoshone
ID
Bannock
ID
Clark
WA
416/Burro Fire Complex
June 8-13 2018
La Plata
CO
Butte Fire
Sept. 11-14, 2015
Calaveras
CA
Placer
CA
Carr/Mendocino/Ferguson Fires
July 28-Aug. 18, 2018
Inyo
CA
Mono
CA
Shasta
CA
Siskiyou
CA
Calaveras
CA
T ehama
CA
Lake
CA
Fresno
CA
Tulare
CA
Butte
CA
Nevada
CA
Appalachian Fires
Nov. 7-24, 2016
Hamilton
TN
Knox
TN
Loudon
TN
Roane
TN
Blount
TN
Swain
NC
Mitchell
NC
Buncombe
NC
Jackson
NC
2A-25
-------
Episode
Dates
Impacted County
State
Walker
GA
Clarke
GA
Richmond
GA
Hall
GA
Greenville
SC
Richland
SC
Edgefield
SC
Lexington
SC
Charleston
SC
Fireworks
July 4-5 and Dec. 31-Jan. 1 (all years)
Weber
UT
Pierce
WA
Snohomish
WA
Clark
NV
St. Louis City
MO
Macomb
MI
Marion
IN
Lake
IN
Allen
IN
Cook
IL
La Plata
CO
Stanislaus
CA
San Joaquin
CA
San Bernardino
CA
Riverside
CA
Merced
CA
Madera
CA
Los Angeles
CA
Kings
CA
Kern
CA
Inyo
CA
Imperial
CA
Fresno
CA
Santa Cruz
AZ
Maricopa
AZ
2A-26
-------
Figure 2A-6 Visible Satellite Imagery from NASA's Worldview Platform Showing
Smoke from the Camp Fire on 11/10/2018
Figure 2A-7 Visible Satellite Imagery from NASA's Worldview Platform Showing
Smoke from the North Bay/Wine Country Fires on 10/09/2017
2A-27
-------
Figure 2A-8 Visible Satellite Imagery from NASA's Worldview Platform Showing
Smoke from Fires Across the Pacific Northwest/Northern California on
08/29/2017
Figure 2A-9 Visible Satellite Imagery from NASA's Worldview Platform Showing
Smoke from Fires in Washington and Oregon on 08/09/2018
2A-28
-------
Figure 2A-10 Visible Satellite Imagery from NASA's Worldview Platform Showing
Smoke from Fires in Montana on 08/19/2018
Figure 2A-11 Visible Satellite Imagery from NASA's Worldview Platform Showing
Smoke from Fires in Montana, Washington and Idaho on 08/22/2015
2A-29
-------
Figure 2A-12 Visible Satellite Imagery from NASA's Worldview Platform Showing
Smoke from the 416/Burro Complex Fires on 06/10/2018
Figure 2A-13 Visible Satellite Imagery from NASA's Worldview Platform Showing
Smoke from the Butte Fire on 09/11/2015
2 A-30
-------
Figure 2A-14 Visible Satellite Imagery from NASA's Worldview Platform Showing
Smoke from the Carr/Mendocino/Ferguson Fires on 08/04/2018
Figure 2A-15 Visible Satellite Imagery from NASA's Worldview Platform Showing
Smoke from Fires in the Appalachians on 11/10/2016
2 A-31
-------
Alameda
100 150
I I
Alameda
100 150
I I
Alameda
0 _
0 _
1 1 1 I
0 100 200 300
I I I I
0 100 200 300
I I I I
0 100 200 300
Alameda
100 150
l I
Alameda
100 150
l I
Alameda <
>
0 50
1 I
fit*
0 50
1 l
fi mnfrumtiii "
1 1 1 1
0 100 200 300
I I I i
0 100 200 300
1 1 1 1
0 100 200 300
Stanislaus
100 150
l I
Stanislaus
.
100 150
I l
San Joaquin
0 50
1 l
A
0 _
A
I I I I
0 100 200 300
I I I I
0 100 200 300
I I I I
0 100 200 300
Sonoma
'
i
200 300 400
I l l
Butte
100 150
l I
Contra Costa
0 100
1 I
0 50
1 l
,aA»
Figure 2A-16 Daily PM2.5 (in ng m3) from a Subset of Monitors Impacted by the
Camp Fire in November 2018
Note: Bottom axis shows day in 2018. Red line indicates extreme value threshold of 61 jig m 3 used for
screening.
2A-32
-------
Napa
c
1 1 1 1
0 100 200 300
Solano
'
0
100
200
I
300
0
Lake
0
0
frPo^o^o0^
0
jP°cP
0
100
200
300
Mendocino
Figure 2A-17 Daily PM2.5 (in [xg m3) from a Subset of Monitors Impacted by the
North Bay/Wine Country Fires in October 2017
Note: Bottom axis shows day in 2017. Red line indicates extreme value threshold of 61 |xg nr3 used for
screening.
2A-33
-------
Missoula
fr mi ifa-ifrtftffi
Jackson
O
O
O
O
O
<§>
O
0 °°o
s%8&
Spokane
WW
Figure 2A-18 Daily PM2.5 (in ng m3) from a Subset of Monitors Impacted by Fires in
the Pacific Northwest/Northern California in August-September 2017
Note: Bottom axis shows day in 2017. Red line indicates extreme value threshold of 61 jig m 3 used for
screening.
2A-34
-------
Spokane
1
—I—
300
King
Okanogan ,
§ -
1 1
0 100
200
I
300
Snohomish
I I
0 100
200
i
300
Kitsap
I I
0 100
200
i
300
King
§ "
i r
0 100
§ -
Skagit
<
I I i
0 100 200
I
300
Snohomish
<
J
0 an n- **
I I I
0 100 200
I
300
King
I
Mm
I I I
0 100 200
I
300
King
~r
100
T
200
Figure 2A-19 Daily PM2.5 (in ng m3) from a Subset of Monitors Impacted by Fires in
Washington and Oregon in July-August 2018
Note: Bottom axis shows day in 2018. Red line indicates extreme value threshold of 61 jig nr3 used for
screening.
2A-35
-------
Lincoln
0 100
200
300
Lewis and Clark
nfftt §£ti iffi Tmi
0 100
200
300
Fergus
Figure 2A-20 Daily PM2.5 (in ng m3) from the Monitors Impacted by Fires and Smoke
in Montana in August 2018
Note: Bottom axis shows day in 2018. Red line indicates extreme value threshold of 61 jig m 3 used for
screening.
2A-36
-------
Figure 2A-21 Daily PM2.5 (in ng m3) from a Subset of Monitors Impacted by Fires in
Montana, Washington and Idaho in August 2015
Note: Bottom axis shows day in 2015. Red line indicates extreme value threshold of 61 jig m3 used for
screening.
0
LO -
La Plata
<
3
)
O
O -
'1
O
LO
<
A Jk
O -
oOooooooof
0 100 200 300
Figure 2A-22 Daily PM2.5 (in ng m3) from the Monitor in Plata, CO Impacted by the
416/Burro Fire Complex in June 2018
Note: Bottom axis shows day in 2018. Red line indicates extreme value threshold of 61 jig m3 used for
screening.
2A-37
-------
Figure 2A-23 Daily PM2.5 (in ng m3) from the Two monitors Impacted by the Butte
Fire in September 2015
Note: Bottom axis shows day in 2015. Red line indicates extreme value threshold of 61 jig m3 used for
screening.
Figure 2A-24 Daily PM2.5 (in ng m3) from a Subset of Monitors Impacted by the
Carr/Mendocino/Ferguson Fires in August 2018
Note: Bottom axis shows day in 2018. Red line indicates extreme value threshold of 61 jig m3 used for
screening.
2A-38
-------
0 100 200 300 0 100 200 300
Figure 2A-25 Daily PM2.5 (in ng m3) from a Subset of Monitors Impacted by Fires in
the Appalachians in November 2016
Note: Bottom axis shows day in 2016. Red line indicates extreme value threshold of 61 jig nr3 used for
screening.
2A.2.2 Future-Year PM2.5 Design Values
PM2.5 DVs were projected to 2032 using air quality modeling as described above and
compared with the existing standard combination, 12/35. Counties with projected 2032
PM2.5 DVs exceeding the existing standards are shown in Figure 2A-26. Counties that
exceed only the 24-hour standard are in northern California, Oregon, Washington, Idaho,
Utah, and Montana. Elevated PM2.5 episodically occurs in winter in these areas due to
meteorological temperature inversions that concentrate PM2.5 in shallow layers, especially
in mountainous terrain. In California, multiple counties exceed both the annual and 24-
2A-39
-------
hour standards and three counties (Los Angeles, San Bernardino, and Imperial) exceed only
the annual standard. Los Angeles and San Bernardino are in the South Coast Air Basin along
with Riverside County, which exceeds both the annual and 24-hour standard.
Figure 2A-26 Counties with Projected 2032 PM2.5 DVs that Exceed the 24-Hour (24-
hr Only), Annual (Annual Only) or Both the 24-Hour and Annual
(Both) Standards for the Combination of Existing Standards (12/35)
12/35
40
fo
35
Both
I Annual Only
24-hr Only
-120 -110 -100 -90 -80 -70
Longitude
As described below in section 2A.3.4, PM2.5 DVs for 2032 were adjusted to
correspond with just meeting the existing standard level to form the 12/35 analytical
baseline used in estimating the incremental costs and benefits of meeting the alternative
standards relative to the existing standards. The county exceedances of the alternative
standards in the 12/35 analytical baseline are shown in Figure 2A-27. Since the PM2.5 DVs
have been adjusted to meet the 24-hour standard level of 35 |j,g m-3 in the analytical
baseline, there are no exceedances of the 24-hour standard for the cases of 10/35, 9/35,
and 8/35. For the 10/35 case, six counties in the east, three in the NW, and fifteen in
California have annual PM2.5 DVs greater than 10 |j,g m-3 in the 12/35 analytical baseline.
For the 10/30 case, twenty-three counties have 24-hr DVs greater than 30 |j,g m-3 and
annual DVs less than 10 |j,g m-3, and eleven counties exceed both the 24-hr and annual
standards. For the 9/35 case, twenty-two counties exceed the annual standard in the
2A-40
-------
eastern US, compared with six for the 10/35 and 10/30 cases. The total number of counties
exceeding the standards increases from 51 to 141 when moving from 9/35 to 8/35, In
Table 2A-8, PM2.5 DVs are shown for the 2032 projections and 12/35 analytical baseline for
sites with the highest annual and 24-hour PM2.5 DVs in counties with 2032 DVs that exceed
an annual standard 8 [ig nr3 or a 24-hour standard of 30 jig nr3.
10/35
10/30
45°N -(1 " 7 K
Both: 0
30°N -Annual Only: 51
24-hr Only: 0
25° N -TDtat51
I
Both
Annual Only
24-hr Only
120°W 110°W 100°W 90°W 8CTW 70°W 120DW 110°W 100°W 90°W 80°W 70°W
Figure 2A-27 Counties with PM2.5 DVs in the 12/35 Analytical Baseline that Exceed
the 24-Hour (24-hr Only), Annual (Annual Only) or Both the 24-Hour
and Annual (Both) Standards for the Combination of Existing
Standards
2A-41
-------
Table 2A-8 PM2.5 DVs for 2032 Projection and 12/35 Analytical Baseline for the
Highest DVs in the County for Counties with Annual 2032 DVs Greater
8 |ig m3 or 24-hour 2032 DVs Greater than 30 (j,g m3
State
County
Annual
2032 DV
fug m-')
24-hour
2032 DV
fug m-')
Annual
12/35 DV
ftigm-3)
24-hour
12/35 DV
ftigm-3)
AL
Jefferson
9.86
20.1
9.86
20.1
AL
Talladega
8.20
16.3
8.20
16.3
AZ
Maricopa
9.47
26.7
9.47
26.7
AZ
Pinal3
8.16
34.2
8.16
34.2
AZ
Santa Cruz
8.99
26.5
8.99
26.5
AR
Pulaski
8.99
19.3
8.99
19.3
AR
Union
8.12
17.0
8.12
17.0
CA
Alameda
10.14
25.4
10.14
25.4
CA
Butte
8.28
27.2
8.28
27.2
CA
Contra Costa
9.16
25.1
9.16
25.1
CA
Fresno
13.34
50.8
11.43
35.4
CA
Imperial
12.45
32.4
12.04
31.5
CA
Kern
16.20
57.2
12.04
32.5
CA
Kings
15.27
48.2
12.04
28.4
CA
Los Angeles
12.73
34.9
12.04
31.1
CA
Madera
12.13
39.8
10.60
31.1
CA
Marin
8.18
23.4
8.18
23.4
CA
Merced
11.88
36.3
10.79
29.1
CA
Napa
10.09
25.7
10.09
25.7
CA
Orange
7.79
31.5
7.47
28.5
CA
Plumas
14.52
47.8
10.60
35.4
CA
Riverside
14.10
39.9
12.04
33.1
CA
Sacramento
9.29
31.0
9.29
31.0
CA
San Bernardino
14.96
35.0
12.04
26.3
CA
San Diego
9.16
22.3
9.16
22.3
CA
San Joaquin
12.01
35.7
10.08
29.1
CA
San Luis Obispo
9.63
25.1
9.63
25.1
CA
Santa Clara
9.56
26.0
9.56
26.0
CA
Siskiyou
7.77
34.8
7.77
34.8
CA
Solano
9.04
24.7
9.04
24.7
CA
Stanislaus
12.43
38.7
11.08
29.8
CA
Sutter
8.82
27.6
8.82
27.6
CA
Tulare
14.66
46.5
12.04
25.3
CA
Ventura
9.23
33.5
9.23
33.5
CO
Denver
9.04
24.1
9.04
24.1
CO
Weld
8.14
24.9
8.14
24.9
DE
New Castle
8.14
21.4
8.14
21.4
DC
District of Columbia
8.21
19.8
8.21
19.8
GA
Bibb
8.80
18.3
8.80
18.3
2A-42
-------
State
County
Annual
2032 DV
fag m-3}
24-hour
2032 DV
fag m-3}
Annual
12/35 DV
fugm-3!
24-hour
12/35 DV
fugm-3!
GA
Clayton
8.57
17.2
8.57
17.2
GA
Cobb
8.09
16.6
8.09
16.6
GA
DeKalb
8.08
18.2
8.08
18.2
GA
Dougherty
8.38
21.3
8.38
21.3
GA
Floyd
8.72
17.3
8.72
17.3
GA
Fulton
9.46
20.4
9.46
20.4
GA
Gwinnett
8.06
18.7
8.06
18.7
GA
Muscogee
8.68
27.3
8.68
27.3
GA
Richmond
8.54
21.0
8.54
21.0
GA
Wilkinson
8.97
19.2
8.97
19.2
ID
Benewah
9.61
35.2
9.61
35.2
ID
Canyon
8.86
31.4
8.86
31.4
ID
Lemhi
11.03
39.4
10.05
35.4
ID
Shoshone
11.04
36.6
10.75
35.4
IL
Cook
9.43
20.7
9.43
20.7
IL
Madison
9.03
19.0
9.03
19.0
IL
Saint Clair
8.99
17.6
8.99
17.6
IN
Allen
8.10
19.6
8.10
19.6
IN
Clark
8.58
19.8
8.58
19.8
IN
Elkhart
8.37
23.5
8.37
23.5
IN
Floyd
8.08
18.0
8.08
18.0
IN
Lake
8.92
22.2
8.92
22.2
IN
Marion
9.61
22.0
9.61
22.0
IN
St Joseph
8.72
20.4
8.72
20.4
IN
Vanderburgh
8.40
17.5
8.40
17.5
IN
Vigo
8.47
19.2
8.47
19.2
KS
Wyandotte
8.15
19.9
8.15
19.9
KY
Jefferson
8.85
19.5
8.85
19.5
LA
Caddo
9.44
19.6
9.44
19.6
LA
East Baton Rouge
8.69
20.7
8.69
20.7
LA
Iberville
8.06
18.6
8.06
18.6
LA
St Bernard
8.11
17.4
8.11
17.4
LA
West Baton Rouge
8.67
18.7
8.67
18.7
MD
Howard
8.21
18.6
8.21
18.6
MD
Baltimore (City)
8.17
21.5
8.17
21.5
MI
Kent
8.49
22.5
8.49
22.5
MI
Wayne
10.06
24.1
10.06
24.1
MS
Hinds
8.08
18.1
8.08
18.1
MO
Buchanan
8.15
17.1
8.15
17.1
MO
Jackson
8.09
18.1
8.09
18.1
MO
Jefferson
8.51
18.4
8.51
18.4
MO
Saint Louis
8.82
19.1
8.82
19.1
2A-43
-------
State
County
Annual
2032 DV
fag m-3}
24-hour
2032 DV
fag m-3}
Annual
12/35 DV
fugm-3!
24-hour
12/35 DV
fugm-3!
MO
St Louis City
8.36
19.8
8.36
19.8
MT
Lewis and Clark
8.57
37.6
8.03
35.4
MT
Lincoln
11.08
33.2
11.08
33.2
MT
Missoula
9.53
29.6
9.53
29.6
MT
Ravalli
8.75
38.0
8.11
35.4
MT
Silver Bow
8.64
30.6
8.64
30.6
NE
Douglas
8.08
17.8
8.08
17.8
NE
Sarpy
8.10
17.5
8.10
17.5
NV
Clark
9.24
23.0
9.24
23.0
NJ
Camden
9.21
22.3
9.21
22.3
NJ
Union
8.62
21.3
8.62
21.3
NM
Dona Ana
8.57
27.6
8.57
27.6
NY
New York
8.95
22.1
8.95
22.1
NC
Davidson
8.29
18.1
8.29
18.1
NC
Mecklenburg
8.15
17.5
8.15
17.5
NC
Wake
8.12
16.7
8.12
16.7
OH
Butler
9.82
20.7
9.82
20.7
OH
Cuyahoga
10.23
21.8
10.23
21.8
OH
Franklin
8.17
17.9
8.17
17.9
OH
Hamilton
8.91
20.1
8.91
20.1
OH
Jefferson
9.26
22.3
9.26
22.3
OH
Lucas
8.70
19.4
8.70
19.4
OH
Mahoning
8.20
19.0
8.20
19.0
OH
Stark
8.92
19.9
8.92
19.9
OH
Summit
8.72
19.9
8.72
19.9
OK
Tulsa
8.13
19.5
8.13
19.5
OR
Crook
8.29
35.5
8.27
35.4
OR
Harney
8.61
30.8
8.61
30.8
OR
Jackson
9.18
17.3
9.18
17.3
OR
Klamath
8.64
31.2
8.64
31.2
OR
Lake
7.89
37.3
7.42
35.4
OR
Lane
8.12
29.0
8.12
29.0
PA
Allegheny
11.19
34.7
11.19
34.7
PA
Armstrong
9.28
19.3
9.28
19.3
PA
Beaver
8.44
19.1
8.44
19.1
PA
Berks
8.18
23.9
8.18
23.9
PA
Cambria
9.08
22.8
9.08
22.8
PA
Chester
8.97
22.1
8.97
22.1
PA
Dauphin
8.37
24.5
8.37
24.5
PA
Delaware
9.96
23.6
9.96
23.6
PA
Lackawanna
8.07
18.6
8.07
18.6
PA
Lancaster
10.14
26.8
10.14
26.8
2A-44
-------
State
County
Annual
2032 DV
fag m-3}
24-hour
2032 DV
fag m-3}
Annual
12/35 DV
fugm-3!
24-hour
12/35 DV
fugm-3!
PA
Lebanon
9.10
27.1
9.10
27.1
PA
Lehigh
8.17
21.0
8.17
21.0
PA
Mercer
8.42
19.6
8.42
19.6
PA
Philadelphia
9.75
22.7
9.75
22.7
PA
Washington
8.37
19.0
8.37
19.0
PA
York
8.56
21.4
8.56
21.4
RI
Providence
8.27
17.9
8.27
17.9
SC
Greenville
8.16
18.6
8.16
18.6
TN
Davidson
8.17
16.9
8.17
16.9
TN
Knox
8.60
19.3
8.60
19.3
TX
Cameron
9.75
24.5
9.75
24.5
TX
Dallas
8.08
17.1
8.08
17.1
TX
El Paso
9.08
23.8
9.08
23.8
TX
Harris
10.37
22.0
10.37
22.0
TX
Hidalgo
10.29
25.8
10.29
25.8
TX
Nueces
9.03
23.9
9.03
23.9
TX
Travis
9.07
18.8
9.07
18.8
UT
Box Elder
6.51
31.7
6.51
31.7
UT
Cache
7.07
32.7
7.07
32.7
UT
Davis
7.27
31.1
7.27
31.1
UT
Salt Lake
8.20
37.4
7.71
35.4
UT
Utah
7.63
31.5
7.63
31.5
UT
Weber
7.99
30.8
7.99
30.8
WA
King
8.31
26.5
8.31
26.5
WA
Kittitas
7.37
38.0
6.73
35.4
WA
Okanogan
-
31.8
-
31.8
WA
Snohomish
7.07
31.3
7.07
31.3
WA
Spokane
8.18
27.2
8.18
27.2
WA
Yakima
8.18
38.8
7.34
35.4
WV
Berkeley
8.21
22.1
8.21
22.1
WV
Brooke
8.41
19.8
8.41
19.8
WV
Marshall
8.46
19.7
8.46
19.7
aThe Hidden Valley site in Pinal County (04-021-3015) was not compared with the annual NAAQS in this
analysis because it is a replacement site for the Cowtown Road site that was not comparable to the annual
NAAQS (PCAQCD, 2020).
2A.3 Developing Air Quality Ratios and Estimating Emission Reductions
As in the RIAs for the 2012 PM2.5 NAAQS review (USEPA, 2012a, USEPA, 2012b), air
quality ratios are used here to estimate the emission reductions beyond the 2032 modeling
case that are needed to meet the existing and alternative standards. Air quality ratios are
2A-45
-------
developed from sensitivity modeling with CMAQ and relate a change in PM2.5 DV to a
change in emissions. Air quality ratios have units of |Lxg nr3 per kton of emissions. The
remainder of this section describes the development of air quality ratios and their
application to estimating emission reductions for meeting the existing and alternative
standards.
2A.3.1 Developing Air Quality Ratios for Primary PM2.5 Emissions
To develop air quality ratios that relate the change in DV in a county to the change in
primary PM2.5 emissions in that county, CMAQ sensitivity modeling was performed with
reductions in primary PM2.5 emissions in selected counties. The modeling was conducted
using CMAQ version 5.2.1 for a 2028 modeling case similar to that of recent regional haze
modeling (USEPA, 2019b) due to the availability of the 2028 (but not 2032) modeling
platform at the time of the work.
To develop air quality ratios for primary PM2.5 emissions, a 2028 CMAQ sensitivity
simulation was conducted with 50% reductions in primary PM2.5 emissions from
anthropogenic sources in counties with annual 2028 DVs greater than 8 |Lxg nr3 (Figure 2A-
28). The change in annual and 24-hour PM2.5 DVs in these counties was then divided by the
change in emissions in the respective counties to determine the air quality ratio at
individual monitors as follows:
AQratioPM2Si j = A£^ty. x 1000 (2A-1)
where ADVis the change in design value (|Lxg nr3) between the 2028 base case and the
simulation with 50% reduction in primary PM2.5 emissions at a monitor / in a county j,
AEmissCty is the change in primary PM2.5 emissions (tons) in county j between the 2028
base case and the simulation with 50% reduction in primary PM2.5 emissions, and the
factor of 1000 converts units from (|Lxg nr3 per ton) to (|Lxg nr3 per kton).
2A-46
-------
50-
|
45"w%«l* ' 1 Yrl r rA >'
3fl— y,: x
0 MF '—r= ' i I / ¦ tju -
rAiL ' ~ * * ~%nw
mj ^ :•
' « i, - i . -
30- * n * » '
s
'** '" I
-120 -110 -100 -90 -80 -70
Longitude
Figure 2A-28 Counties with 50% Reduction in Anthropogenic Primary PM2.5
Emissions in 2028 Sensitivity Modeling
Representative air quality ratios for regions of the US were developed from the
ratios at individual monitors as in the 2012 PM2.5 NAAQS review (USEPA, 2012b), Regional
ratios were calculated as the 75th percentile of air quality ratios at monitors within five
regions: Northeast, Southeast, Northern California, Southern California, and West (Figure
2A-29). The Northeast region was defined by combining the Upper Midwest, Ohio Valley,
and Northeast US climate regions (Figure 2A-2); the Southeast region was defined by
combining the Southeast and South climate regions (Figure 2A-2); and California was
separated into Southern and Northern regions as done previously (USEPA, 2012b). The air
quality ratios for primary PM2.5 emissions used in estimating the emission reductions
needed to just meet standards are listed in Table 2A-9.
2A-47
-------
25°N - • . i . w. ,
120°W 110°W 100°W 90°W 80°W 70°W
Figure 2A-29 Regional Groupings for Calculating Air Quality Ratios
Table 2A-9 Annual and 24-Hour Air Quality Ratios for Primary PM2.5 Emissions
Region Annual Air Quality Ratio 24-hour Air Quality Ratio
flig m ! per kton) (ug m 3 per kton)
Northeast 1.37 4.33
Southeast 1.22 3.51
West 2.14 8.70
Northern California 3.15 9.97
Southern California 1.18 2.56
The air quality ratios in Table 2A-9 relate the change in DV in a given county to a
change in emissions in that county. The ratios are developed for local spatial scales because
concentrations are most responsive to changes in local emissions. However, emission
controls may not always be identified in the local county, and emission reductions in
neighboring counties may sometimes be appropriate, such as in the eastern US where
counties are relatively small, and terrain is relatively flat. To apply emission reductions in
the neighboring counties in the eastern US, the responsiveness of annual PM2.5 DVs to
emission reductions within a county was compared with the responsiveness for
neighboring counties as estimated from the 2028 sensitivity modeling.
First, county groups of most relevance were identified from the 2028 sensitivity
modeling. These groups were selected as eastern counties where emission reductions were
2A-48
-------
applied and whose neighbors were not also neighbors of another county where emission
reductions were applied. This set of county groups was then subset from the full list of
counties and filtered to ensure that at least one monitor was included in the neighbor
counties for the county group. The resulting county groups are shown in Figure 2A-30. The
average relative responsiveness of annual DVs in the east for emission reductions in a core
county to reductions in a neighboring county was then calculated as follows:
mean(ADVcore)
ImpactRatio =
= 4
(2A-2)
mean(ADVneighb
or)
where the numerator is the average impact on annual PM2.5 DVs in the core counties with
50% reduction in anthropogenic primary PM2.5 emissions, and the denominator is the
average impact on annual PM2.5 DVs in neighboring counties. The resulting impact ratio
suggests that primary PM2.5 emission reductions in neighboring counties would be 4x less
effective as in the core county.
50-
Core
| Neighbor
-100 -90
Longitude
Figure 2A-30 Counties Used in Estimating the Relative Impact of Emissions in Core
and Neighboring Counties
2A-49
-------
2A.3.2 Developing Air Quality Ratios for NOx in Southern California
As described above, PM2.5 DVs exceeded the existing standards at monitors in the
South Coast Air Basin in the 2032 modeling case. PM2.5 DVs were adjusted to meet the
existing standards in these counties in creating the 12/35 analytical baseline. Since
concentrations of ammonium nitrate are elevated in South Coast, NOx emission reductions
were applied in these counties in addition to primary PM2.5 emission reductions to meet
12/35. For this purpose, air quality ratios were developed that relate a change in PM2.5 DVs
to a change in NOx emissions at monitors in Southern California.
The air quality ratios were developed from a CMAQ sensitivity simulation with 50%
reductions in anthropogenic NOx emissions relative to the 2028 modeling case. The 50%
emission reductions were applied in counties with annual 2028 DVs greater than 8 |Lxg nr3
and their neighboring counties (Figure 2A-31). The change in annual and 24-hour DVs in
these counties was then divided by the change in emissions in the respective county groups
to determine the air quality ratio at individual monitors as follows:
AQratioPM2 5 / / = —-1 x 1000 (2A-3)
'AEmissCtyGroupj
where ADVis the change in design value (|j,g nr3) between the 2028 base case and the
simulation with 50% reduction in NOx emissions at monitor /, AEmissCtyGroup is the
change in NOx emissions (ton) in the county group associated with county j between the
2028 base case and the simulation with 50% reduction in NOx emissions, and the factor of
1000 converts units from (|Lxg nr3 per ton) to (|Lxg nr3 per kton).
2 A-50
-------
-100 -90
Longitude
Figure 2A-31 Counties with 50% Reduction in Anthropogenic NOx Emissions in
2028 Sensitivity Modeling
The county groups for determining the emission change to associate with the DV
change in Equation 2A-3 are defined in Table 2A-10. These county groups were identified
by first selecting the county of focus plus the neighboring counties to reflect the regional
nature of ammonium nitrate formation from NOx emissions. These county groups were
then refined to account for the influence of terrain, which limits air mixing between
different air basins, on meteorology and air pollution. For instance, although Kern County
neighbors Los Angeles County, Kern is part of the SJV air basin while Los Angeles is part of
the South Coast Air Basin. Kern is therefore not included in the county group associated
with Los Angeles County, because Kern is separated from Los Angeles County by mountain
ranges.
Table 2A-10 County Groups for Calculating Air Quality Ratios for NOx Emission
Changes in Southern California
FIPS
County
County Group
06025
Imperial
Imperial; San Diego
06037
Los Angeles
Los Angeles; Orange; San Bernardino; Ventura
06065
Riverside
Riverside; Orange; San Bernardino
06071
San Bernardino
San Bernardino; Los Angeles; Orange; Riverside
06073
San Diego
San Diego; Imperial; Orange
06111
Ventura
Ventura; Los Angeles; Santa Barbara
2 A-51
-------
To develop representative air quality ratios (USEPA, 2012b) for Southern California,
the 75th percentile of the air quality ratios for individual monitors in the six counties in
Table 2A-10 was calculated. The resulting air quality ratio for the annual standard is 0.004
Hg nr3 per kton and for the 24-hour standard ratio is 0.038 |Lxg nr3 per kton. These ratios
were applied to adjust 2032 PM2.5 DVs according to 75% reductions in anthropogenic NOx
emissions for counties in South Coast Air Basin (i.e., LA, San Bernardino, Riverside, and
Orange). The 75% reduction in emissions corresponded to 78,700 tons. The 2032 DVs and
the NOx-adjusted DVs are shown in Table 2A-11 for the highest annual and 24-hour DV
monitors in the county. Note that these emission reductions were applied in meeting the
existing standards (12/35) and are therefore not part of the incremental cost and benefits
of meeting alternative standards relative to the existing standards.
Table 2A-11 2032 PM2.5 DVs and NOx-adjusted PM2.5 DVs for the Highest Annual
and 24-Hour DV Monitors in South Coast Counties
Site ID
County
AQ Ratio
Annual
Qig nr3 per kton)
AQ Ratio
24-hour
(|Jg [11 ; per kton)
2032 DV
Annual
(ugm')
2032 DV
2 4-hour
(M-gm3)
NOx-Adj DV
Annual
(ugm')
NOx-Adj
DV
2 4-hour
km')
060371302
Los Angeles
0.004
0.038
12.38
34.9
12.06
31.9
060374008
Los Angeles
0.004
0.038
12.73
31.0
12.41
28.0
060592022
Orange
0.004
0.038
7.79
14.6
7.47
11.6
060590007
Orange
0.004
0.038
-
31.5
-
28.5
060658005
Riverside
0.004
0.038
14.10
39.9
13.78
36.9
060710027
San Bernardino
0.004
0.038
14.96
35.0
14.64
32.0
2A.3.3 Developing Air Quality Ratios for NOx in SJV, CA
As in the South Coast Air Basin, PM2.5 DVs exceed existing standards in SJV in the
2032 modeling case, and concentrations of ammonium nitrate are elevated in SJV. To
develop PM2.5 DVs for SJV counties in the 12/35 analytical baseline, NOx emission
reductions were applied in addition to primary PM2.5 emission reductions. To develop air
quality ratios for NOx emission changes in SJV, information was used from Appendix K of
the 2018 SJV PM2.5 Plan (SJVAPCD, 2018). The Plan was based on fine-scale CMAQ modeling
and provides useful information for characterizing the responsiveness of PM2.5 DVs to NOx
emissions.
2A-52
-------
The California Air Resources Board (CARB) modeled PM2.5 concentrations in SJV
corresponding to 30% reductions in NOx emissions relative to a 2024 base case. The
change in annual and 24-hour PM2.5 DVs at monitors in SJV was reported for the sensitivity
simulation. Using this information, along with PM2.5 DVs and emissions information from
the 2032 CMAQ modeling developed here, air quality ratios were calculated at monitors in
SJV from the following equation:
The parenthesis labeled CARB indicates values from the SJV Plan, and the
parenthesis labeled 2032 indicates values from the 2032 CMAQ modeling described above.
%Chg.DVi indicates the percent change in the design value for a given monitor, i, from Table
49 and 50 of Appendix K of the SJV Plan (SJVAPCD, 2018). %Chg.DVi ranged from 2.3% to
7.5% for annual DVs and from 7.0% to 16.3% for 24-hour DVs. %Chg.Emissionsjv indicates
the percent change in NOx emissions in SJV and equaled 30%. DVt corresponds to the
design value at monitor i, and Emissionsjv corresponds to the anthropogenic NOx emissions
in SJV (i.e., 53,500 ton) in the 2032 CMAQ modeling developed here. Equation 2A-4
normalizes the percent changes from CARB's 2024 modeling to the PM2.5 DVs and
emissions from the 2032 case for application here.
Air quality ratios were calculated as above for all monitors in SJV, except for the
Tranquility monitor. The Tranquility monitor is in the Western part of Fresno County, away
from the urban exceedance monitors, and has a low PM2.5 concentration (e.g., 2024 annual
DV for CARB modeling is 5.6 |Lxg nr3). To develop representative air quality ratios for
counties in SJV, the 75th percentile of air quality ratios over monitors in the SJV counties
was calculated. These ratios were applied to adjust 2032 PM2.5 DVs according to 75%
reductions in anthropogenic NOx emissions for counties in SJV. The 75% reduction in
emissions corresponded to 40,200 tons. The 2032 DVs and the NOx-adjusted DVs are
shown in Table 2A-12 for the highest annual and 24-hour DV monitors in the county. Note
that these emission reductions were applied in meeting the existing standards (12/35) and
are therefore not part of the incremental cost and benefits of meeting alternative standards
relative to the existing standards.
(2A-4)
2A-53
-------
Table 2A-12 2032 PM2.5 DVs and NOx-adjusted PM2.5 DVs for the Highest Annual
and 24-Hour DV Monitors in SJV Counties
Site ID County AQ Ratio AQ Ratio 2032 DV 2032 DV NOx-Adj DV NOx-Adj DV
Annual 24-hour Annual 24-hour Annual 24-hour
Qigm 3perkton) Qg m 'per kton) fugm3) (ug 111 ') (ug 111 ') (ug 111 ')
060190011
Fresno
0.033
0.337
13.25
50.8
11.94
37.3
060195025
Fresno
0.033
0.337
13.34
48.3
12.03
34.8
060290010
Kern
0.041
0.418
14.40
57.2
12.74
40.4
060290016
Kern
0.041
0.418
16.20
56.3
14.54
39.5
060311004
Kings
0.072
0.467
15.27
48.2
12.37
29.5
060392010
Madera
0.038
0.216
12.13
39.8
10.60
31.1
060470003
Merced
0.027
0.178
11.88
36.3
10.79
29.1
060771002
San Joaquin
0.048
0.164
12.01
35.7
10.08
29.1
060990006
Stanislaus
0.034
0.222
12.43
38.7
11.08
29.8
061072002
Tulare
0.047
0.472
14.66
46.5
12.76
27.6
2 A.3.4 Applying Air Quality Ratios to Estimate Emission Reductions
The emissions reductions needed to just meet standards were estimated using the
primary PM2.5 air quality ratios in combination with the required incremental change in
concentration. The emission reductions required to meet the DV target for a standard were
calculated as follows:
AEmissionstd = w^e;,std-wTarflet,std x 1QQQ
AQTdtios^d
where AEmissionstd is the emission reduction required to meet an annual or 24-hour
standard; DVTarget,std is the level of the annual or 24-hour standard to be met; DVModeistd is the
modeled PM2.5 DV for the annual or 24-hour standard at the county highest monitor;
AQratiostd is the air quality ratio for that standard; and the factor of 1000 converts units
from kton to ton.
For example, the highest 2032 annual PM2.5 DV in Kern County is 14.54 |Lxg nr3 at site
06-029-0016 after applying the 75% NOx emission reduction to the 2032 DVs. The annual
air quality ratio for primary PM2.5 emissions in Northern California is 3.15 |Lxg nr3 per kton.
Therefore, to meet an annual standard of 12 |Lxg nr3, a total of 794 tons of primary PM2.5
emissions would be needed (i.e., (14.54-12.04)/3.15 x 1000). The highest 2032 24-hour
PM2.5 DV in Kern County is 40.4 |Lxg nr3 at site 06-029-0010 after applying the 75% NOx
emission reduction to the 2032 DVs. The 24-hour air quality ratio for primary PM2.5
2A-54
-------
emissions in Northern California is 9.97 |Lxg nr3 per kton. Therefore, to meet a 24-hour
standard of 35 |Lxg nr3, a total of 502 tons of primary PM2.5 emissions would be needed (i.e.,
(40.4-35.4)/9.97 x 1000). To determine the emission reductions needed to meet an annual
and 24-hour standard combination, the maximum needed emissions across standards is
calculated as follows:
A Emissionstdcombo = max(AEmissionAnnuai, AEmission24fir) (2A-6)
For the Kern County example, a total 794 tons of primary PM2.5 emission reductions are
needed to meet the 12/35 standard combination (i.e., max(794,502)).
The PM2.5 DVs associated with meeting a standard combination at the highest
monitor in a county are calculated using the required emission reductions as follows:
DVAnnual,std.combo ~ ^^Innuai.tntttai — AElTlissiOTlgtd combo X AQrCLtioAnnuai (2A-8)
DVoaily,std.combo ~ ^^Innuai.tntttai — AElTlissiOTlgtd combo X AQratiOjjany (2A-9)
In the Kern County example, the adjusted annual DV for the 12/35 case is 12.04 ngrrr3
(14.54-794*3.15/1000) and the adjusted 24-hour DV is 32.5 [ig nr3 (40.4-794*9.97/1000).
2A.3.4.1 Emission Reductions Needed to Meet 12/35
In the 2032 projections, PM2.5 DVs exceeded the existing standards for some
counties in the west (Figure 2A-26). To create the PM2.5 DVs for 12/35 analytical baseline,
the reductions in primary PM2.5 emissions needed to just meet 12/35 at the highest DV
monitor by county were calculated using the air quality ratios in Table 2A-9. PM2.5 DVs
were then adjusted according to those emission reductions. In Table 2A-13, the primary
PM2.5 emission reductions needed to meet 12/35 is shown by county for counties with
annual DVs greater than 8 |Lxg nr3 or 24-hour DVs greater than 30 |Lxg nr3 (note that required
emission reductions are zero for counties with DVs below 12/35). Table 2A-13 also
includes the corresponding air quality ratios, the 2032 PM2.5 DVs (or NOx-adjusted DVs for
South Coast and SJV counties), and the PM2.5 DVs that define the 12/35 analytical baseline.
2A-55
-------
Table 2A-13 Summary of Primary PM2.5 Emissions Reductions by County Needed to
Meet the Existing Standards (12/35) for Counties with 2032a Annual
DVs greater than 8 (j,g m-3 or 24-Hour DVs Greater than 30 |ig m3
State
County
AEmission
2032 to
12/35
(ton)
AQ Ratio
Annual
Qigm3
perkton)
AQ Ratio
2 4-hour
Qjgnv3per
kton)
2032a DV
Annual
(ugm')
2032a
DV
2 4-hour
km3)
12/35 DV
Annual
(Hgm3)
12/35 DV
2 4-hour
(Hgm3)
AL
Jefferson
0
1.22
3.51
9.86
20.1
9.86
20.1
AL
Talladega
0
1.22
3.51
8.20
16.3
8.20
16.3
AZ
Maricopa
0
2.14
8.70
9.47
26.7
9.47
26.7
AZ
Pinal
0
2.14
8.70
8.16
34.2
8.16
34.2
AZ
Santa Cruz
0
2.14
8.70
8.99
26.5
8.99
26.5
AR
Pulaski
0
1.22
3.51
8.99
19.3
8.99
19.3
AR
Union
0
1.22
3.51
8.12
17.0
8.12
17.0
CA
Alameda
0
3.15
9.97
10.14
25.4
10.14
25.4
CA
Butte
0
3.15
9.97
8.28
27.2
8.28
27.2
CA
Contra Costa
0
3.15
9.97
9.16
25.1
9.16
25.1
CA
Fresno
189
3.15
9.97
12.03
37.3
11.43
35.4
CA
Imperial
349
1.18
2.56
12.45
32.4
12.04
31.5
CA
Kern
791
3.15
9.97
14.54
40.4
12.04
32.5
CA
Kings
104
3.15
9.97
12.37
29.5
12.04
28.4
CA
Los Angeles
313
1.18
2.56
12.41
31.9
12.04
31.1
CA
Madera
0
3.15
9.97
10.60
31.1
10.60
31.1
CA
Marin
0
3.15
9.97
8.18
23.4
8.18
23.4
CA
Merced
0
3.15
9.97
10.79
29.1
10.79
29.1
CA
Napa
0
3.15
9.97
10.09
25.7
10.09
25.7
CA
Orange
0
1.18
2.56
7.47
28.5
7.47
28.5
CA
Plumas
1,244
3.15
9.97
14.52
47.8
10.60
35.4
CA
Riverside
1,478
1.18
2.56
13.78
36.9
12.04
33.1
CA
Sacramento
0
3.15
9.97
9.29
31.0
9.29
31.0
CA
San Bernardino
2,209
1.18
2.56
14.64
32.0
12.04
26.3
CA
San Diego
0
1.18
2.56
9.16
22.3
9.16
22.3
CA
San Joaquin
0
3.15
9.97
10.08
29.1
10.08
29.1
CA
San Luis Obispo
0
3.15
9.97
9.63
25.1
9.63
25.1
CA
Santa Clara
0
3.15
9.97
9.56
26.0
9.56
26.0
CA
Siskiyou
0
3.15
9.97
7.77
34.8
7.77
34.8
CA
Solano
0
3.15
9.97
9.04
24.7
9.04
24.7
CA
Stanislaus
0
3.15
9.97
11.08
29.8
11.08
29.8
CA
Sutter
0
3.15
9.97
8.82
27.6
8.82
27.6
CA
Tulare
230
3.15
9.97
12.76
27.6
12.04
25.3
CA
Ventura
0
1.18
2.56
9.23
33.5
9.23
33.5
CO
Denver
0
2.14
8.70
9.04
24.1
9.04
24.1
CO
Weld
0
2.14
8.70
8.14
24.9
8.14
24.9
DE
New Castle
0
1.37
4.33
8.14
21.4
8.14
21.4
DC
District of Columbia
0
1.22
3.51
8.21
19.8
8.21
19.8
2A-56
-------
State
County
AEmission
2032 to
12/35
(ton)
AQ Ratio
Annual
Cngm3
perkton)
AQ Ratio
2 4-hour
(|Agnv3per
kton)
2032a DV
Annual
(Hgm')
2032a
DV
2 4-hour
km')
12/35 DV
Annual
(M-gm3)
12/35 DV
2 4-hour
(M-gm3)
GA
Bibb
0
1.22
3.51
8.80
18.3
8.80
18.3
GA
Clayton
0
1.22
3.51
8.57
17.2
8.57
17.2
GA
Cobb
0
1.22
3.51
8.09
16.6
8.09
16.6
GA
DeKalb
0
1.22
3.51
8.08
18.2
8.08
18.2
GA
Dougherty
0
1.22
3.51
8.38
21.3
8.38
21.3
GA
Floyd
0
1.22
3.51
8.72
17.3
8.72
17.3
GA
Fulton
0
1.22
3.51
9.46
20.4
9.46
20.4
GA
Gwinnett
0
1.22
3.51
8.06
18.7
8.06
18.7
GA
Muscogee
0
1.22
3.51
8.68
27.3
8.68
27.3
GA
Richmond
0
1.22
3.51
8.54
21.0
8.54
21.0
GA
Wilkinson
0
1.22
3.51
8.97
19.2
8.97
19.2
ID
Benewah
0
2.14
8.70
9.61
35.2
9.61
35.2
ID
Canyon
0
2.14
8.70
8.86
31.4
8.86
31.4
ID
Lemhi
460
2.14
8.70
11.03
39.4
10.05
35.4
ID
Shoshone
138
2.14
8.70
11.04
36.6
10.75
35.4
IL
Cook
0
1.37
4.33
9.43
20.7
9.43
20.7
IL
Madison
0
1.37
4.33
9.03
19.0
9.03
19.0
IL
Saint Clair
0
1.37
4.33
8.99
17.6
8.99
17.6
IN
Allen
0
1.37
4.33
8.10
19.6
8.10
19.6
IN
Clark
0
1.37
4.33
8.58
19.8
8.58
19.8
IN
Elkhart
0
1.37
4.33
8.37
23.5
8.37
23.5
IN
Floyd
0
1.37
4.33
8.08
18.0
8.08
18.0
IN
Lake
0
1.37
4.33
8.92
22.2
8.92
22.2
IN
Marion
0
1.37
4.33
9.61
22.0
9.61
22.0
IN
St. Joseph
0
1.37
4.33
8.72
20.4
8.72
20.4
IN
Vanderburgh
0
1.37
4.33
8.40
17.5
8.40
17.5
IN
Vigo
0
1.37
4.33
8.47
19.2
8.47
19.2
KS
Wyandotte
0
1.22
3.51
8.15
19.9
8.15
19.9
KY
Jefferson
0
1.37
4.33
8.85
19.5
8.85
19.5
LA
Caddo
0
1.22
3.51
9.44
19.6
9.44
19.6
LA
East Baton Rouge
0
1.22
3.51
8.69
20.7
8.69
20.7
LA
Iberville
0
1.22
3.51
8.06
18.6
8.06
18.6
LA
St. Bernard
0
1.22
3.51
8.11
17.4
8.11
17.4
LA
West Baton Rouge
0
1.22
3.51
8.67
18.7
8.67
18.7
MD
Howard
0
1.37
4.33
8.21
18.6
8.21
18.6
MD
Baltimore (City]
0
1.37
4.33
8.17
21.5
8.17
21.5
MI
Kent
0
1.37
4.33
8.49
22.5
8.49
22.5
MI
Wayne
0
1.37
4.33
10.06
24.1
10.06
24.1
MS
Hinds
0
1.22
3.51
8.08
18.1
8.08
18.1
MO
Buchanan
0
1.37
4.33
8.15
17.1
8.15
17.1
MO
Jackson
0
1.37
4.33
8.09
18.1
8.09
18.1
MO
Jefferson
0
1.37
4.33
8.51
18.4
8.51
18.4
2A-57
-------
State
County
AEmission
2032 to
12/35
(ton)
AQ Ratio
Annual
Cngm3
perkton)
AQ Ratio
2 4-hour
(|Agnv3per
kton)
2032a DV
Annual
(Hgm')
2032a
DV
2 4-hour
km')
12/35 DV
Annual
(M-gm3)
12/35 DV
2 4-hour
(M-gm3)
MO
Saint Louis
0
1.37
4.33
8.82
19.1
8.82
19.1
MO
St. Louis City
0
1.37
4.33
8.36
19.8
8.36
19.8
MT
Lewis and Clark
253
2.14
8.70
8.57
37.6
8.03
35.4
MT
Lincoln
0
2.14
8.70
11.08
33.2
11.08
33.2
MT
Missoula
0
2.14
8.70
9.53
29.6
9.53
29.6
MT
Ravalli
299
2.14
8.70
8.75
38.0
8.11
35.4
MT
Silver Bow
0
2.14
8.70
8.64
30.6
8.64
30.6
NE
Douglas
0
2.14
8.70
8.08
17.8
8.08
17.8
NE
Sarpy
0
2.14
8.70
8.10
17.5
8.10
17.5
NV
Clark
0
2.14
8.70
9.24
23.0
9.24
23.0
NJ
Camden
0
1.37
4.33
9.21
22.3
9.21
22.3
NJ
Union
0
1.37
4.33
8.62
21.3
8.62
21.3
NM
Dona Ana
0
2.14
8.70
8.57
27.6
8.57
27.6
NY
New York
0
1.37
4.33
8.95
22.1
8.95
22.1
NC
Davidson
0
1.22
3.51
8.29
18.1
8.29
18.1
NC
Mecklenburg
0
1.22
3.51
8.15
17.5
8.15
17.5
NC
Wake
0
1.22
3.51
8.12
16.7
8.12
16.7
OH
Butler
0
1.37
4.33
9.82
20.7
9.82
20.7
OH
Cuyahoga
0
1.37
4.33
10.23
21.8
10.23
21.8
OH
Franklin
0
1.37
4.33
8.17
17.9
8.17
17.9
OH
Hamilton
0
1.37
4.33
8.91
20.1
8.91
20.1
OH
Jefferson
0
1.37
4.33
9.26
22.3
9.26
22.3
OH
Lucas
0
1.37
4.33
8.70
19.4
8.70
19.4
OH
Mahoning
0
1.37
4.33
8.20
19.0
8.20
19.0
OH
Stark
0
1.37
4.33
8.92
19.9
8.92
19.9
OH
Summit
0
1.37
4.33
8.72
19.9
8.72
19.9
OK
Tulsa
0
1.22
3.51
8.13
19.5
8.13
19.5
OR
Crook
11
2.14
8.70
8.29
35.5
8.27
35.4
OR
Harney
0
2.14
8.70
8.61
30.8
8.61
30.8
OR
Jackson
0
2.14
8.70
9.18
17.3
9.18
17.3
OR
Klamath
0
2.14
8.70
8.64
31.2
8.64
31.2
OR
Lake
218
2.14
8.70
7.89
37.3
7.42
35.4
OR
Lane
0
2.14
8.70
8.12
29.0
8.12
29.0
PA
Allegheny
0
1.37
4.33
11.19
34.7
11.19
34.7
PA
Armstrong
0
1.37
4.33
9.28
19.3
9.28
19.3
PA
Beaver
0
1.37
4.33
8.44
19.1
8.44
19.1
PA
Berks
0
1.37
4.33
8.18
23.9
8.18
23.9
PA
Cambria
0
1.37
4.33
9.08
22.8
9.08
22.8
PA
Chester
0
1.37
4.33
8.97
22.1
8.97
22.1
PA
Dauphin
0
1.37
4.33
8.37
24.5
8.37
24.5
PA
Delaware
0
1.37
4.33
9.96
23.6
9.96
23.6
PA
Lackawanna
0
1.37
4.33
8.07
18.6
8.07
18.6
2A-58
-------
State
County
AEmission
2032 to
12/35
(ton)
AQ Ratio
Annual
Cms 111:1
perkton)
AQ Ratio
2 4-hour
(|Agnv3per
kton)
2032a DV
Annual
(Hgm')
2032a
DV
2 4-hour
km')
12/35 DV
Annual
(M-gm3)
12/35 DV
2 4-hour
(M-gm3)
PA
Lancaster
0
1.37
4.33
10.14
26.8
10.14
26.8
PA
Lebanon
0
1.37
4.33
9.10
27.1
9.10
27.1
PA
Lehigh
0
1.37
4.33
8.17
21.0
8.17
21.0
PA
Mercer
0
1.37
4.33
8.42
19.6
8.42
19.6
PA
Philadelphia
0
1.37
4.33
9.75
22.7
9.75
22.7
PA
Washington
0
1.37
4.33
8.37
19.0
8.37
19.0
PA
York
0
1.37
4.33
8.56
21.4
8.56
21.4
RI
Providence
0
1.37
4.33
8.27
17.9
8.27
17.9
SC
Greenville
0
1.22
3.51
8.16
18.6
8.16
18.6
TN
Davidson
0
1.37
4.33
8.17
16.9
8.17
16.9
TN
Knox
0
1.37
4.33
8.60
19.3
8.60
19.3
TX
Cameron
0
1.22
3.51
9.75
24.5
9.75
24.5
TX
Dallas
0
1.22
3.51
8.08
17.1
8.08
17.1
TX
El Paso
0
1.22
3.51
9.08
23.8
9.08
23.8
TX
Harris
0
1.22
3.51
10.37
22.0
10.37
22.0
TX
Hidalgo
0
1.22
3.51
10.29
25.8
10.29
25.8
TX
Nueces
0
1.22
3.51
9.03
23.9
9.03
23.9
TX
Travis
0
1.22
3.51
9.07
18.8
9.07
18.8
UT
Box Elder
0
2.14
8.70
6.51
31.7
6.51
31.7
UT
Cache
0
2.14
8.70
7.07
32.7
7.07
32.7
UT
Davis
0
2.14
8.70
7.27
31.1
7.27
31.1
UT
Salt Lake
230
2.14
8.70
8.20
37.4
7.71
35.4
UT
Utah
0
2.14
8.70
7.63
31.5
7.63
31.5
UT
Weber
0
2.14
8.70
7.99
30.8
7.99
30.8
WA
King
0
2.14
8.70
8.31
26.5
8.31
26.5
WA
Kittitas
299
2.14
8.70
7.37
38.0
6.73
35.4
WA
Okanogan
0
2.14
8.70
-
31.8
-
31.8
WA
Snohomish
0
2.14
8.70
7.07
31.3
7.07
31.3
WA
Spokane
0
2.14
8.70
8.18
27.2
8.18
27.2
WA
Yakima
391
2.14
8.70
8.18
38.8
7.34
35.4
wv
Berkeley
0
1.37
4.33
8.21
22.1
8.21
22.1
wv
Brooke
0
1.37
4.33
8.41
19.8
8.41
19.8
wv
Marshall
0
1.37
4.33
8.46
19.7
8.46
19.7
a For South Coast and SJV counties, these are DVs that result from applying 75% NOx emission reduction to
the 2032 DVs.
2A.3.4.2 Emission Reductions Needed to Meet 10/35, 9/35,8/35, and 10/30
The primary PM2.5 emission reductions needed to meet the alternative standard
levels of 10/35,10/30, 9/35, and 8/35 relative to the 12/35 analytical baseline were
calculated to inform identification of emission controls. These emission amounts were
2A-59
-------
calculated using Equations 2A-5 and 2A-6 and the air quality ratios in the Table 2A-9 and
are shown in Table 2A-14. The total emission reductions needed in the eastern and
western US is also shown in Figure 2A-32 for the standard combinations.
o
c
50,000-
40,000-
30,000-
£ 20,000-
LU
10,000-
o-
12/35
BB
10/35 10/30 9/35
Standard Level
8/35
East
West
Figure 2A-32 Total Primary PM2.5 Emission Reductions Needed to Meet the
Alternative Standard Levels of 10/35,10/30, 9/35, and 8/35 Relative
to the 12/35 Analytical Baseline in the East and West
Table 2A-14 Primary PM2.5 Emission Reductions Needed to Meet the Alternative
Standard Levels of 10/35,10/30,9/35, and 8/35 Relative to the 12/35
Analytical Baseline
State
County
Emission
10/35
(ton)
Emission
9/35
(ton)
Emission
8/35
(ton)
Emission
10/30
(ton)
Alabama
Jefferson
0
670
1,488
0
Alabama
Talladega
0
0
131
0
Arizona
Maricopa
0
201
669
0
Arizona
Pinal
0
0
56
437
Arizona
Santa Cruz
0
0
444
0
Arkansas
Pulaski
0
0
111
0
Arkansas
Union
0
0
65
0
California
Alameda
32
349
666
32
California
Butte
0
0
76
0
California
Contra Costa
0
38
355
0
California
Fresno
440
757
1,074
502
California
Imperial
1,701
2,551
3,402
1,701
California
Kern
634
951
1,268
634
California
Kings
634
951
1,268
634
California
Los Angeles
1,701
2,551
3,402
1,701
California
Madera
179
496
813
179
2A-60
-------
State
County
Emission
10/35
(ton)
Emission
9/35
(ton)
Emission
8/35
(ton)
Emission
10/30
(ton)
California
Marin
0
0
44
0
California
Merced
237
554
871
237
California
Napa
16
333
650
16
California
Orange
0
0
0
0
California
Plumas
176
493
810
502
California
Riverside
1,701
2,551
3,402
1,701
California
Sacramento
0
79
396
60
California
San Bernardino
1,701
2,551
3,402
1,701
California
San Diego
0
102
953
0
California
San Joaquin
12
329
646
12
California
San Luis Obispo
0
187
504
0
California
Santa Clara
0
165
482
0
California
Siskiyou
0
0
0
441
California
Solano
0
0
317
0
California
Stanislaus
331
648
965
331
California
Sutter
0
0
247
0
California
Tulare
634
951
1,268
634
California
Ventura
0
162
1,012
1,213
Colorado
Denver
0
0
468
0
Colorado
Weld
0
0
47
0
Delaware
New Castle
0
0
73
0
District Of Columbia
District of Columbia
0
0
139
0
Georgia
Bibb
0
0
621
0
Georgia
Clayton
0
0
433
0
Georgia
Cobb
0
0
41
0
Georgia
DeKalb
0
0
33
0
Georgia
Dougherty
0
0
278
0
Georgia
Floyd
0
0
556
0
Georgia
Fulton
0
343
1,161
0
Georgia
Gwinnett
0
0
16
0
Georgia
Muscogee
0
0
523
0
Georgia
Richmond
0
0
409
0
Georgia
Wilkinson
0
0
760
0
Idaho
Benewah
0
267
734
552
Idaho
Canyon
0
0
383
115
Idaho
Lemhi
3
471
939
574
Idaho
Shoshone
330
797
1,265
574
Illinois
Cook
0
285
1,017
0
Illinois
Madison
0
0
724
0
Illinois
Saint Clair
0
0
695
0
Indiana
Allen
0
0
44
0
Indiana
Clark
0
0
395
0
2 A-61
-------
State
County
Emission
10/35
(ton)
Emission
9/35
(ton)
Emission
8/35
(ton)
Emission
10/30
(ton)
Indiana
Elkhart
0
0
241
0
Indiana
Floyd
0
0
29
0
Indiana
Lake
0
0
644
0
Indiana
Marion
0
417
1,149
0
Indiana
St. Joseph
0
0
498
0
Indiana
Vanderburgh
0
0
263
0
Indiana
Vigo
0
0
315
0
Kansas
Wyandotte
0
0
90
0
Kentucky
Jefferson
0
0
593
0
Louisiana
Caddo
0
327
1,145
0
Louisiana
East Baton Rouge
0
0
531
0
Louisiana
Iberville
0
0
16
0
Louisiana
St. Bernard
0
0
57
0
Louisiana
West Baton Rouge
0
0
515
0
Maryland
Howard
0
0
124
0
Maryland
Baltimore (City]
0
0
95
0
Michigan
Kent
0
0
329
0
Michigan
Wayne
15
746
1,478
15
Mississippi
Hinds
0
0
33
0
Missouri
Buchanan
0
0
80
0
Missouri
Jackson
0
0
37
0
Missouri
Jefferson
0
0
344
0
Missouri
Saint Louis
0
0
571
0
Missouri
St. Louis City
0
0
234
0
Montana
Lewis and Clark
0
0
0
574
Montana
Lincoln
486
954
1,422
486
Montana
Missoula
0
229
697
0
Montana
Ravalli
0
0
33
574
Montana
Silver Bow
0
0
281
23
Nebraska
Douglas
0
0
19
0
Nebraska
Sarpy
0
0
28
0
Nevada
Clark
0
94
561
0
New Jersey
Camden
0
124
856
0
New Jersey
Union
0
0
424
0
New Mexico
Dona Ana
0
0
248
0
New York
New York
0
0
666
0
North Carolina
Davidson
0
0
204
0
North Carolina
Mecklenburg
0
0
90
0
North Carolina
Wake
0
0
65
0
Ohio
Butler
0
571
1,303
0
Ohio
Cuyahoga
139
871
1,603
139
Ohio
Franklin
0
0
95
0
2A-62
-------
State
County
Emission
10/35
(ton)
Emission
9/35
(ton)
Emission
8/35
(ton)
Emission
10/30
(ton)
Ohio
Hamilton
0
0
637
0
Ohio
Jefferson
0
161
893
0
Ohio
Lucas
0
0
483
0
Ohio
Mahoning
0
0
117
0
Ohio
Stark
0
0
644
0
Ohio
Summit
0
0
498
0
Oklahoma
Tulsa
0
0
74
0
Oregon
Crook
0
0
105
574
Oregon
Harney
0
0
267
46
Oregon
Jackson
0
65
533
0
Oregon
Klamath
0
0
281
92
Oregon
Lake
0
0
0
574
Oregon
Lane
0
0
37
0
Pennsylvania
Allegheny
842
1,573
2,305
994
Pennsylvania
Armstrong
0
176
907
0
Pennsylvania
Beaver
0
0
293
0
Pennsylvania
Berks
0
0
102
0
Pennsylvania
Cambria
0
29
761
0
Pennsylvania
Chester
0
0
681
0
Pennsylvania
Dauphin
0
0
241
0
Pennsylvania
Delaware
0
673
1,405
0
Pennsylvania
Lackawanna
0
0
22
0
Pennsylvania
Lancaster
73
805
1,537
73
Pennsylvania
Lebanon
0
44
776
0
Pennsylvania
Lehigh
0
0
95
0
Pennsylvania
Mercer
0
0
278
0
Pennsylvania
Philadelphia
0
520
1,251
0
Pennsylvania
Washington
0
0
241
0
Pennsylvania
York
0
0
381
0
Rhode Island
Providence
0
0
168
0
South Carolina
Greenville
0
0
98
0
Tennessee
Davidson
0
0
95
0
Tennessee
Knox
0
0
410
0
Texas
Cameron
0
581
1,398
0
Texas
Dallas
0
0
33
0
Texas
El Paso
0
33
850
0
Texas
Harris
270
1,087
1,905
270
Texas
Hidalgo
204
1,022
1,840
204
Texas
Nueces
0
0
809
0
Texas
Travis
0
25
842
0
Utah
Box Elder
0
0
0
149
Utah
Cache
0
0
0
264
2A-63
-------
State
County
Emission
10/35
(ton)
Emission
9/35
(ton)
Emission
8/35
(ton)
Emission
10/30
(ton)
Utah
Davis
0
0
0
80
Utah
Salt Lake
0
0
0
574
Utah
Utah
0
0
0
126
Utah
Weber
0
0
0
46
Washington
King
0
0
126
0
Washington
Kittitas
0
0
0
574
Washington
Okanogan
0
0
0
161
Washington
Snohomish
0
0
0
103
Washington
Spokane
0
0
65
0
Washington
Yakima
0
0
0
574
West Virginia
Berkeley
0
0
124
0
West Virginia
Brooke
0
0
271
0
West Virginia
Marshall
0
0
307
0
2A.4 Calculating PM2.5 Concentration Fields for Standard Combinations
National PM2.5 concentration fields corresponding to meeting the existing and
alternative standards were developed to inform health benefit calculations. First, a gridded
PM2.5 concentration field for the 2032 CMAQ modeling case was developed using the
enhanced Voronoi Neighbor Average (eVNA) method (Ding et al., 2016, Gold et al., 1997,
USEPA, 2007). Next, the incremental difference in annual PM2.5 DVs between the 2032 case
and cases of meeting standard combinations was calculated at monitors and interpolated
to the spatial grid. The resulting field of incremental PM2.5 concentration was then
subtracted from the 2032 eVNA field to create the gridded field for the standard
combination. The steps in developing the PM2.5 concentration fields are described further
below.
2A.4.1 Creating the PM2.5 Concentration Field for 2032
The gridded field of annual average PM2.5 concentrations for 2032 was developed
using the eVNA method that combines information from the model and monitors to predict
PM2.5 concentrations. The eVNA approach was applied using SMAT-CE, version 1.8, and has
been previously described in EPA's modeling guidance document (USEPA, 2018) and the
user's guide for the predecessor software to SMAT-CE (Abt, 2014). The method is briefly
described here, and more details are available in the primary references.
2A-64
-------
Quarterly average PM2.5 component concentrations measured during the 2015-2017
period were interpolated to the spatial grid using inverse distance-squared-weighting of
monitored concentrations that were further weighted by the ratio of the 2016 CMAQ value
in the prediction grid cell to CMAQ value in the monitor-containing grid cell. Weighting by
the ratio of CMAQ values adjusts the interpolation of monitor data to account for spatial
gradients in the CMAQ fields. This step results in an interpolated field of gradient-adjusted
observed concentrations for each PM2.5 component and each quarter:
eVNAs q 2016 = 1 VKeightxMonitorxs q ^^s,g,2°16 (2A-10)
MoaeiS)q2 016
where eVNAs,q,2016 is the gradient-adjusted quarterly-average concentration of PM2.5
component species, s, during quarter, q, at the prediction grid cell; Weightx is the inverse-
distance-squared weight for monitor, x, at the location of the prediction grid cell;
Monitorx,s,q is the average of the quarterly-average monitored concentrations for species, s,
at monitor, x, during quarter, q, in 2015-2017; Models,q,2016 is the quarterly-average 2016
CMAQ concentration of species, s, during quarter, q, in the prediction grid cell; and
Modelx,s,q,2016 is the quarterly-average 2016 CMAQ concentration of species, s, during
quarter, q, in the grid cell of monitor, x.
The 2016 eVNA fields for quarterly-average PM2.5 component concentrations are the
starting point for developing the 2032 PM2.5 concentration field. To create eVNA fields for
PM2.5 components in 2032, the 2016 eVNA component concentration in each grid cell is
multiplied by the corresponding ratio of the quarterly-average CMAQ concentration
predictions in 2032 and 2016:
eVNAs,q,2032 = eVNAs,q,2016^^ (2A-11)
The PM2.5 concentration fields for quarters in 2032 are calculated by summing the
2032 PM2.5 component concentration by quarter. The 2032 PM2.5 concentration field is
then calculated by averaging the 2032 quarterly PM2.5 concentrations. The resulting 2032
PM2.5 concentration fields is shown in Figure 2A-33.
2A-65
-------
ug/m3
> 15
12
-120 -110 -100
Longitude
-100 -90 -80
-70
Figure 2A-33 PM2.5 Concentration for 2032 based on eVNA Method
2A.4.2 Creating Spatial Fields Corresponding to Meeting Standards
To create spatial fields corresponding to meeting standard levels, the 2032
concentration field was adjusted according to the change in PM2.5 concentrations
associated with the difference in annual PM2.5 DVs between the 2032 case and the cases
where standards are met. To implement this adjustment, the difference in annual PM2.5 DVs
was calculated at the county highest monitor between the 2032 case and cases of meeting
the 12/35,10/30,10/35, 9/35, and 8/35 standard combinations. For the county non-
highest monitors, the difference in PM2.5 DVs was estimated by proportionally adjusting
DVs according to the percent change in PM2.5 DV at the highest monitor.
Due to the relatively large size and complex terrain of counties in the western US,
the proportional adjustment of DVs within counties was limited in some cases.
Proportional adjustment was not applied to seven sites that have 2032 annual PM2.5 DVs
less than 7 j_ig nr3 and are located within counties that exceed 12/35 standard
combination: i.e., 06-029-0011 (Kern, CA), 06-037-9033 (Los Angeles, CA), 06-065-5001
(Riverside, CA), 06-071-8001 (San Bernardino, CA), 30-049-0004 (Lewis and Clark, MT),
49-035-1001 (Salt Lake, UT), 49-035-3013 (Salt Lake, UT). The relatively low annual PM2.5
DVs for these sites compared with the highest-DV monitor suggests they are influenced by
different air pollution processes than the highest-DV monitor. Additionally, the annual
2A-66
-------
PM2.5 DV at the 06-065-2002 site in Riverside County was not adjusted due to its location
outside of the portion of the county within the South Coast Air Basin that contains the
highest-DV monitor.
After adjusting the annual PM2.5 DVs at county monitors and calculating the
difference in annual DVs between the 2032 case and cases of meeting the standard
combinations, the annual PM2.5 DV differences were interpolated to the spatial grid using
inverse-distance-squared VNA interpolation. The interpolated field was then clipped to
grid cells within 50 km of monitors whose design values changed in meeting the standard
level (USEPA, 2012a). An example of a spatial field for the incremental change in PM2.5
concentration between the 2032 case and the case of meeting the existing standard
combination, 12/35, is shown in Figure 2A-34.
HQ m"3
-120 -110 -100 -90 -80 -70
Longitude
Figure 2A-34 PM2.5 Concentration Improvement Associated with Meeting 12/35
Relative to the 2032 Case
National PM2.5 concentration fields were developed for each standard combination
by subtracting the corresponding VNA field of incremental PM2.5 concentration from the
2032 eVNA concentration field. The resulting PM2.5 concentration fields were then
compared with regional estimates of background PM2.5 concentrations based on a previous
2k-61
-------
CMAQ modeling study with North American anthropogenic emissions set to zero (see Table
3-23 of USEPA, 2009). For a small number of grid cells (two for the 9/35 case and seven for
the 8/35 case) in the full attainment scenario, adjusted concentrations were below the
Southern California background level of 0.84 |Lxg nr3 and were reset to that value. These
grid cells are over the mountain ranges downwind of Los Angeles and Bakersfield where
concentrations are much lower than in the South Coast Air Basin and SJV. In the partial
attainment case, all concentrations were above the regional background levels and no
adjustments were applied.
2A.5 Calculating DV Impacts for Further EGU Emission Reductions
Additional EGU emissions reductions are expected to occur between 2016 and 2030
beyond those included in the 2032 CMAQ simulation. These additional emission reductions
are mainly due to planned EGU retirements that were not known at the time of
development of the emission projections. In this section, we consider the potential
influence of these emission reductions on PM2.5 DVs. First, the influence of further primary
PM2.5 emission reductions from EGUs on DVs is estimated for counties with 2032 PM2.5 DVs
that exceed the alternative standard levels. Next, the regional impact on annual PM2.5 DVs
of the estimated total SO2 and NOx emission reductions from EGUs in the eastern US is
estimated. Finally, the influence of these SO2 and NOx emission reductions on annual PM2.5
DVs in nearby county groups is estimated for two areas with the largest SO2 reductions
expected near monitors with 2032 PM2.5 that exceed alternative standard levels.
2A.5.1 Estimating the Influence of Additional Primary PM2.5 EGU Reductions
For ten of the counties with 2032 DVs that exceed the alternative annual standard
level of 8 |Lxg nr3 or the 24-hour standard level of 30 |Lxg nr3, additional reductions in
primary PM2.5 emissions from EGUs beyond the 2032 modeling case are expected. These
counties are shown in Table 2A-15 with the expected emission reductions and the
estimated influence on the annual and 24-hour DV. For reference, the 2032 DVs,
corresponding to projections based on the CMAQ simulation of the 2032 emissions case
(Section 2.2), are also shown in the Table. The DV impacts were calculated by applying the
air quality ratios for these counties (Table 2A-9) to the emission estimates. The largest
influence of the further EGU emission reductions is in Hamilton, OH (0.85 |Lxg nr3), Jefferson,
2A-68
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MO (0.31 |Lxg nr3), and Allegheny, PA (0.18 |Lxg nr3). The significance of these DV reductions
in the context of meeting alternative standard levels is discussed in section 3.2.4.
Table 2A-15 Primary PM2.5 Emission Reductions from EGUs Expected beyond 2032
Modeling Case and Estimated Impact on DVs for Counties Exceeding
Alternative Standards in the 2032 Case
State
County
PM2.5 Emissions
Reduction
(ton)
2032
Annual DVa
Oigm-3)
2032
24-hour DVa
(MB m-3)
ADV
Annual
(ngnr3)
ADV
24-hour
(M-S m-3)
Arizona
Maricopa
6.0
9.47
26.7
0.01
0.1
California
Los Angeles
5.9
12.73
34.9
0.01
0.0
Colorado
Weld
0.1
8.14
24.9
0.00
0.0
Missouri
Jefferson
229.0
8.51
18.4
0.31
1.0
Nevada
Clark
19.4
9.24
23.0
0.04
0.2
Ohio
Hamilton
619.0
8.91
20.1
0.85
2.7
Pennsylvania
Allegheny
133.8
11.19
34.7
0.18
0.6
Texas
Dallas
11.7
8.08
17.1
0.01
0.0
Texas
El Paso
4.5
9.08
23.8
0.01
0.0
Texas
Travis
2.9
9.07
18.8
0.00
0.0
aThe 2032 DVs correspond to projections based on the CMAQ simulation of the 2032 emissions case
(Section 2.2) without any additional DV adjustments.
2A.5.2 Estimating the Regional Influence of Additional SO2 and NOx EGU Emission
Reductions
For states in the eastern US, a combined total of 170,411 tons of SO2 and 52,718 tons
of NOx emission reductions are expected to occur from EGUs beyond the 2032 modeling
case. The emission tons are listed by state and county in Table 2A-16. Sensitivity model
simulations with 50% SO2 and NOx emission reductions relative to the 2028 case described
above were used to estimate the regional influence of these emission reductions on annual
DVs in the eastern US.
The counties with 50% reductions of SO2 and NOx emissions in the 2028 CMAQ
sensitivity simulations are shown in Figure 2A-35. The eastern states considered in this
analysis are shaded in red in the figure. The total change in emissions in these states in the
50% NOx emission reduction simulation was 1,566,554 tons, and the total emission
reduction was 479,342 tons in the 50% SO2 emission reduction simulation. To estimate the
regional influence of the additional EGU emission reductions, the change in DVs for the
sensitivity simulations was calculated at monitors in the eastern states and scaled by the
ratio of the EGU emission reductions to the sensitivity simulation emission reductions (i.e.,
2A-69
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52,718 tons / 1,566,554 tons for NOx, and 170,411 tons / 479,342 tons for SO2). Across
monitors in the eastern states, the median total PM2.5 DV change estimated this way is 0.06
Hg nr3 (25th/75th percentile: 0.05 |Lxg nr3/0.08 |Lxg nr3). The full distribution of the estimated
changes in annual PM2.5 DVs is shown in Figure 2A-36. Due to differences in the spatial
distribution and magnitude of the emission changes in the sensitivity simulations and the
EGU reductions, the PM2.5 DV impacts are rough approximations, and photochemical
modeling of the EGU reductions would be needed to provide better estimates.
Table 2A-16 SO2 and NOx Emission Reductions from EGUs Expected Beyond 2032
Modeling Case by County
State
County
S02
(ton}
NOx
(ton)
Connecticut
Hartford
385
1,101
Florida
Alachua
0
2
Florida
Hillsborough
1,328
414
Illinois
Christian
1,942
1,133
Illinois
Jasper
4,770
1,934
Illinois
Lake
1,024
933
Illinois
Massac
18,793
4,237
Illinois
Randolph
4,206
5,100
Illinois
Will
826
983
Indiana
LaPorte
1,289
1,441
Indiana
Spencer
35
19
Indiana
Warrick
766
442
Iowa
Des Moines
0
5
Iowa
Muscatine
1,039
1,308
Iowa
Winnebago
0
9
Louisiana
Ascension
0
147
Louisiana
Calcasieu
0
656
Louisiana
Pointe Coupee
4,225
1,149
Louisiana
Rapides
14,360
1,278
Maryland
Montgomery
0
14
Massachusetts
Middlesex
0
77
Michigan
Eaton
3,018
892
Michigan
Ottawa
6,799
3,343
Minnesota
Blue Earth
102
602
Minnesota
Cook
877
477
Minnesota
Goodhue
100
543
Missouri
Franklin
35,424
-
Missouri
Jasper
0
76
Missouri
Jefferson
37,421
3,286
New Jersey
Essex
0
11
2 A-70
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State
County
S02
(ton}
NOx
(ton)
New Jersey
Salem
0
8
New Mexico
Lea
0
3
New York
Queens
0
7
North Carolina
Cleveland
1,140
2,082
Ohio
Clermont
17,532
5,849
Ohio
Hamilton
3,663
6,127
Ohio
Lorain
3,799
1,437
Oklahoma
Osage
0
13
Pennsylvania
Allegheny
628
1,254
Pennsylvania
Bucks
0
39
Pennsylvania
Northampton
0
25
Pennsylvania
Somerset
0
16
South Dakota
Minnehaha
0
1
Tennessee
Stewart
720
527
Texas
Bexar
3,726
751
Texas
Dallas
0
59
Texas
El Paso
0
328
Texas
Orange
0
1,246
Texas
Pecos
0
4
Texas
Travis
0
15
Virginia
Dinwiddie
0
30
Wisconsin
Marathon
0
77
Wisconsin
Milwaukee
473
1,206
50-
Longitude
Figure 2A-35 PM2.5 Counties with 50% Reductions of SO2 Emissions in the 2028
CMAQ Sensitivity Simulations (Green) and Eastern States Considered
in the EGU Sensitivity Analysis (Red)
2 A-71
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0,12-
NOx S02 Total
Figure 2A-36 Distributions of the Estimated Changes in Annual PM2.5 DVs in the
Eastern U.S. Associated with NOx and SO2 EGU Emission Reductions in
the Eastern US Beyond the 2032 Modeling Case
2A.5.3 Estimating the Local Influence of Additional SO2 and NOx EGU Emission
Reductions
In addition to estimating potential regional impacts of the additional SO2 and NOx
emissions reductions from EGUs, we considered the relatively local impacts of the
reductions on DVs in nearby counties for two cases with large SO2 reductions. In one case,
the EGU emission reductions in Franklin and Jefferson, MO, and Randolph, IL were grouped
to give a total of 77,100 tons of SO2 and 8,390 tons of NOx emissions. To estimate the
impact of these emission reductions, SO2 and NOx air quality ratios for nearby counties
were developed using the 2028 sensitivity modeling. The change in annual DV at sites
within the relevant cluster of counties with emission reductions in the 2028 sensitivity
modeling (Figure 2A-37) was calculated and divided by the change in emissions in that
county group in the sensitivity modeling. This yielded an average annual air quality ratio
for NOx of 0.002 |Lxg nr3 and for SO2 of 0.006 |Lxg nr3 for estimating the impact of SO2 and
NOx emission reductions in the county group on the DVs in that group. Applying these
ratios to the combined emission reductions in Franklin, Jefferson, and Randolph counties,
yields an increment in the annual PM2.5 DV of about 0.5 |Lxg nr3. The additional EGU
emission reductions may have a DV impact of approximately this amount at the sites listed
in Table 2A-17, although a better estimate could be provided through explicit
2A-72
-------
photochemical modeling of the sources. The significance of these DV reductions in the
context of meeting alternative standard levels is discussed in section 3.2.4.
1
j
id
fT ^
. / 1
Figure 2A-37 County Group in 2028 Sensitivity Modeling Used in Estimating the
Response of DVs to EGU Emission Changes in Franklin and Jefferson,
MO, and Randolph, IL
Table 2A-17 2032 PM2.5 DVs and Estimated Influence of Emission Reductions from
EGUs in Franklin and Jefferson, MO, and Randolph, IL on DVs in Nearby
Counties
Site ID
State
County
2032
Annual DVa
Oigm-3)
ADV
Annual
171191007
Illinois
Madison
9.03
0.5
171630010
Illinois
Saint Clair
8.99
0.5
290990019
Missouri
Jefferson
8.51
0.5
291893001
Missouri
Saint Louis
8.82
0.5
295100085
Missouri
St Louis City
8.36
0.5
aThe 2032 DVs correspond to projections based on the CMAQ simulation of
the 2032 emissions case (Section 2.2) without any additional DV adjustments.
In a second case, emission reductions in Clermont and Hamilton, OH were grouped
to give a total of 21,190 tons of SO2 and 11,980 tons of NOx. To estimate the impact of these
reductions on DVs in nearby counties, the change in annual DV at sites within the relevant
cluster of counties with emission reductions in the 2028 sensitivity modeling (Figure 2A-
38) was calculated and divided by the change in emissions in that county group in the
2A-73
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sensitivity modeling. This yielded an average annual air quality ratio for NOx of 0.003 |j,g nr
3 and for SO2 of 0.014 |j,g m-3 for estimating the impact of SO2 and NOx emission reductions
in the county group on the DVs in that group. Applying these ratios to the combined
emission reductions in Clermont and Hamilton counties, yields an increment in the annual
PM2.5 DV of about 0.3 |j,g m-3. The additional EGU emission reductions may have a DV
impact of approximately this amount at the sites listed in Table 2A-18, although a better
estimate could be provided through explicit photochemical modeling of the sources.
Figure 2A-38 County Group in 2028 Sensitivity Modeling Used in Estimating the
Response of DVs to EGU Emission Changes in Clermont and Hamilton,
OH
Table 2A-18 2032 PM2.5 DVs and Estimated Influence of Emission Reductions from
EGUs in Clermont and Hamilton, OH on DVs in Nearby Counties
Site ID
State
County
2032
Annual DVa
(|j.gm-3)
ADV
Annual
fugm-3!
390170022
Ohio
Butler
9.82
0.3
390610014
Ohio
Hamilton
8.91
0.3
aThe 2032 DVs correspond to projections based on the CMAQ simulation of
the 2032 emissions case (Section 2.2) without any additional DV adjustments.
2 A-74
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CHAPTER 3: CONTROL STRATEGIES AND PM2.5 EMISSIONS REDUCTIONS
Overview
The current annual primary PM2.5 standard is 12 ng/m3, and the current 24-hour
standard is 35 |~ig/m3. The Agency is proposing to revise the current annual PM2.5 standard
to a level within the range of 9-10 |~ig/m3 and is soliciting comment on an alternative
annual standard level down to 8 |~ig/m3 and a level up to 11 ng/m3. The Agency is also
proposing to retain the current 24-hour standard of 35 ng/m3 and is soliciting comment on
an alternative 24-hour standard level of 25 ng/m3. In this Regulatory Impact Analysis
(RIA), we are analyzing the proposed annual and current 24-hour alternative standard
levels of 10/35 ng/m3 and 9/35 ng/m3, as well as the following two more stringent
alternative standard levels: (1) an alternative annual standard level of 8 ng/m3 in
combination with the current 24-hour standard (i.e., 8/35 |j,g/m3), and (2) an alternative
24-hour standard level of 30 ng/m3 in combination with the proposed annual standard
level of 10 ng/m3 (i.e., 10/30 |j,g/m3). Because the EPA is proposing that the current
secondary PM standards be retained, we did not evaluate alternative secondary standard
levels in this RIA.
As discussed in Chapter 1 in the Overview of the Regulatory Impact Analysis, the
analyses in this RIA rely on national-level data (emissions inventory and control measure
information) for use in national-level assessments (air quality modeling, control strategies,
environmental justice, and benefits estimation). However, the ambient air quality issues
being analyzed are highly complex and local in nature, and the results of these national-
level assessments therefore contain uncertainty. It is beyond the scope of this RIA to
develop detailed local information for the areas being analyzed, including populating the
local emissions inventory, obtaining local information to increase the resolution of the air
quality modeling, and obtaining local information on emissions controls, all of which would
reduce some of the uncertainty in these national-level assessments. For example, having
more refined data would be ideal for agricultural dust and burning, prescribed burning,
3-1
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and non-point (area) sources due to their large contribution to primary PM2.5 emissions
and the limited availability of emissions controls.1
We assume that areas will be designated such that they are required to reach
attainment by 2032, and we developed our projected baselines for emissions and air
quality for 2032. To estimate the costs and benefits of the proposed and more stringent
annual and 24-hour PM2.5 alternative standard levels, we first prepared an analytical
baseline for 2032 that assumes full compliance with the current standards of 12/35 |~ig/m3.
From that baseline, we then analyze illustrative control strategies that areas might employ
toward attaining the proposed and more stringent annual and 24-hour PM2.5 alternative
standard levels.2 Because PM2.5 concentrations are most responsive to direct PM emissions
reductions, as discussed in Chapter 2, Section 2.1.3, we analyze direct, local PM2.5 emissions
reductions by individual counties. Section 2.1.3 also includes a discussion of historical and
projected emissions trends for direct PM2.5 and precursor emissions (i.e., SO2, NOx, VOC,
and ammonia), as well as a discussion of the "urban increment" of consistently higher PM2.5
concentrations over urban areas. The projections of additional, large reductions in SO2 and
NOx emissions in the 2032 case further motivate the need for control of local primary PM2.5
sources to address the highest PM2.5 concentrations in urban areas.
For the eastern U.S. where counties are relatively small and terrain is relatively flat,
we identified potential PM2.5 emissions reductions within each county and in adjacent
counties within the same state, where needed. As discussed below in Section 3.2.2, when
we applied the emissions reductions from adjacent counties, we used a |~ig/m3 per ton PM2.5
air quality ratio that was four times less responsive than the ratio used when applying in-
county emissions reductions. Because the counties in the western U.S. are generally large
and the terrain is more complex, we only identified potential PM2.5 emissions reductions
within each county.
1 Examples of area source emissions include area fugitive dust, residential wood combustion, and commercial
cooking emissions.
2 We define control strategy as a group of control measures. In this analysis, we developed a control strategy
for each alternative standard level analyzed.
3-2
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Next, we prepare illustrative control strategies. We apply end-of-pipe control
technologies to non-electric generating unit (non-EGU) stationary sources (e.g., fabric
filters, electrostatic precipitators, venturi scrubbers) and control measures to nonpoint
(area) sources (e.g., installing controls on charbroilers), to residential wood combustion
sources (e.g., converting woodstoves to gas logs), and for area fugitive dust emissions (e.g.,
paving unpaved roads) in analyzing PM2.5 emissions reductions needed toward attaining
the alternative standard levels. We did not apply controls to EGUs or mobile sources;
Chapter 2, Section 2.1.3 includes a discussion of SO2 and NOx emissions decreases reflected
in the projections between 2016 and 2032, noting that over the period (1) NOx emissions
are projected to decrease by 3.8 million tons (40 percent), with the greatest reductions
from mobile source and EGU emissions inventory sectors, and (2) SO2 emissions are
projected to decrease by 1 million tons (38 percent), with the greatest reductions from the
EGU emissions inventory sector. In addition, Chapter 2, Section 2.2.1.2 includes a
discussion of the EGU and non-EGU rules reflected in the projections for this analysis.
Further, Appendix 2A, Section 2A.5 includes a discussion of EGU NOx, SO2, and PM2.5
emissions reductions that are expected to occur from firm retirements between 2016 and
2030; these reductions are beyond those included in the air quality modeling for this
analysis. Lastly, Section 2A.5 includes a discussion of the potential influence of the
reductions from these firm EGU retirements on future PM2.5 design values (DVs) regionally
in the east, as well as locally.
The illustrative control strategy analyses indicate that counties in the northeast and
southeast U.S. do not need additional emissions reductions after the application of controls
to meet alternative standard levels of 10/35 |~ig/m3 and 10/30 |j,g/m3; however, these
counties would need additional PM2.5 emissions reductions to meet alternative standard
levels of 9/35 |~ig/m3 and 8/35 ng/m3. Also, the analysis indicates that counties in the west
and California would need additional PM2.5 emissions reductions after the application of
controls to meet all of the alternative standard levels analyzed.
The remainder of this chapter is organized into four sections. Section 3.1 provides a
summary of the steps that we took to create the analytical baseline. Section 3.2 presents
the illustrative control strategies identified to assess the proposed and more stringent
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annual and 24-hour alternative standard levels in the continental U.S., along with the
resulting estimated emissions reductions. Section 3.3 includes a summary of the key
limitations and uncertainties associated with the control strategy analyses. Finally, Section
3.4 includes the references for the chapter. We present the costs associated with the
estimated PM2.5 emissions reductions in Chapter 4.
3.1 Preparing the 12/35 (ig/m3 Analytical Baseline
In the 2032 projections, PM2.5 DVs exceeded the current standards for some
counties in the west. As a result, we adjusted the PM2.5 DVs for 2032 to correspond with
just meeting the current standards to form the 12/35 |_ig/m3 analytical baseline used in
estimating the incremental costs and benefits associated with control strategies for the
proposed and more stringent alternative standard levels relative to the current standards.
Figure 3-1 includes a map of the U.S. with the areas identified as northeast, southeast, west,
and California; results are summarized for these areas. Table 3-1 presents a summary of
the PM2.5 emissions reductions needed by area to meet the current standards.
Figure 3-1 Geographic Areas Used in Analysis
3-4
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Table 3-1 Summary of PM2.5 Emissions Reductions Needed by Area in 2032 to
Meet Current Primary Annual and 24-hour Standards of 12/35 (ig/m3
(tons/year)
Area
12/35
Northeast
0
Southeast
0
West
2,298
CA
6,907
Total
9,205
Eighteen counties need PM2.5 emissions reductions to meet the current standards in
2032 - 9 counties in California and 9 counties in the west.3 The counties in California
include several counties in the San Joaquin Valley Air Pollution Control District and the
South Coast Air Quality Management District, as well as Plumas County in Northern
California and Imperial County in southern California. No counties in the northeast or
southeast U.S. need PM2.5 emissions reductions to meet the current annual and 24-hour
standards.
3.2 Illustrative Control Strategies and PM2.5 Emissions Reductions from the
Analytical Baseline
To analyze counties projected to exceed the proposed and more stringent annual
and 24-hour alternative standard levels in 2032, we estimate total PM2.5 emissions
reductions needed by county for the alternative standard levels analyzed. To estimate the
PM2.5 emissions reductions needed, we start with projected future DVs, DV targets for each
area, and the sensitivity of PM2.5 DVs to direct PM2.5 emissions reductions. For each of the
alternative standard levels, we estimate PM2.5 emissions reductions needed by county and
then identify control technologies and measures that can achieve PM2.5 emissions
reductions. In Section 3.2.1, we discuss the approach for estimating the direct PM2.5
emissions reductions needed and present them by area for the alternative standard levels
analyzed. In Sections 3.2.2 and 3.2.3, respectively, we present information on the controls
and the estimated emissions reductions, from the analytical baseline, associated with
3 The 18 counties require primary PM emissions reductions to meet the current standards of 12/35 |J.g/m3
following application of the NOx emission reductions in San Joaquin Valley and the South Coast to adjust the
2032 DVs. For additional discussion, see Appendix 2A, Section 2A.3.2.
3-5
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applying controls by area for the alternative standard levels analyzed. In Section 3.2.4, we
discuss EGU emissions reductions from planned retirements and their potential influence
in some areas. In Sections 3.2.5 and 3.2.6, we discuss areas with other types of influences
affecting PM2.5 concentrations. As noted in Chapter 2, Section 2.4, there are certain types of
areas for which our illustrative control strategies may not capture important local
emissions and air quality dynamics. For these areas, we note that local emissions inventory
information and information on potential additional controls for emissions inventory
sectors that are traditionally challenging to control may be needed. Sections 3.2.5 presents
the emissions reductions still needed, and for each area Section 3.2.6 includes a qualitative
discussion of the remaining area-specific air quality challenges. Appendix 3A, Tables 3A.2
through 3A.7 summarize estimated PM2.5 emissions reductions by county for the
alternative standard levels for the northeast, the adjacent counties in the northeast, the
southeast, the adjacent counties in the southeast, the west, and California.
3.2.1 Estimating PM2.5 Emissions Reductions Needed for Annual and 24-hour
Alternative Standard Levels Analyzed
We apply regional PM2.5 air quality ratios to estimate PM2.5 DVs at air quality
monitor locations and then again to estimate the emissions reductions needed to reach the
proposed and more stringent annual and 24-hour alternative standard levels analyzed. To
develop air quality ratios that relate the change in DV in a county to the change in primary
PM2.5 emissions in that county, we performed air quality sensitivity modeling with
reductions in primary PM2.5 emissions in selected counties. More specifically, we conducted
a 2028 Community Multiscale Air Quality Modeling System (CMAQ) sensitivity modeling
simulation with 50 percent reductions in primary PM2.5 emissions from anthropogenic
sources in counties with annual 2028 DVs greater than 8 |j,g/m3.4 We divided the change in
annual and 24-hour PM2.5 DVs in these counties by the change in emissions in the
respective counties to determine the air quality ratio at individual monitors.
4 The modeling sensitivity runs were based on 50 percent reductions in emissions to provide estimates of
PM2.5 sensitivity across the full range of potential emissions changes. Since the response of PM2.5
concentrations to changes in primary PM2.5 emissions is approximately linear (Kelly et al., 2015, 2019), the
air quality ratios are insensitive to the percent emissions change applied in the sensitivity simulations.
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We developed representative air quality ratios for regions of the U.S. from the ratios
at individual monitors as in the 2012 PM2.5 NAAQS review (U.S. EPA, 2012). We calculated
regional ratios as the 75th percentile of air quality ratios at monitors within five regions:
Northeast, Southeast, Northern California, Southern California, and West. The Northeast
region was defined by combining the Upper Midwest, Ohio Valley, and Northeast U.S.
climate regions; the Southeast region was defined by combining the Southeast and South
climate regions; and California was separated into Southern and Northern regions as done
previously. (These regions are shown in Figure 2-7 in Chapter 25, and the air quality ratios
for primary PM2.5 emissions used in estimating the emission reductions needed to just
meet the alternative standard levels analyzed are listed in Table 2-1 in Chapter 2.) We
estimated the emissions reductions needed to just meet the alternative standard levels
analyzed using the primary PM2.5 air quality ratios in combination with the required
incremental change in concentration. (Chapter 2, Section 2.3.1 includes a brief discussion of
developing air quality ratios and estimated emissions reductions needed to just meet the
alternative standard levels analyzed, and Appendix 2A, Section 2A.3 includes more detailed
discussions.)
Table 3-2 presents a summary of the estimated emissions reductions needed by
area to reach the annual and 24-hour alternative standard levels. For each alternative
standard level, Table 3-2 also includes an area's percent of the total estimated emissions
reductions needed nationwide to reach that alternative standard level in all locations. For
example, for the proposed standard level of 10/35 |~ig/m3, California's 10,128 estimated
tons needed is 81 percent of the total estimated emissions reductions needed nationwide
to meet 10/35 |~ig/m3. (See Appendix 2A, Table 2A-14 for the estimated PM2.5 emissions
reductions, from the analytical baseline, needed by county for the alternative standard
levels analyzed.) Figure 3-2 shows the counties projected to exceed the annual and 24-hour
alternative standard levels of 10/35 |~ig/m3, 9/35 |~ig/m3, and 8/35 |~ig/m3 in the analytical
baseline. Figure 3-3 shows the counties projected to exceed the annual and 24-hour
alternative standard levels of 10/30 ng/m3 in the analytical baseline. Additional
5 To present results throughout this RIA, we combined the Northern California and Southern California
regions.
3-7
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information on the air quality modeling, as well as information about projected future DVs,
DV targets, and air quality ratios is provided in Chapter 2 and Appendix 2A.
Table 3-2 By Area, Summary of PM2.5 Emissions Reductions Needed, in
Tons/Year and as Percent of Total Reductions Needed Nationwide, for
Alternative Primary Standard Levels of 10/35 (ig/m3,10/30 |ag/m3,
9/35 (j,g/m3, and 8/35 ng/m3 in 2032
Area
10/35
10/30
9/35
8/35
Northeast
1,068
1,221
6,996
30,843
Southeast
474
474
4,088
18,028
West
820
7,852
3,078
9,708
CA
10,128
12,230
17,750
28,293
Total
12,490
21,776
31,912
86,872
Area
10/35
10/30
9/35
8/35
Northeast
9%
6%
22%
36%
Southeast
4%
2%
13%
21%
West
7%
36%
10%
11%
CA
81%
56%
56%
33%
¦ Reductions required for 10/35, 9/35, arid 8/35
Reductions required for 9/35 and 8/35
¦ Reductions required for 8/35
Figure 3-2 Counties Projected to Exceed in Analytical Baseline for Alternative
Standard Levels of 10/35 (ig/m3,9/35 (ig/m3, and 8/35 jig/m3
3-8
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Reductions required for 10/30
Figure 3-3 Counties Projected to Exceed in Analytical Baseline for Alternative
Standard Levels of 10/30 (J.g/m3
As presented previously, for each alternative standard level, Chapter 2, Section 2.3.3
includes a discussion of the number of counties that are projected to exceed in 2032, and
Figure 2-9 includes maps of counties projected to exceed along with the number of
counties. The following summarizes the number of counties, by alternative standard level,
in each geographic area that need PM2.5 emissions reductions from the analytical baseline.
* 10/35 |j.g/m3" 24 counties need PM2.5 emissions reductions. This includes 4
counties in the northeast, 2 counties in the southeast, 3 counties in the west,
and 15 counties in California.
• 10/30 [ig/m3- 47 counties need PM2.5 emissions reductions. This includes 4
counties in the northeast, 2 counties in the southeast, 23 counties in the west,
and 18 counties in California.
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• 9/35 ng/m3 -- 51 counties need PM2.5 emissions reductions. This includes 14
counties in the northeast, 8 counties in the southeast, 8 counties in the west,
and 21 counties in California.
• 8/35 ng/m3 --141 counties need PM2.5 emissions reductions. This includes 57
counties in the northeast, 35 counties in the southeast, 24 counties in the
west, and 25 counties in California.
3.2.2 Applying Control Technologies and Measures
To identify controls and estimate emissions reductions, we used information about
the emissions reductions needed, by county, in the northeast, southeast, west, and
California. Given the different county sizes between eastern and western states, as well as
different terrain or other topographical features, we estimated potential PM2.5 emissions
reductions for the eastern U.S. and western U.S. as detailed below. Note that we included a
total of 154 counties in the analyses. The total number of counties below (154 counties)
does not directly match the number of counties that would need emissions reductions for
the more stringent alternative standard level of 8/35 |~ig/m3 (141 counties) in Section 3.2.1
above. This difference is because there are thirteen counties that do not need PM2.5
emissions reductions to meet alternative standard levels of 9/35 |~ig/m3 and 8/35 |~ig/m3
but do need PM2.5 emissions reductions to meet an alternative standard level of 10/30 |Lxg/-
m3.
1. Northeast (57 counties) and Southeast (35 counties) - In the eastern U.S. where
counties are relatively small, we were not always able to identify controls within a
given county. We identified controls and emissions reductions from neighboring
counties because the terrain is relatively flat, and the application of these controls is
appropriate in such cases. Any emissions reductions from neighboring counties
were identified in adjacent counties within the same state.
To apply emissions reductions in the neighboring counties in the eastern U.S., we
compared the responsiveness of annual PM2.5 DVs to emissions reductions within a
county to the responsiveness for neighboring counties. The resulting impact ratio
suggests that primary PM2.5 emissions reductions in neighboring counties would be
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4 times less effective as in the core county. (Appendix 2A, Section 2A.3.1 includes a
more detailed discussion of the comparison.) As such, when we applied the
emissions reductions from adjacent counties, we used a |~ig/m3 per ton PIVh.sair
quality ratio that was four times less responsive than the ratio used when applying
in-county emissions reductions (i.e., we applied four tons of PM2.5 emissions
reductions from an adjacent county for one ton of emissions reduction needed in a
given county).
2. West (36 counties) and California (26 counties) - Because these counties are
generally large and the terrain is complex, we only identified potential PM2.5
emissions reductions within each county.
We identified control measures using the EPA's Control Strategy Tool (CoST) (U.S.
EPA, 2019a) and the control measures database.6,7 CoST estimates emissions reductions
and engineering costs associated with control technologies or measures applied to non-
electric generating unit (non-EGU) point, non-point (area), residential wood combustion,
and area fugitive dust sources of air pollutant emissions by matching control measures to
emissions sources by source classification code (SCC). For these control strategy analyses,
to maximize the number of emissions sources included we applied controls to emissions
sources with greater than 5 tons per year of PM2.5 emissions at a marginal cost threshold of
up to a $160,000/ton. Figure 3-4 presents estimated PM2.5 emissions reductions for 5 tons
per year (tpy), 10 tpy, 15 tpy, 25 tpy, and 50 tpy emissions unit/source sizes up to the
$160,000/ton marginal cost threshold; the figure includes all emissions inventory and
control measure data for the counties in the analysis. We selected the $160,000/ton
marginal cost threshold because it is around that cost level that (i) road paving controls get
selected and applied (as seen by the slight uptick in the curves), and (ii) opportunities for
additional emissions reductions diminish (as seen by the flattening of the curve around
that cost threshold). While the 2012 PM NAAQS RIA used a $20,000/ton marginal cost
6 More information about CoST and the control measures database can be found at the following link:
https://www.epa.gov/economic-and-cost-analysis-air-pollution-regulations/cost-analysis-modelstools-air-
pollution.
7 The 2032 emissions inventory data, the CoST run results, the CMDB, and the R code that processed these
data to prepare the summaries in Chapters 3 and 4 are available upon request.
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threshold and a 50 tpy emissions source size threshold, this analysis uses a higher cost per
ton threshold and a lower source size threshold in recognition of the challenges that some
areas will experience in identifying controls to meet both the current and alternative
standard levels analyzed (U.S. EPA, 2012). The estimated costs of the control measures are
presented in Chapter 4.
In some cases, more emissions reductions are selected by CoST than may be needed
for some areas to meet the alternative standard levels. There are two primary reasons this
may occur. First, because CoST employs a least cost algorithm to determine the bundle of
controls that achieves the required emissions reductions at the lowest possible cost, there
are instances when a non-point or area fugitive dust source will be selected for control due
to its cost-effectiveness. Because the emissions from these sources are summarized at the
county level and the controls specify a percent reduction, selection of these sources for
control can sometimes lead to overshooting the emissions reduction target.
Second, for counties in the northeast and southeast, we considered emissions
reductions from adjacent counties. There are some instances where a neighboring county
may be adjacent to multiple counties that need reductions. Furthermore, it is sometimes
the case that one of the multiple counties to which a neighboring county is adjacent needs
substantially more reductions than the other counties. In these cases, an adjacent
[neighboring) county may be called upon to provide reductions to help the county that
needs the most reductions, and in so doing it may cause the other counties to which it is
adjacent to overshoot their emissions reductions targets.
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),000 $100,000 $120,000 $140,000 $160,000 $180,000 $200,000 $220,000 $240,000
CPT Threshold
Figure 3-4 PM2.5 Emissions Reductions and Costs Per Ton (CPT) in 2032 (tons,
2017$)
We identified control technologies and measures for non-electric generating unit
point sources (non-EGU point, oil & gas point), non-point (area) sources, residential wood
combustion sources, and area fugitive dust emissions. Controls applied for the analyses of
the current standards of 12/35 |~ig/m3 and the annual and 24-hour PM2.5 alternative
standard levels of 10/35 |~ig/m3,10/30 |~ig/m3, 9/35 |~ig/m3, and 8/35 ng/m3 are listed in
Table 3-3 by emissions inventory sector, with an "X" indicating which control technologies
were applied in analyzing each standard level. See Appendix 3A, Table 3A.1 for a more
detailed presentation of control technologies applied for the alternative standard levels
both by geographic area and by emissions inventory sector, as well as a discussion of some
of the control measures.
Non-EGU point source controls are applied to individual point sources. Non-point
(area), residential wood combustion, and area fugitive dust emissions data are generated at
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the county level, and therefore controls for these emissions inventory sectors were applied
at the county level. Control measures were applied to non-EGU point, non-point (area),
residential wood combustion, and area fugitive dust sources of PM2.5 emissions including:
industrial, commercial, and institutional boilers; industrial processes located in the cement
manufacturing, chemical manufacturing, pulp and paper, mining, ferrous and non-ferrous
metals, and refining industries; commercial cooking; residential wood combustion; and
fugitive construction and road dust. (Also, see Appendix 2A, Section 2A.5 for a discussion of
electric generating unit NOx, SO2, and PM2.5 emissions reductions that are expected to occur
between 2016 and 2030 beyond those included in the 2032 air quality modeling simulation
for this analysis. These additional emissions reductions will result from planned EGU
retirements that were not known when we developed the 2032 emissions projections.)
Table 3-3 By Inventory Sector, Control Measures Applied in Analyses of the
Current Standards and the Alternative Primary Standard Levels
Inventory
Sector Control Technology 12/35 10/35 10/30 9/35 8/35
Non-EGU Point
Electrostatic Precipitator-All Types
X
X
X
Fabric Filter-All Types
X
X
X
X
X
Install new drift eliminator at 10% RP
X
X
X
Install new drift eliminator at 25% RP
X
X
X
X
X
Venturi Scrubber
X
X
X
X
X
Oil & Gas Point
Fabric Filter-All Types
Install new drift eliminator at 25% RP
X
X
X
Non-Point
Add-on Scrubber at 25% RP
X
X
[Area]
Annual tune-up at 10% RP
X
X
X
Annual tune-up at 25% RP
X
X
X
X
X
Biennial tune-up at 10% RP
X
X
X
X
X
Biennial tune-up at 25% RP
X
X
X
X
X
Catalytic oxidizers at 25% RP
X
X
X
X
X
Electrostatic Precipitator at 10% RP
X
X
Electrostatic Precipitator at 25% RP
X
X
X
X
X
Fabric Filter-All Types
X
X
HEPA filters atl0%RP
X
X
X
X
HEPA filters at25%RP
X
X
X
Smokeless Broiler at 10% RP
X
X
X
X
X
Smokeless Broiler at 25% RP
X
X
Substitute chipping for burning
X
X
X
X
X
Residential
Convert to Gas Logs at 25% RP
X
X
X
X
X
Wood
Combustion
EPA-certified wood stove at 10% RP
EPA Phase 2 Qualified Units at 10% RP
X
X
X
EPA Phase 2 Qualified Units at 25% RP
X
X
X
Install Cleaner Hydronic Heaters at 10% RP
X
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Inventory
Sector Control Technology 12/35 10/35 10/30 9/35 8/35
Install Cleaner Hydronic Heaters at 25% RP
X
X
X
X
X
Install Retrofit Devices at 10% RP
X
X
X
Install Retrofit Devices at 25% RP
X
X
X
New gas stove or gas logs at 10% RP
X
X
X
X
X
New gas stove or gas logs at 25% RP
X
X
X
X
X
Area Source
Chemical Stabilizer at 10% RP
X
X
X
X
Fugitive Dust
Chemical Stabilizer at 25% RP
X
X
X
Dust Suppressants at 10% RP
X
Dust Suppressants at 25% RP
X
Pave existing shoulders at 10% RP
X
Pave existing shoulders at 25% RP
X
X
X
X
X
Pave Unpaved Roads at 25% RP
X
X
X
X
X
Note: The 10% RP and 25% RP indicate the rule penetration (RP) percent, or the percent of the non-point
(area), residential wood combustion, or area fugitive dust inventory emissions that the control measure is
applied to at a specified percent control efficiency.
3.2.3 Estimates of PM2.5 Emissions Reductions Resulting from Applying Control
Technologies and Measures
By area, Table 3-4 includes a summary of the estimated emissions reductions from
control applications for the alternative standards analyzed. These emissions reductions
were used to create the PM2.5 spatial surfaces described in Appendix 2A, Section 2A.4.2 for
the human health benefits assessments presented in Chapter 5.
Table 3-4 Summary of PM2.5 Estimated Emissions Reductions from CoST by Area
for the Alternative Primary Standard Levels of 10/35 ng/m3,10/30
(j,g/m3, 9/35 |ig/m3, and 8/35 jig/m3 in 2032 (tons/year)
PM2.5 Emissions Reductions
Area
10/35
10/30
9/35
8/35
Northeast
1,070
1,222
6,334
19,142
Northeast (Adjacent Counties)
0
0
1,737
15,440
Southeast
475
475
3,040
12,212
Southeast (Adjacent Counties)
0
0
194
4,892
West
224
2,206
947
4,711
CA
1,792
2,481
2,958
4,925
Total
3,561
6,384
15,210
61,321
Note: Totals may not match related tables due to independent rounding. In the northeast and southeast
when we applied the emissions reductions from adjacent counties, we used a ppb/ton PM2.5 air quality
ratio that was four times less responsive than the ratio used when applying in-county emissions
reductions.
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By emissions inventory sector, Table 3-5 includes a summary of PM2.5 emissions and
estimated emissions reductions from control applications for the alternative standard
levels analyzed. The PM2.5 emissions in Table 3-5 are the total emissions associated with
the emissions units/sources that get controls applied within each of the inventory sectors
for each of the alternative standard levels (not the total emissions associated with the
entire inventory sector). Across the alternative standard levels analyzed, overall total
emissions reductions are approximately 30 percent of the PM2.5 emissions from the sources
selected by CoST for control. In general, a large percentage of the emissions are being
controlled for the alternative standard levels analyzed, while additional reductions may be
possible in some areas and different inventory sectors are selected for control in different
areas.
The emissions inventory sector with the highest percent of emissions reductions
relative to total potentially controllable emissions for that sector is the non-EGU point
sector - the estimated emissions reductions are between 65 and 92 percent of total PM2.5
emissions from the sources selected for control, with that percent increasing as the
alternative standard level gets more stringent. The emissions inventory sector with the
lowest percent of emissions reductions relative to total potentially controllable emissions
for that sector is the area fugitive dust sector - the estimated emissions reductions are
between 15 and 19 percent of total PM2.5 emissions from the sources selected for control,
with that percent decreasing as the alternative standard level gets more stringent. The
residential wood combustion sector's emissions reductions relative to total potentially
controllable emissions are between 21 and 23 percent across the alternative standard
levels analyzed. It is worth noting that the control efficiencies associated with control
measures for the non-point (area), area fugitive dust, and residential wood combustion
sectors are generally lower than control efficiencies associated with control measures for
the non-EGU point and oil and gas point inventory sectors, and many of the controls for
these sectors are only applied to a portion of the inventory. As noted in Table 3-3, controls
for emissions from these inventory sectors are applied to a percent of the relevant
inventory (rule penetration) at a specified percent control efficiency. For the proposed
alternative standard levels of 10/35 |~ig/m3 and 9/35 |~ig/m3, the inventory sectors with the
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most potentially controllable emissions are the non-point (area) and area fugitive dust
sectors. The inventory sectors with the most estimated emissions reductions are the non-
point (area) and non-EGU point sectors.
Table 3-5 Summary of PM2.5 Emissions and Estimated Emissions Reductions
from CoST by Inventory Sector for Alternative Primary Standard
Levels of 10/35 (j,g/m3,10/30 |ig/m3,9/35 (j,g/m3, and 8/35 |ig/m3 in
2032 (tons/year)
Emissions Inventory
Sector
10/35
10/30
9/35
8/35
Non-EGU Point
PM2.5 Emissions
1,384
1,823
6,824
19,832
PM2.5 Emissions
Reductions
901
1,326
6,035
18,289
Oil & Gas Point
PM2.5 Emissions
0
0
0
83
PM2.5 Emissions
Reductions
0
0
0
60
Non-Point (Area)
PM2.5 Emissions
6,994
9,987
23,770
80,265
PM2.5 Emissions
Reductions
1,771
2,572
6,269
27,352
Residential Wood
Combustion
PM2.5 Emissions
1,262
2,635
5,808
17,963
PM2.5 Emissions
Reductions
296
556
1,276
4,193
Area Source Fugitive
Dust
PM2.5 Emissions
3,175
10,198
9,127
74,034
PM2.5 Emissions
Reductions
593
1,930
1,630
11,427
Total
PM2.5 Emissions
12,816
24,643
45,529
192,176
PM2.5 Emissions
Reductions
3,561
6,384
15,210
61,321
Note: The PM2.5 emissions in the table are for the emissions sources that get controls applied within
each of the inventory sectors (not the total emissions associated with the entire inventory sector)
for each of the standard levels.
By emissions inventory sector and by control technology, Table 3-6 includes a
summary of estimated PM2.5 emissions reductions from control applications for the
alternative standard levels analyzed. Across alternative standard levels analyzed, estimated
PM2.5 emissions reductions from control applications in the (i) non-EGU point and oil and
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gas point inventory sectors account for between 21 and 40 percent of estimated
reductions; (ii) non-point (area) inventory sector account for between 41 and 50 percent of
estimated reductions; (iii) residential wood combustion inventory sector account for
between 7 and 9 percent; and (iv) area fugitive dust inventory sector account for between
11 and 30 percent.
Also, across alternative standard levels analyzed, six control technologies and
measures comprise between approximately 81 and 87 percent of the estimated emissions
reductions. Those control technologies and measures include:
• Fabric Filter- All Types (non-EGU point inventory sector) - the control
technology is generally applied to industrial, commercial, and institutional
boilers and industrial processes located in the cement manufacturing,
chemical manufacturing, pulp and paper, mining, ferrous and non-ferrous
metals, and refining industries.
• Electrostatic Precipitator at 25% RP (non-point (area) inventory sector) -
the control measure is applied to area source commercial cooking
emissions.8
• Substitute Chipping for Burning (non-point (area) inventory sector) - the
control measure is applied to area source waste disposal emissions.
• Convert to Gas Logs at 25% RP (residential wood combustion inventory
sector) - the control measure is applied to area source residential wood
combustion emissions.
• Pave Existing Shoulders at 25% RP (area fugitive dust inventory sector) -
the control measure is applied to road dust emissions.
• Pave Unpaved Roads at 25% RP (area fugitive dust inventory sector) - the
control measure is applied to road dust emissions.
8 RP indicates the rule penetration (RP) percent, or the percent of the non-point (area), residential wood
combustion, or area fugitive dust inventory emissions that the control measure is applied to at a specified
percent control efficiency.
3-18
-------
The three control measures that result in the most emissions reductions for alternative
standard levels of 10/35 ng/m3, 9/35 ng/m3, and 8/35 |~ig/m3 are Fabric Filter- All Types,
Electrostatic Precipitator at 25% RP, and Substitute Chipping for Burning. The three
control measures that result in the most emissions reductions for alternative standard
levels of 10/30 |~ig/m3 are Fabric Filter- All Types, Substitute Chipping for Burning, and
Pave Unpaved Roads at 25% RP. The 10% RP and 25% RP indicate the rule penetration
(RP) percent, or the percent of the area source inventory emissions that the control
measure is applied to at a specified percent control efficiency.
Table 3-6 Summary of Estimated Emissions Reductions from CoST by Inventory
Sector and Control Technology for Alternative Primary Standard
Levels of 10/35 |jg/m3,10/30 ng/m3,9/35 (ig/m3, and 8/35 ng/m3 in
2032 (tons/year)
Inventory
Sector
Control Technology
10/35
10/30
9/35
8/35
Non-EGU Point
Electrostatic Precipitator-All Types
16
0
27
20
Fabric Filter-All Types
713
1,071
5,026
16,511
Install new drift eliminator at 10% RP
0
0
5
2
Install new drift eliminator at 25% RP
115
115
144
292
Venturi Scrubber
56
139
833
1,464
Oil & Gas Point
Fabric Filter-All Types
0
0
0
55
Install new drift eliminator at 25% RP
0
0
0
5
Non-Point
Add-on Scrubber at 25% RP
5
5
0
0
(Area)
Annual tune-up at 10% RP
0
1
1
1
Annual tune-up at 25% RP
83
96
450
1,589
Biennial tune-up at 10% RP
1
1
0
44
Biennial tune-up at 25% RP
24
58
53
347
Catalytic oxidizers at 25% RP
42
53
151
183
Electrostatic Precipitator at 10% RP
0
0
11
1
Electrostatic Precipitator at 25% RP
849
1,038
1,615
6,395
Fabric Filter-All Types
0
0
77
199
HEPA filters at 10% RP
0
1
1
2
HEPA filters at25%RP
1
0
6
27
Smokeless Broiler at 10% RP
53
79
142
39
Smokeless Broiler at 25% RP
0
0
411
177
Substitute chipping for burning
712
1,240
3,351
18,349
Residential
Convert to Gas Logs at 25% RP
219
369
805
2,446
Wood
EPA-certified wood stove at 10% RP
0
0
0
1
Combustion
EPA Phase 2 Qualified Units at 10% RP
0
0
16
3
EPA Phase 2 Qualified Units at 25% RP
15
20
0
66
Install Cleaner Hydronic Heaters at 10% RP
0
1
0
0
Install Cleaner Hydronic Heaters at 25% RP
22
42
285
901
Install Retrofit Devices at 10% RP
0
0
12
6
3-19
-------
Inventory
Sector
Control Technology
10/35
10/30
9/35
8/35
Install Retrofit Devices at 25% RP
11
11
0
9
New gas stove or gas logs at 10% RP
3
54
45
86
New gas stove or gas logs at 25% RP
25
58
111
675
Area Source
Chemical Stabilizer at 10% RP
22
71
42
1,524
Fugitive Dust
Chemical Stabilizer at 25% RP
0
0
52
1,488
Dust Suppressants at 10% RP
0
0
0
0
Dust Suppressants at 25% RP
0
0
0
126
Pave existing shoulders at 10% RP
0
0
0
49
Pave existing shoulders at 25% RP
200
611
769
4,854
Pave Unpaved Roads at 25% RP
371
1,248
767
3,384
Total
3,561
6,384
15,210
61,321
By emissions inventory sector and by inventory source classification code (SCC)
sector, Table 3-7 includes a summary of estimated PM2.5 emissions reductions from control
applications for the alternative standard levels analyzed. As seen in Table 3-6, across
alternative standard levels analyzed, estimated PM2.5 emissions reductions from control
applications in the (i) non-EGU point and oil and gas point inventory sectors account for
between 21 and 40 percent of estimated reductions; (ii) non-point (area) inventory sector
account for between 41 and 50 percent of estimated reductions; (iii) residential wood
combustion inventory sector account for between 7 and 9 percent; and (iv) area fugitive
dust inventory sector account for between 11 and 30 percent.
Across alternative standard levels analyzed, estimated PM2.5 emissions reductions
from control applications in the Industrial Processes - Ferrous Metals, Industrial Processes -
Not Elsewhere Classified, and Industrial Processes - Petroleum Refineries inventory SCC
sectors account for between 62 percent and 69 percent of reductions from the non-EGU
point and oil and gas point inventory sectors. Estimated PM2.5 emissions reductions from
control applications in the Commercial Cooking and Waste Disposal - All Categories
inventory SCC sectors account for between 78 percent and 88 percent of reductions from
the non-point (area) inventory sector. Estimated PM2.5 emissions reductions from control
applications in the Fuel Combustion - Residential - Wood inventory SCC sector account for
all of the reductions from the residential wood combustion inventory sector, and estimated
PM2.5 emissions reductions from control applications in the Dust - Paved Road Dust and
3-20
-------
Dust - Unpaved Road Dust inventory SCC sectors account for all of the reductions from the
area source fugitive dust inventory sector.
Table 3-7 Summary of Estimated PM2.5 Emissions Reductions from CoST by
Inventory Source Classification Code Sectors for Alternative Primary
Standard Levels of 10/35 |ig/m3,10/30 |ig/m3, 9/35 |ig/m3, and 8/35
(ig/m3 in 2032 (tons/year)
Sector
SCC Sector
10/35
10/30
9/35
8/35
Non-EGU
Agriculture - Livestock Waste
0
6.2
6.8
6.8
Point
Fuel Combustion -
Commercial/Institutional Boilers - Biomass
0
0
0
15.6
Fuel Combustion -
0
0
8.0
8.0
Commercial/Institutional Boilers - Coal
Fuel Combustion -
0
0
0
85.9
Commercial/Institutional Boilers - Natural
Gas
Fuel Combustion -
64.7
64.7
64.7
69.8
Commercial/Institutional Boilers - Other
Fuel Combustion - Industrial Boilers, ICEs -
0
76.0
5.2
402.2
Biomass
Fuel Combustion - Industrial Boilers, ICEs -
0
0
16.4
211.2
Coal
Fuel Combustion - Industrial Boilers, ICEs -
6.1
75.4
81.7
405.8
Natural Gas
Fuel Combustion - Industrial Boilers, ICEs -
0
0
0
18.1
Oil
Fuel Combustion - Industrial Boilers, ICEs -
110.9
140.7
689.5
1,023.9
Other
Industrial Processes - Cement
0
0
89.8
688.5
Manufacturing
Industrial Processes - Chemical
29.3
40.3
136.5
953.8
Manufacturing
Industrial Processes - Ferrous Metals
142.8
150.1
836.0
2,378.0
Industrial Processes - Mining
0
7.4
239.4
326.9
Industrial Processes - Non-ferrous Metals
55.9
55.9
502.1
918.0
Industrial Processes - Not Elsewhere
304.3
456.1
2,169.9
6,818.0
Classified
Industrial Processes - Petroleum Refineries
178.5
216.6
875.8
2,204.2
Industrial Processes - Pulp & Paper
0
18.3
119.5
848.1
Industrial Processes - Storage and Transfer
8.9
18.0
186.7
887.4
Waste Disposal - Excavation/Soils Handling
0
0
0
5.8
Waste Disposal - General Processes
0
0
7.0
7.0
Waste Disposal - Landfill Dump
0
0
0
5.5
Oil & Gas
Industrial Processes - Not Elsewhere
0
0
0
3.6
Point
Classified
Industrial Processes - Oil & Gas Production
0
0
0
54.9
Industrial Processes - Petroleum Refineries
0
0
0
1.8
3-21
-------
Sector
SCC Sector
10/35
10/30
9/35
8/35
Non-Point
Commercial Cooking
950.2
1,176.5
2,336.9
6,823.5
(Area)
Fuel Combustion -
Commercial/Institutional Boilers - Biomass
16.3
20.2
52.8
258.6
Fuel Combustion -
0
0
0
0.5
Commercial/Institutional Boilers - Coal
Fuel Combustion -
18.9
22.2
49.8
95.5
Commercial/Institutional Boilers - Natural
Gas
Fuel Combustion -
0
0
3.0
14.4
Commercial/Institutional Boilers - Oil
Fuel Combustion - Industrial Boilers, ICEs -
66.0
103.3
345.0
1,499.0
Biomass
Fuel Combustion - Industrial Boilers, ICEs -
0
2.4
17.8
39.1
Coal
Fuel Combustion - Industrial Boilers, ICEs -
4.0
4.0
32.7
65.5
Natural Gas
Fuel Combustion - Industrial Boilers, ICEs -
1.0
1.0
1.0
5.4
Oil
Fuel Combustion - Industrial Boilers, ICEs -
2.0
2.0
2.0
2.0
Other
Industrial Processes - Chemical
0
0
77.4
199.1
Manufacturing
Waste Disposal - All Categories
603.2
880.0
2,641.3
14,623.5
Waste Disposal - Residential
109.2
360.5
709.2
3,725.4
Residential
Fuel Combustion - Residential - Wood
296.2
555.6
1,275.9
4,193.4
Wood
Combustion
Area Source
Dust - Paved Road Dust
199.9
611.0
768.9
4,903.3
Fugitive
392.7
1,319.3
861.3
6,523.6
Dust
Dust - Unpaved Road Dust
Total
3,561.0
6,383.7
15,210.0
61,320.7
3.2.4 Potential Influence of EGU Emissions Reductions from Planned
Retirements
As indicated in Appendix 2A and the Overview section above, we did not apply
controls and estimate emissions reductions and costs for EGUs; however, Appendix 2A,
Section 2A.5 includes a discussion of EGU NOx, SO2, and PM2.5 emissions reductions from
planned EGU retirements that are expected to occur between 2016 and 2030 beyond those
included in the air quality modeling for this analysis. Section 2A.5 discusses the potential
influence of these EGU emissions reductions on PM2.5 DVs in three ways - (i) local impact of
the direct PM2.5 emissions reductions from EGUs on DVs for counties with 2032 PM2.5 DVs
that exceed the alternative standard levels, (ii) regional impact of the total EGU SO2 and
3-22
-------
NOx emissions reductions in the eastern U.S. on 2032 PM2.5 DVs, and (iii) relatively local
impact of the EGU NOx and SO2 emissions reductions on 2032 PM2.5 DVs in nearby counties
for two cases with large SO2 reductions. The emissions reductions from the planned EGU
retirements are not expected to have large impacts on PM2.5 DVs in the areas that need
emissions reductions in this analysis. We include brief discussions below; for more detailed
discussions see Appendix 2A, Section 2A.5.
In assessing the local impact of direct PM2.5 emissions reductions on DVs for
counties with 2032 PM2.5 DVs that exceed the alternative standard levels, ten counties had
PM2.5 reductions from the planned EGU retirements (see Table 2A-15). The direct PM2.5
EGU emissions reductions from just three counties (out of the ten counties) account for 95
percent of these EGU PM2.5 reductions from these ten counties. In these three counties,
either emissions reductions were not needed for, or the control strategy analysis identified
sufficient non-EGU emissions reductions for, the alternative standard levels of 10/35
Hg/m3,10/30 ng/m3, and 9/35 |j,g/m3; in all three counties the control strategy analysis
did not identify sufficient non-EGU reductions for an alternative standard level of 8/35
Hg/m3. If the EGU PM2.5 emissions reductions from the planned retirements were directly
included in the control strategy analyses, these reductions may have offset the need for
some of the controls applied for all of the alternative standard levels. In particular, we note
that for Hamilton County, Ohio, Jefferson County, Missouri, and Allegheny County,
Pennsylvania, the planned retirements could offset the need for some of the other non-EGU
controls identified in this analysis.
In assessing the regional impact of the total EGU NOx and SO2 emissions reductions
(see Table 2A-16) on annual 2032 PM2.5 DVs, across monitors in the eastern states the
estimated median annual 2032 PM2.5 DV change is 0.06 |~ig/m3. See Figure 2A-36 for the
distributions of annual 2032 PM2.5 DV changes from the NOx and SO2 emissions reductions.
Therefore, these NOx and SO2 emissions reductions from planned retirements could have a
small impact on PM2.5 DVs regionally across the eastern U.S. but are unlikely to have a
substantial impact on the need for the additional non-EGU controls identified in this
analysis.
3-23
-------
For the areas with the largest SO2 reductions expected near monitors with 2032
PM2.5 DVs that exceed alternative standard levels, we combined the NOx and SO2 EGU
emissions reductions from the relevant counties and estimated their impact on the annual
2032 PM2.5 DVs. For one area, the EGU emissions reductions are estimated to impact the
2032 annual PM2.5 DV at each of the five monitoring sites listed in Table 2A-17 by
approximately 0.5 |~ig/m3. For the other area, the emissions reductions are estimated to
impact the 2032 annual PM2.5 DV at the two monitoring sites listed in Table 2A-18 by
approximately 0.3 |~ig/m3. For a few counties in these two areas, the NOx and SO2 reductions
could offset the need for some of the controls applied in the analysis, particularly for a
standard level of 8/35 |~ig/m3.
3.2.5 Estimates of PM2.5 Emissions Reductions Still Needed after Applying
Control Technologies and Measures
The percent of total PM2.5 emissions reductions estimated from CoST (shown in
Table 3-4 above) relative to total PM2.5 emissions reductions needed (shown in Table 3-2
above) varies by alternative standard level and by area. Note that in the northeast and
southeast when we applied the emissions reductions from adjacent counties, we used a
Hg/m3 per ton PM2.5 air quality ratio that was four times less responsive than the ratio used
when applying in-county emissions reductions (i.e., we applied four tons of PM2.5 emissions
reductions from an adjacent county for one ton of emissions reduction needed in a given
county).
• For the proposed alternative standard level of 10/35 |~ig/m3, the northeast
and southeast have sufficient estimated emissions reductions. For the west,
the estimated emissions reductions are approximately 27 percent of the total
needed, and for California the estimated emissions reductions are
approximately 18 percent of the total needed.
• For the proposed alternative standard level of 9/35 |~ig/m3, for the northeast
we were able to identify approximately 97 percent of the reductions needed.
For the southeast we were able to identify approximately 76 percent of the
reductions needed. For the west, we were able to identify approximately 31
3-24
-------
percent of the reductions needed, and for California the percentage is
approximately 17 percent.
The higher percent of estimated emissions reductions relative to needed reductions in the
northeast and southeast is likely because as the alternative standard level becomes more
stringent, more controls from counties projected to exceed and their adjacent counties are
available and applied. See Appendix 3A, Tables 3A.2 through 3A.7 for more detailed
summaries of PM2.5 emissions reductions by county for the alternative standard levels for
the northeast, the adjacent counties in the northeast, the southeast, the adjacent counties in
the southeast, the west, and California. Table 3A.7 for California presents the counties
organized by air districts.
As indicated, the estimated PM2.5 emissions reductions from control applications do
not fully account for all the emissions reductions needed to reach the proposed and more
stringent alternative standard levels in some counties in the northeast, southeast, west, and
California. By area, Table 3-8 includes a summary of the estimated emissions reductions
still needed after control applications for the alternative standards analyzed. By area and
by county, Table 3-9 includes a more detailed summary of the estimated emissions
reductions still needed after control applications for the alternative standards analyzed. As
seen in Table 3-9, some counties need emissions reductions to meet a standard level of
10/30 ng/m3 that did not need emissions reductions to meet a standard level of 10/35
Hg/m3. These counties are in the west and California, where there are small valleys with
mountainous terrain and wintertime inversions, along with residential woodsmoke
emissions and some wildfire influence, and need emissions reductions to meet the more
stringent 24-hour standard level of 30 |~ig/m3. Figure 3-5 through Figure 3-8 show the
counties that still need emissions reductions after control applications for the alternative
standards analyzed.
The analysis indicates that counties in the northeast and southeast U.S. do not need
additional emissions reductions to meet alternative standard levels of 10/35 |~ig/m3 and
10/30 ng/m3. For the northeast, 1 (out of 14) county needs additional emissions
reductions to reach attainment of the proposed alternative standard level of 9/35 |~ig/m3,
3-25
-------
and 22 (out of 57) counties need additional emissions reductions to reach attainment of the
more stringent alternative standard level of 8/35 |~ig/m3. For the southeast, 2 (out of 8)
counties need additional emissions reductions to reach attainment of the proposed
alternative standard level of 9/35 ng/m3, and 10 (out of 35) counties need additional
emissions reductions to reach attainment of the more stringent alternative standard level
of 8/35 ng/m3.
The analysis also indicates that counties in the west and California need additional
emissions reductions after the application of controls to meet all of the alternative standard
levels. For the west, 3 (out of 3) counties need additional emissions reductions to reach
attainment of the proposed alternative standard level of 10/35 |~ig/m3,16 (out of 23)
counties need additional emissions reductions to reach attainment of the more stringent
alternative standard level of 10/30 |~ig/m3, 4 (out of 8) counties need additional emissions
reductions to reach attainment of the proposed alternative standard level of 9/35 |~ig/m3,
and 8 (out of 24) counties need additional emissions reductions to reach attainment of the
more stringent alternative standard level of 8/35 |~ig/m3. For California, 12 (out of 15)
counties need additional emissions reductions to reach attainment of the proposed
alternative standard level of 10/35 |~ig/m3,14 (out of 18) counties need additional
emissions reductions to reach attainment of the more stringent alternative standard level
of 10/30 ng/m3,15 (out of 21) counties need additional emissions reductions to reach
attainment of the proposed alternative standard level of 9/35 |~ig/m3, and 21 (out of 25)
counties need additional emissions reductions to reach attainment of the more stringent
alternative standard level of 8/35 ng/m3.
Table 3-8 Summary of PM2.5 Emissions Reductions Still Needed by Area for the
Alternative Primary Standard Levels of 10/35 ng/m3,10/30 ng/m3,
9/35 (j,g/m3, and 8/35 jig/m3 in 2032 (tons/year)
Region
10/35
10/30
9/35
8/35
Northeast
0
0
238
6,741
Southeast
0
0
994
4,780
West
595
5,651
2,132
5,023
CA
8,336
9,749
14,793
23,368
Total
8,931
15,400
18,157
39,912
3-26
-------
Table 3-9 Summary of PM2.5 Emissions Reductions Still Needed by Area and by
County for the Alternative Primary Standard Levels of 10/35 (ig/m3,
10/30 |ig/m3,9/35 (j,g/m3, and 8/35 jig/m3 in 2032 (tons/year)
Area
Area Name
10/35
10/30
9/35
8/35
Northeast
Saint Clair County, IL
0
0
0
13
Marion County, IN
0
0
0
390
St. Joseph County, IN
0
0
0
207
Vigo County, IN
0
0
0
63
Wayne County, MI
0
0
0
286
St. Louis City County, MO
0
0
0
77
Camden County, NJ
0
0
0
608
Union County, NJ
0
0
0
76
New York County, NY
0
0
0
266
Butler County, OH
0
0
0
410
Cuyahoga County, OH
0
0
0
436
Hamilton County, OH
0
0
0
36
Jefferson County, OH
0
0
0
680
Allegheny County, PA
0
0
0
382
Armstrong County, PA
0
0
0
294
Cambria County, PA
0
0
0
129
Delaware County, PA
0
0
238
970
Lancaster County, PA
0
0
0
600
Lebanon County, PA
0
0
0
523
Philadelphia County, PA
0
0
0
51
Brooke County, WV
0
0
0
119
Marshall County, WV
0
0
0
124
Southeast
Bibb County, GA
0
0
0
154
Clayton County, GA
0
0
0
304
Floyd County, GA
0
0
0
15
Fulton County, GA
0
0
0
396
Muscogee County, GA
0
0
0
265
Caddo Parish, LA
0
0
0
359
West Baton Rouge Parish, LA
0
0
0
55
Cameron County, TX
0
0
427
1,244
El Paso County, TX
0
0
0
603
Hidalgo County, TX
0
0
567
1,385
West
Pinal County, AZ
0
272
0
0
Santa Cruz County, AZ
0
0
0
431
Denver County, CO
0
0
0
323
Benewah County, ID
0
419
134
601
Lemhi County, ID
3
575
471
939
Shoshone County, ID
330
575
797
1,265
Lewis and Clark County, MT
0
487
0
0
Lincoln County, MT
262
262
730
1,197
Ravalli County, MT
0
514
0
0
Silver Bow County, MT
0
0
0
148
Crook County, OR
0
352
0
0
Harney County, OR
0
0
0
119
Lake County, OR
0
575
0
0
3-27
-------
Area
Area Name
10/35
10/30
9/35
8/35
Cache County, UT
0
29
0
0
Davis County, UT
0
1
0
0
Salt Lake County, UT
0
413
0
0
Weber County, UT
0
7
0
0
Kittitas County, WA
0
575
0
0
Okanogan County, WA
0
22
0
0
Yakima County, WA
0
575
0
0
CA
Alameda County, CA
0
0
0
175
Fresno County, CA
192
253
509
826
Imperial County, CA
1,701
1,701
2,551
3,402
Kern County, CA
634
634
951
1,268
Kings County, CA
634
634
951
1,268
Los Angeles County, CA
542
542
1,393
2,243
Madera County, CA
67
67
384
702
Merced County, CA
136
136
453
770
Napa County, CA
0
0
300
617
Plumas County, CA
176
502
493
810
Riverside County, CA
1,701
1,701
2,551
3,402
Sacramento County, CA
0
0
0
168
San Bernardino County, CA
1,701
1,701
2,551
3,402
San Diego County, CA
0
0
0
337
San Joaquin County, CA
0
0
161
478
San Luis Obispo County, CA
0
0
59
376
Siskiyou County, CA
0
43
0
0
Solano County, CA
0
0
0
167
Stanislaus County, CA
218
218
535
852
Sutter County, CA
0
0
0
56
Tulare County, CA
634
634
951
1,268
Ventura County, CA
0
983
0
783
Total
8,931
15,400
18,157
39,912
Note: The table includes only those counties that still need reductions (e.g., in the Northeast there were 57
counties that needed emissions reductions, and only the 22 counties still need emissions reductions for an
alternative standard level of 8/35 |ag/m3].
3-28
-------
¦ Counties with Sufficient Identified Reductions to Meet 10/35
¦ Counties Still Needing Reductions to Meet 10/35
Figure 3-5 Counties that Still Need PM2.5 Emissions Reductions for Proposed
Alternative Standard Level of 10/35 ng/m3
¦ Counties with Sufficient Identified Reductions to Meet 9/35
¦ Counties Still Needing Reductions to Meet 9/35
Figure 3-6 Counties that Still Need PM2.5 Emissions Reductions for Proposed
Alternative Standard Level of 9/35 (ig/m3
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¦ Counties with Sufficient Identified Reductions to Meet 8/35
¦ Counties Still Needing Reductions to Meet 8/35
Figure 3-7 Counties that Still Need PM2.5 Emissions Reductions for More Stringent
Alternative Standard Level of 8/35 (ig/m3
¦ Counties with Sufficient Identified Reductions to Meet 10/30
¦ Counties Still Needing Reductions to Meet 10/30
Figure 3-8 Counties that Still Need PM2.5 Emissions Reductions for More Stringent
Alternative Standard Level of 10/30 jig/in3
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3.2.6 Qualitative Assessment of the Remaining Air Quality Challenges and
Emissions Reductions Potentially Still Needed
The sections below discuss the remaining air quality challenges for areas in the
northeast and southeast, as well as in the west and California for the proposed alternative
standard levels of 10/35 |~ig/m3 and 9/35 |j,g/m3; the areas include a county in
Pennsylvania potentially affected by local sources, counties in border areas, counties in
small western mountain valleys, and counties in California's air basins and districts. The
characteristics of the air quality challenges for these areas include features of local source-
to-monitor impacts, cross-border transport, effects of complex terrain in the west and
California, and identifying wildfire influence on projected PM2.5 DVs that could potentially
qualify for exclusion as atypical, extreme, or unrepresentative events (U.S. EPA, 2019b).
Consistent with Chapter 2, Section 2.4, to discuss the remaining air quality
challenges for the proposed alternative standard levels of 10/35 |~ig/m3 and 9/35 ng/m3,
we group counties into the following "bins": Delaware County, Pennsylvania, border areas,
small mountain valleys, and California areas. By bin, Table 3-10 below summarizes the
counties that need additional emissions reductions for the proposed alternative standard
levels of 10/35 ng/m3 and 9/35 ng/m3.
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Table 3-10 Summary of Counties by Bin that Still Need Emissions Reductions for
Proposed Alternative Primary Standard Levels of 10/35 (ig/m3 and
9/35 jig/m3
Counties3 for
Additional Counties3 for
Bin
Area
10/35 mg/m3
9/35 mg/m3
Delaware County,
Pennsylvania
Northeast
--
Delaware County, PA
Border Areas
Southeast
--
Cameron County, TX
Hidalgo County, TX
California
Imperial County, CA
—
Small Mountain Valleys
West
Plumas County, CA
Lemhi County, ID
Shoshone County, ID
Lincoln County, MT
Benewah County, ID
California Areas
Fresno County, CA (SJVAPCD)
Kern County, CA (SJVAPCD)
Kings County, CA (SJVAPCD)
Los Angeles County, CA (SCAQMD)
Madera County, CA (SJVAPCD)
Merced County, CA (SJVAPCD)
Riverside County, CA (SCAQMD)
San Bernardino County, CA (SCAQMD)
Stanislaus County, CA (SJVAPCD)
Tulare County, CA (SJVAPCD)
Napa County, CA (BAAQMD)
San Joaquin County, CA (SJVAPCD)
San Luis Obispo County, CA
Note: For California counties that are part of multi-county air districts, the relevant district is indicated in parentheses;
BAAQMD = Bay Area Air Quality Management District, SCAQMD = South Coast Air Quality Management District, and
SJVAPCD= San Joaquin Valley Air Pollution Control District.
a The following counties have no identified PM2.5 emissions reductions because available controls were applied for the
current standard of 12/35 |ig/m3 and additional controls were notavailable: Imperial, Kern, Kings, Lemhi, Plumas,
Riverside, San Bernardino, Shoshone, and Tulare.
3.2.6.1 Delaware County, Pennsylvania (Northeast)
As shown in Table 3-9 above, the analysis indicates that counties in the northeast do
not need additional emissions reductions for the proposed alternative standard level of
10/35 |j,g/m3; Delaware County, Pennsylvania county needs additional emissions
reductions for the proposed alternative standard level of 9/35 |~ig/m3.
In analyzing the proposed alternative standard level of 9/35 |~ig/m3, we estimated
Delaware County would need 673 tons of PM2.5 emissions reductions.9 The control strategy
analysis identified 277 tons of reductions within Delaware County from the application of
several controls, including a potential control at one of the facilities adjacent to a monitor.10
9 Appendix 2A, Table 2A-14 provides a summary of emissions reductions needed by county for the proposed
and more stringent alternative standard levels.
10 Appendix 3A, Table 3A-2 provides a summary of in-county emissions reductions from control applications
by county for the northeast.
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Some additional control applications within Delaware County included: Electrostatic
Precipitator at 25% RP applied to commercial cooking emissions in the non-point (area)
inventory sector; Pave Existing Shoulders at 25% RP applied to road dust emissions in the
area fugitive dust inventory sector; Fabric Filter - All Types applied to industrial,
commercial, and institutional boilers and industrial processes in the non-EGU point
inventory sector; and Convert to Gas Logs at 25% RP and New Gas Stove or Gas Logs at
25% RP applied to area source residential wood combustion emissions in the residential
wood combustion inventory sector.
To analyze the 396 tons of PM2.5 emissions reductions still needed, we identified
633 tons of PM2.5 emissions reductions from adjacent counties11, which is the equivalent of
158 tons of in-county emissions reductions after adjusting for the 4:1 ratio of adjacent
county reductions identified to in-county reductions needed. This left 238 tons of PM2.5
emissions reductions still needed. As shown in Table 3A-8, Delaware County has area
fugitive dust (afdust), non-point (area) (nonpt), non-electric generating unit point source
(ptnonipm), and residential wood combustion (rwc) emissions remaining in the inventory if
additional controls beyond the scope of this analysis can be identified. In addition,
Philadelphia County and Montgomery County, which are adjacent to Delaware County,
have emissions remaining in those inventory sectors if additional controls beyond the
scope of this analysis can be identified.
In Chapter 2, Section 2.4.1 we discuss a monitor located on the property of Evonik
Degussa Corporation in Delaware County, Pennsylvania. The state, in their Commonwealth
of Pennsylvania Department of Environmental Protection 2018 Annual Ambient Air
Monitoring Network Plan, concluded that local emissions sources are impacting this
monitor (Chester monitor) based on comparisons of PM2.5 concentrations from the Chester
monitor and a monitor approximately 2.5 miles away (Marcus Hook monitor). The EPA's
2032 DV projections are consistent with a local source influence on the Chester monitor. It
is possible that controls applied in the illustrative control strategy analysis at one of the
facilities adjacent to the Chester monitor might result in sufficient emissions reductions for
11 Appendix 3A, Table 3A-3 provides a summary of adjacent county emissions reductions from control
applications in the northeast.
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the proposed alternative standard level of 9/35 |~ig/m3 at that monitor because PM2.5
concentrations are more responsive to primary PM2.5 emission reductions located close to a
monitor. However, specifically quantifying the impacts of the CoST-recommended control
at one of the facilities adjacent to the Chester monitor would require a more detailed local
analysis. In addition, the CoST-recommended control may not be applicable if the
underlying emissions inventory did not accurately reflect existing controls at the facility
adjacent to the Chester monitor.
3.2.6.2 Border Areas (Southeast, California)
As shown in Table 3-9 above, the analysis indicates that counties in the southeast do
not need additional emissions reductions for the proposed alternative standard level of
10/35 |j,g/m3; Cameron County and Hidalgo County, Texas need additional emissions
reductions for the proposed alternative standard level of 9/35 |~ig/m3.
We estimated Cameron County would need 581 tons of PM2.5 emissions reductions.
The control strategy analysis identified 148 tons of reductions within Cameron County
from the application of several controls.12 The control applications within Cameron County
included: Electrostatic Precipitator at 25% RP applied to commercial cooking emissions in
the non-point (area) inventory sector; Pave Existing Shoulders at 25% RP and Pave
Unpaved Roads at 25% RP applied to road dust emissions in the area fugitive dust
inventory sector; Convert to Gas Logs at 25% RP applied to area source residential wood
combustion emissions in the residential wood combustion inventory sector; and Substitute
Chipping for Burning applied to waste disposal emissions in the non-point (area) inventory
sector.
To analyze the 433 tons of PM2.5 emissions reductions still needed, we identified 22
tons of PM2.5 emissions reductions from adjacent counties13, which was the equivalent of
5.5 tons of in-county emissions reductions after adjusting for the 4:1 ratio of adjacent
county reductions identified to in-county reductions needed. This left 427 tons of PM2.5
12 Appendix 3A, Table 3A-4 provides a summary of in-county emissions reductions from control applications
by county for the southeast.
13 Appendix 3A, Table 3A-5 provides a summary of adjacent county emissions reductions from control
applications in the southeast.
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emissions reductions still needed. As shown in Table 3A-8, Cameron County has area
fugitive dust (afdust), point source agriculture fire (ptagfire), non-point (area) (nonpt),
non-electric generating unit point source (ptnonipm), and residential wood combustion
[rwc] emissions remaining in the inventory if additional controls beyond the scope of this
analysis can be identified; the majority of the emissions remaining are area fugitive dust
emissions.
In addition, we estimated Hidalgo County would need 1,022 tons of PM2.5 emissions
reductions. The control strategy analysis identified 406 tons of reductions within Hidalgo
County from the application of several controls.14 Some of the control applications within
Hidalgo County included: Electrostatic Precipitator at 25% RP applied to commercial
cooking emissions in the non-point (area) inventory sector; Fabric Filter - All Types
applied to industrial, commercial, and institutional boilers in the non-EGU point inventory
sector; Pave Existing Shoulders at 25% RP and Pave Unpaved Roads at 25% RP applied to
road dust emissions in the area fugitive dust inventory sector; Convert to Gas Logs at 25%
RP and New Gas Stove or Gas Logs at 25% RP applied to area source residential wood
combustion emissions in the residential wood combustion inventory sector; and Substitute
Chipping for Burning applied to waste disposal emissions in the non-point (area) inventory
sector.
To analyze the 616 tons of PM2.5 emissions reductions still needed, we identified
194 tons of PM2.5 emissions reductions from adjacent counties15, which was the equivalent
of 48.5 tons of in-county emissions reductions after adjusting for the 4:1 ratio of adjacent
county reductions identified to in-county reductions needed. This left 567 tons of PM2.5
emissions reductions still needed. As shown in Table 3A-8, Hidalgo County has area fugitive
dust (afdust), point source agriculture fire [ptagfire], non-point (area) [nonpt], non-point
source oil and gas [np_oilgas], non-electric generating unit point source [ptnonipm), point
source oil and gas [pt_oilgas], and residential wood combustion [rwc] emissions remaining
14 Appendix 3A, Table 3A-4 provides a summary of in-county emissions reductions from control applications
by county for the southeast.
15 Appendix 3A, Table 3A-5 provides a summary of adjacent county emissions reductions from control
applications in the southeast.
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in the inventory if additional controls beyond the scope of this analysis can be identified;
the majority of the emissions remaining are area fugitive dust emissions.
In Chapter 2, Section 2.4.2.1 we note that the monitors in Cameron County and
Hidalgo County are in the Lower Rio Grande Valley, which includes the northern portion of
the state of Tamaulipas, Mexico. Addressing emissions reductions needed for the proposed
alternative standard level of 9/35 |~ig/m3 at the monitors is challenging because of the
location of these counties along the U.S.-Mexico border.
Area fugitive dust emissions make up the largest fraction of primary PM2.5 emissions
in Hidalgo County and Cameron County in the 2016 and 2032 air quality modeling cases
(Chapter 2, Figure 2-16). Paved-road dust emissions (in the area fugitive dust inventory
sector) are projected to increase in these counties between 2016 and 2032 as a result of
projected increases in the vehicle miles travelled; non-point (area) sources emissions are
also projected to increase as a result of population-based emissions projection factors.
Increases in area fugitive dust and non-point (area) emissions from 2016 to 2032 offset the
decreases in primary PM2.5 emissions projected for EGUs and mobile sources in the
counties. More detailed local analyses for these counties are needed to better understand
the potential growth in area fugitive dust and non-point (area) source emissions, as well as
the potential contributions of international transport.
Further, for Imperial County, California the control strategy analysis did not identify
any emissions reductions from the application of controls.16 As shown in Table 3A-8,
Imperial County has area fugitive dust (afdust), non-point (area) (nonpt), non-electric
generating unit point source (ptnonipm), point source agriculture fire (ptagflre), and
residential wood combustion (rwc) emissions remaining in the inventory if controls
beyond the scope of this analysis can be identified; the majority of the emissions remaining
are area fugitive dust emissions.
As discussed in Chapter 2, Section 2.4.2, Imperial County is located in the southeast
corner of California and shares a southern border with Mexicali, Mexico. Imperial County
16 As shown in Table 3A-8, for Imperial, CA, CoST identified controls to apply toward the current standard of
12/35 |-Lg/m:i. Additional controls were not available for the proposed or more stringent alternative
standard levels.
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includes three PM2.5 monitoring sites, located in the cities of Calexico, El Centro, and
Brawley (Chapter 2, Figure 2-12). While these three cities are of similar size and have
similar emissions sources, the annual 2032 PM2.5 DV at the Calexico monitor, which is the
southern-most monitor and is less than a mile from the U.S.-Mexico border, is much greater
than the other two monitors (12.45 |~ig/m3, 9.13 |~ig/m3, and 8.02 |~ig/m3, respectively). In
addition, substantially greater NOx, SO2 and sulfate, and primary PM2.5 emissions have been
estimated for Mexicali, Mexico than for Calexico, California. For the proposed alternative
standard levels, Imperial County may not need the additional emissions reductions
estimated because of the potential influence of Mexicali emissions on PM2.5 concentrations
at the Calexico monitor and Section 179B of the Clean Air Act; however, a detailed local
analysis is needed.17
3.2.6.3 Small Mountain Valleys (West)
As shown in Table 3-9 above, the analysis also indicates that counties in the west
need additional emissions reductions after the application of controls for all of the
alternative standard levels analyzed. For the small mountain valleys bin, Table 3-11 below
summarizes the estimated PM2.5 emissions reductions needed and emissions reductions
identified by CoST for each of these counties for the proposed alternative standard level of
9/35 ng/m3.
Table 3-11 Summary of Estimated PM2.5 Emissions Reductions Needed and
Emissions Reductions Identified by CoST for the West for the Proposed
Primary Standard Level of 9/35 |ig/m3 in 2032 (tons/year)
County/State
PM2.5 Emissions Reductions
Needed
In-County PM2.5 Emissions
Reductions Identified by CoST
Plumas, CA
493.2
0
Benewah, ID
266.6
132.8
Lemhi, ID
471.0
0
Shoshone, ID
797.4
0
Lincoln, MT
954.0
224.2
Note: As shown in Table 3A-8, for Plumas, CA and Lemhi and Shoshone, ID, CoST identified controls to apply
toward the current standard of 12/35 \xg/m:i. Additional controls in those counties were not available for the
proposed or more stringent alternative standard levels.
17 Section 179B of the Clean Air Act [CAA] provides that a nonattainment area would be able to attain, or
would have attained, the relevant National Ambient Air Quality Standard but for emissions emanating from
outside the U.S.
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As shown in Table 3-11, the control strategy analysis identified emissions
reductions for two of the counties. Some of the control applications in those counties
included: Pave Existing Shoulders at 25% RP and Pave Unpaved Roads at 25% RP applied
to road dust emissions in the area fugitive dust inventory sector; Install Cleaner Hydronic
Heaters at 25% RP and New Gas Stove or Gas Logs at 25% RP applied to area source
residential wood combustion emissions in the residential wood combustion inventory
sector; and Substitute Chipping for Burning applied to waste disposal emissions in the non-
point (area) inventory sector.
As shown in Table 3A-8, these counties have area fugitive dust (afdust), non-point
(area) (nonpt), non-electric generating unit point source (ptnonipm), and residential wood
combustion (rwc) emissions remaining in the inventory if additional controls beyond the
scope of this analysis can be identified; for each of the counties the majority of the
emissions remaining are area fugitive dust emissions.
Meteorological temperature inversions often occur in small northwestern mountain
valleys in winter and trap pollution emissions in a shallow atmospheric layer at the surface
(Chapter 2, Section 2.1.2). As discussed in Chapter 2, Section 2.4.3, primary PM2.5 emissions
can build up in the surface layer and produce high PM2.5 concentrations in winter (Chapter
2, Figure 2-17). These mountain valleys are often very small in size relative to the area of
the surrounding county and far smaller than the resolution of photochemical air quality
models (e.g., 12km grid cells). See Chapter 2, Figures 2-18 and 2-19 for maps of the Portola
nonattainment area (2012 PM2.5 NAAQS) relative to the city of Portola, California and the
Libby nonattainment area (1997 PM2.5 NAAQS) relative to the city of Libby, Montana. PM2.5
concentrations in these small mountain valleys can be influenced by the temperature
inversions, as well as by residential wood combustion and wildfire smoke.
Also as discussed in Chapter 2, Section 2.4.3, because of the small size of the urban
areas within the northwestern mountain valleys, air quality planning is commonly based
on linear rollback methods. To estimate emissions reductions needed for a standard level,
the linear rollback method relates wood-smoke contribution estimates at an exceeding
monitor to the local, or sub-county, wood combustion emissions totals. The PM2.5 response
factors from linear rollback methods estimate that relatively fewer residential wood
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combustion emissions reductions can greatly influence PM2.5 concentrations in many of
these small mountain valleys. We did not apply linear rollback-based response factors for
the mountain valleys in this RIA because emissions inventory and control measure
information are available at the county level, preventing us from targeting residential wood
combustion controls in the local communities identified in the analyses. To better assess
the emissions reductions needed for the proposed standard levels, more detailed analyses
that include local PM2.5 response factors, emissions estimates, and controls for each local
area are needed.
In addition to air quality challenges related to meteorological temperature
inversions and residential wood combustion, PM2.5 concentrations in these small mountain
valleys may also be influenced by wildfire emissions that could potentially qualify for
exclusion as atypical, extreme, or unrepresentative events.18 We performed sensitivity
projections to assess the potential for wildfire impacts. These projections suggest that
Benewah County, Oregon may be largely affected by wildfires and that annual 2032 DVs in
Lemhi County and Shoshone County, Oregon, and Lincoln County, Montana could be much
lower if detailed analyses resulted in additional data exclusion. Detailed local analyses are
needed to fully characterize the wildfire influence in these areas. For more detailed
discussions of the residential wood combustion and wildfire smoke air quality challenges,
see Chapter 2, Section 2.4.3.
3.2.6.4 California Areas
As shown in Table 3-9 above, the analysis also indicates that counties in California
need additional emissions reductions after the application of controls for all of the
alternative standard levels analyzed. The sections below discuss the air quality challenges
by each air basin and/or district.
In the SJVAPCD, in analyzing the proposed alternative standard level of 9/35 |~ig/m3,
the District needed 5,636 tons of PM2.5 emissions reductions. The control strategy analysis
18 Some wildfire influence likely persists in the projected 2032 PM2.5 DVs despite the exclusion of EPA-
concurred exceptional events and the wildfire screening (Chapter 2, Section 2.2.2).
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identified 741 tons of reductions from the application of several controls.19 Some of the
control applications included: Electrostatic Precipitator at 25% RP applied to commercial
cooking emissions in the non-point (area) inventory sector; Fabric Filter - All Types
applied to industrial, commercial, and institutional boilers and industrial processes in the
non-EGU point inventory sector; Pave Existing Shoulders at 25% RP and Pave Unpaved
Roads at 25% RP applied to road dust emissions in the area fugitive dust inventory sector;
Convert to Gas Logs at 25% RP applied to area source residential wood combustion
emissions in the residential wood combustion inventory sector; and Substitute Chipping
for Burning applied to waste disposal emissions in the non-point (area) inventory sector.
As discussed above, we did not attempt to identify additional PM2.5 emissions reductions in
adjacent counties or air districts.
As discussed in more detail in Chapter 2, Section 2.4.4, the air quality in SJVAPCD is
influenced by complex terrain and meteorological conditions that are best characterized
with a high-resolution air quality modeling platform developed for the specific conditions
of the valley. Air quality in the valley is influenced by emissions from large cities such as
Bakersfield and Fresno, a productive agricultural region, dust exacerbated by drought,
major goods transport corridors, and wildfires. The largest share of 2032 PM2.5 emissions
are from agricultural dust, the production of crops and livestock, agricultural burning,
paved and unpaved road dust, and prescribed burning (Chapter 2, Figure 2-23); wildfire
emissions also influence PM2.5 concentrations.
Specific, local information on control measures to reduce emissions from
agricultural dust and burning and prescribed burning is needed given the magnitude of
emissions from these sources. In addition, more detailed analyses are needed to
characterize the influence of wildfires on PM2.5 concentrations and the potential for some
of these wildfires to be considered as atypical, extreme, or unrepresentative events. Note
that wildfire screening is particularly complex in California because different parts of the
state have different wildfire seasons.
19 Appendix 3A, Table 3A-7 provides a summary of in-county emissions reductions from control applications
by county for California.
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In the SCAQMD, in analyzing the proposed alternative standard level of 9/35 ng/m3,
the District needed 7,654 tons of PM2.5 emissions reductions. The control strategy analysis
identified 1,159 tons of reductions from the application of several controls.20 Some of the
control applications included: Electrostatic Precipitator at 25% RP applied to commercial
cooking emissions in the non-point (area) inventory sector; Fabric Filter - All Types
applied to industrial, commercial, and institutional boilers and industrial processes in the
non-EGU point inventory sector; Convert to Gas Logs at 25% RP applied to area source
residential wood combustion emissions in the residential wood combustion inventory
sector; and Substitute Chipping for Burning applied to waste disposal emissions in the non-
point (area) inventory sector. We did not attempt to identify additional PM2.5 emissions
reductions in adjacent counties or air districts.
As discussed in more detail in Chapter 2, Section 2.4.4, the air quality in the SCAQMD
is influenced by complex terrain and meteorological conditions that are best characterized
with a high-resolution air quality modeling platform developed for the specific conditions
of the air basin. Air quality is influenced by diverse emissions sources associated with the
large population, the ports of Los Angeles and Long Beach, wildfires, and transportation of
goods. The largest share of 2032 PM2.5 emissions are from commercial and residential
cooking, on-road mobile sources, and paved and unpaved road dust (Chapter 2, Figure 2-
26).
Specific, local information on control measures to reduce emissions from many of
the non-point (area) emissions sources (e.g., commercial and residential cooking) is needed
given the magnitude of emissions from these sources. In addition, more detailed analyses
are needed to characterize the influence of wildfires on PM2.5 concentrations and the
potential for some of these wildfires to be considered as atypical, extreme, or
unrepresentative events.
In the BAAQMD, in analyzing the proposed alternative standard level of 9/35 |~ig/m3,
the District needed 884 tons of PM2.5 emissions reductions. The control strategy analysis
20 Appendix 3A, Table 3A-7 provides a summary of in-county emissions reductions from control applications
by county for California.
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identified 586 tons of reductions from the application of several controls.21 Some of the
control applications included: Smokeless Broiler at 25% RP, Catalytic Oxidizers at 25% RP,
and Electrostatic Precipitator at 25% RP applied to commercial cooking emissions in the
non-point (area) inventory sector; Fabric Filter - All Types and Venturi Scrubber applied to
industrial, commercial, and institutional boilers and industrial processes in the non-EGU
point inventory sector; Pave Existing Shoulders at 25% RP and Pave Unpaved Roads at
25% RP applied to road dust emissions in the area fugitive dust inventory sector; Convert
to Gas Logs at 25% RP applied to area source residential wood combustion emissions in the
residential wood combustion inventory sector; and Substitute Chipping for Burning applied
to waste disposal emissions in the non-point (area) inventory sector. We did not attempt to
identify additional PM2.5 emissions reductions in adjacent counties or air districts.
As discussed in Chapter 2, Section 2.4.4, PM2.5 concentrations in Napa County may
have relatively large contributions from local emissions sources, as well as contributions
from wildfires and sources in nearby regions including the BAAQMD and the SJVAPCD. In
addition, previous research reported that modeled concentrations of carbonaceous PM2.5 at
the monitor in Napa County were underestimated. The research suggested that
carbonaceous PM2.5 emissions, possibly from wood burning, may have been strongly
underrepresented in the Napa County emissions inventory. Additional work to develop
local emissions inventories and identify appropriate controls is needed.
In San Luis Obispo County APCD, in analyzing the proposed alternative standard
level of 9/35 |~ig/m3, the District needed 187 tons of PM2.5 emissions reductions. The
control strategy analysis identified 128 tons of reductions from the application of several
controls.22 The control applications included: Electrostatic Precipitator at 25% RP applied
to commercial cooking emissions in the non-point (area) inventory sector; Fabric Filter -
All Types applied to industrial processes in the non-EGU point inventory sector; Convert to
Gas Logs at 25% RP applied to area source residential wood combustion emissions in the
residential wood combustion inventory sector; and Substitute Chipping for Burning applied
21 Appendix 3A, Table 3A-7 provides a summary of in-county emissions reductions from control applications
by county for California.
22 Appendix 3A, Table 3A-7 provides a summary of in-county emissions reductions from control applications
by county for California.
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to waste disposal emissions in the non-point (area) inventory sector. We did not attempt to
identify additional PM2.5 emissions reductions in adjacent counties or air districts.
As discussed in Chapter 2, Section 2.4.4, in recent years the PM2.5 DVs have decreased
at the monitor in San Luis Obispo County APCD -- the annual PM2.5 DVs for the 2018-2020
and 2019-2021 periods are 8.0 and 7.7 ng/m3, respectively (Chapter 2, Figure 2-28). The
projected 2032 annual DV (9.63 |j,g/m3) at the monitor is based on data from the 2014-
2018 period and does not capture these recent air quality improvements. Based on the data
for these two most recent DV periods, the monitor may not need additional emissions
reductions for the proposed alternative standard level of 9/35 |~ig/m3.
3.3 Limitations and Uncertainties
The EPA's analysis is based on its best judgment for various input assumptions that
are uncertain. As a general matter, the Agency selects the best available information from
engineering studies of air pollution controls and has set up what it believes is the most
reasonable modeling framework for analyzing the cost, emissions changes, and other
impacts of emissions controls. However, the control strategies above are subject to
important limitations and uncertainties. In the following, we summarize the limitations and
uncertainties that are most significant.
• Illustrative control strategy: A control strategy is the set of control measures
or actions that States may take to meet a standard, such as which industries
should be required to install end-of-pipe controls or certain types of
equipment and technology. The illustrative control strategy analyses in this
RIA present only one potential pathway for controlling emissions. The control
strategies are not recommendations for how a revised PM2.5 NAAQS should be
implemented, and States will make all final decisions regarding
implementation strategies for a revised NAAQS. We do not presume that the
controls presented in this RIA are an exhaustive list of possibilities for
emissions reductions.
• Emissions inventories and air quality modeling: These serve as a
foundation for the projected PM2.5 DVs, control strategies, and estimated costs
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in this analysis and thus limitations and uncertainties for these inputs impact
the results, especially for issues such as future year emissions projections and
information on controls currently in place at many sources. Limitations and
uncertainties for these inputs are discussed in previous chapters. In addition,
there are factors that affect emissions, such as economic growth and the
makeup of the economy that introduce additional uncertainty.
Projecting level and geographic scope of exceedances: Estimates of the
geographic areas that would exceed alternative standard levels in a future
year, and the level to which those areas would exceed, are approximations
based on several factors. The actual nonattainment determinations that would
result from a revised NAAQS will likely depend on the consideration of local
issues, changes in source operations between the time of this analysis and
implementation of a new standard, and changes in control technologies over
time.
Assumptions about the baseline: There is significant uncertainty about the
illustration of the impact of rules on the baseline. In addition, the February
2022 Proposed Federal Implementation Plan Addressing Regional Ozone
Transport for the 2015 Ozone National Ambient Air Quality Standard and the
firm EGU retirements are not included in the 2032 projections.
Applicability of control measures: The applicability of a control measure to
a specific source varies depending on a number of process equipment factors
such as age, design, capacity, fuel, and operating parameters. These can vary
considerably from source to source and over time. The applicability of control
measures to area sources is also subject to the uncertainty of the area source
emissions estimated.
Control measure advances over time: The control measures applied do not
reflect potential effects of technological change that may be available in future
years. All estimates of impacts associated with control measures applied
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reflect our current knowledge, and not projections, of the measures'
effectiveness or costs.
• Pollutants to be targeted: Local knowledge of atmospheric chemistry in each
geographic area may result in a different prioritization of pollutants for
potential control.
3-45
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3.4 References
Kelly, J. T., CJ Jang, B Timin, B Gantt, A Reff, Y Zhu, S Long, A Hanna. 2019. A System for
Developing and Projecting PM2.5 Spatial Fields to Correspond to Just Meeting National
Ambient Air Quality Standards. Atmospheric Environment: X 100019.
https://doi.org/10.1016/j-aeaoa.2019.100019
Kelly, J. T., K. R. Baker, S. N. Napelenok, and S. R. Roselle. 2015. Examining single-source
secondary impacts estimated from brute-force, decoupled direct method, and advanced
plume treatment approaches. Atmospheric Environment 111:10-19.
https://doi.Org/10.1016/j.atmosenv.2015.04.004
U.S. Environmental Protection Agency (U.S. EPA). 2019a. CoST v3.7 User's Guide. Office of
Air Quality Planning and Standards, Research Triangle Park, NC. November 2019.
Available at <
https://www.cmascenter.org/help/documentation. cfm?model=cost&version=3.7>.
United States Environmental Protection Agency (U.S. EPA). 2019b. Additional Methods,
Determinations, and Analyses to Modify Air Quality Data Beyond Exceptional Events.
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards,
Research Triangle Park, NC, EPA-457/B-19-002. Available:
https://www.epa.gov/sites/default/files/2019-
04/documents/clarification_memo_on_data_modification_methods.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2012. Regulatory Impact Analysis for the
Final Revisions to the National Ambient Air Quality Standards for Particulate Matter,
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards,
Research Triangle Park, NC. EPA-452/R-12-005. Available at:
https://www.epa.gov/sites/default/files/2020-07/documents/naaqs-
pm_ria_final_2 012-12.pdf.
3-46
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APPENDIX 3 A: CONTROL STRATEGIES AND PM2.5 EMISSIONS REDUCTIONS
Overview
Chapter 3 describes the approach that EPA used in applying the illustrative control
strategies for analyzing the following proposed and more stringent alternative annual and
24-hour standard levels --10/35 ng/m3,10/30 ng/m3, 9/35 ng/m3, and 8/35 |~ig/m3. This
Appendix contains additional information about the control technologies and measures
that were applied, as well as additional details on the estimated PM2.5 emissions reductions.
3A.1 Types of Control Measures
Several types of control measures were applied in the analyses for the analytical
baseline and alternative standard levels. We identified control measures using the EPA's
Control Strategy Tool (CoST) (U.S. EPA, 2019) and the control measures database.1 A brief
description of several of the control technologies and measures is below.
3A.1.1 PM Control Measures for Non-EGU Point Sources
Non-EGU point source categories covered in this analysis include industrial boilers,
as well as industrial processes in the cement manufacturing, chemical manufacturing, pulp
and paper, mining, ferrous and non-ferrous metals, and refining industries. Several types of
PM2.5 control technologies were applied for these sources, including venturi scrubbers,
fabric filters, and electrostatic precipitators, which are the primary controls analyzed for
non-EGU point sources.
• Venturi scrubbers - Venturi scrubbers are one of several types of wet
scrubbers that remove both acid gas and PM from waste gas streams of
stationary point sources. The pollutants are removed primarily through the
impaction, diffusion, interception and/or absorption of the pollutant onto
droplets of liquid. The liquid containing the pollutant is then collected for
disposal.
1 More information about CoST and the control measures database can be found at the following link:
https://www.epa.gov/economic-and-cost-analysis-air-pollution-regulations/cost-analysis-modelstools-air-
pollution.
3A-1
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• Fabric filters -- A fabric filter unit consists of one or more isolated
compartments containing rows of fabric bags in the form of round, flat, or
shaped tubes, or pleated cartridges. Particle-laden gas usually passes up
along the surface of the bags then radially through the fabric. Particles are
retained on the upstream face of the bags, and the cleaned gas stream is
vented to the atmosphere. Fabric filters collect particles with sizes ranging
from submicron to several hundred microns in diameter at efficiencies
generally in excess of 99 or 99.9 percent.
• Electrostatic precipitators -- An ESP is a particle control device that uses
electrical forces to move the particles out of the flowing gas stream and onto
collector plates. The particles are given an electrical charge by forcing them
to pass through a corona, a region in which gaseous ions flow. The electrical
field that forces the charged particles to the walls comes from electrodes
maintained at high voltage in the center of the flow lane. Once the particles
are collected on the plates, they must be removed from the plates without re-
entraining them into the gas stream. This is usually accomplished by
knocking them loose from the plates, allowing the collected layer of particles
to slide down into a hopper from which they are evacuated.
3A.1.2 PM Control Measures for Non-point (Area) Sources
The non-point sector of the emissions inventory includes emissions sources that are
generally too small and/or numerous to estimate emissions for individual sources (e.g.,
commercial cooking, residential woodstoves, commercial or backyard waste burning). We
estimate the emissions from these sources for each county overall, typically using an
emissions factor that is applied to a surrogate of activity such as population or number of
houses. Control measures for non-point sources are applied to the county level emissions.
Several control measures were applied to PM2.5 emissions from non-point sources,
including catalytic oxidizers applied to charbroilers in commercial cooking, electrostatic
precipitator applied to under-fire charbroilers in commercial cooking, substitute chipping
for open burning in general and for households, converting to gas logs for residential wood
3 A-2
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combustion, chemical stabilizers to suppress unpaved road dust, and paving existing
shoulders to suppress paved road dust.
3A.2 EGU Trends Reflected in EPA's Integrated Planning Model (IPM) v6 Platform,
Summer 2021 Reference Case Projections
The EPA's Integrated Planning Model (IPM) v6 Platform Summer 2021 Reference
Case projections were used in the air quality modeling done for this RIA.2 A high level
summary of the input assumptions in the Summer 2021 Reference Case is below. This
version features bottom-up comprehensive input data and assumption updates3, including
the following:
• Demand - Annual Energy Outlook (AEO) 2020
• Gas Market Assumptions - Updated as of September 2020
• Coal Market Assumptions - Updated as of September 2020
• Cost and Performance of Fossil Generation Technologies - AEO 2020
• Cost and Performance of Renewable Energy Generation Technologies -
National Renewable Energy Lab Annual Technology Baseline 2020 mid-case
• Nuclear Unit Operational Costs - AEO 2020 with some adjustments
• Environmental Rules and Regulations (On-the-Books) — Revised Cross-State
Air Pollution Rule, Mercury and Air Toxics Standard, BART, California
Assembly Bill 32, Regional Greenhouse Gas Initiative, various renewable
portfolio standards and clean energy standards, non-air rules (Cooling Water
Intake, Steam Electric Power Generating Effluent Guidelines, Coal
Combustion Residuals), State Rules
• Financial Assumptions - Based on 2016-2020 data, reflects tax credit
extensions from Consolidated Appropriations Act of 2021
2 Documentation of the Summer 2021 Reference Case and the corresponding results are available at
https://www.epa.gov/power-sector-modeling/epas-power-sector-modeling-platform-v6-using-ipm-
summer-2 021-reference-case.
3 For a complete summary reference, see Chapter 1, Table 1-1 available at
https://www.epa.gov/system/files/documents/2021-09/chapter-l-introduction.pdf
3 A-3
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• Transmission - Updated data with build options
• Retrofits - carbon capture and storage option for combined cycles
• Operating Reserves (in select runs) - greater detail in representing
interaction of load, wind, and solar, ensuring availability of quick response of
resources at higher levels of renewable energy penetration
• Fleet-NEEDS Summer 2021
The Summer 2021 Reference Case projections show a gradual decline in national-
level annual SO2, NOx, and primary PM emissions because of displacement of retired coal
units with new natural gas generation and renewable energy. Greater near-term renewable
energy penetration is due to increase in actual projects reflected in NEEDS prior to the IPM
projections; long-term increase is largely driven by improved renewable energy technology
costs.
California sees a significant decrease in projected emissions for all pollutants by
2030 due to the state's Clean Energy Standards (CES). California's Senate Bill No. 100
requires expansion of the Renewable Portfolio Standard through 2030 where generation
from qualifying renewables must achieve a 50 percent share of retail sales by 2026 and 60
percent by 2030.4 California's legislation requires a transition from the RPS to CES where
generation from qualifying "zero carbon resources" must equal 100 percent of retail sales
by 2045. Our projections show a significant shift from fossil to renewable energy
generation in California between 2025 and 2030 with the trend continuing thereafter.
3A.3 Applying Control Technologies and Measures
As mentioned in Chapter 3, Section 3.2.2, controls applied for the analyses of the
existing standards of 12/35 |~ig/m3 and the proposed and more stringent annual and 24-
hour PM2.5 alternative standard levels of 10/35 |~ig/m3,10/30 |~ig/m3, 9/35 |~ig/m3, and
8/35 ng/m3 are listed in Table 3A-1 by geographic area and by emissions inventory sector,
with an "X" indicating which control technologies were applied for each standard level.
4 https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=2 0172 0180SB100
3A-4
-------
Table 3A-2 through Table 3A-7 include detailed summaries of PM2.5 emissions
reductions by county for the alternative standard levels for the northeast, the adjacent
counties in the northeast, the southeast, the adjacent counties in the southeast, the west,
and California. Table 3A-7 for California presents counties organized by air districts.
As shown in Table 3A-2 and Table 3A-3 for the northeast counties (57 counties) and
the adjacent counties (75 counties), for the alternative standard levels of 10/35 |~ig/m3 and
10/30 ng/m3, controls were applied in 4 counties and no additional emissions reductions
were needed in adjacent counties. For the alternative standard level of 9/35 |~ig/m3, we
estimated a total of 8,701 tons of PM2.5 emission reductions available from the application
of controls - approximately 78 percent of that total is available from within a county and
22 percent is from an adjacent county. For the alternative standard level of 8/35 |~ig/m3, we
estimated a total of 34,582 tons of PM2.5 emission reductions - approximately 55 percent of
that total is available from within a county and 45 percent is from an adjacent county.
As shown in Table 3A-4 and Table 3A-5 for the southeast counties (35 counties) and
the adjacent counties (32 counties), for the alternative standard levels of 10/35 |~ig/m3 and
10/30 ng/m3, controls were applied in two counties and no additional emissions
reductions were needed in adjacent counties. For the alternative standard level of 9/35
Hg/m3, we estimated a total of 3,235 tons of PM2.5 emission reductions - approximately 94
percent of that total is available from the application of controls from within a county and
six percent is from an adjacent county. For the alternative standard level of 8/35 ng/m3, we
estimated a total of 17,104 tons of PM2.5 emission reductions - approximately 71 percent of
that total is available from within a county and 29 percent is from an adjacent county.
As shown in Table 3A-6 for the west (36 counties), for the alternative standard level
of 10/35 ng/m3 controls were applied in one county. For the alternative standard level of
10/30 ng/m3 controls were applied in 18 counties; for the alternative standard level of
9/35 ng/m3 controls were applied in six counties; and for the alternative standard level of
8/35 ng/m3 controls were applied in 22 counties.
As shown in Table 3A-7 for California (26 counties) of the eight counties in the San
Joaquin Valley Air Pollution Control District, we estimated that five need PM2.5 emissions
3 A-5
-------
reductions. For four counties, we identified some emissions reductions available for an
alternative standard level of 10/35 |~ig/m3 and no additional emissions reductions for
lower alternative standard levels. For one county, we identified some emissions reductions
available for an alternative standard level of 10/35 |~ig/m3 and additional reductions
available for an alternative standard level of 9/35 ng/m3. Of the four counties in the South
Coast Air Quality Management District, we estimated that three need emissions reductions.
For two counties we did not identify any emissions reductions from the application of
controls for any of the alternative standard levels. For one county, we identified some
emissions reductions available for an alternative standard level of 10/35 ng/m3.
Table 3A-8 includes information on PM2.5 emissions by emissions inventory sector,
on counties needing emissions reductions, and on estimated emissions reductions by
alternative standard levels being analyzed. The column labeled Sector uses abbreviations
for emissions inventory sectors from the National Emissions Inventory. The abbreviations
and related sectors include: afdust or area fugitive dust emissions; nonpt or non-point
(area) source emissions; np_oilgas or non-point (area) source oil and gas emissions;
ptagflre or point source agriculture fire emissions; ptnonipm or non-electric generating
unit, point source emissions; pt_oilgas or point source oil and gas emissions; and rwc or
residential wood combustions emissions.
The first column includes names of adjacent counties and counties still needing
emissions reductions. The second column lists any counties that need emissions
reductions. The columns with annual PM2.5 emissions and the PM2.5 emissions reductions
are related to the county in the first column. If the second column is blank, then the annual
PM2.5 emissions serves as an indicator of the county's own PM2.5 emissions that might be
controllable if a state or local jurisdiction knew how to control those emissions; in these
cases the maximum PM2.5 emissions reductions should be equal to the selected PM2.5
emissions reductions for one of the alternative standards being analyzed (e.g., Pinal County,
AZ).
The table is intended to present information about potential nearby emissions
reductions that might be available to help counties attain an alternative standard level. The
list of PM2.5 emissions is not exhaustive, as inventory sectors with reported emissions less
3 A-6
-------
than 5 tons per year are excluded in general, and emissions from rail, airports, and
wildfires of all types are excluded regardless of their emissions because either we do not
have information on potential controls for these sectors or the emissions from these
sectors are not necessarily controllable (i.e., wildfires). While we considered emissions
from adjacent counties in the east, we did not do so in the west and California due to
uncertainty about the air quality impacts of emissions reductions from adjacent counties.
For the west and California, in addition to finding ways of controlling remaining emissions
within a county or adjacent counties (or within the same air district in California), it will be
necessary to determine how much emissions reductions in adjacent counties may impact
the DV at a monitor of interest.
3 A-7
-------
Table 3A-1 By Area and Emissions Inventory Sector, Control Measures Applied in
Analyses of the Current Standards and Alternative Primary Standard
Levels
Area
Inventory
Sector
Control Technology
12/35 10/35 10/30 9/35 8/35
Northeast
Non-EGU Point
Electrostatic Precipitator-All Types
X
X
Fabric Filter-All Types
X
X
X
X
Install new drift eliminator at 25% RP
X
X
Venturi Scrubber
X
X
X
X
Non-Point
Annual tune-up at 10% RP
X
[Area]
Annual tune-up at 25% RP
X
X
X
X
Biennial tune-up at 10% RP
X
X
X
Biennial tune-up at 25% RP
X
X
X
X
Catalytic oxidizers at 25% RP
X
X
X
X
Electrostatic Precipitator at 10% RP
X
Electrostatic Precipitator at 25% RP
X
X
X
X
HEPA filters atl0%RP
X
X
X
HEPA filters at25%RP
X
X
X
Smokeless Broiler at 10% RP
X
Smokeless Broiler at 25% RP
X
X
Substitute chipping for burning
X
X
X
X
Residential
Convert to Gas Logs at 25% RP
X
X
X
X
Wood
EPA-certified wood stove at 10% RP
X
Combustion
EPA Phase 2 Qualified Units at 10% RP
X
X
EPA Phase 2 Qualified Units at 25% RP
X
Install Cleaner Hydronic Heaters at 25% RP
X
X
X
X
Install Retrofit Devices at 10% RP
X
X
Install Retrofit Devices at 25% RP
X
New gas stove or gas logs at 10% RP
X
X
X
New gas stove or gas logs at 25% RP
X
X
X
X
Area Source
Chemical Stabilizer at 10% RP
X
Fugitive Dust
Chemical Stabilizer at 25% RP
X
X
Dust Suppressants at 10% RP
X
Pave existing shoulders at 10% RP
X
Pave existing shoulders at 25% RP
X
X
Pave Unpaved Roads at 25% RP
X
X
Northeast
Non-EGU Point
Fabric Filter-All Types
X
X
[Adjacent
Install new drift eliminator at 25% RP
X
X
Counties]
Venturi Scrubber
X
X
Oil & Gas Point
Fabric Filter-All Types
X
Non-Point
Annual tune-up at 25% RP
X
X
[Area]
Biennial tune-up at 10% RP
X
Biennial tune-up at 25% RP
X
X
Catalytic oxidizers at 25% RP
X
Electrostatic Precipitator at 25% RP
X
X
Fabric Filter-All Types
X
X
Smokeless Broiler at 10% RP
X
Smokeless Broiler at 25% RP
X
3 A-8
-------
Inventory
Area
Sector
Control Technology
12/35 10/35
10/30
9/35
8/35
Substitute chipping for burning
X
X
Residential
Convert to Gas Logs at 25% RP
X
X
Wood
Install Cleaner Hydronic Heaters at 25% RP
X
X
Combustion
New gas stove or gas logs at 25% RP
X
X
Area Source
Chemical Stablizer at 10% RP
X
X
Fugitive Dust
Chemical Stablizer at 25% RP
X
Pave existing shoulders at 25% RP
X
X
Pave Unpaved Roads at 25% RP
X
Southeast
Non-EGU Point
Electrostatic Precipitator-All Types
X
Fabric Filter-All Types
X
X
X
X
Install new drift eliminator at 10% RP
X
X
Install new drift eliminator at 25% RP
X
X
X
X
Venturi Scrubber
X
X
Oil & Gas Point
Install new drift eliminator at 25% RP
X
Non-Point
Annual tune-up at 25% RP
X
X
[Area]
Biennial tune-up at 10% RP
X
Biennial tune-up at 25% RP
X
X
X
Catalytic oxidizers at 25% RP
X
X
X
X
Electrostatic Precipitator at 10% RP
X
X
Electrostatic Precipitator at 25% RP
X
X
X
X
HEPA filters atlO%RP
X
HEPA filters at25%RP
X
Smokeless Broiler at 10% RP
X
X
X
X
Smokeless Broiler at 25% RP
X
X
Substitute chipping for burning
X
X
X
X
Residential
Convert to Gas Logs at 25% RP
X
X
X
X
Wood
EPA Phase 2 Qualified Units at 25% RP
X
X
X
Combustion
Install Cleaner Hydronic Heaters at 25% RP
X
X
Install Retrofit Devices at 10% RP
X
New gas stove or gas logs at 10% RP
X
New gas stove or gas logs at 25% RP
X
X
X
X
Area Source
Chemical Stabilizer at 10% RP
X
X
X
Fugitive Dust
Chemical Stabilizer at 25% RP
X
Pave existing shoulders at 10% RP
X
Pave existing shoulders at 25% RP
X
X
Pave Unpaved Roads at 25% RP
X
X
Southeast
Non-EGU Point
Fabric Filter-All Types
X
[Adjacent
Install new drift eliminator at 25% RP
X
Counties]
Non-Point
Annual tune-up at 25% RP
X
[Area]
Electrostatic Precipitator at 25% RP
X
X
Substitute chipping for burning
X
X
Residential
Convert to Gas Logs at 25% RP
X
Wood
Install Cleaner Hydronic Heaters at 25% RP
X
Combustion
New gas stove or gas logs at 25% RP
X
Area Source
Pave existing shoulders at 25% RP
X
X
Fugitive Dust
Pave Unpaved Roads at 25% RP
X
X
3A-9
-------
Inventory
Area Sector Control Technology 12/35 10/35 10/30 9/35 8/35
West Non-EGU Point Fabric Filter-All Types x x x x
Install new drift eliminator at 10% RP x
Install new drift eliminator at 25% RP x x
Venturi Scrubber
X
X
X
Non-Point
Annual tune-up at 10% RP
X
[Area]
Annual tune-up at 25% RP
Biennial tune-up at 10% RP
X
X
X
X
X
Biennial tune-up at 25% RP
X
X
X
X
Catalytic oxidizers at 25% RP
X
X
X
X
Electrostatic Precipitator at 25% RP
X
X
X
HEPA filters at25%RP
X
Smokeless Broiler at 10% RP
X
X
X
X
Smokeless Broiler at 25% RP
X
Substitute chipping for burning
X
X
X
X
X
Residential
Convert to Gas Logs at 25% RP
X
X
X
Wood
Combustion
EPA Phase 2 Qualified Units at 25% RP
Install Cleaner Hydronic Heaters at 10% RP
X
X
X
Install Cleaner Hydronic Heaters at 25% RP
X
X
X
X
X
Install Retrofit Devices at 10% RP
X
Install Retrofit Devices at 25% RP
X
New gas stove or gas logs at 10% RP
X
X
X
X
New gas stove or gas logs at 25% RP
X
X
X
X
X
Area Source
Chemical Stabilizer at 10% RP
X
X
Fugitive Dust
Chemical Stabilizer at 25% RP
Dust Suppressants at 25% RP
X
X
X
Pave existing shoulders at 25% RP
X
X
X
X
Pave Unpaved Roads at 25% RP
X
X
X
X
X
Non-EGU Point
Electrostatic Precipitator-All Types
X
Fabric Filter-All Types
X
X
X
X
X
Install new drift eliminator at 10% RP
X
Install new drift eliminator at 25% RP
X
Venturi Scrubber
X
X
X
X
X
Oil & Gas Point
Fabric Filter-All Types
X
Non-Point
Add-on Scrubber at 25% RP
X
X
[Area]
Annual tune-up at 10% RP
X
Annual tune-up at 25% RP
X
X
X
X
X
Biennial tune-up at 10% RP
X
Biennial tune-up at 25% RP
X
X
Catalytic oxidizers at 25% RP
X
X
Electrostatic Precipitator at 25% RP
X
X
X
X
X
Fabric Filter-All Types
X
HEPA filters atl0%RP
X
HEPA filters at25%RP
X
Smokeless Broiler at 10% RP
X
X
Smokeless Broiler at 25% RP
X
X
Substitute chipping for burning
X
X
X
X
X
3 A-10
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Area
Inventory
Sector
Control Technology
12/35 10/35 10/30 9/35 8/35
Residential
Wood
Combustion
Area Source
Fugitive Dust
Convert to Gas Logs at 25% RP
Install Retrofit Devices at 10% RP
Install Retrofit Devices at 25% RP
Chemical Stabilizer at 10% RP
Chemical Stabilizer at 25% RP
Pave existing shoulders at 25% RP
Pave Unpaved Roads at 25% RP
x
x
x
x
x x
x x
x
x
x
x
x
x
x
Table 3A-2 Summary of PM2.5 Estimated Emissions Reductions from CoST for the
Northeast (57 counties) for Alternative Primary Standard Levels of
10/35 ng/m3,10/30 ng/m3, 9/35 (ig/m3, and 8/35 ng/m3 in 2032
(tons/year)
County 10/35 10/30 9/35 8/35
New Castle County, DE
0
0
0
73
Cook County, IL
0
0
285
710
Madison County, IL
0
0
0
724
St. Clair County, IL
0
0
0
579
Allen County, IN
0
0
0
44
Clark County, IN
0
0
0
395
Elkhart County, IN
0
0
0
213
Floyd County, IN
0
0
0
40
Lake County, IN
0
0
0
644
Marion County, IN
0
0
405
405
St. Joseph County, IN
0
0
0
205
Vanderburgh County, IN
0
0
0
161
Vigo County, IN
0
0
0
206
Jefferson County, KY
0
0
0
552
Baltimore city, MD
0
0
0
95
Howard County, MD
0
0
0
124
Kent County, MI
0
0
0
330
Wayne County, MI
15
15
645
645
Buchanan County, MO
0
0
0
81
Jackson County, MO
0
0
0
37
Jefferson County, MO
0
0
0
346
St. Louis city, MO
0
0
0
157
St. Louis County, MO
0
0
0
571
Camden County, NJ
0
0
110
110
Union County, NJ
0
0
0
168
New York County, NY
0
0
0
268
Butler County, OH
0
0
571
704
Cuyahoga County, OH
139
139
825
825
Franklin County, OH
0
0
0
96
Hamilton County, OH
0
0
0
439
Jefferson County, OH
0
0
93
93
Lucas County, OH
0
0
0
483
3 A-11
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County
10/35
10/30
9/35
8/35
Mahoning County, OH
0
0
0
117
Stark County, OH
0
0
0
644
Summit County, OH
0
0
0
310
Allegheny County, PA
842
994
1,573
1,613
Armstrong County, PA
0
0
142
142
Beaver County, PA
0
0
0
260
Berks County, PA
0
0
0
103
Cambria County, PA
0
0
34
191
Chester County, PA
0
0
0
598
Dauphin County, PA
0
0
0
242
Delaware County, PA
0
0
277
277
Lackawanna County, PA
0
0
0
66
Lancaster County, PA
73
73
805
937
Lebanon County, PA
0
0
44
181
Lehigh County, PA
0
0
0
95
Mercer County, PA
0
0
0
230
Philadelphia County, PA
0
0
524
896
Washington County, PA
0
0
0
242
York County, PA
0
0
0
381
Providence County, RI
0
0
0
195
Davidson County, TN
0
0
0
95
Knox County, TN
0
0
0
410
Berkeley County, WV
0
0
0
124
Brooke County, WV
0
0
0
120
Marshall County, WV
0
0
0
148
Total
1,070
1,222
6,334
19,142
3A-12
-------
Table 3A-3 Summary of PM2.5 Estimated Emissions Reductions from CoST for the
Adjacent Counties in the Northeast (75 counties) for Alternative
Primary Standard Levels of 10/35 (j,g/m3,10/30 |ig/m3,9/35 (j,g/m3,
and 8/35 (ig/m3 in 2032 (tons/year)
County Adjacent Counties 10/35 10/30 9/35 8/35
Clinton County, IL
Madison County, IL
St Clair County, IL
0
0
0
122
DuPage County, IL
Cook County, IL
0
0
0
124
Kane County, IL
Cook County, IL
0
0
0
98
Lake County, IL
Cook County, IL
0
0
0
434
McHenry County, IL
Cook County, IL
0
0
0
95
Monroe County, IL
St Clair County, IL
0
0
0
110
Randolph County, IL
St Clair County, IL
0
0
0
91
Washington County, IL
St Clair County, IL
0
0
0
90
Will County, IL
Cook County, IL
0
0
0
476
Boone County, IN
Marion County, IN
0
0
3
75
Clay County, IN
Vigo County, IN
0
0
0
65
Gibson County, IN
Vanderburgh County, IN
0
0
0
29
Hamilton County, IN
Marion County, IN
0
0
8
281
Hancock County, IN
Marion County, IN
0
0
3
77
Hendricks County, IN
Marion County, IN
0
0
17
208
Johnson County, IN
Marion County, IN
0
0
4
168
LaPorte County, IN
St Joseph County, IN
0
0
0
186
Marshall County, IN
Elkhart County, IN
St Joseph County, IN
0
0
0
121
Morgan County, IN
Marion County, IN
0
0
12
207
Parke County, IN
Vigo County, IN
0
0
0
30
Posey County, IN
Vanderburgh County, IN
0
0
0
199
Shelby County, IN
Marion County, IN
0
0
3
400
Starke County, IN
St Joseph County, IN
0
0
0
34
Sullivan County, IN
Vigo County, IN
0
0
0
58
Vermillion County, IN
Vigo County, IN
0
0
0
31
Warrick County, IN
Vanderburgh County, IN
0
0
0
182
Bullitt County, KY
Jefferson County, KY
0
0
0
71
Hardin County, KY
Jefferson County, KY
0
0
0
38
Oldham County, KY
Jefferson County, KY
0
0
0
23
Shelby County, KY
Jefferson County, KY
0
0
0
17
Spencer County, KY
Jefferson County, KY
0
0
0
13
Montgomery County, MD
Howard County, MD
0
0
0
2
Macomb County, MI
Wayne County, MI
0
0
59
409
Monroe County, MI
Wayne County, MI
0
0
240
463
Oakland County, MI
Wayne County, MI
0
0
55
954
Washtenaw County, MI
Wayne County, MI
0
0
53
365
Atlantic County, NJ
Camden County, NJ
0
0
7
98
Burlington County, NJ
Camden County, NJ
0
0
26
183
Essex County, NJ
Union County, NJ
0
0
0
116
Gloucester County, NJ
Camden County, NJ
0
0
27
274
Hudson County, NJ
Union County, NJ
0
0
0
73
Middlesex County, NJ
Union County, NJ
0
0
0
299
3A-13
-------
County Adjacent Counties 10/35 10/30 9/35 8/35
Morris County, NJ
Union County, NJ
0
0
0
164
Somerset County, NJ
Union County, NJ
0
0
0
69
Bronx County, NY
New York County, NY
0
0
0
91
Kings County, NY
New York County, NY
0
0
0
215
Queens County, NY
New York County, NY
0
0
0
223
Belmont County, OH
Jefferson County, OH
0
0
81
126
Carroll County, OH
Jefferson County, OH
Stark County, OH
0
0
34
68
Clermont County, OH
Hamilton County, OH
0
0
0
279
Columbiana County, OH
Jefferson County, OH
Mahoning County, OH
Stark County, OH
0
0
144
172
Geauga County, OH
Cuyahoga County, OH
Summit County, OH
0
0
9
256
Harrison County, OH
Jefferson County, OH
0
0
12
109
Lake County, OH
Cuyahoga County, OH
0
0
6
184
Lorain County, OH
Cuyahoga County, OH
0
0
145
301
Medina County, OH
Cuyahoga County, OH
Summit County, OH
0
0
9
340
Montgomery County, OH
Butler County, OH
0
0
0
303
Portage County, OH
Cuyahoga County, OH
Mahoning County, OH
Stark County, OH
Summit County, OH
0
0
15
287
Preble County, OH
Butler County, OH
0
0
0
82
Warren County, OH
Butler County, OH
Hamilton County, OH
0
0
0
366
Bedford County, PA
Cambria County, PA
0
0
0
121
Blair County, PA
Cambria County, PA
0
0
0
365
Bucks County, PA
Lehigh County, PA
Philadelphia County, PA
0
0
0
581
Butler County, PA
Allegheny County, PA
Armstrong County, PA
Beaver County, PA
Mercer County, PA
0
0
34
631
Clarion County, PA
Armstrong County, PA
0
0
4
90
Clearfield County, PA
Cambria County, PA
0
0
0
171
Indiana County, PA
Armstrong County, PA
Cambria County, PA
0
0
55
294
Jefferson County, PA
Armstrong County, PA
0
0
5
260
Montgomery County, PA
Berks County, PA
Chester County, PA
Delaware County, PA
Lehigh County, PA
Philadelphia County, PA
0
0
633
633
Schuylkill County, PA
Berks County, PA
Dauphin County, PA
Lebanon County, PA
Lehigh County, PA
0
0
0
287
3A-14
-------
County Adjacent Counties 10/35 10/30 9/35 8/35
Somerset County, PA
Cambria County, PA
0
0
0
204
Westmoreland County, PA
Allegheny County, PA
Armstrong County, PA
Cambria County, PA
Washington County, PA
0
0
37
609
Hancock County, WV
Brooke County, WV
0
0
0
32
Ohio County, WV
Brooke County, WV
Marshall County, WV
0
0
0
96
Wetzel County, WV
Marshall County, WV
0
0
0
45
Total 0 0 1,737 15,440
3A-15
-------
Table 3A-4 Summary of PM2.5 Estimated Emissions Reductions from CoST for the
Southeast (35 counties) for Alternative Primary Standard Levels of
10/35 |ig/m3,10/30 (j,g/m3, 9/35 |ig/m3, and 8/35 jig/m3 in 2032
(tons/year)
County
10/35
10/30
9/35
8/35
Jefferson County, AL
0
0
671
1,488
Talladega County, AL
0
0
0
131
Pulaski County, AR
0
0
0
777
Union County, AR
0
0
0
66
District of Columbia
0
0
0
140
Bibb County, GA
0
0
0
158
Clayton County, GA
0
0
0
58
Cobb County, GA
0
0
0
42
DeKalb County, GA
0
0
0
34
Dougherty County, GA
0
0
0
481
Floyd County, GA
0
0
0
400
Fulton County, GA
0
0
344
599
Gwinnett County, GA
0
0
0
17
Muscogee County, GA
0
0
0
176
Richmond County, GA
0
0
0
409
Wilkinson County, GA
0
0
0
761
Wyandotte County, KS
0
0
0
90
Caddo Parish, LA
0
0
327
436
East Baton Rouge Parish, LA
0
0
0
531
Iberville Parish, LA
0
0
0
17
St. Bernard Parish, LA
0
0
0
60
West Baton Rouge Parish, LA
0
0
0
393
Hinds County, MS
0
0
0
33
Davidson County, NC
0
0
0
204
Mecklenburg County, NC
0
0
0
91
Wake County, NC
0
0
0
66
Tulsa County, OK
0
0
0
74
Greenville County, SC
0
0
0
98
Cameron County, TX
0
0
148
148
Dallas County, TX
0
0
0
33
El Paso County, TX
0
0
33
240
Harris County, TX
270
270
1,087
1,905
Hidalgo County, TX
205
205
406
406
Nueces County, TX
0
0
0
810
Travis County, TX
0
0
25
842
Total
475
475
3,040
12,212
3A-16
-------
Table 3A-5 Summary of PM2.5 Estimated Emissions Reductions from CoST for the
Adjacent Counties in the Southeast (32 counties) for Alternative
Primary Standard Levels of 10/35 (j,g/m3,10/30 |ig/m3,9/35 (j,g/m3,
and 8/35 (ig/m3 in 2032 (tons/year)
County
Adjacent Counties
10/35
10/30
9/35
8/35
Bartow County, GA
Cobb County, GA
0
0
0
135
Floyd County, GA
Carroll County, GA
Fulton County, GA
0
0
0
154
Chattahoochee County, GA
Muscogee County, GA
0
0
0
37
Chattooga County, GA
Floyd County, GA
0
0
0
116
Cherokee County, GA
Cobb County, GA
0
0
0
151
Fulton County, GA
Coweta County, GA
Fulton County, GA
0
0
0
120
Crawford County, GA
Bibb County, GA
0
0
0
112
Douglas County, GA
Cobb County, GA
0
0
0
71
Fulton County, GA
Fayette County, GA
Clayton County, GA
0
0
0
76
Fulton County, GA
Forsyth County, GA
Fulton County, GA
0
0
0
89
Gwinnett County, GA
Gordon County, GA
Floyd County, GA
0
0
0
123
Harris County, GA
Muscogee County, GA
0
0
0
204
Henry County, GA
Clayton County, GA
0
0
0
88
DeKalb County, GA
Houston County, GA
Bibb County, GA
0
0
0
640
Jones County, GA
Bibb County, GA
0
0
0
145
Wilkinson County, GA
Monroe County, GA
Bibb County, GA
0
0
0
161
Polk County, GA
Floyd County, GA
0
0
0
118
Spalding County, GA
Clayton County, GA
0
0
0
122
Talbot County, GA
Muscogee County, GA
0
0
0
87
Twiggs County, GA
Bibb County, GA
0
0
0
180
Wilkinson County, GA
Walker County, GA
Floyd County, GA
0
0
0
71
Bossier Parish, LA
Caddo Parish, LA
0
0
0
237
De Soto Parish, LA
Caddo Parish, LA
0
0
0
160
East Feliciana Parish, LA
East Baton Rouge Parish, LA
0
0
0
66
West Baton Rouge Parish, LA
Pointe Coupee Parish, LA
Iberville Parish, LA
0
0
0
80
West Baton Rouge Parish, LA
Red River Parish, LA
Caddo Parish, LA
0
0
0
1,001
West Feliciana Parish, LA
West Baton Rouge Parish, LA
0
0
0
121
Brooks County, TX
Hidalgo County, TX
0
0
66
66
Hudspeth County, TX
El Paso County, TX
0
0
0
31
Kenedy County, TX
Hidalgo County, TX
0
0
43
43
Starr County, TX
Hidalgo County, TX
0
0
62
62
Willacy County, TX
Cameron County, TX
0
0
22
22
Hidalgo County, TX
Total
0
0
194
4,892
3A-17
-------
Table 3A-6 Summary of PM2.5 Estimated Emissions Reductions from CoST for the
West (36 counties) for Alternative Primary Standard Levels of 10/35
(j,g/m3,10/30 (j,g/m3,9/35 |ig/m3, and 8/35 |ig/m3 in 2032 (tons/year)
County 10/35 10/30 9/35 8/35
Maricopa County, AZ
0
0
201
669
Pinal County, AZ
0
164
0
61
Santa Cruz County, AZ
0
0
0
13
Denver County, CO
0
0
0
145
Weld County, CO
0
0
0
47
Benewah County, ID
0
133
133
133
Canyon County, ID
0
115
0
384
Lemhi County, ID
0
0
0
0
Shoshone County, ID
0
0
0
0
Lewis and Clark County, MT
0
87
0
0
Lincoln County, MT
224
224
224
224
Missoula County, MT
0
0
229
697
Ravalli County, MT
0
58
0
31
Silver Bow County, MT
0
25
0
133
Douglas County, NE
0
0
0
19
Sarpy County, NE
0
0
0
28
Dona Ana County, NM
0
0
0
248
Clark County, NV
0
0
94
561
Crook County, OR
0
222
0
126
Harney County, OR
0
49
0
148
Jackson County, OR
0
0
66
533
Klamath County, OR
0
94
0
281
Lake County, OR
0
0
0
0
Lane County, OR
0
0
0
37
Box Elder County, UT
0
149
0
0
Cache County, UT
0
236
0
0
Davis County, UT
0
79
0
0
Salt Lake County, UT
0
162
0
0
Utah County, UT
0
127
0
0
Weber County, UT
0
39
0
0
King County, WA
0
0
0
126
Kittitas County, WA
0
0
0
0
Okanogan County, WA
0
139
0
0
Snohomish County, WA
0
104
0
0
Spokane County, WA
0
0
0
66
Yakima County, WA
0
0
0
0
Total
224
2,206
947
4,711
3A-18
-------
Table 3A-7 Summary of PM2.5 Estimated Emissions Reductions from CoST for
California (26 counties) for Alternative Primary Standard Levels of
10/35 |ig/m3,10/30 |ig/m3, 9/35 (j,g/m3, and 8/35 |ig/m3 in 2032
(tons/year)
County
Air District
10/35
10/30
9/35
8/35
Alameda County, CA
Bay Area AQMD
32
32
349
491
Contra Costa County, CA
Bay Area AQMD
0
0
38
355
Marin County, CA
Bay Area AQMD
0
0
0
45
Napa County, CA
Bay Area AQMD
16
16
33
33
Santa Clara County, CA
Bay Area AQMD
0
0
166
482
Solano County, CA
Bay Area AQMD
0
0
0
150
Butte County, CA
Butte County AQMD
0
0
0
76
Sutter County, CA
Feather River AQMD
0
0
0
191
Imperial County, CA
Imperial County APCD
0
0
0
0
Plumas County, CA
Northern Sierra AQMD
0
0
0
0
Sacramento County, CA
Sacramento Metro AQMD
0
60
79
228
San Diego County, CA
San Diego County APCD
0
0
102
615
Fresno County, CA
San Joaquin Valley APCD
248
248
248
248
Kern County, CA
San Joaquin Valley APCD
0
0
0
0
Kings County, CA
San Joaquin Valley APCD
0
0
0
0
Madera County, CA
San Joaquin Valley APCD
111
111
111
111
Merced County, CA
San Joaquin Valley APCD
101
101
101
101
San Joaquin County, CA
San Joaquin Valley APCD
12
12
168
168
Stanislaus County, CA
San Joaquin Valley APCD
113
113
113
113
Tulare County, CA
San Joaquin Valley APCD
0
0
0
0
San Luis Obispo County, CA
San Luis Obispo County APCD
0
0
128
128
Siskiyou County, CA
Siskiyou County APCD
0
398
0
0
Los Angeles County, CA
South Coast AQMD
1,159
1,159
1,159
1,159
Riverside County, CA
South Coast AQMD
0
0
0
0
San Bernardino County, CA
South Coast AQMD
0
0
0
0
Ventura County, CA
Ventura County APCD
0
229
162
229
Total
1,792
2,481
2,958
4,925
3A-19
-------
Table 3A-8 Remaining PM2.5 Emissions and Potential Additional Reduction Opportunities
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30
9/35 8/35
Cochise County, AZ
Santa Cruz County, AZ
afdust
1,516
-
-
-
nonpt
128
54
-
-
ptnonipm
117
55
-
-
rwc
38
3
.
-
Gila County, AZ
Pinal County, AZ
afdust
900
-
-
-
nonpt
70
30
-
-
ptnonipm
361
240
-
-
rwc
22
-
.
-
Graham County, AZ
Pinal County, AZ
afdust
718
49
-
-
nonpt
38
13
-
-
rwc
9
-
-
-
Pima County, AZ
Pinal County, AZ
afdust
3,446
-
-
-
Santa Cruz County, AZ
nonpt
739
269
-
-
ptnonipm
79
11
-
-
rwc
244
25
.
-
Pinal County, AZ
-
afdust
3,385
-
-
-
nonpt
297
156
156
61
ptagfire
19
-
-
-
ptnonipm
94
-
-
-
rwc
103
8
8
-
Santa Cruz County, AZ
-
afdust
167
-
-
-
nonpt
47
13
-
13
rwc
13
-
.
-
Alameda County, CA
Napa County, CA
afdust
543
60
-
60
Solano County, CA
nonpt
885
134
-
86 134
ptnonipm
450
208
32 32
173 208
rwc
368
90
.
90 90
Contra Costa County, CA
Alameda County, CA
afdust
405
47
-
-
Napa County, CA
nonpt
646
82
-
-
Solano County, CA
pt_oilgas
6
-
-
-
ptnonipm
1,798
999
-
38 355
rwc
812
169
-
-
3 A-20
-------
Adjacent Counties Maximum
(NE,SE,W) or Counties in Annual PM2.5 Selected PM2.5 Emissions Reductions
V*" J ...
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35
10/35
10/30
9/35
8/35
Fresno County, CA
Kern County, CA
afdust
2,277
224
-
224
224
224
224
Kings County, CA
nonpt
519
81
79
-
-
-
-
Madera County, CA
pt_oilgas
36
-
-
-
-
-
-
Merced County, CA
ptagfire
882
-
-
-
-
-
-
San Joaquin County, CA
ptnonipm
275
108
82
24
24
24
24
Stanislaus County, CA
rwc
289
29
29
-
-
-
-
Tulare County, CA
Imperial County, CA
-
afdust
3,596
-
-
-
-
-
-
nonpt
221
9
9
-
-
-
-
ptagfire
198
-
-
-
-
-
-
ptnonipm
134
80
80
-
-
-
-
rwc
18
3
3
-
-
-
-
Kern County, CA
Fresno County, CA
afdust
1,396
-
-
-
-
-
-
Kings County, CA
nonpt
823
276
276
-
-
-
-
Madera County, CA
pt_oilgas
331
51
51
-
-
-
-
Merced County, CA
ptagfire
332
-
-
-
-
-
-
San Joaquin County, CA
ptnonipm
517
209
209
-
-
-
-
Stanislaus County, CA
rwc
224
27
27
_
_
_
_
Tulare County, CA
Kings County, CA
Fresno County, CA
afdust
849
30
30
-
-
-
-
Kern County, CA
nonpt
57
9
9
-
-
-
-
Madera County, CA
ptagfire
210
-
-
-
-
-
-
Merced County, CA
ptnonipm
69
-
-
-
-
-
-
San Joaquin County, CA
rwc
31
4
4
-
-
-
-
Stanislaus County, CA
Tulare County, CA
Los Angeles County, CA
Riverside County, CA
afdust
2,240
-
-
-
-
-
-
San Bernardino County, CA
nonpt
5,052
723
0
722
722
722
722
pt_oilgas
18
-
-
-
-
-
-
ptnonipm
2,087
638
313
325
325
325
325
rwc
947
112
-
112
112
112
112
3 A-21
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30
9/35
8/35
Madera County, CA
Fresno County, CA
afdust
672
68
68 68
68
68
Kern County, CA
nonpt
197
27
27 27
27
27
Kings County, CA
ptagfire
415
-
-
-
-
Merced County, CA
ptnonipm
52
12
12 12
12
12
San Joaquin County, CA
rwc
52
4
4 4
4
4
Stanislaus County, CA
Tulare County, CA
Marin County, CA
Alameda County, CA
afdust
168
18
-
-
-
Napa County, CA
nonpt
144
23
-
-
-
Solano County, CA
ptnonipm
74
54
-
-
45
rwc
220
10
.
-
-
Merced County, CA
Fresno County, CA
afdust
1,304
73
73 73
73
73
Kern County, CA
nonpt
111
19
19 19
19
19
Kings County, CA
ptagfire
152
-
-
-
-
Madera County, CA
ptnonipm
67
-
.
-
-
San Joaquin County, CA
rwc
114
10
10 10
10
10
Stanislaus County, CA
Tulare County, CA
Napa County, CA
Alameda County, CA
afdust
112
10
-
10
10
Solano County, CA
nonpt
63
7
5 5
7
7
ptagfire
7
-
-
-
-
ptnonipm
37
-
-
-
-
rwc
123
16
11 11
16
16
Nevada County, CA
Plumas County, CA
afdust
343
44
-
-
-
nonpt
72
6
-
-
-
ptnonipm
6
-
-
-
-
rwc
279
18
.
-
-
Orange County, CA
Los Angeles County, CA
afdust
672
-
-
-
-
Riverside County, CA
nonpt
1,862
288
-
-
-
San Bernardino County, CA
ptnonipm
200
20
-
-
-
rwc
305
54
-
-
-
3A-22
-------
Adjacent Counties Maximum
(NE,SE,W) or Counties in Annual PM2.5 Selected PM2.5 Emissions Reductions
V*" J ...
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35
10/35 10/30
9/35
8/35
Plumas County, CA
-
afdust
483
99
99
-
-
-
nonpt
43
-
-
-
-
-
ptnonipm
7
-
-
-
-
-
rwc
326
9
9
-
-
-
Riverside County, CA
Los Angeles County, CA
afdust
2,589
-
-
-
-
-
San Bernardino County, CA
nonpt
973
137
137
-
-
-
ptagfire
34
-
-
-
-
-
ptnonipm
128
21
21
-
-
-
rwc
468
34
34
-
-
-
Sacramento County, CA
-
afdust
1,023
-
-
-
-
-
nonpt
713
109
-
32
50
109
ptagfire
46
-
-
-
-
-
ptnonipm
92
29
-
29
29
29
rwc
1,790
90
-
-
-
90
San Bernardino County, CA
Los Angeles County, CA
afdust
2,424
-
-
-
-
-
Riverside County, CA
nonpt
1,094
144
144
-
-
-
pt_oilgas
56
-
-
-
-
-
ptagfire
7
-
-
-
-
-
ptnonipm
2,642
1,965
1,965
-
-
-
rwc
470
31
31
-
-
-
San Diego County, CA
-
afdust
2,485
194
-
-
-
194
nonpt
1,949
371
-
-
81
371
ptnonipm
489
12
-
-
11
12
rwc
678
39
-
-
11
39
San Francisco County, CA
Alameda County, CA
afdust
108
13
-
-
-
-
Napa County, CA
nonpt
588
107
-
-
-
-
Solano County, CA
ptnonipm
45
7
-
-
-
-
rwc
49
10
-
-
-
-
3A-23
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30
9/35
8/35
San Joaquin County, CA
Fresno County, CA
afdust
1,110
80
-
80
80
Kern County, CA
nonpt
290
40
4 4
40
40
Kings County, CA
ptagfire
126
-
-
-
-
Madera County, CA
ptnonipm
167
19
8 8
19
19
Merced County, CA
rwc
217
28
.
28
28
Stanislaus County, CA
Tulare County, CA
San Luis Obispo County, CA
-
afdust
133
-
-
-
-
nonpt
226
57
-
57
57
ptagfire
13
-
-
-
-
ptnonipm
42
6
-
6
6
rwc
475
65
.
65
65
San Mateo County, CA
Alameda County, CA
afdust
249
26
-
-
-
Napa County, CA
nonpt
419
61
-
-
-
Solano County, CA
ptnonipm
131
42
-
-
-
rwc
167
26
.
-
-
Santa Clara County, CA
Alameda County, CA
afdust
717
85
-
-
83
Napa County, CA
nonpt
945
173
-
93
173
Solano County, CA
ptnonipm
244
111
-
72
103
rwc
614
122
.
-
122
Sierra County, CA
Plumas County, CA
afdust
240
48
-
-
-
nonpt
35
-
-
-
-
rwc
11
-
.
-
-
Siskiyou County, CA
-
afdust
901
166
166
-
-
nonpt
480
217
217
-
-
ptagfire
38
-
-
-
-
rwc
217
15
15
-
-
Solano County, CA
Alameda County, CA
afdust
414
34
-
-
34
Napa County, CA
nonpt
251
40
-
-
40
ptagfire
23
-
-
-
-
ptnonipm
185
35
-
-
35
rwc
328
42
-
-
42
3A-24
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35
10/30
9/35
8/35
Sonoma County, CA
Alameda County, CA
afdust
420
34
-
-
-
-
Napa County, CA
nonpt
355
54
-
-
-
-
Solano County, CA
ptnonipm
103
20
-
-
-
-
rwc
572
66
-
-
-
-
Stanislaus County, CA
Fresno County, CA
afdust
1,139
-
-
-
-
-
Kern County, CA
nonpt
236
31
31
31
31
31
Kings County, CA
ptagfire
150
-
-
-
-
-
Madera County, CA
ptnonipm
146
60
60
60
60
60
Merced County, CA
rwc
188
22
22
22
22
22
San Joaquin County, CA
Tulare County, CA
Sutter County, CA
-
afdust
280
25
-
-
-
25
nonpt
386
149
-
-
-
149
ptagfire
195
-
-
-
-
-
ptnonipm
33
5
-
-
-
5
rwc
199
11
-
-
-
11
Tulare County, CA
Fresno County, CA
afdust
2,106
137
137
-
-
-
Kern County, CA
nonpt
222
28
28
-
-
-
Kings County, CA
ptagfire
560
-
-
-
-
-
Madera County, CA
ptnonipm
96
-
-
-
-
-
Merced County, CA
rwc
139
13
13
-
-
-
San Joaquin County, CA
Stanislaus County, CA
Ventura County, CA
-
afdust
529
51
-
51
5
51
nonpt
354
63
-
63
41
63
pt_oilgas
6
-
-
-
-
-
ptnonipm
94
7
-
7
7
7
rwc
677
108
-
108
108
108
Yolo County, CA
Solano County, CA
afdust
808
30
-
-
-
-
nonpt
335
35
-
-
-
-
ptagfire
66
-
-
-
-
-
ptnonipm
105
6
-
-
-
-
rwc
248
13
-
-
-
-
3A-25
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35 8/35
Yuba County, CA
Sutter County, CA
afdust
177
21
-
nonpt
78
19
-
ptagfire
47
-
-
ptnonipm
17
-
-
rwc
157
9
.
Adams County, CO
Denver County, CO
afdust
1,876
65
-
nonpt
233
57
-
np_oilgas
8
-
-
pt_oilgas
21
-
-
ptagfire
6
-
-
ptnonipm
346
112
-
rwc
360
36
.
Arapahoe County, CO
Denver County, CO
afdust
1,602
115
-
nonpt
274
63
-
ptnonipm
500
7
-
rwc
450
43
.
Denver County, CO
-
afdust
1,453
-
-
nonpt
389
88
88
ptnonipm
204
43
43
rwc
177
13
13
Jefferson County, CO
Denver County, CO
afdust
1,285
205
-
nonpt
355
93
-
ptnonipm
242
129
-
rwc
601
64
.
Bartow County, GA
Floyd County, GA
afdust
464
59
59
nonpt
147
43
43
ptnonipm
44
23
23
rwc
93
10
10
Bibb County, GA
-
afdust
232
34
34
nonpt
150
33
33
pt_oilgas
18
-
-
ptnonipm
157
81
81
rwc
90
9
9
3A-26
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35 8/35
Carroll County, GA
Fulton County, GA
afdust
590
89
89
nonpt
126
43
43
ptnonipm
40
11
11
rwc
104
11
11
Chattahoochee County, GA
Muscogee County, GA
afdust
99
18
18
nonpt
26
19
19
Chattooga County, GA
Floyd County, GA
afdust
207
34
34
nonpt
99
81
81
ptnonipm
8
-
-
rwc
30
1
1
Cherokee County, GA
Fulton County, GA
afdust
525
78
78
nonpt
181
51
51
ptnonipm
8
-
-
rwc
179
21
21
Clayton County, GA
Fulton County, GA
afdust
258
33
33
nonpt
88
16
16
ptnonipm
8
-
-
rwc
103
9
9
Coweta County, GA
Fulton County, GA
afdust
364
62
62
nonpt
128
46
46
ptagfire
12
-
-
rwc
110
13
13
Crawford County, GA
Bibb County, GA
afdust
141
25
25
nonpt
100
88
88
ptagfire
8
-
-
rwc
14
-
.
Douglas County, GA
Fulton County, GA
afdust
235
35
35
nonpt
88
25
25
rwc
91
10
10
Fayette County, GA
Clayton County, GA
afdust
209
29
29
Fulton County, GA
nonpt
96
27
27
ptnonipm
20
11
11
rwc
84
10
10
3A-27
-------
Adjacent Counties Maximum
(NE,SE,W) or Counties in Annual PM2.5 Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction 12/35 10/35 10/30
9/35
8/35
Floyd County, GA
-
afdust
402
65 ...
-
65
nonpt
109
31 ...
-
31
ptagfire
6
-
-
-
ptnonipm
316
294 ...
-
294
rwc
89
10 ...
-
10
Forsyth County, GA
Fulton County, GA
afdust
342
40 ...
-
40
nonpt
127
33 ...
-
33
ptnonipm
6
-
-
-
rwc
136
16 ...
-
16
Fulton County, GA
Clayton County, GA
afdust
1,329
159 ...
-
159
nonpt
729
168 ...
150
168
ptnonipm
289
237 ...
157
237
rwc
371
36 ...
36
36
Gordon County, GA
Floyd County, GA
afdust
341
43 ...
-
43
nonpt
123
75 ...
-
75
rwc
54
6 ...
-
6
Harris County, GA
Muscogee County, GA
afdust
304
59 ...
-
59
nonpt
173
140 ...
-
140
pt_oilgas
17
-
-
-
ptagfire
9
-
-
-
rwc
47
5 ...
-
5
Henry County, GA
Clayton County, GA
afdust
278
35 ...
-
35
nonpt
130
37 ...
-
37
pt_oilgas
54
-
-
-
rwc
138
15 ...
-
15
Houston County, GA
Bibb County, GA
afdust
282
38 ...
-
38
nonpt
271
189 ...
-
189
ptagfire
9
-
-
-
ptnonipm
460
403 ...
-
403
rwc
111
11 ...
-
11
Jones County, GA
Bibb County, GA
afdust
303
54 ...
-
54
nonpt
111
88 ...
-
88
ptagfire
8
-
-
-
rwc
33
3 ...
-
3
3A-28
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30
9/35
8/35
Monroe County, GA
Bibb County, GA
afdust
281
51
-
-
51
nonpt
134
107
-
-
107
ptagfire
13
-
-
-
-
rwc
33
3
.
-
3
Muscogee County, GA
-
afdust
206
28
-
-
28
nonpt
121
38
-
-
38
ptnonipm
111
99
-
-
99
rwc
108
11
.
-
11
Polk County, GA
Floyd County, GA
afdust
218
33
-
-
33
nonpt
117
81
-
-
81
ptnonipm
6
-
-
-
-
rwc
45
4
.
-
4
Spalding County, GA
Clayton County, GA
afdust
176
29
-
-
29
nonpt
132
88
-
-
88
ptagfire
6
-
-
-
-
rwc
50
5
.
-
5
Talbot County, GA
Muscogee County, GA
afdust
138
25
-
-
25
nonpt
68
62
-
-
62
ptagfire
8
-
-
-
-
rwc
10
-
.
-
-
Twiggs County, GA
Bibb County, GA
afdust
208
32
-
-
32
nonpt
150
116
-
-
116
ptagfire
10
-
-
-
-
ptnonipm
59
32
-
-
32
rwc
10
-
.
-
-
Walker County, GA
Floyd County, GA
afdust
316
41
-
-
41
nonpt
66
24
-
-
24
ptagfire
11
-
-
-
-
rwc
68
7
.
-
7
Benewah County, ID
Shoshone County, ID
afdust
859
131
131
131
131
nonpt
33
2
2
2
2
ptnonipm
30
-
-
-
-
rwc
21
-
-
-
-
3A-29
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35 8/35
Bonner County, ID
Shoshone County, ID
afdust
2,200
424
-
nonpt
149
49
-
pt_oilgas
6
-
-
ptnonipm
13
-
-
rwc
97
9
.
Butte County, ID
Lemhi County, ID
afdust
689
102
-
rwc
8
-
-
Clark County, ID
Lemhi County, ID
afdust
299
36
-
ptagfire
7
-
.
Clearwater County, ID
Shoshone County, ID
afdust
457
89
-
nonpt
21
1
-
ptagfire
48
-
-
rwc
22
-
.
Custer County, ID
Lemhi County, ID
afdust
681
108
-
nonpt
7
-
-
rwc
15
-
.
Idaho County, ID
Lemhi County, ID
afdust
1,509
237
-
nonpt
44
9
-
ptagfire
138
-
-
ptnonipm
14
5
-
rwc
46
3
.
Kootenai County, ID
Benewah County, ID
afdust
3,418
689
-
Shoshone County, ID
nonpt
501
237
-
ptnonipm
90
62
-
rwc
150
13
.
Latah County, ID
Benewah County, ID
afdust
1,850
215
-
Shoshone County, ID
nonpt
54
15
-
ptagfire
32
-
-
ptnonipm
78
72
-
rwc
37
2
.
Lemhi County, ID
-
afdust
728
116
116 -
nonpt
10
-
-
rwc
19
-
-
3 A-30
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35
8/35
Shoshone County, ID
Benewah County, ID
afdust
573
96
96
-
nonpt
24
11
11
-
rwc
28
1
1 - - -
-
Valley County, ID
Lemhi County, ID
afdust
786
174
-
-
nonpt
25
12
-
-
rwc
28
2
.
-
Clinton County, IL
St. Clair County, IL
afdust
1,326
72
-
72
nonpt
92
43
-
43
pt_oilgas
15
-
-
-
ptnonipm
15
-
-
-
rwc
52
7
.
7
Monroe County, IL
St. Clair County, IL
afdust
889
68
-
68
nonpt
79
37
-
37
rwc
43
6
.
6
Randolph County, IL
St. Clair County, IL
afdust
964
49
_ _ _ _
49
nonpt
80
36
-
36
ptnonipm
35
-
-
-
rwc
43
6
.
6
St. Clair County, IL
-
afdust
3,376
498
.
498
nonpt
218
57
-
57
ptnonipm
120
14
-
14
rwc
107
10
.
10
Washington County, IL
St. Clair County, IL
afdust
1,249
69
-
69
nonpt
45
16
-
16
np_oilgas
5
-
-
-
ptnonipm
5
-
-
-
rwc
32
5
.
5
Boone County, IN
Marion County, IN
afdust
448
23
-
23
nonpt
94
47
-
47
rwc
73
5
3
5
3 A-31
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35
8/35
Clay County, IN
Vigo County, IN
afdust
230
9
-
9
nonpt
35
12
-
12
ptnonipm
42
40
-
40
rwc
50
4
.
4
Hamilton County, IN
Marion County, IN
afdust
786
62
-
62
nonpt
350
195
-
195
rwc
275
24
8
24
Hancock County, IN
Marion County, IN
afdust
324
23
-
23
nonpt
86
46
-
46
rwc
92
9
3
9
Hendricks County, IN
Marion County, IN
afdust
426
37
-
37
nonpt
197
115
-
115
ptnonipm
124
40
11
40
rwc
169
15
6
15
Johnson County, IN
Marion County, IN
afdust
396
32
-
32
nonpt
206
123
-
123
rwc
139
13
4
13
LaPorte County, IN
St. Joseph County, IN
afdust
581
46
-
46
nonpt
160
82
-
82
ptnonipm
107
43
-
43
rwc
139
15
.
15
Marion County, IN
-
afdust
1,534
146
146
146
nonpt
521
92
92
92
pt_oilgas
17
-
-
-
ptnonipm
235
135
135
135
rwc
330
32
32
32
Marshall County, IN
St. Joseph County, IN
afdust
305
18
-
18
nonpt
94
42
-
42
ptnonipm
78
55
-
55
rwc
66
5
.
5
Morgan County, IN
Marion County, IN
afdust
376
28
-
28
nonpt
120
71
-
71
ptnonipm
105
99
8
99
rwc
101
9
4
9
3A-32
-------
Adjacent Counties
(NE,SE,W) or Counties in
Same Air District (CA) Still
Annual
Maximum
PM2.5
Selected PM2.5 Emissions Reductions
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35 8/35
Parke County, IN
Vigo County, IN
afdust
233
8
8
nonpt
36
20
20
rwc
35
2
2
Shelby County, IN
Marion County, IN
afdust
279
15
15
nonpt
69
31
31
ptnonipm
410
350
350
rwc
64
4
3 4
St. Joseph County, IN
-
afdust
531
45
45
nonpt
266
116
116
ptnonipm
72
18
18
rwc
249
26
26
Starke County, IN
St. Joseph County, IN
afdust
134
9
9
nonpt
46
22
22
pt_oilgas
6
-
-
rwc
43
4
4
Sullivan County, IN
Vigo County, IN
afdust
479
12
12
nonpt
38
13
13
ptnonipm
44
32
32
rwc
31
1
1
Vermillion County, IN
Vigo County, IN
afdust
167
-
-
nonpt
22
7
7
ptnonipm
63
22
22
rwc
30
1
1
Vigo County, IN
-
afdust
314
24
24
nonpt
135
65
65
ptnonipm
189
106
106
rwc
128
12
12
Bossier Parish, LA
Caddo Parish, LA
afdust
433
58
58
nonpt
423
174
174
np_oilgas
46
-
-
pt_oilgas
11
-
-
ptnonipm
11
-
-
rwc
52
5
5
3A-33
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35
8/35
Caddo Parish, LA
-
afdust
970
108
20
108
nonpt
815
196
196
196
np_oilgas
90
-
-
-
ptnonipm
243
123
102
123
rwc
87
9
9
9
De Soto Parish, LA
Caddo Parish, LA
afdust
444
57
.
57
nonpt
120
38
-
38
np_oilgas
112
-
-
-
pt_oilgas
40
-
-
-
ptnonipm
439
64
-
64
rwc
15
-
.
-
East Feliciana Parish, LA
West Baton Rouge Parish, LA
afdust
281
38
-
38
nonpt
68
29
-
29
pt_oilgas
25
-
-
-
rwc
11
-
.
-
Pointe Coupee Parish, LA
West Baton Rouge Parish, LA
afdust
553
53
.
53
nonpt
63
19
-
19
pt_oilgas
11
-
-
-
ptagfire
89
-
-
-
ptnonipm
318
8
-
8
rwc
10
-
.
-
Red River Parish, LA
Caddo Parish, LA
afdust
202
22
.
22
nonpt
52
10
-
10
np_oilgas
22
-
-
-
pt_oilgas
41
-
-
-
ptnonipm
987
970
.
970
West Baton Rouge Parish, LA
-
afdust
255
35
.
35
nonpt
265
68
-
68
pt_oilgas
35
2
-
2
ptagfire
44
-
-
-
ptnonipm
420
288
-
288
rwc
9
-
.
-
3A-34
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30
9/35
8/35
West Feliciana Parish, LA
West Baton Rouge Parish, LA
afdust
196
27
-
-
27
nonpt
56
24
-
-
24
ptnonipm
144
70
-
-
70
rwc
6
-
-
-
-
Macomb County, MI
Wayne County, MI
afdust
689
104
-
-
104
nonpt
1,338
264
-
56
264
pt_oilgas
9
-
-
-
-
ptnonipm
120
-
-
-
-
rwc
500
42
.
3
42
Monroe County, MI
Wayne County, MI
afdust
829
112
-
-
112
nonpt
254
82
-
-
82
ptnonipm
309
251
-
233
251
rwc
172
17
.
7
17
Oakland County, MI
Wayne County, MI
afdust
1,425
176
-
-
176
nonpt
1,955
691
-
43
691
ptnonipm
140
5
-
-
5
rwc
897
82
.
13
82
Washtenaw County, MI
Wayne County, MI
afdust
784
112
-
-
112
nonpt
610
222
-
42
222
pt_oilgas
5
-
-
-
-
ptnonipm
40
-
-
-
-
rwc
273
30
.
10
30
Wayne County, MI
-
afdust
945
-
-
-
-
nonpt
1,719
214
-
214
214
ptnonipm
1,106
376
15 15
376
376
rwc
506
55
.
55
55
St. Louis city, MO
-
afdust
682
55
.
-
55
nonpt
240
35
-
-
35
ptnonipm
237
58
-
-
58
rwc
82
9
.
-
9
Beaverhead County, MT
Ravalli County, MT
afdust
1,211
89
.
-
-
Silver Bow County, MT
nonpt
17
3
-
-
-
ptnonipm
5
-
-
-
-
rwc
19
1
-
-
-
3A-35
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35 8/35
Broadwater County, MT
Lewis and Clark County, MT
afdust
967
162
-
nonpt
16
4
-
ptnonipm
30
13
-
rwc
16
-
-
Cascade County, MT
Lewis and Clark County, MT
afdust
2,387
331
-
nonpt
118
39
-
ptagfire
52
-
-
ptnonipm
50
19
-
rwc
84
9
.
Deer Lodge County, MT
Ravalli County, MT
afdust
336
58
-
Silver Bow County, MT
nonpt
12
-
-
rwc
14
-
.
Flathead County, MT
Lewis and Clark County, MT
afdust
4,042
760
.
Lincoln County, MT
nonpt
276
109
-
ptagfire
5
-
-
ptnonipm
136
71
-
rwc
180
21
.
Granite County, MT
Ravalli County, MT
afdust
317
37
-
nonpt
11
-
-
rwc
9
-
-
Jefferson County, MT
Lewis and Clark County, MT
afdust
613
86
-
Silver Bow County, MT
nonpt
30
8
-
ptnonipm
138
123
-
rwc
31
2
.
Lewis and Clark County, MT
-
afdust
1,677
302
252 17
nonpt
138
64
64
ptagfire
5
-
-
rwc
86
10
1 - 5 - -
Lincoln County, MT - afdust 1,023 206 - 206 206 206 206
nonpt 43 12 - 12 12 12 12
rwc 67 7 - 1 1 1 1
3A-36
-------
Adjacent Counties Maximum
(NE,SE,W) or Counties in Annual PM2.5 Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35
10/35 10/30
9/35
8/35
Madison County, MT
Silver Bow County, MT
afdust
1,280
182
-
-
-
-
nonpt
19
6
-
-
-
-
ptnonipm
83
-
-
-
-
-
rwc
25
2
-
-
-
-
Meagher County, MT
Lewis and Clark County, MT
afdust
441
36
-
-
-
-
rwc
6
-
-
-
-
-
Powell County, MT
Lewis and Clark County, MT
afdust
677
104
-
-
-
-
nonpt
18
3
-
-
-
-
ptnonipm
22
10
-
-
-
-
rwc
11
-
-
-
-
-
Ravalli County, MT
-
afdust
1,755
358
301
18
-
-
nonpt
100
29
-
29
-
26
rwc
94
11
-
11
-
6
Sanders County, MT
Lincoln County, MT
afdust
999
190
-
-
-
-
nonpt
29
8
-
-
-
-
ptnonipm
12
-
-
-
-
-
rwc
43
5
-
-
-
-
Silver Bow County, MT
-
afdust
461
76
-
-
-
76
nonpt
54
19
-
-
-
19
ptnonipm
62
34
-
25
-
34
rwc
44
5
-
-
-
5
Teton County, MT
Lewis and Clark County, MT
afdust
1,188
67
-
-
-
-
nonpt
13
4
-
-
-
-
ptagfire
221
-
-
-
-
-
ptnonipm
5
-
-
-
-
-
rwc
15
-
-
-
-
-
Atlantic County, NJ
Camden County, NJ
afdust
264
48
-
-
-
48
nonpt
129
20
-
-
-
20
ptnonipm
17
-
-
-
-
-
rwc
262
31
-
-
7
31
Burlington County, NJ
Camden County, NJ
afdust
435
70
-
-
-
70
nonpt
229
34
-
-
-
34
ptnonipm
49
12
-
-
12
12
rwc
562
67
-
-
13
67
3A-37
-------
Adjacent Counties
(NE,SE,W) or Counties in
Same Air District (CA) Still
Annual
Maximum
PM2.5
Selected PM2.5 Emissions Reductions
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35
8/35
Camden County, NJ
-
afdust
251
37
37
37
nonpt
245
37
37
37
ptnonipm
18
-
-
-
rwc
240
35
35
35
Essex County, NJ
Union County, NJ
afdust
317
46
-
46
nonpt
388
59
-
59
ptnonipm
35
-
-
-
rwc
155
10
.
10
Gloucester County, NJ
Camden County, NJ
afdust
250
34
-
34
nonpt
147
22
-
22
ptnonipm
262
185
20
185
rwc
296
33
7
33
Hudson County, NJ
Union County, NJ
afdust
181
24
-
24
nonpt
305
50
-
50
ptnonipm
21
-
-
-
rwc
11
-
.
-
Middlesex County, NJ
Union County, NJ
afdust
540
78
-
78
nonpt
442
69
-
69
ptnonipm
202
115
-
115
rwc
267
39
.
39
Morris County, NJ
Union County, NJ
afdust
346
52
-
52
nonpt
281
48
-
48
ptnonipm
6
-
-
-
rwc
624
64
.
64
Somerset County, NJ
Union County, NJ
afdust
234
7
-
7
nonpt
189
28
-
28
ptnonipm
8
-
-
-
rwc
313
34
.
34
Union County, NJ
-
afdust
314
47
-
47
nonpt
282
43
-
43
pt_oilgas
11
-
-
-
ptnonipm
246
66
-
66
rwc
100
12
-
12
3A-38
-------
Adjacent Counties
(NE,SE,W) or Counties in
Same Air District (CA) Still
Annual
Maximum
PM2.5
Selected PM2.5 Emissions Reductions
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35
8/35
Bronx County, NY
New York County, NY
afdust
275
30
-
30
nonpt
476
61
-
61
ptnonipm
17
-
-
-
Kings County, NY
New York County, NY
afdust
455
55
-
55
nonpt
1,232
160
-
160
ptnonipm
35
-
-
-
rwc
5
-
-
-
New York County, NY
-
afdust
996
-
.
-
nonpt
1,640
261
-
261
ptnonipm
51
7
.
7
Queens County, NY
New York County, NY
afdust
678
70
-
70
nonpt
1,212
153
-
153
ptnonipm
21
-
-
-
rwc
13
-
.
-
Belmont County, OH
Jefferson County, OH
afdust
488
54
10
54
nonpt
126
59
59
59
np_oilgas
18
-
-
-
pt_oilgas
9
-
-
-
rwc
120
12
12
12
Butler County, OH
Hamilton County, OH
afdust
643
68
21
68
nonpt
376
160
159
160
ptnonipm
627
446
360
446
rwc
350
31
31
31
Carroll County, OH
Jefferson County, OH
afdust
311
35
7
35
nonpt
50
16
15
16
np_oilgas
18
-
-
-
pt_oilgas
28
-
-
-
ptnonipm
22
13
6
13
rwc
64
5
5
5
Clermont County, OH
Hamilton County, OH
afdust
499
64
-
64
nonpt
329
192
-
192
ptnonipm
8
-
-
-
rwc
262
23
-
23
3A-39
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30
9/35
8/35
Columbiana County, OH
Jefferson County, OH
afdust
522
60
-
31
60
nonpt
194
95
-
95
95
np_oilgas
8
-
-
-
-
pt_oilgas
9
-
-
-
-
ptnonipm
41
-
-
-
-
rwc
181
18
.
18
18
Cuyahoga County, OH
-
afdust
949
-
-
-
-
nonpt
986
157
40 40
157
157
ptnonipm
948
616
96 96
616
616
rwc
457
52
3 3
52
52
Geauga County, OH
Cuyahoga County, OH
afdust
567
85
-
-
85
nonpt
265
151
-
-
151
rwc
196
20
.
9
20
Hamilton County, OH
Butler County, OH
afdust
1,192
92
-
-
92
nonpt
829
295
-
-
295
ptnonipm
155
11
-
-
11
rwc
372
41
.
-
41
Harrison County, OH
Jefferson County, OH
afdust
308
31
-
-
31
nonpt
34
10
-
10
10
np_oilgas
16
-
-
-
-
pt_oilgas
102
55
-
-
55
ptnonipm
12
12
-
-
12
rwc
40
2
.
2
2
Jefferson County, OH
-
afdust
239
-
-
-
-
nonpt
115
61
-
61
61
ptnonipm
72
19
-
19
19
rwc
130
13
.
13
13
Lake County, OH
Cuyahoga County, OH
afdust
338
33
-
-
33
nonpt
297
120
-
-
120
ptnonipm
66
7
-
-
7
rwc
237
24
-
6
24
3A-40
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35
10/35 10/30
9/35
8/35
Lorain County, OH
Cuyahoga County, OH
afdust
644
85
-
-
-
85
nonpt
323
155
-
-
107
155
ptnonipm
115
27
-
-
27
27
rwc
337
34
-
-
11
34
Medina County, OH
Cuyahoga County, OH
afdust
692
93
-
-
-
93
nonpt
373
221
-
-
-
221
ptnonipm
40
-
-
-
-
-
rwc
245
26
-
-
9
26
Montgomery County, OH
Butler County, OH
afdust
752
70
-
-
-
70
nonpt
515
179
-
-
-
179
ptnonipm
44
15
-
-
-
15
rwc
426
38
-
-
-
38
Portage County, OH
Cuyahoga County, OH
afdust
558
73
-
-
-
73
nonpt
296
157
-
-
-
157
ptnonipm
121
35
-
-
7
35
rwc
216
22
-
-
8
22
Preble County, OH
Butler County, OH
afdust
461
46
-
-
-
46
nonpt
76
29
-
-
-
29
ptnonipm
27
-
-
-
-
-
rwc
72
6
-
-
-
6
Warren County, OH
Butler County, OH
afdust
521
59
-
-
-
59
Hamilton County, OH
nonpt
446
284
-
-
-
284
pt_oilgas
24
-
-
-
-
-
ptnonipm
9
-
-
-
-
-
rwc
252
23
-
-
-
23
Crook County, OR
Harney County, OR
afdust
1,126
209
-
209
-
126
nonpt
28
16
9
7
-
-
rwc
92
9
3
5
-
-
Deschutes County, OR
Crook County, OR
afdust
4,882
1,093
-
-
-
-
Harney County, OR
nonpt
292
214
-
-
-
-
Lake County, OR
pt_oilgas
6
-
-
-
-
-
ptnonipm
7
-
-
-
-
-
rwc
689
72
-
-
-
-
3A-41
-------
Adjacent Counties Maximum
(NE,SE,W) or Counties in Annual PM2.5 Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35
10/35
10/30
9/35
8/35
Grant County, OR
Crook County, OR
afdust
679
110
-
-
-
-
-
Harney County, OR
nonpt
23
6
-
-
-
-
-
rwc
48
5
-
-
-
-
-
Harney County, OR
Crook County, OR
afdust
1,332
146
-
-
49
-
146
Lake County, OR
nonpt
7
-
-
-
-
-
-
rwc
32
2
-
-
-
-
2
Jefferson County, OR
Crook County, OR
afdust
1,423
300
-
-
-
-
-
nonpt
32
17
-
-
-
-
-
ptagfire
60
-
-
-
-
-
-
rwc
93
9
-
-
-
-
-
Lake County, OR
Harney County, OR
afdust
1,106
141
141
-
-
-
-
nonpt
11
4
4
-
-
-
-
rwc
36
3
3
-
-
-
-
Malheur County, OR
Harney County, OR
afdust
2,371
336
-
-
-
-
-
nonpt
30
11
-
-
-
-
-
ptagfire
16
-
-
-
-
-
-
ptnonipm
51
42
-
-
-
-
-
rwc
78
8
-
-
-
-
-
Wheeler County, OR
Crook County, OR
afdust
222
34
-
-
-
-
-
rwc
10
-
-
-
-
-
-
Allegheny County, PA
Armstrong County, PA
afdust
1,401
-
-
-
-
-
-
nonpt
1,865
664
-
664
663
664
664
np_oilgas
19
-
-
-
-
-
-
ptnonipm
1,269
864
-
93
246
824
864
rwc
878
85
-
85
85
85
85
Armstrong County, PA
Allegheny County, PA
afdust
279
18
-
-
-
18
18
nonpt
125
49
-
-
-
49
49
np_oilgas
132
-
-
-
-
-
-
pt_oilgas
12
-
-
-
-
-
-
ptnonipm
80
61
-
-
-
61
61
rwc 130 15 - - - 15 15
3A-42
-------
Adjacent Counties Maximum
(NE,SE,W) or Counties in Annual PM2.5 Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction 12/35 10/35 10/30
9/35
8/35
Bedford County, PA
Cambria County, PA
afdust
419
28 ...
-
28
nonpt
155
79 ...
-
79
pt_oilgas
6
-
-
-
rwc
142
14 ...
-
14
Blair County, PA
Cambria County, PA
afdust
298
19 ...
-
19
nonpt
424
264 ...
-
264
ptnonipm
94
59 ...
-
59
rwc
203
22 ...
-
22
Bucks County, PA
Philadelphia County, PA
afdust
829
68 ...
-
68
nonpt
1,043
401 ...
-
401
ptnonipm
111
65 ...
-
65
rwc
502
47 ...
-
47
Butler County, PA
Allegheny County, PA
afdust
549
43 ...
-
43
Armstrong County, PA
nonpt
np_oilgas
695
42
419 ...
419
pt_oilgas
16
-
-
-
ptnonipm
413
140 ...
24
140
rwc
274
29 ...
10
29
Cambria County, PA
-
afdust
260
27 ...
-
27
nonpt
273
124 ...
34
124
np_oilgas
7
-
-
-
pt_oilgas
5
-
-
-
ptnonipm
29
13 ...
-
13
rwc
253
27 ...
-
27
Clarion County, PA
Armstrong County, PA
afdust
230
17 ...
-
17
nonpt
114
56 ...
-
56
np_oilgas
43
-
-
-
ptnonipm
38
7 ...
-
7
rwc
86
10 ...
4
10
Clearfield County, PA
Cambria County, PA
afdust
265
26 ...
-
26
nonpt
197
92 ...
-
92
np_oilgas
62
-
-
-
ptnonipm
47
35 ...
-
35
rwc
186
19 ...
-
19
3A-43
-------
Adjacent Counties Maximum
(NE,SE,W) or Counties in Annual PM2.5 Selected PM2.5 Emissions Reductions
V*" J ...
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35
10/30
9/35
8/35
Delaware County, PA
Philadelphia County, PA
afdust
388
38
-
-
38
38
nonpt
478
58
-
-
58
58
ptnonipm
270
165
-
-
165
165
rwc
136
17
-
-
17
17
Indiana County, PA
Armstrong County, PA
afdust
356
29
-
-
-
29
Cambria County, PA
nonpt
206
91
-
-
-
91
np_oilgas
163
-
-
-
-
-
pt_oilgas
8
-
-
-
-
-
ptnonipm
171
158
-
-
48
158
rwc
147
16
-
-
6
16
Jefferson County, PA
Armstrong County, PA
afdust
226
16
-
-
-
16
nonpt
133
55
-
-
-
55
np_oilgas
73
-
-
-
-
-
ptnonipm
192
177
-
-
-
177
rwc
99
11
-
-
5
11
Lancaster County, PA
Lebanon County, PA
afdust
1,871
95
-
-
-
95
nonpt
1,310
530
1
1
529
530
pt_oilgas
10
-
-
-
-
-
ptnonipm
494
272
58
58
235
272
rwc
419
41
15
15
41
41
Lebanon County, PA
Lancaster County, PA
afdust
441
31
-
-
-
31
nonpt
310
135
-
-
34
135
ptnonipm
28
-
-
-
-
-
rwc
152
15
-
-
10
15
Montgomery County, PA
Delaware County, PA
afdust
1,057
75
-
-
75
75
Philadelphia County, PA
nonpt
1,352
377
-
-
377
377
pt_oilgas
11
-
-
-
-
-
ptnonipm
328
143
-
-
143
143
rwc
433
38
-
-
38
38
Philadelphia County, PA
Delaware County, PA
afdust
633
57
-
-
-
57
nonpt
1,098
162
-
-
-
162
ptnonipm
988
674
-
-
524
674
rwc
42
4
-
-
-
4
3A-44
-------
Adjacent Counties Maximum
(NE,SE,W) or Counties in Annual PM2.5 Selected PM2.5 Emissions Reductions
V*" J ...
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35
10/30
9/35
8/35
Schuylkill County, PA
Lebanon County, PA
afdust
430
38
-
-
-
38
nonpt
427
214
-
-
-
214
ptnonipm
104
10
-
-
-
10
rwc
255
25
-
-
-
25
Somerset County, PA
Cambria County, PA
afdust
479
25
-
-
-
25
nonpt
257
146
-
-
-
146
ptnonipm
89
15
-
-
-
15
rwc
173
17
-
-
-
17
Westmoreland County, PA
Allegheny County, PA
afdust
640
58
-
-
-
58
Armstrong County, PA
nonpt
765
356
-
-
-
356
Cambria County, PA
np_oilgas
88
-
-
-
-
-
pt_oilgas
33
-
-
-
-
-
ptnonipm
228
135
-
-
18
135
rwc
561
60
-
-
20
60
Brooks County, TX
Hidalgo County, TX
afdust
467
66
-
-
66
66
np_oilgas
9
-
-
-
-
-
Cameron County, TX
Hidalgo County, TX
afdust
910
83
-
-
83
83
nonpt
200
63
-
-
63
63
ptagfire
94
-
-
-
-
-
ptnonipm
26
-
-
-
-
-
rwc
36
2
-
-
2
2
El Paso County, TX
-
afdust
1,592
-
-
-
-
-
nonpt
442
169
-
-
10
169
pt_oilgas
6
-
-
-
-
-
ptnonipm
234
65
-
-
21
65
rwc
60
6
-
-
1
6
Hidalgo County, TX
Cameron County, TX
afdust
1,758
170
22
22
170
170
nonpt
430
156
156
156
156
156
np_oilgas
30
-
-
-
-
-
pt_oilgas
9
-
-
-
-
-
ptagfire
128
-
-
-
-
-
ptnonipm
117
74
21
21
74
74
rwc
60
6
6
6
6
6
3A-45
-------
Adjacent Counties Maximum
(NE,SE,W) or Counties in Annual PM2.5 Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35
10/35 10/30
9/35
8/35
Hudspeth County, TX
El Paso County, TX
afdust
245
31
-
-
-
31
pt_oilgas
19
-
-
-
-
-
Kenedy County, TX
Hidalgo County, TX
afdust
269
43
-
-
43
43
Starr County, TX
Hidalgo County, TX
afdust
474
47
-
-
47
47
nonpt
47
16
-
-
16
16
np_oilgas
28
-
-
-
-
-
pt_oilgas
9
-
-
-
-
-
ptagfire
5
-
-
-
-
-
rwc
8
-
-
-
-
-
Willacy County, TX
Cameron County, TX
afdust
355
22
-
-
22
22
Hidalgo County, TX
nonpt
10
-
-
-
-
-
ptagfire
32
-
-
-
-
-
Cache County, UT
Weber County, UT
afdust
1,603
225
-
225
-
-
nonpt
53
9
-
9
-
-
rwc
26
2
-
2
-
-
Davis County, UT
Salt Lake County, UT
afdust
455
43
-
43
-
-
Weber County, UT
nonpt
125
23
-
23
-
-
ptnonipm
95
9
-
9
-
-
rwc
67
5
-
5
-
-
Morgan County, UT
Davis County, UT
afdust
201
32
-
-
-
-
Salt Lake County, UT
nonpt
6
-
-
-
-
-
Weber County, UT
ptnonipm
26
-
-
-
-
-
Rich County, UT
Cache County, UT
afdust
345
29
-
-
-
-
Weber County, UT
Salt Lake County, UT
Davis County, UT
afdust
1,649
83
-
83
-
-
nonpt
445
84
22
12
-
-
ptnonipm
789
263
206
57
-
-
rwc
234
14
2
10
-
-
Summit County, UT
Salt Lake County, UT
afdust
635
92
-
-
-
-
nonpt
40
8
-
-
-
-
ptnonipm
61
-
-
-
-
-
rwc
12
-
-
-
-
-
3A-46
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35 8/35
Tooele County, UT
Davis County, UT
afdust
641
104
-
Salt Lake County, UT
nonpt
26
2
-
Weber County, UT
ptnonipm
773
42
-
rwc
15
-
.
Wasatch County, UT
Salt Lake County, UT
afdust
756
144
-
nonpt
15
2
-
rwc
9
-
-
Weber County, UT
Cache County, UT
afdust
557
19
19
Davis County, UT
nonpt
91
15
15
ptnonipm
65
-
-
rwc
59
5
5
Benton County, WA
Yakima County, WA
afdust
1,539
63
-
nonpt
139
24
-
ptagfire
71
-
-
rwc
108
9
.
Chelan County, WA
Kittitas County, WA
afdust
329
44
-
Okanogan County, WA
nonpt
81
14
-
ptagfire
26
-
-
rwc
227
27
.
Douglas County, WA
Kittitas County, WA
afdust
2,049
186
-
Okanogan County, WA
nonpt
23
2
-
ptagfire
12
-
-
ptnonipm
10
-
-
rwc
84
9
.
Ferry County, WA
Okanogan County, WA
afdust
397
63
-
nonpt
9
-
-
rwc
41
5
.
Grant County, WA
Kittitas County, WA
afdust
3,242
169
-
Okanogan County, WA
nonpt
78
13
-
Yakima County, WA
ptagfire
264
-
-
ptnonipm
68
43
-
rwc
109
10
-
3A-47
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35 8/35
Kittitas County, WA
Yakima County, WA
afdust
472
47
47 ...
nonpt
51
8
8 - - - -
ptagfire
9
-
-
rwc
113
13
13
Klickitat County, WA
Yakima County, WA
afdust
568
54
-
nonpt
20
1
-
ptagfire
14
-
-
ptnonipm
44
8
-
rwc
55
6
.
Lewis County, WA
Yakima County, WA
afdust
550
65
-
nonpt
82
15
-
ptnonipm
94
20
-
rwc
226
24
.
Lincoln County, WA
Okanogan County, WA
afdust
2,537
130
-
nonpt
10
-
-
ptagfire
40
-
-
rwc
26
3
.
Okanogan County, WA
-
afdust
771
113
113
nonpt
42
9
9
ptagfire
12
-
-
rwc
154
17
17
Pierce County, WA
Kittitas County, WA
afdust
1,540
7
-
Yakima County, WA
nonpt
574
103
-
ptnonipm
200
99
-
rwc
1,047
93
.
Skagit County, WA
Okanogan County, WA
afdust
626
67
-
nonpt
131
24
-
pt_oilgas
6
-
-
ptnonipm
252
137
-
rwc
237
26
.
Skamania County, WA
Yakima County, WA
afdust
122
18
-
nonpt
8
-
-
rwc
57
6
-
3A-48
-------
Adjacent Counties
Maximum
(NE,SE,W) or Counties in
Annual
PM2.5
Selected PM2.5 Emissions Reductions
Same Air District (CA) Still
PM2.5
Emissions
County
Needing Reductions
Sector
Emissions
Reduction
12/35 10/35 10/30 9/35
8/35
Whatcom County, WA
Okanogan County, WA
afdust
874
69
-
-
nonpt
227
48
-
-
pt_oilgas
8
-
-
-
ptnonipm
851
762
-
-
rwc
365
35
.
-
Yakima County, WA
Kittitas County, WA
afdust
1,845
100
100 -
-
nonpt
193
41
41
-
ptagfire
177
-
-
-
rwc
345
39
39
-
Brooke County, WV
-
afdust
90
12
-
12
nonpt
44
19
-
19
ptnonipm
127
76
-
76
rwc
103
13
.
13
Hancock County, WV
Brooke County, WV
afdust
58
8
-
8
nonpt
51
9
-
9
ptnonipm
32
-
-
-
rwc
122
15
.
15
Marshall County, WV
-
afdust
179
24
-
24
nonpt
46
13
-
13
np_oilgas
14
-
-
-
pt_oilgas
13
-
-
-
ptnonipm
109
92
-
92
rwc
158
19
.
19
Ohio County, WV
Brooke County, WV
afdust
238
40
-
40
Marshall County, WV
nonpt
97
38
-
38
np_oilgas
26
-
-
-
rwc
143
18
.
18
Wetzel County, WV
Marshall County, WV
afdust
77
12
-
12
nonpt
41
22
-
22
np_oilgas
35
-
-
-
pt_oilgas
8
-
-
-
rwc
82
11
-
11
3A-49
-------
CHAPTER 4: ENGINEERING COST ANALYSIS AND QUALITATIVE DISCUSSION OF
SOCIAL COSTS
Overview
This chapter provides estimates of the engineering costs of the illustrative control
strategies identified in Chapter 3 for the proposed annual and current 24-hour alternative
standard levels of 10/35 |~ig/m3 and 9/35 ng/m3, as well as the following two more
stringent alternative standard levels: 8/35 |~ig/m3 and 10/30 ng/m3. Because the EPA is
proposing that the current secondary PM standards be retained, we did not evaluate
alternative secondary standard levels in this RIA. The chapter summarizes the methods,
tools, and data sources used to estimate the engineering costs presented. As discussed in
Chapter 3, for the alternative standards analyzed we applied control measures to sources
in the following emissions inventory sectors: non-electric generating unit (non-EGU) point,
oil and gas point, non-point (area), residential wood combustion, and area fugitive dust.
The estimated costs for the alternative standard levels are a function of (i)
assumptions used in the analysis, including assumptions about which areas will require
emissions controls and the sources and controls available in those areas; (ii) the level of
sufficient, detailed information on emissions sources and control measures needed to
estimate engineering costs; and (iii) the future year baseline emissions from which the
emissions reductions are measured.
For the proposed alternative standard level of 10/35 ng/m3, because 15 of the 24
counties that need emissions reductions are counties in California, the majority of the
estimated costs are incurred in California. In addition, as the alternative standard levels
become more stringent, more counties in the northeast and southeast need emissions
reductions. As additional controls are applied in those areas (and less so in the west and
California because availability of additional controls is limited), those areas account for a
relatively higher proportion of estimated costs. For example, for alternative standard levels
of 9/35 ng/m3 and 8/35 ng/m3, more controls are available to apply in the northeast and
their adjacent counties and the southeast and their adjacent counties. The estimated costs
for those areas are higher than the estimated costs for the west and California. Note that in
the northeast and southeast we identified control measures and associated emissions
4-1
-------
reductions from adjacent counties and used a ppb/ton PM2.5 air quality ratio that was four
times less responsive than the ratio used when applying in-county emissions reductions
(i.e., applied four tons of PM2.5 emissions reductions from an adjacent county for one ton of
emissions reduction needed in a given county); the cost of the additional reductions from
adjacent counties also contributes to the higher proportion of the estimated costs. Lastly,
for the more stringent alternative standard level of 8/35 |~ig/m3, across all areas the largest
share of estimated costs is from controls for area fugitive dust emissions.
The remainder of the chapter is organized as follows. Section 4.1 presents the
engineering costs associated with the application of controls identified in EPA's national-
scale analysis. Section 4.2 provides a discussion of the uncertainties and limitations
associated with the engineering cost estimates. Section 4.3 includes a qualitative discussion
on social costs. Section 4.4 includes references.
4.1 Estimating Engineering Costs
The engineering costs described in this chapter generally include the costs of
purchasing, installing, operating, and maintaining the control technologies applied. The
costs associated with monitoring, testing, reporting, and recordkeeping for potentially
affected sources are not included in the annualized cost estimates. These cost estimates are
presented for 2032 but reflect the annual cost that is expected to be incurred each year
over a longer time horizon. We calculate the present value of these annual costs over 20
years in Chapter 8 using 3 and 7 percent discount rates.
This analysis focuses on emissions reductions needed for the proposed and more
stringent alternative standard levels. As discussed in this analysis, the control technologies
and strategies selected for analysis were from information available in EPA's control
measures database; these control strategies illustrate one way in which nonattainment
areas could work toward meeting a revised standard. There are many ways to construct
and evaluate potential control programs for a revised standard, and the EPA anticipates
that state and local governments will consider programs best suited for local conditions.
The EPA understands that some states will incur costs both designing State
Implementation Plans (SIPs) and implementing new control strategies to meet a revised
4-2
-------
standard. However, the EPA does not know what specific actions states will take to design
their SIPs to meet a revised standard. Therefore, we do not present estimated costs that
government agencies may incur for managing the requirement or implementing these (or
other) control strategies.
4.1.1 Methods, Tools, and Data
The EPA uses the Control Strategy Tool (CoST) (U.S. EPA, 2019a) to estimate
engineering control costs. CoST models emissions reductions and control costs associated
with the application of control technologies or measures by matching the controls in the
control measures database (CMDB) to emissions sources in the future year projected
emissions inventory by source classification code (SCC).1*2 CoST was used in two ways in
the analysis. First, CoST was used to identify controls and related potential PM2.5 emissions
reductions in counties projected to exceed the proposed and more stringent alternative
annual and 24-hour standard levels of 10/35 |~ig/m3,10/30 |~ig/m3, 9/35 |~ig/m3, and 8/35
Hg/m3 in the analytical baseline (see Chapter 3, Section 3.2.1 for a discussion of the
counties and areas). Second, CoST was used to estimate the control costs for the measures
identified. As indicated in Chapter 3, Section 3.2.2., for the control strategy analyses in this
RIA, to maximize the number of emissions sources included we applied controls to
emissions sources with greater than 5 tons per year of PM2.5 emissions at a marginal cost
threshold of up to a $160,000/ton.
CoST calculates engineering costs using one of two different methods: (1) an
equation that incorporates key operating unit information, such as unit design capacity or
stack flow rate, or (2) an average annualized cost-per-ton factor multiplied by the total tons
of reduction of a pollutant. Most control cost information within CoST was developed based
on the cost-per-ton approach because (1) parameters used in the engineering equations
are not readily available or broadly representative across emissions sources within the
emissions inventory and (2) estimating engineering costs using an equation requires data
from the emissions inventory, which may not be available. The cost equations used in CoST
1 More information about CoST and the control measures database can be found at the following link:
https://www.epa.gov/economic-and-cost-analysis-air-pollution-regulations/cost-analysis-modelstools-air-
pollution.
2 We used a 2016-based modeling platform to project future-year emissions and air quality for 2032.
4-3
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estimate annual, capital and/or operating and maintenance (O&M) costs and are used
primarily for some larger emissions sources such as industrial, commercial, and
institutional (ICI) boilers, glass manufacturing furnaces, and cement kilns.
CoST gets key operating unit information from the emissions inventory data
submitted by state, local, and tribal air agencies (S/L/T), including detailed information by
source on emissions, installed control devices, and control device efficiency. Much of this
underlying emissions inventory data serves as key inputs into CoST and the control
strategy analyses. The information on whether a source is currently controlled, by what
control device, and control device efficiency is required under the Air Emissions Reporting
Rule (AERR) used to collect the emissions inventory data. However, control information
may not be fully reported by S/L/T agencies and would not be available for purposes of the
control strategy analyses, introducing the possibility that CoST applies controls to already
controlled emissions sources.
When sufficient information is available to estimate control costs using equations,
the capital costs of the control equipment must be annualized. Capital costs are converted
to annual costs using the capital recovery factor (CRF).3 The engineering cost analysis uses
the equivalent uniform annual costs (EUAC) method, in which annualized costs are
calculated based on the equipment life for the control measure and the interest rate
incorporated into the CRF. Annualized costs represent an equal stream of yearly costs over
the period the control technology is expected to operate. For more information on the
EUAC method, refer to the EPA Air Pollution Control Cost Manual (U.S. EPA, 2017a).
4.1.2 Cost Estimates for the Control Strategies
In this section, we provide engineering cost estimates for the control technologies
and measures presented in Chapter 3 that include control technologies for non-EGU point
sources, oil and gas point, non-point (area) sources, residential wood combustion sources,
and area fugitive dust emissions. The cost estimates presented in Table 4-1 through Table
3 The capital recovery factor incorporates the interest rate and equipment life (in years) of the control
equipment. The capital recovery factor formula is expressed as r*(l+r]n/[(l+rOn -1], where r is the real rate
of interest and n is the number of time periods. The annualized costs assumed a 7 percent interest rate.
4-4
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4-5 reflect the engineering costs annualized at 7 percent, to the extent possible.4 When
calculating the annualized costs we would like to use the interest rates faced by firms;
however, we do not know what those rates are. As such we use 7 percent as a conservative
estimate.
By area, Table 4-1 includes a summary of estimated control costs from control
applications for the alternative standard levels analyzed. Tables 4A-1 through 4A-6 in
Appendix 4A include detailed information on estimated costs by area and by county.
Table 4-1 By Area, Summary of Annualized Control Costs for Alternative Primary
Standard Levels of 10/35 |ig/m3,10/30 |ig/m3, 9/35 |ig/m3, and 8/35
(ig/m3 for 2032 (millions of 2017$)
Area
10/35
10/30
9/35
8/35
Northeast
$7.3
$12.8
$183.5
$560.2
Northeast (Adjacent Counties)
$0
$0
$22.3
$539.7
Southeast
$4.1
$4.1
$50.4
$250.6
Southeast (Adjacent Counties)
$0
$0
$18.2
$186.5
West
$19.0
$150.0
$34.2
$121.8
CA
$64.1
$90.4
$84.7
$162.9
Total
$94.5
$257.2
$393.3
$1,821.7
For the proposed alternative standard level of 10/35 |~ig/m3, the majority of the
estimated costs are incurred in California because 15 of the 24 counties that need
emissions reductions are located in California. Looking at the more stringent alternative
standard level of 10/30 |~ig/m3 in the west, an additional 20 counties need emissions
reductions, and the estimated costs increase significantly; estimated costs for the proposed
alternative standard level of 9/35 |~ig/m3 are higher than for 10/35 |~ig/m3 but lower than
for 10/30 ng/m3 in this area. For alternative standard levels of 9/35 |~ig/m3 and 8/35
Hg/m3, more controls are available to apply in the northeast and the southeast as compared
to in California and the west. Therefore, the estimated costs for the northeast and the
4 Because we obtain control cost data from many sources, we are not always able to obtain consistent data
across original data sources. As a result, we do not know the interest rates used to calculate costs for some
of the controls included in this analysis. If disaggregated control cost data is available (i.e., where capital,
equipment life value, and O&M costs are separated out) we can calculate costs using a specified percent
interest rate. EPA may not know the interest rates used to calculate costs when disaggregated control cost
data is unavailable (i.e., where we only have a $/ton value and where capital, equipment life value, and O&M
costs are not separated out).
4-5
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southeast are significantly higher for 9/35 |~ig/m3 and 8/35 |~ig/m3. See Tables 3A.2 through
3A.7 for more details on emissions reductions available by area and county.
As discussed in Chapter 3, in the northeast and southeast when we applied the
emissions reductions from adjacent counties, we applied a ratio of 4:1. That is, it is
assumed that four tons of PM2.5 emissions reductions from an adjacent county are needed
to produce the equivalent air quality change of one ton of emissions reduction if it had
occurred within the county needing the reduction. Application of this ratio contributes to
the higher cost estimates for alternative standard levels of 9/35 |~ig/m3 and 8/35 ng/m3.
Naturally, it is anticipated that states will first attempt to find emissions reductions within
the counties that actually need the reductions. To the extent that states are able to identify
control opportunities within those counties beyond the reductions identified by CoST, the
need for reductions from adjacent counties will be reduced. Also, depending on local air
quality factors, the resulting air quality impact may be greater than a 4:1 ratio suggests. As
a result, the estimate of costs for adjacent counties may be an overestimate.
By emissions inventory sector, Table 4-2 includes a summary of the estimated costs
from control applications for the alternative standard levels analyzed. For all of the
alternative standard levels analyzed, controls for area fugitive dust emissions comprise the
largest share of the estimated costs, ranging from 49 to 81 percent of the cost estimates.
Non-EGU point and non-point (area) controls represent the next largest shares of the cost
estimates.
By area and by emissions inventory sector, Table 4-3 includes a summary of the
estimated costs from control applications for the alternative standard levels analyzed. For
the more stringent alternative standard level of 8/35 ng/m3 across all areas the largest
share of estimated costs is from controls for area fugitive dust emissions. In addition, as the
alternative standard levels become more stringent, more counties in the northeast and
southeast need emissions reductions and controls are applied in those areas (and less so in
the west and California because availability of additional controls is limited), resulting in a
relatively higher proportion of estimated costs for those areas.
4-6
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Table 4-2 By Emissions Inventory Sector, Summary of Annualized Control Costs
for Alternative Primary Standard Levels of 10/35 ng/m3,10/30 |jg/m3,
9/35 (j,g/m3, and 8/35 jig/m3 for 2032 (millions of 2017$)
Sector
10/35
10/30
9/35
8/35
Non-EGU Point
$10.2
$21.2
$144.3
$423.7
Oil & Gas Point
$0
$0
$0
$5.0
Non-Point (Area)
$15.8
$21.4
$46.3
$189.2
Residential Wood Combustion
$3.1
$5.6
$11.3
$36.7
Area Source Fugitive Dust
$65.4
$209.1
$191.5
$1,167.0
Total
$94.5
$257.2
$393.3
$1,821.7
Table 4-3 By Area and by Emissions Inventory Sector, Summary of Annualized
Control Costs for Alternative Primary Standard Levels of 10/35 |ig/m3,
10/30 ng/m3,9/35 (ig/m3, and 8/35 (ig/m3 for 2032 (millions of
2017$)
Area
Sector
10/35
10/30
9/35
8/35
Northeast
Non-EGU Point
$1.7
$7.3
$125.0
$232.8
Non-Point (Area)
$4.6
$4.6
$16.8
$56.4
Residential Wood Combustion
$1.0
$1.0
$4.1
$10.5
Area Source Fugitive Dust
$0
$0
$37.7
$260.5
Northeast
Non-EGU Point
$0
$0
$4.0
$65.3
(Adjacent
Oil & Gas Point
$0
$0
$0
$5.0
Counties)
Non-Point (Area)
$0
$0
$4.4
$50.5
Residential Wood Combustion
$0
$0
$0.8
$10.6
Area Source Fugitive Dust
$0
$0
$13.1
$408.4
Southeast
Non-EGU Point
$1.2
$1.2
$6.2
$81.4
Oil & Gas Point
$0
$0
$0
$0.02
Non-Point (Area)
$2.0
$2.0
$10.1
$37.7
Residential Wood Combustion
$0.3
$0.3
$0.6
$2.4
Area Source Fugitive Dust
$0.7
$0.7
$33.6
$129.0
Southeast
Non-EGU Point
$0
$0
$0
$17.9
(Adjacent
Non-Point (Area)
$0
$0
$0.1
$10.0
Counties)
Residential Wood Combustion
$0
$0
$0
$1.4
Area Source Fugitive Dust
$0
$0
$18.1
$157.3
West
Non-EGU Point
$0
$5.4
$0.6
$11.9
Non-Point (Area)
$0.06
$3.6
$2.1
$13.4
Residential Wood Combustion
$0.03
$1.1
$0.4
$2.8
Area Source Fugitive Dust
$19.0
$139.9
$31.0
$93.7
CA
Non-EGU Point
$7.3
$7.3
$8.4
$14.5
Non-Point (Area)
$9.2
$11.2
$12.8
$21.2
Residential Wood Combustion
$1.9
$3.3
$5.5
$9.0
Area Source Fugitive Dust
$45.8
$68.5
$58.0
$118.2
Total
$94.5
$257.2
$393.3
$1,821.7
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By control technology, Table 4-4 includes a summary of the estimated costs from
control applications for the alternative standard levels analyzed. Across all of the
alternative standard levels analyzed, the control technologies that comprise more than 80
percent of the cost estimates include Pave Existing Shoulders at 25% rule penetration (RP)
(area fugitive dust inventory sector), Pave Unpaved Roads at 25% RP (area fugitive dust
inventory sector), Fabric Filter-All Types (non-EGU point inventory sector), and
Electrostatic Precipitator at 25% RP (non-point (area) inventory sector).
By emissions inventory sector and by control technology, Table 4-5 includes a
summary of the cost estimates. Across all of the alternative standard levels analyzed, for
the non-EGU point sector, the application of Fabric Filter-All Types results in the highest
portion of estimated costs for that inventory sector; for the non-point (area) sector, the
application of Electrostatic Precipitator at 25% RP and Substitute Chipping for Burning
result in the highest portion of estimated costs for that inventory sector; for the residential
wood combustion sector, the application of Convert to Gas Logs at 25% RP results in the
highest portion of estimated costs for that inventory sector; and for the area fugitive dust
sector, the application of Pave Existing Shoulders at 25% and Pave Unpaved Roads at 25%
result in the highest portion of estimated costs for that inventory sector.
4-8
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Table 4-4 By Control Technology, Summary of Annualized Control Costs for
Alternative Primary Standard Levels of 10/35 ng/m3,10/30 ng/m3,
9/35 (j,g/m3, and 8/35 jig/m3 for 2032 (millions of 2017$)
Control Technology
10/35
10/30
9/35
8/35
Add-on Scrubber at 25% RP
$0.06
$0.06
$0
$0
Annual tune-up at 10% RP
$0
$0.01
$0.01
$0.01
Annual tune-up at 25% RP
$0.6
$0.7
$3.4
$12.0
Biennial tune-up at 10% RP
$0.0
$0.0
$0.0
$0.3
Biennial tune-up at 25% RP
$0.1
$0.3
$0.3
$2.0
Catalytic oxidizers at 25% RP
$0.3
$0.4
$1.1
$1.4
Chemical Stabilizer at 10% RP
$0.7
$2.2
$1.3
$46.8
Chemical Stabilizer at 25% RP
$0
$0
$1.6
$49.8
Convert to Gas Logs at 25% RP
$2.6
$4.4
$9.5
$29.0
Dust Suppressants at 10% RP
$0
$0
$0
$0.02
Dust Suppressants at 25% RP
$0
$0
$0
$5.4
Electrostatic Precipitator-All Types
$0.4
$0
$0.4
$0.7
Electrostatic Precipitator at 10% RP
$0
$0
$0.1
$0.01
Electrostatic Precipitator at 25% RP
$10.7
$13.1
$20.4
$80.6
EPA-certified wood stove at 10% RP
$0
$0
$0
$0.01
EPA Phase 2 Qualified Units at 10% RP
$0
$0
$0.2
$0.03
EPA Phase 2 Qualified Units at 25% RP
$0.2
$0.2
$0
$0.7
Fabric Filter-All Types
$9.0
$18.9
$129.1
$397.2
HEPA filters at 10% RP
$0.01
$0.01
$0.01
$0.02
HEPA filters at25%RP
$0.02
$0
$0.09
$0.4
Install Cleaner Hydronic Heaters at 10% RP
$0
$0.0
$0
$0
Install Cleaner Hydronic Heaters at 25% RP
$0.02
$0.03
$0.2
$0.7
Install new drift eliminator at 10% RP
$0
$0
$0.02
$0.01
Install new drift eliminator at 25% RP
$0.5
$0.5
$0.6
$1.3
Install Retrofit Devices at 10% RP
$0
$0
$0.1
$0.06
Install Retrofit Devices at 25% RP
$0.1
$0.1
$0
$0.08
New gas stove or gas logs at 10% RP
$0.03
$0.4
$0.4
$0.7
New gas stove or gas logs at 25% RP
$0.2
$0.5
$0.9
$5.4
Pave existing shoulders at 10% RP
$0
$0
$0
$7.6
Pave existing shoulders at 25% RP
$31.1
$95.0
$119.6
$755.0
Pave Unpaved Roads at 25% RP
$33.7
$111.8
$69.0
$302.5
Smokeless Broiler at 10% RP
$0.4
$0.6
$1.1
$0.3
Smokeless Broiler at 25% RP
$0
$0
$3.1
$1.3
Substitute chipping for burning
$3.5
$6.1
$16.6
$90.6
Venturi Scrubber
$0.3
$1.7
$14.3
$29.7
Total
$94.5
$257.2
$393.3
$1,821.7
Note - The 10% RP and 25% RP indicate the rule penetration percent, or the percent of the non-point (area),
residential wood combustion, or area fugitive dust inventory emissions that the control measure is applied to
at a specified percent control efficiency.
4-9
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Table 4-5 By Emissions Inventory Sector and Control Technology, Summary of
Annualized Control Costs for Alternative Primary Standard Levels of
10/35 |ig/m3,10/30 |ig/m3, 9/35 (j,g/m3, and 8/35 |ig/m3 for 2032
(millions of 2017$)
Inventory
Sector
Control Technology
10/35
10/30
9/35
8/35
Non-EGU Point
Electrostatic Precipitator-All Types
$0.4
$0
$0.4
$0.7
Fabric Filter-All Types
$9.0
$18.9
$129.0
$392.0
Install new drift eliminator at 10% RP
$0
$0
$0.02
$0.01
Install new drift eliminator at 25% RP
$0.5
$0.5
$0.6
$1.3
Venturi Scrubber
$0.3
$1.7
$14.3
$29.7
Oil & Gas Point
Fabric Filter-All Types
$0
$0
$0
$5.0
Install new drift eliminator at 25% RP
$0
$0
$0
$0.02
Non-Point
Add-on Scrubber at 25% RP
$0.06
$0.06
$0
$0
(Area)
Annual tune-up at 10% RP
$0
$0.01
$0.01
$0.01
Annual tune-up at 25% RP
$0.6
$0.7
$3.4
$12.0
Biennial tune-up at 10% RP
$0.0
$0.0
$0.0
$0.3
Biennial tune-up at 25% RP
$0.1
$0.3
$0.3
$2.0
Catalytic oxidizers at 25% RP
$0.3
$0.4
$1.1
$1.4
Electrostatic Precipitator at 10% RP
$0
$0
$0.1
$0.01
Electrostatic Precipitator at 25% RP
$10.7
$13.1
$20.4
$80.6
Fabric Filter-All Types
$0
$0
$0.09
$0.2
HEPA filters at 10% RP
$0.01
$0.01
$0.01
$0.02
HEPA filters at25%RP
$0.02
$0
$0.09
$0.4
Smokeless Broiler at 10% RP
$0.4
$0.6
$1.1
$0.3
Smokeless Broiler at 25% RP
$0
$0
$3.1
$1.3
Substitute chipping for burning
$3.5
$6.1
$16.6
$90.6
Residential
Convert to Gas Logs at 25% RP
$2.6
$4.4
$9.5
$29.0
Wood
EPA-certified wood stove at 10% RP
$0
$0
$0
$0.01
Combustion
EPA Phase 2 Qualified Units at 10% RP
$0
$0
$0.2
$0.03
EPA Phase 2 Qualified Units at 25% RP
$0.2
$0.2
$0
$0.7
Install Cleaner Hydronic Heaters at 10% RP
$0
$0.0
$0
$0
Install Cleaner Hydronic Heaters at 25% RP
$0.02
$0.03
$0.2
$0.7
Install Retrofit Devices at 10% RP
$0
$0
$0.1
$0.06
Install Retrofit Devices at 25% RP
$0.1
$0.1
$0
$0.08
New gas stove or gas logs at 10% RP
$0.03
$0.4
$0.4
$0.7
New gas stove or gas logs at 25% RP
$0.2
$0.5
$0.9
$5.4
Area Source
Chemical Stabilizer at 10% RP
$0.7
$2.2
$1.3
$46.8
Fugitive Dust
Chemical Stabilizer at 25% RP
$0
$0
$1.6
$49.8
Dust Suppressants at 10% RP
$0
$0
$0
$0.02
Dust Suppressants at 25% RP
$0
$0
$0
$5.4
Pave existing shoulders at 10% RP
$0
$0
$0
$7.6
Pave existing shoulders at 25% RP
$31.1
$95.0
$119.6
$755.0
Pave Unpaved Roads at 25% RP
$33.7
$111.8
$69.0
$302.5
Total
$94.5
$257.2
$393.3
$1,821.7
Note - The 10% RP and 25% RP indicate the rule penetration percent, or the percent of the non-point (area),
residential wood combustion, or area fugitive dust inventory emissions that the control measure is applied to
at a specified percent control efficiency.
4-10
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As discussed in Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6, for the proposed
alternative standard levels of 10/35 |~ig/m3 and 9/35 |~ig/m3 there are remaining air quality
challenges for areas in the northeast and southeast, as well as in the west and California;
the areas include a county in Pennsylvania affected by local sources, 3 counties in border
areas, 5 counties in small western mountain valleys, and 13 counties in California's air
basins and districts. The characteristics of the air quality challenges for these areas include
features of local source-to-monitor impacts, cross-border transport, effects of complex
terrain in the west and California, and identifying wildfire influence on projected PM2.5 DVs
that could qualify for exclusion as atypical, extreme, or unrepresentative events (U.S. EPA,
2019b). To the extent that state and local areas are able to find alternative lower-cost
approaches to reducing emissions, the annualized control costs above may be
overestimated. To the extent that additional PM2.5 emissions reductions are required that
were not identified in our analysis of these areas, the annualized control costs above may
be underestimated.
4.2 Limitations and Uncertainties in Engineering Cost Estimates
The EPA acknowledges several important limitations of this analysis, which include
the following:
• Exclusions from the cost analysis: As mentioned above, recordkeeping,
reporting, testing and monitoring costs are not included. The costs some
states will incur both designing SIPs and implementing new control
strategies to meet a revised standard are also not included.
• Cost and effectiveness of control measures: We are not able to account for
regional or local variation in capital and annual cost items such as energy,
labor, or materials. Our estimates of control measure costs may over- or
under-estimate the costs depending on how the difficulty of actual
retrofitting and equipment life compares with our control assumptions. In
addition, our estimates of control efficiencies for the controls assume that the
control devices are properly installed and maintained. Further, our estimates
of control efficiencies do not account for differences in individual
4-11
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applications as we use a single value for each control that does not account
for differences in individual applications - sometimes a control operates
more or less effectively than the specified efficiency. There is also variability
in scale of application that is difficult to reflect for small area sources of
emissions.
• Interest rate: Because we obtain control cost data from many sources, we
are not always able to obtain consistent data across original data sources. If
disaggregated control cost data is available (i.e., where capital, equipment life
value, and O&M costs are separated out) we can calculate costs using a
specified percent interest rate. The EPA may not know the interest rates used
to calculate costs if disaggregated control cost data is unavailable (i.e., where
we only have a $/ton value and where capital, equipment life value, and O&M
costs are not separated out). In general, we have some disaggregated data
available for non-EGU point source controls, but we do not have any
disaggregated control cost data for non-point (area) source controls.
• Differences between ex ante and ex post compliance cost estimates: In
comparing regulatory cost estimates before and after regulation, ex ante cost
estimate predictions may differ from actual costs. Harrington etal. (2000)
surveyed the predicted and actual costs of 28 federal and state rules,
including 21 issued by the U.S. Environmental Protection Agency and the
Occupational Safety and Health Administration (OSHA). In 14 of the 28 rules,
predicted total costs were overestimated, while analysts underestimated
costs in three of the remaining rules. In EPA rules where per-unit costs were
specifically evaluated, costs of regulations were overestimated in five cases,
underestimated in four cases, and accurately estimated in four cases
(Harrington et al., 2000). The collection of literature regarding the accuracy
of cost estimates seems to reflect these splits. A recent EPA report, the
"Retrospective Study of the Costs of EPA Regulations" that examined the
4-12
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compliance costs of five EPA regulations in four case studies,5 found that
several of the case studies suggested that cost estimates were over-estimated
ex ante and did not find the evidence to be conclusive. The EPA stated in the
report that the small number of regulatory actions covered, as well as
significant data and analytical challenges associated with the case studies
limited the certainty of this conclusion (U.S. EPA, 2014).
4.3 Social Costs
As discussed in EPA's Guidelines for Preparing Economic Analyses, social costs are
the total economic burden of a regulatory action (U.S. EPA, 2010). This burden is the sum of
all opportunity costs incurred due to the regulatory action, where an opportunity cost is
the value lost to society of any goods and services that will not be produced and consumed
as a result of reallocating some resources toward pollution mitigation. Estimates of social
costs may be compared to the social benefits expected as a result of a regulation to assess
its net impact on society.
Computable General Equilibrium (CGE) models are analytical tools that can be used
to evaluate the broad impacts of a regulatory action and are therefore often used to
estimate social costs. While this section includes a qualitative discussion of social costs and
economic impact modeling, CGE modeling was not conducted for this analysis because
EPA's current CGE model, discussed later in this section, does not have the resolution
needed to accurately model the emissions inventory sectors being controlled (e.g., area
fugitive dust inventory sector, residential wood combustion inventory sector). However,
the EPA continues to be committed to the use of CGE models to evaluate the economy-wide
effects of its regulations.
Economic impacts focus on the behavioral response to the costs imposed by a policy
being analyzed. The responses typically analyzed are market changes in prices, quantities
5 The four case studies in the 2014 Retrospective Study of the Costs of EPA Regulations examine five EPA
regulations: the 2001/2004 National Emission Standards for Hazardous Air Pollutants and Effluent
Limitations Guidelines, Pretreatment Standards, and New Source Performance Standards on the Pulp and
Paper Industry; Critical Use Exemptions for Use of Methyl Bromide for Growing Open Field Fresh
Strawberries in California for the 2004-2008 Seasons; the 2001 National Primary Drinking Water
Regulations for Arsenic; and the 1998 Locomotive Emission Standards.
4-13
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produced and purchased, changes in international trade, changes in profitability, facility
closures, and employment. Sometimes these behavioral changes can be used to estimate
social costs if there is indication that the social costs differ from the estimate of control
costs because behavioral change results in other ways of meeting the requirements (e.g.,
facilities choosing to reduce emissions by producing less rather than adding pollution
control devices).
Changes in production in a directly regulated sector may have indirect effects on a
myriad of other markets when output from that is used as an input in the production of
many other goods. It may also affect upstream industries that supply goods and services to
the sector, along with labor and capital markets, as these suppliers alter production
processes in response to changes in factor prices. In addition, households may change their
demand for particular goods and services due to changes in the price of those goods.
When new regulatory requirements are expected to result in effects outside of
regulated and closely related sectors, a key challenge is determining whether they are of
sufficient magnitude to warrant explicit evaluation (Hahn and Hird 1990). It is not possible
to estimate the magnitude and direction of all of these potential effects outside of the
regulated sector(s) without an economy-wide modeling approach. For example, studies of
air pollution regulations for the power sector have found that the social costs and benefits
may be greater or lower than when secondary market impacts are taken into account, and
that the direction of the estimates may depend on the form of the regulation (e.g., Goulder
et al. 1999, Williams 2002, Goulder et al. 2016).
The alternative standard levels analyzed are anticipated to impact multiple markets
in many places over time. CGE models are one possible tool for evaluating the impacts of a
regulation on the broader economy because this class of models explicitly captures
interactions between markets across the entire economy. While a CGE model captures the
effects of behavioral responses on the part of consumers or other producers to changes in
price that are missed by an engineering estimate of compliance costs, most CGE models do
not model the environmental externality or the benefits that accrue to society from
mitigating the externality. When benefits from a regulation are expected to be substantial,
4-14
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social cost cannot be interpreted as a complete characterization of economic welfare. To
the extent that the benefits affect behavioral responses in markets, the social cost measure
may also be potentially biased.
A CGE-based approach to cost estimation concurrently considers the effect of a
regulation across all sectors in the economy. It is structured around the assumption that,
for some discrete period of time, an economy can be characterized by a set of equilibrium
conditions in which supply equals demand in all markets. When the imposition of a
regulation alters conditions in one market, a general equilibrium approach will determine a
new set of prices for all markets that will return the economy to equilibrium. These prices
in turn determine the outputs and consumption of goods and services in the new
equilibrium. In addition, a new set of prices and demands for the factors of production
(labor, capital, and land), the returns to which compose the income of businesses and
households, will be determined in general equilibrium. The social cost of the regulation can
then be estimated by comparing the value of variables in the pre-regulation "baseline"
equilibrium with those in the post-regulation, simulated equilibrium.
In 2015, the EPA established a Science Advisory Board (SAB) panel to consider the
technical merits and challenges of using economy-wide models to evaluate costs, benefits,
and economic impacts in regulatory development. In its final report (U.S. EPA, 2017b), the
SAB recommended that the EPA begin to integrate CGE modeling into regulatory analysis
to offer a more comprehensive assessment of the effects of air regulations. The SAB noted
that CGE models can provide insight into the likely social costs of a regulation even when
they do not include a characterization of the likely social benefits of the regulation. CGE
models may also offer insights into the ways costs are distributed across regions, industry
sectors, or households.
The SAB also noted that the case for using CGE models to evaluate a regulation's
effects is strongest when the industry sector has strong linkages to the rest of the economy.
The report also noted that the extent to which CGE models add value to the analysis
depends on data availability. CGE models provide aggregated representations of the entire
economy and are designed to capture substitution possibilities between production,
4-15
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consumption, and trade; interactions between economic sectors; and interactions between
a policy shock and pre-existing distortions, such as taxes. However, one also needs to
adequately represent a regulation in the model to estimate its effects.
In response to the SAB's recommendations, the EPA built a new CGE model called
SAGE. A second SAB panel performed a peer review of SAGE, and the review concluded in
2020. While the EPA now has a peer-reviewed CGE model for analyzing the potential
economy-wide effects of regulations, we did not use the model in the RIA for this proposal,
but the EPA continue to be committed to the use of CGE models to evaluate the economy-
wide effects of its regulations.
Lastly, the EPA included specific types of health benefits in a CGE model for the
prospective analysis — The Benefits and Costs of the Clean Air Act from 1990 to 2020 (EPA
2011) - and demonstrated the importance of their inclusion when evaluating the economic
welfare effects of policy. However, while the external Council on Clean Air Compliance
Analysis (Council) peer review of this the EPA report (Hammitt, 2010) stated that inclusion
of benefits in an economy-wide model, specifically adapted for use in that study,
"represented] a significant step forward in benefit-cost analysis", serious technical
challenges remain when attempting to evaluate the benefits and costs of potential
regulatory actions using economy-wide models.
4-16
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4.4 References
Goulder, L., I. Parry, R. Williams, and D. Burtraw (1999). The cost-effectiveness of
alternative instruments for environmental protection in a second-best setting. Journal
of Public Economics, 72(3): 329-360.144
Goulder, L., M. Hafstead, and R. Williams III (2016). General equilibrium impacts of a
federal clean energy standard. American Economic Journal - Economic Policy 8(2):
186-218.
Hahn, R., and J. Hird (1990). The costs and benefits of regulation: review and synthesis. Yale
Journal of Regulation 8: 233-278.
Hammitt, J.K. (2010). Review of the final integrated report for the second section 812
prospective study of the benefits and costs of the clean air act. Available at:
https://council.epa.gov/ords/sab/apex_util.get_blob?s=3667362893551&a=104&c=45
494121954152467&p=18&kl=946&k2=&ck=7rX81JLu6VlfAmvI_4bpsKFHvLUkoKf4dq
oGFPV89m216Y5SNmFmlOLePlqgIxlBHlu8weBKnW7yvfsOXfV25Q&rt=IR.
Harrington, W., R.D. Morgenstern, and P. Nelson (2000). On the accuracy of regulatory cost
estimates. Journal of Policy Analysis and Management 19, 297-322.
U.S. EPA (2010). EPA Guidelines for Preparing Economic Analyses. Available at:
https://www.epa.gov/sites/default/files/2017-08/documents/ee-0568-50.pdf.
U.S. EPA (2011). The Benefits and Costs of the Clean Air Act from 1990 to 2020. Final
Report. Office of Air and Radiation, Washington, DC. Available at:
https://www.epa.gov/sites/default/files/2015-07/documents/fullreport_rev_a.pdf.
U.S. EPA (2014). Retrospective Study of the Costs of EPA Regulations: A Report of Four Case
Studies. Available at http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-
0575.pdf/$file/EE-0575.pdf.
U.S. EPA (2017a). EPA Air Pollution Control Cost Manual, Section 1, Chapter 2. Office of Air
Quality Planning and Standards, Research Triangle Park, NC. Available at
https://www.epa.gov/sites/default/files/2017-
12/documents/epaccmcostestimationmethodchapter_7thedition_2017.pdf.
U.S. EPA (2017b). SAB Advice on the Use of Economy-Wide Models in Evaluating the Social
Costs, Benefits, and Economic Impacts of Air Regulations. EPA-SAB-17-012.
U.S. EPA (2019a). CoST v3.7 User's Guide. Office of Air Quality Planning and Standards,
Research Triangle Park, NC. November 2019. Available at:
https://www.cmascenter.org/help/documentation. cfm?model=cost&version=3.7.
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U.S. EPA (2019b). Additional Methods, Determinations, and Analyses to Modify Air Quality
Data Beyond Exceptional Events. U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards, Research Triangle Park, NC, EPA-457/B-19-002.
Available: https://www.epa.gov/sites/default/files/2019-
04/documents/clarification_memo_on_data_modification_methods.pdf.
Williams III, R. (2002). Environmental tax interactions when pollution affects health or
productivity. Journal of Environmental Economics and Management 44(2): 261-270.
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APPENDIX 4A: ENGINEERING COST ANALYSIS
Overview
Chapter 4 describes the engineering cost analysis approach that EPA used to analyze
the following alternative annual and 24-hour standard levels in this regulatory impact
analysis (RIA) --10/35 ng/m3,10/30 ng/m3, 9/35 ng/m3, and 8/35 |~ig/m3. This Appendix
contains more detailed information about the estimated costs from application of controls
by area and by county for the northeast and their adjacent counties, the southeast and their
adjacent counties, the west, and California.
4A.1 Estimated Costs by County for Alternative Standard Levels
The cost estimates presented in Table 4A-1 through Table 4A-6 reflect the
engineering costs annualized at 7 percent, to the extent possible.1 When calculating the
annualized costs we would like to use the interest rates faced by firms; however, we do not
know what those rates are. As such we use 7 percent as a conservative estimate.
Table 4A-1 and Table 4A-2 present the cost estimates for the northeast counties and
their adjacent counties. Table 4A-3 and Table 4A-4 present the cost estimates for the
northeast counties and their adjacent counties. Table 4A-5 presents the cost estimates for
the counties in the west, and Table 4A-6 presents the cost estimates for the counties in
California, organized by air district.
Table 4A-1 Summary of Estimated Annual Control Costs for the Northeast (57
counties) for Alternative Primary Standard Levels of 10/35 |ig/m3,
10/30 |ig/m3,9/35 (j,g/m3, and 8/35 jig/m3 for 2032 (millions of
2017$)
County
10/35
10/30
9/35
8/35
New Castle County, DE
$0
$0
$0
$0.8
Cook County, IL
$0
$0
$2.1
$13.7
Madison County, IL
$0
$0
$0
$23.8
St. Clair County, IL
$0
$0
$0
$48.8
Allen County, IN
$0
$0
$0
$0.2
1 Because we obtain control cost data from many sources, we are not always able to obtain consistent data
across original data sources. As a result, we do not know the interest rates used to calculate costs for some
of the controls included in this analysis. If disaggregated control cost data is available (i.e., where capital,
equipment life value, and O&M costs are separated out) we can calculate costs using a specified percent
interest rate. EPA may not know the interest rates used to calculate costs when disaggregated control cost
data is unavailable (i.e., where we only have a $/ton value and where capital, equipment life value, and O&M
costs are not separated out).
4A-1
-------
County 10/35 10/30 9/35 8/35
Clark County, IN
$0
$0
$0
$1.9
Elkhart County, IN
$0
$0
$0
$9.7
Floyd County, IN
$0
$0
$0
$0.2
Lake County, IN
$0
$0
$0
$0.7
Marion County, IN
$0
$0
$31.1
$31.1
St. Joseph County, IN
$0
$0
$0
$10.0
Vanderburgh County, IN
$0
$0
$0
$7.4
Vigo County, IN
$0
$0
$0
$6.6
Jefferson County, KY
$0
$0
$0
$22.0
Baltimore city, MD
$0
$0
$0
$0.3
Howard County, MD
$0
$0
$0
$10.0
Kent County, MI
$0
$0
$0
$1.3
Wayne County, MI
$0.02
$0.02
$15.1
$15.1
Buchanan County, MO
$0
$0
$0
$0.9
Jackson County, MO
$0
$0
$0
$0.07
Jefferson County, MO
$0
$0
$0
$1.4
St. Louis city, MO
$0
$0
$0
$10.5
St. Louis County, MO
$0
$0
$0
$11.0
Camden County, NJ
$0
$0
$6.6
$6.6
Union County, NJ
$0
$0
$0
$8.3
New York County, NY
$0
$0
$0
$4.0
Butler County, OH
$0
$0
$13.3
$31.8
Cuyahoga County, OH
$0.4
$0.4
$23.5
$23.5
Franklin County, OH
$0
$0
$0
$0.5
Hamilton County, OH
$0
$0
$0
$16.9
Jefferson County, OH
$0
$0
$1.0
$1.0
Lucas County, OH
$0
$0
$0
$11.5
Mahoning County, OH
$0
$0
$0
$0.6
Stark County, OH
$0
$0
$0
$18.4
Summit County, OH
$0
$0
$0
$11.5
Allegheny County, PA
$6.8
$12.3
$60.3
$65.8
Armstrong County, PA
$0
$0
$4.1
$4.1
Beaver County, PA
$0
$0
$0
$7.5
Berks County, PA
$0
$0
$0
$0.4
Cambria County, PA
$0
$0
$0.2
$5.5
Chester County, PA
$0
$0
$0
$17.1
Dauphin County, PA
$0
$0
$0
$1.4
Delaware County, PA
$0
$0
$15.8
$15.8
Lackawanna County, PA
$0
$0
$0
$0.08
Lancaster County, PA
$0.08
$0.08
$8.1
$27.2
Lebanon County, PA
$0
$0
$0.2
$5.6
Lehigh County, PA
$0
$0
$0
$0.4
Mercer County, PA
$0
$0
$0
$6.3
Philadelphia County, PA
$0
$0
$2.2
$22.5
Washington County, PA
$0
$0
$0
$1.2
York County, PA
$0
$0
$0
$1.6
Providence County, RI
$0
$0
$0
$1.0
Davidson County, TN
$0
$0
$0
$0.4
Knox County, TN
$0
$0
$0
$1.3
4A-2
-------
County
10/35
10/30
9/35
8/35
Berkeley County, WV
$0
$0
$0
$0.5
Brooke County, WV
$0
$0
$0
$6.4
Marshall County, WV
$0
$0
$0
$6.0
Total
$7.3
$12.8
$183.5
$560.2
Table 4A-2 Summary of Estimated Annual Control Costs for Adjacent Counties in
the Northeast (75 counties) for Alternative Primary Standard Levels of
10/35 ng/m3,10/30 ng/m3, 9/35 (ig/m3, and 8/35 ng/m3 for 2032
(millions of 2017$)
County Adjacent Counties 10/35 10/30 9/35 8/35
Clinton County, IL
Madison County, IL
St Clair County, IL
$0
$0
$0
$7.1
DuPage County, IL
Cook County, IL
$0
$0
$0
$1.5
Kane County, IL
Cook County, IL
$0
$0
$0
$1.1
Lake County, IL
Cook County, IL
$0
$0
$0
$11.3
McHenry County, IL
Cook County, IL
$0
$0
$0
$0.9
Monroe County, IL
St Clair County, IL
$0
$0
$0
$6.7
Randolph County, IL
St Clair County, IL
$0
$0
$0
$4.8
Washington County, IL
St Clair County, IL
$0
$0
$0
$6.7
Will County, IL
Cook County, IL
$0
$0
$0
$11.7
Boone County, IN
Marion County, IN
$0
$0
$0.0
$3.9
Clay County, IN
Vigo County, IN
$0
$0
$0
$2.2
Gibson County, IN
Vanderburgh County, IN
$0
$0
$0
$0.2
Hamilton County, IN
Marion County, IN
$0
$0
$0.01
$11.1
Hancock County, IN
Marion County, IN
$0
$0
$0.0
$3.8
Hendricks County, IN
Marion County, IN
$0
$0
$0.02
$10.3
Johnson County, IN
Marion County, IN
$0
$0
$0.0
$5.8
LaPorte County, IN
St Joseph County, IN
$0
$0
$0
$8.9
Marshall County, IN
Elkhart County, IN
St Joseph County, IN
$0
$0
$0
$5.8
Morgan County, IN
Marion County, IN
$0
$0
$0.01
$7.4
Parke County, IN
Vigo County, IN
$0
$0
$0
$1.3
Posey County, IN
Vanderburgh County, IN
$0
$0
$0
$3.9
Shelby County, IN
Marion County, IN
$0
$0
$0.0
$10.1
Starke County, IN
St Joseph County, IN
$0
$0
$0
$1.5
Sullivan County, IN
Vigo County, IN
$0
$0
$0
$2.8
Vermillion County, IN
Vigo County, IN
$0
$0
$0
$0.06
Warrick County, IN
Vanderburgh County, IN
$0
$0
$0
$4.8
Bullitt County, KY
Jefferson County, KY
$0
$0
$0
$0.08
Hardin County, KY
Jefferson County, KY
$0
$0
$0
$0.1
Oldham County, KY
Jefferson County, KY
$0
$0
$0
$0.06
Shelby County, KY
Jefferson County, KY
$0
$0
$0
$0.07
Spencer County, KY
Jefferson County, KY
$0
$0
$0
$0.06
Montgomery County, MD
Howard County, MD
$0
$0
$0
$0.0
Macomb County, MI
Wayne County, MI
$0
$0
$0.3
$15.8
Monroe County, MI
Wayne County, MI
$0
$0
$0.9
$14.4
Oakland County, MI
Wayne County, MI
$0
$0
$0.2
$30.5
4A-3
-------
County
Adjacent Counties
10/35
10/30
9/35
8/35
Washtenaw County, MI
Wayne County, MI
$0
$0
$0.2
$14.9
Atlantic County, NJ
Camden County, NJ
$0
$0
$0.01
$6.4
Burlington County, NJ
Camden County, NJ
$0
$0
$0.02
$10.3
Essex County, NJ
Union County, NJ
$0
$0
$0
$8.0
Gloucester County, NJ
Camden County, NJ
$0
$0
$0.03
$8.1
Hudson County, NJ
Union County, NJ
$0
$0
$0
$4.3
Middlesex County, NJ
Union County, NJ
$0
$0
$0
$13.5
Morris County, NJ
Union County, NJ
$0
$0
$0
$8.7
Somerset County, NJ
Union County, NJ
$0
$0
$0
$1.5
Bronx County, NY
New York County, NY
$0
$0
$0
$5.3
Kings County, NY
New York County, NY
$0
$0
$0
$10.4
Queens County, NY
New York County, NY
$0
$0
$0
$12.6
Belmont County, OH
Jefferson County, OH
$0
$0
$0.7
$7.1
Carroll County, OH
Jefferson County, OH
$0
$0
$0.3
$4.7
Stark County, OH
Clermont County, OH
Hamilton County, OH
$0
$0
$0
$9.2
Columbiana County, OH
Jefferson County, OH
$0
$0
$1.6
$7.6
Mahoning County, OH
Stark County, OH
Geauga County, OH
Cuyahoga County, OH
$0
$0
$0.01
$10.8
Summit County, OH
Harrison County, OH
Jefferson County, OH
$0
$0
$0.05
$9.1
Lake County, OH
Cuyahoga County, OH
$0
$0
$0.01
$6.0
Lorain County, OH
Cuyahoga County, OH
$0
$0
$0.6
$11.8
Medina County, OH
Cuyahoga County, OH
$0
$0
$0.01
$13.0
Summit County, OH
Montgomery County, OH
Butler County, OH
$0
$0
$0
$14.2
Portage County, OH
Cuyahoga County, OH
$0
$0
$0.04
$11.7
Mahoning County, OH
Stark County, OH
Summit County, OH
Preble County, OH
Butler County, OH
$0
$0
$0
$5.9
Warren County, OH
Butler County, OH
$0
$0
$0
$9.6
Hamilton County, OH
Bedford County, PA
Cambria County, PA
$0
$0
$0
$4.9
Blair County, PA
Cambria County, PA
$0
$0
$0
$8.3
Bucks County, PA
Lehigh County, PA
$0
$0
$0
$14.1
Philadelphia County, PA
Butler County, PA
Allegheny County, PA
$0
$0
$0.03
$14.5
Armstrong County, PA
Beaver County, PA
Mercer County, PA
Clarion County, PA
Armstrong County, PA
$0
$0
$0.0
$3.4
Clearfield County, PA
Cambria County, PA
$0
$0
$0
$5.6
Indiana County, PA
Armstrong County, PA
$0
$0
$0.06
$6.8
Cambria County, PA
Jefferson County, PA
Armstrong County, PA
$0
$0
$0.0
$6.9
4A-4
-------
County
Adjacent Counties
10/35
10/30
9/35
8/35
Montgomery County, PA
Berks County, PA
Chester County, PA
Delaware County, PA
Lehigh County, PA
Philadelphia County, PA
$0
$0
$17.2
$17.2
Schuylkill County, PA
Berks County, PA
Dauphin County, PA
Lebanon County, PA
Lehigh County, PA
$0
$0
$0
$7.2
Somerset County, PA
Cambria County, PA
$0
$0
$0
$5.2
Westmoreland County, PA
Allegheny County, PA
Armstrong County, PA
Cambria County, PA
Washington County, PA
$0
$0
$0.03
$17.4
Hancock County, WV
Brooke County, WV
$0
$0
$0
$0.9
Ohio County, WV
Brooke County, WV
Marshall County, WV
$0
$0
$0
$4.4
Wetzel County, WV
Marshall County, WV
$0
$0
$0
$1.4
Total
$0
$0
$22.3
$539.7
Table 4A-3 Summary of Estimated Annual Control Costs for the Southeast (35
counties) for Alternative Primary Standard Levels of 10/35 |ig/m3,
10/30 ng/m3,9/35 (ig/m3, and 8/35 (ig/m3 for 2032 (millions of
2017$)
County 10/35 10/30 9/35 8/35
Jefferson County, AL
$0
$0
$0.7
$4.5
Talladega County, AL
$0
$0
$0
$0.3
Pulaski County, AR
$0
$0
$0
$12.8
Union County, AR
$0
$0
$0
$0.3
District of Columbia
$0
$0
$0
$7.1
Bibb County, GA
$0
$0
$0
$7.8
Clayton County, GA
$0
$0
$0
$5.4
Cobb County, GA
$0
$0
$0
$0.3
DeKalb County, GA
$0
$0
$0
$0.3
Dougherty County, GA
$0
$0
$0
$2.4
Floyd County, GA
$0
$0
$0
$15.4
Fulton County, GA
$0
$0
$3.1
$29.9
Gwinnett County, GA
$0
$0
$0
$0.1
Muscogee County, GA
$0
$0
$0
$8.5
Richmond County, GA
$0
$0
$0
$5.6
Wilkinson County, GA
$0
$0
$0
$14.0
Wyandotte County, KS
$0
$0
$0
$0.2
Caddo Parish, LA
$0
$0
$2.9
$16.7
East Baton Rouge Parish, LA
$0
$0
$0
$2.9
Iberville Parish, LA
$0
$0
$0
$0.02
St. Bernard Parish, LA
$0
$0
$0
$0.9
West Baton Rouge Parish, LA
$0
$0
$0
$11.7
4A-5
-------
County
10/35
10/30
9/35
8/35
Hinds County, MS
$0
$0
$0
$0.2
Davidson County, NC
$0
$0
$0
$3.3
Mecklenburg County, NC
$0
$0
$0
$0.5
Wake County, NC
$0
$0
$0
$0.3
Tulsa County, OK
$0
$0
$0
$0.4
Greenville County, SC
$0
$0
$0
$0.6
Cameron County, TX
$0
$0
$11.2
$11.2
Dallas County, TX
$0
$0
$0
$0.2
El Paso County, TX
$0
$0
$0.2
$4.7
Harris County, TX
$1.4
$1.4
$5.5
$25.0
Hidalgo County, TX
$2.7
$2.7
$26.6
$26.6
Nueces County, TX
$0
$0
$0
$25.4
Travis County, TX
$0
$0
$0.2
$4.9
Total
$4.1
$4.1
$50.4
$250.6
Table 4A-4 Summary of Estimated Annual Control Costs for Adjacent Counties in
the Southeast (32 counties) for Alternative Primary Standard Levels of
10/35 |ig/m3,10/30 |ig/m3, 9/35 (j,g/m3, and 8/35 |ig/m3 for 2032
(millions of 2017$)
County Adjacent Counties 10/35 10/30 9/35 8/35
Bartow County, GA
Cobb County, GA
$0
$0
$0
$8.4
Floyd County, GA
Carroll County, GA
Fulton County, GA
$0
$0
$0
$11.1
Chattahoochee County, GA
Muscogee County, GA
$0
$0
$0
$1.9
Chattooga County, GA
Floyd County, GA
$0
$0
$0
$4.0
Cherokee County, GA
Cobb County, GA
$0
$0
$0
$10.3
Fulton County, GA
Coweta County, GA
Fulton County, GA
$0
$0
$0
$7.8
Crawford County, GA
Bibb County, GA
$0
$0
$0
$3.0
Douglas County, GA
Cobb County, GA
$0
$0
$0
$5.0
Fulton County, GA
Fayette County, GA
Clayton County, GA
$0
$0
$0
$4.1
Fulton County, GA
Forsyth County, GA
Fulton County, GA
$0
$0
$0
$6.8
Gwinnett County, GA
Gordon County, GA
Floyd County, GA
$0
$0
$0
$5.3
Harris County, GA
Muscogee County, GA
$0
$0
$0
$7.0
Henry County, GA
Clayton County, GA
$0
$0
$0
$5.9
DeKalb County, GA
Houston County, GA
Bibb County, GA
$0
$0
$0
$11.0
Jones County, GA
Bibb County, GA
$0
$0
$0
$6.1
Wilkinson County, GA
Monroe County, GA
Bibb County, GA
$0
$0
$0
$6.1
Polk County, GA
Floyd County, GA
$0
$0
$0
$4.1
Spalding County, GA
Clayton County, GA
$0
$0
$0
$4.0
Talbot County, GA
Muscogee County, GA
$0
$0
$0
$2.9
4A-6
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County
Adjacent Counties
10/35
10/30
9/35
8/35
Twiggs County, GA
Bibb County, GA
$0
$0
$0
$5.9
Wilkinson County, GA
Walker County, GA
Floyd County, GA
$0
$0
$0
$4.8
Bossier Parish, LA
Caddo Parish, LA
$0
$0
$0
$8.0
De Soto Parish, LA
Caddo Parish, LA
$0
$0
$0
$8.2
East Feliciana Parish, LA
East Baton Rouge Parish, LA
$0
$0
$0
$4.2
West Baton Rouge Parish, LA
Pointe Coupee Parish, LA
Iberville Parish, LA
$0
$0
$0
$5.9
West Baton Rouge Parish, LA
Red River Parish, LA
Caddo Parish, LA
$0
$0
$0
$5.4
West Feliciana Parish, LA
West Baton Rouge Parish, LA
$0
$0
$0
$7.8
Brooks County, TX
Hidalgo County, TX
$0
$0
$6.6
$6.6
Hudspeth County, TX
El Paso County, TX
$0
$0
$0
$3.3
Kenedy County, TX
Hidalgo County, TX
$0
$0
$4.3
$4.3
Starr County, TX
Hidalgo County, TX
$0
$0
$5.1
$5.1
Willacy County, TX
Cameron County, TX
$0
$0
$2.3
$2.3
Hidalgo County, TX
Total
$0
$0
$18.2
$186.5
Table 4A-5 Summary of Estimated Annual Control Costs for the West (36
counties) for Alternative Primary Standard Levels of 10/35 |ig/m3,
10/30 ng/m3,9/35 (ig/m3, and 8/35 (ig/m3 for 2032 (millions of
2017$)
County 10/35 10/30 9/35 8/35
Maricopa County, AZ
$0
$0
$1.4
$7.4
Pinal County, AZ
$0
$1.0
$0
$0.3
Santa Cruz County, AZ
$0
$0
$0
$0.09
Denver County, CO
$0
$0
$0
$2.2
Weld County, CO
$0
$0
$0
$0.05
Benewah County, ID
$0
$12.1
$12.1
$12.1
Canyon County, ID
$0
$1.1
$0
$9.7
Lemhi County, ID
$0
$0
$0
$0
Shoshone County, ID
$0
$0
$0
$0
Lewis and Clark County, MT
$0
$3.1
$0
$0
Lincoln County, MT
$19.0
$19.0
$19.0
$19.0
Missoula County, MT
$0
$0
$1.1
$15.8
Ravalli County, MT
$0
$3.0
$0
$0.1
Silver Bow County, MT
$0
$0.03
$0
$7.8
Douglas County, NE
$0
$0
$0
$0.02
Sarpy County, NE
$0
$0
$0
$0.1
Dona Ana County, NM
$0
$0
$0
$6.9
Clark County, NV
$0
$0
$0.3
$5.5
Crook County, OR
$0
$19.3
$0
$5.4
Harney County, OR
$0
$1.5
$0
$13.4
Jackson County, OR
$0
$0
$0.3
$8.4
Klamath County, OR
$0
$0.5
$0
$6.2
Lake County, OR
$0
$0
$0
$0
4A-7
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County
10/35
10/30
9/35
8/35
Lane County, OR
$0
$0
$0
$0.04
Box Elder County, UT
$0
$14.6
$0
$0
Cache County, UT
$0
$22.0
$0
$0
Davis County, UT
$0
$7.1
$0
$0
Salt Lake County, UT
$0
$16.0
$0
$0
Utah County, UT
$0
$12.3
$0
$0
Weber County, UT
$0
$3.2
$0
$0
King County, WA
$0
$0
$0
$0.8
Kittitas County, WA
$0
$0
$0
$0
Okanogan County, WA
$0
$13.3
$0
$0
Snohomish County, WA
$0
$0.7
$0
$0
Spokane County, WA
$0
$0
$0
$0.4
Yakima County, WA
$0
$0
$0
$0
Total
$19.0
$150.0
$34.2
$121.8
Table 4A-6 Summary of Estimated Annual Control Costs for California (26
counties) for Alternative Primary Standard Levels of 10/35 (ig/m3,
10/30 ng/m3,9/35 (ig/m3, and 8/35 (ig/m3 for 2032 (millions of
2017$)
County
Air District
10/35
10/30
9/35
8/35
Alameda County, CA
Bay Area AQMD
$0.03
$0.03
$2.0
$14.5
Contra Costa County, CA
Bay Area AQMD
$0
$0
$0.04
$0.4
Marin County, CA
Bay Area AQMD
$0
$0
$0
$0.05
Napa County, CA
Bay Area AQMD
$0.2
$0.2
$1.7
$1.7
Santa Clara County, CA
Bay Area AQMD
$0
$0
$1.0
$16.8
Solano County, CA
Bay Area AQMD
$0
$0
$0
$6.7
Butte County, CA
Butte County AQMD
$0
$0
$0
$0.3
Sutter County, CA
Feather River AQMD
$0
$0
$0
$3.6
Imperial County, CA
Imperial County APCD
$0
$0
$0
$0
Plumas County, CA
Northern Sierra AQMD
$0
$0
$0
$0
Sacramento County, CA
Sacramento Metro AQMD
$0
$0.2
$0.4
$2.3
San Diego County, CA
San Diego County APCD
$0
$0
$0.7
$30.3
Fresno County, CA
San Joaquin Valley APCD
$30.1
$30.1
$30.1
$30.1
Kern County, CA
San Joaquin Valley APCD
$0
$0
$0
$0
Kings County, CA
San Joaquin Valley APCD
$0
$0
$0
$0
Madera County, CA
San Joaquin Valley APCD
$9.1
$9.1
$9.1
$9.1
Merced County, CA
San Joaquin Valley APCD
$9.0
$9.0
$9.0
$9.0
San Joaquin County, CA
San Joaquin Valley APCD
$0.03
$0.03
$11.9
$11.9
Stanislaus County, CA
San Joaquin Valley APCD
$2.9
$2.9
$2.9
$2.9
Tulare County, CA
San Joaquin Valley APCD
$0
$0
$0
$0
San Luis Obispo County, CA
San Luis Obispo County APCD
$0
$0
$1.2
$1.2
Siskiyou County, CA
Siskiyou County APCD
$0
$16.9
$0
$0
Los Angeles County, CA
South Coast AQMD
$12.9
$12.9
$12.9
$12.9
Riverside County, CA
South Coast AQMD
$0
$0
$0
$0
San Bernardino County, CA
South Coast AQMD
$0
$0
$0
$0
Ventura County, CA
Ventura County APCD
$0
$9.1
$1.8
$9.1
Total
$64.1
$90.4
$84.7
$162.9
4A-8
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CHAPTER 5: BENEFITS ANALYSIS APPROACH AND RESULTS
Overview
This chapter presents the estimated human health-related and welfare benefits of
meeting the proposed National Ambient Air Quality Standards (NAAQS) for particulate
matter (PM). In this Regulatory Impact Analysis (RIA), we are analyzing the proposed
annual and current 24-hour alternative standard levels of 10/35 |~ig/m3 and 9/35 ng/m3, as
well as the following two more stringent alternative standard levels: (1) an alternative
annual standard level of 8 |~ig/m3 in combination with the current 24-hour standard (i.e.,
8/35 |j,g/m3), and (2) an alternative 24-hour standard level of 30 ng/m3 in combination
with the proposed annual standard level of 10 ng/m3 (i.e., 10/30 |j,g/m3). We quantify the
number and economic value of the estimated avoided premature deaths and illnesses
attributable to applying hypothetical national control strategies for the more stringent
annual PM2.5 NAAQS standards with a sensitivity analysis for a more stringent 24-hour
standard that reduces fine particulate matter (PM2.5) concentrations in 2032. Reducing
directly emitted PM2.5 and PM2.5 precursor emissions would also improve environmental
quality (U.S. EPA, 2019c, U.S. EPA, 2022a) and reduce the ecological effects of nitrogen and
sulfur deposition. Because the EPA is proposing that the current secondary PM NAAQS
standards be retained, we did not evaluate alternative secondary standard levels in this
RIA, or any visibility-, climate change-, or materials-damage-related benefits of the
proposed rule (Cox, 2019, U.S. EPA, 2019c).
The analysis in this chapter aims to characterize the benefits of the air quality
changes resulting from the implementation of revised PM standard levels by answering
two key questions:
1. What is the estimated number and geographic distribution of avoided PM2.5-
attributable premature deaths and illnesses expected to result from applying
hypothetical national control strategies for a more stringent PM2.5 NAAQS? This
chapter presents these results. As discussed in Chapter 3, Section 3.2.5, the
estimated PM2.5 emissions reductions from control applications do not fully
account for all the emissions reductions needed to reach the proposed and more
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stringent alternative standard levels in some counties in the northeast,
southeast, west, and California. In Chapter 2, Section 2.4 and Chapter 3, Section
3.2.6, we discuss the remaining air quality challenges for areas in the northeast
and southeast, as well as in the west and California for the proposed alternative
standard levels of 10/35 |~ig/m3 and 9/35 |~ig/m3.
2. What is the estimated number and geographic distribution of avoided PM2.5-
attributable premature deaths and illnesses expected to result if we assume that
areas identify all of the controls needed for compliance with the proposed and
alternative PM2.5 NAAQS? Appendix 5A presents these results.
3. What is the estimated economic value of these avoided impacts?
To answer these questions we perform a human health benefits analysis (NRC,
2002). Starting first with the Integrated Science Assessment (ISA) for Particulate Matter
(U.S. EPA, 2019b) and the Supplement to the ISA for Particulate Matter (U.S. EPA, 2022a),
we identify the human health effects associated with ambient particles, which include
premature death and a variety of morbidity effects associated with acute (hours-long) and
chronic (months- or years- long) exposures. Table 5-2 summarizes human health
categories monetized and reflected in the total value of the benefits reported and those
categories not monetized due to limited data or resources. The list of benefits categories is
neither exhaustive nor completely quantified. We excluded effects not identified as having
a causal or likely to be causal relationship with the affected pollutants in the most recent
PM ISA (U.S. EPA, 2019b,U.S. EPA, 2022a). In a Technical Support Document (TSD)
accompanying this RIA we specify in detail our approach for identifying, selecting, and
parametrizing concentration-response relationships and economic unit values to support
this benefits analysis. Below in Section 5.1 we summarize this information for readers,
describing how we updated our methods for quantifying the number and value of PM-
related benefits to reflect the information reported in the PM ISA and supplement to the PM
ISA.
This chapter contains a subset of the estimated health benefits of the proposed and
alternative PM2.5 standard levels in 2032 that EPA was able to quantify, given available
5-2
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resources and methods. This benefits analysis relies on an array of data inputs—including
air quality modeling, health impact functions and valuation estimates—which are
themselves subject to uncertainty and may also in turn contribute to the overall
uncertainty in this analysis. We employ several techniques to characterize this uncertainty,
which are described in detail in section 5.5.
As described in Chapter 1, the analytical objectives of the NAAQS RIA are unique as
compared to other RIAs, such as the recent Revised Cross-State Air Pollution Rule Update
(U.S. EPA, 2020c). The NAAQS RIAs illustrate the potential costs and benefits of attaining
one or more revised air quality standard(s) nationwide; these estimated costs and benefits
are estimated after we first assume the current standards have been attained. In this RIA,
we illustrate the potential costs and benefits for the proposed and more stringent
alternative standard levels nationwide. The NAAQS RIAs hypothesize the control strategies
that States may choose to enact when implementing a revised NAAQS, but they cannot do
so with perfect foresight; individual states will formulate air quality management plans
whose mix of emissions controls may differ substantially from those we simulate here.
Hence, NAAQS RIAs are illustrative. The benefits and costs estimated in a NAAQS RIA are
not intended to be added to the costs and benefits of other regulations that result in
specific costs of control and emissions reductions. By contrast, EPA is generally confident
in the emissions projected to be reduced from rules affecting specific and well-
characterized sources—such as mobile and Electric Generating Units (U.S. EPA, 2019a).
Hence, the emissions reduced by final rules affecting such sources are accounted for when
simulating attainment with alternative NAAQS.
In the following sections of this chapter, we estimate health benefits occurring as an
increment to a 2032 baseline in which the nation fully attains the current primary PM2.5
standards (i.e., an annual standard of 12 |ig/m3 and a 24-hour standard of 35 |ig/m3,
hereafter referred to as "12/35"). This baseline accounts for: (1) promulgated regulations
(Chapter 1, Section 1.3.); and (2) any additional illustrative emissions reductions needed to
simulate attainment with 12/35 (Chapter 3, Section 3.1). We project PM2.5 levels in 2032 in
certain areas would exceed 10/35,10/30, 9/35 and 8/35, even after illustrative controls
applied to simulate attainment with 12/35 and estimate emissions reductions needed to
5-3
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attain the alternative standard levels (Chapter 3, Table 3-2). Table 5-1 summarizes the total
national monetized benefits resulting from applying the control strategies in 2032. Because
the analyses in the RIA are national-level assessments and the ambient air quality issues
are complex and local in nature, we do not currently have sufficiently detailed local
information for the areas being analyzed, including local inventory information on
emissions sources, higher resolution air quality modeling, and local information on
emissions controls to estimate the control measures or strategies that might result in
meeting the range of revised annual and 24-hour alternative standard levels in the
proposal.
Whereas the main analysis in this chapter presents the benefits of the applied
control strategies for the standards levels (Table 5-5 through Table 5-9), in Appendix 5A,
we present the potential health and monetized benefits of full compliance with the
alternative standard levels; the tables in Appendix 5A present potential health benefits
regardless of whether the technology or control measures to achieve them is currently
available or whether an agency submits information on cross-border transport or wildfire
influence on projected PM2.5 DVs that could potentially qualify for exclusion as atypical,
extreme, or unrepresentative events, potentially affecting the amount of any additional
control needed. The estimates reflect the value of the avoided PIVh.s-attributable deaths and
the value of morbidity impacts, including, for example, hospital admissions and emergency
department visits for cardiovascular and respiratory health issues.
5-4
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Table 5-1 Estimated Monetized Benefits of the Applied Control Strategies for the
Proposed and Alternative Combinations of Primary PM2.5 Standard
Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)
ft r l*g/m3 annual & 10 jig/m3 annual & 9 jig/m3 annual & 8 ng/m3 annual &
Benefits Estimate 3 5 ng/m3 24-hour 30 iig/m3 24-hour 35 ng/m3 24-hour 35 ng/m3 24-hour
Economic value of avoided PIVh.s-related morbidities and premature deaths using PM2.5 mortality
estimate from Pope III et al., 2019
3% discount $17+ B $20+ B $43 + B $95 + B
rate
7% discount $16+ B $18 + B $39 + B $86 + B
rate
Economic value of avoided PIVh.s-related morbidities and premature deaths using PM2.5 mortality
estimate from Wu et al., 2020
3% discount $8.5 + B $9.6 + B $21 + B $46 + B
rate
7% discount $7.6+ B $8.6 + B $19 + B $41 + B
rate
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized
benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not possible to
quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and
welfare benefits.
Because the method used in this analysis to simulate the control strategies does not
also simulate changes in ambient concentrations of other pollutants, we were not able to
quantify the additional benefits associated with reduced exposure to other pollutants. We
also did not estimate the additional benefits from improvements in welfare effects, such as
climate effects, ecosystem effects, and visibility (Cox, 2019, U.S. EPA, 2019c). With regard to
potential climate benefits, we note that because the available evidence suggests direct PM
control measures will be most effective in reducing ambient PM2.5 concentrations, and
because we lack information on the CCh-related emissions changes that may result from
such measures, we do not quantitatively estimate C02-related climate benefits in this RIA.
5.1 Updated Methodology Presented in the RIA
In 2021, EPA published a TSD titled "Estimating PM2.5- and Ozone-Attributable
Health Benefits" that accompanied the RIA for the Revised Cross-State Air Pollution Rule
Update (U.S. EPA, 2021). As noted above, that TSD described the EPA's approach for
quantifying the number and value of air pollution-related premature deaths and illnesses.
Since publishing the Revised Cross-State Air Pollution Rule Update TSD, the EPA released a
Supplement to the PM ISA (U.S. EPA, 2022a). EPA evaluated the new evidence reported in
the Supplement to the PM ISA and revised the TSD accordingly; this process is described in
5-5
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detail within the TSD. The updated TSD will be published as a new document alongside this
RIA. Key changes from the most recent version of the TSD are summarized below:
1. Incorporated alternative long-term exposure mortality studies. We selected a
hazard ratio from an analysis of the National Health Interview Survey (NHIS)
(Pope III et al., 2019). Compared to the American Cancer Society study it
replaces (Turner et al., 2016), the NHIS cohort reflects more recent years of
PM2.5 concentrations and produces a larger number of estimated PM-
attributable deaths. We also selected a hazard ratio from an extended
analysis of the Medicare cohort (Wu et al., 2020). Compared to the study it
replaces (Di et al., 2017), the Wu et al., 2020 analysis includes additional, and
more current, years of PM2.5 concentrations and more person-time; this
newer study produces a similar number of estimated PM-attributable deaths.
We elaborate on our rationale for these choices in section 5.3.3.1 of the TSD.
2. Altered our approach for estimating counts of Acute Myocardial Infarctions.
We selected a risk estimate from an analysis of the Medicare cohort (Wei et
al., 2019), in which the authors performed a case-crossover analysis of over
95 million Medicare inpatient hospital claims from 2000-2012. The risk
estimate from this study replaces a pooled estimate of single- and multi-city
studies that accounted for a smaller population, more limited geographic
coverage and less recent PM2.5 concentrations; that latter approach yielded a
range of estimated non-fatal heart attacks whose upper bound was
significantly larger than the estimate reported in this RIA.
5.2 Human Health Benefits Analysis Methods
We estimate the quantity and economic value of air pollution-related effects using a
"damage-function." This approach quantifies counts of air pollution-attributable cases of
adverse health outcomes and assigns dollar values to those counts, while assuming that
each outcome is independent of one another. We construct this damage function 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
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endpoints to quantify; (2) calculating counts of air pollution effects using a health impact
function; (3) calculating the economic value of the health impacts.
5.2.1 Selecting Air Pollution Health Endpoints to Quantify
As a first step in quantifying PIVh.s-related human health impacts, the Agency
consults the most recent PM ISA and the Supplement to the ISA for Particulate Matter (U.S.
EPA, 2019b, U.S. EPA, 2022a). This document synthesizes the toxicological, clinical and
epidemiological evidence to determine whether PM is causally related to an array of
adverse human health outcomes associated with either acute (i.e., hours or days-long) or
chronic (i.e., years-long) exposure; for each outcome, the ISA reports this relationship to be
causal, likely to be causal, suggestive of a causal relationship, inadequate to infer a causal
relationship or not likely to be a causal relationship. Historically, the Agency estimates the
incidence of air pollution effects for those health endpoints that the ISA classified as either
causal or likely-to-be-causal.
Consistent with economic theory, the willingness-to-pay (WTP) for reductions in
exposure to environmental hazard will depend on the expected impact of those reductions
on human health and other outcomes. All else equal, WTP is expected to be higher when
there is stronger evidence of a causal relationship between exposure to the contaminant
and changes in a health outcome (McGartland et al., 2017). For example, in the case where
there is no evidence of a potential relationship the WTP would be expected to be zero and
the effect should be excluded from the analysis. Alternatively, when there is some evidence
of a relationship between exposure and the health outcome, but that evidence is
insufficient to definitively conclude that there is a causal relationship, individuals may have
a positive WTP for a reduction in exposure to that hazard (U.S. EPA-SAB, 2020; Kivi and
Shogren, 2010). Lastly, the WTP for reductions in exposure to pollutants with strong
evidence of a relationship between exposure and effect are likely positive and larger than
for endpoints where evidence is weak, all else equal. Unfortunately, the economic literature
currently lacks a settled approach for accounting for how WTP may vary with uncertainty
about causal relationships.
Given this challenge, the Agency draws its assessment of the strength of evidence on
the relationship between exposure to PM2.5 and potential health endpoints from the ISAs
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that are developed for the NAAQS process as discussed above. The focus on categories
identified as having a "causal" or "likely to be causal" relationship with the pollutant of
interest is to estimate the pollutant-attributable human health benefits in which we are
most confident. All else equal, this approach may underestimate the benefits of PM2.5
exposure reductions as individuals may be willing to pay to avoid specific risks where the
evidence is insufficient to conclude they are "likely to be caus[ed]" by exposure to these
pollutants.6 At the same time, WTP may be lower for those health outcomes for which
causality has not been definitively established. This approach treats relationships with ISA
causality determinations of "likely to be causal" as if they were known to be causal, and
therefore benefits could be overestimated. Table 5-2 reports the effects we quantified and
those we did not quantify in this RIA. The list of benefit categories not quantified is not
exhaustive. The table below omits welfare effects such as acidification and nutrient
enrichment.
5-8
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Table 5-2 Human Health Effects of Pollutants Potentially Affected by Attainment
of the Primary PM2.5 NAAQS
Pollutant
Effect (age)
Effect
Quantified
Effect
Monetized
More
Information
Adult premature mortality based on cohort study
estimates [>17 or >64]
~
~
PM ISA
Infant mortality (<1]
~
~
PM ISA
Non-fatal heart attacks (>18]
~
V
PM ISA
Hospital admissions - cardiovascular (all]
~
V
PM ISA
Hospital admissions - respiratory (<19 and >64]
~
V
PM ISA
Hospital admissions - Alzheimer's disease (>64]2
~
V
PM ISA
Hospital admissions - Parkinson's disease (>64]2
~
V
PM ISA
Emergency department visits - cardiovascular (all]
~
V
PM ISA
Emergency department visits - respiratory (all]
~
~
PM ISA
Emergency hospital admissions (>65]
~
~
PM ISA
Non-fatal lung cancer (>29]2
~
~
PM ISA
Out-of-hospital cardiac arrest (all]2
~
—
PM ISA
Stroke incidence (50-79]2
~
V
PM ISA
PM2.5
New onset asthma (<12]2
~
~
PM ISA
Exacerbated asthma - albuterol inhaler use (asthmatics,
6-13]
~
~
I'M ISA
Lost work days (18-64]
~
~
PM ISA
Minor restricted-activity days (18-64]
~
—
PM ISA
Other cardiovascular effects (e.g., doctor's visits,
prescription medication]
—
—
PM ISA1
Other respiratory effects (e.g., pulmonary function, other
ages]
—
—
PM ISA1
Other cancer effects (e.g., mutagenicity, genotoxicity]
—
—
PM ISA1
Other nervous system effects (e.g., dementia]
—
—
PM ISA1
Metabolic effects (e.g., diabetes, metabolic syndrome]
—
—
PM ISA1
Reproductive and developmental effects (e.g., low birth
weight, pre-term births]
—
PM ISA1
1 We assess these benefits qualitatively due to epidemiological or economic data limitations.
2 Quantified endpoints have been added since the 2021 version of the Estimating PM2.5- and Ozone-Attributable Health
Benefits TSD. Full details of the updates can be found in the TSD published alongside this RIA.
5.2.2 Calculating Counts of Air Pollution Effects Using the Health Impact
Function
We use the environmental Benefits Mapping and Analysis Program—Community
Edition (BenMAP-CE) software program to quantify counts of premature deaths and
illnesses attributable to photochemical modeled changes in annual mean PM2.5 for the year
2032 using a health impact function (Sacks et al., 2018).1 A health impact function
1 The 2032 air quality modeling surface input files, configuration files and BenMAP script to produce the
health benefits analyses in Chapters 5 and Appendix 5A are available upon request.
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combines information regarding: the concentration-response relationship between air
quality changes and the risk of a given adverse outcome; the population exposed to the air
quality change; the baseline rate of death or disease in that population; and, the air
pollution concentration to which the population is exposed.
The following provides an example of a PM2.5 mortality risk health impact function.
We estimate counts of PIVh.s-related total deaths (yij) during each year i (i=2032) among
adults aged 18 and older (a) in each county in the contiguous U.S. j (j=l,...,J where J is the
total number of counties) as
yij= Eayija
yija = moija x(eP ACij-l) x Pija, Eq[l]
where moija is the baseline total mortality rate for adults aged a= 18-99 in county j in
year i stratified in 10-year age groups, (3 is the risk coefficient for total mortality for adults
associated with annual average PM2.5 exposure, Cij is the annual mean PM2.5 concentration
in county j in year i, and Pija is the number of county adult residents aged a= 18-99 in county
j in year i stratified into 5-year age groups.2
To assess economic value in a damage-function framework, the changes in
environmental quality must be translated into effects on people or on the things that
people value. In some cases, the changes in environmental quality can be directly valued. In
other cases, such as for changes in ozone and PM, a health and welfare impact analysis
must first be conducted to convert air quality changes into effects that can be assigned
dollar values. For the purposes of this RIA, the health impacts analysis is limited to those
health effects that are directly and specifically linked to PM2.5.
We note at the outset that EPA rarely has the time or resources to perform extensive
new research to measure directly either the health outcomes or their values for regulatory
2 In this illustrative example, the air quality is resolved at the county level. For this RIA, we simulate air
quality concentrations at 12km by 12km grids. The BenMAP-CE tool assigns the rates of baseline death and
disease stored at the county level to the 12km by 12km grid cells using an area-weighted algorithm. This
approach is described in greater detail in the appendices to the BenMAP-CE user manual appendices (U.S.
EPA, 2022b).
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analyses. Thus, similar to Kiinzli et al., 2000 and other, more recent health impact analyses,
our estimates are based on the best available methods of benefits transfer.
5.2.3 Calculating the Economic Valuation of Health Impacts
After quantifying the change in adverse health impacts, the final step is to estimate
the economic value of these avoided impacts. The appropriate economic value for a change
in a health effect depends on whether the health effect is viewed ex ante (before the effect
has occurred) or ex post (after the effect has occurred). Reductions in ambient
concentrations of air pollution generally lower the risk of future adverse health effects by a
small amount for a large population. The appropriate economic measure is therefore ex
ante WTP for changes in risk. However, epidemiological studies generally provide
estimates of the relative risks of a particular health effect avoided due to a reduction in air
pollution. A convenient way to use these data in a consistent framework is to convert
probabilities to units of avoided statistical incidences. This measure is calculated by
dividing individual WTP for a risk reduction by the related observed change in risk. For
example, suppose a regulation reduces the risk of premature mortality from 2 in 10,000 to
1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100,
then the WTP for an avoided statistical premature mortality amounts to $1 million
($100/0.0001 change in risk). The same type of calculation can produce values for
statistical incidences of other health endpoints.
For some health effects, such as hospital admissions, WTP estimates are generally
not available. In these cases, we instead use the cost of treating or mitigating the effect to
economically value the health impact. For example, for the valuation of hospital admissions
we use the avoided medical costs as an estimate of the value of avoiding the health effects
causing the admission. These cost-of-illness (COI) estimates generally (although not in
every case) understate the true value of reductions in risk of a health effect. They tend to
reflect the direct expenditures related to treatment but not the value of avoided pain and
suffering from the health effect.
5.3 Benefits Analysis Data Inputs
In Figure 5-1, we summarize the key data inputs to the health impact and economic
valuation estimates, which were calculated using BenMAP-CE model version 1.5.1 (Sacks et
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al., 2018). In the sections below we summarize the data sources for each of these inputs,
including demographic projections, incidence and prevalence rates, effect coefficients, and
economic valuation. We indicate where we have updated key data inputs since the benefits
analysis conducted for the Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2020c).
Census
Population
Data
Modeled
Baseline and
Post-Control
Ambient PM2.5
2032
Population
Projections
PM2.5 Incremental
Air Quality Change
Woods & Poole
Population
Projections
PM2.5 Health
Functions
PM2.5-Related
Health Impacts
Background
Incidence and
Prevalence Rates
Economic
Valuation
Functions
Monetized PM2.5-
related Benefits
Blue identifies a user-selected input within the BenMAP model
Green identifies a data input generated outside of the BenMAP
Figure 5-1 Data Inputs and Outputs for the BenMAP-CE Model
5.3.1 Demographic Data
Quantified and monetized human health impacts depend on the demographic
characteristics of the population, including age, location, and income. We use projections
based on economic forecasting models developed by Woods & Poole, Inc. (Woods & Poole,
2015). The Woods & Poole database contains county-level projections of population by age,
sex, and race out to 2060, relative to a baseline using the 2010 Census data. Projections in
each county are determined simultaneously with every other county in the U.S. to consider
patterns of economic growth and migration. The sum of growth in county-level populations
is constrained to equal a previously determined national population growth, based on
Bureau of Census estimates (Hollmann et al., 2000). According to Woods & Poole, linking
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county-level growth projections together and constraining the projected population to a
national-level total growth avoids potential errors introduced by forecasting each county
independently (for example, the projected sum of county-level populations cannot exceed
the national total). County projections are developed in a four-stage process:
• First, national-level variables such as income, employment, and populations are
forecasted.
• Second, employment projections are made for 179 economic areas defined by the
Bureau of Economic Analysis (U.S. BEA, 2004)3, using an "export-base" approach,
which relies on linking industrial-sector production of non-locally consumed
production items, such as outputs from mining, agriculture, and manufacturing with
the national economy. The export-based approach requires estimation of demand
equations or calculation of historical growth rates for output and employment by
sector.
• Third, population is projected for each economic area based on net migration rates
derived from employment opportunities and following a cohort-component method
based on fertility and mortality in each area.
• Fourth, employment and population projections are repeated for counties, using the
economic region totals as bounds. The age, sex, and race distributions for each
region or county are determined by aging the population by single year by sex and
race for each year through 2060 based on historical rates of mortality, fertility, and
migration.
5.3.2 Baseline Incidence and Prevalence Estimates
Epidemiological studies of the association between pollution levels and adverse
health effects generally provide a direct estimate of the relationship of air quality changes
to the relative risk of a health effect, rather than estimating the absolute number of avoided
cases. For example, a typical result might be that a 5 |ig/m3 decrease in daily PM2.5 levels is
associated with a decrease in hospital admissions of 3%. A baseline incidence rate,
3 According to the Bureau of Economic Analysis (BEA) website, due to the impact of sequestration and
reduced FY 2013 funding levels, statistics will not be updated or made available after November 21, 2013.
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necessary to convert this relative change into a number of cases, is the estimate of the
number of cases of the health effect per year in the assessment location, as it corresponds
to baseline pollutant levels in that location. To derive the total baseline incidence per year,
this rate must be multiplied by the corresponding population number. For example, if the
baseline incidence rate is the number of cases per year per million people, that number
must be multiplied by the millions of people in the total population.
Table 12 from the TSD (reproduced below as Table 5-3) summarizes the sources of
baseline incidence rates and reports average incidence rates for the endpoints included in
the analysis. For both baseline incidence and prevalence data, we used age-specific rates
where available. We applied concentration-response functions to individual age groups and
then summed over the relevant age range to provide an estimate of total population
benefits. National-level incidence rates were used for most morbidity endpoints4, whereas
county-level data are available for premature mortality. Whenever possible, the national
rates used are national averages, because these data are most applicable to a national
assessment of benefits. For some studies, however, the only available incidence
information comes from the studies themselves; in these cases, incidence in the study
population is assumed to represent typical incidence at the national level.
Table 5-3 Baseline Incidence Rates for Use in Impact Functions
Endpoint
Parameter
Rates
Value
Source
Mortality1
Daily or annual projected
incidence to 2060 in 5-year
increments fO—99")
Age-, cause-, race-, and
county-stratified rates
CDC WONDER (2012-2014)
U.S. Census Bureau, 2012
Hospitalizations2
Daily incidence rates for all
ages
Age-,
region/state/county-, and
cause- stratified rates
2011-2014 HCUP data files
and data requested from and
supplied by individual states
Emergency
Department Visits2
Daily emergency
department visit incidence
rates for all ages
Age-, region-, state-,
county-, and cause-
stratified rates
2011-2014 HCUP data files
and data requested from and
supplied by individual states
Nonfatal Acute
Myocardial
Infarction
Daily nonfatal AMI
incidence rate per person
aged 18-99
Age-, region-, state-, and
county- stratified rates
AHRQ, 2016
4 Data availability from HCUP has changed since the last PM NAAQS RIA, with state-level incidence data
replacing regional-level data. As some states have low populations, many data points are unavailable, either
because they are missing or have been censored to protect health record privacy. To avoid interpolating the
missing values, we used national-level incidence data, which corresponds appropriately with the national-
level epidemiology effect coefficients used in these analyses.
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Rates
Endpoint
Parameter
Value
Source
Asthma Symptoms
Daily incidence among
asthmatic children
Age- and race- stratified
rates
Ostro etal., 2001
Wheeze (ages 5-12)
Cough (ages 5-12)
Shortness of breath (ages 5-
12)
Albuterol use (ages 6-13)
2.2 puffs per day
Rabinovitch et al., 2006
Asthma Onset
Annual incidence
0-4
5-11
12-17
0.0234
0.0111
0.0044
Winer etal., 2012
Alzheimer's Disease
Daily incidence rates for all
ages
Age-, region-, state-, and
county- stratified rates
2011-2014 HCUP data files
Parkinson's Disease
Annual incidence
18-44
45-64
65-84
85-99
0.0000011
0.0000366
0.0002001
0.0002483
HCUPnet
Allergic Rhinitis
Respondents aged 3-17
experiencing allergic
rhinitis/hay fever
symptoms within the year
prior to the survey
0.192
Parker etal., 2009
Cardiac Arrest
Daily nonfatal incidence
rates
0-17
18-39
40-64
65-99
0.00000002
0.00000009
0.00000056
0.00000133
Ensor et al., 2013, Rosenthal
et al., 2008, Silverman et al.,
2010
Lung Cancer
Annual nonfatal incidence
25-34
35-44
45-54
55-64
65-74
75-84
95-99
0.000001746
0.000014919
0.000067463
0.000208053
0.000052370
0.000576950
0.000557130
SEER, 2015 and Gharibvand
et al., 2017
Stroke
Annual nonfatal incidence
in ages 65-99
0.00446
Kloog et al., 2012
Work Loss Days
Daily incidence rate per
person (18-64)
Aged 18-24
Aged 25-44
Aged 45-64
0.00540
0.00678
0.00492
Adams et al., 1999, Table 41;
U.S. Census Bureau, 2000
School Loss Days
Rate per person per year,
assuming 180 school days
per year
9.9
Adams et al., 1999, Table 47
Minor Restricted-
Activity Days
Daily MRAD incidence rate
per person (18-64)
0.02137
Ostro and Rothschild, 1989,
p. 243
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CDC-Centers for Disease Control; NHS-National Health Interview Survey. Detailed references associated with
this table are located in the TSD.
'Mortality rates are only available in 5-year increments. The Healthcare Cost and Utilization Program [HCUP]
database contains individual level, state and regional-level hospital and emergency department discharges
for a variety of International Classification of Diseases (ICD) codes (AHRQ, 2016).
2Baseline incidence rates now include corrections from the states of Indiana and Montana.
We projected mortality rates such that future mortality rates are consistent with our
projections of population growth (U.S. EPA, 2018). To perform this calculation, we began
first with an average of 2007-2016 cause-specific mortality rates. Using Census Bureau
projected national-level annual mortality rates stratified by age range, we projected these
mortality rates to 2060 in 5-year increments (U.S. Census Bureau, 2009, U.S. EPA, 2018).
Further information regarding this procedure may be found in the TSD for this RIA and the
appendices to the BenMAP user manual (U.S. EPA, 2022b).
The baseline incidence rates for hospital admissions and emergency department
visits reflect the revised rates first applied in the Revised Cross-State Air Pollution Rule
Update (U.S. EPA, 2021). In addition, we revised the baseline incidence rates for acute
myocardial infarction. These revised rates are more recent (AHRQ, 2016) than the rates
they replace and more accurately represent the rates at which populations of different
ages, and in different locations, visit the hospital and emergency department for air
pollution-related illnesses. Lastly, these rates reflect unscheduled hospital admissions only,
which represents a conservative assumption that most air pollution-related visits are likely
to be unscheduled. If air pollution-related hospital admissions are scheduled, this
assumption would underestimate these benefits.
5.3.3 Effect Coefficients
Our approach for selecting and parametrizing effect coefficients for the benefits
analysis is described fully in the TSD accompanying this RIA. Because of the substantial
economic value associated with estimated counts of PIVh.s-attributable deaths, we describe
our rationale for selecting among long-term exposure epidemiologic studies below; a
detailed description of all remaining endpoints may be found in the TSD.
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5.3.3.1 PM2.5 Premature Mortality Effect Coefficients for Adults
A substantial body of published scientific literature documents the association
between PM2.5 concentrations and the risk of premature death (U.S. EPA, 2019b U.S. EPA,
2022a). This body of literature reflects thousands of epidemiology, toxicology, and clinical
studies. The PM ISA, completed as part of this review of the PM standards and reviewed by
the Clean Air Scientific Advisory Committee (CASAC) (Sheppard, 2022), concluded that
there is a causal relationship between mortality and both long-term and short-term
exposure to PM2.5 based on the full body of scientific evidence (U.S. EPA, 2019b U.S. EPA,
2022a). The size of the mortality effect estimates from epidemiologic studies, the serious
nature of the effect itself, and the high monetary value ascribed to prolonging life make
mortality risk reduction the most significant health endpoint quantified in this analysis.
EPA selects Hazard Ratios from cohort studies to estimate counts of PM-related premature
death, following a systematic approach detailed in the TSD accompanying this RIA that is
generally consistent with previous RIAs (e.g., U.S. EPA, 2011a, U.S. EPA, 2011b, U.S. EPA,
2011c, U.S. EPA, 2012a, U.S. EPA, 2012b, U.S. EPA, 2015a, U.S. EPA, 2019a).
As premature mortality typically constitutes the vast majority of monetized benefits
in a PM2.5 benefits assessment, quantifying effects using risk estimates reported from
multiple long-term exposure studies using different cohorts helps account for uncertainty
in the estimated number of PM-related premature deaths. Below we summarize the three
identified studies and hazard ratios and then describe our rationale for quantifying
premature PM-attributable deaths using two of these studies.
Wu et al., 2020 evaluated the relationship between long-term PM2.5 exposure and
all-cause mortality in more than 68.5 million Medicare enrollees (over the age of 64), using
Medicare claims data from 2000-2016 representing over 573 million person-years of
follow up and over 27 million deaths. This cohort included over 20% of the U.S. population
and was, at the time of publishing, the largest air pollution study cohort to date. The
authors modeled PM2.5 exposure at a 1-km2 grid resolution using a hybrid ensemble-based
prediction model that combined three machine learning models and relied on satellite data,
land-use information, weather variables, chemical transport model simulation outputs, and
monitor data. Wu et al., 2020 fit five different statistical models: a Cox proportional hazards
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model, a Poisson regression model, and three causal inference approaches (GPS estimation,
GPS matching, and GPS weighting). All five statistical approaches provided consistent
results; we report the results of the Cox proportional hazards model here. The authors
adjusted for numerous individual-level and community-level confounders, and sensitivity
analyses suggest that the results are robust to unmeasured confounding bias. In a single-
pollutant model, the coefficient and standard error for PM2.5 are estimated from the hazard
ratio (1.066) and 95% confidence interval (1.058-1.074) associated with a change in
annual mean PM2.5 exposure of 10.0 ug/m3 (Wu et al., 2020, Table S3, Main analysis, 2000-
2016 Cohort, Cox PH). We use a risk estimate from this study in place of the risk estimate
from Di et al., 2017. These two epidemiologic studies share many attributes, including the
Medicare cohort and statistical model used to characterize population exposure to PM2.5. As
compared to Di et al., 2017, Wu et al., 2020 includes a longer follow-up period and reflects
more recent PM2.5 concentrations.
Pope III et al., 2019 examined the relationship between long-term PM2.5 exposure
and all-cause mortality in a cohort of 1,599,329 U.S. adults (aged 18-84 years) who were
interviewed in the National Health Interview Surveys (NHIS) between 1986 and 2014 and
linked to the National Death Index (NDI) through 2015. The authors also constructed a sub-
cohort of 635,539 adults from the full cohort for whom body mass index (BMI) and
smoking status data were available. The authors employed a hybrid modeling technique to
estimate annual-average PM2.5 concentrations derived from regulatory monitoring data
and constructed in a universal kriging framework using geographic variables including
land use, population, and satellite estimates. Pope III et al., 2019 assigned annual-average
PM2.5 exposure from 1999-2015 to each individual by census tract and used complex
(accounting for NHIS's sample design) and simple Cox proportional hazards models for the
full cohort and the sub-cohort. We select the Hazard Ratio calculated using the complex
model for the sub-cohort, which controls for individual-level covariates including age, sex,
race-ethnicity, inflation-adjusted income, education level, marital status, rural versus
urban, region, survey year, BMI, and smoking status. In a single-pollutant model, the
coefficient and standard error for PM2.5 are estimated from the hazard ratio (1.12) and
95% confidence interval (1.08-1.15) associated with a change in annual mean PM2.5
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exposure of 10.0 ug/m3 (Pope III et al., 2019, Table 2, Subcohort). This study exhibits two
key strengths that makes it particularly well suited for a benefits analysis: (1) it includes a
long follow-up period with recent (and thus relatively low) PM2.5 concentrations; (2) the
NHIS cohort is representative of the U.S. population, especially with respect to the
distribution of individuals by race, ethnicity, income, and education.
EPA has historically used estimated Hazard Ratios from extended analyses of the ACS
cohort (Pope etal., 1995, Pope III etal., 2002, Krewski etal, 2009) to estimate PM-related
risk of premature death. More recent ACS analyses (Pope et al., 2015, Turner et al., 2016):
• extended the follow-up period of the ACS CSP-II to 22 years (1982-2004),
• evaluated 669,046 participants over 12,662,562 person-years of follow up and
237,201 observed deaths, and
• applied a more advanced exposure estimation approach than had previously been
used when analyzing the ACS cohort, combining the geostatistical Bayesian
Maximum Entropy framework with national-level land use regression models.
The total mortality hazard ratio best estimating risk from these ACS cohort studies
was based on a random-effects Cox proportional hazard model incorporating multiple
individual and ecological covariates (relative risk =1.06, 95% confidence intervals 1.04-
1.08 per 10[ig/m3 increase in PM2.5) from Turner et al., 2016. The relative risk estimate is
identical to a risk estimate drawn from earlier ACS analysis of all-cause long-term exposure
PM2.5-attributable mortality (Krewski et al., 2009). However, as the ACS hazard ratio is
quite similar to the Medicare estimate of (1.066,1.058-1.074), especially when considering
the broader age range (>29 vs >64), only the Wu et all., 2020 and Pope III et al., 2019 are
included in the main benefits assessments, with Wu et al., 2020 representing results from
both the Medicare and ACS cohorts.
5.3.4 Unquantified Human Health Benefits
Although we have quantified many of the health benefits associated with reducing
exposure to PM2.5, as shown in Table 5-2, we are unable to quantify the health benefits of
implementing the illustrative control strategies described in Chapter 3 associated with
reducing ozone exposure, SO2 exposure, or NO2 exposure. This is because we focused on
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reducing direct PM emissions and do not have air quality modeling data for these
pollutants. Although we used air quality surfaces that reflect applying the control strategies
for the impact of each alternative combination of standard levels on ambient levels of PM2.5,
this method does not simulate how the illustrative emissions reductions would affect
ambient levels of ozone, SO2, or NO2. Below we provide a qualitative description of these
health benefits. In general, previous analyses have shown that the monetized value of these
additional health benefits is much smaller than PIVh.s-related benefits (U.S. EPA, 2010, U.S.
EPA, 2015a). The extent to which ozone, SO2, and/or NOx would be reduced would depend
on the specific control strategies used to reduce PIVh.sin a given area.
Exposure to ambient ozone is associated with human health effects, including
respiratory and metabolic morbidity (U.S. EPA, 2020a). Epidemiological researchers have
associated ozone exposure with adverse health effects in numerous toxicological, clinical
and epidemiological studies (U.S. EPA, 2020a). When adequate data and resources are
available, EPA generally quantifies several health effects associated with exposure to ozone
(e.g., U.S. EPA, 2014b, U.S. EPA, 2015a). These health effects include respiratory morbidity
such as asthma attacks, hospital admissions, emergency department visits, and school loss
days. The scientific literature suggests that exposure to ozone is also associated with
chronic respiratory damage and premature aging of the lungs, but EPA has not quantified
these effects in benefits analyses previously.
Following an extensive evaluation of health evidence from epidemiologic and
laboratory studies, the Integrated Science Assessment for Sulfur Dioxide—Health Criteria
(SO2 ISA) concluded that there is a causal relationship between respiratory health effects
and short-term exposure to SO2 (U.S. EPA, 2017). The immediate effect of SO2 on the
respiratory system in humans is bronchoconstriction. Asthmatics are more sensitive to the
effects of SO2 likely resulting from preexisting inflammation associated with this disease. A
clear concentration-response relationship has been demonstrated in laboratory studies
following exposures to SO2, both in terms of increasing severity of effect and percentage of
asthmatics adversely affected. Based on our review of this information, we identified three
short-term morbidity endpoints that the SO2 ISA identified as a "causal relationship":
asthma exacerbation, respiratory-related emergency department visits, and respiratory -
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related hospitalizations. The differing evidence and associated strength of the evidence for
these different effects is described in detail in the SO2 ISA (U.S. EPA, 2017). The SO2 ISA also
concluded that the relationship between short-term SO2 exposure and premature mortality
was "suggestive of a causal relationship" because it is difficult to attribute the mortality risk
effects to SO2 alone. Although the SO2 ISA stated that studies are generally consistent in
reporting a relationship between SO2 exposure and mortality, the number of studies was
limited. Because we focused on reducing primary PM emissions, we did not quantify these
benefits.
Epidemiological researchers have associated NO2 exposure with adverse health
effects in numerous toxicological, clinical and epidemiological studies, as described in the
Integrated Science Assessment for Oxides of Nitrogen—Health Criteria (NO2 ISA) (U.S. EPA,
2016). The NO2 ISA provides a comprehensive review of the current evidence of health and
environmental effects of NO2. The NO2 ISA concluded that "evidence for asthma attacks
supports a causal relationship between short-term NO2 exposure and respiratory effects,"
and "evidence for development of asthma supports a likely to be causal relationship
between long-term NO2 exposure and respiratory effects." These are stronger conclusions
than those determined in the 2008 NO2 ISA (U.S. EPA, 2008).These epidemiologic and
experimental studies encompass a number of endpoints including emergency department
visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway
inflammation, and lung function. These are stronger conclusions than those determined in
the 2008 NO2 ISA (U.S. EPA, 2008). These epidemiologic and experimental studies
encompass a number of endpoints including emergency department visits and
hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation,
and lung function. Effect estimates from epidemiologic studies conducted in the United
States and Canada generally indicate a 2-20% increase in risks for ED visits and hospital
admissions and higher risks for respiratory symptoms. The NO2 ISA concluded that the
relationship between short-term NO2 exposure and premature mortality was "suggestive
but not sufficient to infer a causal relationship" because it is difficult to attribute the
mortality risk effects to NO2 alone. Although the NO2 ISA stated that studies consistently
reported a relationship between NO2 exposure and mortality, the effect was generally
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smaller than that for other pollutants such as PM. Because we focused on reducing primary
PM emissions, we did not quantify these benefits.
Illustrative controls to meet the alternative standard levels are expected to reduce
PM2.5 emissions from fossil fuel and wood combustion, as well as industrial processes, and
consequentially is expected to lead to reduced Hazardous Air Pollutant (HAP) emissions.
HAP emissions from EGUs and other industrial sources may contribute to increased cancer
risks and other serious health effects, including damage to the immune system, as well as
neurological, reproductive (e.g., reduced fertility), developmental, respiratory and other
health problems. These public health implications of exposure to HAPs can be particularly
pronounced for segments of the population that are especially vulnerable to some of these
effects [e.g., children are especially vulnerable to neurological effects because their brains
are still developing). Some HAPs can also detrimentally affect ecosystems used for
recreational and commercial purposes.
5.3.5 Unquantified Welfare Benefits
The Clean Air Act definition of welfare effects includes, but is not limited to, effects
on soils, water, wildlife, vegetation, visibility, weather, and climate, as well as effects on
man-made materials, economic values, and personal comfort and well-being. Detailed
information regarding the ecological effects of nitrogen and sulfur deposition is available in
the Integrated Science Assessment for Oxides of Nitrogen, Oxides of Sulfur, and Particulate
Matter— Ecological Criteria (ISA) (U.S. EPA, 2020b).
Particulate matter (PM) is composed of some or all of the following components:
nitrate (NO3-), sulfate (SO42-), ammonium (NH4+), metals, minerals (dust), and organic and
elemental carbon. Nitrate, sulfate, and ammonium contribute to nitrogen (N) and sulfur (S)
deposition, which causes substantial ecological effects. The ecological effects of deposition
are grouped into three main categories: acidification, N enrichment/N driven
eutrophication, and S enrichment. Ecological effects are further subdivided into terrestrial,
wetland, freshwater, and estuarine/near-coastal ecosystems. These ecosystems and effects
are linked by the connectivity of terrestrial and aquatic habitats through biogeochemical
pathways of N and S.
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In the ISA, information on ecological effects from controlled exposure, field addition,
ambient deposition, and toxicological studies, among others, are integrated to form
conclusions about the causal relationships between NOy, SOx, and PM and ecological
effects. A consistent and transparent framework (U.S. EPA, 2015b, Table II) is applied to
classify the ecological effect evidence according to a five-level hierarchy:
1. Causal relationship
2. Likely to be a causal relationship
3. Suggestive of, but not sufficient to infer, a causal relationship
4. Inadequate to infer a causal relationship
5. Not likely to be a causal relationship
Table 5-4 summarizes the causal determinations for relationships between N and S
deposition and ecological effects. Though not quantified in this RIA, it is reasonable to infer
that reducing fine particle levels by controlling emissions of NOx and SOx will yield the
ecological benefits detailed below.
Table 5-4 Causal Determinations Identified in Integrated Science Assessment for
Oxides of Nitrogen, Oxides of Sulfur, and Particulate Matter —
Ecological Criteria 2020b
Effect Category
Causal Determination
N and acidifying deposition to terrestrial
ecosystems
N and S deposition and alteration of soil
Causal relationship
biogeochemistry in terrestrial ecosystems
Section IS.5.1 and Appendix 4.1
N deposition and the alteration of the physiology and
Causal relationship
growth of terrestrial organisms and the productivity
of terrestrial ecosystems
Section IS.5.2 and Appendix 6.6.1
N deposition and the alteration of species richness,
Causal relationship
community composition, and biodiversity in
terrestrial ecosystems
Section IS.5.2 and Appendix 6.6.2
Acidifying N and S deposition and the alteration of the
Causal relationship
physiology and growth of terrestrial organisms and
the productivity of terrestrial ecosystems
Section IS.5.3 and Appendix 5.7.1
Acidifying N and S deposition and the alteration of
Causal relationship
species richness, community composition, and
biodiversity in terrestrial ecosystems
Section IS.5.3 and Appendix 5.7.2
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Effect Category
Causal Determination
N and acidifying deposition to freshwater
ecosystems
N and S deposition and alteration of freshwater
Causal relationship
biogeochemistry
Section IS.6.1 and Appendix 7.1.7
Acidifying N and S deposition and changes in biota,
Causal relationship
including physiological impairment and alteration of
species richness, community composition, and
biodiversity in freshwater ecosystems
Section IS.6.3 and Appendix 8.6
N deposition and changes in biota, including altered
Causal relationship
growth and productivity, species richness, community
composition, and biodiversity due to N enrichment in
freshwater ecosystems
Section IS.6.2 and Appendix 9.6
N deposition to estuarine ecosystems
N deposition and alteration of biogeochemistry in
Causal relationship
estuarine and near-coastal marine systems
Section IS.7.1 and Appendix 7.2.10
N deposition and changes in biota, including altered
Causal relationship
growth, total primary production, total algal
community biomass, species richness, community
composition, and biodiversity due to N enrichment in
estuarine environments
Section IS.7.2 and Appendix 10.7
N deposition to wetland ecosystems
N deposition and the alteration of biogeochemical
Causal relationship
cycling in wetlands
Section IS.8.1 and Appendix 11.10
N deposition and the alteration of growth and
Causal relationship
productivity, species physiology, species richness,
community composition, and biodiversity in wetlands
Section IS.8.2 and Appendix 11.10
S deposition to wetland and freshwater
ecosystems
S deposition and the alteration of mercury
Causal relationship
methylation in surface water, sediment, and soils in
wetland and freshwater ecosystems
Section IS.9.1 and Appendix 12.7
S deposition and changes in biota due to sulfide
Causal relationship
phytotoxicity, including alteration of growth and
productivity, species physiology, species richness,
community composition, and biodiversity in wetland
and freshwater ecosystems
Section IS.9.2 and Appendix 12.7
5.3.5.1 Visibility Impairment Benefits
Reducing PM2.5 would improve levels of visibility in the U.S. because suspended
particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 2009).
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Fine particles with significant light-extinction efficiencies include sulfates, nitrates, organic
carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to
people's enjoyment of daily activities and their overall sense of wellbeing. Good visibility
increases the quality of life where individuals live and work, and where they engage in
recreational activities. Particulate sulfate is the dominant source of regional haze in the
eastern U.S. and particulate nitrate is an important contributor to light extinction in
California and the upper Midwestern U.S., particularly during winter (U.S. EPA, 2009).
Previous analyses (U.S. EPA, 2011d) show that visibility benefits can be a significant
welfare benefit category. Without air quality modeling, we are unable to estimate visibility-
related benefits, and we are also unable to determine whether the emission reductions
associated with the proposal would be likely to have a significant impact on visibility in
urban areas or Class I areas.
5.3.6 Climate Effects of PM2.5
In the climate section of Chapter 5 of the 2020 PM2.5 Primary NAAQS Policy
Assessment it states "Thus, as in the last review, the data remain insufficient to conduct
quantitative analyses for PM effects on climate in the current review." (U.S. EPA, 2020d)
Pollutants that affect the energy balance of the earth are referred to as climate forcers. A
pollutant that increases the amount of energy in the Earth's climate system is said to exert
"positive radiative forcing," which leads to warming and climate change. In contrast, a
pollutant that exerts negative radiative forcing reduces the amount of energy in the Earth's
system and leads to cooling.
Atmospheric particles influence climate in multiple ways: directly absorbing light,
scattering light, changing the reflectivity ("albedo") of snow and ice through deposition,
and interacting with clouds. Depending on the particle's composition, the timing of
emissions, and where it is in the atmosphere determine if it contributes to cooling or
warming. The short atmospheric lifetime of particles, lasting from days to weeks, and the
mechanisms by which particles affect climate, distinguish it from long-lived greenhouse
gases like CO2. This means that actions taken to reduce PM2.5 will have near term effects on
climate change. The Intergovernmental Panel on Climate Change Sixth Assessment Report
concludes that for forcers with short lifetimes, "the response in surface temperature occurs
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strongly, as soon as a sustained change in emissions is implemented" (Naik et al., 2021).
The potential to affect near-term climate change and the rate of climate change with
policies to address these emissions is gaining attention nationally and internationally (e.g.,
Black Carbon Report to Congress, Arctic Council, Climate and Clean Air Coalition, and
Convention on Long-Range Transboundary Air Pollution of the United Nations Economic
Commission for Europe). Recent reports have concluded that short-lived compounds play a
prominent role in keeping global warming below 1.5° C (IPCC, 2018), and are especially
important in the rapidly warming Arctic (AMAP, 2021). While reducing long-lived GHGs
such as CO2 is necessary to protect against long-term climate change, reducing short-lived
forcers and would slow the rate of climate change within the first half of this century
(UNEP, 2011).
5.3.6.1 Climate Effects of Carbonaceous Particles
The illustrative control strategies are focused on emissions sources that are
significant sources of carbonaceous particles, including black carbon and organic carbon.
Black Carbon (BC), also called soot, is the most strongly light-absorbing component of
PM2.5, and is formed by incomplete combustion of fossil fuels, biofuels, and biomass.
Another contributor to carbonaceous particles is organic carbon (OC), which in addition to
carbon are also composed of oxygen and hydrogen. Organic carbon particles can be directly
emitted from the same sources as black carbon or formed in the atmosphere from chemical
reactions. They can be light-absorbing, but most have a larger light-scattering component.
Both BC and organic carbon in the atmosphere influence climate in multiple ways:
directly absorbing or reflecting light, modifying the rate of vertical mixing, and interacting
with clouds. Light-absorbing particles also have an additional climate effect when
deposited on snow and ice. These particles darken the surface and decrease albedo,
thereby increasing absorption and accelerating melting (Hock et al., 2019; Meredith et al.,
2019). Regional climate impacts of BC are highly variable, and sensitive regions such as the
Arctic are particularly vulnerable to the warming and melting effects of BC. Snow and ice
cover in the western U.S. has also been affected by BC. Specifically, deposition of BC on
mountain glaciers and snowpacks produces a positive snow and ice albedo effect,
contributing to the melting of snowpack earlier in the spring and reducing the amount of
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snowmelt that normally would occur later in the spring and summer (Hadley et al. 2010).
This has implications for freshwater resources in regions of the U.S. dependent on snow-
fed or glacier-fed water systems. In the Sierra Nevada mountain range, Hadley et al. (2010)
found BC at different depths in the snowpack, deposited over the winter months by
snowfall. In the spring, the continuous uncovering of the BC contributed to the early melt. A
model capturing the effects of soot on snow in the western U.S. shows significant decreases
in snowpack between December and May (Qian et al., 2009). Snow water equivalent (the
amount of water that would be produced by melting all the snow) is reduced 2-50
millimeters (mm) in mountainous areas, particularly over the Central Rockies, Sierra
Nevadas, and western Canada. A study found that biomass burning emissions in Alaska and
the Rocky Mountain region during the summer can enhance snowmelt (McKenzie Skiles et
al 2018). Light-absorbing particles and especially BC can have an additional warming effect
when deposited on snow and ice, and this effect is highly seasonal and regional.
Relative to greenhouse gases, the net effect of carbonaceous particles is both more
regionally variable and more uncertain (Naik et al., 2021). Particles have a relatively short
lifetime in the atmosphere, leading to spatial concentration differences, while greenhouse
gases are more well mixed and have less global variability. The amount of light absorption
by particles depends on the season, with different effects in the summer and winter. Lastly,
even light-absorbing particles can also contribute to cooling (e.g., by shading the surface).
5.3.6.2 Climate Effects: Summary and Conclusions
The net climate change effect of carbonaceous aerosols in the illustrative control
strategies depends on the location, timing, and type of the emissions controls. As described
above, the black carbon emissions are more likely to contribute to warming and organic
aerosols more likely to contribute to cooling. Emissions sources with larger amounts of
light-absorbing aerosols, like diesel vehicles, or with emissions near snow or the Arctic, like
residential wood combustion, are more likely to contribute to warming (Bond et al., 2013).
However, assessing the net effect is beyond the scope of this RIA and requires
climate atmospheric modeling that has not been undertaken. Furthermore, there are
uncertainties relevant to the assessment of the net climate change effects of PM2.5,
especially at a regional scale (U.S. EPA, 2019b). Strategies that could be implemented by
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State and Local governments that would likely provide climate change mitigation benefits
include prioritizing (/) emissions control actions that also achieve emissions reductions for
warming agents like carbon dioxide, methane, and ozone precursors (carbon monoxide and
volatile organic compounds), and (/'/') sources of light-absorbing carbonaceous aerosols,
especially diesel engines and residential wood combustion.
5.3.7 Economic Valuation Estimates
To directly compare benefits estimates associated with a rulemaking to cost
estimates, the number of instances of each air pollution-attributable health impact must be
converted to a monetary value. This requires a valuation estimate for each unique health
endpoint, and potentially also discounting if the benefits are expected to accrue over more
than a single year, as recommended by the Guidelines for Preparing Economic Analyses (U.S.
EPA, 2014a).
5.4 Characterizing Uncertainty
In any complex analysis using estimated parameters and inputs from numerous
models, there are likely to be many sources of uncertainty. This analysis is no exception.
The TSD accompanying this RIA details our approach to characterizing uncertainty in both
quantitative and qualitative terms. That TSD describes the sources of uncertainty
associated with key input parameters including emissions inventories, air quality data from
models (with their associated parameters and inputs), population data, population
estimates, health effect estimates from epidemiology studies, economic data for monetizing
benefits, and assumptions regarding the future state of the country (i.e., regulations,
technology, and human behavior). Each of these inputs is uncertain and affects the size and
distribution of the estimated benefits. When the uncertainties from each stage of the
analysis are compounded, even small uncertainties can have large effects on the total
quantified benefits.
To characterize uncertainty and variability into this assessment, we incorporate
three quantitative analyses described below and in greater detail within the TSD (Section
7.1):
1. A Monte Carlo assessment that accounts for random sampling error and
between study variability in the epidemiological and economic valuation studies;
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2. The quantification of PM-related mortality using alternative PM2.5 mortality
effect estimates drawn from two long-term cohort studies; and
3. Presentation of 95th percentile confidence interval around each risk estimate.
Quantitative characterization of other sources of PM2.5 uncertainties are discussed
only in Section 7.1 of the TSD:
1. For adult all-cause mortality:
a. The distributions of air quality concentrations experienced by the
original cohort population (TSD Section 7.1.2.1);
b. Methods of estimating and assigning exposures in epidemiologic studies
(TSD Section 7.1.2.2);
c. Confounding by ozone (TSD Section 7.1.2.3); and
d. The statistical technique used to generate hazard ratios in the
epidemiologic study (TSD Section 7.1.2.4).
Plausible alternative risk estimates for asthma onset in children (TSD Section 7.1.3),
cardiovascular hospital admissions (TSD Section 7.1.4,), and respiratory hospital
admissions (TSD Section 7.1.5);
Effect modification of PM2.5-attributable health effects in at-risk populations (TSD
Section 7.1.6).
Quantitative consideration of baseline incidence rates and economic valuation
estimates are provided in Section 7.3 and 7.4 of the TSD, respectively. Qualitative
discussions of various sources of uncertainty can be found in Section 7.5 of the TSD.
5.4.1 Monte Carlo Assessment
Similar to other recent RIAs, we used Monte Carlo methods for characterizing
random sampling error associated with the concentration response functions from
epidemiological studies and random effects modeling to characterize both sampling error
and variability across the economic valuation functions. The Monte Carlo simulation in the
BenMAP-CE software randomly samples from a distribution of incidence and valuation
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estimates to characterize the effects of uncertainty on output variables. Specifically, we
used Monte Carlo methods to generate confidence intervals around the estimated health
impact and monetized benefits. The reported standard errors in the epidemiological
studies determined the distributions for individual effect estimates for endpoints estimated
using a single study. For endpoints estimated using a pooled estimate of multiple studies,
the confidence intervals reflect both the standard errors and the variance across studies.
The confidence intervals around the monetized benefits incorporate the epidemiology
standard errors as well as the distribution of the valuation function. These confidence
intervals do not reflect other sources of uncertainty inherent within the estimates, such as
baseline incidence rates, populations exposed, and transferability of the effect estimate to
diverse locations. As a result, the reported confidence intervals and range of estimates give
an incomplete picture about the overall uncertainty in the benefits estimates.
5.4.2 Sources of Uncertainty Treated Qualitatively
Although we strive to incorporate as many quantitative assessments of uncertainty
as possible, there are several aspects we are only able to address qualitatively. These
attributes are summarized below and described more fully in the TSD.
Key assumptions underlying the estimates for premature mortality, which account
for over 98% of the total monetized benefits in this analysis, include the following:
1. We assume that all fine particles, regardless of their chemical composition,
are equally potent in causing premature mortality. This is an important
assumption, because PM2.5 varies considerably in composition across
sources, but the scientific evidence is not yet sufficient to allow
differentiation of effect estimates by particle type. The PM ISA, which was
reviewed by CASAC, concluded that "across exposure durations and health
effects categories ... 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, 2019b).
2. We assume that the health impact function for fine particles is log-linear
down to the lowest air quality levels modeled in this analysis. Thus, the
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estimates include health benefits from reducing fine particles in areas with
varied concentrations of PM2.5, including both regions that are in attainment
with the fine particle standard and those that do not meet the standard down
to the lowest modeled concentrations. The PM ISA concluded that "the
majority of evidence continues to indicate a linear, no-threshold
concentration-response relationship for long-term exposure to PM2.5 and
total (nonaccidental) mortality" U.S. EPA, 2019b .
3. We assume that there is a "cessation" lag between the change in PM
exposures and the total realization of changes in mortality effects.
Specifically, we assume that some of the incidences of premature mortality
related to PM2.5 exposures occur in a distributed fashion over the 20 years
following exposure based on the advice of the SAB-HES (Cameron, 2004),
which affects the valuation of mortality benefits at different discount rates.
Similarly, we assume there is a cessation lag between the change in PM
exposures and both the development and diagnosis of lung cancer.
5.5 Benefits Results
5.5.1 Benefits of the Applied Control Strategies for the Alternative Combinations
of Primary PM2.5 Standard Levels
Applying the impact and valuation functions described previously in this chapter to
the estimated changes in PM2.5 yields estimates of the changes in physical damages (e.g.,
premature mortalities, cases of hospital admissions and emergency department visits) and
the associated monetary values for those changes. Not all known PM health effects could be
quantified or monetized.
We present two sets of tables - one set in this chapter and one set in Appendix 5A.
First, Table 5-5 through Table 5-9 present benefits associated with the illustrative control
strategies identified in Chapter 3. More specifically, for the proposed alternative standard
level of 9/35 |~ig/m3, for the northeast we were able to identify approximately 97 percent of
the reductions needed. For the southeast we were able to identify approximately 76
percent of the reductions needed. For the west, we were able to identify approximately 31
percent of the reductions needed, and for California the percentage is approximately 17
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percent. As such, these tables present the benefits associated with the illustrative control
strategies and reflect the remaining air quality challenges (discussed in Chapter 2, Section
2.4 and Chapter 3, Section 3.2.6). Second, Table 5A-1 through 5A-5 in Appendix 5A present
the potential benefits associated with fully meeting the proposed and alternative
standards.
Table 5-5 through Table 5-9 present the benefits results of applying the control
strategies for the proposed annual and current 24-hour alternative standard levels of
10/35 ng/m3 and 9/35 |~ig/m3, as well as the following two more stringent alternative
standard levels: (1) an alternative annual standard level of 8 |~ig/m3 in combination with
the current 24-hour standard (i.e., 8/35 |j,g/m3), and (2) an alternative 24-hour standard
level of 30 |~ig/m3 in combination with the proposed annual standard level of 10 ng/m3 (i.e.,
10/30 |j,g/m3).
Table 5-5 presents the estimated avoided incidences of PM-related illnesses and
premature mortality resulting from the control strategies applied to each of the alternative
standard levels in 2032. Table 5-6 and Table 5-7 present the monetized valuation benefits
(discounted at a 3% and 7% discount rate, respectively) of the avoided health outcomes
presented in Table 5-5.
Table 5-8 and Table 5-9 present a summary of the monetized benefits associated
with each of the alternative standard levels, both nationally and by region. The regional
monetized benefits in Table 5-8 are presented in four regions: California (CA), the
Northeastern (NE) states, the Southeastern (SE) states, and the Western (W) states. For
Table 5-8 and Table 5-9, the monetized value of unquantified effects is represented by
adding an unknown "B" to the aggregate total. This B represents both uncertainty and a
bias in this analysis, as it reflects health and welfare benefits that we are unable to
quantify.5
For a more detailed description of the geographic distribution of the emissions
reductions needed for each of the alternative standard levels, see the discussion in Chapter
5 The health and monetized benefits of fully attaining the alternative standard levels in all areas can be found
in Appendix 5A.
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3, Section 3.2.5. The estimated PM2.5 emissions reductions from control applications do not
result in all counties in the northeast, southeast, west, and California meeting the proposed
and more stringent alternative standard levels. For the proposed alternative standard level
of 10/35 ng/m3, the northeast and southeast have sufficient estimated emissions
reductions to reach attainment. For the west, the estimated emissions reductions are
approximately 27 percent of the total needed to reach attainment, and for California the
estimated emissions reductions are approximately 18 percent of the total needed to reach
attainment.
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Table 5-5 Estimated Avoided PM-Related Premature Mortalities and Illnesses of
the Applied Control Strategies for the Proposed and More Stringent
Alternative Primary PM2.5 Standard Levels for 2032 (95% Confidence
Interval)
Avoided Mortality3
10/35
10/30
9/35
8/35
Pope III et al., 2019 (adult
mortality ages 18-99
years]
1,700
(1,200 to 2,100)
1,900
(1,400 to 2,400)
4,200
(3,000 to 5,300)
9,200
(6,600 to 12,000)
Wu et al., 2020 (adult
mortality ages 65-99
years]
810
(710 to 900)
920
(810 to 1,000)
2,000
(1,800 to 2,200)
4,400
(3,900 to 4,900)
Woodruff et al., 2008
(infant mortality")
1.6
(-0.99 to 4.0)
1.8
(-1.1 to 4.6)
4.7
(-3.0 to 12)
11
(-6.9 to 28)
Avoided Morbidity
10/35
10/30
9/35
8/35
Hospital admissions—
cardiovascular (age > 18)
140
(100 to 170)
150
(110 to 190)
310
(230 to 400)
660
(480 to 840)
Hospital admissions—
respiratory
93
(31 to 150)
100
(35 to 170)
210
(74 to 350)
460
(160 to 740)
ED visits-cardiovascular
260
(-100 to 610)
290
(-110 to 670)
630
(-240 to 1,500)
1,400
(-530 to 3,200)
ED visits—respiratory
490
(95 to 1,000)
530
(100 to 1,100)
1,200
(240 to 2,600)
2,700
(540 to 5,700)
Acute Myocardial
Infarction
29
(5.9 to 17)
32
(19 to 45)
67
(39 to 94)
140
(83 to 200)
Cardiac arrest
15
(-5.9 to 33)
16
(-6.6 to 37)
34
(-14 to 76)
72
(-29 to 160)
Hospital admissions-
Alzheimer's Disease
360
(270 to 440)
390
(300 to 480)
850
(640 to 1,000)
1,900
(1,500 to 2,400)
Hospital admissions-
Parkinson's Disease
48
(25 to 70)
54
(28 to 79)
120
(63 to 180)
270
(140 to 390)
Stroke
55
(14 to 94)
61
(16 to 110)
130
(33 to 220)
270
(71 to 470)
Lung cancer
65
(20 to 110)
73
(22 to 120)
150
(46 to 250)
320
(99 to 530)
Hay Fever/Rhinitis
15,000
(3,500 to 25,000)
16,000
(4,000 to 28,000)
35,000
(8,500 to 60,000)
75,000
(18,000 to 130,000)
Asthma Onset
2,200
(2,100 to 2,300)
2,500
(2,400 to 2,600)
5,400
(5,100 to 5,600)
11,000
(11,000 to 12,000)
Asthma symptoms -
Albuterol use
310,000
(-150,000 to
750,000)
350,000
(-170,000 to
850,000)
740,000
(-360,000 to
1,800,000)
1,600,000
(-780,000 to
3,900,000)
Lost work days
110,000
(97,000 to
130,000)
130,000
(110,000 to
150,000)
270,000
(230,000 to
310,000)
580,000
(490,000 to
660,000)
Minor restricted-activity
daysd'f
680,000
(550,000 to
800,000)
750,000
(610,000 to
890,000)
1,600,000
(1,300,000 to
1,900,000)
3,400,000
(2,700,000 to
4,000,000)
Note: Values rounded to two significant figures.
a Reported here are two alternative estimates of the number of premature deaths among adults due to long-
term exposure to PM2.5. These values should not be added to one another.
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Table 5-6 Monetized PM-Related Premature Mortalities and Illnesses of the Applied
Control Strategies for the Proposed and More Stringent Alternative
Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 3% discount
rate; 95% Confidence Interval)
Avoided Mortality3
10/35
10/30
9/35
8/35
Pope III etal., 2019 (adult
mortality ages 18-99 years)
17,000
(1,600 to 47,000)
20,000
(1,800 to 53,000)
43,000
(3,900 to 120,000)
94,000
(8,600 to 260,000)
Wu etal., 2020 (adult
mortality ages 65-99 years)
8,300
(770 to 22,000)
9,400
(870 to 25,000)
20,000
(1,900 to 54,000)
45,000
(4,200 to 120,000)
Woodruff etal., 2008 (infant
mortality)
18
(-9.9 to 70)
20
(-11 to 80)
53
(-30 to 210)
120
(-69 to 490)
Avoided Morbidity
10/35
10/30
9/35
8/35
Hospital admissions—
cardiovascular [age > 18)
2.3
(1.7 to 2.9)
2.5
(1.8 to 3.2)
5.2
(3.7 to 6.5)
11
(7.9 to 14)
Hospital admissions—
respiratory
1.6
(0.35 to 2.7)
1.7
(0.39 to 3.0)
3.6
(0.81 to 6.2)
7.6
(1.7 to 13)
ED visits-cardiovascular
0.32
(-0.12 to 0.75)
0.35
(-0.14 to 0.83)
0.78
(-0.3 to 1.8)
1.7
(-0.65 to 4)
ED visits—respiratory
0.45
(0.089 to 0.94)
0.5
(0.098 to 1)
1.2
(0.23 to 2.4)
2.6
(0.5 to 5.3)
Acute Myocardial Infarction
1.5
(0.88 to 2.1)
1.7
(0.97 to 2.4)
3.5
(2.0 to 4.9)
7.4
(4.3 to 10)
Cardiac arrest
0.55
(-0.23 to 1.3)
0.62
(-0.25 to 1.4)
1.3
(-0.52 to 2.9)
2.7
(-1.1 to 6.2)
Hospital admissions-
Alzheimer's Disease
4.6
(3.5 to 5.7)
5
(3.8 to 6.2)
11
(8.3 to 13)
25
(19 to 31)
Hospital admissions-
Parkinson's Disease
0.66
(0.34 to 0.96)
0.74
(0.38 to 1.1)
1.7
(0.86 to 2.4)
3.7
(1.9 to 5.3)
Stroke
2
(0.51 to 3.4)
2.2
(0.58 to 3.8)
4.6
(1.2 to 7.8)
9.9
(2.6 to 17)
Lung cancer
1
(0.31 to 1.7)
1.1
(0.35 to 1.9)
2.3
(0.71 to 3.8)
4.9
(1.5 to 8.1)
Hay Fever/Rhinitis
9.3
(2.3 to 16)
11
(2.5 to 18)
22
(5.4 to 38)
48
(12 to 82)
Asthma Onset
100
(98 to 110)
120
(110 to 130)
250
(240 to 270)
540
(510 to 570)
Asthma symptoms -
Albuterol use
0.11
(-0.055 to 0.28)
0.13
(-0.062 to 0.31)
0.27
(-0.13 to 0.66)
0.59
(-0.29 to 1.4)
Lost work days
21
(17 to 24)
23
(19 to 26)
48
(41 to 56)
100
(88 to 120)
Minor restricted-activity
days
53
(28 to 80)
59
(31 to 89)
120
(64 to 190)
260
(140 to 400)
Note: Values rounded to two significant figures.
a Reported here are two alternative estimates of the number of premature deaths among adults due to long-
term exposure to PM2.5. These values should not be added to one another.
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Table 5-7 Monetized PM-Related Premature Mortalities and Illnesses of the
Applied Control Strategies for the Proposed and More Stringent
Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$,
7% discount rate; 95% Confidence Interval)
Avoided Mortality3
10/35
10/30
9/35
8/35
Pope III et al., 2019 (adult
mortality ages 18-99
years]
16,000
(1,400 to 42,000)
18,000
(1,600 to 47,000)
38,000
(3,500 to
100,000)
85,000
(7,700 to 230,000)
Wu et al., 2020 (adult
mortality ages 65-99
years]
7,500
(690 to 20,000)
8,500
(780 to 22,000)
18,000
(1,700 to 49,000)
41,000
(3,800 to 110,000)
Woodruff et al., 2008
(infant mortality")
18
(-9.9 to 70)
20
(-11 to 80)
53
(-30 to 210)
120
(-69 to 490)
Avoided Morbidity
10/35
10/30
9/35
8/35
Hospital admissions—
cardiovascular (age > 18)
2.3
(1.7 to 2.9)
2.5
(1.8 to 3.2)
5.2
(3.7 to 6.5)
11
(7.9 to 14)
Hospital admissions—
respiratory
1.6
(0.35 to 2.7)
1.7
(0.39 to 3.0)
3.6
(0.81 to 6.2)
7.6
(1.7 to 13)
ED visits-cardiovascular
0.32
(-0.12 to 0.75)
0.35
(-0.14 to 0.83)
0.78
(-0.3 to 1.8)
1.7
(-0.65 to 4)
ED visits—respiratory
0.45
(0.089 to 0.94)
0.5
(0.098 to 1)
1.2
(0.23 to 2.4)
2.6
(0.5 to 5.3)
Acute Myocardial
Infarction
1.5
(0.86 to 2.1)
1.6
(0.97 to 2.4)
3.4
(2.0 to 4.8)
7.3
(4.2 to 10)
Cardiac arrest
0.55
(-0.22 to 1.2)
0.61
(-0.25 to 1.4)
1.3
(-0.51 to 2.8)
2.7
(-1.1 to 6.1)
Hospital admissions-
Alzheimer's Disease
4.6
(3.5 to 5.7)
5
(3.8 to 6.2)
11
(8.3 to 13)
25
(19 to 31)
Hospital admissions-
Parkinson's Disease
0.66
(0.34 to 0.96)
0.74
(0.38 to 1.1)
1.7
(0.86 to 2.4)
3.7
(1.9 to 5.3)
Stroke
2
(0.51 to 3.4)
2.2
(0.58 to 3.8)
4.6
(1.2 to 7.8)
9.9
(2.6 to 17)
Lung cancer
0.72
(0.22 to 1.2)
0.8
(0.25 to 1.3)
1.6
(0.5 to 2.7)
3.4
(1.1 to 5.7)
Hay Fever/Rhinitis
9.3
(2.3 to 16)
11
(2.5 to 18)
22
(5.4 to 38)
48
(12 to 82)
Asthma Onset
65
(60 to 69)
73
(68 to 78)
160
(150 to 170)
340
(310 to 360)
Asthma symptoms -
Albuterol use
0.11
(-0.055 to 0.28)
0.13
(-0.062 to 0.31)
0.27
(-0.13 to 0.66)
0.59
(-0.29 to 1.4)
Lost work days
21
(17 to 24)
23
(19 to 26)
48
(41 to 56)
100
(88 to 120)
Minor restricted-activity
days
53
(28 to 80)
59
(31 to 89)
120
(64 to 190)
260
(140 to 400)
Note: Values rounded to two significant figures.
a Reported here are two alternative estimates of the number of premature deaths among adults due to long-
term exposure to PM2.5. These values should not be added to one another.
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Table 5-8 Estimated Monetized Benefits of the Applied Control Strategies for the
Proposed and More Stringent Alternative Combinations of Primary
PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35
(billions of 2017$)
ft r l*g/m3 annual & 10 jig/m3 annual & 9 jig/m3 annual & 8 ng/m3 annual &
Benefits Estimate 3 5 ng/m3 24-hour 30 iig/m3 24-hour 35 ng/m3 24-hour 35 ng/m3 24-hour
Economic value of avoided PIVh.s-related morbidities and premature deaths using PM2.5 mortality
estimate from Pope III et al., 2019
3% discount $17+ B $20 + B $43 + B $95 + B
rate
7% discount $16 +B $18 + B $39 + B $86 + B
rate
Economic value of avoided PIVh.s-related morbidities and premature deaths using PM2.5 mortality
estimate from Wu et al., 2020
3% discount $8.5+ B $9.6 + B $21 + B $46 + B
rate
7% discount $7.6 + B $8.6+ B $19 + B $41 + B
rate
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized
benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not all
possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified
health and welfare benefits.
Table 5-9 is a summary of the monetized benefits associated with applying the
control strategies for each of the alternative standard levels by four regions: California, the
Northeast, the Southeast, and the West. The monetized benefits differ regionally and by
each alternative standard level. For the proposed alternative standard level of 10/35 |Lxg/-
m3, because 15 of the 24 counties that need emissions reductions are counties in California,
the majority of the benefits are incurred in California (Table 5-9). For California, we were
able to identify approximately 18 percent of the reductions needed. In addition, as the
alternative standard levels become more stringent, more counties in the northeast and
southeast need emissions reductions. As additional controls are applied in those areas,
those areas account for a relatively higher proportion of the benefits. For example, for
alternative standard levels of 9/35 |~ig/m3 and 8/35 |~ig/m3, more controls are available to
apply in the northeast and their adjacent counties and the southeast and their adjacent
counties6. The benefits for those areas are higher than the costs for the west and California.
6 Note that in the northeast and southeast we identified control measures and associated emissions
reductions from adjacent counties and used a ppb/ton PM2.5 air quality ratio that was four times less
responsive than the ratio used when applying in-county emissions reductions (i.e., applied four tons of PM2.5
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Table 5-9 Estimated Monetized Benefits by Region of the Applied Control
Strategies for the Proposed and More Stringent Alternative
Combinations of Primary PM2.5 Standard Levels in 2032, Incremental
to Attainment of 12/35 (billions of 2017$)
Benefits
Region
10 jig/m3
annual &
10 jig/m3
annual &
9 Jig/m3
annual &
8 Jig/m3
annual &
Estimate
35 Jig/m3 24-
hour
30 Jig/m3 24-
hour
35 Jig/m3 24-
hour
35 Jig/m3 24-
hour
Economic value of avoided PM2.5
-related morbidities and premature deaths using PM2.5 mortality estimate
from Pope III et al., 2019
3%
discount
rate
California
$13+ B
$14+ B
$17+ B
$23+ B
Northeast
$2.3+ B
$2.6+ B
$15+ B
$40+ B
Southeast
$1.8 + B
$1.8 + B
$8.8 + B
$22+ B
West
$0.018+ B
$1.1+ B
$2.2 + B
$11+ B
7%
discount
rate
California
$12+ B
$13+ B
$16+ B
$21+ B
Northeast
Southeast
$2 + B
$1.6+ B
$2.3+ B
$1.6+ B
$13+ B
$7.9 + B
$36+ B
$20+ B
West
$0.016+ B
$1 + B
$2 + B
$9.5 + B
Economic value of avoided PM2.5
-related morbidities and premature deaths using PM2.5 mortality estimate
from Wu et al., 2020
30/0
discount
rate
California
$6.5 + B
$6.9 + B
$8.4 + B
$11+ B
Northeast
$1.1+ B
$1.3+ B
$7.3 + B
$19+ B
Southeast
$0.84+ B
$0.84+ B
$4.1 + B
$10+ B
West
$0.0092 + B
$0.56 + B
$1.1+ B
$5.1+ B
7%
discount
rate
California
$5.8 + B
$6.2 + B
$7.5 + B
$10+ B
Northeast
$1 + B
$1.2 + B
$6.6+ B
$17+ B
Southeast
$0.75+ B
$0.75+ B
$3.6+ B
$9.2 + B
West
$0.0082 + B
$0.5 + B
$0.97+ B
$4.6 + B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized
benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not possible to
quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and
welfare benefits.
5.6 Discussion
The estimated benefits to human health and the environment of the alternative
PM2.5 daily and annual standard levels are substantial. We estimate that by 2032 the
emissions reduced by the applied control strategies for the proposed annual primary
standards would decrease the number of PIVh.s-related premature deaths and illnesses. The
emissions reduction strategies will also yield significant welfare benefits (see Section
5.3.5), though this RIA does not quantify those endpoints.
emissions reductions from an adjacent county for one ton of emissions reduction needed in a given county);
the benefits of the additional reductions from adjacent counties also contributes to the higher proportion of
the benefits.
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Inherent to any complex analysis quantifying the benefits of improved air quality,
such as this one, are multiple sources of uncertainty. Some of these we characterized
through our use of Monte Carlo techniques to sample the statistical error reported in the
epidemiologic and economic studies supplying concentration-response parameters and
economic unit values. Other key sources of uncertainty that affect the size and distribution
of the estimated benefits—including projected atmospheric conditions and source-level
emissions, projected baseline rates of illness and disease, incomes and expected advances
in healthcare—remain unquantified. When evaluated within the context of these
uncertainties, the estimated health impacts and monetized benefits in this RIA provide
important information regarding the public health benefits associated with a revised PM
NAAQS.
There are two important differences worth noting in the design and analytical
objectives of NAAQS RIAs compared to RIAs for implementation rules, such as the Revised
Cross-State Air Pollution Rule Update (U.S. EPA, 2020c). First, the NAAQS RIAs illustrate the
potential costs and benefits of a revised air quality standard nationwide based on an array
of emission reduction strategies for different sources. Second, those costs and benefits are
calculated incremental to implementation of existing regulations as well as additional
controls applied to reach the current standards and create the analytical baseline for the
analysis. In short, NAAQS RIAs hypothesize, but do not predict, the strategies that States
may follow to reduce emissions when implementing previous and revised NAAQS options.
Setting a NAAQS does not directly result in costs or benefits, and as such, NAAQS RIAs
illustrate potential benefits and costs; these estimated values cannot be added, or directly
compared, to the costs and benefits of regulations that require specific emissions control
strategies to be implemented.
This latter type of regulatory action—often referred to as an implementation rule—
reduces emissions for specific, well-characterized sources (see: Revised Cross-State Air
Pollution Rule Update (U.S. EPA, 2020c)). In general, the EPA is more confident in the
magnitude and location of the emissions reductions for these implementation rules. As
such, emissions reductions achieved under promulgated implementation rules such as the
RCU have been reflected in the baseline of this NAAQS analysis. For this reason, the benefits
5-39
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estimated in this RIA and all other NAAQS RIAs should not be added to the benefits
estimated for implementation rules.
In setting the NAAQS, the EPA accounts for the variability in PM2.5 concentrations
over space and time. While the standard is designed to limit concentrations at the highest
monitor in an area, EPA acknowledges that emissions controls implemented to meet the
standard at the highest monitor will simultaneously result in lower PM2.5 concentrations in
neighboring areas. In fact, the Policy Assessment for the Review of the National Ambient
Air Quality Standards for Particulate Matter (U.S. EPA, 2022c) shows how different
standard levels would affect the distribution of PM2.5 concentrations, as well as people's
risk, across urban areas. For this reason, it is inappropriate to use the NAAQS level as a
bright line for health effects.
The NAAQS are not set at levels that eliminate the risk of air pollution completely.
Instead, the Administrator sets the NAAQS at a level requisite to protect public health with
an adequate margin of safety, taking into consideration effects on susceptible populations
based on the scientific literature. The risk analysis prepared in support of this PM NAAQS
reported risks below these levels, while acknowledging that the confidence in those effect
estimates is higher at levels closer to the standard (U.S. EPA, 2022c). While benefits
occurring below the standard may be somewhat more uncertain than those occurring
above the standard, the EPA considers these to be legitimate components of the total
benefits estimate. Though there are greater uncertainties at lower PM2.5 concentrations,
there is no evidence of a threshold in PIVh.s-related health effects in the epidemiology
literature. Given that the epidemiological literature in most cases has not provided
estimates based on threshold models, there would be additional uncertainties imposed by
assuming thresholds or other non-linear concentration response functions for the purposes
of benefits analysis.
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Research Triangle Park, NC. U.S. EPA. EPA/600/R-15/068. January 2016. Available at:
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https://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=338596.
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Edition User's Manual. Office of Air Quality Planning and Standards. Research Triangle
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04/documents/benmap-ce_user_manual_march_2015.pdf.
U.S. EPA (2019a). Regulatory Impact Analysis for the Repeal of the Clean Power Plan, and
the Emission Guidelines for Greenhouse Gas Emissions from Existing Electric Utility
Generating Units. U.S. Environmental Protection Agency, Office of Air Quality Planning
and Standards, Health and Environmental Impact Division. Research Triangle Park, NC.
U.S. EPA. EPA-452/R-19-003. June 2019. Available at:
https://www.epa.gov/sites/production/files/2019-
06/documents/utilities_ria_final_cpp_repeal_and_ace_2019-06.pdf.
U.S. EPA (2019b). Integrated Science Assessment (ISA) for Particulate Matter (Final
Report). U.S. Environmental Protection Agency, Office of Research and Development,
Center for Public Health and Environmental Assessment. Research Triangle Park, NC.
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https://www.epa.gov/naaqs/particulate-matter-pm-standards-integrated-science-
assessments-current-review.
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Standards for Particulate Matter, External Review Draft. U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, Health and Environmental
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2019. Available at: https://www.epa.gov/naaqs/particulate-matter-pm-standards-
policy-assessments-current-review-O.
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Oxidants (Final Report). U.S. Environmental Protection Agency. Washington, DC. Office
of Research and Development. EPA/600/R-20/012. April 2020. Available at:
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photochemical-oxidants.
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Sulfur and Particulate Matter Ecological Criteria. U.S. Environmental Protection Agency.
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10/documents/revised_csapr_update_ria_proposal.pdf.
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APPENDIX 5A: BENEFITS OF THE PROPOSED AND ALTERNATIVE STANDARD LEVELS
Overview
In this Appendix, we estimate the potential health benefits resulting from
identifying controls and emissions reductions to comply with the proposed and alternative
standard levels, incremental to a 2032 baseline in which the nation fully attains the current
primary PM2.5 standards (i.e., an annual standard of 12 |ig/m3 and a 24-hour standard of 35
|ig/m3). In contrast the main analysis in Chapter 5, we present the national health impacts
and monetized benefits resulting only from the applied control strategies identified in
Chapter 3 for each of the alternative PM2.5 standard levels in 2032. After applying the
control strategies for the main analysis, we estimated that PM2.5 emissions reductions
would still be needed in certain areas to meet the 10/35,10/30, 9/35 and 8/35 alternative
standard levels. Additional information on estimating the emission reductions needed to
meet each of the alternative standards is available in section 2A.3.4.2 of Appendix 2A. Also,
additional information on the emissions reductions still needed is available in Chapter 3,
Section 3.2.5. Lastly, Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6 discuss the
remaining air quality challenges for areas in the northeast and southeast, as well as in the
west and California that may still need emissions reductions. These challenges limit our
ability to characterize how standard levels might be met given highly local influences that
require more specific information beyond what is available for this type of national
analysis. In this Appendix, we assume the remaining emissions reductions are identified to
meet the proposed and more stringent alternative standard levels, and we present the
resulting health and monetized benefits below. To the extent that the additional PM2.5
emissions reductions are not achieved, the health benefits reported below may be
overestimated.
For this appendix, the annual-mean PM2.5 concentration fields where existing and
alternative NAAQS standard levels are just met were developed to estimate the emission
changes resulting from fully meeting each of the proposed and more stringent alternative
standard levels. Using the methods described in Chapter 5 of this RIA and the "Technical
Support Document (TSD) for the PM2.5 NAAQS Proposal: Estimating PM2.5- and Ozone-
Attributable Health Benefits" that will be published with this RIA, we estimate health
5A-1
-------
benefits from achieving the proposed and more stringent alternative standard levels
occurring as an increment to a 12/35 baseline. These benefits reflect the value of the
avoided PIVh.s-attributable deaths and the value of avoided morbidity impacts, including,
for example, hospital admissions and emergency department visits for cardiovascular and
respiratory health issues.
5A.1 Benefits of the Proposed and More Stringent Alternative Standard Levels of
Primary PM2.5 Standards
Applying the impact and valuation functions described in Chapter 5 and the TSD to
the projected changes in PM2.5 yields estimates of the changes in physical damages (e.g.,
premature mortalities, cases of hospital admissions and emergency department visits) and
the associated monetary values for those changes. Not all known PM health effects could be
quantified or monetized. Tables 5A-1 through 5A-5 present the benefits results for the
proposed and more stringent alternative annual primary PM2.5 standard levels. Table 5A-1
presents the estimated avoided incidences of PM-related illnesses and premature mortality
for achieving each alternative standard level in 2032. Tables 5A-2 and 5A-3 present the
monetized valuation benefits of the avoided morbidity and premature mortality (at a 3%
and 7% discount rate respectively) of the health outcomes in Table 5A-1 for each
alternative standard level in 2032.
Tables 5A-4 and 5A-5 present a summary of the monetized benefits nationally and
by region of achieving the alternative standard levels. The regional monetized benefits in
Table 5A-5 are presented in four regions: California, the Northeast, the Southeast, and the
West. For Tables 5A-4 and 5A-5, the monetized value of unquantified effects is represented
by adding an unknown "B" to the aggregate total. The estimate of total monetized health
benefits is thus equal to the subset of monetized PM-related health benefits plus B, the sum
of the non-monetized health and welfare benefits; this B represents both uncertainty and a
bias in this analysis, as it reflects those benefits categories that we are unable to quantify in
this analysis.
5 A-2
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Table 5A-1 Estimated Avoided PM-Related Premature Mortalities and Illnesses of
Meeting the Proposed and More Stringent Alternative Primary PM2.5
Standard Levels for 2032 (95% Confidence Interval)
Avoided Mortality
10/35
10/30
9/35
8/35
Pope et al. (adult mortality
ages 18-99 years)
3,200
(2,300 to 4,100)
3,800
(2,700 to 4,800)
7,300
(5,200 to 9,300)
15,000
(11,000 to 20,000)
Wu et al. (adult mortality
ages 65-99 years)
1,500
(1,300 to 1,700)
1,800
(1,600 to 2,000)
3,500
(3,100 to 3,900)
7,400
(6,500 to 8,200)
Woodruff et al. (infant
mortality)
3.4
(-2.1 to 8.6)
3.9
(-2.5 to 10)
8.3
(-5.2 to 21)
18
(-11 to 45)
Avoided Morbidity
10/35
10/30
9/35
8/35
Hospital admissions—
cardiovascular (age > 18)
260
(190 to 330)
300
(220 to 380)
570
(410 to 720)
1,200
(840 to 1,500)
Hospital admissions—
respiratory
180
(64 to 300)
210
(72 to 330)
400
(140 to 650)
810
(280 to 1,300)
ED visits-cardiovascular
500
(-190 to 1,200)
570
(-220 to 1,300)
1,100
(-430 to 2,600)
2,300
(-900 to 5,500)
ED visits—respiratory
990
(200 to 2,100)
1,100
(220 to 2,300)
2,300
(450 to 4,700)
4,700
(920 to 9,800)
Acute Myocardial
Infarction
57
(33 to 80)
65
(38 to 91)
120
(72 to 170)
250
(150 to 350)
Cardiac arrest
28
(-11 to 63)
32
(-13 to 73)
61
(-25 to 140)
130
(-51 to 280)
Hospital admissions-
Alzheimer's Disease
610
(470 to 740)
690
(520 to 840)
1,400
(1,000 to 1,700)
3,000
(2,300 to 3,600)
Hospital admissions-
Parkinson's Disease
87
(45 to 120)
100
(53 to 150)
200
(100 to 290)
430
(220 to 610)
Stroke
100
(27 to 180)
120
(31 to 210)
230
(59 to 390)
470
(120 to 810)
Lung cancer
120
(38 to 200)
140
(44 to 230)
270
(83 to 440)
550
(170 to 890)
Hay Fever/Rhinitis
30,000
(7,400 to 52,000)
35,000
(8,500 to 60,000)
66,000
(16,000 to
110,000)
130,000
(33,000 to 230,000)
Asthma Onset
4,600
(4,400 to 4,800)
5,300
(5,100 to 5,500)
10,000
(9,700 to 10,000)
20,000
(19,000 to 21,000)
Asthma symptoms -
Albuterol use
650,000
(-320,000 to
1,600,000)
750,000
(-360,000 to
1,800,000)
1,400,000
(-690,000 to
3,400,000)
2,900,000
(-1,400,000 to
7,000,000)
Lost work days
230,000
(190,000 to
260,000)
260,000
(220,000 to
300,000)
500,000
(420,000 to
570,000)
1,000,000
(850,000 to
1,200,000)
Minor restricted-activity
days
1,300,000
(1,100,000 to
1,600,000)
1,500,000
(1,200,000 to
1,800,000)
2,900,000
(2,400,000 to
3,400,000)
5,900,000
(4,800,000 to
7,000,000)
Note: Values rounded to two significant figures.
5A-3
-------
Table 5A-2 Monetized Avoided PM-Related Premature Mortalities and Illnesses of
Meeting the Proposed and More Stringent Alternative Primary PM2.5
Standard Levels for 2032 (Millions of 2017$, 3% discount rate; 95%
Confidence Interval)
Avoided Mortality
10/35
10/30
9/35
8/35
Pope et al. (adult mortality
ages 18-99 years)
33,000
(3,000 to 89,000)
39,000
(3,500 to 100,000)
75,000
(6,800 to
200,000)
160,000
(14,000 to 430,000)
Wu et al. (adult mortality
ages 65-99 years)
16,000
(1,400 to 41,000)
18,000
(1,700 to 49,000)
36,000
(3,300 to 94,000)
76,000
(7,000 to 200,000)
Woodruff et al. (infant
mortality)
38
(-21 to 150)
44
(-25 to 180)
94
(-52 to 370)
200
(-110 to 800)
Avoided Morbidity
10/35
10/30
9/35
8/35
Hospital admissions—
cardiovascular (age > 18)
4.3
(3.1 to 5.4)
4.9
(3.5 to 6.2)
9.3
(6.8 to 12)
19
(14 to 24)
Hospital admissions—
respiratory
3.0
(0.70 to 5.3)
3.4
(0.79 to 5.9)
6.6
(1.5 to 11)
13
(3.1 to 23)
ED visits-cardiovascular
0.62
(-0.24 to 1.4)
0.7
(-0.27 to 1.6)
1.4
(-0.54 to 3.2)
2.9
(-1.1 to 6.7)
ED visits—respiratory
0.92
(0.18 to 1.9)
1
(0.2 to 2.2)
2.1
(0.42 to 4.4)
4.4
(0.86 to 9.1)
Acute Myocardial
Infarction
3.0
(1.7 to 4.1)
3.4
(2.0 to 4.7)
6.4
(3.7 to 9.0)
13
(7.6 to 18)
Cardiac arrest
1.1
(-0.43 to 2.4)
1.2
(-0.5 to 2.8)
2.3
(-0.95 to 5.2)
4.8
(-2 to 11)
Hospital admissions-
Alzheimer's Disease
7.8
(6 to 9.5)
8.8
(6.7 to 11)
18
(13 to 21)
38
(29 to 46)
Hospital admissions-
Parkinson's Disease
1.2
(0.62 to 1.7)
1.4
(0.72 to 2)
2.7
(0.86 to 2.4)
5.8
(3.1 to 8.3)
Stroke
3.7
(0.97 to 6.4)
4.4
(1.1 to 7.5)
8.3
(2.1 to 14)
17
(4.4 to 29)
Lung cancer
1.9
(0.59 to 3.1)
2.2
(0.68 to 3.6)
4.1
(1.3 to 6.7)
8.4
(2.6 to 14)
Hay Fever/Rhinitis
19
(4.7 to 33)
22
(5.4 to 38)
42
(10 to 73)
85
(21 to 150)
Asthma Onset
220
(200 to 230)
250
(230 to 260)
470
(440 to 500)
950
(890 to 1,000)
Asthma symptoms -
Albuterol use
0.24
(-0.12 to 0.58)
0.27
(-0.13 to 0.67)
0.52
(-0.25 to 1.3)
1.1
(-0.51 to 2.6)
Lost work days
41
(35 to 47)
47
(40 to 54)
90
(76 to 100)
180
(150 to 210)
Minor restricted-activity
days
100
(55 to 160)
120
(63 to 180)
230
(120 to 350)
460
(240 to 700)
Note: Values rounded to two significant figures.
5 A-4
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Table 5A-3 Monetized Avoided PM-Related Premature Mortalities and Illnesses of
Meeting the Proposed and More Stringent Alternative Primary PM2.5
Standard Levels for 2032 (Millions of 2017$, 7% discount rate; 95%
Confidence Interval)
Avoided Mortality
10/35
10/30
9/35
8/35
Pope et al. (adult mortality
ages 18-99 years)
30,000
(2,700 to 80,000)
35,000
(3,100 to 94,000)
67,000
(6,100 to
180,000)
140,000
(13,000 to 380,000)
Wu et al. (adult mortality
ages 65-99 years)
14,000
(1,300 to 37,000)
17,000
(1,500 to 44,000)
32,000
(3,000 to 85,000)
68,000
(6,300 to 180,000)
Woodruff et al. (infant
mortality)
38
(-21 to 150)
44
(-25 to 180)
94
(-52 to 370)
200
(-110 to 800)
Avoided Morbidity
10/35
10/30
9/35
8/35
Hospital admissions—
cardiovascular (age > 18)
4.3
(3.1 to 5.4)
4.9
(3.5 to 6.2)
9.3
(6.8 to 12)
19
(14 to 24)
Hospital admissions—
respiratory
3.0
(0.70 to 5.3)
3.4
(0.79 to 5.9)
6.6
(1.5 to 11)
13
(3.1 to 23)
ED visits-cardiovascular
0.62
(-0.24 to 1.4)
0.7
(-0.27 to 1.6)
1.4
(-0.54 to 3.2)
2.9
(-1.1 to 6.7)
ED visits—respiratory
0.92
(0.18 to 1.9)
1
(0.2 to 2.2)
2.1
(0.42 to 4.4)
4.4
(0.86 to 9.1)
Acute Myocardial
Infarction
2.9
(1.7 to 4.0)
3.3
(1.9 to 4.6)
6.3
(3.6 to 8.8)
13
(7.4 to 18)
Cardiac arrest
1
(-0.43 to 2.4)
1.2
(-0.5 to 2.7)
2.3
(-0.94 to 5.2)
4.7
(-1.9 to 11)
Hospital admissions-
Alzheimer's Disease
7.8
(6 to 9.5)
8.8
(6.7 to 11)
18
(13 to 21)
38
(29 to 46)
Hospital admissions-
Parkinson's Disease
1.2
(0.62 to 1.7)
1.4
(0.72 to 2)
2.7
(1.4 to 3.9)
5.8
(3.1 to 8.3)
Stroke
3.7
(0.97 to 6.4)
4.4
(1.1 to 7.5)
8.3
(2.1 to 14)
17
(4.4 to 29)
Lung cancer
1.3
(0.41 to 2.2)
1.5
(0.48 to 2.5)
2.9
(0.9 to 4.7)
5.9
(1.8 to 9.6)
Hay Fever/Rhinitis
19
(4.7 to 33)
22
(5.4 to 38)
42
(10 to 73)
85
(21 to 150)
Asthma Onset
130
(130 to 140)
160
(140 to 160)
290
(270 to 310)
590
(550 to 630)
Asthma symptoms -
Albuterol use
0.24
(-0.12 to 0.58)
0.27
(-0.13 to 0.67)
0.52
(-0.25 to 1.3)
1.1
(-0.51 to 2.6)
Lost work days
41
(35 to 47)
47
(40 to 54)
90
(76 to 100)
180
(150 to 210)
Minor restricted-activity
days
100
(55 to 160)
120
(63 to 180)
230
(120 to 350)
460
(240 to 700)
Note: Values rounded to two significant figures.
5A-5
-------
Table 5A-4 Total Estimated Monetized Benefits of Meeting the Proposed and More
Stringent Alternative Primary Standard Levels in 2032, Incremental to
Attainment of 12/35 (billions of 2017$)
ft r l*g/m3 annual & 10 jig/m3 annual & 9 jig/m3 annual & 8 ng/m3 annual &
Benefits Estimate 35 ng/m3 24-hour 30 iig/m3 24-hour 35 ng/m3 24-hour 35 ng/m3 24-hour
Economic value of avoided PIVh.s-related morbidities and premature deaths using PM2.5 mortality
estimate from Pope (2019)
3% discount $33 + B $39 + B $76 + B $160 + B
rate
7% discount $30+ B $35 + B $68 + B $140+B
rate
Economic value of avoided PIVh.s-related morbidities and premature deaths using PM2.5 mortality
estimate from Wu et al. (2020)
3% discount $16 +B $19 + B $36 + B $77 + B
rate
7% discount $14+ B $17 + B $33 + B $69 + B
rate
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized
benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not possible to
quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and
welfare benefits.
5 A-6
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Table 5A-5
Total Estimated Monetized Benefits by Region of Meeting the
Proposed and More Stringent Alternative Primary Standard Levels in
2032, Incremental to Attainment of 12/35 (billions of 2017$)
Benefits
Estimate
Region
10 jig/m3
annual &
35 Jig/m3 24-
hour
10 jig/m3
annual &
30 Jig/m3 24-
hour
9 jig/m3
annual &
35 Jig/m3 24-
hour
8 jig/m3
annual &
35 Jig/m3 24-
hour
Economic value of avoided PIVh.s-related morbidities and premature deaths using PM2.5 mortality estimate
from Pope (2019)
California
Northeast
Southeast
West
3%
discount
rate
$29+ B
$2.3+ B
$1.8 + B
$0,086 + B
$32 + B
$2.6+ B
$1.8 + B
$2.8 + B
$49+ B
$15+ B
$9.6+ B
$2.4+ B
$76+ B
$46+ B
$26+ B
$12+ B
California $26 + B $28 + B $44 + B $68 + B
Northeast $2 + B $2.3 + B $13 + B $41 + B
Southeast $1.6 + B $1.6 + B $8.6 + B $23 + B
West $0,077 + B $2.6 + B $2.2 + B $11 + B
Economic value of avoided PIVh.s-related morbidities and premature deaths using PM2.5 mortality estimate
from Wu etal. (2020)
California $14 + B $15 + B $24 + B $37 + B
Northeast $1.1 + B $1.3 + B $7.2 + B $23 + B
Southeast $0.84 + B $0.84 + B $4.4 + B $12 + B
West $0,044 + B $1.4 + B $1.2 + B $5.9 + B
7%
discount
rate
30/0
discount
rate
7%
discount
rate
California
Northeast
Southeast
West
$13+ B
$1 + B
$0.75+ B
$0.04+ B
$14+ B
$1.2 + B
$0.75+ B
$1.3+ B
$21+ B
$6.4+ B
$4 + B
$1.1+ B
$33+ B
$20+ B
$11+ B
$5.3+ B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized
benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not all
possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified
health and welfare benefits.
5A-7
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5A.2 References
Pope III, CA, Lefler, JS, Ezzati, M, Higbee, JD, Marshall, JD, Kim, S-Y, Bechle, M, Gilliat, KS,
Vernon, SE and Robinson, AL (2019). Mortality risk and fine particulate air pollution in
a large, representative cohort of US adults. Environmental health perspectives 127(7):
077007.
Wu, X, Braun, D, Schwartz, J, Kioumourtzoglou, M and Dominici, F (2020). Evaluating the
impact of long-term exposure to fine particulate matter on mortality among the elderly.
Science advances 6(29): eaba5692.
Woodruff, TJ, Darrow, LA and Parker, JD (2008). Air pollution and postneonatal infant
mortality in the United States, 1999-2002. Environmental Health Perspectives 116(1):
110-115.
5 A-8
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CHAPTER 6: ENVIRONMENTAL JUSTICE
Introduction
Executive Order 12898 directs the EPA to "achiev[e] environmental justice (EJ) by
identifying and addressing, as appropriate, disproportionately high and adverse human
health or environmental effects" (59 FR 7629, February 16,1994), termed
disproportionate impacts in this chapter. Additionally, Executive Order 13985 was signed
to advance racial equity and support underserved communities through Federal
government actions (86 FR 7009, January 20, 2021). The EPA defines EJ as the fair
treatment and meaningful involvement of all people regardless of race, color, national
origin, or income with respect to the development, implementation, and enforcement of
environmental laws, regulations, and policies. The EPA further defines the term fair
treatment to mean that "no group of people should bear a disproportionate burden of
environmental harms and risks, including those resulting from the negative environmental
consequences of industrial, governmental, and commercial operations or programs and
policies".1 Meaningful involvement means that: (1) potentially affected populations have an
appropriate opportunity to participate in decisions about a proposed activity that will
affect their environment and/or health; (2) the public's contribution can influence the
regulatory Agency's decision; (3) the concerns of all participants involved will be
considered in the decision-making process; and (4) the rule-writers and decision-makers
seek out and facilitate the involvement of those potentially affected.
The term "disproportionate impacts" refers to differences in impacts or risks that
are extensive enough that they may merit Agency action.2 In general, the determination of
whether a disproportionate impact exists is ultimately a policy judgment which, while
informed by analysis, is the responsibility of the decision-maker. The terms "difference" or
"differential" indicate an analytically discernible distinction in impacts or risks across
population groups. It is the role of the analyst to assess and present differences in
anticipated impacts across population groups of concern for both the baseline and
1 See, e.g., "Environmental Justice." Epa.gov, U.S. Environmental Protection Agency, 4 Mar. 2021,
https: //www.epa.gov/ environmentaljustice.
2 See https://www.epa.gov/environmentaljustice/technical-guidance-assessing-environmental-justice-
regulatory-analysis.
6-1
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proposed regulatory options, using the best available information (both quantitative and
qualitative) to inform the decision-maker and the public.
A regulatory action may involve potential EJ concerns if it could: (1) create new
disproportionate impacts on minority populations, low-income populations, and/or
Indigenous peoples; (2) exacerbate existing disproportionate impacts on minority
populations, low-income populations, and/or Indigenous peoples; or (3) present
opportunities to address existing disproportionate impacts on minority populations, low-
income populations, and/or Indigenous peoples through the action under development.
The Presidential Memorandum on Modernizing Regulatory Review (86 FR 7223;
January 20, 2021) calls for procedures to "take into account the distributional
consequences of regulations, including as part of a quantitative or qualitative analysis of
the costs and benefits of regulations, to ensure that regulatory initiatives appropriately
benefit, and do not inappropriately burden disadvantaged, vulnerable, or marginalized
communities." Under Executive Order 13563, federal agencies may consider equity, human
dignity, fairness, and distributional considerations, where appropriate and permitted by
law. For purposes of analyzing regulatory impacts, the EPA relies upon its June 2016
"Technical Guidance for Assessing Environmental Justice in Regulatory Analysis,"3 which
provides recommendations that encourage analysts to conduct the highest quality analysis
feasible, recognizing that data limitations, time, resource constraints, and analytical
challenges will vary by media and circumstance.
A reasonable starting point for assessing the need for a more detailed EJ analysis is
to review the available evidence from the published literature and from community input
on what factors may make population groups of concern more vulnerable to adverse effects
(e.g., underlying risk factors that may contribute to higher exposures and/or impacts). It is
also important to evaluate the data and methods available for conducting an EJ analysis. EJ
analyses can be grouped into two types, both of which are informative, but not always
feasible for a given rulemaking:
3 See https://www.epa.gov/environmentaljustice/technical-guidance-assessing-environmental-justice-
regulatory-analysis.
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1. Baseline: Describes the current (pre-control) distribution of exposures and risk,
identifying potential disparities.
2. Policy: Describes the distribution of exposures and risk after the regulatory
option(s) have been applied (post-control), identifying how potential disparities
change in response to the rulemaking.
EPA's 2016 Technical Guidance does not prescribe or recommend a specific
approach or methodology for conducting EJ analyses, though a key consideration is
consistency with the assumptions underlying other parts of the regulatory analysis when
evaluating the baseline and regulatory options.
6.1 Analyzing EJ Impacts in This Proposal
In addition to the benefits assessment (Chapter 5), the EPA considers potential EJ
concerns of this proposed rulemaking. A potential EJ concern is defined as "the actual or
potential lack of fair treatment or meaningful involvement of minority populations, low-
income populations, tribes, and indigenous peoples in the development, implementation
and enforcement of environmental laws, regulations and policies" (U.S. EPA, 2015). For
analytical purposes, this concept refers more specifically to "disproportionate impacts on
minority populations, low-income populations, and/or indigenous peoples that may exist
prior to or that may be created by the proposed regulatory action" (U.S. EPA, 2015).
Although EJ concerns for each rulemaking are unique and should be considered on a case-
by-case basis, the EPA's EJ Technical Guidance (U.S. EPA, 2015) states that "[t]he analysis of
potential EJ concerns for regulatory actions should address three questions:
1. Are there potential EJ concerns associated with environmental stressors affected by
the regulatory action for population groups of concern in the baseline?
2. Are there potential EJ concerns associated with environmental stressors affected by
the regulatory action for population groups of concern for the regulatory option(s)
under consideration?
3. For the regulatory option(s) under consideration, are potential EJ concerns created
[, exacerbated,] or mitigated compared to the baseline?"
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To address these questions, the EPA developed an analytical approach that
considers the purpose and specifics of this proposed rulemaking, as well as the nature of
known and potential exposures and health impacts. The purpose of this Regulatory Impact
Analysis (RIA) is to provide estimates of the potential costs and benefits of the illustrative
national control strategies in 2032 for the alternative standard levels analyzed. The
alternative standard levels evaluated in the RIA are more stringent than the current
standards. This means that in reducing emissions to reach lower standard levels, some
areas above or near the current standards are expected to experience greater air quality
improvements, and thus health improvements, than other areas already at or below lower
alternative standard levels. As differences in both exposure and susceptibility (i.e., intrinsic
individual risk factors) contribute to environmental impacts, the analytical approach used
here first determines whether exposure (Section 6.2) and health effect (Section 6.3)
disparities exist under the baseline scenario. The approach then evaluates if and how
disparities are impacted when illustrative emissions control strategies are analyzed. Both
the exposure and health effects analyses were developed using available scientific evidence
from the current PM NAAQS reconsideration, for the future year 2032, and are associated
with various uncertainties. Consistent with the methods the EPA uses to fully characterize
the benefits of a regulatory action, these EJ analyses evaluate the full set of exposure and
health outcome distributions resulting from this proposed action at the national scale.
Recognizing, however, that only some areas of the U.S. are projected to exceed the
proposed alternative standard levels, the EPA conducted a case study analysis to further
examine the impacts of this proposed action on populations living in areas with the highest
exposures and health risks in the baseline. By focusing on locations that are projected to
exceed one of the analytical alternatives examined, this case study analysis considers the
magnitude of exposure and health effect disparities across the smaller geographical scale
where the impacts of alternative standard levels are expected (Section 6.4).4
The EJ exposure assessment portion of the analysis focuses on associating ambient
PM2.5 concentrations with various demographic variables. Because this type of analysis
4 Input data (e.g., air quality surfaces, configuration files, and command line scripts) used to prepare the EJ
analysis described in this chapter are available upon request.
6-4
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requires less a priori information, we were able to include a broad array of demographic
characteristics. Estimating actual health outcomes modified by demographic population
requires additional scientific information, which constrained the scope of the second
portion of the assessment. We focused the EJ health effects analysis on populations and
health outcomes with the strongest scientific support (U.S. EPA, 2019, U.S. EPA, 2020, U.S.
EPA, 2022a). However, the EJ health effects analysis does not include information about
differences in other factors that could affect the likelihood of adverse impacts (e.g., access
to health care, BMI, etc.) across groups, due to limitations on the underlying data.5 Both the
EJ exposure and health effects analyses are subject to uncertainties related to input
parameters and assumptions. For example, both analyses focus on annual PM2.5
concentrations and do not evaluate whether concentrations experienced by different
groups persist across the distribution of daily PM2.5 exposures. Additionally, the EJ health
effects analysis is subject to additional uncertainties related to concentration-response
relationships and baseline incidence data.
Since NAAQS RIAs are national-level assessments and air quality issues are complex
and local in nature, the RIA presents costs and benefits of PM2.5 emission reductions
associated with illustrative control strategies. Correspondingly, the main EJ analyses in this
chapter also evaluates implications of air quality surfaces associated with the illustrative
emission control strategies for both current (i.e., baseline) and alternative standard levels.
However, the illustrative control strategies do not result in all counties identifying
emissions reductions needed to meet either the current or more stringent alternative
standard levels (Chapters 3). As such, the appendix to this chapter provides EJ implications
of air quality scenarios associated with meeting the standards (labelled in some Section 6.6
figures as "Standards") and allows for direct comparison with results associated with the
illustrative emissions control strategies (labelled in some Section 6.6 figures as "Controls").
Complex analyses using estimated parameters and inputs from numerous models
are likely to include multiple sources of uncertainty. As this analysis is based on the same
PM2.5 spatial fields as the benefits assessment (Appendix 2A), it is subject to similar types
5 We do not ascribe differential health effects to be caused by race or ethnicity. Instead, race and ethnicity
likely serve as proxies for a variety of environmental and social stressors.
6-5
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of uncertainty (Chapter 5, Section 5.4). A particularly germane limitation is the illustrative
nature of the emission reductions in NAAQS RIAs; as a result, the EJ analyses in this chapter
illustrate the estimated EJ impacts of the illustrative control strategies and may not reflect
state-level implementation decisions. Relatedly, while proximity analyses can sometimes
provide limited EJ information regarding the demographics of populations living near
emissions sources, in this case state-level implementation decisions are unknown.
Therefore, proximity analyses of populations living near individual sources that could
potentially install controls would be highly uncertain and were not conducted in this EJ
assessment. However, the EJ exposure and health analyses included in this chapter provide
more relevant and high-confidence information than a proximity analysis, since these
analyses relate actual PM2.5 concentrations (not just emissions) to various demographic
populations.
As with all EJ analyses, data limitations make it quite possible that there exist
additional disparities unidentified in this analysis. This is especially relevant for potential
EJ characteristics and more granular spatial resolutions that were not evaluated. For
example, results are provided here at national- and county-levels, potentially masking
tract- or block-level EJ impacts. Additional uncertainties are briefly discussed in the
summary of this analysis (Section 6.5).
6.2 EJ Analysis of Exposures Under Current Standard and Alternative Standard
Levels
This EJ PM2.5 exposure6 analysis aims to evaluate the potential for EJ concerns
related to PM2.5 exposures7 among potentially vulnerable populations8 from three
perspectives, which correspond to the three EJ questions listed in Section 6.1. Specifically,
the following questions are addressed:
6 The term exposure is used here to describe estimated PM2.5 concentrations and not individual dosage.
7 Air quality surfaces used to estimate exposures are based on 12 km x 12 km grids. Additional information on
air quality modeling can be found in Chapter 2.
8 Race, ethnicity, sex, and age population input information is at the tract level, whereas poverty status and
educational attainment population input information is at the county level.
6-6
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1) Are there disproportionate PM2.5 exposures under baseline/current PM NAAQS
standard levels (question 1)?
2) Are there disproportionate PM2.5 health effects under illustrative alternative PM
NAAQS standard levels (question 2)?
3) Are PM2.5 exposure disparities created, exacerbated, or mitigated under illustrative
alternative PM NAAQS standard levels as compared to the baseline (question 3)?
Population variables considered in this EJ exposure assessment include
race/ethnicity, poverty status, educational attainment, age, and sex (Table 6-1). The results
presented below reflect the control strategies described in Chapter 3.
Table 6-1 Populations Included in the PM2.5 Exposure Analysis
Population
Groups
Ethnicity
Hispanic; Non-Hispanic
Race
Asian; American Indian; Black; White
Educational Attainment
High school degree or more; No high school degree
Poverty Status
Above the poverty line; Below the poverty line
Age
Children (0-17); Adults (18-64); Older Adults (65-99)
Sex
Female; Male
6.2.1 Total Exposure
We begin by considering the first two questions from EPA's EJ Technical Guidance
(i.e., are there potential EJ concerns 1) in the baseline, and 2) for the regulatory option(s)
under consideration) with respect to PM2.5 exposures. Estimated exposures as measured by
the projected national and regional ambient PM2.5 concentrations experienced by various
demographic populations for the current standards or alternative standard levels analyzed
are provided in Sections 6.2.1.1 and 6.2.1.2, respectively. Information regarding identified
emissions controls, as well as areas where air quality has been adjusted, is available in
Chapters 2 and 3.
6.2.1.1 National
As NAAQS are national rules, we begin by evaluating annual average PM2.5
concentrations in absolute terms projected to be experienced by various demographic
6-7
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groups that maybe of EJ concern, averaged across the contiguous US (national).9 Figure 6-1
shows the national average annual PM2.5 concentrations associated with the control
strategy baseline scenario for the current annual standard of 12 |ig/m3 and current 24-
hour standard of 35 |ig/m3 (12/35) as a heat map, with higher estimated annual PM2.5
concentrations shown in darker shades of blue. Populations with potential EJ concerns can
be compared to the reference/overall population and/or other populations (i.e., White,
Non-Hispanic, above the poverty line, more educated, and adults 18-64). On average,
Asians, Blacks, Hispanics, and those over 25 without a high school education live in areas
with higher annual PM2.5 concentrations than the reference population, with Hispanic and
Asian populations experiencing the highest relative concentrations. The most substantial
discrepancy in national average annual PM2.5 exposures is noted between Hispanic
populations and non-Hispanic populations. It is noteworthy that the national average
annual exposures for all demographic groups are well below the current annual NAAQS.
9 We initially included children (ages 0-18) for each demographic group in the analyses, but as the 0-18 age
range PM2.5 concentrations appeared very similar to the 0-99 age range PM2.5 concentrations, only the 0-99
age range is presented.
6-8
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Population
Groups
Populations (Ages)
12/35
10/35
10/30
9/35
8/35
Reference
All (0-99)
7.2
7.1
7.1
7.0
6.9
Race
White (0-99)
7.1
7.0
7.0
7.0
6.8
American Indian (0-99)
6.7
6.6
6.6
6.6
6.5
Asian (0-99)
7.7
7.6
7.5
7.4
7.2
Black (0-99)
7.4
7.4
7.4
7.3
7.1
Ethnicity
Non-Hispanic (0-99)
7.0
6.9
6.9
6.9
6.7
Hispanic (0-99)
7.9
7.7
7.7
7.6
7.5
Poverty
Above the poverty line (0-99)
7.2
7.1
7.1
7.0
6.9
Status
Below poverty line (0-99)
7.2
7.2
7.2
7.1
7.0
Educational More educated (HS or more) (25-99)
7.1
7.1
7.0
7.0
6.8
Attainment
Less educated (no HS) (25-99)
7.3
7.3
7.3
7.2
7.0
Age
Children (0-17)
7.2
7.2
7.2
7.1
6.9
Adults (18-64)
7.2
7.2
7.2
7.1
6.9
Older Adults (64-99)
7.0
6.9
6.9
6.9
6.7
Sex
Females (0-99)
7.2
7.1
7.1
7.1
6.9
Males (0-99)
7.2
7.1
7.1
7.0
6.9
Figure 6-1 Heat Map of National Average Annual PM2.5 Concentrations (fig/m3) by
Demographic for Current and Alternative PM NAAQS Levels (10/35,
10/30,9/35, and 8/35) After Application of Controls
Figure 6-1 also shows the national average total PM2.5 concentrations associated
with control strategies applied for the potential alternative annual and 24-hour standard
levels: 10/35,10/30, 9/35, and 8/35, Although average concentrations under 10/35 and
10/30 are similar, most demographic groups are projected to experience greater annual
PM2.5 concentration reductions after implementing the illustrative control strategies for
lower alternative annual standard levels. However, after implementing the illustrative
control strategies associated with all alternative standard levels evaluated, Asians, Blacks,
Hispanics, those over 25 without a high school education, and those under the poverty level
live in areas with higher projected annual PM2.5 concentrations than the reference
population, again with Hispanic and Asian populations experiencing the highest average
concentrations. This suggests that while emissions reductions associated with more
stringent standard levels will result in air quality improvements across the board,
disparities seen in the baseline likely remain, at least when considering the average
national exposure levels by demographic group. These annual average exposures are also
well below the current standards and all alternative standard levels evaluated.
6-9
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While average PM2.5 concentrations can provide some insight when comparing
population impacts, information on the full distribution of concentrations affords a more
comprehensive understanding. This is because both demographic groups and ambient
concentrations are unevenly distributed, meaning that average exposures may mask
important disparities that occur on a more localized basis. To evaluate how the distribution
of annual exposures varies within and across demographic groups at the county level, we
plot the full array of exposures (including very high and very low exposures) projected to
be experienced by different subpopulations. Distributional figures present the running sum
of each population, converted to a percentage, on the y-axes (i.e., cumulative percent).
Conversion of each total population to a percent of the total permits direct comparison of
annual PM2.5 exposures across demographic populations with different absolute numbers.
The x-axes show annual PM2.5 concentrations ([ig/m3) from low to high. For Figure 6-2,
PM2.5 concentrations are county-level averages from all counties in the contiguous U.S. In
other words, plots compare the running sum of each population against increasing annual
PM2.5 concentrations.
Information on the distribution of county-level PM2.5 concentrations associated with
the illustrative control strategies associated with the current and alternative PM standard
levels across and within populations can be found in Figure 6-2. The reference population
in the top row shows that emissions reductions associated with the current or alternative
standard levels yields a fairly smooth S-curve, with the majority of the population
experiencing annual PM2.5 concentrations between 4 and 10 |ig/m3 under air quality
scenarios associated with the control strategies for current standards (12/35). Lower PM2.5
concentrations remain similar across lower alternative standard levels, while higher
concentrations are reduced.
To evaluate differential exposures, populations of potential EJ concern are shown
with a colored line and can be compared to the respective reference population shown with
a black line. Colored lines to the right of a black line suggest that the potential EJ population
is experiencing disproportionately higher PM2.5 concentrations. The greatest
disproportionate exposures are observed when considering ethnicity. The Hispanic
population (dark orange) is predicted to experience higher PM2.5 concentrations than the
6-10
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non-Hispanic population (black) across a large portion of the exposure distribution. This
difference is approximately 1 |ig/m3 at all concentrations above 6 |ig/m3.
Similarly, when considering race across the various standard levels evaluated,
portions of the Asian (bright orange) and Black (blue) populations live in areas with higher
PM2.5 concentrations than the White (black) population, and portions of the American
Indian (light orange) population live in areas with lower PM2.5 concentrations.
Interestingly, Black and White population exposures are very similar at concentrations
above about 8 |ig/m3 under air quality scenarios associated with controls for 12/35 and
about 7.5 |ig/m3 air quality scenarios associated with controls for 8/35. This could suggest
that exposure disparities in the Black population occur in rural areas with lower PM2.5
concentrations. The Asian population experiences higher PM2.5 concentrations across a
larger portion of the distribution, but higher exposures become more similar to the White
distribution at lower alternative PM standard levels. Those living below the poverty level,
those over 25 without a high school diploma, and the two sexes experience virtually
identical distributions of exposure of all standard levels.
6-11
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Figure 6-2 National Distributions of Annual PM2.5 Concentrations by
Demographic for Current and Alternative PM NAAQS Levels After
Application of Controls
§ 100%
Reference -5 50%
a
o
0%
g 100%
Race -5 50%
Q.
a- 0%
§ 100%
Ethnicity -5 50%
Poverty
Status
a- 0%
§ 100%
3 50%
§ 100%
Educational %
Attainment 3 50'c
a- 0%
§ 100%
Age -5 50%
cl
& 0%
= 100%
Sex -5 50%
Q.
O
a 0%
All (0-99)
White (0-99)
American Indian (0-99)
Asian (0-99)
Black (0-99)
Non-Hispanic (0-99)
Hispanic (0-99)
Above the poverty line (0-99)
Below poverty line (0-99)
More educated (HS or more) (25-99)
Less educated (no HS) (25-99)
Adults (18-64)
Children (0-17)
Older Adults (64-99)
Females (0-99)
Males (0-99)
6.2.1.2 Regional
As both emissions changes and overrepresentation of people/communities of color
(POC/COC) vary with respect to location, we also parse the aggregated and distributional
absolute PM2.5 concentration by geographic region (southeast [SE], northeast [NE], west
[W], and California [CA|) (Figure 6-3 and Figure 6-4).ian Across all current and alternative
standard levels, average annual reference PM2.5 concentrations are highest in CA, followed
by the SE and NE, and are lowest in the W (Figure 6-3). Comparing populations of potential
EJ concern with their respective references within each region, disparities are observed in
all four regions, though not all for the same demographic populations.
10 Regions used here are consistent with regions used in the costs and benefits chapters of this RIA and were
selected for reasons associated with identification of emission controls.
11 Distributions for the reference, male, and female populations were excluded from Figure 6-4- as they closely
reflect overall distributions.
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Regarding racial and ethnic disparities, annual PM2.5 concentrations for Black
populations are substantially higher in the NE across the full distribution, but only slightly
higher in the W and in CA, Also, concentrations for Black populations are slightly higher
than concentrations for White populations only in the lowest ~50 percent of the
populations in the SE. PM2.5 concentrations among Hispanics are higher than
concentrations for Non-Hispanic populations in all four regions, although disparities are
largest at higher PM2.5 concentrations in CA and smallest at lower PM2.5 concentrations in
the NE, Total PM2.5 concentrations for Asian populations in the NE and SE are higher than
the reference PM2.5 concentrations, but similar in the W and CA.
People living below the poverty level and people over 25 without a high school
diploma experience similar annual PM2.5 concentrations to those above the poverty line
and with a high school diploma in the NE, SE, and W, but experience higher PM2.5
concentrations in CA under controls associated with the current standards (12/35). Older
adults (65-99) experience slightly lower PM2.5 concentrations associated with the
illustrative control strategies for the more stringent alternative standard levels in all
regions. Children experience higher annual PM2.5 concentrations in some areas in the W.
12/35 | 10/35 10/30 9/35 8/35
Groups™" P°Pulations (A9es) | NE SE W CA | NE SE W CA NE SE W CA NE SE W CA NE SE W CA
Reference All (0-99)
6.9 7.1
6.6
8.9
6.9 7.1 6.6
8.6
6.9 7.1 6.5
8.6
6.8 7.0
6.5
8.5
6.7 6.9
6.3
8.3
Race White (0-99)
6.8 7.0
6.6
8.9
6.7 7.0 6.6
8.6
6.7 7.0 6.6
8.6
6.7 6.9
6.5
8.5
6.6 6.8
6.4
8.4
American Indian (0-99)
6.7 7.0
5.4
8.8
6.7 7.0 I 5.4
85
6.7 7.0 1 5.4
8.5
6.6 6.9
5.4
8.4
6.5 6.9
5.3
8.3
Asian (0-99)
7.2 7.5
6.5
8.8
7.2 7.5 1 6.5
8.5
7.2 7.5 6.5
8.5
7.1 7.3
6.4
8.3
7.0 7.1
6.2
8.1
Black (0-99)
7.5 7.2
6.9
9.3
7.5 7.2 6.9
8.9
7.5 7.2 6.8
8.9
7.3 7.1
6.8
8.8
7.1 7.0
6.5
8.6
Ethnicity Non-Hispanic (0-99)
6.8 6.9
6.4
8.6
6.8 6.9 6.4
8.3
6.8 6.9 6.4
8.3
6.7 6.9
6.4
8.2
6.6 6.7
6.2
8.0
Hispanic (0-99)
7.3 7.6
6.9
9.4
7.3 7.5 6.9
9.0
7.3 7.5 6.9
8.9
7.2 7.4
6.8
8.9
7.1 7.2
6.6
8.8
Poverty Above the poverty 1 i ne (0-99)
6.9 7.1
6.6
8.9
6.9 7.0 6.6
8.6
6.9 7.0 6.5
8.5
6.8 7.0
6.5
8.5
6.7 6.9
6.3
8.3
Status Below poverty line (0-99)
7.0 7.1
6.5
9.1
7.0 7.1 6.5
8.7
7.0 7.1 6.5
8.7
6.9 7.0
6.5
8.7
6.7 6.9
6.3
8.6
Educational More educated (HS or more) (25-99)
6.9 7.0
6.5
8.8
6.9 7.0 6.5
8.5
6.8 7.0 6.5
8.5
6.8 6.9
6.5
8.4
6.6 6.8
6.3
8.2
Attainment Less educated (no HS) (25-99)
6.9 7.1
6.6
9 2
6.9 7.1 6.6
8.8
6.9 7.1 6.5
8.7
6.9 7.0
6.5
8.7
6.7 6.9
6.3
8.6
Age Children (0-17)
6.9 7.1
6.6
9.0
6.9 7.1 6.6
8.7
6.9 7.1 6.6
8.7
6.8 7.0
6.6
8.6
6.7 6.9
6.4
8.4
Adults (18-64)
6.9 7.1
6.6
9.0
6.9 7.1 6.6
8.6
6.9 7.1 6.6
8.6
6.8 7.0
6.5
8.5
6.7 6.9
6.4
8.4
Older Adults (64-99)
6.8 6.8
6.4
8.7
6.7 6.8 6.4
8.4
6.7 6.8 6.4
8.4
6.7 6.8
6.3
8.3
6.5 6.7
6.2
8.2
Sex Females (0-99)
6.9 7.1
6.6
8.9
6.9 7.1 6.6
8.6
6.9 7.1 6.5
8.6
6.8 7.0
6.5
8.5
6.7 6.9
6.3
8.3
Males (0-99)
6.9 7.1
6.6
8.9
6.9 7.0 6.6
8.6
6.9 7.0 6.5
8.6
6.8 7.0
6.5
8,5
6.7 6.8
6.3
8.3
Figure 6-3 Heat Map of Regional Average Annual PM2.5 Concentrations (|ig/m3) by
Demographic for Current (12/35) and Alternative PM NAAQS Levels
(10/35,10/30,9/35, and 8/35) After Application of Controls
6-13
-------
NEi
i o%
1100%
<5 SE 3 50%
o 51
!"f
* i'
LU *
CA i
&
a.
ne|
&
a.
2 1
SE 1
o ^
o> ^
< Jl
W 1
&
a.
Sioo^
CA I 50%
§. 0%
4 5 6 7 8 9 45678 9
PM2.5 (pg/m*) PM2.5 (^g/m1)
White (0-99)
American Indian (0-99)
Asian (0-99)
Black (0-99)
Non-Hispanic (0-99)
Hispanic (0-99)
Above the poverty line (0-99)
Below poverty line (0-99)
More educated (HS or more) (25-99)
Less educated (no HS) (25-99)
Adults (18-64)
Children (0-17)
Older Adults (64-99)
Figure 6-4 Regional Distributions of Annual PM2.5 Concentrations by
Demographic for Current and Alternative PM NAAQS Levels After
Application of Controls
6-14
-------
6.2.2 Exposure Changes
In addition to evaluating total/absolute exposures under control strategies
associated with current/baseline and potential alternative standard levels (Section 6.2), we
evaluate the extent to which exposures change for each demographic population, to
compare improvements in air quality among populations. This begins to address the third
question from EPA's EJ Technical Guidance: how disparities observed between
demographic groups in the baseline scenario (12/35) are impacted (e.g.,
exacerbated/mitigated) under alternative standard levels. The national and regional
changes in PM2.5 concentrations experienced by different demographic populations for the
current and alternative standard levels are provided in Sections 6.2.2.1 and 6.2.2.2,
respectively.
6.2.2.1 National
First, we consider how average exposures change across different demographic
groups at the national level. Figure 6-5 shows the average PM2.5 concentration reduction
and Figure 6-6 shows the distributions of county-level PM2.5 concentration exposure
reductions for each population when moving from the current standard to alternative
standard levels. The magnitude of these numbers is quite small because they are national
averages and include individuals residing in 12km x 12km gridded areas not predicted to
experience PM2.5 concentration reductions. For example, Figure 6-6 shows that only ~15%
of the non-Hispanic population will experience PM2.5 concentration reductions when
moving from the baseline of control strategies associated with the current standards to
control strategies associated with the alternative standard levels of 10/35, whereas ~30%
of the Hispanic population will experience PM2.5 concentration reductions under air quality
scenarios associated with the same control strategies. Figure 6-6 also shows that greater
reductions are expected in the ~30% of the Hispanic population projected to experience
PM2.5 concentration reductions than the ~15% of the non-Hispanic population projected to
experience PM2.5 concentration reductions. Together, these differences lead to an estimated
four-fold greater reduction in average PM2.5 concentrations when moving from the baseline
of air quality associated with control strategies for the current standards of 12/35 to
control strategies associated with the proposed alternative standard level of 10/35 (12/35-
6-15
-------
10/35) in Figure 6-5, Colored lines again represent potential populations of EJ concern and
black lines the respective reference population; however, in these figures, colored lines to
the right of the black line now indicate greater relative air quality improvements.
In general, populations with higher total PM2.5 exposures (Section 6.2.1) are also
expected to see the greatest reductions in average PM2.5 concentrations under the
alternative standard levels. On average nationwide, Asians, Hispanics, and those over 25
without a high school diploma are predicted to experience substantially greater PM2.5
concentration reductions under air quality scenarios associated with control strategies for
all alternative standard levels as compared to the reference population. Black populations
may experience slightly smaller PM2.5 concentration reductions for alternative standard
levels of 12/35-10/35 and 12/35-10/30 as compared to either the reference/overall
population or other populations (Asian, Hispanic, and those over 25 without a high school
diploma), but that disparity is smaller for control strategies associated with 12/35-9/35 or
12/35-8/35, and in fact average PM2.5 concentration improvements are on par or slightly
greater than in the reference population for these more stringent alternative standard
levels.
Population
Groups
Populations (Ages)
12/35-10/35 12/35-10/30
12/35-9/35
12/35-8/35
Reference
All (0-99)
0.05
0.06
0.12
0.27
Race
White (0-99)
American Indian (0-99)
0.05
0.05
0.06
0.06
0.12
0.10
0.25
0.21
Asian (0-99)
0.11
0.12
0.23
0.42
Black (0-99)
0.04
0.04
0.13
0.29
Ethnicity
Non-Hispanic (0-99)
0.03
0.04
0.10
0.24
Hispanic (0-99)
0.12
0.12
0.21
0.38
Poverty
Status
Above the poverty line (0-99)
Below poverty line (0-99)
0.05
0.06
0.06
0.06
0.12
0.13
0.27
0.27
Educational More educated (HS or more) (25-99)
0.05
0.06
0.12
0.26
Attainment
Less educated (no HS) (25-99)
0.08
0.09
0.16
0.30
Age
Children (0-17)
Adults (18-64)
Older Adults (64-99)
0.05
0.06
0.05
0.06
0.06
0.05
0.13
0.13
0.11
0.27
0.28
0.24
Sex
Females (0-99)
Males (0-99)
0.05
0.05
0.06
0.06
0.13
0.12
0.27
0.27
Figure 6-5 Heat Map of National Reductions in Average Annual PM2.5
Concentrations (ng/m3) for Demographic Groups When Moving from
Current to Alternative PM NAAQS Levels After Application of Controls
6-16
-------
12/35-10/35
12/35-10/30
12/35-9/35
12/35-8/35
¦ All (0-99)
c 100%
% 80%
Reference f 6Q%
40%
r*
c 100%
1 80%
RaCe | 60%
40%
J
¦ White (0-99)
American Indian (0-99)
¦ Asian (0-99)
¦ Black (0-99)
c 100%
•| 80%
E tonicity 3 60%
a 40%
r
r
¦ Non-Hispanic (0-99)
I Hispanic (0-99)
c 100%
Poverty | 80%
Status i. 60%
O
a 40%
¦ Above the poverty line (0-99)
¦ Below poverty line (0-99)
c 100%
_o
Educational H 80%
Attainment i. 60%
0
40%
¦ More educated (HS or more) (25-1
¦ Less educated (no HS) (25-99)
c 100%
5 80%
AQe "1 60%
a 40%
¦ Adults (18-64)
¦ Children (0-17)
¦ Older Adults (64-99)
c 100%
I 80%
SeX 1 60%
O
40%
p
¦ Females (0-99)
¦ Males (0-99)
0.51.01.5 2.0
PM2.5 (ng/m3)
0.51.01.5 2.0
PM2.5 (ng/m3)
0.5 1.0 1.5 2.0
PM2.5 (ng/m3)
0.51.01.5 2.0
PM2.5 (|ig/m3)
Figure 6-6 National Distributions of Annual PM2.5 Concentration Reductions for
Demographic Groups When Moving from Current to Alternative PM
NAAQS Levels After Application of Controls
6.2.2.2 Regional
Next, we consider how average exposures change across different demographic
groups at the regional level. Information on average and distributional exposure changes
by region when moving from control strategies associated with the current standard to
control strategies associated with alternative standard levels are available in Figure 6-7
and Figure 6-8, respectively.12 Similar to the average annual PM2.5 concentrations going
from highest in CA, followed by the SE and NE, and being lowest in the W (Section 6.2.1.2),
average PM2.5 concentration reductions also follow the same order. Comparing how these
12 Distributions for the reference, male, and female populations were excluded from Figure 6-8 as they closely
reflect overall distributions.
6-17
-------
reductions affect populations of potential EJ concern with each region, we note that there
are differences across regions in terms of which demographic populations benefit the most
(or least), particularly for 12/35-9/35 or 12/35-8/35,
Going through each region, the largest regional PM2.5 concentration reductions
occur in CA, where Blacks, Hispanics, those below the poverty line, and those less educated
are expected to experience greater PM2.5 concentration reductions when moving from the
baseline to alternative standard levels. In the SE, there are greater PM2.5 concentration
reductions for Asians, Hispanics, and those less educated under all alternative standard
levels. Asian and Black populations in CA experience greater PM2.5 concentration
reductions when moving from 12/35-8/35. In the NE for 12/35-9/35 and 12/35-8/35
there are greater PM2.5 concentration reductions for Blacks, and slightly greater PM2.5
concentration reductions for Asians. This is similar to the W, where Blacks, Hispanics, and
those less educated are predicted to see greater PM2.5 concentration reductions for 12/35-
9/35 and 12/35-8/35.
Population
Groups
Reference
Race
Ethnicity
Poverty
Status
Educational
Attainment
Age
Sex
Populations (Ages)
All (0-99)
White (0-99)
American Indian (0-99)
Asian (0-99)
Black (0-99)
Non-Hispanic (0-99)
Hispanic (0-99)
Above the poverty line (0-99)
Below poverty line (0-99)
More educated (HS or more) (25-99)
Less educated (no HS) (25-99)
Children (0-17)
Adults (18-64)
Older Adults (64-99)
Females (0-99)
Males (0-99)
12/35-10/35 12/35-10/30
NE SE W CA I NE SE W CA
12/35-9/35 12/35-8/35
NE SE W CA NE SE W CA
0.01
0.01
0.00
0.01
0.01
0.01
0.00
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.02
0.02
0.02
0.04
0.02
0.01
0.04
0.02
0.02
0.02
0.03
0.02
0.02
0.01
0.02
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.34 0.01
0.34 0.01
0.30 0.00
0.35 0.01
0.40 0.01
0.29 0.01
0.40 0.00
0.34 0.01
0.37 0.01
0.33 0.01
0.41 0.01
0.33 0.01
0.35 0.01
0.34 0.01
0.35 0.01
0.34 0.01
0.02
0.02
0.02
0.04
0.02
0.01
0.04
0.02
0.02
0.02
0.03
0.02
0.02
0.01
0.02
0.02
0.36
0.35
0.32
0.36
0.42
0.03
0.03
0.01
0.03
0.02
0.03 0.31
0.02 0.41
0.03 0.35
0.02 0.39
0.03 0.35
0 02 0.43
0.03 0.35
0.03 0.36
0.02 0.36
0.03 0.36
0.03 0.35
0.08
0.07
0.05
0.09
0.12
0.08
0.07
0.08
0.09
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.09
0.09
0.07
0.18
0.09
0.07
0.17
0.09
0.09
0.08
0.11
0.10
0.10
0.07
0.09
0.09
0.05
0.05
0.04
0.05
0.08
0.04
0.08
0.05
0.06
0.05
0.06
0.05
0.05
0.05
0.05
0.05
0.44
0.42
0.37
0.51
0.51
0.41
0.47
0.44
0.45
0.44
0.49
0.43
0.45
0.43
0.45
0.44
0.22
0.21
0.17
0.24
0.32
0.22
0.24
0.22
0.24
0.22
0.22
0.23
0.23
0.21
0.23
0.22
0.22
0.21
0.17
0.38
0.21
0.17
0.34
0.22
0.21
0.20
0.23
0.24
0.22
0.16
0.22
0.22
0.59
0.55
0.49
0.24
0.23
0.17
0.30
0.38
0.20
0.34 0.59
0.241
0 75
0.67
0.26
0.23
0.29
0.23
0.25
0.22
0.24
0.24
0.60
0.57
0.60
0.60
0.57
0.60
0.59
0.60
0.59
Figure 6-7 Heat Map of Regional Reductions in PM2.5 Concentrations (ng/m3) for
Demographic Groups When Moving from Current to Alternative PM
NAAQS Levels After Application of Controls
6-18
-------
12/35-10/35
12/35-10/30
12/35-9/35
12/35-3/35
I 100%
Race NE 3 50%
a
zi 0%
§ 100%
SE 50%
a.
8. o%
^ 1
§ 100%
W ¦§ 50%
2.
a. 0%
r
r1 1
J 100%
CA 3 50%
el
& 0%
£
£
/
/
o 100%
Ethnicity NE -§ 50%
<£ o%
r
1 100%
SE 3 50%
fik
& 0%
¦3
73
i
1 100%
W 3 50%
a.
S. o%
r
r* 1
1 100%
CA 3 50%
CL
S. 0%
c
£
/
/
1 100%
Poverty WE -= 50%
Status 1 0%
o 100%
SE 3 S0%
D.
<£ 0%
o 100%
W 3 50%
a.
8. o%
r
r* 1
§ 100%
CA 3 50%
£ o%
£
/
/
J 100%
Educational NE 3 50%
Attainment | Q%
J 100%
SE 3 50%
Q.
a. 0%
r
§ 100%
W 3 50%
Q.
<£ 0%
r
/
1
J 100%
CA | 50%
a.
a. 0%
/
/
0.0 1.0 2.0 0.0 1.0 2.0 0.0 1.0 2.0 0.0 1.0 2.0
PM2.5 ReductionPM2.5 ReductionPM2.5 ReductionPM2.5 Reduction
ftig/m1) (pg/ni3) (Mg/ni3) (PO/m3)
| White (0-99)
American Indian (0-99)
I Asian (0-99)
I Black (0-99)
I Non-Hispanic (0-99)
I Hispanic (0-99)
I Above the poverty line (0-99)
I Below poverty line (0-99)
I More educated (HS or more) (2S-99)
I Less educated (no HS) (25-99)
Figure 6-8 Regional Distributions of Total PM2.5 for Demographic Groups When
Moving from Current to Alternative PM NAAQS Levels After
Application of Controls
6-19
-------
6.2.3 Proportional Changes in Exposure
To put the changes in exposure discussed in section 6.2.2 in perspective, especially
in light of the disparities in the exposure baseline across population groups as discussed in
section 6.2.1, it helps to consider whether the absolute changes represent equivalent
(proportional) reductions in exposure. In some cases, moving to more stringent control
strategies could both reduce total average exposures and reduce disparities in exposure
across groups. However, it can be difficult to determine the relative proportionality of
changes in PM2.5 concentrations for demographic populations using just the absolute
exposure changes when moving from the current standard to a potential alternative
standard level, like those shown in section 6.2.2.
In this section, the proportionality of PM2.5 concentration changes when moving
from the current (baseline) to alternative standard levels under air quality scenarios
associated with the illustrative emission control strategies is directly calculated.13 To
compare air quality improvements on a percentage basis, first exposures under the current
standard are divided by exposures under the alternative standard levels at the national and
regional levels. Those results are then subtracted from 1 to get the remainder, and then
multiplied by 100 to get the percent change. For example, if the average annual PM2.5
concentration in population A is 7 under control strategies associated with the current
standard and 6 under an alternative standard level, the proportional change would be (1-
(6/7)) x 100 = (1-0.857) x 100 = 0.143 x 100 = 14.3%. If the average annual PM2.5
concentration in population B is 6 under the current standard and 5 under an alternative
standard level, the proportional change would be (l-(5/6)) x 100 = (1-0.833) x 100 = 0.167
x 100 = 16.7%. Therefore, even though the absolute reduction is equivalent, population B
would experience a proportionally larger reduction under controls strategies associated
with the alternate standard level because the starting concentration was lower. As average
PM2.5 concentrations have been representative of the distributions, for simplicity we only
present the average proportional reduction for each population and scenario, at the
national and regional levels (6.2.3.1 and 6.2.3.2).
13 Results for air quality scenarios associated with meeting the standards can be found in the Appendix to this
chapter.
6-20
-------
6.2.3.1 National
Nationally, alternative PM standard levels associated with control strategies reduce
the average PM2.5 exposure concentrations experienced by the reference population by an
increasing percentage as the alternative standards are lowered, with a 0.7% improvement
for 12/35-10/35 and a 3.8% improvement for 12/35-8/35 (Figure 6-9). Non-Hispanics
experience slightly smaller proportional reductions, 0.5% for 12/35-10/35 and 3.4% for
12/35-8/35. Hispanics and Asian populations are predicted to experience the
proportionally largest reductions in PM2.5 concentrations under all alternative standard
levels evaluated, followed by those less educated. Black populations experience smaller
proportional PM2.5 concentration improvements than Whites when moving from 12/35-
10/35 or 12/35-10/30, but greater proportional PM2.5 concentration improvements than
Whites when moving from 12/35-9/35 or 12/35-8/35. This is likely due to the fact that
gaps between the PM2.5 concentrations experienced by Black populations vs. those
experienced by White populations in the baseline is greater at lower ambient PM2.5
concentrations (Figure 6-2, Figure 6-4, Figure 6-6, and Figure 6-8), with Black populations
experiencing higher PM2.5 levels relative to Whites throughout the distribution but
particularly at lower ambient concentrations. This leads to proportionally greater
improvements for Black populations (i.e., a narrowing of disparities as compared to White
populations) at lower alternative PM2.5 standards. Native Americans are estimated to
experience the opposite, with slightly greater proportional PM2.5 concentration
improvements than Whites when moving from 12/35-10/35 or 12/35-10/30, and smaller
proportional PM2.5 concentration improvements than Whites when moving from 12/35-
9/35 or 12/35-8/35. Older adults are estimated to experience proportionally smaller
reductions in PM2.5 concentrations under all alternative standard levels evaluated; however
older adults experience lower PM2.5 concentrations under air quality scenarios associated
with control strategies for the baseline and all alternative NAAQS (Figure 6-1 through
Figure 6-8).
6-21
-------
Population
Groups
Populations (Ages)
12/35-10/35 12/35-10/30
12/35-9/35
12/35-8/35
Reference
All (0-99)
0.7
0.8
1.7
3.8
Race
White (0-99)
American Indian (0-99)
0.7
0.7
0.8
0.9
1.6
1.5
3.5
3.2
Asian (0-99)
1.5
1.6
3.0
5.5
Black (0-99)
0.5
0.5
1.7
3.9
Ethnicity
Non-Hispanic (0-99)
0.5
0.6
1.4
3.4
Hispanic (0-99)
1.5
1.6
2.7
4.8
Poverty
Status
Above the poverty line (0-99)
Below poverty line (0-99)
0.7
0.8
0.8
0.9
1.7
1.8
3.7
3.8
Educational
Attainment
More educated (HS or more) (25-99)
Less educated (no HS) (25-99)
0.7
1.1
0.8
1.2
1.7
2.2
3.7
4.1
Age
Children (0-17)
Adults (18-64)
Older Adults (64-99)
0.7
0.8
0.7
0.8
0.9
0.8
1.8
1.8
1.6
3.8
3.8
3.4
Sex
Females (0-99)
Males (0-99)
0.8
0.7
0.8
0.8
1.8
1.7
3.8
3.7
Figure 6-9 Heat Map of National Percent Reductions in Average Annual PM2.5
Concentrations (|ig/m3) for Demographic Groups When Moving from
Current to Alternative PM NAAQS Levels After Application of Controls
6.2.3.2 Regional
Regionally the greatest proportional reductions are estimated for CA when moving
from the current to all alternative standards under air quality associated with the
illustrative emission control strategies (Figure 6-10). Like the national analysis, percent
reductions get larger as alternative standard levels decrease. In addition to trends
observed at the national level (Section 6.2.3.1), there are notable proportional reductions
of PM2.5 concentrations for Hispanic populations in CA, the SE, and the W, as well as for
Asian populations in the SE and CA for all alternative standard levels and in the W for
12/35-8/35.
6-22
-------
Population
Groups
Populations (Ages)
NE
12/35-10/35
SE W CA
NE
12/35-10/30
SE W CA
NE
12/35-9/35
SE W
CA
NE
12/35-8/35
SE W
CA
Reference
All (0-99)
0.1
0.3
0.0
3.8
0.2
0.3
0.4
4.0
1.2
1.3
0.8
4.9
3.3
3.0
3.6
6.6
Race
White (0-99)
American Indian (0-99)
0.1
0.1
0.3
0.2
0.0
0.0
3.8
3.4
0.2
0.1
0.3
0.2
0.4
0.3
4.0
3.6
1.1
0.8
1.2
1.0
0.8
0.8
4.7
4.3
3.0
2.5
2.9
2.4
3.5
3.1
6.2
5.6
Asian (0-99)
0.1
0.6
0.0
4.0
0.1
0.6
0.4
4.1
1.2
2.4
0.8
5.8
3.3
5.1
4.7
8.5
Black (0-99)
0.2
0.2
0.0
4.3
0.2
0.2
0.2
4.5
1.7
1.2
1.1
5.5
4.3
3.0
5.5
7.2
Ethnicity
Non-Hispanic (0-99)
0.2
0.2
0.0
3.4
0.2
0.2
0.5
3.6
1.2
1.0
0.7
4.8
3.2
2.5
3.2
7.0
Hispanic (0-99)
0.0
0.6
0.0
4.3
0.0
0.6
0.3
4.4
1.0
2.3
1.1
5.1
3.3
4.6
4.9
6.3
Poverty
Status
Above the poverty line (0-99)
Below poverty line (0-99)
0.1
0.1
0.3
0.3
0.0
0.0
3.8
4.1
0.2
0.2
0.3
0.3
0.4
0.4
4.0
4.3
1.1
1.2
1.3
1.3
0.8
0.9
4.9
5.0
3.2
3.4
3.1
3.0
3.6
3.9
6.7
6.2
Educational
Attainment
More educated (HS or more) (25-99)
Less educated (no HS) (25-99)
0.2
0.1
0.3
0.4
0.0
0.0
3.8
4.5
0.2
0.1
0.3
0.4
0.4
0.3
4.0
4.7
1.2
1.1
1.2
1.5
0.8
1.0
5.0
5.3
3.3
3.2
2.9
3.3
3.6
4.5
6.8
6.6
Age
Children (0-17)
Adults (18-64)
Older Adults (64-99)
0.1
0.1
0.2
0.3
0.3
0.2
o o o
boo
3.7
3.9
3.9
0.2
0.2
0.2
0.3
0.3
0.2
0.4
0.4
0.4
3.9
4.1
4.1
1.1
1.2
1.2
1.5
1.3
1.0
0.8
0.8
0.7
4.7
5,0
5.0
3.3
3.3
3.2
3.3
3.2
2.4
3.5
3.7
3.5
6.4
6.7
6.7
Sex
Females (0-99)
Males (0-99)
0.1
0.1
0.3
0.3
0.0
0.0
3.9
3.8
0.2
0.2
0.3
0.3
0.4
0.4
4.1
4.0
1.2
1.1
1.3
1.3
0.8
0.8
5.0
4.9
3.3
3.2
3.0
3.1
3.6
3.6
6.7
6.6
Figure 6-10 Heat Map of Regional Percent Reductions in Average Annual PM2.5
Concentrations (ng/m3) for Demographic Groups When Moving from
Current (12/35) to Alternative PM NAAQS Level (10/35,10/30, 9/35,
and 8/35 After Application of Controls
6.3 EJ Analysis of Health Effects under Current Standards and Alternative Standard
Levels
In addition to comparing PM2.5 concentrations for potential demographic
populations of concern in the EJ exposure analysis (Section 6.2.1), we conducted an EJ
analysis of health effects. This analysis aims to evaluate the potential for EJ concerns
related to PM2.5 health outcomes among populations potentially at increased risk of or to
PM2.5 exposures from three perspectives, which correspond to the three EJ questions listed
in Section 6.1. Specifically the following questions are addressed:
1) Are there disproportionate PM2.5 health effects (e.g., mortality) under
baseline/current PM NAAQS standard levels (question 1)?
2) Are there disproportionate PM2.5 health effects under illustrative alternative PM
NAAQS standard levels (question 2)?
3) Are disparities in PM2.5 health effects created, exacerbated, or mitigated under
illustrative alternative PM NAAQS standard levels as compared to the baseline
(question 3)?
There is considerable scientific evidence that specific populations and lifestages are
at increased risk of PM2.5-related health effects (Section 1.5.5 and Chapter 1.2 of U.S. EPA,
6-23
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2019). Factors that may contribute to increased risk of PIVh.s-related health effects include
lifestage (e.g., children), pre-existing diseases (e.g., cardiovascular disease and respiratory
disease), race/ethnicity, and socioeconomic status.14 Of these factors, the ISA found
"adequate evidence" indicating that children and some races are at increased risk of
PM2.5-related health effects, in part due to disparities in exposure. However, we lack
associated epidemiologic information that would enable us to conduct a health effects
analysis for children.
Therefore, due to the limited availability of both new scientific evidence in this
NAAQS review and input information (U.S. EPA, 2019, U.S. EPA, 2022a), the one health
endpoint for which we evaluate EJ implications is premature mortality. The PM ISA and PM
ISA Supplement provided evidence that there are consistent racial and ethnic disparities in
PM2.5 exposure across the U.S., particularly for Black/African Americans, as compared to
non-Hispanic White populations. Additionally, some studies provided evidence of
increased PIVh.s-related mortality and other health effects from long-term exposure to PM2.5
among Black populations. Taken together, the 2019 PM ISA concluded that the evidence
was adequate to conclude that race and ethnicity modify PIVh.s-related risk, and that non-
White individuals, particularly Black individuals, are at increased risk for PIVh.s-related
health effects, in part due to disparities in exposure (U.S. EPA, 2019, U.S. EPA, 2022a).
As such, this EJ health analysis assesses long-term PM2.5-attributable mortality rates
stratified by racial and ethnic demographic populations.15'16 Mortality is presented as a rate
14 As described in the 2019 ISA, other factors that have the potential to contribute to increased risk include
obesity, diabetes, genetic factors, smoking status, sex, diet, and residential location (U.S. EPA, 2019, chapter
12).
15 As the ISA and ISA Supplement found that mortality studies evaluated continued to support a linear,
no-threshold concentration-response relationship, mortality rates are calculated here using exposure
estimates across all PM2.5 concentrations (U.S. EPA, 2019, U.S. EPA, 2022a). However, uncertainties remain
regarding the shape of mortality concentration-response functions, particularly at low concentrations.
Additional uncertainties are related to this analysis, as a single epidemiologic study was used to relate
exposure to mortality health effects that applies only to older adults aged 65 and over (Di et al., 2017).
16 The epidemiologic study and concentration-response functions used here to estimate PIVh.s-attributable
mortality rates were identified using criteria that consider factors such as study design, geographic
coverage, demographic populations, and health endpoints. Of the studies available from the 2019 PM ISA
and 2022 Supplement, Di et al., 2017 was identified as best characterizing potentially at-risk racial- and
ethnicity-stratified populations across the U.S. (U.S. EPA, 2019, U.S. EPA, 2022a). The overall response
function was applied to non-Hispanics, as a non-Hispanic-specific concentration-response function was not
provided by Di etal., 2017.
6-24
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per 100,000 (100k) individuals to permit direct comparisons between population
demographics with different total population counts.17 Additional information on the
concentration-response functions and baseline incidence rates used as input information in
this health EJ analysis can be found in Section 6.6.1.2 and Appendix C of the draft PM Policy
Assessment (U.S. EPA, 2021).
6.3.1 Total Mortality Rates
National and regional relative disparities between the demographic-specific
mortality rates under air quality scenarios associated with control strategies for the
current and potential alternative lower standard levels are provided in Sections 6.3.1.1 and
6.3.1.2, respectively.
6.3.1.1 National
Figure 6-11 and Figure 6-12 show the national averages and distributions of
estimated mortality rates per 100k individuals for each demographic population over the
age of 64. These estimates are calculated using various inputs, including air quality
changes, concentration-response functions, and baseline incidence. The greater magnitude
concentration-response relationship between exposure and mortality for the Black
population of older adults found by Di et al., 2017 results in estimated higher mortality
rates in Blacks. Higher estimated average PM2.5 concentrations among Hispanics, as
discussed in the previous sections, leads to larger mortality rates in Hispanics than in non-
Hispanics even though the baseline incidence rate in Hispanics is slightly lower than the
overall rate (U.S. EPA, 2021, Appendix C).
17 Current Agency VSL practices do not differentiate based on race or ethnicity, so the health analysis did not
include monetization. Separately, although the valuation of morbidity outcomes may differ by race or
ethnicity (e.g., someone without insurance may delay seeing seen by a medical professional until the
situation requires more expensive treatment), available scientific evidence for race/ethnicity-stratified
valuation estimates is lacking.
6-25
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12/35
10/35
10/30
9/35
8/35
White
186
185
185
184
181
American Indian
190
188
188
187
185
Asian
165
160
160
158
154
Black
581
579
578
572
559
Non-Hispanic
217
215
215
214
210
Hispanic
236
232
232
230
226
Figure 6-11 Heat Map of National Average Annual Total Mortality Rates (per 100K)
for Demographic Groups for Current and Alternative PM NAAQS Levels
After Application of Controls
12/35 10/35 10/30 9/35 8/35
0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800
Mortality Rate (per Mortality Rate (per Mortality Rate (per Mortality Rate (per Mortality Rate (per
100k) 100k) 100k) 100k) 100k)
Figure 6-12 National Distributions of Total Annual Mortality Rates for
Demographic Groups for Current and Alternative PM NAAQS Levels
After Application of Controls
6.3.1.2 Regional
Regionally, the highest mortality rates for reference populations are in CA under air
quality scenarios associated with control strategies for both current and alternative PM
standard levels, followed by the NE, SE, and then the W (Figure 6-13 and Figure 6-14).
Total mortality rates in the reference populations decrease slightly under alternative
standard levels in all regions, and the most in CA. Within each of the four regions, average
and distributional mortality rates are highest among Blacks and lowest among Asians,
although there are differences in the ordinality of other races and ethnicities across
regions. Interestingly, the distribution of Hispanic mortality rates in the SE suggests there
maybe a subset of locations in which Hispanics have higher baseline incidence rates, as the
PM2.5 concentration differentials between Hispanic and non-Hispanic populations
remained fairly constant across PM2.5 concentration distributions (Figure 6-4).
6-26
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12/35
10/35
10/30
9/35
8/35
NE
SE
W
CA
NE
SE
W
CA
NE
SE
W
CA
NE
SE
W
CA
NE
SE
W
CA
White
188
182
170
216
187
182
170
209
187
182
169
209
186
181
169
207
182
179
164
204
American Indian
168
203
165
249
168
202
165
241
168
202
165
240
167
201
164
239
164
200
162
236
Asian
127
121
137
221
127
120
137
211
127
120
137
210
125
116
136
207
122
112
132
202
Black
594
561
498
699
593
560
498
669
593
560
498
668
583
556
492
660
566
547
468
649
Non-Hispanic
217
213
196
255
217
212
196
246
217
212
195
245
215
211
195
243
210
208
190
238
Hispanic
188
238
207
283
188
237
207
270
188
237
207
270
186
235
205
268
182
231
198
265
Figure 6-13 Heat Map of Regional Average Annual Total Mortality Rates (per
100K) for Demographic Groups for Current and Alternative PM NAAQS
Levels After Application of Controls
12/35
10/35
10/30
9/35
8/35
80%-
NE 40%-
0%.
17
[/
If
[f
u
80%-
>> SE
±: 40%-
y
Je 0%.
¦u
IU
"g" 80%-
ra
£* W
40%-
0%.
ir
If
if
If
If
u
j
V
y
V
V
1
ff f"
\( 1m White
0 J ¦ Non-Hispanic
f [ American Indian
J ¦ Asian
¦ Black
80%-
CA 40%-
0%.
fj
L
IJ
U
J
f/
u
1 i 1 r-
0 200 400 600
Mortality Rate
(per 100k)
1 1 1 r~
0 200 400 600
Mortality Rate
(per 100k)
1 1 1 T—
0 200 400 600
Mortality Rate
(per 100k)
1 1 1 1—
0 200 400 600
Mortality Rate
(per 100k)
1 1 1 r
0 200 400 600
Mortality Rate
(per 100k)
Figure 6-14 Regional Distributions of Total Annual Mortality Rates for
Demographic Groups for Current and Alternative PM NAAQS Levels
After Application of Controls
6.3.2 Mortality Rate Changes
National and regional relative changes in disparities between the demographic-
specific mortality rates when moving from air quality associated with control strategies for
6-27
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the current to alternative standard levels are provided in Sections 6.3,2.1 and 6.3.2.2,
respectively.
6.3.2.1 National
Nationally, the rate of PM2.5-attributable mortality is estimated to decrease for all
races and ethnicities when moving from current alternative standard levels, and more so
under lower alternate standard levels (Figure 6-15 and Figure 6-16). In addition,
reductions in mortality rates are larger for all other races as compared to Whites, and for
Hispanics as compared to non-Hispanics.
12/35-10/35 12/35-10/30 12/35-9/35 12/35-8/35
White
1.0
1.2
2.6
6.0
American Indian
1.4
1.6
2.6
5.2
Asian
4.8
5.0
7.5
11.9
Black
3.4
3.6
11.5
Non-Hispanic
1.2
1.3
3.2
7.3
Hispanic
4.1
4.3
6.5
11.0
Figure 6-15 Heat Map of National Average Annual Mortality Rate Reductions (per
100k) for Demographic Groups When Moving from Current to
Alternative PM NAAQS Levels After Application of Controls
,ioo%-
¦| 80%-
-C
£ 60%
cj 40%-
CY
20%
0 50 100 150
Mortality Rate Reduction
(per 100k)
0 50 100 150
Mortality Rate Reduction
(per 100k)
0 50 100 150
Mortality Rate Reduction
(per 100k)
I White
I Non-Hispanic
American Indian
I Asian
I Black
I Hispanic
1 1 1
0 50 100 150
Mortality Rate Reduction
(per 100k)
12/35-10/35 12/35-10/30 12/35-9/35 12/35-8/35
Figure 6-16 National Distributions of Annual Mortality Rate Reductions for
Demographic Groups When Moving from Current to Alternative PM
NAAQS Levels After Application of Controls
6.3.2.2 Regional
Of the four regions, the largest mortality rate reductions for the greatest percent of
each population are estimated in CA when moving from the current to alternative standard
6-28
-------
levels (Figure 6-17 and Figure 6-18). Reductions are smaller in the other three regions,
although reductions become more substantial in the other three regions for 12/35-9/35 or
12/35-8/35. When comparing across race and ethnicities, Blacks are predicted to see the
largest mortality rate reductions and Whites are predicted to see the smallest rate
reductions.
12/35-10/35
12/35-10/30
12/35-9/35
12/35-8/35
NE SE W
CA
NE SE W
CA
NE SE W
CA
NE SE W
CA
White
0.4 0.3 0.0
7.5
0.4 0.3 0.7
8.0
2.2 1.5 1.2
9.7
6.0 3.8 5.7
13.2
American Indian
0.1 0.3 0.0
9.0
0.1 0.3 0.4
9.7
1.5 1.3 0.8
10.9
4.6 3.2 3.5
14.0
Asian
0.1 1.1 0.0
11.4
0.1 1.1 0.5
11.8
1.7 4.6 1.0
15.1
4.7 8.8 6.1
20.3
Black
1.2 1.1 0.0
36,5
1.3 1.1 0.8
37.9
12.6 6.2 6.7
46.3
31.5 15.7 34.4
Non-Hispanic
0 5 0 3 0.0
9.1
0.5 0.3 0.8
9.7
2.8 1.7 1.4
12.3
7.4 4.6 6,5
17.3
Hispanic
0.1 1.1 0.0
13.8
0.1 1.1 0.5
14.2
1.9 4.1 2.2
15.8
6.4 8.3 10.0
19.1
Figure 6-17 Heat Map of Regional Average Annual Mortality Rate Reductions (per
100k) for Demographic Groups When Moving from Current and
Alternative PM NAAQS Levels After Application of Controls
6-29
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12/35-10/35
12/35-10/30
12/35-9/35
12/35-8/35
100%"
80%-
Mr- 60%-
NE
40%-
20%-
r
r
TWhite
tf ¦ Non-Hispanic
y L American Indian
1 ¦ Asian
¦ Black
¦ Hispanic
100%"
80%-
gg 60%-
$ 40%-
E 20%-
_C
r
r
f—
r-
^ 100%"
8 80%-
Sw 60%-
40%-
20%-
r
If
r
100%"
80%-
CA 60%"
40%-
20%-
V
fj
V
V..
0 50 100 150
Mortality Rate Reduction
(per 100k)
0 50 100 150
Mortality Rate Reduction
(per 100k)
0 50 100 150
Mortality Rate Reduction
(per 100k)
0 50 100 150
Mortality Rate Reduction
(per 100k)
Figure 6-18 Regional Distributions of Annual Mortality Rate Reductions for
Demographic Groups When Moving from Current to Alternative PM
NAAQS Levels After Application of Controls
6.3.3 Proportional Changes in Mortality Rates
The proportional change in mortality rate for different demographic groups when
moving from current to alternative PM2.5 standard levels associated with the illustrate
control strategies is calculated in the same way we estimated proportional changes in PM2.5
concentrations in Section 6.2.3. Briefly the mortality rate under the alternative standard
level is divided by the mortality rate under the current standard, then subtracted from 1,
and multiplied by 100 to get a percent. As the average mortality rates have been
representative of the distributions, for simplicity we again only present the average
proportional change for each population and scenario, at the national and regional levels
(6.3.3.1 and 6.3.3.2).
6-30
-------
6.3.3.1 National
Hispanics and Asians are estimated to experience proportionally larger reductions
in mortality rates when moving from the current to alternative standard levels associated
with control strategies, with the percent relative improvement increasing as standards are
lowered (Figure 6-19).
12/35-10/35 12/35-10/30
12/35-9/35
12/35-8/35
White
0.6
0.6
1.4
3.2
¦H
'u
American Indian
0.7
0.9
1.4
2.7
"c
JZ
Asian
2.9
3.1
4.6
LLJ
~OT
u
ro
Black
0.6
0.6
2.0
4.4
Non-Hispanic
0.5
0.6
1.5
3.4
CtL
Hispanic
1.8
1.8
2.8
4.7
Figure 6-19 Heat Map of National Average Percent Mortality Rate Reductions (per
100k People) for Demographic Groups When Moving from Current to
Alternative PM NAAQS Levels After Application of Controls
6.3.3.2 Regional
Hispanics and Asians are estimated to experience proportionally larger reductions
in mortality rates when moving from current standard to alternative standard levels
associated with control strategies in the SE and CA. Blacks experience proportionally larger
reductions in mortality rates for 12/35-9/35 and 12/35-8/35.
White
£ Asian
iid, Black
cu
12/35-10/35
12/35-10/30
12/35-9/35
12/35-8/35
NE SE W
CA
NE SE W
CA
NE SE W
CA
NE
SE
W
CA
0.2 0.2 0.0
3.5
0.2 0.2 0.4
3.7
1.2 0.8 0.7
4.5
3.2
2.1
3.3
6.1
0.1 0.1 0.0
3.6
0.1 0.1 0.2
3.9
0.9 0.6 0.5
4.4
2.7
1.6
2.1
5.6
0.1 0.9 0.0
5.2
0.1 0.9 0.4
5.3
1.3 3.8 0.7
6.8
3.7
7.3
4.4
9.2
0.2 0.2 0.0
5.2
0.2 0.2 0.2
5.4
2.1 1.1 1.3
6.6
5.3
2.8
6.9
8.5
0.0 0.5 0.0
4.9
0.0 0.5 0.2
5.0
1.0 1.7 1.1
5.6
3.4
3.5
4.8
6.7
0.2 0.2 0.0
3.6
0.2 0.2 0.4
3.8
1.3 0.8 0.7
4.8
3.4
2.1
3.3
6.8
to Hispanic
^ Non-Hispanic
Figure 6-20 Heat Map of Regional Average Percent Mortality Rate Reductions (per
100k) for Demographic Groups When Moving from Current to
Alternative PM NAAQS Levels After Application of Controls
6-31
-------
6.4 EJ Case Study of Exposure and Health Effects in Impacted Areas
The analyses presented above in sections 6.2 and 6.3 encompass the entire
contiguous U.S., including areas that already meet potential alternative standards. Such
areas would not be required to reduce emissions to meet the proposed more stringent
standards, and therefore PM2.5 concentrations in these areas would not be expected to
change as a result of EPA adopting more stringent PM2.5 standard level(s). Including such
areas in the analysis reduces the resulting average exposure and mortality rate change
estimates and potentially masks proportionally greater changes (i.e., reductions) in
exposure and health impacts in areas that are projected to exceed the proposed alternative
standards in the baseline. Areas that exceed the proposed alternative standards can be
expected to experience the greatest PM2.5 concentration changes following the application
of control strategies. Therefore, in addition to analyses of the whole contiguous U.S.
(Sections 6.2 and 6.3), here we perform an EJ case study focusing on areas that are
predicted to experience PM2.5 concentration changes when moving from the current
standard of 12/35 to the alternative standard 9/35 under the emission control scenario
described in Chapter 3.
This case study is intended to illustrate how changes in higher concentration areas
compare to changes at the national scale; for purposes of this illustration, we focus on the
single lower alternative standard of 9/35. The specific areas in which PM2.5 concentrations
change when moving to a lower standard differ with each alternative lower standard, with
the number of areas increasing as the standard lowers. As such, fewer areas would be
included if we analyzed 10/35 or 10/30, and additional areas would be included if we
analyzed 8/35. Also, the case study analysis is limited to the assessment of average PM2.5
exposures and risks and does not include all of the distributional information presented in
the national analysis above. It is important to note that some of the limitations and caveats
that affect the national scale analysis become even more relevant to this case study
analysis. For example, 12 km grid scale air quality information may not be sufficiently
resolved to detect hyperlocal differences in population exposures; this limitation becomes
more important as we try to dial in on changes in exposure and risk in the considerably
smaller areas included in the case study. Finally, the illustrative nature of the emission
6-32
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control strategy leading to emissions reductions in this NAAQS RIA may lead to increased
uncertainties when looking only at areas in which PM2.5 concentrations are predicted to
change, as PM2.5 concentrations in this analysis may not reflect state-level implementation
decisions.
The subset of areas in which PM2.5 concentrations are predicted to change when
moving from 12/35 to 9/35 are colored blue in Figure 6-21. The subset of areas constitutes
approximately 5% of the area across the contiguous U.S. and just over a quarter of the
population. Information regarding the other —95% of areas, which are projected to already
meet a standard of 9/35 and therefore are not projected to experience a change in PM2.5
concentrations under this more stringent standard, is also provided in certain figures for
context.
Figure 6-21 Map of Areas in which PM2.5 Concentrations Change when Moving from
12/35 to 9/35 After Application of Controls
6-33
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6.4.1 Exposures
Average annual PM2.5 concentrations and concentration changes for the various
demographic populations analyzed are presented for the subset of areas in Figure 6-22.
Columns labelled '12/35 (Subset)' and '9/35 (Subset)' provide average PM2.5
concentrations experienced by populations residing in the subset of ~5% of areas (~25%
of people) where PM2.5 concentration changes when moving from 12/35 to 9/35. The far-
right column labeled 'No PM Changes' provides the average PM2.5 concentrations
experienced by populations residing in the other ~95% of areas (~75% of people) that do
not experience a change in PM2.5 concentration under the more stringent standard of 9/35.
The column labelled '12/35-9/35 (Subset)' also shows the average PM2.5 concentration
reduction afforded to each population residing in the subset of areas where concentrations
change, when moving from 12/35 to 9/35.
Comparing these averages to national-level estimates (Figure 6-1), we note that as
expected, we observe higher average baseline exposures in areas where air quality
changes, but the overall pattern of exposure across groups is fairly similar to the national
pattern. Like Figure 6-1, Figure 6-22 shows that the most substantial disparity in average
annual PM2.5 exposures occurs between Hispanic populations and non-Hispanic
populations. Further, in comparing the subset of areas where air quality changes to areas
where it does not change, we note that average exposures in the subset of areas where air
quality does change are at least 1 |ig/m3 higher than averages in the areas where air quality
does not change under both the baseline and 9/35 scenarios. In addition, disparities are
pronounced among certain demographics (e.g., the average baseline exposure among
Hispanics living in areas where air quality does change is almost 2 |ig/m3 higher than
exposures among Hispanics in areas that already meet 9/35). Similarly, the average air
quality improvements experienced by populations living in areas where air quality does
change are 2-4 times larger than when such changes are averaged over the entire
contiguous U.S. (Figure 6-5).
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Populations
Populations (ages)
12/35
(Subset)
9/35
(Subset)
12/35-9/35
(Subset)
No PM
Changes
All
Reference (0-99)
8.5
8.0
0.5
6.7
Race
White (0-99)
8.5
8.0
0.4
6.6
American Indian (0-99)
8.6
8.2
0.4
6.1
Asian (0-99)
8.6
8.1
0.5
6.9
Black (0-99)
8.5
8.0
0.5
7.1
Ethnicity
Non-Hispanic (0-99)
8.2
7.8
0.4
6.6
Hispanic (0-99)
9.0
8.5
0.5
7.1
Educational
More educated (>24; high school or more)
8.4
8.0
0.5
6.6
Attainment
Less educated (>24; no high school)
8.7
8.2
0.5
6.7
Poverty Status
Above poverty line (0-99)
8.5
8.0
0.5
6.7
Below poverty line (0-99)
8.6
8.1
0.5
6.7
Age
Children (0-17)
8.5
8.1
0.5
6.7
Adults (18-64)
8.5
8.0
0.5
6.7
Older Adults (64-99)
8.4
7.9
0.5
6.5
Sex
Females (0-90)
8.5
8.0
0.5
6.7
Males (0-99)
8.5
8.0
0.5
6.7
Figure 6-22 Heat Map of National Average Annual PM2.5 Concentrations and
Concentration Changes (jig/m3) by Demographic for 12/35, 9/35, and
12/35-9/35 in the Subset of Areas that Do and Do Not Experience
Changes in Air Quality When Moving from 12/35 to 9/35
Average exposures of the subset of areas where air quality changes in each of the
four regions analyzed show similar results, with larger average annual PM2.5
concentrations and concentration reductions for this subset of areas in all regions (Figure
6-23), In the subset of areas where air quality does change moving from 12/35 to 9/35,
absolute concentration reductions are more similar across the regions than when all areas
are included as in Sections 6.2 and 6.3, with the largest reductions predicted in the SE,
followed by CA, the NE, and the W. We note that this is tied to the control strategy, which
identified different available measures in each region.
6-35
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Populations Populations (ages)
12/35 (Subset)
NE SE W CA
9/35 (Subset)
NE SE W CA
12/35-9/35 (Subset)
NE SE W CA
No PM Changes
NE SE W CA
All Reference (0-99)
8.0 8.5
7.5
9.1 |
7.6 7.9
7.3
8.6
0.4 0.6 0.3 0.5
6.6 6.8
6.3
7.9
Race White (0-99)
American Indian (0-99)
Asian (0-99)
Black(0-99)
7.9 8.5
8.1 8.6
8.1 8.5
8.1 8.5
7.5
7.8
7.2
7.6
9.1
9.2
8.9
9.4
7.5 7.9
7.7 7.9
7.7 7.8
7.6 8.0
7.3
7.5
tm
7.4
8.6
8.7
8.3
8.9
0.4 0.6 0.3 0.5
0.4 0.7 0.3 0.5
0.4 0.7 0.2 0.5
0.5 0.6 0.3 0.6
6.5 6.7
6.4 6.8
7.0 mm
fel 7.0
6.4
5.0
6.3
6.5
7.9
7.5
8.3
8.3
Ethnicity Non-Hispanic (0-99)
Hispanic (0-99)
7.9 8.3
8.2 8.8
7.3
7.9
8.8 |
9.5
7.5 7.8
7.9 8.1
7.1
7.6
8.3
8.9
0.4 0.5 0.2 0.5
0.3 0.6 0.3 0.6
6.6 6.7
7.0 M
6.3
6.5
7.2
8.6
Educational More educated (>24; high school or more)
Attainment Less educated (>24; no high school)
7.9 8.5
8.0 8.7
7.5
7.4
9.0
9.3
7.5 7.9
7.6 8.0
7.2
7.2
8.5
8.8
0.4 0.6 0.3 0.5
0.4 0.6 0.2 0.6
6.6 6.8
6.7 6.8
6.3
6.3
7.7
8.1
Poverty Status Above poverty line (0-99)
Below poverty line (0-99)
7.9 8.5
8.0 8.6
7.5
7.5
9.1
9.3
7.6 7.9
7.6 8.1
7.3
7.3
8.6
8.8
0.4 0.6 0.3 0.5
0.4 0.6 0.3 0.5
6.6 6.8
6.7 6.8
6.3
6.3
7.9
8.0
Age Children (0-17)
Adults (18-64)
Older Adults (64-99)
8.0 8.5
8.0 8.5
7.9 8.5
7.6
7.6
7.3
9.1
9.1
9.0
7.6 7.9
7.6 7.9
7.5 7.9
7.4
7.3
7.1
8.6
8.6
8.5
0.4 0.6 0.3 0.5
0.4 0.6 0.3 0.5
0.4 0.6 0.2 0.5
6.7 6.9
6.7 6.8
6.5 6.6
6.4
6.4
6.1
8.2
8.0
7.4
Sex Females (0-90)
Males (0-99)
8.0 8.5
7.9 8.5
7.5
7.5
9.1
9.1
7.6 7.9
7.6 7.9
7.3
73
8.6
8.6
0.4 0.6 0.3 0.5
0.4 0.6 0.3 0.5
6.6 6.8
6.6 6.8
6.3
6.3
i
Figure 6-23 Heat Map of Regional Average Annual PM2.5 Concentrations and
Concentration Changes (|xg/m3) by Demographic for 12/35, 9/35, and
12/35-9/35 in the Subset of Areas that Do and Do Not Experience
Changes in Air Quality When Moving from 12/35 to 9/35
While absolute exposure and exposure reduction estimates are necessary
foundational information, the proportionality of the reductions more clearly answers
question 3 of the EJ Technical Guidance (U.S. EPA, 2015). Proportional exposure reductions
(i.e., the percent change in PM2.5 concentrations when moving from 12/35 to 9/35 divided
by the total exposure under 12/35) for the national and regional subset of areas in which
PM2.5 concentrations changed when moving from 12/35 to 9/35 are shown in Figure 6-24.
As expected, proportional reductions are also greater than the national proportions (Figure
6-9 and Figure 6-10). Nationally, all populations with exposures higher than the overall
reference (i.e., Hispanic, Asian, Black, and those less educated) are predicted to have larger
proportional exposure decreases than the reference population. CA reflects the national
trend, although there are variations in the NE, SE, and W. For example, ethnic exposure
disparities in the NE, Black exposure disparities in the SE, and educational attainment
disparities in the W are not proportionally mitigated in the subset of areas with air quality
improvements when moving from the current standard to 9/35. However, it is also
important to note thatHispanics are underrepresented in the NE, and population counts
are lowest in the W.
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Geographic Area
Populations
Populations (ages)
Nation
NE
SE
W
CA
All
Reference (0-99)
5.5
4.9
6.9
3.4
5.6
Race
White (0-99)
5.3
4.7
6.8
3.4
5.4
American Indian (0-99)
5.2
4.3
7.8
3.6
5.2
Asian (0-99)
lift.
4.9
8.0
3.2
6.1
Black (0-99)
5.7
6,7
3.4
6.1
Ethnicity
Non-Hispanic (0-99)
5.3
5.1
3.3
5.4
Hispanic (0-99)
5.8
3.9
si
3.4
Educational
More educated (>24; high school or more)
5.4
5.0
3.4
5.6
Attainment
Less educated (>24; no high school)
5.9
4.8
ma
3.3
6.1
Poverty
Above poverty line (0-99)
5.4
4.9
6.9
3.4
56
Status
Below poverty line (0-99)
5.7
5.4
6.8
3.3
Age
Children (0-17)
5.5
4.9
7.1
3.4
5.4
Adults (18-64)
5.5
4.9
6.9
3.4
5.6
Older Adults (64-99)
5.4
5.1
6.5
3.3
5.6
Sex
Females (0-90)
5.5
4.9
6.9
3.4
5.6
Males (0-99)
5.5
4.9
6.9
3.4
5.5
Figure 6-24 Heat Map of National Percent Reductions in Average Annual PM2.5
Concentrations for Demographic Groups in the Subset of Areas in
which PM2.5 Concentrations Change When Moving from 12/35 to 9/35
6,4,2 Mortality Rates
Although the mitigation of exposure disparities is predicted for all demographic
groups at the national level and most demographic groups at the regional scale in areas in
which PM2.5 concentrations are expected to change in moving from 12/35 to 9/35, it is also
important to translate exposure disparities into health disparities when feasible,
acknowledging that additional uncertainties are associated with estimating population-
stratified health effects. To exemplify the potential importance of stratifying health impacts
within various demographic of potential EJ concern, when employing the Di et al., 2017
population-stratified mortality hazard ratios (Table 6-2), the same PM2.5 exposure
reduction will reduce the hazard of mortality ~3-fold more in Black populations than in
White populations. Therefore, we also separate mortality rate impacts in the subset of
areas where PM2.5 concentrations are expected to change when moving from 12/35 to
9/35 from areas that are not associated with PM2.5 concentration changes.
Average national annual mortality rates and mortality rate changes for the various
demographic populations analyzed are presented in Figure 6-25. Similar to average PM2.5
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concentrations (Figure 6-22), average mortality rates in the subset of areas where air
quality changes are higher, and averages in the areas where air quality does not change are
lower than in the analysis of all areas (Figure 6-11 and Figure 6-13). The mortality rate
reductions are also 2-5 times larger (Figure 6-15 and Figure 6-17),
Race/Ethnicity
12/35
(Subset)
9/35
(Subset)
12/35-9/35
(Subset)
No PM
Changes
White
219
208
11
177
American Indian
240
228
13
177
Asian
203
190
14
122
Black
676
637
45
549
Non-Hispanic
256
243
14
205
Hispanic
274
259
16
211
Figure 6-25 Heat Map of National Average Annual Total Mortality Rates and
Mortality Rate Reductions (per 100K) by Demographic for 12/35,
9/35, and 12/35-9/35 in the Subset of Areas that Do and Do Not
Experience Changes in Air Quality when Moving from 12/35 to 9/35
In the subset of areas in which PM2.5 air quality changes in moving from 12/35 to
9/35, absolute mortality rate reductions are larger and also more similar across the
regions than when all areas are included as in Sections 6.2 and 6.3 (Figure 6-26).
Race/Ethnicity
12/35 (Subset)
NE SE W CA
9/35 (Subset)
NE SE W CA
12/35-9/35 (Subset)
NE SE W CA
No PM Changes
NE SE W CA
White
American Indian
Asian
Black
Non-Hispanic
Hispanic
230 210 191 221
220 201 225 259
159 150 128 223
218 197 185 210
210 187 218 245
152 134 125 208
12 14 6 12
11 16 8 14
8 17 4 16
44 47 18 52
14 17 7 14
9 18 8 18
178 179 165 190
159 203 158 218
118 110 140 187
694 638 541 710
656 598 525 667
553 549 474 604
265 249 222 260
219 289 224 285
251 232 215 247
210 273 217 269
205 209 190 221
180 223 201 267
Figure 6-26 Heat Map of Regional Average Annual Total Mortality Rates and
Mortality Rate Reductions (per 100K) by Demographic for 12/35
9/35, and 12/35-9/35, in the Subset of Areas that Do and Do Not
Change When Moving from 12/3 5-9/3 5
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Proportionally, mortality rate reductions associated with the change between the
12/35 and 9/35 scenarios are greatest for Black and Hispanic populations, helping to
alleviate some of the disparities in the baseline (Figure 6-27). While mortality rate
disparities for Blacks are predicted to be reduced in each region, impacts on disparities for
Hispanics vary by region, with the greatest percent reduction in CA and the W. In
comparing these reductions to the overall reductions in mortality rates nationally (Figure
6-19 and Figure 6-20), we note that the percent reductions are larger in the areas in which
air quality changes when moving from 12/35 to 9/35, and that the pattern of results also
varies somewhat by region (e.g., the greatest proportional rate reductions are seen among
Asians in the SE, as compared to Blacks and Asians in CA in the analysis of all areas).
Geographic Area
Populations
Nation NE
SE
W
CA
White
5.2 5.2
6.4
3.3
5.2
American Indian
5.3 4.8
7.7
3.4
5.5
Asian
7.0 5.0
11.2
3.2
7.0
Black
6.7 6.3
7.3
3.4
7.3
Non-Hispanic
5.5 5.4
7.0
3.3
5.5
Hispanic
5.9 4.3
6.1
3 4
6.4
Figure 6-27 Heat Map of National and Regional Percent Reductions in Average
Annual Total Mortality Rates (per 100K) by Demographic in the
Subset of Areas in which PM2.5 Concentrations Change When Moving
from 12/35-9/35
6.5 Summary
For this proposal, we quantitatively evaluate the potential for disparities in PM2.5
concentrations and mortality effects across different demographic populations for the
current (12/35; baseline) and potential alternative PM2.5 NAAQS levels (10/35,10/30,
9/35, and 8/35) under air quality scenarios associated with illustrative emission control
strategies. Specifically, we provide information on totals, changes, and proportional
changes in 1) annual average PM2.5 concentrations and 2) premature mortality as rates per
100,000 individuals across and within various demographic populations. Each type of
analysis has strengths and weaknesses, but when taken together, can respond to the three
questions from EPA's EJ Technical Guidance. Total concentration/mortality rate analyses
provide information on absolute PM2.5 concentrations and mortality rates; however, it can
6-39
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be difficult to compare improvements in air quality/mortality rates among populations
from total information. In contrast, comparing changes in concentration/mortality rates
provides information on how improvements compare across and within populations, but
does not provide information on which populations experience higher total
concentration/mortality rates. Proportional changes are provided as a percent of the total
concentration/mortality rate information, so although they are similar to absolute changes,
they are more closely related to total concentration/mortality rate information.
EJ analyses were performed both at national and regional scales, as geography is
relevant both to PM NAAQS attainment and population demographics. We also conducted a
case study to examine the subset of areas in which air quality is projected to change after
the application of controls outlined in Chapter 3 to illustrate how air quality improvements
in the areas with the highest starting concentrations might be distributed demographically.
For all of these analyses, we note that the results should be considered illustrative only,
Further, as with all EJ analyses, data limitations make it possible that disparities may exist
that our analysis did not identify. This is especially relevant for potential EJ characteristics,
environmental impacts, and more granular spatial resolutions that were not evaluated. We
note again that this analysis is based on air quality modeling conducted on a 12 by 12 km
grid scale, which may mask more local disparities in exposure and risk. Additionally, EJ
concerns for each rulemaking are unique and should be considered on a case-by-case basis.
Whereas all populations experience reductions in PM2.5 concentrations and health
effects at lower PM standard levels, to make conclusions regarding EJ impacts of this
proposed rule we refer back to the three questions that EPA's EJ Technical Guidance (U.S.
EPA, 2015) states should be addressed, which for purposes of the PM NAAQS RIA EJ
analysis are:
1) Are there disproportionate PM2.5 exposures/health effects under baseline/current
PM NAAQS standard levels?
2] Are there disproportionate PM2.5 exposures/health effects under illustrative
alternative PM NAAQS standard levels?
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3) Are PM2.5 exposure/health effect disparities created, exacerbated, or mitigated
under illustrative alternative PM NAAQS standard levels as compared to the
baseline?
Considering results of both the EJ exposure analysis (Section 6.2) and the EJ health
effects analysis (Section 6.3), responses to the above three questions can be summarized
as:
1) Disparities in the baseline: Under air quality scenarios associated with control
strategies for the baseline (12/35) PM NAAQS scenario, some populations are
predicted to experience disproportionately higher annual PM2.5 concentrations
nationally than the reference (overall) population, both in terms of aggregated
average concentrations and across the distribution of air quality (Figure 6-1 and
Figure 6-2). Specifically, Hispanics, Asians, Blacks, and those less educated (no high
school) have higher national annual concentrations, on average and across the
distributions, than both the overall reference population or other populations (e.g.,
non-Hispanic, White, and more educated). In particular, the Hispanic population is
estimated to experience the highest concentrations, both on average and across
PM2.5 concentration distributions, of all demographic groups analyzed. These
disproportionalities are also observed at the regional level, though to different
extents, as Asian concentrations in the W and CA are similar to the reference group,
and those less educated are exposed to higher PM2.5 concentrations only in CA
(Figure 6-3 and Figure 6-4). Similar, but magnified, trends are observed when
evaluating only the areas in which air quality improvements are predicted.
In terms of health effects, some demographic populations are also predicted to
experience disproportionately higher rates of premature mortality than reference
populations (Figure 6-11 through Figure 6-14). Black populations are estimated to
have the highest national and regional mortality rates, both on average and across
population distributions. This may be partly due to higher PM2.5 concentrations for
this population, which could contribute to the higher magnitude concentration-
response relationship between exposure concentrations and premature mortality
(Di et al., 2017), as well as other underlying health factors which may increase
6-41
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susceptibility to adverse outcomes among Black populations. Hispanic mortality
rates are disproportionately higher in the SE, W, and CA. Higher mortality rates are
predicted for Asians and American Indians in CA and for American Indians in the SE.
Similar, but larger, trends are also observed when evaluating only the areas in which
air quality improvements are predicted.
2) Disparities under alternative policy options: While more stringent control
strategies reduce PM2.5 concentrations and mortality rates across all demographic
groups, disparities seen in the baseline are also reflected in the policy options under
consideration. Specifically, disproportionately higher PM2.5 concentrations and
health effects remain for some populations estimated under air quality scenarios
associated with the illustrative control strategies (10/35,10/30, 9/35, and 8/35)
(Figure 6-1 through Figure 6-4 and Figure 6-11 through Figure 6-14). Nationally
and regionally, these patterns and the populations affected are similar to those seen
in the baseline, and larger when considering only the subset of areas in which air
quality improvements are expected.
3) Relative impact of alternative policy options on disparities in the baseline: For
most populations assessed, PM2.5 concentration disparities are mitigated in the
illustrative air quality scenarios associated with control strategies for more
stringent PM2.5 control strategies (10/35,10/30, 9/35, and 8/35) as compared to
the baseline (12/35), to differing degrees (Figure 6-1 through Figure 6-10). This
conclusion is strengthened when restricting analyses to areas in which PM2.5
concentrations are predicted to decrease (Figure 6-29 through Figure 6-34). More
specifically, increasing portions of certain populations of potential EJ concern are
expected to experience greater PM2.5 concentration reductions as the control
strategies become more stringent (Figure 6-6). At the national scale, Hispanics,
Asians, and those less educated are estimated to see greater proportional reductions
in PM2.5 concentrations than reference populations under all lower standard levels
evaluated, with proportional reductions increasing as the standard levels decrease.
However, concentrations in the Black population are estimated to proportionally
decrease on par with reference concentrations. Average concentration reductions
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were also similar across Black and White populations when the spatial scale of the
analysis was limited to those areas affected by the illustrative control strategies.
Considering the four geographic regions, proportionally greater reductions in PM2.5
concentrations experienced by Asian, Hispanic, and less educated populations are
most notable in the SE and CA, whereas PM2.5 concentration reductions among Black
populations tend to be proportionally larger than among the reference population in
CA, W, and the NE, especially under lower standard levels. Due to the higher
prevalence of Black populations in the SE, the lack of proportional concentration
reductions in that region may mask increased concentration reductions in other
regions at the national level.
In general, more stringent control strategies are also associated with reductions in
mortality rate disparities. Specifically, the analysis shows that as the PM2.5 control
strategies become increasingly stringent, differences in mortality rates across
demographic groups decline, particularly for the lowest alternatives evaluated
(12/35-9/35 and 12/35-8/35). Similar to the estimated changes in PM2.5
concentrations following reductions in PM2.5 concentrations under alternative
standards, disparities in PM2.5 mortality rates across demographic groups are
mitigated nationally for Hispanics in all the alternative PM standard levels (10/35,
10/30, 9/35, and 8/35) as compared to the baseline (Figure 6-19). Nationally, Black
populations are predicted to experience proportionally similar mortality rate
reductions to White populations under control strategies associated with 12/35-
10/35 or 12/35-10/30, but greater reductions in mortality rates than White
populations under control strategies associated with 12/35-9/35 or 12/35-8/35.
While Asians are estimated to experience the greatest proportional mortality rate
reductions of the races/ethnicities analyzed, they are predicted to initially
experience disproportionally lower mortality rates under the baseline scenario.
When the spatial scale of the analysis was limited to those areas affected by the
illustrative control strategies for 9/35, Asian, Black and Hispanics experienced the
greatest reduction in mortality rates nationally and in most regions.
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6.6 Environmental Justice Appendix
6.6.1 Input Information
6.6.1.1 EJ Exposure Analysis Input Data
In Appendix 2A, the exposure assessment involves demographic population data
projected out to the future year 2032. We use population projections based on economic
forecasting models developed by Woods and Poole, Inc. (Woods & Poole, 2015). The Woods
and Poole database contains county-level projections of population by age, sex, and race
out to 2060, relative to a baseline using the 2010 Census data. Projections in each county
are determined simultaneously with every other county in the U.S to consider patterns of
economic growth and migration. The sum of growth in county-level populations is
constrained to equal a previously determined national population growth, based on Bureau
of Census estimates (Hollmann et al., 2000). According to Woods and Poole, linking county-
level growth projections together and constraining to a national-level total growth avoids
potential errors introduced by forecasting each county independently (Woods & Poole,
2015).
6.6.1.2 EJ Health Effects Analysis Input Data
The health assessment requires input data in addition to the information used in the
exposure assessment (Section 6.6.1.1). As such, there are additional uncertainties, albeit
similar to the benefits assessment results (Chapter 5). We evaluated the available studies
and concentration-response functions to determine if sufficient information exists for use
in a quantitative analysis and to determine which study or studies best characterizes at-
risk nonwhite populations across the U.S. Of the available studies, Di et al., 2017 was a
nationwide study, evaluated the largest study size over one of the most recent time spans,
used a sophisticated exposure estimation technique, and provided sufficient information to
apply risk models quantifying increased risks to the following nonwhite groups: Black,
Asian, Native American, and Hispanic populations. Although Di et al., 2017 effect estimates
were derived from a cohort aged 65 and older and the study did not provide a non-
Hispanic concentration-response function, it was identified as best characterizing
populations potentially at increased risk of long-term exposure and all-cause mortality.
Health impact functions, including beta parameters and standard errors (SE), were
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developed for each at-risk population demographic described by Di etal., 2017 and are
provided in Table 6-2.
Table 6-2 Hazard Ratios, Beta Coefficients, and Standard Errors (SE) from Di et
al, 2017
Demographic
Population
3
Risk of Death Associated with a 10 jig/m Increase in
PM
2.5
Beta Coefficient
(SE)
White
1.063 (1.060,1.065)
0.0061 (0.0001)
All
1.073 (1.071,1.075)
0.0070 (0.0001)
Hispanic
1.116 (1.100,1.133)
0.0110 (0.0008)
Black
1.208 (1.199,1.217)
0.0189 (0.0004)
Asian
1.096 (1.075,1.117)
0.0092 (0.0010)
Native American
1.100 (1.060,1.140)
0.0095 (0.0019)
Concentration-response functions stratified by race and ethnicity were only
available for ages greater than 64. While BenMAP-CE includes population information for
5-year age spans up to 84 and Di et al., 2017 provides stratified concentration-response
functions for 10-year age spans (65-74, 75-84, and 85-99), the stratified concentration-
response functions for 10-year age spans were not also stratified by race or ethnicity.
Therefore, this analysis only evaluated a single age range group of 65-99 years.
BenMAP-CE includes baseline incidence rates at the most geographically- and age-
specific levels available for each health endpoint assessed. For many locations within the
U.S., these data are resolved at the county- or state-level, providing a better
characterization of the geographic distribution of mortality rates than the national-level
rates. Race- and ethnicity-stratified baseline incidence rates from 2007-2016 Census data
were recently improved for the all-cause mortality health endpoint, by adding the
geographic level option of rural/urban state between county-level and state-level. Both
overall and race/ethnicity-stratified baseline rates are used in this analysis of EJ health
impacts analysis.
To estimate race-stratified and age-stratified incidence rates at the county level, we
downloaded all-cause and respiratory mortality data from 2007 to 2016 from the CDC
WONDER mortality database.18 Race-stratified incidence rates were calculated for the
18 https://wonder.cdc.gov/
6-45
-------
following age groups: < lyear, 1-4 years, 5-14 years, 15-24 years, 25-34 years, 35-44 years,
45-54 years, 55-64 years, 65-74 years, 75-84 years, and 85+ years. To address the frequent
county-level data suppression for race-specific death counts, we stratified the county-level
data into two broad race categories, White and Non-White populations. In a later step, we
stratified the non-White incidence rates by race (Black, Asian, Native American) using the
relative magnitudes of incidence values by race at the regional level, described in more
detail below.
We followed methods outlined in Section D.l.l of the BenMAP User Manual with one
notable difference in methodology; we included an intermediate spatial scale between
county and state for imputation purposes.19 We designated urban and rural counties within
each state using CDC WONDER and, where possible, imputed missing data using the state-
urban and state-rural classifications before relying on broader statewide data. We followed
methods for dealing with suppressed and unreliable data at each spatial scale as described
in Section D.l.l.
A pooled non-White incidence rate masks important differences in mortality risks
by race. To estimate county-level mortality rates by individual race (Black, Asian, Native
American), we applied regional race-specific incidence relationships to the county-level
pooled non-White incidence rates. We calculated a weighted average of race-specific
incidence rates using regional incidence rates for each region/age/race group normalized
to one reference population (the Asian race group) and county population proportions
based on race-specific county populations from CDC WONDER where available. In cases of
population suppression across two or more races per county, we replaced all three race-
specific population proportions derived from CDC WONDER with population proportions
derived from 2010 Census data in BenMAP-CE (e.g., 50 percent Black, 30 percent Asian, 20
percent Native American).
To estimate ethnicity-stratified and age-stratified incidence rates at the county level,
we downloaded all-cause and respiratory mortality data from 2007 to 2016 from the CDC
WONDER mortality database.20 Ethnicity-stratified incidence rates were calculated for the
19 https://www.epa.gov/sites/default/files/2015-04/documents/benmap-ce_user_manual_march_2 015.pdf
20 https://wonder.cdc.gov/
6-46
-------
following age groups: < lyear, 1-4 years, 5-14 years, 15-24 years, 25-34 years, 35-44 years,
45-54 years, 55-64 years, 65-74 years, 75-84 years, and 85+ years. We stratified county-
level data by Hispanic origin (Hispanic and non-Hispanic). We followed the methods
outlined in Section D.l.l to deal with suppressed and unreliable data. We also included an
intermediate spatial scale between county and state designating urban and rural counties
for imputation purposes, described in detail in Section D.1.3 of the BenMAP User Manual.21
6.6.2 EJ Analysis of Total Exposures Associated with Meeting the Standards
In addition to air quality surfaces associated with the illustrative emission control
strategies evaluated in the main EJ chapter, PM2.5 air quality surfaces associated with
meeting the current and alternative standard levels were also developed. Air quality
associated with meeting the standards was based on assumptions that emission controls
could be identified to meet the required emission amounts (Appendix 2A). Results for both
air quality scenarios are included in this appendix, to allow for direct comparisons. In
general, for populations experiencing higher baseline PM2.5 concentrations and mortality
rates, air quality scenarios associated with meeting the standards reduce disparities more
so than air quality scenarios associated with the control strategies, especially for Hispanics
populations in CA.
National and regional PM2.5 concentrations by demographic populations for air
quality scenarios associated with both the control strategies and meeting the standards are
provided in Sections 6.6.2.1 and 6.6.2.2, respectively.
6.6.2.1 National
At the national level, air quality scenarios associated with meeting the standards led
to similar and/or slightly lower PM2.5 concentrations under the current and lower
alternative standard levels than air quality scenarios associated with control strategies
(Figure 6-28 and Figure 6-29). This may narrow disproportionate PM2.5 concentrations for
certain populations, such as Hispanics, under air quality associated with more stringent
alternative standard levels.
21 https://www.epa.gov/sites/default/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf
6-47
-------
Populations Populations (Ages)
Controls ^
JSJ
UJ
Standards01
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1/)
ID
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9/35
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en
Standards
Reference All (0-99)
7.2 7.2
7.1 7.0
7.1 7.0
7.0 6.9
6.9
6.7
Race White (0-99)
7.1 7.1
7.0 7.0
7.0 6.9
7.0 6.8
6.8
6.6
American Indian (0-99)
Asian (0-99)
Black (0-99)
6.7 6.7 6.6 6.5 6.6 6.5 6.6 6.4 6.5
6,2
7.7 7.7
7.4 7.4
7.6 7.5
7.4 7.4
7.5 7.4
7.4 7.4
7.4 7.2
7.3 7.2
7.2
7.1
6.9
7.0
6.6
7.0
Ethnicity Non-Hispanic (0-99)
Hispanic (0-99)
7.0 7.0
6.9 6,9
6.9 6.9
6.9 6.8
6.7
7.9 7.8
7.7 7.6
7.7 7 5
7.6 7.3
7.5
Poverty Above the poverty line (0-99)
Status Below poverty line (0-99)
7.2 7.1
7.2 7.2
7.1 7.0
7.2 7.1
7.1 7.0
7.2 7.1
7.0 6.9
7.1 7.0
6.9
7.0
6.7
6.7
Educational More educated (HS or more) (25-99)
7.1 7.1
7.1 7.0
7.0 7.0
7.0 6.9
6.8
6,6
Attainment Less educated (no HS) (25-99)
7.3 7.3
7.3 7.2
7.3 7.1
7.2 7.0
7.0
6.7
Age Children (0-17)
Adults (18-64)
Older Adults (64-99)
7.2 7.2
7.2 7.2
7.0 7.0
7.2 7.1
7.2 7.1
6.9 6.9
7.2 7.1
7.2 7.1
6.9 6.9
7.1 7.0
7.1 7.0
6.9 6.8
6.9
6.9
6.7
6.7
6.7
6,5
Sex Females (0-99)
Males (0-99)
7.2 7.2
7.2 7.1
7.1 7.1
7.1 7.0
7.1 7.0
7.1 7.0
7.1 6.9
7.0 6.9
6.9
6.9
6.7
6.7
Figure 6-28 Heat Map of National Average Annual PM2.5 Concentrations ([ig/m3)
Associated Either with Control Strategies (Controls) or with Meeting
the Standards (Standards) by Demographic for Current (12/35) and
Alternative PM NAAQS Levels (10/35,10/30, 9/35, and 8/35)
6-48
-------
12/35
10/35
10/30
9/35
8/35
I All (0-99)
I white (0-99)
American Indian (0-99)
I Asian (0-99)
I Black (0-99)
I Non-Hispanic (0-99)
I Hispanic (0-99)
| Above the poverty line (0-99)
I Below poverty line (0-99)
More educated (HS or more) (25-99)
I Less educated (no HS) (25-99)
| Adults (18-64)
| Children (0-17)
I Older Adults (64-99)
I Females (0-99)
[ Males (0-99)
3456789 10 34S6789 10 |3 456789 10 3456789 10 3456789 10
PM (|ig/m3) * PM (|ig/m3) * PM (ng/m3) * PM (ng/m3) * PM (ng/m3) *
Figure 6-29 National Distributions of Annual PM2.5 Concentrations Associated
Either with Control Strategies or with Meeting the Standards by
Demographic for Current and Alternative PM NAAQS Levels
6.6.2.2 Regional
Regionally, air quality scenarios associated with meeting the standards also led to
similar or slightly lower PM2.5 concentrations as air quality scenarios associated with the
current standards for more stringent standard levels, except for in CA, where air quality
6-49
-------
associated with the standards resulted in substantially lower PM2.5 concentrations (Figure
6-30 and Figure 6-31).22
Comparing 'Controls' and 'Standards' in CA associated with the lower alternative
standard levels allows for some insight into areas without known emission control
strategies. For example, for the alternative standard level of 9/35 in CA, more than 90% of
non-Hispanics and Hispanics are projected to experience annual PM2.5 concentrations <9
[ig/m3 when meeting an alternative standard level of 9/35 and a similar percentage of non-
Hispanics are expected to experience annual PM2.5 concentrations <9 |ig/m3 associated
with emission control strategies for 9/35. In contrast, only about 60% of Hispanics are
expected to experience annual PM2.5 concentrations <9 |ig/m3 with controls associated
with 9/35. Therefore, disparities between Hispanics and non-Hispanics predicted with
controls at 9/35 are mitigated if CA were to meet the alternative standard level of 9/35.
22 The overall reference, ages, and sex population groups were excluded from Figure 6-30 to so that the figure
could fit on a single page.
6-50
-------
NE
12/35
SE
W
CA
NE
10/35
SE
W
CA
NE
10/30
SE
W
CA
NE
SE
9/35
W
CA
NE
SE
8/35
W
CA
Populations
Reference
Race
Ethnicity
Poverty
Status
Educational
Attainment
Age
Sex
Populations (Ages)
All (0-99)
White (0-99)
American Indian (0-99)
Asian (0-99)
Black (0-99)
Non-Hispanic (0-99)
Hispanic (0-99)
Above the poverty line (0-99)
Below poverty line (0-99)
More educated (HS or more) (25-99)
Less educated (no HS) (25-99)
Children (0-17)
Adults (18-64)
Older Adults (64-99)
Females (0-99)
Males (0-99)
CO
CO
CO
CO
CO
CO
CO
CO
6.9 6.9 7.17.1 6.6 6.6
6.8 6.8 7.0 7.0 6.6 6.6
6.7 6.7 7.0 7.0 5.4 5.4
7.2 7.2 7.5 7.5 6.5 6.5
8 9 8.9 6.9 6.9 7.1 7.1 6.6 6.6
~^H6.7 6.7 7.0 7.0 6.6 6.6
.8 8 786.7 6.7 7.0 7.0 5.4 5.4
8 8.8 7.2 7.2 7.5 7.5 6.5 6.5
8.6 8.1
7.5 7.5 7.2 7.2 6.9 6.81
6.8 6.8 6.9 6.9 6.4 6.4 8.6 8.i
7.3 7.3 7.6 7.6 6.9 6.9 \ a 9J
6.9 6.9 7.17.1 6.6 6.6 8.9 8.1
7.0 7.0 7.1 7.1 6.5 6.5 Jl 9.(
6.9 6.9 7.0 7.0 6.5 6.5 ~~
6.9 6.9 7.1 7.1 6.6 6.6
8.6 8.1
8.5 8.0
8.5 8.1
17.5 7.5 7.2 7.2 6.9 6.8 8.9 8.3
6 8 6.8 6.9 6.9 6.4 6.4 8.3 7.9
17.3 7.3 7.5 7.5 6.9 6.9 9.0 8.3
6.9 6.9 7.17.1 6.6 6.61.0 8.9
6.9 6.9 7.1 7.1 6.6 6.6 9.0 8.9
6.8 6.8 6.8 6.8 6.4 6.4 8.7 8.7
6.9 6.9 7.1 7.1 6.6 6.6 8.9 8.9
6.9 6.9 7.1 7.1 6.6 6.5 8.9 8.8
6.9 6.9 7.0 7.0 6.6 6.6 8.6 8.1
7.0 7.0 7.1 7.1 6.5 6.5
6.9 6.9 7.0 7.0 6.5 6.5 8.5 8.1
6.9 6.9 7.1 7.1 6.6 6.6 |.8 8.2
6.9 6.9 7.1 7.1 6.6 6.6^HI
6.9 6.9 7.1 7.1 6.6 6.6 8.6 8.1
6.7 6.7 6.8 6.8 6.4 6.4 8.4 8.0
6.9 6.9 7.1 7.1 6.6 6.6 1&.6 8.1
6.9 6.9 7.0 7.0 6.6 6.5 8.6 8.1
6.9 6.9 7.1 7.1 6.5 6.5
6.7 6.7 7.0 7.0 6.6 6.5
6.7 6.7 7.0 7.0 5.4 5.4
7.2 7.2 7.5 7.5 6.5 6.4
8.6 8.0
8.6 8.0
8.5 7.9
8.5 8.1
7.5 7.5 7.2 7.2 6.8 6.8
6.8 6.8 6.9 6.9 6.4 6.4
7.3 7.3 7.5 7.5 6.9 6.9
6.9 6.9 7.0 7.0 6.5 6.5
7.0 7.0 7.1 7.1 6.5 6.5
6.8 6.8 7.0 7.0 6.5 6.5
6.9 6.9 7.1 7.1 6.5 6.5
6.9 6.9 7.1 7.1 6.6 6.6
6.9 6.9 7.1 7.1 6.6 6.5
6.7 6.7 6.8 6.8 6.4 6.3
6.9 6.9 7.1 7.1 6.5 6.5
6.9 6.9 7.0 7.0 6.5 6.5
8.9 8.3
8.3 7.9
8.9 8.2
8.5 8.0
8.7 8.1
8.5 8.0
8.7 8.1
8.7 8.1
8.6 8.1
8.4 7.9
8.6 8.0
8.6 8.0
6.8 6.8 7.0 7.0 6.5 6.57.6 6.7 6.6 6.9 6.8 6.3 6.3
6.7 6.7 6.9 6.9 6.5 6.5B|7.5 6.6 6.5 6.8 6.8 6.4 6.3
6.6 6.6 6.9 6.9 5.4 5.4 8.4 7.4 6.5 6.5 6.9 6.8 5.3 5.2
7.1 7.1 7.3 7.3 6.4 6.4 8.3 7.6 7.0 6.9 7.1 7.1 6.2 6.2
7.3 7.3 7.1 7.1 6.8 6.8 8.8 7.8
6.7 6.7 6.9 6.9 6.4 6.4 8.2 7.5
7.2 7.2 7.4 7.3 6.8 6.8 8.9 7.7
7.1 7.1 7.0 7.0 6.5 6.4
6.6 6.6 6.7 6.7 6.2 6.2
7.1 7.0 7.2 7.1 6.6 6.5
6.8 6.8 7.0 7.0 6.5 6.5 8.5 7.6 6.7 6.6 6.9 6.8 6.3 6.3
6.9 6.9 7.0 7.0 6.5 6.5 8.7 7.6 6.7 6.7 6.9 6.8 6.3 6.2
6.8 6.8 6.9 6.9 6.5 6.5 8.4 7.5 6.6 6.6 6.8 6.8 6.3 6.2
6.9 6.9 7.0 7.0 6.5 6.5 8.7 7.6 6.7 6.7 6.9 6.8 6.3 6.2
6.8 6.8 7.0 7.0 6.6 6.6 8.6 7.6 6.7 6.7 6.9 6.9 6.4 6.4
6.8 6.9 7.0 7.0 6.5 6.5 8.5 7.6 6.7 6.7 6.9 6.9 6.4 6.3
6.7 6.7 6.8 6.8 6.3 6.3(5.3 7.5 6.5 6.5 6.7 6.7 6.2 6.1
6.8 6.8 7.0 7.0 6.5 6.5 8.5 7.6 6.7 6.6 6.9 6.8 6.3 6.3
6.8 6.8 7.0 7.0 6.5 6.5 8.5 7.6 6.7 6.6 6.8 6.8 6.3 6.3
6.9
6.9
6.8
8.1 6.9
8.6 7.0
8.0 6.8
8.8 7.0
8.3
8.6
8.2
8.6
8.4
8.4
8.2
8.3
8.3
6.9
6.9
6.9
,
6.9
69
6.9
6.8
69
6.9
Figure 6-30 Heat Map of Regional Average Annual PM2.5 Concentrations (|ig/m3) Associated Either with Control
Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS
Levels
6-51
-------
White (0-99)
American Indian (0-99)
Asian (0-99)
Black (0-99)
Non-Hispanic (0-99)
Hispanic (0-99)
Above the poverty line (0-99)
Below poverty line (0-99)
More educated (HS or more) (25-99)
Less educated (no HS) (25-99)
3456789 10 3*56789 10 3*56789 10 3*56789 10 J456789 10 |
* PM (|*fl/Vn*) * PM * PM *
Figure 6-31 Regional Distributions of Annual PM2.5 Concentrations Associated
Either with Control Strategies or with Meeting the Standards by
Demographic for Current and Alternative PM NAAQS Levels
6-52
-------
6.6.3 EJ Analysis of Exposure Changes Associated with Meeting the Standards
National and regional changes in PM2.5 concentrations for demographic populations
when moving from current to more stringent alternative standard levels for air quality
scenarios associated with meeting the standards, and the ability to compare them with air
quality scenarios associated with the illustrative emission control strategies, are provided
in Sections 6.2.3.1 and 6.2.3.2, respectively.
6.6.3.1 National
Nationally, PM2.5 concentration reductions for air quality scenarios associated with
the illustrative emission control strategies are estimated to be similar or slightly greater
than PM2.5 concentration reductions for air quality scenarios associated with meeting the
standards when moving from current to more stringent standard levels (Figure 6-32 and
Figure 6-33).
Populations Populations (Ages)
12/35-10/35
l/>
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Race White (0-99)
American Indian (0-99)
Asian (0-99)
0.1
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2 0.3
0.2 0.2
0.5
0.4
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0.1
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0.2
0.4
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0.8
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0.0
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0.3
0.4
Ethnicity Non-Hispanic (0-99)
Hispanic (0-99)
0.0
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0.0
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0.1 0.2 0.2
0.4
0.1
0.3
0.1
0.3
0.2
0.5
0.4
0.9
Poverty Above the poverty line (0-99)
Status Below poverty line (0-99)
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2 0.3
0.3 0.3
0.5
0.5
Educational More educated (HS or more) (25-99)
Attainment Less educated (no HS) (25-99)
0.0
0.1
0.1
0.2
0.1
0.1
0.1
0.2
0.1
0.2
0.2
0.3
0.3
0.3
0.4
0.6
Age Children (0-17)
Adults (18-64)
Older Adults (64-99)
0.1
0.1
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2 0.3
0.2 0.3
0.2 0.2
0.5
0.5
0.4
Sex Females (0-99)
Males (0-99)
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2 0.3
0.2 0.3
0.5
0.5
Figure 6-32 Heat Map of National Average Annual Reductions in PM2.5
Concentrations (lig/m3) Associated Either with Control Strategies or
with Meeting the Standards by Demographic When Moving from
Current to Alternative PM NAAQS Levels
6-53
-------
12/35-10/35
12/35-10/30
12/35-9/35
12/35-8/35
100%"
0 80%-
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Figure 6-33 National Distributions of Annual Reductions in PM2.5 Concentrations
Associated Either with Control Strategies or with Meeting the
Standards by Demographic When Moving from Current to Alternative
PM NAAQS Levels
6-54
-------
6.6.3.2 Regional
Regionally, air quality scenarios associated with meeting the standards led to
similar PM2.5 concentration changes as air quality scenarios associated with control
strategies under more stringent alternative standard levels in the NE, SE, and W (Figure
6-34 and Figure 6-35).23 In CA, PM2.5 concentration reductions were substantially greater
under air quality scenarios associated with meeting the standards.
23 Overall reference, ages, and sex population groups were excluded from Figure 6-34 to restrict the figure to
a single page.
6-55
-------
NE
12/35-10/35
SE
W
CA
NE
12/35-10/30
SE
W
CA
NE
12/35-9/35
SE
W
Populations
Reference
Race
Ethnicity
Poverty
Status
Educational
Attainment
Age
Sex
Populations (Ages)
All (0-99)
White (0-99)
American Indian (0-99)
Asian (0-99)
Black (0-99)
Non-Hispanic (0-99)
Hispanic (0-99)
Above the poverty I i ne (0-99)
Below poverty line (0-99)
More educated (HS or more) (25-99)
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0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.6 0.0 0.0 0.0 0.0 0.0 0.1 0.3 0.7 0.1 0.1 0.1 0.1 0.0 0.0 0.4
0.0 0.0 0.0 0.0 0.0 0.0 0.4 1.0 0.0 0.0 0.0 0.0 0.0 0.1 0.4 1.0 0.1 0.1 0.2 0.2 0.1 0.1 0.5
0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.7 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.8 0.1 0.1 0.1 0.1 0.1 0.1 0.4
0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.9 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.9 0.1 0.1 0.1 0.1 0.1 0.1 0.5
0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.7 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.8 0.1 0.1 0.1 0.1 0.0 0.1 0.4
0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.9 0.0 0.0 0.0 0.0 0.0 0.1 0.4 1.0 0.1 0.1 0.1 0.1 0.1 0.1 0.5
0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.8 0.0 0.0 0.0 0.0 0.0 0.1 0.3 0.9 0.1 0.1 0.1 0.1 0.1 0.1 0.4
0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.8 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.8 0.1 0.1 0.1 0.1 0.1 0.1 0.4
0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.7 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.8 0.1 0.1 0.1 0.1 0.0 0.1 0.4
0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.8 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.8 0.1 0.1 0.1 0.1 0.1 0.1 0.4
0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.8 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.8 0.1 0.1 0.1 0.1 0.1 0.1 0.4
0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.8 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.8 0.1 0.1 0.1 0.1 0.1 0.1 0.4^0.2 0.3 0.2 0.3 0.2 0.3 0.6|
0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.8 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.8 0.1 0.1 0.1 0.1 0.0 0.1 0.4 1.3 0.2 0.2 0.2 0.2 0.2 0.3 0.6 2.0
1.3 0.2 0.2 0.2 0.2 0.2 0.2 0.5 1.9
1.2 0.2 0.3 0.4 0.4 0.3 0.3 0.8 1.9
1.5 0.3 0.4 0.2 0.2 0.4 0.4 0.7J.2
1.1 0.2 0.3 0.2 0.2 0.2 0.2 0.6 1.7
0.2 0.3 0.3 0.5 0.3 0.4 0.e|2.3
0.2 0.3 0.2 0.2 0.2 0.3 0.6
2.2
1.9
¦
2.0
2.0
1.9
2.0
2.0
1.6
1.3
1.5
1.2
1.5
1.3
1.3
1.2
1.3
1.3
0.2 0.3 0.2 0.3 0.3 0.3 0.6
0.2 0.3 0.2 0.2 0.2 0.3 0.6
0.2 0.3 0.2 0.3 0.3 0.3 0.6
0.2 0.3 0.2 0.3 0.2 0.3 0.6
0.2 0.3 0.2 0.3 0.2 0.3 0.6
0.2 0.3 0.2 0.2 0.2 0.3 0.6
0.2 0.3 0.2 0.3 0.2 0.3 0.6
0.2 0.3 0.2 0.3 0.2 0.3 0.6
Figure 6-34 Heat Map of National Reductions in Average Annual PM2.5 Concentrations (|ig/m3) Associated Either
with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to
Alternative PM NAAQS Levels
6-56
-------
12/35-10/35 12/35-10/30
Race
NE Controls 50*
100*
Standards 50*
SE Control* 50*
W Controls SO*
Standards
100*
CA Controls so*
Standards 50*
Ethnicity NE Controls so*
Standards SO*
100*
SE Controls so*
100*
Standards 50*
W Controls 5#
100*
Standards w
Poverty NE Controls '
Status
Standards e
Standards SO*
W Controls so*
100*
Standards so*
100*
Standards 50*
100*
Educational ne contra so*
Attainment i«»
Standards 50*
.Qt
10O9
SE Controls 50»
04
100*
Standards 50*
100*
W Controls 50*
100*
Standards 50*
100*
CA Controls so1
100*
Standards 50*
12/35-9/35 12/35-8/35
— fT-
| White (0-99)
American Indian (0-99)
| As;an (0-99)
I Black (0-99)
| Non-Hispanic (0-99)
I Hispanic (0-99)
I Above the poverty line (0-99)
| Below poverty line (0-99)
I More educated (hs or more) (25-99)
I Less educated (no HS) (25-99)
0123401234012340123 4
PM Reduction (itg/m1) PM Redixttoo (pa/m1) pm Reduction (jig/m') PM Rpductlon (im/in*)
Figure 6-35 National Distributions of Reductions in Annual PM2.5 Concentrations
Associated Either with Control Strategies or with Meeting the
Standards by Demographic When Moving from Current to Alternative
PM NAAQS Levels
6-57
-------
6.6.4 Proportionality of Exposure Changes Associated with Meeting the
Standards
The proportionality of national and regional changes in demographic-specific PM2.5
concentrations when moving from air quality scenarios associated with meeting the
standards, as opposed to air quality scenarios associated with control strategies, when
moving from current to more stringent alternative standard levels are provided in Sections
6.6.4.1 and 6.6.4.2, respectively.
6.6.4.1 National
Nationally, air quality scenarios associated with meeting the standards
proportionally reduce PM2.5 concentrations in the reference population by a larger amount
than air quality scenarios associated with the illustrative control strategies as alternative
standard levels are lowered (Figure 6-36).
Population
Group
Population
12/35-10/35
Controls Standards
12/35-10/30
Controls Standards,
12/35-9/35
Controls Standards
12/35-8/35
Controls Standards
Reference
All (0-99)
0.7
1.5
0.8
1.7
1.7
3.3
3.8
6.6
Race
White (0-99)
American Indian (0-99)
0.7
0.7
1.5
1.7
0.8
0.9
1.7
2.1
1.6
1.5
3.2
3.5
3.5
3.2
6.4
6.6
Asian (0-99)
1.5
2.6
1.6
2.8
3.0
5.4
5.5
10.1
Black (0-99)
0.5
0.9
0.5
1.0
1.7
2.5
3.9
5.8
Ethnicity
Non-Hispanic (0-99)
0.5
0.9
0.6
1.1
1.4
2.2
3.4
5.2
Hispanic (0-99)
1.5
3.3
1.6
3.6
2.7
6.3
4.8
11.0
Poverty
Status
Above the poverty line (0-99)
Below poverty line (0-99)
0.7
0.8
1.5
1.7
0.8
0.9
1.7
1.9
1.7
1.8
3.2
3.6
3.7
3.8
6.5
7.1
Educational More educated (HS or more) (25-99)
0.7
1.3
0.8
1.6
1.7
3.0
3.7
6.3
Attainment
Less educated (no HS) (25-99)
1.1
2.3
1.2
2.6
2.2
4.6
4.1
8.5
Age
Children (0-17)
Adults (18-64)
Older Adults (64-99)
0.7
0.8
0.7
1.5
1.5
1.3
0.8
0.9
0.8
1.8
1.7
1.5
1.8
1.8
1.6
3.4
3.3
2.9
3.8
3.8
3.4
6.8
6.8
6.0
Sex
Females (0-99)
Males (0-99)
0.8
0.7
1.5
1.5
0.8
0.8
1.7
1.7
1.8
1.7
3.3
3.2
3.8
3.7
6.6
6.6
Figure 6-36 Heat Map of National Percent Reductions in Average Annual PM2.5
Concentrations (ng/m3) Associated Either with Control Strategies or
with Meeting the Standards by Demographic When Moving From
Current to Alternative PM NAAQS Levels
6-58
-------
6.6.4.2 Regional
Dividing the country into the four regions shows that air quality associated with
meeting the standards in CA would lead to substantially greater proportional PM2.5
concentration reductions under all scenarios evaluated (Figure 6-37). Also, differences
between air quality scenarios associated control strategies versus meeting the standards
are greater when moving to lower alternative standard levels.
6-59
-------
NE
12/35-10/35
SE W
CA
NE
12/35-10/30
SE W
CA
NE
12/35-9/35
SE W
CA
NE
12/35-8/35
SE W
CA
J/3
O
CO
y<
03
10
0
in
-a
03
U)
0
-0
as
0
w
T>
03
_u]
0
if)
T5
OJ
10
0
(/)
P
03
o)
0
0
-O
OJ
-52
0
U3
03
J£)
O
1/3
T3
03
U)
0
U)
~o
03
Population
C
c
c:
c
c
c
c
C
C
C
c
c
c
c
c
C
c
c
c
c
c
c
c
C
c
c
c
C
C
c
c
Group
Population
u
CO
4—>
CO
CJ
IB
i/)
u
-M
CO
CJ
¦M
CO
CJ
*->
I/)
CJ
CO
u
-M
CO
u
CO
u
CO
u
CO
u
CO
CO
CJ
¦M
CO
U
CO
u
+->
CO
Reference
All (0-99)
0.1
0.1
0.3
0.3
0.0
0.0
3.8
8.7
0.2
0.2
0.3
0.3
0.4
1.0
4.0
9.4
1.2
1.1
1.3
1.4
0.8
0.8
4.9
14.6
3.3
3.8
3.0
3.6
3.6
4.2
6.6
22.4
Race
White (0-99)
0.1
0.1
0.3
0.3
0.0
0.0
3.8
8.8
0.2
0.2
0.3
0.3
0.4
1.0
4.0
9.6
1.1
1.0
1.2
1.4
0.8
0.8
4.7
14.7
3.0
3.5
2.9
3.5
3.5
4.0
6.2
22.4
American Indian (0-99)
0.1
0.1
0.2
0.2
0.0
0.0
3.4
8.8
0.1
0.1
0.2
0.2
0.3
0.8
3.6
9.5
0.8
0.8
1.0
1.1
0.8
0.8
4.3
14.6
2.5
2.9
2.4
2.6
3.1
3.5
5.6
22.1
Asian (0-99)
0.1
0.1
0.6
0.6
0.0
0.0
4.0
7.5
0.1
0.1
0.6
0.6
0.4
0.8
4.1
8.0
1.2
1.1
2.4
2.4
0.8
0.9
5.8
13.3
3.3
3.9
5.1
5.4
4.7
5.1
8.5
21.4
Black (0-99)
0.2
0.2
0.2
0.2
0.0
0.0
4.3
9.6
0.2
0.2
0.2
0.2
0.2
0.6
4.5
10.1
1.7
1.6
1.2
1.2
1.1
1.1
5.5
16.0
4.3
5.1
3.0
3.3
5.5
6.2
7.2
24.3
Ethnicity
Non-Hispanic (0-99)
0.2
0.2
0.2
0.2
0.0
0.0
3.4
7.1
0.2
0.2
0.2
0.2
0.5
1.0
3.6
7.9
1.2
1.2
1.0
1.0
0.7
0.7
4.8
12.6
3.2
3.8
2.5
2.7
3.2
3.7
7.0
m
Hispanic (0-99)
0.0
0.0
0.6
0.6
0.0
0.0
4.3
10.3
0.0
0.0
0.6
0.6
0.3
0.9
4.4
11.0
1.0
1.0
2.3
2.7
1.1
1.1
5.1
16.8
3.3
3.8
4.6
6.0
4.9
5.6
6.3
Poverty
Above the poverty line (0-99)
0.1
0.1
0.3
0.3
0.0
0.0
3.8
8.5
0.2
0.2
0.3
0.3
0.4
1.0
4.0
9.2
1.1
1.1
1.3
1.4
0.8
0.8
4.9
14.3
3.2
3.8
3.1
3.5
3.6
4.2
6.7
22.2
Status
Below poverty line (0-99)
0.1
0.1
0.3
0.3
0.0
0.0
4.1
9.8
0.2
0.2
0.3
0.3
0.4
0.9
4.3
10.4
1.2
1.2
1.3
1.6
0.9
0.9
5.0
16.1
3.4
4.0
3.0
3.9
3.9
4.4
6.2
Educational More educated (HS or more) (25-99)
0.2
0.2
0.3
0.3
0.0
0.0
3.8
8.2
0.2
0.2
0.3
0.3
0.4
0.9
4.0
8.9
1.2
1.1
1.2
1.3
0.8
0.8
5.0
14.0
3.3
3.8
2.9
3.3
3.6
4.2
6.8
21.8
Attainment
Less educated (no HS) (25-99)
0.1
0.1
0.4
0.4
0.0
0.0
4.5
10.2
0.1
0.1
0.4
0.4
0.3
1.0
4.7
10.9
1.1
1.1
1.5
1.8
1.0
1.0
5.3
16.6
3.2
3.8
3.3
4.2
4.5
5.1
6.6
Age
Children (0-17)
0.1
0.1
0.3
0.3
0.0
0.0
3.7
8.9
0.2
0.2
0.3
0.3
0.4
1.0
3.9
9.6
1.1
1.1
1.5
1.6
0.8
0.8
4.7
14.9
3.3
3.8
3.3
3.9
3.5
4.1
6.4
22.8
Adults (18-64)
0.1
0.1
0.3
0.3
0.0
0.0
3.9
8.7
0.2
0.2
0.3
0.3
0.4
0.9
4.1
9.4
1.2
1.1
1.3
1.5
0.8
0.8
5.0
14.7
3.3
3.8
3.2
3.7
3.7
4.3
6.7
22.5
Older Adults (64-99)
0.2
0.2
0.2
0.2
0.0
0.0
3.9
8.2
0.2
0.2
0.2
0.2
0.4
0.9
4.1
8.9
1.2
1.1
1.0
1.1
0.7
0.8
5.0
13.9
3.2
3.7
2.4
2.8
3.5
4.0
6.7
21.5
Sex
Females (0-99)
0.1
0.1
0.3
0.3
0.0
0.0
3.9
8.7
0.2
0.2
0.3
0.3
0.4
0.9
4.1
9.4
1.2
1.1
1.3
1.4
0.8
0.8
5.0
14.6
3.3
3.8
3.0
3.6
3.6
4.2
6.7
£1
Males (0-99)
0.1
0.1
0.3
0.3
0.0
0.0
3.8
8.6
0.2
0.2
0.3
0.3
0.4
1.0
4.0
9.3
1.1
1.1
1.3
1.4
0.8
0.8
4.9
14.5
3.2
3.8
3.1
3.6
3.6
4.2
6.6
Figure 6-37 Heat Map of Regional Percent Reductions in Average Annual PM2.5 Concentrations (ng/ni3) Associated
Either with Control Strategies or with Meeting the Standards by Demographic When Moving From
Current to Alternative PM NAAQS Levels
6-60
-------
6.6.5 EJ Analysis of Total Mortality Rates Associated with Meeting the Standards
National and regional total demographic-specific mortality rates for both air quality
scenarios associated with control strategies and meeting the standards are provided in
Sections 6.6.5.1 and 6.6.5.2, respectively.
6.6.5.1 National
Using concentration-response relationships derived from Di et al., 2017, the older
(>64 years) Black population is estimated to have the highest mortality rates per 100k of
all races and ethnicities evaluated. This is the case under air quality scenarios associated
with either the illustrative emission control scenarios or under air quality scenarios
associated with meeting the standards for current and alternative standard levels (Figure
6-38 and Figure 6-39). Older Hispanics and older American Indians are also predicted to
have a higher rate of mortality than older non-Hispanics and older Whites, respectively,
under all air quality scenarios evaluated.
Controls ^
UJ
Standards
Controls ^
.o
UJ
Standards
Controls ^
.o
UJ
Standards
Controls
UJ
cn
Standards
Controls
.CO
UJ
cn
Standards
>, White
4—'
American Indian
jz Asian
iid. Black
O)
^ Non-Hispanic
^ ui-
H ispanic
186 186
190 189
165 164
185 184
188 186
160 157
185 184
188 186
160 156
184 182
187 183
158 150
181 177
185 179
154 141
581 581
579 576
578 576
572 567
559 549
217 216
236 235
215 214
232 227
215 214
232 226
214 212
230 220
210 206
226 209
Figure 6-38 Heat Map of National Average Annual Total Mortality Rates (per 100K
People) Associated Either with Control Strategies or with Meeting the
Standards by Demographic for Current and Alternative PM NAAQS
Levels
6-61
-------
White
Non-Hispanic
American Indian
Asian
Black
Hispanic
0 200 400 600 800 0
Mortality Rate ^
(per 100k)
200 400 600 800 0
Mortality Rate ^
(per 100k)
200 400 600 800 0
Mortality Rate ^
(per 100k)
200 400 600 800 0
Mortality Rate ^
(per 100k)
200 400 600 800
Mortality Rate ^
(per 100k)
Figure 6-39 National Distributions of Total Mortality Rates Associated Either with
Control Strategies or with Meeting the Standards by Demographic for
Current and Alternative PM NAAQS Levels
6.6.5.2 Regional
Similar to PM2.5 concentrations, regional average mortality rates are lowest in the W
and highest in CA (Figure 6-40). Black populations are estimated to have the highest
mortality rates in all regions. Hispanic mortality rates are lower in the NE and higher in the
other three regions.
NE
12/35
SE W
CA
NE
10/35
SE W
CA
NE
10/30
SE W
CA
NE
9/35
SE W
CA
NE
8/35
SE W
CA
White
Controls
188
182
170
216
187
182
170
209
187
182
169
209
186
181
169
207
182
179
164
204
Standards
188
182
170
215
187
182
170
198
187
182
168
197
186
181
169
187
181
178
163
171
American
Controls
168
203
165
249
168
202
165
241
168
202
165
240
167
201
164
239
164
200
162
236
>
Indian
Standards
168
203
165
247
168
202
165
226
168
202
164
224
167
201
164
212
163
199
161
195
Asian
Controls
127
121
137
221
127
120
137
211
127
120
137
210
125
116
136
207
122
112
132
202
'c
JZ.
Standards
127
121
137
221
127
120
137
202
127
120
136
201
125
116
136
189
121
112
131
172
E
~aT
u
ro
Black
Controls
594
561
498
¦
593
560
498
669
593
560
498
668
583
556
492
660
566
547
468
649
Standards
594
561
498
593
560
498
632
593
560
496
629
583
556
492
589
561
546
464
533
Oi
Non-Hispanic Controls
217
213
196
255
217
212
196
246
217
212
195
245
215
211
195
243
210
208
190
238
Standards
217
213
196
253
217
212
196
235
217
212
194
234
215
211
195
222
209
208
189
204
Hispanic
Controls
188
238
207
283
188
237
207
270
188
237
207
270
186
235
205
268
182
231
198
265
Standards
188
238
207
280
188
237
207
251
188
237
206
249
186
233
205
233
181
226
197
212
Figure 6-40
Heat Map of Regional Average Annual Total Mortality Rates (per 100K
People) Associated Either with Control Strategies or with Meeting the
Standards by Demographic for Current and Alternative PM NAAQS
Levels
6-62
-------
II
0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800; 0 200 400 600 800
Mortality Rate Reduction Mortality Rate Reduction Mortality Rate Reduction Mortality Rate Reduction Mortality Rate Reduction
(per 100k) (per 100k) (per 100k) (per 100k) (per 100k)
Figure 6-41 Regional Distributions of Total Mortality Rates Associated Either with
Control Strategies or with Meeting the Standards by Demographic for
Current and Alternative PM NAAQS Levels
100%"
80%-
60%-
40%-
100%'
80%-
60%-
40%-
20%-
100%"
80%-
60%-
40%-
20%-
100%'
80%-
60%-
40%-
20%-
6-63
-------
6.6.6 EJ Analysis of Mortality Rate Change Associated with Meeting the
Standards
National and regional changes in demographic-specific mortality rates when moving
from current to alternate standard levels under air quality surfaces associated with either
control strategies or meeting the standards levels are provided in Sections 6.6.6.1 and
6.6.6.2, respectively.
6.6.6.1 National
Nationally, mortality rate reductions are larger for Asians and Hispanics under air
quality associated with the standards, as compared to air quality associated with the
illustrative emission control strategies (Figure 6-42 and Figure 6-43). Mortality rate
reductions increase in absolute terms for Black as alternative standard levels become more
stringent.
Oi
u
ro
a.
White
American Indian
Asian
Black
Non-Hispanic
Hispanic
H
Controls
cn
h-1
Standards'^-
cn
Controls
cn
h-1
Standards'^-
o
12/35-9/35
i/i
8 ™
-O
c c
o to
in
H1
Controls
UJ
U1
A
Standards lu
cn
1.0 2.0
1.4 3.4
1.2 2.4
1.6 4.1
2.6
2.6
4.6
6.4
6.0
5.2
10.0
11.5
4.8 8.2
3.4 5.8
5.0 8.7
3.6 6.2
7.5
15.0
16.2
11.9
25.1
11.5
25.6
36.9
1.2 2.1
1.3 2.5
3.2
5.0
7.3
11.3
4.1 9.0
4.3 9.7
6.5
16.5
11.0
28.1
Figure 6-42 Heat Map of National Average Annual Total Mortality Rate Reductions
(per 100K People) Associated Either with Control Strategies or with
Meeting the Standards by Demographic When Moving from Current to
Alternative PM NAAQS Levels
6-64
-------
12/35-10/35
12/35-10/30
12/35-9/35
12/35-8/35
ioo%-
80%~
o
£ 60%-
> o
.y 40%-
JC 20%
r
f
r
Sff^S~w\Nhite
1/ ¦ Non-Hispanic
V/ American Indian
¦ Asian
¦ Black
¦ Hispanic
ioo%-
o
xj 80%-
QZ >-
fTJ
c 60%-
ro
1/1 40%-
20%
r
r
0 50 100 150
Mortality Rate Reduction
(per 100k)
0 50 100 150
Mortality Rate Reduction
(per 100k)
0 50 100 150
Mortality Rate Reduction
(per 100k)
0 50 100 150
Mortality Rate Reduction
(per 100k)
Figure 6-43 National Distributions of Annual Total Mortality Rate Reductions
Associated Either with Control Strategies or with Meeting the
Standards by Demographic When Moving from Current to Alternative
PM NAAQS Levels
6.6.6.2 Regional
Absolute mortality rate reductions per 100k individuals are most notable in CA and
for Hispanic, Asian, and Black populations under full attainment scenarios at lower
alternative standard levels (Figure 6-44-and Figure 6-45). Note that we did not specifically
evaluate the areas that would not meet the alternative standard levels through application
of existing controls.
12/35-10/35
12/35-10/30
12/35-9/35
12/35-8/35
NE
SE
W
CA
NE
SE
W
CA
NE
SE
W
CA
NE
SE
W
CA
White
Controls
0.4
0.3
0.0
7.5
0.4
0.3
0.7
8.0
2.2
1.5
1.2
9.7
6.0
3.8
5.7
13.2
Standards
0.4
0.3
0.1
17.1
0.4
0.3
1.6
18.9
2.2
1.7
1.3
29.0
6.9
4.6
6.6
45.2
American
Controls
0.1
0.3
0.0
9.0
0.1
0.3
0.4
9.7
1.5
1.3
0.8
10.9
4.6
3.2
3.5
14,0
< Indian
Standards
0.1
0.3
0.0
22.3
0.1
0.3
1.4
24.4
1.5
1.3
0.9
36.8
5.2
3.5
4.1
55.2
Asian
Controls
0.1
1.1
0.0
11.4
0.1
1.1
0.5
11.8
1.7
4.6
1.0
15.1
4.7
8.8
6.1
20.3
Standards
0.1
1.1
0.0
19.9
0.1
1.1
0.9
20.9
1.6
4.7
1.0
33.8
5.6
9.0
6.7
52.1
_ Black
Controls
1.2
1.1
0.0
36.5
1.3
1.1
0.8
37.9
12.6
6.2
6.7
46.3
31.5
15.7
34.4
59.5
Standards
1.2
1.1
0.0
73.7
1.3
1.1
2.0
76.9
12.0
6.2
6.7
122.1
36.8
17.5
38.8
Non-Hispanic Controls
0.5
0.3
0.0
9.1
0.5
0.3
0.8
9.7
2.8
1.7
1.4
12.3
7.4
4.6
6.5
17.3
Standards
0.5
0.3
0.1
18.8
0.5
0.3
1.9
20.8
2.8
1.8
1.5
32.6
8.6
5.0
7.5
52.0
Hispanic
Controls
0.1
1.1
0.0
13.8
0.1
1.1
0.5
14.2
1.9
4.1
2.2
15.8
6.4
8.3
10.0
19.1
Standards
0.1
1.1
0.0
31.7
0.1
1.1
1.4
33.7
1.8
5.7
2.2
50.7
7.5
13.4
11.6
73.7
Figure 6-44 Heat Map of Regional Average Annual Total Mortality Rate Reductions
(per 100K People) Associated Either with Control Strategies or with
Meeting the Standards by Demographic When Moving from Current to
Alternative PM NAAQS Levels
6-65
-------
12/35-10/35
12/35-10/30
12/35-9/35
12/35-8/35
100%"
m 80%-
£ 60%-
4->
o 40%-
20%-
NE
100%"
80%-
60%-
ro 40%-
00 20%-
r
r
r^ ¦ Non-Hispanic
' ¦ American Indian
¦ Asian
¦ Black
¦ Hispanic
r~
100%"
„ 80%-
2 60%-
o 40%-
20%-
SE 100%'
^ 80%-
J5 60%-
>» ^
Si 40%-
,S£ &
C 20%-
JZ
r
r
r~
r~~
r
r
r'
r~
i
100%"
8 „ so%-
-------
6.6.7 Proportionality of Mortality Rate Changes Associated with Meeting the
Standards
The proportionality of national and regional changes in demographic-specific
mortality rates when moving from current to more stringent alternative standard levels
under air quality scenarios associated with control strategies and with meeting the
standards are provided in Sections 6.6.7.1 and 6.6.7.2, respectively
6.6.7.1 National
Proportional reductions when moving to more stringent alternative PM NAAQS
reduce mortality rate disparities for Hispanics under all air quality scenarios evaluated at
the national scale. Proportional reductions when moving to more stringent alternative
standards reduce mortality rate disparities at the national level for Blacks are similar to
Whites for 12/35-10/35 and 10/30, but larger than Whites for 12/35-9/35 and 12/35-
8/35 (Figure 6-46).
12/35-10/35
12/35-10/30
12/35-9/35
12/35-8/35
Controls
Standards
Controls
Standards
Controls
Standards
Controls
Standards
>, White
0.6
1.1
0.6
1.3
1.4
2.5
3.2
5.4
1
U American Indian
0.7
1.8
0.9
2.2
1.4
3.4
2.7
6.1
-H Asian
2.9
5.0
3.1
5.3
4.6
9.1
7.2
15.2
Black
aj
Hispanic
0.6
1.0
0.6
1.1
2.0
2.8
4.4
6.4
1.8
3.8
1.8
4.1
2.8
7.0
4.7
11.9
^ Non-Hispanic
0.5
1.0
0.6
1.2
1.5
2.3
3.4
5.2
Figure 6-46 Heat Map of National Percent Changes in Average Mortality Rate
Reductions Associated Either with Control Strategies or with Meeting
the Standards by Demographic When Moving from Current to
Alternative PM NAAQS Levels
6-67
-------
6.6.7.2 Regional
Proportional changes also demonstrate that mortality rates disparities are expected
to be reduced for Hispanics and Blacks in CA, especially under more stringent alternative
standard levels and under air quality scenarios associated with meeting the standards
(Figure 6-47). Note that we did not specifically evaluate the areas that would not meet the
alternative standard levels through application of existing controls.
6-68
-------
12/35-10/35
12/35-10/30
12/35-9/35
12/35-8/35
NE
SE
W
CA
NE
SE
W
CA
NE
SE
W
CA
NE
SE
W
CA
in
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American Indian
0.1
0.1
0.1
0.1
0.0
0.0
3.6
9.1
0.1
0.1
0.1
0.1
0.2
0.8
3.9
9.9
0.9
0.9
0.6
0.7
0.5
0.6
4.4
14.9
2.7
3.1
1.6
1.7
2.1
2.5
5.6
22.4
u
Asian
0.1
0.1
0.9
0.9
0.0
0.0
5.2
9.0
0.1
0.1
0.9
0.9
0.4
0.7
5.3
9.5
1.3
1.2
3.8
3.9
0.7
0.7
6.8
15.3
3.7
4.5
7.3
7.4
4.4
4.9
9.2
:
'E
Black
0.2
0.2
0.2
0.2
0.0
0.0
5.2
10.6
0.2
0.2
0.2
0.2
0.2
0.4
5.4
11.1
2.1
2.0
1.1
1.1
1.3
1.3
6.6
17.6
5.3
6.2
2.8
3.1
6.9
7.8
8.5
1:
LU
u
03
Hispanic
0.0
0,0
0.5
0.5
0.0
0.0
4.9
11.3
0.0
0.0
0.5
0.5
0.2
0.7
5.0
12.0
1.0
1.0
1.7
2.4
1.1
1.1
5.6
18.1
3.4
4.0
3.5
5.6
4.8
5.6
6.7
26.3
Non-Hispanic
0.2
0,2
0.2
0.2
0.0
0.0
3.6
7.4
0.2
0,2
0.2
0.2
0.4
1.0
3.8
8.2
1.3
1.3
0.8
0.8
0.7
0.8
4.8
12.9
3.4
4.0
2.1
2.3
3.3
3.8
6.8
20.6
QtL
White
0.2
0.2
0.2
0.2
0.0
0.0
3.5
8.0
0.2
0.2
0.2
0.2
0.4
1.0
3.7
8.8
1.2
1.2
0.8
0.9
0.7
0.8
4.5
13.5
3.2
3.7
2.1
2.5
3.3
3.9
6.1
21.1
Figure 6-47 Heat Map of Regional Percent Reductions in Average Mortality Rate Reductions Associated Either with
Control Strategies or with Meeting the Standards by Demographic When Moving from Current to
Alternative PM NAAQS Levels
6-69
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6.7 References
Di, Q, Wang, Y, Zanobetti, A, Wang, Y, Koutrakis, P, Choirat, C, Dominici, F and Schwartz, JD
(2017). Air pollution and mortality in the Medicare population. New England Journal of
Medicine 376(26): 2513-2522.
Hollmann, F, Mulder, T and Kalian, JJW, DC: US Bureau of the Census (2000). Methodology
and assumptions for the population projections of the United States: 1999 to 2100
(Population Division Working Paper No. 38). 338.
U.S. EPA (2015). Guidance on Considering Environmental Justice During the Development
of Regulatory Actions, https://www.epa.gov/sites/default/files/2016-
06/documents/ejtg_5_6_16_v5.1.pdf.
U.S. EPA (2019). Integrated Science Assessment (ISA) for Particulate Matter (Final Report).
U.S. Environmental Protection Agency, Office of Research and Development, Center for
Public Health and Environmental Assessment. Research Triangle Park, NC. U.S. EPA.
EPA/600/R-19/188. December 2019. Available at:
https://www.epa.gov/naaqs/particulate-matter-pm-standards-integrated-science-
assessments-current-review.
U.S. EPA (2020). Policy Assessment for the Review of the National Ambient Air Quality
Standards for Particulate Matter. U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards, Health and Environmental Impacts Division. Research
Triangle Park, NC. U.S. EPA. EPA-452/R-20-002. January 2020. Available at:
https://www.epa.gov/naaqs/particulate-matter-pm-standards-policy-assessments-
current-review-0.
U.S. EPA (2021). Draft Policy Assessment for the Reconsideration of the National Ambient
Air Quality Standards for Particulate Matter. U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, Health and Environmental Impacts
Division. Research Triangle Park, NC. U.S. EPA. EPA-452/P-21-001. October 2021.
Available at: https://www.epa.gov/system/files/documents/2021-10/draft-policy-
assessment-for-the-reconsideration-of-the-pm-naaqs_october-2021_0.pdf.
U.S. EPA (2022a). Supplement to the 2019 Integrated Science Assessment for Particulate
Matter (Final Report). U.S. Environmental Protection Agency, Office of Research and
Development, Center for Public Health and Environmental Assessment. Research
Triangle Park, NC. U.S. EPA. EPA/600/R-22/028. May 2022. Available at:
https://www.epa.gov/isa/integrated-science-assessment-isa-particulate-matter.
Woods & Poole (2015). Complete Demographic Database.
6-70
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CHAPTER 7: LABOR IMPACTS
Overview
This chapter discusses baseline employment in some of the industries potentially
affected by this proposal. As economic activity shifts in response to a regulation, typically
there will be a mix of declines and gains in employment in different parts of the economy
over time and across regions. To present a complete picture, an employment impact
analysis will describe the potential positive and negative changes in employment levels.
Significant challenges arise however when trying to evaluate the employment effects due to
an environmental regulation and separate them from employment effects due to a wide
variety of other concurrent economic changes, including such important macroeconomic
events as the coronavirus pandemic, or the state of the macroeconomy generally. Despite
these challenges, the economics literature provides a constructive framework and
empirical evidence that sheds light on the labor impacts of environmental regulation. To
simplify, we focus on potential impacts on labor demand related to compliance behavior.
Environmental regulation may also have important effects on labor supply through
changes in worker health and productivity (Graff Zivin and Neidell, 2018).
7.1 Labor Impacts
Economic theory of labor demand indicates that employers affected by
environmental regulation may increase their demand for some types of labor, decrease
demand for other types, or for still other types, not change it at all (Morgenstern et al.,
2002, Deschenes, 2018, Berman and Bui, 2001). To study labor demand impacts
empirically, a growing literature has compared employment levels at facilities subject to an
environmental regulation to employment levels at similar facilities not subject to that
environmental regulation; some studies find no employment effects, and others find
significant differences. For example, see (Berman and Bui 2001), (Greenstone 2002),
(Ferris, Shadbegian and Wolverton 2014), and (Curtis 2018, 2020).
A variety of conditions can affect employment impacts of environmental regulation,
including baseline labor market conditions and employer and worker characteristics such
7-1
-------
as occupation and industry. This baseline labor analysis is illustrative and focused on
potential labor impacts in the emissions inventory sectors and industries that may apply
control technologies, as identified in Chapter 3. We present information on baseline
characteristics of labor markets in the affected emissions inventory sectors: non-electric
generating unit (non-EGU) point, oil and gas point, non-point (area), residential wood
combustion, and area fugitive dust. Baseline information presented includes employment
levels, recent trends in employment, and labor intensity of production. We do not have
detailed information on the industries that may require pollution controls, and in which
states they may be required. Thus, the presentation of nationwide baseline information is
merely suggestive of employment conditions in the industries that might be affected.
Table 7-1 presents baseline employment for industries that fall into the emissions
inventory sectors of non-EGU point, oil and gas point, residential wood combustion, and
area fugitive dust. The table shows national employment levels in 2020 and the percent
change in employment over the ten years from 2011 to 2020 for the industries and North
American Industry Classification System (NAICS) codes identified as potentially affected
industries under each emissions inventory sector. Non-EGU point sources include
emissions units in the cement and concrete product manufacturing, basic chemical
manufacturing, pulp, paper, and paperboard mills, iron and steel mills and ferroalloy
manufacturing, non-ferrous metals production and processing, petroleum and coal
products manufacturing, and mining industries. The oil and gas point emissions inventory
sector includes oil and gas extraction. The residential wood combustion emissions
inventory sector reflects HVAC and commercial refrigeration equipment manufacturing,
and hardware, and plumbing and heating equipment supplies merchant wholesalers as
both of those industries include establishments engaged in manufacturing and repairing
heating equipment, including wood stoves, fireplaces, and wood furnaces. Because
potential control measures that could reduce fugitive road dust are to apply asphalt or
concrete to roadbeds or roadsides, we included asphalt paving, roofing, and saturated
materials under the area fugitive dust emissions inventory sector.
7-2
-------
Table 7-1 Baseline Industry Employment
Potentially Affected Industries by Emissions
Inventory Sector and by Industry
NAICS
Employment in
2020
(thousands)
Percent Change
in Employment
2011-2020
Non-EGU Point
Cement and Concrete Product Manufacturing
3273
194.5
18
Basic Chemical Manufacturing
3251
150.1
5
Pulp, Paper, and Paperboard Mills
3221
92.6
-15
Iron and Steel Mills and Ferroalloy Manufacturing
3311
83.2
-10
Non-ferrous Metal (except Aluminum) Production and
Processing
3314
58.2
-6
Petroleum and Coal Products Manufacturing
3241
106.5
-5
Mining (except Oil and Gas)
212
179.4
-19
Oil and Gas Point
Oil and Gas Extraction
2111
138.6
-20
Residential Wood Combustion
Ventilation, Heating, Air Conditioning and Commercial
Refrigeration Equipment Manufacturing
3334
134.4
3
Hardware, and Plumbing and Heating Equipment
Supplies Merchant Wholesalers
4237
280.2
18
Area Fugitive Dust
Asphalt Paving, Roofing, and Saturated Materials
Manufacturing
32412
N/Aa
N/A
Note: NAICS is North American Industry Classification System. The source of the information is the U.S.
Bureau of Labor Statistics and is available at https://www.bls.gov/emp/data/industry-out-and-emp.htm.
aN/A - not available. The U.S. Bureau of Labor Statistics only provides information at the 4-digit NAICS code.
By Standard Industrial Classification (SIC) code, we located information on employment for paving, surfacing
and tamping equipment operators (47-2071), which is briefly discussed below.
Cement and concrete product manufacturing, hardware and plumbing and heating
equipment supplies merchant wholesalers, and mining are the largest industries in terms
of number people employed. The basic chemical manufacturing and oil and gas extraction
industries also have high employment. Each of the industries has had different trends in
employment over the past decade. Cement and concrete product manufacturing and
hardware and plumbing and heating equipment supplies merchant wholesalers have had
sizable increases in employment over the past decade, while pulp, paper, and paperboard
mills, oil and gas extraction, and mining have experienced a decline in employment over
the last decade.
Under the area fugitive dust emissions inventory sector, potential control measures
that could reduce fugitive road dust are to apply asphalt or concrete to roadbeds or
7-3
-------
roadsides, i.e., shoulders. Associated with these control measures, the overall employment
for paving, surfacing and tamping equipment operators in 2021 was 44,200.1 The industry
with the highest concentration of employment in paving, surfacing and tamping equipment
operators is highway, street and bridge construction which employs 16,410 workers.
Texas, California, New York, Illinois, and Florida are the states with the highest
employment level in paving, surfacing and tamping equipment operators.
Understanding the relative use of labor and capital in potentially affected industries
can shed light on potential labor impacts. Many of these manufacturing industries are
capital intensive. We rely on three public sources to get a range of estimates of employment
per output by industry. Two of the public sources are provided by the U.S. Census Bureau:
the Economic Census (EC) and the Annual Survey of Manufacturers (ASM). The EC is
conducted every 5 years and was most recently conducted in 2017. The ASM is an annual
subset of the EC and is based on a sample of establishments. The latest set of data from the
ASM is from 2020. Both sets of U.S. Census Bureau data provide detailed industry data,
providing estimates at the 4-digit NAICS level. The data sets provide separate estimates of
the number of employees and the value of shipments at the 4-digit NAICS, which we
convert to a ratio in this analysis. The third public source that gives an estimate of
employment per output by industry is the U.S. Bureau of Labor Statistics (BLS). Table 7-2
provides estimates of employment per $1 million of products sold by the industry for each
data source in 2017$. While the ratios are not the same, they are similar across time for
each data source. Cement and concrete products manufacturing and ventilation, heating,
air conditioning and commercial refrigeration equipment manufacturing appear to be the
most labor-intensive industries.
1 The source of the information is the U.S. Bureau of Labor Statistics and is available at
(https://www.bls.gov/oes/current/oes472071.htm).
7-4
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Table 7-2 Employment per $1 Million Output (2017$) by Industry (4-digit
NAICS)
Source of Estimate
Emissions Inventory Sector and Industry Sector
BLS
Economic
Census
ASM (2020)
Non-EGU Point
Cement and Concrete Product Manufacturing
3.39
2.92
2.88
Basic Chemical Manufacturing
0.57
0.68
0.85
Pulp, and Paper, and Paperboard Mills
1.18
1.24
1.41
Iron and Steel Mills and Ferroalloy Manufacturing
0.97
0.97
1.14
Non-ferrous Metals (except Aluminum) Production and
Processing
1.33
1.21
1.25
Petroleum and Coal Products Manufacturing
N/A
0.20
0.31
Mining (except Oil and Gas)
N/A
2.02
N/A
Oil and Gas Point
Oil and Gas Extraction
N/A
0.54
N/A
Residential Wood Combustion
Ventilation, Heating, Air Conditioning and Commercial
Refrigeration Equipment Manufacturing
2.84
3.04
3.38
Hardware, and Plumbing and Heating Equipment
Supplies Merchant Wholesalers
N/A
1.39
N/A
Area Fugitive Dust
Asphalt Paving, Roofing, and Saturated Materials
Manufacturing
N/A
1.12
1.28
Note: N/A - not available. The source of the information is the U.S. Bureau of Labor Statistics: BLS and is
available at https://www.bls.gov/emp/data/industry-out-and-emp.htm.
In general, there are significant challenges when trying to evaluate the employment
effects due to an environmental regulation. Employment effects must be evaluated in light
of a wide variety of dynamic economic and social factors that also influence employment in
the U.S. economy. In addition to these challenges, the EPA does not have detailed
information on the industries that may require pollution controls for this proposal. Thus,
the EPA did not estimate potential employment impacts associated with this proposal.
However, to provide information about baseline conditions in relevant employment
markets that might experience incremental impacts, this chapter presented employment
levels, trends, and labor intensities of production in potentially affected industries.
7-5
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7.2 References
Berman, E. and L. T. M. Bui (2001). Environmental regulation and labor demand: evidence
from the South Coast Air Basin. Journal of Public Economics. 79(2): 265-295.
Curtis, E. M. (2018). Who loses under cap-and-trade programs? The labor market effects of
the NOx budget trading program. Review of Economics and Statistics 100 (1): 151-66.
Curtis, E.M. (2020). Reevaluating the ozone nonattainment standards: evidence from the
2004 expansion. Journal of Environmental Economics and Management, 99: 102261.
Deschenes, O. (2018). Environmental regulations and labor markets. IZA World of Labor:
22: 1-10;
Ferris, A. E., R. Shadbegian, A. Wolverton (2014). The effect of environmental regulation on
power sector employment: phase I of the Title IV S02 trading program. Journal of the
Association of Environmental and Resource Economics 1(4): 521-553.
Graff Zivin, J. and M. Neidell (2018). Air pollution's hidden impacts. Science. 359(6371). 39-
40.
Greenstone, M. (2002). The impacts of environmental regulations on industrial activity:
evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of
Manufactures. Journal of Political Economy 110(6): 1175-1219.
Harrington, W., R.D. Morgenstern, and P. Nelson (2000). On the accuracy of regulatory cost
estimates. Journal of Policy Analysis and Management 19, 297-322.
Morgenstern, R.D., W.A. Pizer, and J. Shih (2002). Jobs versus the environment: an industry-
level perspective. Journal of Environmental Economics and Management 43: 412-436.
7-6
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CHAPTER 8: COMPARISON OF BENEFITS AND COSTS
Overview
As discussed in Chapter 1, the Agency is proposing to revise the current annual
PM2.5 standard to a level within the range of 9-10 |~ig/m3 and is soliciting comment on an
alternative annual standard level down to 8 |~ig/m3 and a level up to 11 ng/m3. The Agency
is also proposing to retain the current 24-hour standard of 35 ng/m3 and is soliciting
comment on an alternative 24-hour standard level of 25 ng/m3. OMB Circular A-4 requires
analysis of one potential alternative standard level more stringent than the proposed
standard and one less stringent than the proposed standard. In this Regulatory Impact
Analysis (RIA), we are analyzing the proposed annual and current 24-hour alternative
standard levels of 10/35 ng/m3 and 9/35 ng/m3, as well as the following two more
stringent alternative standard levels: (1) an alternative annual standard level of 8 ng/m3 in
combination with the current 24-hour standard (i.e., 8/35 |j,g/m3), and (2) an alternative
24-hour standard level of 30 ng/m3 in combination with the proposed annual standard
level of 10 ng/m3 (i.e., 10/30 |j,g/m3). Because the EPA is proposing that the current
secondary PM standards be retained, we did not evaluate alternative secondary standard
levels in this RIA. The docket for the proposed rulemaking is EPA-HQ-OAR-2015-0072.
The analyses in this RIA rely on national-level data (emissions inventory and control
measure information) for use in national-level assessments (air quality modeling, control
strategies, environmental justice, and benefits estimation). However, the ambient air
quality issues being analyzed are highly complex and local in nature, and the results of
these national-level assessments therefore contain uncertainty. It is beyond the scope of
this RIA to develop detailed local information for the areas being analyzed, including
populating the local emissions inventory information, obtaining local information to
increase the resolution of the air quality modeling, and obtaining local information on
emissions controls, all of which would reduce some of the uncertainty in these national-
level assessments. For example, having more refined data would be ideal for agricultural
dust and burning, prescribed burning, and non-point (area) sources due to their large
8-1
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contribution to primary PM2.5 emissions and the limited availability of emissions controls.1
The estimated benefits and costs associated with applying emissions controls are
incremental to a baseline of attaining the current primary annual and 24-hour PM2.5
standards of 12/35 |~ig/m3 in ambient air and incorporate air quality improvements
achieved through the projected implementation of existing regulations.
8.1 Results
The EPA prepared an illustrative control strategy analysis to estimate the costs and
human health benefits associated with the control strategies applied toward reaching the
proposed and more stringent alternative PM2.5 standard levels. The control strategies
presented in this RIA are an illustration of one possible set of control strategies states
might choose to implement toward meeting the proposed standard levels. States, not the
EPA, will implement the proposed NAAQS and will ultimately determine appropriate
emissions control strategies and measures. This section summarizes the results of the
analyses.
As shown in Chapter 4, the estimated costs associated with the control strategies for
the proposed alternative standard levels are approximately $95 million for the proposed
alternative standard level of 10/35 |~ig/m3 and $390 million for the proposed alternative
standard level of 9/35 ng/m3 in 2032 (2017$, 7 percent interest rate).2 As shown in
Chapter 5, the estimated monetized benefits associated with these control strategies for the
proposed alternative standard levels are approximately $7.6 billion and $16 billion for the
proposed alternative standard level of 10/35 ng/m3 and $19 billion and $39 billion for the
proposed alternative standard level of 9/35 ng/m3 in 2032 (2017$, based on a real
discount rate of 7 percent).3 The benefits are associated with two point estimates from two
1 Examples of area source emissions include area fugitive dust, residential wood combustion, and commercial
cooking emissions.
2 When calculating the annualized costs, we would like to use the interest rates faced by firms; however, we
do not know what those rates are. As such we use 7 percent as a conservative estimate.
3 As indicated in Chapter 5, we assume that there is a "cessation" lag between the change in PM exposures and
the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of
premature mortality related to PM2.5 exposures occur in a distributed fashion over the 20 years following
exposure, which affects the valuation of mortality benefits at different discount rates. Similarly, we assume
there is a cessation lag between the change in PM exposures and both the development and diagnosis of
lung cancer.
8-2
-------
different epidemiologic studies discussed in more detail in Chapter 5, Section 5.3.3. It is
expected that some costs and benefits will begin occurring before 2032, as states begin
implementing control measures to attain earlier or to show progress towards attainment.
As discussed in Chapter 3, Section 3.2.5, the estimated PM2.5 emissions reductions
from control applications do not fully account for all the emissions reductions needed to
reach the proposed and more stringent alternative standard levels in some counties in the
northeast, southeast, west, and California. In Chapter 2, Section 2.4 and Chapter 3, Section
3.2.6, we discuss the remaining air quality challenges for areas in the northeast and
southeast, as well as in the west and California for the proposed alternative standard levels
of 10/35 ng/m3 and 9/35 |~ig/m3. The EPA calculates the monetized net benefits of the
proposed alternative standard levels by subtracting the estimated monetized compliance
costs from the estimated monetized benefits in 2032. These estimates do not fully account
for all of the emissions reductions needed to reach the proposed and more stringent
alternative standard levels. In 2032, the monetized net benefits of the proposed alternative
standard level of 10/35 |~ig/m3 are approximately $8.4 billion and $17 billion using a 3
percent real discount rate for the benefits estimates and the monetized net benefits of the
proposed alternative standard level of 9/35 |~ig/m3 are approximately $20 billion and $43
billion using a 3 percent real discount rate for the benefits estimates (in 2017$). The
benefits are associated with two point estimates from two different epidemiologic studies
discussed in more detail in Chapter 5, Section 5.3.3. Table 8-1 presents a summary of these
impacts for the proposed alternative standard levels and the more stringent alternative
standard levels for 2032.
Table 8-1 Estimated Monetized Benefits, Costs, and Net Benefits of the Control
Strategies Applied Toward Primary Alternative Standard Levels of
10/35 ng/m3,10/30 ng/m3, 9/35 (ig/m3, and 8/35 ng/m3 in 2032 for
the U.S. (millions of 2017$)
10/35
10/30
9/35
8/35
Benefits3
$8,500 and $17,000
$9,600 and $20,000
$21,000 and $43,000
$46,000 and $95,000
Costsb
$95
$260
$390
$1,800
Net Benefits
$8,400 and $17,000
$9,300 and $19,000
$20,000 and $43,000
$44,000 and $93,000
Notes: Rows may not appear to add correctly due to rounding. We focus results to provide a snapshot of costs
and benefits in 2032, using the best available information to approximate social costs and social benefits
recognizing uncertainties and limitations in those estimates.
8-3
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a We assume that there is a cessation lag between the change in PM exposures and the total realization of
changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related
to PM2.5 exposures occur in a distributed fashion over the 20 years following exposure, which affects the
valuation of mortality benefits at different discount rates. Similarly, we assume there is a cessation lag between
the change in PM exposures and both the development and diagnosis of lung cancer. The benefits are associated
with two point estimates from two different epidemiologic studies, and we present the benefits calculated at a
real discount rate of 3 percent. The benefits exclude additional health and welfare benefits that could not be
quantified (see Chapter 5, Sections 5.3.4 and 5.3.5).
b The costs are annualized using a 7 percent interest rate.
As part of fulfilling analytical guidance with respect to E.0.12866, the EPA presents
estimates of the present value (PV) of the monetized benefits and costs over the twenty-
year period 2032 to 2051. To calculate the present value of the social net benefits of the
proposed alternative standard levels, annual benefits and costs are discounted to 2022 at 3
percent and 7 percent discount rates as directed by OMB's Circular A-4. The EPA also
presents the equivalent annualized value (EAV), which represents a flow of constant annual
values that, had they occurred in each year from 2032 to 2051, would yield a sum
equivalent to the PV. The EAV represents the value of a typical cost or benefit for each year
of the analysis, in contrast to the 2032-specific estimates.
For the twenty-year period of 2032 to 2051, for the proposed alternative standard
level of 10/35 |j,g/m3the PV of the costs, in 2017$ and discounted to 2022, is $1.1 billion
when using a 3 percent discount rate and $540 million when using a 7 percent discount
rate. The EAV is $72 million per year when using a 3 percent discount rate and $51 million
when using a 7 percent discount rate. For the twenty-year period of 2032 to 2051, for the
proposed alternative standard level of 9/35 |j,g/m3the PV of the costs, in 2017$ and
discounted to 2022, is $4.5 billion when using a 3 percent discount rate and $2.3 billion
when using a 7 percent discount rate. The EAV is $300 million per year when using a 3
percent discount rate and $210 million when using a 7 percent discount rate. The costs in
PV and EAV terms for the proposed alternative standard levels can be found in Table 8-2
and Table 8-3.
8-4
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Table 8-2 Summary of Present Values and Equivalent Annualized Values for
Estimated Monetized Compliance Costs of the Control Strategies
Applied Toward the Primary Alternative Standard Levels of 10/35
(ig/m3,10/30 (ig/m3, 9/35 ng/m3 8/35 ng/m3 (millions of 2017$,
2032-2051, discounted to 2022, 3 percent discount rate)
Year
10/35
10/30
9/35
8/35
2032
$70
$190
$290
$1,400
2033
$68
$190
$280
$1,300
2034
$66
$180
$280
$1,300
2035
$64
$180
$270
$1,200
2036
$63
$170
$260
$1,200
2037
$61
$170
$250
$1,200
2038
$59
$160
$250
$1,100
2039
$57
$160
$240
$1,100
2040
$56
$150
$230
$1,100
2041
$54
$150
$220
$1,000
2042
$52
$140
$220
$1,000
2043
$51
$140
$210
$980
2044
$49
$130
$210
$950
2045
$48
$130
$200
$920
2046
$47
$130
$190
$900
2047
$45
$120
$190
$870
2048
$44
$120
$180
$840
2049
$43
$120
$180
$820
2050
$41
$110
$170
$800
2051
$40
$110
$170
$770
Present Value
Equivalent
Annualized Value
$1,100
$72
$2,900
$200
$4,500
$300
$21,000
$1,400
8-5
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Table 8-3 Summary of Present Values and Equivalent Annualized Values for
Estimated Monetized Compliance Costs of the Control Strategies
Applied Toward the Primary Alternative Standard Levels of 10/35
(ig/m3,10/30 (ig/m3, 9/35 ng/m3 8/35 ng/m3 (millions of 2017$,
2032-2051, discounted to 2022, 7 percent discount rate)
Year
10/35
10/30
9/35
8/35
2032
$48
$130
$200
$930
2033
$45
$120
$190
$870
2034
$42
$110
$170
$810
2035
$39
$110
$160
$760
2036
$37
$100
$150
$710
2037
$34
$93
$140
$660
2038
$32
$87
$130
$620
2039
$30
$81
$120
$580
2040
$28
$76
$120
$540
2041
$26
$71
$110
$500
2042
$24
$66
$100
$470
2043
$23
$62
$95
$440
2044
$21
$58
$89
$410
2045
$20
$54
$83
$380
2046
$19
$51
$78
$360
2047
$17
$47
$72
$340
2048
$16
$44
$68
$310
2049
$15
$41
$63
$290
2050
$14
$39
$59
$270
2051
$13
$36
$55
$260
Present Value
$540
$1,500
$2,300
$10,000
Equivalent
Annualized Value
$51
$140
$210
$990
For the twenty-year period of 2032 to 2051, for the proposed alternative standard
level of 10/35 |j,g/m3the PV of the benefits, in 2017$ and discounted to 2022, is $200
billion when using a 3 percent discount rate and $91 billion when using a 7 percent
discount rate. The EAV is $13 billion per year when using a 3 percent discount rate and
$8.5 billion when using a 7 percent discount rate. For the twenty-year period of 2032 to
2051, for the proposed alternative standard level of 9/35 |j,g/m3the PV of the benefits, in
2017$ and discounted to 2022, is $490 billion when using a 3 percent discount rate and
$220billion when using a 7 percent discount rate. The EAV is $33 billion per year when
using a 3 percent discount rate and $21 billion when using a 7 percent discount rate. The
8-6
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benefits in PV and EAV terms for the proposed alternative standard levels can be found in
Table 8-4 and Table 8-5.
Table 8-4 Summary of Present Values and Equivalent Annualized Values for
Estimated Monetized Benefits of the Control Strategies Applied
Toward the Primary Alternative Standard Levels of 10/35 |ig/m3,
10/30 ng/m3, 9/35 |ig/m3 8/35 \xg/m3 (millions of 2017$, 2032-2051,
discounted to 2022, 3 percent discount rate)
Year
10/35
10/30
9/35
8/35
2032
$13,000
$15,000
$32,000
$71,000
2033
$13,000
$14,000
$31,000
$69,000
2034
$12,000
$14,000
$30,000
$67,000
2035
$12,000
$13,000
$29,000
$65,000
2036
$12,000
$13,000
$29,000
$63,000
2037
$11,000
$13,000
$28,000
$61,000
2038
$11,000
$12,000
$27,000
$59,000
2039
$11,000
$12,000
$26,000
$58,000
2040
$10,000
$12,000
$25,000
$56,000
2041
$9,900
$11,000
$25,000
$54,000
2042
$9,700
$11,000
$24,000
$53,000
2043
$9,400
$11,000
$23,000
$51,000
2044
$9,100
$10,000
$23,000
$50,000
2045
$8,800
$10,000
$22,000
$48,000
2046
$8,600
$9,700
$21,000
$47,000
2047
$8,300
$9,400
$21,000
$46,000
2048
$8,100
$9,100
$20,000
$44,000
2049
$7,900
$8,900
$19,000
$43,000
2050
$7,600
$8,600
$19,000
$42,000
2051
$7,400
$8,400
$18,000
$40,000
Present Value
Equivalent
Annualized Value
$200,000
$13,000
$220,000
$15,000
$490,000
$33,000
$1,100,000
$73,000
8-7
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Table 8-5 Summary of Present Values and Equivalent Annualized Values for
Estimated Monetized Benefits of the Control Strategies Applied
Toward the Primary Alternative Standard Levels of 10/35 |ig/m3,
10/30 ng/m3, 9/35 |ig/m3 8/35 \xg/m3 (millions of 2017$, 2032-2051,
discounted to 2022, 7 percent discount rate)
Year
10/35
10/30
9/35
8/35
2032
$8,000
$9,000
$20,000
$44,000
2033
$7,500
$8,400
$18,000
$41,000
2034
$7,000
$7,900
$17,000
$38,000
2035
$6,500
$7,400
$16,000
$36,000
2036
$6,100
$6,900
$15,000
$33,000
2037
$5,700
$6,400
$14,000
$31,000
2038
$5,300
$6,000
$13,000
$29,000
2039
$5,000
$5,600
$12,000
$27,000
2040
$4,600
$5,200
$11,000
$25,000
2041
$4,300
$4,900
$11,000
$24,000
2042
$4,100
$4,600
$10,000
$22,000
2043
$3,800
$4,300
$9,400
$21,000
2044
$3,500
$4,000
$8,800
$19,000
2045
$3,300
$3,700
$8,200
$18,000
2046
$3,100
$3,500
$7,700
$17,000
2047
$2,900
$3,300
$7,200
$16,000
2048
$2,700
$3,100
$6,700
$15,000
2049
$2,500
$2,900
$6,300
$14,000
2050
$2,400
$2,700
$5,800
$13,000
2051
$2,200
$2,500
$5,500
$12,000
Present Value
$91,000
$100,000
$220,000
$490,000
Equivalent
Annualized Value
$8,500
$9,600
$21,000
$47,000
For the twenty-year period of 2032 to 2051, for the proposed alternative standard
level of 10/35 |j,g/m3the PV of the net benefits, in 2017$ and discounted to 2022, is $200
billion when using a 3 percent discount rate and $90 billion when using a 7 percent
discount rate. The EAV is $13 billion per year when using a 3 percent discount rate and
$8.5 billion when using a 7 percent discount rate. For the twenty-year period of 2032 to
2051, for the proposed alternative standard level of 9/35 |j,g/m3the PV of the net benefits,
in 2017$ and discounted to 2022, is $490 billion when using a 3 percent discount rate and
$220 billion when using a 7 percent discount rate. The EAV is $33 billion per year when
using a 3 percent discount rate and $21 billion when using a 7 percent discount rate. The
comparison of benefits and costs in PV and EAV terms for the proposed alternative
8-8
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standard levels can be found in Table 8-6 and Table 8-7. Estimates in the tables are
presented as rounded values.
Table 8-6 Summary of Present Values and Equivalent Annualized Values for
Estimated Monetized Compliance Costs, Benefits, and Net Benefits of
the Control Strategies Applied Toward the Proposed Primary
Alternative Standard Level of 10/35 ng/m3 (millions of 2017$, 2032-
2051, discounted to 2022 using 3 and 7 percent discount rates)
Benefits3
Costsb
Net Benefits
Year
3%
7%
3%
7%
3%
7%
2032
$13,000
$8,000
$70
$48
$13,000
$7,900
2033
$13,000
$7,500
$68
$45
$13,000
$7,400
2034
$12,000
$7,000
$66
$42
$12,000
$6,900
2035
$12,000
$6,500
$64
$39
$12,000
$6,500
2036
$12,000
$6,100
$63
$37
$11,000
$6,100
2037
$11,000
$5,700
$61
$34
$11,000
$5,700
2038
$11,000
$5,300
$59
$32
$11,000
$5,300
2039
$11,000
$5,000
$57
$30
$10,000
$4,900
2040
$10,000
$4,600
$56
$28
$10,000
$4,600
2041
$9,900
$4,300
$54
$26
$9,900
$4,300
2042
$9,700
$4,100
$52
$24
$9,600
$4,000
2043
$9,400
$3,800
$51
$23
$9,300
$3,800
2044
$9,100
$3,500
$49
$21
$9,100
$3,500
2045
$8,800
$3,300
$48
$20
$8,800
$3,300
2046
$8,600
$3,100
$47
$19
$8,500
$3,100
2047
$8,300
$2,900
$45
$17
$8,300
$2,900
2048
$8,100
$2,700
$44
$16
$8,000
$2,700
2049
$7,900
$2,500
$43
$15
$7,800
$2,500
2050
$7,600
$2,400
$41
$14
$7,600
$2,300
2051
$7,400
$2,200
$40
$13
$7,400
$2,200
Present Value
$200,000
$91,000
$1,100
$540
$200,000
$90,000
Equivalent
Annualized Value
$13,000
$8,500
$72
$51
$13,000
$8,500
Notes: Rows may not appear to add correctly due to rounding. The annualized present value of costs and
benefits are calculated over a 20-year period from 2032 to 2051.
a The benefits values use the larger of the two avoided premature deaths estimates presented in Chapter 5,
Table 5-7, and are discounted at a rate of 3 percent over the SAB-recommended 20-year segmented lag. The
benefits exclude additional health and welfare benefits that could not be quantified (see Chapter 5, Sections
5.3.4 and 5.3.5).
b The costs are annualized using a 7 percent interest rate.
8-9
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Table 8-7 Summary of Present Values and Equivalent Annualized Values for
Estimated Monetized Compliance Costs, Benefits, and Net Benefits of
the Control Strategies Applied Toward the Proposed Primary
Alternative Standard Level of 9/35 (ig/m3 (millions of 2017$, 2032-
2051, discounted to 2022 using 3 and 7 percent discount rates)
Benefits3
Costsb
Net Benefits
Year
3%
7%
3%
7%
3%
7%
2032
$32,000
$20,000
$290
$200
$32,000
$20,000
2033
$31,000
$18,000
$280
$190
$31,000
$18,000
2034
$30,000
$17,000
$280
$170
$30,000
$17,000
2035
$29,000
$16,000
$270
$160
$29,000
$16,000
2036
$29,000
$15,000
$260
$150
$28,000
$15,000
2037
$28,000
$14,000
$250
$140
$27,000
$14,000
2038
$27,000
$13,000
$250
$130
$27,000
$13,000
2039
$26,000
$12,000
$240
$120
$26,000
$12,000
2040
$25,000
$11,000
$230
$120
$25,000
$11,000
2041
$25,000
$11,000
$220
$110
$24,000
$11,000
2042
$24,000
$10,000
$220
$100
$24,000
$9,900
2043
$23,000
$9,400
$210
$95
$23,000
$9,300
2044
$23,000
$8,800
$210
$89
$22,000
$8,700
2045
$22,000
$8,200
$200
$83
$22,000
$8,100
2046
$21,000
$7,700
$190
$78
$21,000
$7,600
2047
$21,000
$7,200
$190
$72
$20,000
$7,100
2048
$20,000
$6,700
$180
$68
$20,000
$6,600
2049
$19,000
$6,300
$180
$63
$19,000
$6,200
2050
$19,000
$5,800
$170
$59
$19,000
$5,800
2051
$18,000
$5,500
$170
$55
$18,000
$5,400
Present Value
$490,000
$220,000
$4,500
$2,300
$490,000
$220,000
Equivalent
Annualized Value
$33,000
$21,000
$300
$210
$33,000
$21,000
Notes: Rows may not appear to add correctly due to rounding. The annualized present value of costs and
benefits are calculated over a 20-year period from 2032 to 2051.
•' The benefits values use the larger of the two avoided premature deaths estimates presented in Chapter 5,
Table 5-7, and are discounted at a rate of 3 percent over the SAB-recommended 20-year segmented lag. The
benefits exclude additional health and welfare benefits that could not be quantified (see Chapter 5, Sections
5.3.4 and 5.3.5).
b The costs are annualized using a 7 percent interest rate.
8.2 Limitations of Present Value Estimates
The net present value (NPV) estimates presented reflect the costs and benefits
associated with the illustrative control strategies; as discussed in Chapter 3, Section 3.2.5,
some areas still need emissions reductions after control applications for the alternative
standards analyzed. Additionally, there are methodological complexities associated with
calculating the NPV of a stream of costs and benefits for national ambient air quality
standards. The estimated NPV can better characterize the stream of benefits and costs over
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a multi-year period; however, calculating the PV of improved air quality is generally quite
data-intensive and costly. While NPV analysis allows evaluation of alternatives by summing
the present value of all future costs and benefits, insights into how costs will occur over
time are limited by underlying assumptions and data. Calculating a PV of the stream of
future benefits also poses special challenges, which we describe below. Further, the results
are sensitive to assumptions regarding the time period over which the stream of benefits is
discounted.
To estimate engineering costs, the EPA employs the equivalent uniform annual cost
(EUAC) method, which annualizes costs over varying lifetimes of control measures applied
in the analysis. Using the EUAC method results in a stream of annualized costs that is equal
for each year over the lifetime of control measures, resulting in a value similar to the value
associated with an amortized mortgage or other loan payment. Control equipment is often
purchased by incurring debt rather than through a single up-front payment. Recognizing
this led the EPA to estimate costs using the EUAC method instead of a method that mimics
firms paying up front for the future costs of installation, maintenance, and operation of
pollution control devices.
Further, because we do not know when a facility will stop using a control measure
or change to another measure based on economic or other reasons, the EPA assumes the
control equipment and measures applied in the illustrative control strategies remain in
service for their full useful life. As a result, the annualized cost of controls in a single future
year is the same throughout the lifetimes of control measures analyzed, allowing the EPA to
compare the annualized control costs with the benefits in a single year for consistent
comparison.
The theoretically appropriate approach for characterizing the PV of benefits is the
life table approach. The life table, or dynamic population, approach explicitly models the
year-to-year influence of air pollution on baseline mortality risk, population growth and
the birth rate—typically for each year over the course of a 50-to-100 year period (U.S. EPA
SAB, 2010; Miller, 2003). In contrast to the pulse approach that is employed in this
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analysis4, a life table models these variables endogenously by following a population cohort
over time. For example, a life table will "pass" the air pollution-modified baseline death rate
and population from year to year; impacts estimated in year 50 will account for the
influence of air pollution on death rates and population growth in the preceding 49 years.
Calculating year-to-year changes in mortality risk in a life table requires some
estimate of the annual change in air quality levels. It is both impractical to model air quality
levels for each year and challenging to account for changes in federal, state, and local
policies that will affect the annual level and distribution of pollutants. For each of these
reasons the EPA does not always report the PV of benefits for air rules but has instead
pursued a pulse approach.
4 The pulse approach assumes changes in air pollution in a single year and affects mortality estimates over a
20-year period.
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8.3 References
Miller BG (2003). Life table methods for quantitative impact assessments in chronic
mortality. Journal of Epidemiology & Community Health, 57(3):200-206.
U.S. EPA Science Advisory Board (2010). Review of EPA's DRAFT Health Benefits of the
Second Section 812 Prospective Study of the Clean Air Act. Washington, DC.
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United States Office of Air Quality Planning and Standards Publication No. EPA-452/P-22-001
Environmental Protection Health and Environmental Impacts Division December 2022
Agency Research Triangle Park, NC
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