&EPA
llnted Sates
Ettvirainwilal ProtecSwi
Agency
Regulatory Impact Analysis of the
Proposed Revisions to the
National Ambient Air Quality Standards for
Ground-Level Ozone
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EPA-452/R-07-008
July 2007
Regulatory Impact Analysis of the
Proposed Revisions to the
National Ambient Air Quality Standards
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impact Division
Air Benefit-Cost Group
Research Triangle Park, North Carolina
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Table of Contents
Chapter Page
1 Introduction and Background
Synopsis 1-1
1.1 Background 1-1
1.2 Role of the Regulatory Impact Analysis in the NAAQS Setting Process 1-2
1.2.1 Legislative Roles 1-2
1.2.2 Role of Statutory and Executive Orders 1-2
1.2.3 Market Failure or Other Social Purpose 1-3
1.2.4 Illustrative Nature of the Analysis 1-4
1.3 Overview and Design of the RIA 1-5
1.3.1 Baseline and Years of Analysis 1-5
1.3.2 Control Scenarios Considered in this RIA 1-6
1.3.3 Evaluating Costs and Benefits 1-6
1.4 Ozone Standard Alternatives Considered 1-7
1.5 References 1-9
2 Characterizing Ozone and Modeling Tools Used in This Analysis
Synopsis 2-1
2.1 Ozone Chemistry 2-1
2.1.1 Temporal Scale 2-2
2.1.2 Geographic Scale and Transport 2-2
2.1.3 Effects of Ozone 2-3
2.2 Sources of Ozone 2-3
2.3 Modeling Ozone Levels in the Future 2-4
2.3.1 Emissions Inventory 2-4
2.3.2 CMAQ Model 2-5
2.4 References 2-7
3 Modeled Control Strategy: Design and Analytical Results
Synopsis 3-1
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3.1 Establishing the Baseline 3-2
3.1.1 National Rules 3-4
3.1.2 Additional Controls 3-6
3.1.3 Ozone Levels for Baseline 3-12
3.2 Developing the Control Strategy Analysis 3-14
3.2.1 Controls Applied for a 0.070 ppm Standard: Non-EGU and Area
Sectors 3-17
3.2.2 Controls Applied for a 0.070 ppm Standard: ECU Sector 3-19
3.2.3 Controls Applied for a 0.070 ppm Standard: Onroad and Nonroad
Mobile Sectors 3.21
3.2.4 Data Quality for this Analysis 3-22
3.3 Geographic distribution of Emissions reductions 3-23
3.4 Ozone Design Values for partial attainment 3-27
3.5 References 3-31
Appendix Chapter 3
3a. 1 Non-EGU and Area Source Controls Applied in the Baseline and Control
Scenarios 3a-l
3a.l.l Non-EGU and Area Source Control Strategies for Ozone NAAQS
Proposal 3a-l
3a.l.2NOx Control Measures for Non-EGU Point Sources 3a-l
3a.l.3 VOC Control Measures for Non-EGU Point Sources 3a-2
3a.l.4 NOx Control Measures for Area Sources 3a-2
3a.l.5 VOC Control Measures for Area Sources 3a-2
3a. 1.6 Supplemental Controls 3a-3
3a.2 Mobile Controls/Rules Used in Baseline and Control Scenarios 3a-l 1
3a.2.1 Diesel Retrofits and Vehicle Replacement 3a-l 1
3a.2.2 Implement Continuous Inspection and Maintenance Using Remote
Onboard Diagnostics (OBD) 3a-14
3a.2.3 Eliminating Long Duration Truck Idling 3a-15
3a.2.4 Commuter Programs 3a-16
3a.2.5 Reduce Gasoline RVP from 7.8 to 7.0 in Remaining Nonattainment
Areas 3a-18
3a.2.6 Application order for Onroad and Nonroad Mobile Controls 3a-18
3a.3 EGU Controls Used in the Control Strategy 3a-19
3a.4 Emissions Reductions by Sector 3a-22
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3a.5 Change in Ozone Concentrations between Baseline and Post-0.070 ppm Control
Strategy Modeling 3a.27
4 Approach for Estimating Reductions for Full Attainment Scenario
Synopsis 4-1
4.1 Development of Air Quality Impact Ratios for Determination of Extrapolated
Costs 4-1
4.1.1 Approach A: Use of Sensitivity Modeling of Local Emissions
Reductions 4-1
4.1.2 ApproachB: Use of 2020 Baseline andRIA Control Scenario 4-3
4.2 Results from Impact Ratio Analyses 4-5
4.3 Determination of Extrapolated Tons Control Areas 4-7
4.4 Selection of Air Quality Goal for this analysis 4-9
4.5 National 2020 Estimates of Additional Emissions Reductions Needed to
Meet Four Potential Air Quality Targets 4-12
4.6 Estimates of Additional Tons Needed for Four Potential Air Quality Targets
(California Only, Post-2020 Attainment) 4-16
5 Cost Estimates
Synopsis 5-1
5.1 Modeled Controls 5.2
5.1.1 Sector Methodology 5-2
5.1.1.1 Non-EGU Point and Area Sources: AirControlNet 5-2
5.1.1.2 EGU Sources: the Integrated Planning Model 5-3
5.1.1.3 Onroad and Nonroad Mobil Sources: MOBILE Model 5-4
5.1.2 Known Controls- Cost by Sector 5-4
5.1.3 Limitations and Uncertainties Associated with Engineering Cost
Estimates 5-6
5.2 Extrapolated Costs 5-7
5.2.1 Increasing Marginal Cost Methodology 5-9
5.2.1.1 Marginal Cost Regions 5-9
5.2.1.2 Derivation of the Marginal Cost Slopes 5-11
5.2.1.3 Calculating Extrapolated Costs using Marginal Cost
Approach 5-12
5.2.2 Fixed Cost per Ton Values 5-12
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5.2.3 Results 5-13
5.3 Summary of Costs 5-19
5.4 Technology Innovation and Regulatory Cost Estimates 5-20
5.4.1 Examples of Technological Advances in Pollution Control 5-22
5.4.2 Influence on Regulatory Cost Estimates 5-23
5.5 References 5-26
Appendix Chapter 5
5a.l Cost Information forNon-EGU and area sources 5a.l
5a.2 Cost Information for EGU sources 5a-2
5a.3 Cost information for Onroad and Nonroad Mobile Sources 5a-3
Incremental Benefits of Attaining Alternative Ozone Standards Relative to the
Current 8-hour Standard (0.08 ppm)
Synopsis 6-1
6.1 Background 6-3
6.2 Characterizing Uncertainty: Moving Toward a Probabilistic Framework for
Benefits Assessment 6-5
6.3 Health Impact Functions 6-6
6.3.1 Potentially Affected Populations 6-7
6.3.2 Effect Estimate Sources 6-7
6.3.2.1 Premature Mortality Effects Estimates 6-13
6.3.2.2 Respiratory Hospital Admissions Effect Estimates 6-14
6.3.2.3 Asthma-Related Emergency Room Visits Effect Estimates 6-15
6.3.2A Minor Restricted Activity Days Effect Estimates 6-16
6.3.2.5 School Absences Effect Estimate 6-16
6.3.2.6 Worker Productivity 6-17
6.3.2.7 Visibility Benefits 6-17
6.3.2.8 Other Unquantified Effects 6-17
6.3.2.8.1 Direct Ozone Effects on Vegetation 6-17
6.3.2.8.2 Nitrogen Deposition 6-18
6.3.2.8.3 Ultraviolet Radiation 6-19
6.3.2.8.4 Climate Implications of Tropospheric Ozone 6-21
6.3.3 Baseline Incidence Rates 6-22
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6.4 Economic Values for Health Outcomes 6-24
6.4.1 Mortality Valuation 6-25
6.4.2 Hospital Admissions Valuation 6-25
6.4.3 Asthma-Related Emergency Room Visits Valuation 6-25
6.4.4 Minor Restricted Activity Days Valuation 6-25
6.4.5 School Absences 6-26
6.5 Results and Implications 6-31
6.5.1 Glidepath Incidence and Valuation Estimates for 0.065 ppm and
0.075 ppm Alternatives 6-31
6.5.2 PM2.5 Co-Benefits Estimates 6-32
6.5.3 PM2.5 Co-Benefits Resulting from Attainment of 0.070 ppm
incremental to 0.08 ppm 6-74
6.5.4 Estimate of Full Attainment Benefits 6-77
6.5.5 Discussion of Results and Uncertainties 6-98
6.5.6 Summary of Total Benefits 6-101
6.6 References 6-104
Appendix Chapter 6
6a Additional Benefits Information
Summary 6a-l
6a. 1 Developing an air quality estimate of full attainment with the alternative
ozone standards 6a-1
6a.2 Partial Attainment PM2.s Incidence and Valuation Estimates 6a-2
6b Health-Based Cost-Effectiveness of Reductions in Ambient PM2.s Associated
with Illustrative Ozone NAAQS O.OTOppm Attainment Strategy
6b. 1 Summary 6b-1
6b.2 Introduction 6b-4
6b.3 Effectiveness Measures 6b-7
6b.4 Changes in Premature Death, Life Years, and Quality of Life 6b-9
6b.4.1 Calculating Reductions in Premature Deaths 6b-10
6b.4.2 Calculating Changes in Life Years from Direct Reductions in
PM2.5-Related Mortality Risk 6b-l 1
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6b.4.2.1 Should Life Years Gained Be Adjusted for Initial Health
Status? 6b-14
6b.5 Calculating Changes in the Quality of Life Years (Morbidity) 6b-16
6b.5.1 Calculating QALYs Associated with Reductions in the Incidence
of Chronic Bronchitis 6b-17
6b.5.2 Calculating QALYs Associated with Reductions in the Incidence
of Nonfatal Myocardial Infarctions 6b-21
6b.6 Cost-Effectiveness Analysis 6b-28
6b.6.1 Aggregating Life Expectancy and Quality-of-Life Gains 6b-28
6b.6.2 Dealing with Acute Health Effects and Non-health Effects... .6b-31
6b.6.3 Cost-Effectiveness Ratios 6b-33
6b.7 Discount Rate Sensitivity Analysis 6b-35
6b.8 Conclusions 6b-37
6b.9 References 6b-38
6c Additional Sensitivity Analyses Related To the Benefits Analysis
6c.l Premature Mortality Cessation Lag Structure 6c-l
6c.2 Threshold Sensitivity Analysis 6c-5
6c.3 Income Elasticity of Willingness to Pay 6c-8
6c.4 References 6c-9
7 Discussion of Ozone Secondary Standard
8 Conclusions and Implications of the Illustrative Benefit-Cost Anlaysis
Synopsis 8-1
8.1 Results 8-1
8.2 Discussion of Results 8-10
8.3 What did we Learn Through this Analysis? 8-13
9 Statutory and Executive Order Impact Analysis
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Synopsis 9-1
9.1 Executive Order 12866: Regulatory Planning and Review 9-1
9.2 Paperwork Reduction Act 9-1
9.3 Regulatory Flexibility Act 9-2
9.4 Unfunded Mandates Reform Act 9-2
9.5 Executive Order 13132: Federalism 9-3
9.6 Executive Order 13175: Consultation and Coordination with Indian Tribal
Governments 9-3
9.7 Executive Order 13045: Protection of Children from Environmental Health
& Safety Risks 9-4
9.8 Executive Order 13211: Actions that Significantly Affect Energy Supply,
Distribution, or Use 9-4
9.9 National Technology Transfer Advancement Act 9-5
9.10 Executive Order 12898: Federal Actions to Address Environmental Justice
in Minority Populations and Low-Income Populations 9-5
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List of Tables
Number Page
2.1 Geographic Specifications of Modeling Domains 2-7
3.1 National Rules and Control Measures, by Sector, Contributing to the Baseline..3-5
3.2 Controls by Sector Included in the Baseline Determination for 2020 3-10
3.3 Controls for Emissions Reductions, by Sector, for the 0.070 ppm Control Strategy
(Incremental to Baseline) 3-15
3.4 Annual Tons of Emissions Remaining after Application of the 0.070 ppm Control
Strategy (35 States + DC Analysis Area) 3-29
4.1 Summary of site-specific impact ratios over the five analysis zones of
Approach A 4-3
4.2 Summary of site-specific impact ratios over the four analysis zones of
Approach B 4-4
4.3 The NOx impact ratios at the controlling counties for each methodology over the
analysis zones 4-5
4.4 List of counties that did not reach 0.070 in the RIA control scenario and how they
were aggregated into extrapolated tons control areas 4-8
4.5 2020 Air Quality Glidepath Targets for LA and Kern County 4-11
4.6 Estimated Annual Incremental Tons Needed for an 0.065 ppm Air Quality Target
in 2020 (29 areas) 4-13
4.7. Estimated Annual Incremental Tons Needed for an 0.070 ppm air quality target in
2020 (20 areas) 4-14
4.8 Estimated Annual Incremental Tons Needed for an 0.075 ppm air quality target in
2020(11 areas) 4-15
4.9 Estimated Annual Incremental Tons Needed for an 0.079 ppm air quality target in
2020 (6 areas) 4-15
4.10 California: Estimated Tons Needed for Attainment of 0.065 ppm Air Quality
Target (beyond 2020) 4-16
4.11 California: Estimated Tons Needed for Attainment of 0.070 ppm Air Quality
Target (Beyond 2020) 4-16
4.12 California: Estimated Tons Needed for Attainment of 0.075 ppm air quality target
(beyond 2020) 4-16
4.13 California: Estimated Tons Needed for Attainment of 0.079 ppm air quality
target (beyond 2020) 4-16
5.1 Comparison of Modeled Annual Control Costs Nationwide, by sector, for a 0.070
ppm control scenario ($1999) 5-6
5.2 Regions and Slopes for Extrapolated Costs 5-10
5.3 Data Selection Criteria for Extrapolated Costs 5-11
5.4 Extrapolated Costs of Meeting the 0.079 ppm Standard 5-15
5.5 Extrapolated Costs of Meeting the 0.075 ppm Standard 5-16
5.6 Extrapolated Costs of Meeting the 0.070 ppm Standard 5-17
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5.7 Extrapolated Costs of Meeting the 0.065 ppm Standard 5-18
5.8 Total Costs of Attainment in 2020 for Different Levels of the Ozone Standard
(National Attainment in 2020) 5-19
5.9 California Extrapolated Costs ($M) 5-20
5.10 Comparison of Inflation-Adjusted Estimated Costs and Actual Price Changes
for EPA Fuel Control Rules 5-25
6.1 Human Health and Welfare Effects of Pollutants Affected by the Alternate
Standards 6-9
6.2 Ozone and PM Related Health Endpoints basis for the concentration-response
function associated with that endpoint, and sub-populations for which they were
computed 6-11
6.3 National Average Baseline Incidence Rates 6-23
6.4 Unit Values for Economic Valuation of Health Endpoints (2000$) 6-27
6.5 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the
Incidence of Premature Mortality Associated with Ozone Exposure in 2020
(Incremental to Current Ozone Standard) 6-33
6.6 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the
Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current
Ozone Standard, 95% Confidence Intervals in Parentheses) 6-34
6.7 Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual
Reductions in the Incidence of Premature Mortality Associated with Ozone
Exposure (Incremental to Current Ozone Standard) 6-35
6.8 Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual
Reductions in the Incidence of Morbidity Associated with Ozone Exposure.. .6-36
6.9 Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Reductions in
the Incidence of PM Premature Mortality associate with PM co-benefit 6-37
6.10 Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Reductions in
the Incidence of Morbidity Associated with PM Co-benefit 6-38
6.11 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in the
Incidence of Premature Mortality Associated with Ozone Exposure (Incremental
to Current Ozone Standard) 6-39
6.12 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in the
Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current
Ozone Standard, 95% Confidence Intervals in Parentheses) 6-40
6.13 Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual
Reductions in the Incidence of Premature Mortality Associated with Ozone
Exposure (Incremental to Current Ozone Standard) 6-41
6.14 Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual
Reductions in the Incidence of Morbidity Associated with Ozone Exposure
(Incremental to Current Ozone Standard, 95% Confidence Intervals in
Parentheses) 6-42
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6.15 Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Reductions in
the Incidence of PM Premature Mortality associate with PM co-benefit 6-43
6.16 Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Reductions in
the Incidence of Morbidity Associated with PM Co-benefit 6-44
6.17 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in the
Incidence of Premature Mortality Ozone Exposures (Incremental to Current
Ozone Standard) 6-45
6.18 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in the
Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current
Ozone Standard) 6-46
6.19 Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual
Reductions in the Incidence of Premature Mortality Associated with Ozone
Exposure (Incremental to Current Ozone Standard) 6-47
6.20 Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual
Reductions in the Incidence of Morbidity Associated with Ozone Exposure
(Incremental to Current Ozone Standard, 95% Confidence Intervals in
Parentheses) 6-48
6.21 Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Reductions in
the Incidence of PM Premature Mortality associate with PM co-benefit 6-49
6.22 Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Reductions in
the Incidence of Morbidity Associated with PM Co-benefit 6-50
6.23 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in the
Incidence of Premature Mortality Ozone Exposures (Incremental to Current
Ozone Standard) 6-51
6.24 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in the
Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current
Ozone Standard) 6-52
6.25 Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual
Reductions in the Incidence of Premature Mortality Associated with Ozone
Exposure (Incremental to Current Ozone Standard) 6-53
6.26 Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual
Reductions in the Incidence of Morbidity Associated with Ozone Exposure
(Incremental to Current Ozone Standard, 95% Confidence Intervals in
Parentheses) 6-54
6.27 Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Reductions in
the Incidence of PM Premature Mortality associate with PM co-benefit 6-55
6.28 Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Reductions in
the Incidence of Morbidity Associated with PM Co-benefit (95th percentile
confidence intervals provided in parentheses) 6-56
6.29 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Premature Mortality Associated with Ozone
Exposure (Incremental to Current Ozone Standard, Millions of 1999$) 6-57
6.30 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the
Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current
Ozone Standard, 95% Confidence Intervals in Parentheses,
Millions of 1999$) 6-58
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6.31 Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual
Valuation of Reductions in the Incidence of Premature Mortality Associated with
Ozone Exposure (Incremental to Current Ozone Standard) 6-59
6.32 Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual
Valuation of Reductions in the Incidence of Morbidity Associated with Ozone
Exposure (Incremental to Current Ozone Standard) 6-60
6.33 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Premature Mortality Associated with Ozone
Exposure (Incremental to Current Ozone Standard, Millions of 1999$) 6-61
6.34 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with Ozone Exposure
(Incremental to Current Ozone Standard, 95% Confidence Intervals in
Parentheses, Millions of 1999$) 6-62
6.35 Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual
Valuation of Reductions in the Incidence of Premature Mortality Associated with
Ozone Exposure (Incremental to Current Ozone Standard) 6-63
6.36 Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual
Valuation of Reductions in the Incidence of Morbidity Associated with Ozone
Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in
Parentheses) 6-64
6.37 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Monetary Value of
Reductions in the Incidence of Mortality Associated with Exposure to Ozone
(Millions of 1999$, Incremental to Current Standard) 6-65
6.38 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Monetary Value of
Reductions in the Incidence of Morbidity Associated with Exposure to Ozone
(Millions of 1999$, Incremental to Current Standard) 6-66
6.39 Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual
Valuation of Reductions in the Incidence of Premature Mortality Associated with
Ozone Exposure (Incremental to Current Ozone Standard) 6-67
6.40 Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual
Valuation of Reductions in the Incidence of Morbidity Associated with Ozone
Exposure (Incremental to Current Ozone Standard) 6-68
6.41 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Monetary Value of
Reductions in the Incidence of Premature Mortality Associated with Exposure to
Ozone (Millions of 1999$, Incremental to Current Standard) 6-69
6.42 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Monetary Value of
Reductions in the Incidence of Morbidity Associated with Exposure to Ozone
(Millions of 1999$, Incremental to Current Standard) 6-70
6.43 Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual
Valuation of Reductions in the Incidence of Premature Mortality Associated with
Ozone Exposure (Incremental to Current Ozone Standard) 6-71
6.44 Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual
Valuation of Reductions in the Incidence of Morbidity Associated with Ozone
Exposure (Incremental to Current Ozone Standard) 6-72
6.45 Estimated PM2.5 Co-Benefits Associated with Full Attainment of 0.070 ppm
incremental to 0.08 ppm 6-76
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6.46 Estimate of Total Annual Ozone and PIVb.s Benefits (95% Confidence Intervals,
Millions of $1999) for the 0.065 ppm Standard Alternative: National Glidepath
Attainment 6-78
6.47 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals,
Millions of $1999) for the 0.065 ppm Standard Alternative: California
Attainment 6-79
6.48 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals,
Millions of $1999) for the 0.070 ppm Standard Alternative: National Glidepath
Attainment 6-80
6.49 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals,
Millions of $1999) for the 0.070 ppm Standard Alternative: California
Attainment 6-81
6.50 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals,
Millions of $1999) for the 0.075 ppm Standard Alternative: National Glidepath
Attainment 6-82
6.51 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals,
Millions of $1999) for the 0.075 ppm Standard Alternative: California
Attainment 6-83
6.52 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals,
Millions of $1999) for the 0.079 ppm Standard Alternative: National Glidepath
Attainment 6-84
6.53 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals,
Millions of $1999) for the 0.079 ppm Standard Alternative: California
Attainment 6-85
6.54 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: National
Glidepath Attainment 6-86
6.55 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: California
Glidepath Attainment 6-87
6.56 Combined Estimate of Annual Ozone and PM2.s Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: Incremental
Benefits of California Post 2020 Attainment 6-88
6.57 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: Total
California Benefits of Post 2020 Attainment 6-89
6.58 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: National
Glidepath Attainment 6-90
6.59 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: California
Glidepath Attainment 6-91
6.60 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: Incremental
Benefits of California Post 2020 Attainment 6-92
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6.61 Combined Estimate of Annual Ozone and PIVb.s Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: California
Post 2020 Attainment 6-93
6.62 Combined Estimate of Annual Ozone and PM2.s Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.075 ppm Alternative Standard: National
Glidepath Attainment 6-94
6.63 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.075 ppm Alternative Standard: California
Post 2020 Attainment 6-94
6.64 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.079 ppm Alternative Standard: National
Glidepath Attainment 6-96
6.65 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.079 ppm Alternative Standard: California
Post 2020 Attainment 6-97
6.66 Summary of Total Number of Annual Ozone and PM2.s-Related Premature
Mortalities and Premature Morbidity Avoided: 2020 National Benefits 6-102
6.67 Summary of Total Number of Annual Ozone and PM2.s-Related Premature
Mortalities and Premature Morbidity Avoided: California Post 2020
Attainment 6-103
7.1 Comparison of number of counties exceeding various W126 levels when meeting
various levels of the 8-hr standard for the 3-year period 2003-2005 7.3
8.la National Annual Costs and Benefits: 0.079 ppm Standard in 2020 (including
California glidepath) 8-4
8.1b National Annual Costs and Benefits: 0.075 ppm Standard in 2020
(including California glidepath) 8-4
8.1c National Annual Costs and Benefits: 0.070 ppm Standard in 2020 (including
California glidepath) 8-4
8.Id National Annual Costs and Benefits : 0.065 ppm Standard in 2020 (including
California glidepath) 8-5
8.2: Summary of Total Number of Annual Ozone and PM2.5-Related Premature
Mortalities and Premature Morbidity Avoided: 2020 National Benefits 8-6
8.3a California: Annual Costs and Benefits of Attaining 0.079 ppm Standard (beyond
2020) 8-7
8.3b California: Annual Costs and Benefits of Attaining 0.070 ppm Standard (beyond
2020) 8-7
8.3c California: Annual Costs and Benefits of Attaining 0.075 ppm Standard (beyond
2020) 8-7
8.3d California: Annual Costs and Benefits of Attaining 0.065 ppm Standard (beyond
2020) 8-8
8.4: Summary of Total Number of Annual Ozone and PM2.s-Related Premature
Mortalities and Premature Morbidity Avoided: California Post 2020
Attainment 8-9
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List of Figures
Number Page
1.1 Process used to create this RIA 1-5
2.1 Map of the CMAQ Modeling Domains Used for Ozone NAAQS RIA 2-6
3.1 Counties Where Controls for Nitrogen Oxides (NOx) Were Included for Non-
EGU Point and Area Sources, for the Baseline (Current Standard, 0.08 ppm)....3-7
3.2 Counties Where Controls for Volatile Organic Chemicals (VOCs) Were Applied
to Non-EGU Point and Area Sources in Baseline (Current Standard, 0.08
ppm) 3-8
3.3 Areas Where NOx and VOC Controls Were Included for Mobile Onroad and
Nonroad Sources in Addition to National Mobile Controls in Baseline (Current
Standard, 0.08 ppm) 3-9
3.4 Baseline Annual Ozone Air Quality in 2020 3-13
3.5 Counties Where Controls for Nitrogen Oxides (NOx) Were Applied to Non-EGU
Point and Areas Sources for RIA Control Strategy Designed to meet 0.070 ppm
(Incremental to Baseline) 3-18
3.6 Counties Where VOC Controls Were Applied to Non-EGU Point and Areas
Sources for the Control Strategy Designed to Meet 0.070 ppm (Incremental to
Baseline) 3-19
3.7 States Where Nitrogen Oxide (NOx) Controls Were Applied to Electrical
Generating Units (EGUs) for the Control Strategy Designed to Meet 0.070 ppm
(Incremental to Baseline) 3-21
3.8 Areas Where NOx and VOC Controls Were Applied to Mobile Onroad and
Nonroad Sources in Addition to National Mobile Controls for the 0.070 ppm
Control Strategy (incremental to Baseline) 3-22
3.9 Annual Tons of Nitrogen Oxide (NOx) Emission Reductions from Controls
Designed to Meet 0.070 ppm Standard 3-24
3.10 Percentage of Total Annual NOx Emissions Reduced from Various Sources...3-25
3.11 Annual Tons of Volatile Organic Compound (VOC) Emission Reductions from
Controls Designed to Meet 0.070 ppm Standard 3-26
3.12 Percentage of Total Annual VOC Emissions Reduced from Various Sources..3-27
3.13 Projected Ozone Air Quality in 2020 After Application of Known Controls.. .3-28
3.14 Annual NOx Emissions Remaining after PM NAAQS 15/35, Ozone Current
Standard, and 0.070 ppm Control Strategies (35 States + DC Analysis Area)..3-30
3.15 Annual VOC Emissions Remaining after PM NAAQS 15/35, Ozone Current
Standard, and 0.070 ppm Control Strategies (35 States + DC Analysis Area)..3-31
4.1 Nine Local Control Areas in Existing 2010 Sensitivity Runs 4-2
4.2 The NOx impact ratios at each county (sorted from lowest to highest) for the
Approach B over the four analysis areas 4-6
5.1 Extrapolated Cost Example (MC Approach) 5-12
5.2 Technological Innovation Reflected by Marginal Cost Shift 5-22
-------
Acronyms
AHRQ
AQS
BenMAP
BWC
CAAA
CAIR
CAMR
CAPMS
CAVR
CDC
CDC WONDER
CFR
CI
CMAQ
COI
COPD
CPI-U
C-R
DOE
DOI
DPF
ECU
EIA
EO
EPA
ER
HIS
ICD
IPM
km
kWh
MACT
MRAD
MWRPO
NAAQS
NAS
NCHS
NEI
Agency for Healthcare Research and Quality
Air Quality System
Benefits Mapping and Analysis Program
Best workplaces for commuters
Clean Air Act Amendments
Clean Air Interstate Rule
Clean Air Mercury Rule
Criteria Air Pollutant Modeling System
Clean Air Visibility Rule
Centers for Disease Control
Centers for Disease Control Wide-Ranging Online Data for
Epidemiological Research
Code of Federal Regulations
Confidence interval
Community Multi-Scale Air Quality
Cost of illness
Chronic obstructive pulmonary disease
Consumer price index - urban
concentration-response
Department of Energy
Department of the Interior
Diesel Particulate Filters
electric generating unit
Economic Impact Analysis
Executive Order
Environmental Protection Agency
Emergency room
National Health Interview Survey
International Classification of Disease
Integrated Planning Model
kilometer
kilowatt hour
Maximum Achievable Control Technology
Minor restricted activity days
Mid-West Regional Planning Organization
National Ambient Air Quality Standards
National Academy of Sciences
National Center for Health Statistics
National Emissions Inventory
-------
NHAMCS
NHANES
NHDS
NMMAPS
Non-EGU
NOx
O&M
OAQPS
OH
OMB
ORD
OTAQ
OTC
PM
PM2.5
POC
ppb
ppm
RIA
RVP
SCR
SIP
SMR
SNCR
SO2
us
use
USDA
USEPA
USFWS
USGS
UV-B
VNA
voc
VSL
WHO
WTP
National Hospital Ambulatory Medical Care Survey
National Health and Nutrition Examination Survey
National Hospital Discharge Survey
National Morbidity, Mortality and Air Pollution Study
Non-Electricity Generating Unit
nitrogen oxides
operation and maintenance
Office of Air Quality Planning and Standards (EPA)
Hydroxide
Office of Management and Budget
Office of Research and Development (EPA)
Office of Transportation and Air Quality (EPA)
Ozone Transport Commission
particulate matter
Particulate matter less than or equal to 10 microns
Particulate matter less than or equal to 2.5 microns
Parameter occurrence code
parts per billion
parts per million
Regulatory Impact Analysis
Reid vapor pressure
selective catalytic reduction
state implementation plan
standard mortality rate
selective non-catalytic reduction
Sulfur dioxide
United States
US code
United States Department of Agriculture
United States Environmental Protection Agency
United States Fish and Wildlife Service
United States Geological Survey
ultraviolet light, type B
Voronoi neighbor averaging
Volatile Organic Compounds
Value of statistical life
World Health Organization
Willingness to pay
-------
Executive Summary
Overview
EPA has performed an illustrative analysis of the potential costs and human health benefits of
nationally attaining alternative ozone standards. Per Executive Order 12866 and the guidelines of
OMB Circular A-4, this Regulatory Impact Analysis (RIA) presents analyses of the range of
standards proposed by the Administrator in the Notice of Proposed Rulemaking (0.070 - 0.075
ppm), as well as one more stringent option (0.065 ppm). The less stringent option is the
baseline, or the current primary standard for ozone (0.08 ppm, effectively 0.084 ppm due to
current rounding conventions). The benefit and cost estimates below are calculated incremental
to a 2020 baseline that incorporates air quality improvements achieved through the projected
implementation of existing regulations and full attainment of the existing ozone and particulate
matter (PM) National Ambient Air Quality Standards (NAAQS). The baseline includes the
Clean Air Interstate Rule and mobile source programs, which will help many areas move toward
attainment of the current standard.
We present two sets of results. The first reflects full attainment of the alternative ozone
standards in all locations of the U.S. except two areas of California in 2020. These two areas of
California are not planning to meet the current standard by 2020, so the estimated costs and
benefits for these areas are based on reaching an estimated attainment point in 2020 (their
"glidepath" targets). The second set of results, for California only, estimate the costs and
benefits from California fully attaining the alternative standards in a year beyond 2020 (glidepath
estimates, plus the increment needed to reach full attainment beyond 2020, added together for a
California total). Further explanation about these unique circumstances is provided in Chapter 4.
In addition, EPA designed a two-stage approach to estimating costs and benefits because we
recognized from the outset that known and reasonably anticipated emissions controls would
likely be insufficient to bring many areas into attainment with either the current, or alternative,
more stringent ozone standards. The individual chapters of the RIA present more detail
regarding estimated costs and benefits based on both partial attainment (manageable with current
technologies) and full attainment (manageable in some locations only with hypothetical
technologies). The post-2020 estimates for California are entirely based on hypothetical
technologies.
In setting primary ambient air quality standards, EPA's responsibility under the law is to
establish standards that protect public health. The Clean Air Act ("Act") requires EPA, for each
criteria pollutant, to set a standard that protects public health with "an adequate margin of
safety." As interpreted by the Agency and the courts, the Act requires EPA to base this decision
on health considerations only; economic factors cannot be considered.
The prohibition against the consideration of cost in the setting of the primary air quality
standards, however, does not mean that costs, benefits or other economic considerations are
unimportant or should be ignored. The Agency believes that consideration of costs and benefits
is an essential decision making tool for the efficient implementation of these standards. The
ES-1
-------
impacts of cost, benefits, and efficiency are considered by the States when they make decisions
regarding what timelines, strategies, and policies make the most sense.
This RIA is focused on development and analyses of illustrative control strategies to meet these
alternative standards in 2020. This analysis does not prejudge the attainment dates that will
ultimately be assigned to individual areas under the Clean Air Act, which contains a variety of
potential dates and flexibility. For purposes of this analysis, though, we assume attainment by
2020 for all areas except for two areas in California
Because States are ultimately responsible for implementing strategies to meet revised standards,
this RIA provides insights and analysis of a limited number of illustrative control strategies that
states might adopt to meet any revised standard. These illustrative strategies are subject to a
number of important assumptions, uncertainties and limitations, which we document in the
relevant portions of the analysis.
ES.l Approach to the Analysis
This RIA consists of multiple analyses including an assessment of the nature and sources of
ambient ozone; estimates of current and future emissions of relevant precursors that contribute to
the problem; air quality analyses of baseline and alternative strategies; development of
illustrative control strategies to attain the standard alternatives in future years; estimates of the
incremental costs and benefits of attaining the alternative standards, together with an
examination of key uncertainties and limitations; and a series of conclusions and insights gained
from the analysis.
The air quality modeling results for the regulatory baseline (explained in Chapter 3) provide the
starting point for developing illustrative control strategies to attain the alternative standards that
are the focus of this RIA. The baseline shows that by 2020, while ozone air quality would be
significantly better than today under current requirements, several eastern and western states
would need to develop and adopt additional controls to attain the alternative standards.
In selecting controls, we focused more on ozone cost-effectiveness (measured as $/ ppb) than on
the NOx or VOC cost-effectiveness (measured as $/ton). Most of the overall reductions in NOx
achieved our illustrative control strategy were from non-EGU point sources. The NOx based
illustrative control strategies we analyzed are also expected to reduce ambient PM 2.5 levels in
many locations. The total benefits estimates described here include the co-benefits of reductions
in fine particulate levels (PM) associated with year-round application of NOx control strategies
beyond those in the regulatory baseline.
Estimated reductions in premature mortality from reductions in ambient ozone and PM dominate
the benefits estimates. For this reason, our assessment provides a range of estimates for both PM
and ozone premature mortality. Although we note that there are uncertainties that are not fully
captured by this range of estimates, and that additional research is needed to more fully establish
underlying mechanisms by which such effects occur, such ranges are illustrative of the extent of
uncertainly associated with some different modeling assumptions.
ES-2
-------
Fig ES.l Projected Ozone Air Quality in 2020 After Application of Known Controls
counties exceed 0.084
15 additional counties exceed 0.079 ppni for a total of 24
EH 26 additional counties exceed 0.075 ppni for a total of 50
HI 76 additional counties exceed 0.070 ppm for a total of 126
•i 154 additional counties exceed 0,065 ppni for a total of 280
[HI1211 counties meet 0.065 ppm standard for a total of 491
monitored in 2003 - 2005 but not projected
1 Modeled emissions reflect the expected reductions from federal programs including the Clean Air
Interstate Rule, the Clean Air Mercury Rule, the Clean Air Visibility Rule, the Clean Air Nonroad Diesel
Rule, the Light-Duty Vehicle Tier 2 Rule, the Heavy Duty Diesel Rule, proposed rules for Locomotive
and Marine Vessels and for Small Spark-Ignition Engines, and state and local level mobile and stationary
source controls identified for additional reductions in emissions for the purpose of attaining the current
PM 2.5 and Ozone standards.
2 Controls applied are illustrative. States may choose to apply different control strategies for
implementation.
3 The current standard of 0.08 ppm is effectively expressed as 0.084 ppm when rounding conventions are
applied.
4 Modeled design values in ppm are only interpreted up to 3 decimal places.
5 Map shows results from a total of 491 counties with projected design values. Consistent with current
modeling guidance, EPA did not project 2020 concentrations for counties where 2001 base year
concentrations were less than recommended criterion. Such projections may not represent expected
future levels.
ES-3
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ES-2. Results of Benefit-Cost Analysis
There are two sets of results presented below. The first set of national results assumes
attainment of revised standards by 2020 in all areas, except for two areas in Southern California.
It is expected that benefits and costs will begin occurring earlier, as states begin implementing
control measures to show progress towards attainment. Some areas with high ozone levels, such
as the two areas in Southern California, are not planning to attain even the current standard until
after 2020. In these locations, our national 2020 estimate includes the cost and benefits of
reaching an estimated progress point in 2020 (known as a "glidepath" target). The 2020 results
will thus not represent a true "full attainment" scenario for the entire nation. In order to gain an
understanding of the possible additional costs and benefits of fully attaining in California in a
year beyond 2020, we provide an additional set of results for California only. Tables ES-1 to
ES-3 present national benefits and costs in 2020, including the "glidepath" targets for California;
companion Table ES-4 provides the national estimated reductions in premature mortality and
morbidity in 2020, including the "glidepath" targets for California.
Tables ES-5 to ES-7 present the costs and benefits of full attainment for California ("glidepath"
in 2020 plus the additional increment achieved between 2020 and a future year added together
into one California total); Table ES-8 is the companion table showing estimated reductions in
premature mortality and morbidity for California. Because various mobile source rules, such as
the onroad and nonroad diesel rule, among others, would be expected to be implemented
between 2020 and a future year, the tons of emission reduction expected to occur as a result of
those rules has been taken out of the calculated costs and benefits for the estimates of additional
tons of emission reduction needed in California between 2020 and a future year. EPA did the
analysis this way because to force full attainment in an earlier year than would be required under
the Clean Air Act would likely lead to an overstatement of costs because those areas might
benefit from these existing federal or state programs that would be implemented between 2020
and the attainment year; because additional new technologies may become available between
2020 and the attainment year; and because the cost of existing technologies might fall over time.
As such, we use the best available data to estimate costs and benefits of full attainment for
California in a future year, while recognizing that the estimates of costs and benefits for
California in a year between 2020 and a future year are likely to be relatively more uncertain
than the national attainment estimates for 2020. It is not appropriate to add together the 2020
national attainment, California glidepath estimate and the estimate of California full attainment
as an estimate of national full attainment in 2020. The extra increment of attainment that is
estimated for California will not occur in 2020, so it is not accurate to add it to our nationwide
estimate of the "glidepath" benefits and costs to arrive at a "full attainment" estimate for 2020 \
It is also not accurate to add the two estimates together to arrive at an estimate of future, post-
2020 full attainment benefits and costs, because our nationwide full attainment estimates do not
1 The California full attainment costs calculated using the offset in NOx emissions from mobile
programs would understate the costs of fully attaining in 2020, however, California will not be
required to attain in 2020.
ES-4
-------
allow other areas of the nation to take credit for the reductions in NOx from the mobile source
rules that will occur after 2020.2
In these tables, the individual row estimates reflect the different studies available to describe the
ozone premature mortality relationship. Ranges within the total benefits column reflect
variability in the studies upon which the estimates associated with premature mortality were
derived. PM co-benefits account for between 13 and 99 percent of co-benefits, depending on the
standard analyzed and on the choice of ozone and PM mortality functions used.
Ranges in the total costs column reflect different assumptions about the extrapolation of costs.
The low end of the range of net benefits is constructed by subtracting the highest cost from the
lowest benefit, while the high end of the range is constructed by subtracting the lowest cost from
the highest benefit. The presentation of the net benefit estimates represents the widest possible
range from this analysis.
Table ES-1 National Annual Costs and Benefits: 0.079 ppm Standard in 2020
(including California glidepath )
Premature
Mortality
Function or
Assumption
NMMAPS
Meta-analysis
Assumption that
causal***
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits*
$1.2to$ll
$1.6 to $12
$1.7 to $12
$1.6 to $12
$1.1 to$ll
Total Costs**
$3 to $3.3
$3 to $3.3
$3 to $3.3
$3 to $3.3
$3 to $3.3
Net Benefits
-$2.1 to $8.5
-$1.7 to $8.9
-$1.7 to $8.9
-$1.7 to $8.9
-$2.2 to $8.4
This approach would be an overestimate of national full attainment costs in a future year after
2020 because it would not take into account that other states (not just California) could replace
more expensive NOx reductions from other sources with the post-2020 reductions obtained from
implementation of mobile source rules that are included in the regulatory baseline.
ES-5
-------
Table ES-2 National Annual Costs and Benefits: 0.075 ppm Standard in 2020
(including California glidepath )
Premature
Mortality
Function or
Assumption
NMMAPS
Meta-analysis
Assumption that
causal***
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits*
$3 to $16
$7.3 to $20
$7.8 to $21
$8.7 to $22
$1.5 to $15
Total Costs**
$5.5 to $8. 8
$5.5 to $8. 8
$5.5 to $8. 8
$5.5 to $8. 8
$5.5 to $8. 8
Net Benefits
-$5.8 to $10.5
-$1.5 to $15
-$l.to$15
-$0.1 to $16
-$7.3 to $9
Table ES-3 National Annual Costs and Benefits: 0.070 ppm Standard in 2020
(including California glidepath)
Premature
Mortality
Function or
Assumption
NMMAPS
Meta-analysis
Assumption that
causal***
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits*
$4.3 to $26
$9.7 to $31
$10 to $32
$11 to $33
$2.5 to $24
Total Costs**
$10 to $22
$10 to $22
$10 to $22
$10 to $22
$10 to $22
Net Benefits
-$17 to $16
-$12 to $21
-$11 to $22
-$10 to $23
-$20 to $14
Table ES-4 National Annual Costs and Benefits : 0.065 ppm Standard in 2020
(including California glidepath)
Premature
Mortality
Function or
Assumption
NMMAPS
Meta-analysis
Assumption that
causal***
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits*
$7.7 to $45
$18 to $55
$19 to $56
$20 to $57
$4.3 to $42
Total Costs**
$17 to $46
$17 to $46
$17 to $46
$17 to $46
$17 to $46
Net Benefits
-$38 to $28
-$28 to $38
-$27 to $39
-$27 to $40
-$42 to $25
*Includes ozone benefits, and PM 2.5 co-benefits. Range was developed by adding the estimate
from the ozone premature mortality function to both the lower and upper ends of the range of the
PM2.5 premature mortality functions characterized in the expert elicitation
**Range reflects lower and upper bound cost estimates
***Total includes ozone morbidity benefits only
ES-6
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Table ES-5: Summary of Total Number of Annual Ozone and PM2.5-Related Premature
Mortalities and Premature Morbidity Avoided: 2020 National Benefits
Combined Estimate of Mortality
Standard Alternative and
Model or Assumption
NMMAPS Bell (2004)
Bell (2005)
Meta-Analysis Ito (2005)
Levy (2005)
No Causality
Combined Estimate of Morbidity
Acute Myocardial Infarction
Hospital and ER Visits
Chronic Bronchitis
Acute Bronchitis
Asthma Exacerbation
Lower Respiratory Symptoms
Upper Respiratory Symptoms
School Loss Days
Work Loss Days
Minor Restricted Activity Days
Combined Range
0.079 ppm
200 to 1,900
260 to 2,000
270 to 2,000
260 to 2,000
180 to 1,900
1,100
1,300
370
950
7,300
8,100
5,900
50,000
5 1 ,000
430,000
PM2.s
0.075 ppm
430 to 2,600
1,100 to 3,300
1,200 to 3,300
1,300 to 3,500
230 to 2,400
1,400
5,600
470
1,200
9,400
10,000
7,500
610,000
65,000
2,000,000
of Ozone Benefits and
Co-Benefits
0.070 ppm
670 to 4,300
1,500 to 5,100
1,600 to 5,200
1,800 to 5,400
390 to 4,000
2,300
7,600
780
2,000
16,000
1 7,000
13,000
780,000
110,000
2,700,000
0.065 ppm
1,200 to 7,400
2,800 to 9,000
3,000 to 9,200
3,000 to 9,200
660 to 6,900
4,000
13,000
1,300
3,500
27,000
29,000
22,000
1 ,300,000
190,000
4,700,000
ES-7
-------
Table ES-6 California: Annual Costs and Benefits of Attaining 0.079 ppm Standard
(beyond 2020)*
Premature
Mortality
Function
or Assumption
NMMAPS
Meta-analysis
Assumption that
causal
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits**
$0.1 to $0.6
$0.2 to $0.7
$0.3 to $0.7
$0.2 to $0.7
$0.05 to $0.5
Total Costs***
$0.3 to $1.7
$0.3 to $1.7
$0.3 to $1.7
$0.3 to $1.7
$0.3 to $1.7
Net Benefits
-$1-6 to $0.2
-$1.5 to $0.4
-$1.4 to $0.4
-$1.5 to $0.4
-$1.6 to $0.2
Table ES-7 California: Annual Costs and Benefits of Attaining 0.070 ppm Standard
(beyond 2020)"
Premature
Mortality
Function
or Assumption
NMMAPS
Meta-analysis
Assumption that
causal****
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits**
$0.7 to $3.5
$1.9 to $4.7
$2. Ito $4.8
$2.1 to $4.8
$0.4 to $3.1
Total Costs***
$2 to $13
$2 to $13
$2 to $13
$2 to $13
$2 to $13
Net Benefits
-$12 to $1.5
-$11 to $2.7
-$11 to $2.9
-$11 to $2.9
-$13 to $1.2
Table ES-8 California: Annual Costs and Benefits of Attaining 0.075 ppm Standard
(beyond 2020)*
Premature
Mortality
Function
or Assumption
NMMAPS
Meta-analysis
Assumption that
causal
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits**
$0.4 to $1.9
$1.1 to $2.6
$1.2 to $2.7
$1.2 to $2.7
$0.2 to $1.7
Total Costs***
$1.1 to $6.2
$1.1 to $6.2
$1.1 to $6.2
$1.1 to $6.2
$1.1 to $6.2
Net Benefits
-$5.8 to $0.8
-$5.1 to $1.5
-$5.1 to $1.6
-$5to$1.6
-$6to$0.6
ES-8
-------
Table ES-9 California: Annual Costs and Benefits of Attaining 0.065 ppm Standard
(beyond 2020)"
Premature
Mortality
Function
or Assumption
NMMAPS
Meta-analysis
Assumption that
causal****
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits**
$1.1 to $5.2
$3.1 to $7.2
$3.4 to $7.4
$3.3 to $7.4
$0.5 to $4.6
Total Costs***
$2.9 to $21
$2.9 to $21
$2.9 to $21
$2.9 to $21
$2.9 to $21
Net Benefits
-$19 to $2.3
-$17 to $4.3
-$17 to $4.5
-$17 to $4.5
-$20 to $1.7
* Tables present the total of CA glidepath in 2020, plus the additional increment needed to reach
full attainment in a year beyond 2020
** Includes ozone benefits and PM 2.5 co-benefits. Range was developed by adding the
estimate from the ozone premature mortality function to both the lower and upper ends of the
range of the PM2.5 premature mortality functions characterized in the expert elicitation
***Range reflects lower and upper bound cost estimates
****Total includes ozone morbidity benefits only
ES-9
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Table ES-10: Summary of Total Number of Annual Ozone and PM2.5-Related Premature
Mortalities and Premature Morbidity Avoided: California Post 2020 Attainment
Combined Estimate of Mortality
Standard Alternative and Combined Range of Ozone Benefits and
Model or Assumption PM2.s Co-Benefits
0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm
NMMAPS
Meta-Analysis
No Causality
Bell (2004)
Bell (2005)
Ito (2005)
Levy (2005)
17 to 93
42 to 120
45 to 120
46 to 120
8.2 to 84
61 to 310
170 to 410
180 to 430
180 to 430
26 to 270
110 to 570
300 to 760
320 to 780
320 to 780
49 to 500
180 to 840
490 to 1,200
530 to 1,200
520 to 1,200
72 to 740
Combined Estimate of Morbidity
Acute Myocardial Infarction
Hospital and ER Visits
Chronic Bronchitis
Acute Bronchitis
Asthma Exacerbation
Lower Respiratory Symptoms
Upper Respiratory Symptoms
School Loss Days
Work Loss Days
Minor Restricted Activity Days
49
200
17
43
330
360
270
30,000
2,300
87,000
160
790
53
140
1,100
1,200
850
120,000
7,400
340,000
290
1,400
99
260
2,000
2,200
1,600
210,000
14,000
600,000
430
2,200
ISO
380
2,900
3,200
2,300
340,000
20,000
960,000
***Range was developed by adding the estimate from the ozone premature mortality function to both the lower and upper ends of the
range of the PM2.5 premature mortality functions characterized in the expert elicitation
ES-10
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ES-3. Caveats and Conclusions
Of critical importance to understanding these estimates of future costs and benefits is that they
not intended to be forecasts of the actual costs and benefits of implementing revised standards.
There are many challenges in estimating the costs and benefits of attaining a tighter ozone
standard, which are fully discussed in Chapter 8. Analytically, the characterization of ozone
mortality benefits and the estimation of the costs and benefits of the nation fully attaining a
tighter standard are being subject to further review by science advisory boards.
There are significant uncertainties in both cost and benefit estimates. Below we summarize some
of the more significant sources of uncertainty.
• Benefits estimates are influenced by our ability to correctly model relationships between
ozone and PM and their associated health effects (e.g., premature mortality).
• Benefits estimates are also heavily dependent upon the choice of statistical estimates for
values associated with each of the health benefits.
• EPA has requested advice from the National Academy of Sciences on how best to
quantify uncertainty in the relationship between ozone exposure and premature mortality
in the context of quantifying benefits associated with alternative ozone control strategies.
• PM co-benefits are derived primarily from reductions in nitrates (associated with NOx
controls). As such, these estimates are strongly influenced by the assumption that all PM
components are equally toxic. Co-benefit estimates are also influenced by the extent to
which a particular area chooses to use NOx controls rather than VOC controls.
• EPA employed a monitor rollback approach to estimate the benefits of attaining an
alternative standard of 0.079 ppm nationwide. This approach likely understates the
benefits that would occur due to implementation of actual controls because controls
implemented to reduce ozone concentrations at the highest monitor would likely result in
some reductions in ozone concentrations at attaining monitors down-wind (i.e. the
controls would lead to concentrations below the standard in down-wind locations). The
estimated benefits of attaining a standard of 0.075 ppm, however, are likely overstated.
EPA will develop and present consistent approaches for the alternative standards for the
final RIA.
• There are several nonqualified benefits (e.g. effects of reduced ozone on forest health
and agricultural crop production) and disbenefits (e.g. decreases in tropospheric ozone
lead to reduced screening of UV-B rays and reduced nitrogen fertilization of forests and
cropland) discussed in this analysis in chapter 6.
• Changes in air quality as a result of controls are not expected to be uniform over the
country. In our hypothetical control scenario some increases in ozone levels occur in
areas already in attainment, though not enough to push the areas into nonattainment
• As explained in chapter 5, there are several uncertainties in our cost estimates. For
example, the states are likely to use different approaches for reducing NOx and VOCs in
their state implementation plans to reach a tighter standard. In addition, since we are
unable to use known controls to get all areas into attainment, we needed to use simple
$/ton costs to estimate the overall national cost of meeting the tighter alternatives.
• As discussed in chapter 5, recent advice from EPA's Science Advisory Board has
questioned the appropriateness of an approach similar to that used here for estimating
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extrapolated costs. EPA will consider this advice and other guidance as it develops the
methodology for analyzing the final rule.
Both extrapolated costs and benefits have additional uncertainty relative to modeled costs
and benefits. The extrapolated costs and benefits will only be realized to the extent that
unknown extrapolated controls are economically feasible and are implemented.
Technological advances over time will tend to increase the economic feasibility of
reducing emissions, and will tend to reduce the costs of reducing emissions.
These sources of uncertainty are discussed in more detail in subsequent chapters of the
RIA. In addition to considering any advice which comes from advisory bodies prior to
the publication of the final ozone NAAQS, EPA will undertake an updated approach with
improvements to emissions inventories, models and control strategies for the RIA which
will accompany that rulemaking.
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Chapter 1; Introduction and Background
Synopsis
This document estimates the incremental costs and monetized human health and welfare
benefits of attaining possible revised primary ozone National Ambient Air Quality
Standards (NAAQS) nationwide. This document contains illustrative analyses that
consider limited emission control scenarios that states, tribes and regional planning
organizations might implement to achieve a revised ozone NAAQS. In some cases, EPA
weighed the available empirical data to make judgments regarding the proposed
attainment status of certain urban areas in the future. According to the Clean Air Act,
EPA must use health-based criteria in setting the NAAQS and cannot consider estimates
of compliance cost. This Regulatory Impact Analysis (RIA) is intended to provide the
public a sense of the benefits and costs of meeting new alternative ozone NAAQS, and to
meet the requirements of Executive Order 12866 and OMB Circular A-4 (described
below in Section 1.2.2).
1.1 Background
Two sections of the Clean Air Act ("Act") govern the establishment and revision of
NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify pollutants
which "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." Ozone is one of six pollutants for which 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)(l) defines a primary standard as "the attainment and maintenance of which in the
judgment of the Administrator, based on [the] criteria and allowing an adequate margin
of safety, [are] 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, [are] 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 Act directs the Administrator to review existing criteria and
standards at 5-year intervals. When warranted by such review, the Administrator is to
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retain or revise the NAAQS. After promulgation or revision of the NAAQS, the
standards are implemented by the States.
1.2 Role of the Regulatory Impact Analysis in the NAAQS Setting Process
1.2.1 Legislative Roles
In setting primary ambient air quality standards, EPA's responsibility under the law is to
establish standards that protect public health. The Clean Air Act requires EPA, for each
criteria pollutant, to set a standard that protects public health with "an adequate margin of
safety." As interpreted by the Agency and the courts, the Act requires EPA to create
standards based on health considerations only. Economic factors cannot be considered.
The prohibition against the consideration of cost in the setting of the primary air quality
standard, however, does not mean that costs or other economic considerations are
unimportant or should be ignored. The Agency believes that consideration of costs and
benefits are essential to making efficient, cost effective decisions for implementation of
these standards. The impact of cost and efficiency are considered by states during this
process, as they decide what timelines, strategies, and policies make the most sense. This
PJA is intended to inform the public about the potential costs and benefits that may result
when a new ozone standard is implemented, but is not relevant to establishing the
standards themselves.
1.2.2 Role of Statutory and Executive Orders
There are several statutory and executive orders that dictate the manner in which EPA
considers rulemaking and public documents. This document is separate from the
NAAQS decision making process, but there are several statutes and executive orders that
still apply to any public documentation. A summary of the pertinent orders is included in
Appendix 1. The analysis required by these statutes and executive orders is presented in
Chapter 9.
EPA presents this RIA pursuant to Executive Order 12866 and the guidelines of OMB
Circular A-4.l These documents present guidelines for EPA to assess the benefits and
costs of the selected regulatory option, as well as one less stringent and one more
stringent option. OMB circular A-4 also requires both a cost-benefit, and a cost-
effectiveness analysis for rules where health is the primary effect. Within this RIA we
provide a cost benefit analysis. We also provide a cost-effectiveness analysis for that
portion of the benefits which occur from concurrent reductions in particulate matter as a
result of controls on NOx emissions to reduce ozone levels (see Appendix 6b). We are
investigating options for conducting a cost-effectiveness analysis for the ozone portion of
the benefits and expect to provide estimates based on that analysis in the final RIA.
1 U.S. Office of Management and Budget. Circular A-4, September 17, 2003. Found on
the Internet at .
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1.2.3 Market Failure or Other Social Purpose
OMB Circular A-4 indicates that one of the reasons a regulation such as the NAAQS may
one may be issued is to address 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.
An externality occurs when one party's actions impose uncompensated benefits or costs
on another party. Environmental problems are a classic case of externality. For example,
the smoke from a factory may adversely affect the health of local residents while soiling
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. From this perspective, externalities arise from high
transaction costs and/or poorly defined property rights that prevent people from reaching
efficient outcomes through market transactions.
Firms exercise market power when they reduce output below what would be offered in a
competitive industry in order to obtain higher prices. They may exercise market power
collectively or unilaterally. Government action can be a source of market power, such as
when regulatory actions exclude low-cost imports. Generally, regulations that increase
market power for selected entities should be avoided. However, there are some
circumstances in which government may choose to validate a monopoly. If a market can
be served at lowest cost only when production is limited to a single producer of local gas
and electricity distribution services, a natural monopoly is said to exist. In such cases, the
government may choose to approve the monopoly and to regulate its prices and/or
production decisions. Nevertheless, it should be noted that technological advances often
affect economies of scale. This can, in turn, transform what was once considered a
natural monopoly into a market where competition can flourish.
Market failures may also result from inadequate or asymmetric information. Because
information, like other goods, is costly to produce and disseminate, an evaluation will
need to do more than demonstrate the possible existence of incomplete or asymmetric
information. Even though the market may supply less than the full amount of
information, the amount it does supply may be reasonably adequate and therefore not
require government regulation. Sellers have an incentive to provide information through
advertising that can increase sales by highlighting distinctive characteristics of their
products. Buyers may also obtain reasonably adequate information about product
characteristics through other channels, such as a seller offering a warranty or a third party
providing information.
There are justifications for regulations in addition to correcting market failures. A
regulation may be appropriate when there are clearly identified measures that can make
government operate more efficiently. In addition, Congress establishes some regulatory
programs to redistribute resources to select groups. Such regulations should be examined
to ensure that they are both effective and cost-effective. Congress also authorizes some
regulations to prohibit discrimination that conflicts with generally accepted norms within
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our society. Rulemaking may also be appropriate to protect privacy, permit more
personal freedom or promote other democratic aspirations.
From an economics perspective, setting an air quality standard is a straightforward case
of addressing an externality, in this case where firms are emitting pollutants, which cause
health and environmental problems without compensation for those suffering the
problems. Although this economics perspective is reflected in Clean Air Act legislative
history, there is also legislative history in which members of Congress stated that the
purpose of setting national air quality standards solely based on health considerations
(without considering costs) is to protect a fundamental right of Americans to be protected
from air pollution levels that adversely affect their health. Setting a standard with a
reasonable 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.
1.2.4 Illustrative Nature of the Analysis
This ozone NAAQS RIA is an illustrative analysis that provides useful insights into a
limited number of emissions control scenarios that states might implement to achieve a
revised ozone NAAQS. Because states are ultimately responsible for implementing
strategies to meet any revised standard, the control scenarios in this RIA are necessarily
hypothetical in nature. They are not forecasts of expected future outcomes. Important
uncertainties and limitations, are documented in the relevant portions of the analysis.
The illustrative goals of this RIA are somewhat different from other EPA analyses of
national rules, or the implementation plans states develop, and the distinctions are worth
brief mention. This RIA does not assess the regulatory impact of an EPA-prescribed
national or regional rule such as the Clean Air Interstate Rule, nor does it attempt to
model the specific actions that any state would take to implement a revised ozone
standard. This analysis attempts to estimate the costs and human and welfare benefits of
cost-effective implementation strategies which might be undertaken to achieve national
attainment of new standards. These hypothetical strategies represent a scenario where
states use one set of cost-effective controls to attain a revised ozone NAAQS. Because
states—not EPA—will implement any revised NAAQS, they will ultimately determine
appropriate emissions control scenarios. State implementation plans would likely vary
from EPA's estimates due to differences in the data and assumptions that states use to
develop these plans.
The illustrative attainment scenarios presented in this RIA were constructed with the
understanding that there are inherent uncertainties in projecting emissions and controls.
Furthermore, certain emissions inventory, control, modeling and monitoring limitations
and uncertainties inhibit EPA's ability to model full attainment in all areas. An
additional limitation is that this analysis is carried out for the year 2020, before some
areas are required to reach the current ozone standard. Section 1.3.1 below explains why
EPA selected the analysis year of 2020. Despite these limitations, EPA has used the best
available data and methods to produce this RIA.
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1.3 Overview and Design of the RIA
This Regulatory Impact Analysis evaluates the costs and benefits of hypothetical national
strategies to attain several potential revised primary ozone standards. The document is
intended to be straightforward and written for the lay person with a minimal background
in chemistry, economics, and/or epidemiology. Figure 1.1 provides an illustration of the
framework of this RIA.
Estimate 2020 Emissions
Model 2020 Baseline Air Quality
Estimate Modeled
Control Costs
Identify Areas Projected to Exceed
Alternate Standard and Calculate
Needed Reduction Targets
Select Control Strategies
Determine Post-Control Air
Quality and Compare pre- and
post-control strategy air quality
(partial attainment in most areas)
I
Extrapolate Tons to Reach Full
Attainment and Compare pre-and
post- extrapolation air quality
Estimate Modeled Human
Health Effects and Dollar
Benefits
Estimate Extrapolated
Control Costs
Estimate Extrapolated Human
Health Effects and Dollar Benefits
Figure 1.1: the process used to create this RIA
1.3.1 Baseline and Years of Analysis
The analysis year for this regulatory impact analysis is 2020, which allows EPA to build
the ozone RIA analysis on the previously completed PM NAAQS RIA analysis. Many
areas will reach attainment of the current ozone standard or any alternative standard by
2020. For purposes of this analysis, we assume attainment by 2020 for all areas except
for two areas in California with unique circumstances described in chapter 4. Some
areas for which we assume 2020 attainment may in fact need more time to meet one or
more of the analyzed standards, while others will need less time. This analysis does not
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prejudge the attainment dates that will ultimately be assigned to individual areas under
the Clean Air Act, which contains a variety of potential dates and flexibility to move to
later dates (up to 20 years), provided that the date is as expeditious as practicable.
The methodology first estimates what baseline ozone levels might look like in 2020 with
existing Clean Air Act programs, including application of controls to meet the current
ozone standard and the newly revised PM NAAQS standard, and then models how ozone
levels would be predicted to change following the application of additional controls to
reach a tighter standard. This allows for an analysis of the incremental change between
the current standard and an alternative standard. This timeline is also consistent with
expected attainment in 2020 of the revised Particulate Matter (PM) NAAQS covered in
the PM NAAQS RIA issued in September 2006. As explained in Chapter 2, since one of
the principal precursors for ozone, NOx, is also a precursor for PM, it is important that
we account for the impact on ozone concentrations of NOx controls used in the
hypothetical control scenario used in the PM NAAQS RIA, so as to avoid double
counting the benefits and costs of these controls.
1.3.2 Control Scenarios Considered in this RIA
A hypothetical control strategy was developed for an alternative 8-hr ozone standard of
0.070 ppm, in order to illustrate one national scenario for how such a tighter standard
might be met. First, EPA modeled the predicted air quality changes that would result
from the application of emissions control options that are known to be available to
different types of sources in portions of the country that were predicted to be in non-
attainment with 0.070 ppm in 2020. However, given the limitations of current
technology and the amount of improvement in air quality needed to reach a standard of
0.070 ppm in some areas, it was also expected that modeling these known controls would
not reduce ozone concentrations sufficiently to allow all areas to reach the more stringent
standard. This required a second step to calculate the number of tons of emission
reductions that would be needed to reach full attainment. This required calculating a
conversion factor to quantify the estimated tons of emissions that needed to be reduced to
generate a particular change in air quality concentrations of ozone (in ppm). This factor,
coupled with the estimated remaining increment (in ppm) of ozone necessary to reach the
alternative standard in each area, allowed for an extrapolation of how many tons of
additional emissions reductions were estimated to be needed to reach the alternate
standard.
1.3.3 Evaluating Costs and Benefits
Applying a two step methodology for estimating emission reductions needed to reach full
attainment enabled EPA to evaluate nationwide costs and benefits of attaining a tighter
ozone standard, albeit with substantial additional uncertainty regarding the second step
estimates. Costs and benefits are presented in this RIA in the same two steps that
emissions reductions were estimated. First, the costs associated with applying known
controls were quantified, and presented along with an estimate of their economic impact.
Second, EPA estimated costs of the additional tons of extrapolated emission reductions
estimated which were needed to reach full attainment. The analysis of the benefits of
setting an alternative standard included both mortality and morbidity calculations
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matching the costs of applying known controls and then the benefits of reaching full
attainment. The costs and monetized benefits were then compared to provide an estimate
of net benefits nationwide.
The RIA presents two sets of results for estimated costs and benefits. The first reflects
full attainment in 2020 in all locations of the U.S. except two areas of California. These
two areas are not planning to meet the current standard by 2020, so the estimated costs
and benefits for these areas are based on reaching an estimated progress point in 2020
(their "glidepath" targets). The second set of results for California only, estimate the
costs and benefits from California fully attaining the alternative standards in a year
beyond 2020 (glidepath estimates for 2020, plus further increments needed to reach full
attainment beyond 2020, added together for California total).
To streamline this RIA, it refers to several previously published documents, including
two technical documents EPA produced to prepare for the ozone NAAQS proposal. The
first was a Criteria Document created by EPA's Office of Research and Development
(published in 2006), which presented the latest available pertinent information on
atmospheric science, air quality, exposure, dosimetry, health effects, and environmental
effects of ozone. The second was a "Staff Paper" (published in 2007) that evaluated the
policy implications of the key studies and scientific information contained in the Criteria
Document, as well as presented a risk assessment for various standard levels. The Staff
Paper also includes staff conclusions and recommendations to the Administrator
regarding potential revisions to the standards. In addition to the Criteria Document and
Staff Paper, this ozone RIA relies heavily on the 2006 RIA for particulate matter (PM).
Many of the models and methodology used here are the same as in the PM NAAQS RIA.
This RIA identifies methodologies used to generate data, but refers readers to the PM
NAAQS RIA for many technical details. The focus of this RIA is to explain in detail
how the approach or methodologies have changed from the PM NAAQS RIA analysis,
and to present the results of the methodologies employed in this analysis, which
compares attainment of tighter levels of the ozone standard to the baseline of the current
standard.
1.4 Ozone Standard Alternatives Considered
Per Executive Order 12866 and the guidelines of OMB Circular A-4, this RIA presents
analyses of the range of standards proposed by the Administrator in the Notice of
Proposed Rulemaking (0.070 - 0.075 ppm), as well as one more stringent option (0.065
ppm), and one less stringent option (0.079 ppm). EPA will also model a baseline as the
current primary standard for ozone (0.08 parts per million (ppm), calculated as the 3-year
average of the annual fourth-highest daily maximum 8-hour average ozone concentration
measured at each monitor within an area — effectively 0.084 ppm using current data
rounding conventions).
The EPA Administrator received recommendations to revise the current primary ozone
standard from both the Clean Air Act Scientific Advisory Council (CASAC) and EPA
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staff. Both CAS AC and staff expressed the view that the current standard was not
adequately protective of human health and should be tightened to provide additional
public health protection. Specifically, CAS AC recommended that ".. .the current
primary ozone NAAQS [should] be revised and that the level that should be considered
for the revised standard be from 0.060 to 0.070 ppm." In the Staff Paper, EPA staff
suggested a slightly broader range, recommending "that consideration be given to a
standard level within the range of somewhat below 0.080 ppm to 0.060 ppm." Both of
these recommendations encourage maintaining the same averaging time and form as the
current standard. Additionally, both recommendations suggested it was appropriate to
specify the level of the standard out to three significant digits.
In the concurrent ozone NAAQS proposal, EPA proposes to revise the 8-hour standard to
a level within the range of 0.070 to 0.075 ppm, and to request comment on a wider range
of an 8-hour standard from 0.060 ppm to 0.084 ppm, to provide increased protection for
children and other sensitive populations against an array of ozone-related adverse health
effects that range from decreased lung function and increased respiratory symptoms to
serious indicators of respiratory morbidity including emergency department visits and
hospital admissions for respiratory causes, and possibly cardiovascular-related morbidity
as well as total nonaccidental and cardiopulmonary mortality. The EPA also proposes to
specify the level of the primary standard to the nearest thousandth ppm.
This RIA presents benefit and cost estimates for both ends of the proposal range of 0.070
to 0.075 ppm. It also assesses the costs and benefits of attaining one more stringent
standard option (0.065 ppm), and will include a less stringent option of 0.079 ppm..
Since EPA is not considering loosening the standard, the less stringent option to the
proposed range is the current standard itself. Since this RIA presents an analysis of the
costs and benefits incremental to the current standard, retaining the current standard is
assumed to have no additional incremental costs or benefits.
For the secondary standard, EPA proposes to revise the current 8-hour standard with one
of two options to provide increased protection against ozone-related adverse impacts on
vegetation and forested ecosystems. One option is to replace the current standard with a
cumulative, seasonal standard expressed as an index (called the W126) of the annual sum
of weighted hourly concentrations, cumulated over 12 hours per day (8:00 am to 8:00
pm) during the consecutive 3-month period within the ozone season with the maximum
index value, set at a level within the range of 7 to 21 ppm-hours. The other option is to
make the secondary standard identical to the proposed primary 8-hour standard in all
respects. This RIA provides a limited discussion of an alternative secondary standard
showing the number of additional counties with monitors that may violate the standards,
2 (Henderson, 2006c, p. 5). Henderson, R. (2006c) Letter from CAS AC Chairman
Rogene Henderson to EPA Administrator Stephen Johnson, October 24, 2006, EPA-
CASAC-07-001.
3 U.S EPA. 2007. Review of the National Ambient Air Quality Standards for Ozone:
Policy Assessment of Scientific and Technical Information. OAQPS Staff Paper. North
Carolina. EPA-452/R-07-003
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depending on the level of the secondary standard. Costs and benefits of attaining the
different levels of the secondary were not estimated in this RIA; this analysis focused on
the health benefits of the primary standard. An analysis of a separate secondary will be
included in the final RIA as appropriate. Hereafter, any reference to the ozone standard
will be assumed to be the primary standard, unless otherwise noted.
1.5 References:
Henderson, R. 2006. October 24, 2006. Letter from CASAC Chairman Rogene
Henderson to EPA Administrator Stephen Johnson,, EPA-CASAC-07-001.
U.S. EPA. 1970. Clean Air Act. 40CFR50.
U.S. EPA. 2006 .Air Quality Criteria for Ozone and Related Photochemical Oxidants
(Final). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-
cF,
U.S EPA. 2007. Review of the National Ambient Air Quality Standards for Ozone:
Policy Assessment of Scientific and Technical Information. OAQPS Staff Paper. North
Carolina. EPA-452/R-07-003
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Pertinent Statutory and Executive Orders
1. Executive Order 12866: Regulatory Planning and Review
http://www.archives.gov/federal-register/executive-orders/pdf/12866.pdf
2. Paperwork Reduction Act, 44 U.S.C. 3501 et seq
http://www.archives.gov/federal-register/laws/paperwork-reduction/
3. Regulatory Flexibility Act, 5 U.S.C. 601 et seq.
http://www.archives.gov/federal-register/laws/regulatory-flexibility/
4. Unfunded Mandates Reform Act, Public Law 104-4
http://frwebgate.access.gpo.gov/cgi-
bin/getdoc.cgi?dbname=l04 cong_public Iaws&docid=f:publ4.104.pdf
5. Executive Order 13132: Federalism, Executive Order 13132
http://frwebgate.access.gpo.gov/cgi-
bin/getdoc.cgi?dbname=l999 register&docid=fr 1 Oau99-l33.pdf
6. Executive Order 13175: Consultation and Coordination with Indian Tribal
Governments
http://frwebgate.access.gpo.gov/cgi-
bin/getdoc.cgi?dbname=2000_register&docid=fr09noOO-l 67.pdf
7. Executive Order 13045: Protection of Children from Environmental Health &
Safety Risks
http://frwebgate.access.gpo.gov/cgi-
bin/getdoc.cgi?dbname=1997_register&docid=fr23ap97-l 30.pdf
8. Executive Order 13211: Actions that Significantly Affect Energy Supply,
Distribution or Use
http://frwebgate.access.gpo.gov/cgi-
bin/getdoc.cgi?dbname=2001 register&docid=fr22myO 1 -133 .pdf
9. National Technology Transfer Advancement Act, Public Law No. 104-113, §12(d)
http://frwebgate.access.gpo.gov/cgi-
bin/getdoc.cgi?dbname=104 cong_public Iaws&docid=f:publll3.104.pdf
10. Executive Order 12898: Federal Actions to Address Environmental Justice in
Minority Populations and Low-Income Populations
http://www.archives.gov/federal-register/executive-orders/pdf/12898.pdf
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Chapter 2: Characterizing Ozone and Modeling Tools Used in This Analysis
Synopsis
This chapter describes the chemical and physical properties of ozone, general ozone air
quality patterns, key health and environmental impacts associated with exposure to
ozone, and key sources of ozone precursor emissions. In order to evaluate the health and
environmental impacts of trying to reach a tighter ozone standard in the year 2020, it was
necessary to use models to predict concentrations in the future. The tools and
methodology used for the air quality modeling are described in this chapter. Subsequent
chapters of this RIA rely heavily on the results of this modeling.
2.1 Ozone Chemistry
Ozone occurs both naturally in the stratosphere to provide a protective layer high above
the earth, and at ground-level (troposphere) as the prime ingredient of smog.
Tropospheric ozone, which is regulated by the NAAQS, is formed by both naturally
occurring and anthropogenic sources. Ozone is not emitted directly into the air, but is
created when its two primary components, volatile organic compounds (VOC) and oxides
of nitrogen (NOx), combine in the presence of sunlight. VOC and NOx are often referred
to as ozone precursors, which are, for the most part, emitted directly into the atmosphere.
Ambient ozone concentrations are directly affected by temperature, solar radiation, wind
speed and other meteorological factors. Ultraviolet radiation from the sun plays a key
role in initiating the processes leading to ozone formation. However, there is little
empirical evidence directly linking day-to-day variations in observed surface ultraviolet
radiation levels with variations in tropospheric ozone levels.
The rate of ozone production can be limited by either VOCs or NOX. In general, ozone
formation using these two precursors is reliant upon the relative sources of hydroxide
(OH) and NOx. When the rate of OH production is greater than the rate of production of
NOX, indicating that NOX is in short supply, the rate of ozone production is NOX-limited.
In this situation, ozone concentrations are most effectively reduced by lowering current
and future NOX emissions, rather than lowering emissions of VOCs. When the rate of OH
production is less than the rate of production of NOX, ozone production is VOC-limited.
Here, ozone is most effectively reduced by lowering VOCs. Between the NOx- and
VOC- limited extremes there is a transitional region where ozone is nearly equally
sensitive to each species. However ozone is relatively insensitive to marginal changes in
both NOx and VOC in this situation. In urban areas with a high population
concentration, ozone is often VOC-limited. Ozone is generally NOx-limited in rural areas
and downwind suburban areas.
Due to the complex photochemistry of ozone production, NOx emissions lead to both the
formation and destruction of ozone, depending on the local quantities of NOx, VOC, and
ozone catalysts such as the OH and HO2 radicals. In areas dominated by fresh emissions
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of NOx, ozone catalysts are removed via the production of nitric acid, which slows the
ozone formation rate. Because NOx is generally depleted more rapidly than VOC, this
effect is usually short-lived and the emitted NOx can lead to ozone formation later and
further downwind. The terms "NOx disbenefits" or "ozone disbenefits" refer to the
ozone increases that can result from NOx emission reductions in these localized areas.1
2.1.1 Temporal Scale
Ground-level ozone forms readily in the atmosphere, usually during hot weather. The
effects of sunlight on ozone formation depend on its intensity and its spectral distribution.
Ozone levels tend to be highest during the daytime, during the summer or warm season.
Changing weather patterns contribute to day to day and interannual differences in ozone
concentrations. Differences in climatic regime, amount and mixture of emissions, and
the extent of transport contribute to variations in ozone from city to city.
2.1.2 Geographic Scale and Transport
In many urban areas, ozone nonattainment is not caused by emissions from the local area
alone. Due to atmospheric transport, contributions of precursors from the surrounding
region can also be important. Thus, in designing control strategies to reduce ozone
concentrations in a local area, it is often necessary to account for regional transport
within the U.S.
In some areas, such as California, global transport of ozone from beyond North America
can contribute to nonattainment areas. In a very limited number of areas, including
areas such as Buffalo, Detroit and El Paso, which are located near borders, emissions
from Canada or Mexico may contribute to nonattainment. In these areas, our illustrative
implementation strategies may have included more controls on domestic sources than
would be required if cross-border transport did not occur. However, we have not
conducted formal analysis, and as such cannot determine the contribution of non-U.S.
sources to ozone design values. The transport of ozone is determined by meteorological
and chemical processes which typically extend over spatial scales of several hundred
kilometers. Additionally, convection is capable of transporting ozone and its precursors
vertically through the troposphere, with resulting mixing of stratospheric ozone for
periods of a month or more with tropospheric ozone.
The Technical Support Document (TSD) for the Clean Air Interstate Rule (CAIR)
suggests that ozone transport constitutes a sizable portion of projected nonattainment in
most eastern areas based on a 2010 analysis. A listing of Eastern states and the extent of
transported ozone they receive in the CAIR analysis is located in the CAIR TSD. We
used this information to help guide the design of emissions control strategies in this
analysis.
1 U.S. EPA. Final Regulatory Impact Analysis: Control of Emissions fromNonroad
Diesel Engines. EPA420-R-04-007. May 2004.
" http://www.epa.gov/interstateairquality/pdfs/finaltech02.pdf, table VI-2
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2.1.3 Effects of Ozone
Exposure to ground-level ozone is associated with a wide array of human health effects.
Short-term exposure to ozone can cause acute respiratory problems; aggravate asthma;
cause significant temporary decreases in lung capacity; cause inflammation of lung
tissue; lead to hospital admissions and emergency room visits; and impair the body's
immune system defenses, making people more susceptible to respiratory illnesses,
including bronchitis and pneumonia. In addition, recent studies also provide evidence of
additional health impacts associated with exposure to ozone, including premature
mortality and possibly cardiac-related effects. (For a complete discussion of these
effects, see Chapter 3 of the Staff Paper.)
Ground-level ozone is also associated with numerous environmental impacts. For
example, ozone interferes with the ability of plants to produce and store food, so that
growth, reproduction and overall plant health are compromised. By weakening sensitive
vegetation, ozone makes plants more susceptible to disease, pests, and environmental
stresses. Ground-level ozone has been shown to reduce agricultural yields for many
economically important crops (e.g., soybeans, kidney beans, wheat, cotton). The effects
of ground-level ozone on long-lived species such as trees are believed to add up over
many years so that whole forests or ecosystems can be affected. For example, ozone can
adversely impact ecological functions such as water movement, mineral nutrient cycling,
and habitats for various animal and plant species. Furthermore, one of the key
components of ozone, nitrogen oxides, contributes to fish kills and algae blooms in
sensitive waterways, such as the Chesapeake Bay. (For a complete discussion of these
effects, see Chapter 7 of the Staff Paper.)
2.2 Sources of Ozone
The anthropogenic precursors of ozone originate from a wide variety of stationary and
mobile sources. In urban areas, both biogenic (natural) and anthropogenic VOCs are
important for ozone formation. Hundreds of VOCs are emitted by evaporation and
combustion processes from a large number of anthropogenic sources. Current data show
that solvent use and highway vehicles are the two main sources of VOCs, with roughly
equal contributions to total emissions. Emissions of VOCs from highway vehicles
account for roughly two-thirds of the transportation-related emissions3 By 2020, EPA
emission projections show that VOC emissions from highway vehicles decrease
significantly. Solvent use VOC decreases as well, but by 2020 solvent use VOC is
projected to be a slightly more significant VOC contributor than mobile VOC. On the
regional and global scales, emissions of VOCs from vegetation are much larger than
those from anthropogenic sources.
Anthropogenic NOx emissions are associated with combustion processes. The two
largest sources of NOx are electric power generation plants (EGUs) and motor vehicles.
3 U.S EPA. 2007. Review of the National Ambient Air Quality Standards for Ozone:
Policy Assessment of Scientific and Technical Information. OAQPS Staff Paper. North
Carolina. EPA-452/R-07-003
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EGU NOx is approximately 40% less than onroad mobile NOx in 2001. Both decrease
between 2001 and 2020, with onroad mobile NOx decreasing more, so that their
emissions are similar in 2020. It is not possible to make an overall statement about their
relative impacts on ozone in all local areas because EGUs are more sparse than mobile
sources, particularly in the west and south (See chapter 3 for a discussion of emission
reductions projected in 2020 for the 8-hr ozone current standard baseline and the more
stringent alternative control scenario). Natural NOx sources include stratospheric
intrusions, lightning, soils, and wildfires. Lightning, fertilized soils, and wildfires are the
major natural sources of NOx in the United States. Uncertainties in natural NOx
inventories are much larger than for anthropogenic NOx emissions.
A complete list of emissions source categories, for both NOx and VOCs, is compiled in
the final ozone Staff Paper (EPA, 2007, pp. 2-3 to 2-6).
2.3 Modeling Ozone Levels in the Future
In order to evaluate the predicted air quality in 2020, it is necessary to use modeling to
derive estimated air quality concentrations. The modeling analysis uses an emissions
inventory and historical meteorological conditions to simulate pollutant concentrations.
The predictions from the modeling are used to (a) project future ozone design values (a
representation of the resultant air quality concentration in 2020 equal to the 4th highest
maximum 8-hr concentration) and (b) create spatial fields of ozone and PIVb.s for
characterizing human health impacts from reducing ozone precursors, which in the case
of NOx will also affect the formation of PlV^.s. The air quality model used in this PJA is
the Community Multi-Scale Air Quality (CMAQ) model4. The modeling to capture the
PM2.5 was performed for a one year time period. Modeling to calculate ozone-related
benefits and the projection of ozone design values was performed for the period June
through August. All controls in the illustrative 0.070 scenario were applied similarly to
all months. There were no controls applied specifically for PM2.5 co-benefits because the
controls developed to reduce summer ozone were applied to all months (see Chapter 3).
2.3.1 Emissions Inventory
The 2020 inventory from the Final PM NAAQS emissions platform was used as the
starting point for the baseline and all subsequent analyses5. This included emissions from
Canada as of 20006, and Mexico as of 1999.7 As first discussed in the PM NAAQS PJA,
an examination of the historical data suggests our previous methods have over-predicted
future-year emissions for stationary non-EGU point and non-point sources, especially in
the longer-forecast periods required for the NAAQS and other programs. To address this
issue, we developed an 'interim' emission projection approach that assumes no growth to
4 See CMAQ references listed at end of this chapter
5 Final PM NAAQS Inventory is in the public docket EPA-HQ-OAR-2006-0834-0048.3
6 http://www.epa.gov/ttn/chief/net/canada.html#data
7 http://www.epa.gov/ttn/chief/net/mexico.html
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emissions for many stationary non-EGU sources in estimating future-year emissions. In
the future, we intend to pursue improved methods and models that provide more
consistency with the historical record and reasonable assumptions regarding future
conditions. More information is provided in Appendix D of the PM NAAQS RIA on the
interim approach and a sensitivity analysis of the implications of this method relative to
our previous forecasting methods. An updated 2020 inventory based on the EPA's 2002
modeling platform is currently being developed and will be used for the final ozone
NAAQS RIA In addition, all national and local controls used in the illustrative control
scenario for the revised PM NAAQS RIA were included in this 2020 baseline. These
controls were included to prevent double counting of costs and benefits, especially in the
case of NOx controls which are precursors to both PM2.5 and ozone.
2.3.2 CMAQ Model
A national scale air quality modeling analysis was performed to estimate future year
attainment/nonattainment of the current and alternative ozone standards. In addition, the
model-based projections of ozone and PM2.s were used as inputs to the calculation of
expected incremental benefits from the alternative ozone standards considered in this
assessment. The 2001-based CMAQ modeling platform (version 4.5) was used as the
basis for air quality modeling of future baseline emissions and control scenarios designed
to bring areas into attainment with specific standards. This modeling platform was used
in the 2006 PM NAAQS RIA. In addition to the CMAQ model, the modeling platform
includes the emissions, meteorology, and initial and boundary condition data which are
inputs to this model. The model produces spatial fields of gridded air quality
concentrations on an hourly basis for the entire modeling domain. The concentrations
that are produced can be averaged to produce a number of air quality metrics, including
the 8-hr ozone design values, and can be used as inputs for the analysis of costs and
benefits.
The key inputs to the CMAQ model include emissions from anthropogenic and biogenic
sources, meteorological data, and initial and boundary conditions. The CMAQ
meteorological input files were derived from simulations of the Pennsylvania State
University / National Center for Atmospheric Research Mesoscale Model (Grell, Dudhia,
and Stauffer, 1994). This model, commonly referred to as MM5, is a limited-area,
nonhydrostatic, terrain-following system that solves for the full set of physical and
thermodynamic equations which govern atmospheric motions. The lateral boundary and
initial species concentrations were obtained from a three-dimensional global atmospheric
chemistry model, the GEOS-CHEM model (Yantosca, 2004). The global GEOS-CHEM
model simulates atmospheric chemical and physical processes driven by assimilated
meteorological observations from the NASA's Goddard Earth Observing System
(GEOS).
EPA performed an extensive evaluation of the 2001-based CMAQ modeling platform as
part of the analyses for CAIR and the PM NAAQS RIA. These evaluations have been
updated as part of the Locomotive/Marine Proposed Rule to focus on model performance
for ozone from the 12 km CMAQ base year simulations in the East. Details of the model
performance methodology are described in the Locomotive/Marine Rule Air Quality
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Modeling Technical Support Document. For the months of June, July, and August 2001,
which were used as the basis for the Ozone NAAQS modeling, the 8-hour daily
maximum modeled concentrations underestimate the corresponding observed values by
10 to 15 percent in the East and West. As in the evaluation for previous model
applications, the "acceptability" of model performance for the ozone RIA modeling was
judged by comparing the results to those found in recent regional ozone model
applications for other EPA and non-EPA studies. Overall, the performance for the
CMAQ application is generally within the range of these other applications.
Figure 2-1 shows the modeling domains that were used as a part of this analysis. The
geographic specifications for these domains are provided in Table 2-1. All three
modeling domains contain 14 vertical layers with a top at about 16,200 meters, or 100
mb. Two domains with 12 km horizontal resolution were used for episodic ozone
modeling. These domains are labeled as the Eastern and Western 12 km domains in
Figure 2-1. Also shown in this figure is the 36 km domain which was used for modeling
PM2.5 concentrations. For this analysis, predictions from the Eastern domain were used
to provide data for all areas east of 100 degrees longitude. Model predictions from the
Western domain we used for all areas west of this longitude.
Figure 2.1. Map of the CMAQ Modeling Domains Used for Ozone NAAQS RIA
The selection of 12 km grid resolution for ozone modeling and 36 km resolution for
PM2.s modeling is consistent with recommendations on grid resolution for regional
analyses in EPA's air quality modeling guidance for ozone and PM. Specifically, the
guidance recommends modeling at 12 km or finer resolution, but not greater than 36 km
resolution for regional scale modeling. The recommendations in the guidance are based
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largely on analyses of model performance and model response at various grid resolutions
which indicate that results are generally similar at these grid resolutions for secondarily
formed pollutants like ozone, nitrate, and sulfate which are components of PIVb.s.
Another factor weighed in the selection of grid resolution is the computation requirement
for modeling at 12 km versus 36 km. Specifically, national modeling at 12 km resolution
requires roughly 10 times more computer time compared to modeling at 36 km. We were
able to minimize the computer burden of modeling at 12 km for ozone because we
limited the duration of the ozone model simulations to just the summer months when
ozone concentrations are typically at their peak. For PIVb.s, however, it is useful to model
a full year in order to determine annual average PIVb.s concentrations in a manner
consistent with the annual PM NAAQS. In view of the computer requirements for
modeling at 12 km, it would not have been possible to model a full year at 12 km
resolution for the Eastern and Western domains as part of the analysis for this proposal
RIA.
Table 2.1. Geographic Specifications of Modeling Domains.
36 km Domain 12 km Eastern Domain 12 km Western Domain
(148x112 Grid Cells) (279 x 240 Grid Cells) (213 x 192 Grid Cells)
sw
NE
Lon
-121.77
-58.54
lat
18.17
52.41
SW
NE
Ion
-106.79
-65.32
lat
24.99
47.63
SW
NE
Ion
-121.65
-94.94
lat
28.29
51.91
2.4 References
Amar, P., R. Bornstein, H. Feldman, H. Jeffries, D. Steyn, R. Yamartino, and Y. Zhang.
2004. Final Report Summary: December 2003 Peer Review of the CMAQ Model, pp. 7.
Byun, D.W., and K.L. Schere. 2006. "Review of the Governing Equations,
Computational Algorithms, and Other Components of the Models-3 Community
Multiscale Air Quality (CMAQ) Modeling System." J. Applied Mechanics Reviews
59(2):51-77.
Dennis, R.L., D.W. Byun, J.H. Novak, K.J. Galluppi, CJ. Coats, and M.A. Vouk. 1996.
"The next generation of integrated air quality modeling: EPA's Models-3." Atmospheric
Environment 30:1925-1938.
Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth-Generation Penn
State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center
for Atmospheric Research, Boulder CO.
U.S. Environmental Protection Agency (EPA). 1999. "Science Algorithms of EPA
Models-3 Community Multiscale Air Quality." (CMAQ Modeling System D.W. Byun
and J.K.S. Ching, Eds. EPA/600/R-99/030, Office of Research and Development).
U.S. Environmental Protection Agency (EPA). March 2005. CMAQ Model Evaluation
Report, Office of Air Quality Planning and Standard, Research Triangle Park, NC.
(Docket No. OAR-2005-0053-2149).
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U.S. Environmental Protection Agency (EPA). 2006 .Air Quality Criteria for Ozone and
Related Photochemical Oxidants (Final). U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R-05/004aF-cF,
U.S Environmental Protection Agency (EPA). 2007. Review of the National Ambient Air
Quality Standards for Ozone: Policy Assessment of Scientific and Technical Information.
OAQPS Staff Paper. North Carolina. EPA-452/R-07-003
U.S. Environmental Protection Agency (EPA). 2007. "Guidance on the Use of Models
and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone,
PM2.5, and Regional Haze." EPA-454/B-07-002,
http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf
U.S. Environmental Protection agency (EPA). "Technical Support Document for the
Proposed Locomotive/Marine Rule: Ozone Modeling." EPA-454/R-07-004.
Yantosca, B. 2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric Chemistry
Modeling Group, Harvard University, Cambridge, MA, October 15, 2004.
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Chapter 3: Modeled Control Strategy: Design and Analytical Results
Synopsis
In order to estimate the costs and benefits of alternate ozone standards, EPA has analyzed
one possible hypothetical scenario to illustrate the control strategies that areas across the
country might employ to attain an alternative more stringent primary standard of 0.070
ppm. Specifically, EPA has modeled the impact that additional emissions controls across
numerous sectors would have on predicted ambient ozone concentrations, incremental to
meeting the current standard (baseline). Thus, the modeled analysis for a revised
standard focuses specifically on incremental improvements beyond the current standard,
and uses control options that might be available to states for application by 2020. The
hypothetical scenario presented in this RIA is one illustrative option for achieving
emissions reductions to move towards a national attainment of a tighter standard. It is not
a recommendation for how a tighter ozone standard should be implemented, and states
will make all final decisions regarding implementation strategies once a final NAAQS
has been set.
In order to model a hypothetical control strategy to achieve national attainment of 0.070
ppm incremental to attainment of the current standard, EPA approached the analysis in
stages. First, EPA identified controls to be included in the baseline (current state and
federal programs plus controls to attain the current ozone and PM standards). Then, EPA
applied additional known controls within geographic areas designed to bring areas
predicted to exceed 0.070 ppm in 2020 into attainment. This chapter presents the
hypothetical control strategy, the geographic areas where controls were applied, and the
results of the modeling which predicted ozone concentrations in 2020 after application of
the strategy. The strategy to attain a 0.070 ppm level was the only strategy modeled by
EPA. EPA did not expect the modeled control strategy to result in attainment at 0.070
ppm everywhere, so the control will result in only partial attainment. Chapter 4 will
explain how EPA used the results of the modeled control strategy for 0.070 ppm to
estimate total tons of emissions reductions needed to achieve ozone concentrations for the
bounds of the range of the proposed more stringent standard (0.075 and 0.070 ppm, and
the more stringent option analyzed of 0.065 ppm). Chapters 5 and 6 present the estimated
costs and benefits of the modeled costs and benefits for partial attainment.
Because EPA's baseline indicated that some areas were not likely to be in attainment
with the current standard by 2020 (0.08 ppm, effectively 0.084 ppm based on current
rounding conventions) - (Fig 3.4) EPA expected that known controls would not be
enough to bring those areas, and likely others, into attainment with 0.070 ppm in 2020.
Modeling results showed that to be the case (see Fig 3.13).
Because it was impossible to meet either the current or any tighter ozone standard
nationwide using only known controls, EPA conducted a second step in the analysis, and
estimated the number of further tons of emission reductions needed to attain 0.070 ppm
(presented in Chapter 4). It is uncertain what controls States would put in place to attain
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a tighter standard, since additional control measures are not currently recognized as being
commercially available. However, existing emissions inventories for the areas that were
predicted to be in non-attainment after application of all known controls, do indicate that
substantial amounts of ozone precursor emissions (i.e. tons of NOx or VOC) are available
for control, pending future technology. Chapter 4 describes the methodology EPA used
to estimate the amount of tons available for control to reach attainment, and Chapters 5
and 6 present the extrapolation-based costs and benefits of achieving the reductions in
ozone necessary to fully attain the standards, except for a few areas in California, which
will be more fully explained in Chapter 4.
3.1 Establishing the Baseline
The regulatory impact analysis (RIA) is intended to evaluate the costs and benefits of
reaching attainment with potential alternative ozone standards. In order to develop and
evaluate a control strategy for attaining a more stringent (0.070 ppm) primary standard, it
is important to first estimate ozone levels in 2020 given the current ozone standard and
trends (more information is provided in chapter 1). This scenario is known as the
baseline. Establishing this baseline allows us to estimate the incremental costs and
benefits of attaining any alternative standard.
This focus on the assessment of the incremental costs and benefits of attaining any
alternative standard is an important difference from the focus of the risk assessment used
in developing the standard. For purposes of the Staff Paper-risk assessment, risks are
estimated associated with just meeting recent air quality and upon just meeting the
current and alternative standards as well as incremental reductions in risks in going from
the current standard to more stringent alternative standards. When considering risk
estimates remaining upon attaining a given standard, EPA is only interested in the risks in
excess of policy relevant background (PRB). PRB is defined in the ozone Criteria
Document and Staff Paper as including (1) O3 in the U.S. from natural sources of
emissions in the U.S., Canada, and Mexico, and (2) O3 in the U.S. from the transport of
O3 or the transport of emissions from both natural and man-made sources, from outside
of the U.S. and its neighboring countries (Staff Paper, p.2-54). Emissions of ozone
precursors from natural sources (e.g. isoprenes emitted from trees) and from sources
outside of the U.S. are uncertain, as are the specific impacts those emissions will have on
ozone concentrations in areas exceeding alternative standards. Our models use available
information on these emissions in generating future projections of baseline ozone
concentrations, and our modeled reductions in U.S. emissions of NOx and VOC are
based on these baseline levels that include the contribution of natural and non-U.S.
emissions. To the extent that these emissions contribute a greater (lesser) proportion of
ozone on high ozone days, more (less) reductions in emissions from U.S. sources might
be required to reduce ozone levels below the analyzed alternative standards.
In contrast, the RIA only examines the incremental reduction, not the remaining risk,
which results from changes in U.S. anthropogenic emissions. The air quality modeling
used to establish the baseline for the RIA explicitly includes contributions from natural
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and anthropogenic emissions in Canada, Mexico, and other countries abroad, as well as
the contributions to ozone levels from natural sources in the U.S. Since the RIA does not
attempt to estimate the risk remaining upon meeting a given standard, and the alternative
standards are clearly above the Staff Paper estimates of PRB, we do not consider PRB a
component of the RIA costs and benefits estimates.
In developing the baseline it was important to recognize that there are several areas that
are not required to meet the current standard by 2020. The Clean Air Act allows areas
with more significant air quality problems to take additional time to reach the current
standard. Two areas in Southern California, are not planning to meet the current standard
by 2020, so the estimated emission reductions for these areas are based on reaching an
estimated progress point in 2020 (their "glidepath" targets). We provide an estimate of
the additional amount of tons these areas would need to reduce to meet the standard and
the additional costs and benefits of reducing those tons in those few areas.
The baseline includes controls which EPA estimates need to be included to attain the
current standard (0.08 ppm, effectively 0.084 ppm based on current rounding
conventions) for 2020. Two steps were used to develop the baseline. First, the
reductions expected in national ozone concentrations from national rules in effect or
proposed today were considered. Because these alone were not predicted to bring all
areas into attainment with the tighter standard, EPA used a hypothetical control strategy
to apply additional known controls. Additional control measures were used in four
sectors to establish the baseline1: Non-Electricity Generating Unit Point Sources (Non-
EGUs), Non-Point Area Sources (Area), Onroad Mobile Sources and Nonroad Mobile
Sources. A fifth sector was used in the subsequent control strategy for a tighter
alternative standard: Electricity Generating Unit Point Sources (EGUs). Each of these
sectors is defined below for clarity.
• NonEGU point sources are stationary sources that emit at least one criteria
pollutant with emissions of 100 tons per year or higher. NonEGU point sources
are found across a wide variety of industries, such as chemical manufacturing,
cement manufacturing, petroleum refineries, and iron and steel mills.
• Non-Point Area Sources (Area) are stationary sources that are too numerous or
whose emissions are too small to be individually included in a stationary source
emissions inventory. Area sources are the activities where aggregated source
emissions information is maintained for the entire source category instead of each
point source, and are reported at the county level.
• Onroad Mobile Sources are mobile sources that travel on roadways. These
sources include automobiles, buses, trucks, and motorcycles traveling on roads
and highways.
1 In establishing the baseline, EPA selected a set of cost-effective controls to simulate
attainment of the current ozone and PM2.5 standards. These control sets are hypothetical
as states will ultimately determine controls as part of the SIP process.
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• Nonroad Mobile Sources are any portable engine that travels by other means than
roadways. These sources include railroad locomotives; marine vessels; aircraft;
off-road motorcycles; snowmobiles; pleasure craft; and farm, construction,
industrial and lawn/garden equipment.
• Electricity Generating Unit Point Sources (EGUs) are stationary sources
producing electricity, such as fossil-fuel-fired boilers and combustion turbines.
3.1.1 National Rules
To reduce ambient ozone concentrations, it was necessary to control emissions of ozone
precursors, NOx and VOC. Establishing the baseline required identifying the national
rules which were expected to contribute to reductions in NOx and VOCs between now
and 2020. Some of these include the Clean Air Interstate Rule (CAIR), Clean Air
Mercury Rule (CAMR), and the Clean Air Visibility Rule (CAVR); and the 2007
proposed Locomotive/Marine rule. A complete listing of these rules is provided in table
3.1. In addition, EPA included the control set developed for the hypothetical national
attainment strategy presented in the PM NAAQS RIA in the baseline for this ozone
analysis.
At the time that EPA established the regulatory baseline — to capture how existing rules
affect the emissions inventory over time even in the absence of this new NAAQS
standard — EPA focused on information that was readily available in the emission
inventories and other data sources. Typically, a RIA analysis baseline includes only
reductions from final rules and not reductions from regulatory proposals or other actions
being contemplated. However, for this analysis, EPA did not include the recently
promulgated Renewable Fuel Standard (RFS), due to a lack of readily available
quantitative information. In addition, EPA did include reductions from some upcoming
rules in an attempt to better characterize reductions that we anticipate to occur in the
future (e.g. Ocean Going Vessel Rule). For the analysis to support the Final Rule, EPA
will be using an updated emission inventory and improved models and sets of control
information. The starting point for the analysis will include only and all promulgated
rules, including the Renewable Fuel Standard rule. Any potential reductions resulting
from proposed or upcoming rules will be discussed separately.
The RFS RIA provides an analysis of the energy, emissions, air quality, and economic
impacts of expanding the use of renewable fuels in comparison to a reference case of 4
billion gallons of renewable fuel use that represents 2004 conditions projected out to
2012. Depending on the anticipated volume of renewable fuel usage in 2012, EPA
estimates that this transition to renewable fuels will reduce petroleum consumption
between 2.0 and 3.9 billion gallons or roughly 0.8 to 1.6 percent of the petroleum that
would otherwise be used by the transportation sector2.
http;//www.epa.gov/otaq/renewablefuels
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With regard to emissions impacts, carbon monoxide emissions from gasoline-powered
vehicles and equipment will be reduced between 0.9 and 2.5 percent. Emissions of
benzene (a mobile source air toxic) will be reduced between 1.8 and 4.0 percent. Further,
the use of renewable fuel will reduce carbon dioxide equivalent greenhouse gas emissions
between 8.0 and 13.1 million metric tons, about 0.4 to 0.6 percent of the anticipated
greenhouse gas emissions from the transportation sector in the United States in 2012.
At the same time, other vehicle emissions may increase as a result of greater renewable
fuel use. Nationwide, EPA estimates an increase in total emissions of volatile organic
compounds and nitrogen oxides (VOC + NOx) between 41,000 and 83,000 tons.
However, the effects will vary significantly by region. Areas that already are using
ethanol will experience little or no change in emissions or air quality. In some contexts
and situations, however, the use of renewable fuels may impact compliance with a
reduced ozone NAAQS standard.
In addition to changes in NOx and VOC emissions resulting from increased use of
ethanol in gasoline, fugitive ethanol emissions may also increase peroxacetyl nitrate
(PAN) concentrations. Fugitive emissions of ethanol in a photochemical smog polluted
environment will generate acetaldehyde, a precursor to PAN. PAN, in turn, can lead to
increase ozone levels. As part of the analysis to support the final rule, EPA will examine
whether this increase in PAN will affect baseline ozone concentrations in some areas and
how this effect can be quantified and incorporated into the baseline.
For the final analysis, EPA will be using an updated emission inventory and improved
models and sets of control information. The starting point for the analysis will include all
promulgated rules, including the increases in regional VOC and NOx emissions from
increased combustion of ethanol.
Table 3.1 National Rules and Control Measures, by Sector, Contributing to the
Baseline3'4
Sector
Non-EGUs
Area
Onroad
Mobile
Sources
NOx
PM 15/35* (west only)
PM 15/35* (west only)
-Onroad Diesel Particulate
Filters and Retirement
- Commuter Reduction
Strategies
-Idling Elimination
-Intermodal Transfer from
Trucks to Rail
of Controls-National
VOC
(none used)
(none used)
-Onroad Diesel Particulate Filters
and Retirement
- Commuter Reduction Strategies
References for these rules are provided at the end of this chapter. Controls are
explained in Appendix 3.
4 0.08 ppm, effectively 0.084 ppm based on current rounding conventions
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Nonroad
Mobile
EGU
-Diesel Marine & Locomotives
Rule
-Ocean-Going Vessels Rule
-Small Spark-Ignition Engine
Rule
-Nonroad Diesel Particulate
Filters & Engine Rebuilds
-CAIR/CAMR/ CAVR
-PM 15/35* (West only)
-Small Spark-Ignition Engine Rule
-Nonroad Diesel Particulate Filters
& Engine Rebuilds
(none used)
*NOx controls are included as part of the hypothetical control scenario modeled
in the 2006 PM NAAQS RIA. These controls included low NOx burners and SNCR for
industrial boilers. Further examples can be found in Table 3-5 (page 3-18) of the 2006
PM NAAQS RIA. http://www.epa.gov/ttn/ecas/ria.html
3.1.2 Additional Controls
Additional known controls were also included as needed in the baseline, to simulate
attainment with current ozone NAAQS. The applicable controls and their respective
sectors are listed in table 3.2 and described below. Details regarding the individual
controls are provided in appendix 3. Due to the extensive reductions from EGUs already
implemented in CAIR/CAMR/CAVR, no additional EGU controls were included in the
baseline. The East was evaluated separately from the West, due to the nature of the
controls available in each area and the specific features of the areas needing reductions in
ozone, as explained in more detail below.
In the East, controls included in the baseline for Non-EGU and area sources came from a
variety of geographic areas and scales. Almost all available controls in Chicago,
Houston, and the Northeast Corridor were included in the baseline because these areas
contain counties that were projected to be nonattainment of the current ozone NAAQS in
2020 (based on air quality modeling performed as part of the PM NAAQS RIA).
NOx controls from Non-EGU/Area sources were included in two ways in the East. First,
controls were included in 22 counties with monitors that were projected to violate the
current standard in 2020. Second, controls were included in all surrounding counties
within the same state that were completely contained within 200 km of the county
containing the projected violating monitor. These counties were chosen based upon an
examination of previous ozone air quality modeling and emissions inventories as well as
existing EPA guidance. VOC controls were applied (for area sources only) in 26
counties where VOC emissions were high (>5,000 tpy), and screening analysis indicated
that mean ozone concentrations were predicted to be markedly reduced by local VOC
controls (> 0.5 ppb) by local VOC controls of 25%. Two additional counties that did not
meet these criteria were also included.5 In the West, Non-EGU and Area Controls were
5 Porter County, IN, was included, despite being below the emissions threshold, due to its
close proximity to Chicago. Harris County, TX, was included because of local
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included in the baseline only for California, where they were included state-wide. In
California, all controllable tons of NOx and VOC emissions were reduced using known
Non-EGU and Area Controls in the baseline. (See Fig 3.1 and Fig 3.2)
Fig. 3.1 Counties Where Controls for Nitrogen Oxides (NOx) Were Included for
Non-EGU Point and Area Sources, for the Baseline
(Current Standard, 0.08 ppm)
Nitrogen oxide (NOx) controls applied to non-EGU and area sources
Fig. 3.2 Counties Where Controls for Volatile Organic Chemicals (VOCs) Were
Applied to Non-EGU Point and Area Sources in Baseline
(Current Standard, 0.08 ppm)
information about the benefits of VOC control, and concerns about the screening tool
performance in the 36km region of which Houston is a part.
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Areas where YOC controls were applied
In the Onroad Mobile sector, local controls were included as necessary in the baseline for
both East and West. Counties projected to have a monitor that exceeded the current
standard were surrounded by a 200km buffer zone, and controls were included in the
counties within this buffer that were within the same state as the exceeding monitor.
Where some control measures overlapped for a given county, controls with the lowest
costs were included first. This is the only instance in which controls were included in a
certain order. For a complete list of the controls and the order in which they were
included, see Appendix 3. Both onroad and nonroad diesel retrofits and idling
elimination were included statewide in California with an assumed 75% market
penetration, and elsewhere in the nation with an assumed 25% market penetration for all
states with a county projected to be in nonattainment with the current standard in 2020.
EPA determined that 25% would have a significant impact, but was reasonably easy to
achieve and was applied for reduction areas outside of California. EPA further
determined that for southern California a higher level of reduction was required. 75%
was the highest penetration rate that EPA felt could be reasonably accomplished. The
remainder of mobile controls were included statewide in Ozone Transport Commission
(OTC) states (see section 3.2.2 for more information on OTC states), with the exceptions
of Vermont, Maine, New Hampshire, and Massachusetts, which were not projected to
have counties in nonattainment with the current standard in 2020. These additional
mobile controls were included statewide in California (See Fig. 3.3)
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Fig. 3.3 Areas Where NOx and VOC Controls Were Included for Mobile Onroad
and Nonroad Sources in Addition to National Mobile Controls in Baseline
(Current Standard, 0.08 ppm)
CH statewide controls*
• additional local measures**
* Onroad retrofits and elimination of long duration idling
**Onroad retrofits, elimination of long duration idling, nonroad retrofits,
Commuter Reduction Strategies and Reid Vapor Pressure (RVP)
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Table 3.2 Controls by Sector Included in the Baseline Determination for 2020
Sector
Controls- East
NOx
VOC
Controls- West
NOx
VOC
Non-EGUs
-LEC (Low Emission
Combustion)
-LNB (Low NOx Burner)
-LNB + FOR (Flu-Gas
Sulfurization)
-LNB + SCR (Selective
Catalytic Reduction)
-Mid-Kiln Firing
-NSCR (Non-selective
Catalytic Reduction)
-OXY-Firing
-SCR
-SCR + Steam Injection
-SCR + Water Injection
-SNCR (Selective Non-
catalytic Reduction)
-SNCR - Urea
-SNCR - Urea Based
(none used)
-LNB
-Mid-Kiln Firing
-NSCR
-OXY-Firing
-SCR
-SCR + Steam Injection
-SNCR
-SNCR - Urea Based
(none used)
Area
-RACT to 25 tpy (LNB)
-Water Heater + LNB Space
Heaters
-CARS Long-Term Limits
-Catalytic Oxidizer
-Equipment and Maintenance
-Gas Collection (SCAQMD/
BAAQMD)
-Incineration
-Incineration > 100,000 Ibs bread
-Low Pressure/Vacuum Relief
Valve
-OTC Mobile Equipment Repair
-RACT to 25 tpy (LNB)
-Switch to Low Sulfur Fuel
-Water Heater + LNB Space
Heaters
-Add-On Controls
-Airtight Degreasing System
-Catalytic Oxidizer
-Equipment and Maintenance
-FIP Rule (VOC content & TE)
-Gas Collection
(SCAQMD/BAAQMD)
-Incineration
-Incineration > 100,000 Ibs bread
-Low Pressure/ Vacuum Relief
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Table 3.2 Controls by Sector Included in the Baseline Determination for 2020 (continued)
Onroad
Mobile
Nonroad
Mobile
ECU
; and Refinishing Rule
: -OTC Solvent Cleaning Rule
1 -SCAQMD - Low VOC
; -SCAQMD Limits
: -SCAQMD Rule 1168
-Switch to Emulsified Asphalts
-Use of Low or No VOC Materials
-Onroad Selective Catalytic Reduction (SCR) and Diesel
Particulate Filters (DPF)6
-Reduce Gasoline Reid Vapor Pressure (RVP)
-Nonroad Selective Catalytic Reduction (SCR) and Diesel
Particulate Filters (DPF) 6
-Reduce Gasoline Reid Vapor Pressure (RVP)
-Aircraft NOx Engine (none used for VOC only)
Standard
-Ocean-Going Vessels -
reductions for vessels burning
residual fuels7
(none used) (none used)
; Valve
; -OTC Solvent Cleaning Rule
• -Reformulation - FIP Rule
; -SCAQMD Limits
-SCAQMD Rule 1168
-South Coast Phase III
-Switch to Emulsified Asphalts
-Use of Low or No VOC Materials
-Onroad Selective Catalytic Reduction (SCR) and Diesel
Particulate Filters (DPF) 6
-Reduce Gasoline Reid Vapor Pressure (RVP)
-Nonroad Selective Catalytic Reduction (SCR) and Diesel
Particulate Filters (DPF) 6
-Reduce Gasoline Reid Vapor Pressure (RVP)
-Aircraft NOx Engine (none used for VOC only)
Standard
(none used) (none used)
6 Onroad and Nonroad DPF were applied in the baseline, and SCR retrofit technologies were chosen because of the need to reduce
NOx emissions..
7 Reductions from Ocean-Going Vessels burning residual fuels were applied in the Baseline analysis for the east, but inadvertently
omitted for the west. The omission was not identified in time to include it in the initial Baseline analysis for the west.
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3.1.3 Ozone Levels for Baseline
Establishing the baseline required design values (predicted concentrations) of ozone
across the country. Because the intention of this evaluation was to achieve attainment of
the current ozone standard, controls were included to reduce ambient ozone
concentrations to 0.08 ppm (effectively 0.084 ppm based on current rounding
conventions). A map of the country is presented in figure 3.4, which shows predicted
concentrations for the 491 counties with ozone monitors that were included in the
baseline. Modeling projections were developed for all appropriate counties according to
procedures outline in EPA modeling guidance8.
The baseline shows that 10 counties would not meet the current ozone standard in 2020,
even after inclusion of all known controls. After including known controls as described
above, the analysis predicted that the remaining 481 counties would attain the current
standard by 2020. The baseline forms the foundation for the cost-benefit analysis
conducted in this RIA, where EPA compares more stringent primary ozone standard
alternatives incrementally to national attainment of the current standard.
' Available online at: http://www.epa. gov/scramO01 /guidance/guide/final-03 -pm-rh-guidance.pdf
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Fig. 3.4 Baseline Annual Ozone Air Quality in 2020
10 counties exceed 0.084 ppm
19 additional counties exceed 0.079 ppm for a total of 29
HZ153 additional counties exceed 0,075 ppm for a total of 82
dl 150 additional counties exceed 0,070 ppm for a total of 203
••210 additional counties exceed 0.065 ppm for a total of 360
EH131 counties meet ,065 ppm standard for a total of 491
(ZH monitored in 2003 - 2005 but not projected
a Modeled emissions reflect the expected reductions from federal programs including the
Clean Air Interstate Rule, the Clean Air Mercury Rule, the Clean Air Visibility Rule, the
Clean Air Nonroad Diesel Rule, the Light-Duty Vehicle Tier 2 Rule, the Heavy Duty
Diesel Rule, proposed rules for Locomotive and Marine Vessels and for Small Spark-
Ignition Engines, and state and local level mobile and stationary source controls
identified for additional reductions in emissions for the purpose of attaining the current
PM 2.5 and Ozone standards.
b Controls applied are illustrative. States may choose to apply different control strategies
for implementation.
c The current standard of 0.08 ppm is effectively expressed as 0.084 ppm when rounding
conventions are applied.
d Modeled design values in ppm are only interpreted up to 3 decimal places.
e Map shows results from a total of 491 counties with projected design values. Consistent
with current modeling guidance, EPA did not project 2020 concentrations for counties
where 2001 base year concentrations were less than recommended criterion. Such
projections may not represent expected future levels.
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3.2 Developing the Control Strategy Analysis
After developing the baseline, EPA developed a hypothetical control strategy to illustrate
one possible national control strategy that could be adopted to reach an alternative
primary standard of 0.070 ppm by 2020. The stricter standard alternative of 0.070 ppm
was chosen as being representative of the set of alternatives being considered by EPA in
its notice of proposed rulemaking on the ozone NAAQS. Controls for five sectors were
used in developing the control analysis, as discussed previously: non-EGU stationary,
Area, onroad mobile and nonroad mobile, along with EGU controls only in the East
(EGU controls for the West were included in the hypothetical PM NAAQS 15/35
national control strategy, and were therefore already in the ozone baseline). Reductions
in both NOx and VOC ozone precursors were needed in all four remaining sectors to
meet a tighter standard.
As depicted in the flow diagram in figure 1.1, the control strategy modeled in this RIA
first applied and exhausted nearly all known controls (see section 3.2.1 an explanation of
which controls were excluded from this analysis). After controls were identified, the
expected emissions reductions were input to an air quality model that projected design
values for ozone in 2020. Following the control strategy, there were some areas
projected not to attain 0.070 ppm in 2020 using all known control measures. EPA was
then required to extrapolate the additional emission reductions required to reach
attainment. The methodology used to develop those estimates and those calculations are
presented in Chapter 4.
As in the analysis for the baseline, parts of the hypothetical national control strategy for
0.070 ppm focused on the Eastern (East) United States (U.S.) separately from the
Western U.S. (West). However, this RIA presents estimates of the costs and benefits of
attaining alternative ozone standards on a national basis. Table 3.3 presents the specific
control technologies that were applied within each sector for the 0.070 ppm control
strategy.
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Table 3.3: Controls for Emissions Reductions, by Sector, for the 0.070 ppm Control Strategy (Incremental to Baseline)
Controls- East
Controls- West
Sector
NOx
VOC
NOx
VOC
Non- -Biosolid Injection Technology
EGUs -LEC (Low Emission
Combustion)
-LNB
-LNB + FOR
-LNB + SCR
-LNB+SCR
-Mid-Kiln Firing
-NGR
-NSCR
-OXY-Firing
-SCR
-SCR + Steam Injection
-SCR + Water Injection
-SNCR
-SNCR - Urea
-SNCR - Urea Based
-LDAR (Leak Detection and Repair)
-Enhanced LDAR
-Flares Gas Recovery
Monitoring Program
-Permanent Total Enclosure (PTE)
-Wastewater Drain Control
-Biosolid Injection Technology
-LNB
-LNB + FGR
-LNB + SCR
-Mid-Kiln Firing
-NSCR
-OXY-Firing
-SCR
-SCR + Steam Injection
-SCR + Water Injection
-SNCR
-SNCR - Urea Based
(none used)
Area -RACT to 25 tpy (LNB)
-Water Heater + LNB Space
Heaters
-CARB Long-Term Limits
-Catalytic Oxidizer
-Equipment and Maintenance
-Gas Collection (SCAQMD/BAAQMD)
-Incineration
-Incineration > 100,000 Ibs bread
-Low Pressure/Vacuum Relief Valve
-OTC Mobile Equipment Repair and
Refinishing Rule
-OTC Portable Gas Container Rule
-RACT to 25 tpy (LNB)
-Switch to Low Sulfur Fuel
-Water Heater + LNB Space
Heaters
(none used)
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Table 3.3 (Continued): Controls for Emissions Reductions, by Sector, for the 0.070 ppm Control Strategy
(Incremental to Baseline)
-OTC Solvent Cleaning Rule
-SCAQMD - Low VOC
-SCAQMD Limits
-SCAQMD Rule 1168
-Switch to Emulsified Asphalts
-Use of Low or No VOC Materials
Onroad
Mobile9
-Increased Penetration of Onroad SCR and DPF from 25% to 75%
-Continuous Inspection and Maintenance (OBD)
-Continuous Inspection and Maintenance (OBD)
Nonroad
Mobile9
-Increased Penetration of Nonroad SCR and DPF from 25% to 75%
-Ocean-Going Vessels -
reductions for vessels burning
residual fuels
10
EGU
-Lower nested caps in OTC and
MWRPO states
-Application of SCR and SNCR in
coal fired units in NA counties
outside of OTC and MWRPO
(none used)
(none used)
(none used)
9 For Onroad and Nonroad Mobile Source control measures, all measures applied for the Baseline analysis were applied to additional
geographic areas in the .070 analysis.
10 Reductions from Ocean-Going Vessels burning diesel fuel were applied in the Base Case analysis. However, we inadvertently
omitted the associated reductions that would occur in vessels burning residual fuels. These additional reductions were applied in the
Baseline analysis for the east and in the .070 analyses for the east and west. The omission was not identified in time to include it in
the initial Baseline analysis for the west, but was included in the Baseline national PM co-benefits analysis for the east and west.
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3.2.1 Controls Applied for a 0.070 ppm Standard: Non-EGU and Area Sectors
Non-EGU and Area control measures were identified using AirControlNET 4.1 .u'12 To
reduce NOx and VOC levels, all known control measures, within a given cost-cap, were
applied, allowing for the largest emission reduction per source over the widest
geographic area. The cost-caps were pollutant specific and applicable only in the East
portion of the analysis. For reductions of NOx emissions the cap was $16,000/ton, based
upon the approximate benefit per ton of reductions. In some instances, controls were too
costly due to the large capital component of installing these controls. A similar process
was followed for reductions from VOCs. The marginal cost curve was analyzed, and
there was a clear break in the curve at approximately $6,000/ton. Because all available
controls up to the cost cap were used in counties needing emission reductions, there was
no ordering of which controls were applied first. VOCs were cut at this level because
approximately 75% of reductions were coming from controls below that number.
Additionally, the relative effectiveness of VOC controls is not high. See Chapter 5 for
more information on cost caps
Additionally, controls were added that appeared in preliminary State Implementation
Plans (SIPs) from States and Regional Planning Bodies. Supplemental controls that
estimated near-term source controls based on similar technology were included in the
Non-EGU and Area Source sectors as well. Supplemental controls are described in
further detail in Appendix 3.
NOx controls were applied in the East for the 233 counties that were projected to have
concentrations of greater than 0.070 ppm in the 2020 baseline. Additional controls were
applied in surrounding counties within 200 km of the county projected to be out of
attainment (at 0.070 ppm), but not crossing state boundaries. In the West, NOx controls
were applied statewide, rather than only to counties with violating monitors and their
immediate neighbors (See Fig. 3.5). This was due to modeling methodology, in which
the 200 km buffer was only validated for the East.
11 See http://www.epa. gov/ttnecas 1 /AirControlNET .htm for a description of how
AirControlNET operates and what data is included in this tool.
12 While AirControlNET has not undergone a formal peer review, this software tool has
undergone substantial review within EPA's OAR and OAQPS, and by technical staff in
EPA's Regional offices. Much of the control measure data has been included in a control
measure database that will be distributed to EPA Regional offices for use by States as
they prepare their ozone, regional haze, and PM2.5 SIPs over the next 10 months. In
addition, the control measure data within AirControlNET has been used by Regional
Planning Organizations (RPOs) such as the Lake Michigan Air District Commission
(LADCO), the Ozone Transport Commission (OTC), and the Visibility Improvement
State and Tribal Assocation of the Southeast (VISTAS) as part of their technical analyses
associated with SIP development over the last 3 years. All of their technical reports are
available on their web sites.
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Fig 3.5 Counties Where Controls for Nitrogen Oxides (NOx) Were Applied to Non-
EGU Point and Areas Sources for RIA Control Strategy Designed to Meet 0.070
ppm (Incremental to Baseline)
Nitrogen oxide (NOx) controls applied to non-EGU and area sources
In the East, VOC controls were applied (for area sources only) in 47 counties where the
following criteria were met (including the 26 counties which included VOC controls in
their baselines): VOC emissions within the county or an adjacent county were high (e.g.
>5000 tons per year of area source emissions), and screening analyses indicated that
ozone design values would be markedly reduced (> 0.5 ppb) by local VOC controls of
25%, and the county design value was projected to be > 0.070 ppm in the 2020 baseline
(See Fig 3.6). No VOC controls were used in the West.
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Fig. 3.6 Counties Where VOC Controls Were Applied to Non-EGU Point and Areas
Sources for the Control Strategy Designed to Meet 0.070 ppm (Incremental to
Baseline)
Areas where YOG controls were applied
3.2.2 Controls Applied for a 0.070 ppm Standard: EGU Sector
For the East only, a control strategy was applied for the EGU sector (Fig. 3.7) (EGU
controls for the West were already included in the ozone baseline since they were applied
for the hypothetical national control strategy in the PM NAAQS RIA.) Annual and
ozone season CAIR caps remained unchanged, but coal-fired units were targeted for this
shifted strategy within those caps. This strategy was appropriate to consider because
transport of NOx pollution is more of a concern in the East, and NOx from EGUs still
accounts for a significant portion of emissions in this region. California, while in need of
reductions as well, was not included in this strategy because all known controls
(including EGU controls) had already been applied in the baseline. The development of
an EGU-component to this control strategy was based exclusively on NOx emissions
during the ozone season, although the hypothetical controls applied would operate year-
round. The EGU sector used the Integrated Planning Model (IPM) to evaluate the
reductions that are predicted from a specific control strategy. Details of this tool and
subsequent analysis can be found in appendix 3.4.
Reductions in the EGU sector are influenced significantly by the 2003 Clean Air
Interstate Rule (CAIR) (see appendix 3.4 for more details on CAIR). CAIR will bring
significant emission reductions in NOx, and a result, ambient ozone concentrations in the
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eastern U.S. by 202013. A map of the CAIR region is presented in appendix 3.4.
Emissions and air quality impacts of CAIR are documented in detail in the Regulatory
Impact Analysis of the Final Clean Air Interstate Rule14
To address nonattainment in the CAIR region (especially the Midwest, Mid-Atlantic, and
Northeast), lower nested caps (a limit lower than the current CAIR cap) were applied in
these areas for NOx, while holding the CAIR cap unchanged for the entire region. This
provides an opportunity to reduce emissions in a cost effective manner in targeted
regions. Two geographic regions were targeted for emissions reductions: the Midwest
Regional Planning Organization (MWRPO) consisting WI, IL, IN, MI, and OH; and the
Ozone Transport Commission (OTC), consisting of DC, MD, PA, DE, NJ, CT, NY, RI,
MA, VT, NH, and ME. These areas were chosen because the MWRPO and OTC states
are currently investigating ways of reducing EGU emissions further in their states and
because most of the potential ozone nonattainment areas are found within these two
regions. Considering transport, as well as the local effects, reducing emissions in these
areas expected to help bringing the Lake Michigan and Northeast corridor nonattainment
areas into attainment.
Lower nested caps were applied in the MWRPO and OTC states, for the ozone season
only. The caps that were applied lead to reductions that could be obtained by installing
post-combustion controls to all of the coal-fired units that were not projected to have
previously installed post-combustion controls in the base-case. Following this, 75% of
the reduction that could be obtained from these units was subtracted from the sum of
State level ozone control season NOx caps in CAIR15. The CAIR cap for the entire
region was kept unchanged.
In order to address non-attainment in the CAIR region outside of the MWRPO and OTC,
a "command and control" type strategy for coal-fired units has been designed. Annual
and ozone season CAIR caps remained unchanged, and coal-fired units were targeted for
this reduction. Preliminary analysis showed that most of the needed NOx reductions in
the EGU sector can be achieved through application of post-combustion controls (e.g.
Selective Catalytic Reductions (SCR) and Selective Non-Catalytic Reductions (SNCR))
on coal units that are projected to remain without controls under the
CAIR/CAMR/CAVR cap-and-trade scheme.
13 See http://www.epa.gov/airmarkets/progress/progress-reports.html for more
information
14 See http://www.epa.gov/CAIR/technical.html
15 Detailed analysis showed that 75% reduction provides the most cost-effective way of
reducing emissions at the targeted non-attainment areas, considering transport, with the
most air quality impacts.
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Fig 3.7 States Where Nitrogen Oxide (NOx) Controls Were Applied to Electrical
Generating Units (EGUs) for the Control Strategy Designed to Meet 0.070 ppm
(Incremental to Baseline)
States where a tighter cap than the CAIR cap were applied
•ICounties where NOx local controls were applied to electrical generating units (EGUs)
3.2.3 Controls Applied for a 0.070 ppm Standard: Onroad and Nonroad Mobile Sectors
As in other sectors, there are several mobile source control strategies that have been, or
are expected to be, implemented through previous national or regional rules. Although
many expected reductions from these rules are included in the baseline, additional mobile
source controls were required to illustrate attainment of a 0.070 ppm standard (See Fig
3.8). Modeling of the onroad and nonroad mobile sectors was done using MOBILE6.
See Appendix 3 for more information.
All of the local mobile source controls included in the ozone baseline were expanded for
the hypothetical national control strategy to attain 0.070 ppm standard. In the case of
onroad and nonroad Selective Catalytic Reduction (SCR) and Diesel Particulate Filters
(DPF), the measure was applied at a greater penetration rate - to 75% of the equipment
population. 75% was the highest penetration rate that EPA felt could be reasonably
accomplished. All local measures were applied to sources in additional geographic areas.
Continuous inspection and maintenance, which allows for much more rapid identification
of vehicles failing their emissions standard, was added. Descriptions of the mobile
source rules and measures can be found in appendix 3.3.
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As in the baseline, onroad SCR and DPF and elimination of idling were applied statewide
for all states with a county projected to exceed the 0.070 ppm standard. All other
controls were applied to counties within a 200 km buffer from counties projected to
exceed the 0.070 ppm alternative standard with the following exceptions:
• counties in neighboring states were omitted from the buffer zone
• controls were applied statewide to Ozone Transport Commission (OTC) states,
with the exception of Vermont
• controls were applied statewide in California, Colorado, Utah, New Mexico,
Arizona, and Nevada.
Fig. 3.8 Areas Where NOx and VOC Controls Were Applied to Mobile Onroad
and Nonroad Sources in Addition to National Mobile Controls for the 0.070 ppm
Control Strategy (incremental to Baseline)
CH statewide controls*
••additional local measures **
* Onroad retrofits and elimination of long duration idling
**Onroad retrofits, elimination of long duration idling, nonroad retrofits, Best Workplace
for Commuters programs (BWC), low Reid Vapor Pressure (RVP)
3.2.4 Data Quality for this Analysis
The estimates of emission reductions associated with our control strategies above are
subject to important limitations and uncertainties. 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 available engineering studies of air
pollution controls and has set up what it believes is the most reasonable framework for
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analyzing the cost, emission changes, and other impacts of regulatory controls. EPA is
working on approaches to quantify the uncertainties in these areas and will incorporate
them in future RIAs as appropriate.
3.3 Geographic Distribution of Emissions Reductions
The following maps break out NOx and VOC reductions into the controlling sectors. The
maps for NOx and VOC reductions are presented in Figures 3.9 and 3.11, respectively.
Figures 3.10 and 3.12 indicate the emission reductions attributed to each sector.
Appendix 3 contains maps of emissions reductions by sector, nationwide.
Prior to reading the maps, there is an important caveat to consider. The control strategy
above focuses on reducing emissions of VOCs and NOx, the two precursors to ozone
formation. However, in some cases, the application of the control strategy actually
increased the level of NOx or VOC emissions. This is due to controls that affect multiple
pollutants and complex interactions between air pollutants, as well as trading aspects
under the CAIR rule.
Emissions of NOx do not decrease everywhere within the CAIR region. As explained
earlier, the NOx EGU control strategy was designed to achieve emission reductions
specifically in the non-attainment areas, while retaining the overall CAIR cap.
Application of nested and lower (ozone season) caps for the states in the MWRPO and
OTC regions and local controls (SCR and SNCR) on the uncontrolled coal units in the
non-attainment counties outside of the OTC and MWRPO within CAIR region result in
increase of emissions elsewhere within CAIR region. While there are substantial NOx
emission reductions within the OTC and MWRPO expected for the 2020 ozone season
(roughly 55,500 tons) as a result of cap-and-trade program with lower caps and local
command-and-control reductions in other non-attainment counties where uncontrolled
coal units exist, there is the possibility of increased emissions from the remainder of
sources within CAIR region. This approach provides a cost effective opportunity for
reducing emissions where the reductions are most needed to help reach attainment. It is
important to recall that this is a hypothetical control strategy, the states or other
authorities may take additional steps to minimize these increases if warranted.
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Fig 3.9 Annual Tons of Nitrogen Oxide (NOx) Emission Reductions From Controls
Designed to Meet 0.070 ppm Standard,* incremental to the current standard
-26.048 - -2.500
-2,499 - -500
. 499--100
;-99-+100**
a+101-+500
M +501 - +2.500
• +2,501-+10,768
* Reductions are negative and increases are positive
**The -99 - +100 range is shown without color because these are small county-level NOx
reductions or increases that likely had little to no impact on ozone estimates. Most
counties in this range had NOx differences less than 1 ton.
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Fig. 3.10 Percentage of Total Annual NOx Emissions Reduced from Various Sources
On-Road(16%)
Non-Road (<1%)
Area (3%)
Electrical
Generating Unit
Point(
-------
Fig. 3.11 Annual Tons of Volatile Organic Compound (VOC) Emission Reductions
From Controls Designed to Meet 0.070 ppm Standard*, incremental to the current
standard
I -11,216 --2,500
| -2,499 - -500
-499--100
-99-+57**
* Reductions are negative and increases are positive
**The -99 - +57 range is shown without color because these are small county-level VOC
reductions or increases that likely had little to no impact on ozone estimates. Most
counties in this range had VOC differences less than 1 ton.
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Fig. 3.12 Percentage of Total Annual VOC Emissions Reduced from Various
Sources*
Non-Electrical Generating
Unit Point (2%)
On-Road
(46%)
Area
(45%)
Non-Road
(7%)
3.4 Ozone Design Values for partial attainment
After determining the emissions reductions from NOx and VOC, we used modeling tools
(see section 2.3.2) to determine ozone design values for 2020. Figure 3.13 shows a map
of the design values after modeling the control strategy to reach 0.070 ppm. The map
legend is broken out to demonstrate under this control strategy, with no adjustments,
which counties would reach the targeted standard of 0.070 ppm, the more stringent
alternative standard analyzed (0.065 ppm), and the other end of the proposal range (0.075
ppm). It is understood that this illustrative strategy would not be the exact hypothetical
strategy used to try to attain either of these alternative standards, due to over- and under-
attainment in many counties. (Chapter 4 describes EPA's methodology for estimating
tons of reductions needed to hypothetically attain these other two possible alternative
standards.) In addition, because ozone formation is dependent on a variety of factors, it is
not possible to directly attribute changes in predicted ozone concentrations to emission
reductions of a specific precursor from a specific sector.
A full listing of the counties and their design values is provided in Appendix 3.
Figures 3.14 and 3.15 show the tons of emissions reduced by the hypothetical RIA 0.070
ppm control strategy, and the tons of emissions remaining after application of those
controls, by sector.
Using this strategy, it is possible to reach attainment in 365 counties. However, there are
still an additional 126 counties that will remain out of attainment with an alternative
standard of 0.070 ppm using this control strategy. All known controls were applied to
this scenario, but attainment was not achieved everywhere. Because of this partial
attainment outcome, it will be necessary to identify additional reductions in NOx and
VOC in order to assess the costs and benefits of full attainment nationwide. Chapter 4
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will address the methodology for determining the additional tons that were needed to
reach full attainment.
Fig. 3.13 Projected Ozone Air Quality in 2020 After Application of Known Controls
counties exceed 0.084
15 additional counties exceed 0.079 ppm for a total of 24
EH 26 additional counties exceed 0.075 ppm for a total of 50
HI 76 additional counties exceed 0,070 ppm for a total of 126
• 154 additional counties exceed 0.065 ppm for a total of 280
EH 211 counties meet 0.065 ppm standard for a total of 491
CD monitored in 2003 - 2005 but not projected
1 Modeled emissions reflect the expected reductions from federal programs including the Clean
Air Interstate Rule, the Clean Air Mercury Rule, the Clean Air Visibility Rule, the Clean Air
Nonroad Diesel Rule, the Light-Duty Vehicle Tier 2 Rule, the Heavy Duty Diesel Rule, proposed
rules for Locomotive and Marine Vessels and for Small Spark-Ignition Engines, and state and
local level mobile and stationary sovirce controls identified for additional reductions in emissions
for the purpose of attaining the current PM 2.5 and Ozone standards.
2 Controls applied are illustrative. States may choose to apply different control strategies for
implementation.
3 The current standard of 0.08 ppm is effectively expressed as 0.084 ppm when rounding
conventions are applied.
4 Modeled design values in ppm are only interpreted up to 3 decimal places.
5 Map shows results from a total of 491 counties with projected design values. Consistent with
current modeling guidance, EPA did not project 2020 concentrations for counties where 2001
base year concentrations were less than recommended criterion. Svich projections may not
represent expected future levels.
3-28
-------
Table 3.4 Annual Tons of Emissions Remaining after Application of the 0.070 ppm
Control Strategy (35 States + DC Analysis Area)
16
Pollutant Sector
2020 Emissions
After Controls
Applied/or
PM2.5 15/35
(tons)
2020 Emissions
After Controls
Applied/or
PM2.5 15/35 and
Ozone 0.084
Control Strategy
Baseline (tons)
0.070 ppm
Reductions (tons)
2020 Emissions
After Controls
Applied for
PM2.5 15/35 and
Ozone 0.070 ppm
Control Strategy
(tons)
NOX
VOC
Area
Onroad
Nonroad
ECU
Non-EGU
Area
Onroad
Nonroad
ECU
Non-EGU
1,200,000
1,800,000
1,900,000
1,500,000
2,200,000
5,800,000
1,500,000
1,000,000
39,000
1,100,000
1,200,000
1,700,000
1,800,000
1,500,000
1,900,000
5,600,000
1,500,000
1,000,000
39,000
1,100,000
30,000
170,000
8,000
7,800
800,000
84,000
86,000
12,000
26
3,400
1,200,000
1,600,000
1,800,000
1,500,000
1,100,000
5,500,000
1,400,000
1,000,000
38,000
1,100,000
16
Numbers may not add up due to rounding
3-29
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Fig 3.14 Annual NOx Emissions Remaining after PM NAAQS 15/35, Ozone
Current Standard, and 0.070 ppm Control Strategies
(35 States + DC Analysis Area)
2020 Emissions After Controls
Applied for PM2.5 15/35 and
Ozone 0.070 Control Strategy
2020 Emissions After Controls
Applied for PM2.5 15/35 and
Ozone 0.084 Control Strategy
Baseline
2020 Emissions After Controls
Applied for PM 2.5 15/35 and
Accounting for National Rules
on the Books
D NOX Area DNOXOnroad BNOXNonroad DNOXEGU BNOXNonEGU
Tons
3-30
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Fig. 3.15 Annual VOC Emissions Remaining after PM NAAQS 15/35, Ozone
Current Standard, and 0.070 ppm Control Strategies
(35 States + DC Analysis Area)
2020 Emissions After Controls
Applied for PM2.5 15/35, O3
Current Standard and 0.070
Control Strategies
2020 Emissions After Controls
Applied for PM2.5 15/35 and
Ozone 0.084 Control Strategy
Baseline
2020 Emissions After Controls
Applied for PM 2. 5 15/35 and
Accounting for National Rules
on the Books
D VOC Area D VOC Onroad • VOC Nonroad
• VOC EGU • VOC NonEGU
/
/
/
/
&
*$
/
^
*l
Q
n.
I
1
^
0#
0*
/
L_
0
/
iv?
#
Tons
3.5 References
Michigan Department of Environmental Quality and Southeast Michigan Council of
Governments. Proposed Revision to State of Michigan State Implementation Plan for 7.0
Low Vapor Pressure Gasoline Vapor Request for Southeast Michigan. May 24, 2006.
National Ambient Air Quality Standards for Particulate Matter, 40 CFR Part 50 (2006)
Rule To Reduce Interstate Transport of Fine Particulate Matter and Ozone (Clean Air
Interstate Rule); Revisions to Acid Rain Program; Revisions to the NOX SIP Call; Final
Rule, 40 CFR Parts 51, 72, 73, 74, 77, 78 and 96 (2005).
Standards of Performance for New and Existing Stationary Sources: Electric Utility
Steam Generating Units, 40 CFR Parts 60, 63, 72, and 75 (2005)
3-31
-------
Regional Haze Regulations and Guidelines for Best Available Retrofit Technology
(BART) Determinations, 40 CFR Part 51 (2005)
Control of Emissions of Air Pollution from Locomotive Engines and Marine
Compression-Ignition Engines Less than 30 Liters per Cylinder, Proposed rule, 40 CFR
Parts 92, 94, 1033, 1039, 1042, 1065 and 1068 (2007)
Control of Emissions fromNonroad Spark-Ignition Engines and Equipment; proposed
rule, 40 CFR Parts 60, 63, 85, 89, 90, 91, 1027, 1045, 1048, 1051, 1054, 1060, 1065,
1068, and 1074 (2007)
USEPA. Guide on Federal and State Summer RVP Standards for Conventional Gasoline
Only. EPA420-B-05-012. November 2005
USEPA. 2007, Regulatory Announcement: EPA Proposal for More Stringent Emissions
Standards for Locomotives and Marine Compression-Ignition Engines. EPA420-F-07-
015
USEPA. 2007, Proposed Emission Standards for New Nonroad Spark-Ignition Engines,
Equipment, and Vessels. EPA420-F-07-032
3-32
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Appendix- Chapter 3
3a.l Non-EGU and Area Source Controls Applied in the Baseline and Control
Scenarios
Ba.1.1 Non-EGU and Area Source Control Strategies for Ozone NAAQS Proposal
In the Non-EGU and Area Sources portion of the control strategy, maximum control
scenarios were used from the existing control measure dataset from AirControlNET 4.1
for 2020 (for Geographic Areas defined for each level of the standard being analyzed).
This existing control measure dataset reflects changes and updates made as a result of the
reviews performed for the final PM2.5 RIA. Following this, an internal review was
performed by the OAQPS engineers in the Sector Policies and Programs Division (SPPD)
to examine the controls applied by AirControlNET and decide if these controls were
sufficient or could be more aggressive in their application, given the 2020 analysis year.
This review was performed for non-EGU NOx control measures. The result of this
review was an increase in control efficiencies applied for many control measures, and
more aggressive control measures for over 80 SCC's. For example, SPPD recommended
that we apply SCR to cement kilns to reduce NOx emissions in 2020. Currently, there
are no SCRs in operation at cement kilns in the U.S, but there are several SCRs in
operation at cement kilns in France now. Based on the SCR experience at cement kilns
in France, SPPD believes SCR could be applied at U.S. cement kilns by 2020. Following
this, it was recommended that supplemental controls could be applied to 8 additional
SCC's from non-EGU NOx sources. We also looked into sources of controls for highly
reactive VOC non-EGU sources. Four additional controls were applied for highly
reactive VOC non-EGU sources not in AirControlNET.
3a.l.2 NOx Control Measures for Non-EGU Point Sources.
Several types of NOx control technologies exist for non-EGU sources: SCR, selective
noncatalytic reduction (SNCR), natural gas reburn (NGR), coal reburn, and low-NOx
burners. In some cases, LNB accompanied by flue gas recirculation (FGR) is applicable,
such as when fuel-borne NOx emissions are expected to be of greater importance than
thermal NOx emissions. When circumstances suggest that combustion controls do not
make sense as a control technology (e.g., sintering processes, coke oven batteries, sulfur
recovery plants), SNCR or SCR may be an appropriate choice. Finally, SCR can be
applied along with a combustion control such as LNB with overfire air (OFA) to further
reduce NOx emissions. All of these control measures are available for application on
industrial boilers.
Besides industrial boilers, other non-EGU source categories covered in this RIA include
petroleum refineries, kraft pulp mills, cement kilns, stationary internal combustion
engines, glass manufacturing, combustion turbines, and incinerators. NOx control
measures available for petroleum refineries, particularly process heaters at these plants,
include LNB, SNCR, FGR, and SCR along with combinations of these technologies.
3a-l
-------
NOx control measures available for kraft pulp mills include those available to industrial
boilers, namely LNB, SCR, SNCR, along with water injection (WI). NOx control
measures available for cement kilns include those available to industrial boilers, namely
LNB, SCR, and SNCR. Non-selective catalytic reduction (NSCR) can be used on
stationary internal combustion engines. OXY-firing, a technique to modify combustion at
glass manufacturing plants, can be used to reduce NOx at such plants. LNB, SCR, and
SCR + steam injection (SI) are available measures for combustion turbines. Finally,
SNCR is an available control technology at incinerators. For more information on these
measures, please refer to the AirControlNET 4.1 control measures documentation report.
3a. 1.3 VOC Control Measures for Non-EGU Point Sources.
VOC controls were applied to a variety of non-EGU point sources as defined in the emissions
inventory in this RIA. These controls are: permanent total enclosure (PTE) applied to paper
and web coating operations and fabric operations, and incinerators or thermal oxidizers
applied to wood products and marine surface coating operations. A PTE confines VOC
emissions to a particular area where can be destroyed or used in a way that limits emissions
to the outside atmosphere, and an incinerator or thermal oxidizer destroys VOC emissions
through exposure to high temperatures (2,000 degrees Fahrenheit or higher). For more
information on these measures, refer to the AirControlNET 4.1 control measures
documentation report.
3a. 1.4 NOx Control Measures for Area Sources
There were two controls applied for NOx emissions from area sources. The first is
RACT (reasonably available control technology) to 25 tpy (LNB). This control is the
addition of a low NOx burner to reduce NOx emissions. This control is applied to
industrial oil, natural gas, and coal combustion sources. The second control is water
heaters plus LNB space heaters. This control is based on the installation of low-NOx
space heaters and water heaters in commercial and institutional sources for the reduction
of NOx emissions. For additional information regarding these controls please refer to the
AirControlNET 4.1 control measures documentation report.
3a.l.5 VOC Control Measures for Area Source.
The most frequently applied control to reduce VOC emissions from area sources was
CARS Long-Term Limits. This control, which represents controls available in VOC
rules promulgated by the California Air Resources Board, applies to commercial solvents
and commercial adhesives, and depends on future technological innovation and market
incentive methods to achieve emission reductions. The next most frequently applied
controls was the use of low or no VOC materials for graphic art source categories. The
South Coast Air District's SCAQMD Rule 1168 control applies to wood furniture and
solvent source categories sets limits for adhesive and sealant VOC content. The OTC
solvent cleaning rule control establishes hardware and operating requirements for
specified vapor cleaning machines, as well as solvent volatility limits and operating
practices for cold cleaners. The Low Pressure/Vacuum Relief Valve control measure is
the addition of low pressure/vacuum (LP/V) relief valves to gasoline storage tanks at
3a-2
-------
service stations with Stage II control systems. LP/V relief valves prevent breathing
emissions from gasoline storage tank vent pipes. SCAQMD Limits control establishes
VOC content limits for metal coatings along with application procedures and equipment
requirements. Switch to Emulsified Asphalts control is a generic control measure
replacing VOC-containing cutback asphalt with VOC-free emulsified asphalt. The
equipment and maintenance control measure applies to oil and natural gas production.
The Reformulation - FIP Rule control measure intends to reach the VOC limits by
switching to and/or encouraging the use of low-VOC pesticides and better Integrated Pest
Management (IPM) practices. For additional information regarding these controls please
refer to the AirControlNET 4.1 control measures documentation report.
3a. 1.6 Supplemental Controls
The table below summarizes the supplemental control measures added to our control
measures database by providing the pollutant it controls and its control efficiency. These
controls were applied in the baseline scenario to Houston and Chicago, and the Northeast
as well as in the incremental control strategy applied to the Eastern U.S. However, these
controls are not located in AirControlNET.
Table 3a.l Supplemental Emission Control Measures Applied in Modeled
Attainment Strategies for the Ozone NAAQS RIA - New Control Technologies
Added to the Control Measures Database
Pollutant
NOx
VOC*
sec
20200252
20200254
3018001-
30600701
and
30600999
SCC Description
Internal Comb.
Engines/Industrial/Natur
al Gas/2-cycle Lean Burn
Internal Comb.
Engines/Industrial/Natur
al Gas/4-cycle Lean Burn
Fugitive Leaks
Flares
Control
Technology
LEC (Low
Emission
Combustion)
LEC (Low
Emission
Combustion)
Enhanced
LDAR
Percent
Reduction
(%)
87
87
50
98
3018001 -
30600702
Fugitive Leaks
Cooling towers
LDAR
Monitoring
Program
80
No one
general
estimate
3a-3
-------
30600503 Wastewater Drains and Inspection 65
Separators and
Maintenance
Program
(Separators)
Water Seals
(Drains)
*Note: the cost of these measures are not included in our incremental annualized cost
estimates since these controls are found in the Harris-Galveston-Brazoria Cos. SIP
(Texas), and they will be incurred by 2020 in any event. We do quantify the
emission reductions since these controls are not accounted for in our baseline
inventory for 2020, however.
Low Emission Combustion (LEG)
Overview: LEC technology is defined as the modification of a natural gas fueled,
spark ignited, reciprocating internal combustion engine to reduce emissions of
NOx by utilizing ultra-lean air-fuel ratios, high energy ignition systems and/or
pre-combustion chambers, increased turbocharging or adding a turbocharger, and
increased cooling and/or adding an intercooler or aftercooler, resulting in an
engine that is designed to achieve a consistent NOX emission rate of not more than
1.5-3.0 g/bhp-hr at full capacity (usually 100 percent speed and 100 percent load).
This type of retrofit technology is fairly widely available for stationary internal
combustion engines.
For control efficiency, EPA estimates that it ranges from 82 to 91 percent for LEC
technology applications. The EPA believes application of LEC would achieve
average NOX emission levels in the range of 1.5-3.0 g/bhp-hr. This is an 82-91
percent reduction from the average uncontrolled emission levels reported in the
ACT document. An EPA memorandum summarizing 269 tests shows that 96
percent of 1C engines with installed LEC technology achieved emission rates of
less than 2.0 g/bhp-hr.1 The 2000 EC/R report on 1C engines summarizes 476
tests and shows that 97% of the 1C engines with installed LEC technology achieve
emission rates of 2.0 g/bhp-hr or less.2
1 "Stationary Reciprocating Internal Combustion Engines Technical Support Document
for NOx SIP Call Proposal," U.S. Environmental Protection Agency. September 5, 2000.
Available on the Internet at http://www.epa.gov/ttn/naaqs/ozone/rto/sip/data/tsd9-00.pdf.
"Stationary Internal Combustion Engines: Updated Information on NOx Emissions and
Control Techniques," Ec/R Incorporated, Chapel Hill, NC, September 1, 2000.
Available on the Internet at
http://www.epa.gov/ttn/naaqs/ozone/ozonetech/ic engine nox update 09012000.pdf.
3a-4
-------
Major Uncertainties: The EPA acknowledges that specific values will vary from
engine to engine. The amount of control desired and number of operating hours
will make a difference in terms of the impact had from a LEC retrofit. Also, the
use of LEC may yield improved fuel economy and power output, both of which
may affect the emissions generated by the device.
Leak Detection and Repair (LDAR) for Fugitive Leaks
Overview: This control measure is a program to reduce leaks of fugitive VOC
emissions from chemical plants and refineries. The program includes special
"sniffer" equipment to detect leaks, and maintenance schedules that affected
facilities are to adhere to. This program is one that is contained within the
Houston-Galveston-Brazoria 8-hour Ozone SIP.
Major Uncertainties: The degree of leakage from pipes and processes at
chemical plants is always difficult to quantify given the large number of such
leaks at a typical chemical manufacturing plant. There are also growing
indications based on tests conducted by TCEQ and others in Harris County, Texas
that fugitive leaks have been underestimated from chemical plants by a factor of 6
to 20 or greater. 3
Enhanced LDAR for Fugitive Leaks
Overview: This control measure is a more stringent program to reduce leaks of
fugitive VOC emissions from chemical plants and refineries that presumes that an
existing LDAR program already is in operation.
Major Uncertainties: The calculations of control efficiency and cost presume use
of LDAR at a chemical plant. This should not be an unreasonable assumption,
however, given that most chemical plants are under some type of requirement to
have an LDAR program. However, as mentioned earlier, there is growing
evidence that fugitive leak emissions are underestimated from chemical plants by
a factor of 6 to 20 or greater.4
3 VOC Fugitive Losses: New Monitors, Emissions Losses, and Potential Policy Gaps.
2006 International Workshop. U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards and Office of Solid Waste and Emergency Response.
October 25-27, 2006.
4 VOC Fugitive Losses: New Monitors, Emissions Losses, and Potential Policy Gaps.
2006 International Workshop. U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards and Office of Solid Waste and Emergency Response.
October 25-27, 2006.
3a-5
-------
Flare Gas Recovery
Overview: This control measure is a condenser that can recover 98 percent of the
VOC emitted by flares that emit 20 tons per year or more of the pollutant.
Major Uncertainties: Flare gas recovery is just gaining commercial acceptance in
the US and is only in use at a small number of refineries.
Cooling Towers
Overview: The control measure is continuous monitoring of VOC from the
cooling water return to a level of 10 ppb. This monitoring is accomplished by
using a continuous flow monitor at the inlet to each cooling tower.
There is not a general estimate of control efficiency for this measure; one is to
apply a continuous flow monitor until VOC emissions have reached a level of 1.7
tons/year for a given cooling tower.5
Major Uncertainties: The amount of VOC leakage from each cooling tower can
greatly affect the overall cost-effectiveness of this control measure.
Wastewater Drains and Separators
Overview: This control measure includes an inspection and maintenance
program to reduce VOC emissions from wastewater drains and water seals on
drains. This measure is a more stringent version of measures that underlie
existing NESHAP requirements for such sources.
Major Uncertainties: The reference for this control measures notes that the VOC
emissions inventories for the five San Francisco Bay Area refineries whose data
was a centerpiece of this report are incomplete. In addition, not all VOC species
from these sources were included in the VOC data that is a basis for these
calculations.6
In addition to the new supplemental controls presented above, there were a number of
changes made to existing AirControlNET controls. These changes were made based
upon an internal review performed by EPA engineers to examine the controls applied by
AirControlNET and determine if these controls were sufficient or could be more
aggressive in their application, given the 2020 analysis year. This review was performed
for non-EGU NOx control measures. The result of this review was an increase in control
5 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of
Regulation 8, Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004.
6 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of
Regulation 8, Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004.
3a-6
-------
efficiencies applied for many control measures, and more aggressive control measures for
over 70 SCCs. The changes apply to the control strategies performed for the Eastern US
only. These changes are listed in the table below.
Table 3a.2 Supplemental Emission Control Measures Applied in Modeled
Attainment Strategies for the Ozone NAAQS RIA - Changes to Control
technologies currently in our Control Measures Database
Pollutant SCC
NOX
NOX
NOX
NOX
NOX
NOX
AirControlNET AirControlNET New New
Source Control Control Control
Description Technology Technology Efficiency
10200104
10200204
10200205
10300207
10300209
10200217
10300216
10200901
10200902
10200903
10200907
10300902
10300903
10200401
10200402
10200404
10200405
10300401
10200501
10200502
10200504
10200601
10200602
10200603
10200604
10300601
10300602
10300603
10500106
10500206
30500606
ICI Boilers -
Coal-Stoker
ICI Boilers -
Wood/Bark/
Waste
ICI Boilers -
Residual Oil
ICI Boilers -
Distillate Oil
ICI Boilers -
Natural Gas
Cement
Manufacturing
Dry
SNCR
SCR
90.0
Old
Control
Efficiency
40.0
SNCR
SCR
90.0
55.0
SCR
SCR
90.0
80
SCR
SCR
SCR
SCR
90.0
90.0
80
80
SCR
SCR
90.0
80
3a-7
-------
Pollutant SCC
NOX
NOX
NOX
AirControlNET AirControlNET New New Old
Source Control Control Control Control
Description Technology Technology Efficiency Efficiency
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
30500706
30300934
10200701
10200704
10200707
10200710
10200799
10201402
10300701
10300799
10200802
10200804
10201002
10201301
10201302
30700110
30100306
30500622
30500623
30590013
30190013
30190014
39990013
30101301
30101302
30600201
30590003
30600101
30600103
30600111
Cement
Manufacturing -
Wet
Iron & Steel
Mills - Annealing
ICI Boilers -
Process Gas
ICI Boilers -
Coke
ICI Boilers -
LPG
ICI Boilers -
Liquid Waste
Sulfate Pulping -
Recovery
Furnaces
Ammonia
Production -
Pri. Reformer,
Nat. Gas
Cement Kilns
Industrial and
Manufacturing
Incinerators
Nitric Acid
Manufacturing
Fluid Cat.
Cracking Units
Process Heaters -
Process Gas
Process Heaters -
Distillate Oil
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
Biosolid
Injection
SNCR
SNCR
LNB + FGR
LNB + SCR
LNB + SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
Biosolid
Injection
SCR
SCR
SCR
LNB + SCR
LNB + SCR
90.0
90.0
90.0
90.0
90.0
90.0
90.0
90.0
40.0
90.0
90.0
90.0
90.0
90.0
80
85
80
70
80
80
80
80
23
45
908
901
88
90
-------
Pollutant SCC
AirControlNET AirControlNET New New
Source Control Control Control
Description Technology Technology Efficiency
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
Process Heaters -
Residual Oil
Process Heaters -
Natural Gas
Sulfate Pulping -
Recovery
Furnaces
Pulp and Paper -
Natural Gas -
Incinerators
In-Process;
Bituminous Coal;
Cement Kiln
In-Process;
Bituminous Coal;
Lime Kiln
In-Process Fuel
Use;Bituminous
Coal; Gen
In-Process Fuel
Use; Residual
Oil; Gen
In-Process Fuel
Use; Natural Gas;
Gen
In-Proc;Process
Gas;Coke
Oven/Blast Furn
In-Process;
Process Gas;
Coke Oven Gas
Solid Waste
Disp;Gov;Other
Incin; Sludge
LNB + SCR 90.0
LNB + SCR 90.0
SCR 90.0
30600106
30600199
30600102
30600105
30700104
30790013
39000201
39000203
39000289
39000489
39000689
39000701
39000789
50100101
50100506
50200506
50300101
50300102
50300104
50300506
50100102
The last category of supplemental controls is control technologies currently in our control
measures database being applied to SCCs not controlled currently in AirControlNET.
LNB + SCR
LNB + SCR
SCR
SNCR
SNCR - urea
based
SNCR - urea
based
SNCR
LNB
LNB
LNB + FGR
LNB
SNCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
90.0
90.0
90.0
90.0
90.0
90.0
90.0
90.0
90.0
Old
Control
Efficiency
80
80
80
45
50
50
40
37
50
55
50
45
3a-9
-------
Table 3a.3 Supplemental Emission Control Measures Applied in Modeled
Attainment Strategies for the Ozone NAAQS RIA -Control technologies currently
in our Control Measures Database Applied to New Source types
Pollutant SCC
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
39000602
30501401
30302351
30302352
30302359
10100101
NOX 10100202
10100204
10100212
10100401
10100404
10100501
10100601
10100602
10100604
SCC Description
Cement Manufacturing - Dry
Glass Manufacturing - General
Taconite Iron Ore Processing -
Induration - Coal or Gas
External Combustion Boilers;Electric
Generation;Anthracite Coal;Pulverized
Coal
External Combustion Boilers;Electric
Generation;Bituminous/Subbituminous
Coal;Pulverized Coal: Dry Bottom
(Bituminous Coal)
External Combustion Boilers;Electric
Generation;Bituminous/Subbituminous
Coal; Spreader Stoker (Bituminous Coal)
External Combustion Boilers;Electric
Generation;Bituminous/Subbituminous
Coal;Pulverized Coal: Dry Bottom
(Tangential) (Bituminous Coal)
External Combustion Boilers;Electric
Generation;Residual Oil;Grade 6 Oil:
Normal Firing
External Combustion Boilers;Electric
Generation;Residual Oil;Grade 6 Oil:
Tangential Firing
External Combustion Boilers;Electric
Generation;Distillate Oil;Grades 1 and 2
Oil
External Combustion Boilers;Electric
Generation;Natural Gas;Boilers > 100
Million Btu/hr except Tangential
External Combustion Boilers;Electric
Generation;Natural Gas;Boilers < 100
Million Btu/hr except Tangential
External Combustion Boilers;Electric
Generation;Natural Gas;Tangentially
Fired Units
Control Control
Technology Efficiency
SCR 90.0
OXY-Firing 85.0
SCR 90.0
SNCR
SNCR
SNCR
SNCR
SNCR
SNCR
SNCR
NGR
NGR
NGR
40.0
40.0
40.0
40.0
50.0
50.0
50.0
50.0
50.0
50.0
3a-10
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NOX 10101202 External Combustion Boilers;Electric SNCR 50.0
Generation;Solid Waste;Refuse Derived
Fuel
NOX 20200253 Internal Comb. NSCR 90
Engines/Industrial/Natural Gas/4-cycle
Rich Burn
3a.2Mobile Controls/Rules Used in Baseline and Control Scenarios
3a.2.1 Diesel Retrofits and Vehicle Replacement
Retrofitting heavy-duty diesel vehicles and equipment manufactured before stricter
standards are in place - in 2007-2010 for highway engines and in 2011-2014 for most
nonroad equipment - can provide NOx and HC benefits. The retrofit strategies included
in the RIA retrofit measure are:
• Installation of emissions after-treatment devices called selective catalytic
reduction ("SCRs")
• Rebuilding nonroad engines ("rebuild/upgrade kit")
We chose to focus on these strategies due to their high NOx emissions reduction potential
and widespread application. Additional retrofit strategies include, but are not limited to,
lean NOx catalyst systems - which are another type of after-treatment device - and
alternative fuels. Additionally, SCRs are currently the most likely type of control
technology to be used to meet EPA's NOx 2007-2010 requirements for HD diesel trucks
and 2008-2011 requirements for nonroad equipment. Actual emissions reductions may
vary significantly by strategy and by the type and age of the engine and its application.
To estimate the potential emissions reductions from this measure, we applied a mix of
two retrofit strategies (SCRs and rebuild/upgrade kits) for the 2020 inventory of:
• Heavy-duty highway trucks class 6 & above, Model Year 1995-2009
• All diesel nonroad engines, Model Year 1991-2007, except for locomotive,
marine, pleasure craft, & aircraft engines
Class 6 and above trucks comprise the bulk of the NOx emissions inventory from heavy-
duty highway vehicles, so we did not include trucks below class 6. We chose not to
include locomotive and marine engines in our analysis since EPA has proposed
regulations to address these engines, which will significantly impact the emissions
inventory and emission reduction potential from retrofits in 2020. There was also not
enough data available to assess retrofit strategies for existing aircraft and pleasure craft
engines, so we did not include them in this analysis. In addition, EPA is in the process of
negotiating standards for new aircraft engines.
The lower bound in the model year range - 1995 for highway vehicles and 1991 for
nonroad engines - reflects the first model year in which emissions after-treatment devices
can be reliably applied to the engines. Due to a variety of factors, devices are at a higher
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risk of failure for earlier model years. We expect the engines manufactured before the
lower bound year that are still in existence in 2020 to be retired quickly due to natural
turnover, therefore, we have not included strategies for pre-1995/1991 engines because of
the strategies' relatively small impact on emissions. The upper bound in the model year
range reflects the last year before more stringent emissions standards will be fully
phased-in.
We chose the type of strategy to apply to each model year of highway vehicles and
nonroad equipment based on our technical assessment of which strategies would achieve
reliable results at the lowest cost. After-treatment devices can be more cost-effective
than rebuild and vice versa depending on the emissions rate, application, usage rates, and
expected life of the engine. The performance of after-treatment devices, for example,
depends heavily upon the model year of the engine; some older engines may not be
suitable for after-treatment devices and would be better candidates for rebuild/upgrade
kit. In certain cases, nonroad engines may not be suitable for either after-treatment
devices or rebuild, which is why we estimate that retrofits are not suitable for 5% of the
nonroad fleet. The mix of strategies employed in this RIA for highway vehicles and
nonroad engines are presented in Table 3a.4 and Table 3a.5, respectively. The groupings
of model years for highway vehicles reflect changes in EPA's published emissions
standards for new engines.
Table 3a.4 Application of Retrofit Strategy for Highway Vehicles by Percentage of
Fleet
Model Year SCR
<1995 0%
1995-2006 100%
2007-2009 50%
>2009 0%
Table 3a.5 Application of Retrofit Strategy for Nonroad Equipment by Percentage
of Fleet
Model Year Rebuild/Upgrade kit SCR
1991-2007
50%
50%
The expected emissions reductions from SCR's are based on data derived from EPA
regulations (Control of Emissions of Air Pollution from 2004 and Later Model Year
Heavy-duty Highway Engines and Vehicles published October 2000), interviews with
component manufacturers, and EPA's Summary of Potential Retrofit Technologies. This
information is available at www.epa.gov/otaq/retrofit/retropotentialtech.htm. The
estimates for highway vehicles and nonroad engines are presented in Table 3a.6 and
Table 3a.7, respectively.
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Table 3a.6: Percentage Emissions Reduction by Highway Vehicle Retrofit Strategy
SCR (+DPF)
PM
90%
CO
90%
HC
90%
NOx
70%
Table 3a.7: Percentage Emissions Reduction by Nonroad Equipment Retrofit
Strategy
Strategy
SCR (+DPF)
Rebuild/Upgrade Kit
PM
90%
30%
CO
90%
15%
HC
90%
70%
NOx
70%
40%
It is important to note that there is a great deal of variability among types of engines
(especially nonroad), the applicability of retrofit strategies, and the associated emissions
reductions. We applied the retrofit emissions reduction estimates to engines across the
board (e.g. retrofits for bulldozers are estimated to produce the same percentage reduction
in emissions as for agricultural mowers). We did this in order to simplify model runs,
and, in some cases, where we did not have enough data to differentiate emissions
reductions for different types of highway vehicles and nonroad equipment. We believe
the estimates used in the RIA, however, reflect the best available estimates of emissions
reductions that can be expected from retrofitting the heavy-duty diesel fleet.
Using the retrofit module in EPA's National Mobile Inventory Model (NMIM) available
at http://www.epa.gov/otaq/nmim.htm, we calculated the total percentage reduction in
emissions (PM, NOx, HC, and CO) from the retrofit measure for each relevant engine
category (source category code, or SCC) for each county in 2020. To evaluate this
change in the emissions inventory, we conducted both a baseline and control analysis.
Both analyses were based on NMIM 2005 (version NMIM20060310), NONROAD2005
(February 2006), and MOBILE6.2.03 which included the updated diesel PM file
PMDZML.csv dated March 17, 2006.
For the control analysis, we applied the retrofit measure corresponding to the percent
reductions of the specified pollutants in Tables 3a.6 and 3a.7 to the specified model years
in Tables 1 and 2 of the relevant SCCs. Fleet turnover rates are modeled in the NMIM,
so we applied the retrofit measure to the 2007 fleet inventory, and then evaluated the
resulting emissions inventory in 2020. The timing of the application of the retrofit
measure is not a factor; retrofits only need to take place prior to the attainment date target
(2020 for this RIA). For example, if retrofit devices are installed on 1995 model year
bulldozers in 2007, the only impact on emissions in 2020 will be from the expected
inventory of 1995 model year bulldozer emissions in 2020.
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We then compared the baseline and control analyses to determine the percent reduction in
emissions we estimate from this measure for the relevant SCC codes in the targeted
nonattainment areas.
Pollutants and Source Categories Affected by Measure (SCC)
NOx, and HC
3a. 2.2 Implement Continuous Inspection and Maintenance Using Remote Onboard
Diagnostics (OBD)
Continuous Inspection and Maintenance (I/M) is a new way to check the status of OBD
systems on light-duty OBD-equipped vehicles. It involves equipping subject vehicles
with some type of transmitter that attaches to the OBD port. The device transmits the
status of the OBD system to receivers distributed around the I/M area. Transmission may
be through radio-frequency, cellular or wi-fi means. Radio frequency and cellular
technologies are currently being used in the states of Oregon, California and Maryland.
Current I/M programs test light-duty vehicles on a periodic basis - either annually or
biennially. Emission reduction credit is assigned based on test frequency. Using
Continuous I/M, vehicles are continuously monitored as they are operated throughout the
non-attainment area. When a vehicle experiences an OBD failure, the motorist is notified
and is required to get repairs within the normal grace period - typically about a month.
Thus, Continuous I/M will result in repairs happening essentially whenever a malfunction
occurs that would cause the check engine light to illuminate. The continuous I/M
program is applied to the same fleet of vehicles as the current periodic I/M programs.
Currently, MOBILE6 provides an increment of benefit when going from a biennial
program to an annual program. The same increment of credit applies going from an
annual program to a continuous program.
Pollutants and Source Categories Affected by Measure (SCC):
• All 1996 and newer light-duty gasoline vehicles and trucks:
• All 1996 and newer 2201001000 Light Duty Gasoline Vehicles (LDGV), Total:
All Road Types
• All 1996 and newer 2201020000 Light Duty Gasoline Trucks 1 (LDGT1), Total:
All Road Types
• All 1996 and newer 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Total:
All Road Types
OBD systems on light duty vehicles are required to illuminate the malfunction
indicator lamp whenever emissions of HC, CO or NOx would exceed 1.5 times the
vehicle's certification standard. Thus, the benefits of this measure will affect all three
criteria pollutants. MOBILE6 was used to estimate the emission reduction benefits of
Continuous I/M, using the methodology discussed above.
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3a.2.3 Eliminating Long Duration Truck Idling
Virtually all long duration truck idling - idling that lasts for longer than 15 minutes -
from heavy-duty diesel class 8a and 8b trucks can be eliminated with two strategies:
• truck stop & terminal electrification (TSE)
• mobile idle reduction technologies (MIRTs) such as auxiliary power units,
generator sets, and direct-fired heaters
TSE can eliminate idling when trucks are resting at truck stops or public rest areas and
while trucks are waiting to perform a task at private distribution terminals. When truck
spaces are electrified, truck drivers can shut down their engines and use electricity to
power equipment which supplies air conditioning, heat, and electrical power for on-board
appliances.
MIRTs can eliminate long duration idling from trucks that are stopped away from these
central sites. For a more complete list of MIRTs see EPA's Idle Reduction Technology
page at http://www.epa.gov/otaq/smartwav/idlingtechnologies.htm.
This measure demonstrates the potential emissions reductions if every class 8a and 8b
truck is equipped with a MIRT or has dependable access to sites with TSE in 2020.
To estimate the potential emissions reduction from this measure, we applied a reduction
equal to the full amount of the emissions attributed to long duration idling in the
MOBILE model, which is estimated to be 3.4% of the total NOx emissions from class 8a
and 8b heavy duty diesel trucks. Since the MOBILE model does not distinguish between
idling and operating emissions, EPA estimates idling emissions in the inventory based on
fuel conversion factors. The inventory in the MOBILE model, however, does not fully
capture long duration idling emissions. There is evidence that idling may represent a
much greater share than 3.4% of the real world inventory, based on engine control
module data from long haul trucking companies. As such, we believe the emissions
reductions demonstrated from this measure in the RIA represent ambitious but realistic
targets. For more information on determining baseline idling activity see EPA's
"Guidance for Quantifying and Using Long-Duration Truck Idling Emission Reductions
in State Implementation Plans and Transportation Conformity" available at
http://www.epa. gov/smartway/idle-guid.htm.
Pollutants and Source Categories Affected by Measure (SCC): NOX
Table 3a.8 Class 8a and 8b heavy duty diesel trucks (decrease NOx for all SCCs)
| SCC [Note: All SCC Descriptions below begin with "Mobile Sources; Highway Vehicles - Diesel;"
2230074110 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rural Interstate: Total
2230074130 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rural Other Principal Arterial: Total
2230074150 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rural Minor Arterial: Total
2230074170 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rural Major Collector: Total
2230074190 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rural Minor Collector: Total
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2230074210 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rural Local: Total
2230074230 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Urban Interstate: Total
2230074250 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Urban Other Freeways and Expressways: Total
2230074270 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Urban Other Principal Arterial: Total
2230074290 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Urban Minor Arterial: Total
2230074310 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Urban Collector: Total
2230074330 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Urbau Local: Total
Estimated Emissions Reduction from Measure (%): 3.4 % decrease inNOx for all SCCs
affected by measure
3a. 2.4 Commuter Programs
Commuter programs recognize and support employers who provide incentives to
employees to reduce light-duty vehicle emissions. Employers implement a wide range of
incentives to affect change in employee commuting habits including transit subsidies,
bike-friendly facilities, telecommuting policies, and preferred parking for vanpools and
carpools. The commuter measure in this RIA reflects a mixed package of incentives.
This measure demonstrates the potential emissions reductions from providing commuter
incentives to 10% and 25% of the commuter population in 2020.
We used the findings from a recent Best Workplaces for Commuters survey, which was
an EPA sponsored employee trip reduction program, to estimate the potential emissions
reductions from this measure.' The BWC survey found that, on average, employees at
workplaces with comprehensive commuter programs emit 15% fewer emissions than
employees at workplaces that do not offer a comprehensive commuter program.
We believe that getting 10-25% of the workforce involved in commuter programs is
realistic. For modeling purposes, we divided the commuter programs measure into two
program penetration rates: 10% and 25%. This was meant to provide flexibility to model
a lower penetration rate for areas that need only low levels of emissions reductions to
achieve attainment.
According to the 2001 National Household Transportation Survey (NHTS) published by
DOT, commute VMT represents 27% of total VMT. Based on this information, we
calculated that BWC would reduce light-duty gasoline emissions by 0.4% and 1% with a
10% and 25% program penetration rate, respectively.
Pollutants and Source Categories Affected by Measure (SCC): NOX, and VOC
7 Herzog, E., Bricka, S., Audette, L., and Rockwell, J., 2005. Do Employee Commuter
Benefits Reduce Vehicle Emissions and Fuel Consumption? Results of the Fall 2004 Best
Workplaces for Commuters Survey, Transportation Research Record, Journal of the
Transportation Research Board: Forthcoming.
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Table 3a.9 All light-duty gasoline vehicles and trucks
sec
2201001110
2201001130
Note: All SCC Descriptions below begin with "Mobile Sources; Highway Vehicles - Gasoline;"
Light Duty Gasoline Vehicles (LDGV);Rural Interstate: Total
Light Duty Gasoline Vehicles (LDGV);Rural Other Principal Arterial: Total
2201001 150 (Light Duty Gasoline Vehicles (LDGV);Rural Minor Arterial: Total
2201001 170|Light Duty Gasoline Vehicles (LDGV);Rural Major Collector: Total
2201001 190 |Light Duty Gasoline Vehicles (LDGV);Rural Minor Collector: Total
2201001210
2201001230
2201001250
2201001270
Light Duty Gasoline Vehicles (LDGV);Rural Local: Total
Light Duty Gasoline Vehicles (LDGV);Urban Interstate: Total
Light Duty Gasoline Vehicles (LDGV);Urban Other Freeways and Expressways: Total
Light Duty Gasoline Vehicles (LDGV);Urban Other Principal Arterial: Total
220 1001 290 |Light Duty Gasoline Vehicles (LDGV);Urban Minor Arterial: Total
2201 00 13 10 (Light Duty Gasoline Vehicles (LDGV);Urban Collector: Total
2201001330
2201020110
2201020130
2201020150
Light Duty Gasoline Vehicles (LDGV);Urban Local: Total
Light Duty Gasoline Tracks 1 & 2 (M6) = LDGT1 (M5);Rural Interstate: Total
Light Duty Gasoline Tracks 1 & 2 (M6) = LDGT1 (M5);Rural Other Principal Arterial: Total
Light Duty Gasoline Tracks 1 & 2 (M6) = LDGT1 (M5);Rural Minor Arterial: Total
2201020170|Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Rural Major Collector: Total
2201020190
2201020210
2201020230
2201020250
Light Duty Gasoline Tracks 1 & 2 (M6) = LDGT1 (M5);Rural Minor Collector: Total
Light Duty Gasoline Tracks 1 & 2 (M6) = LDGT1 (M5);Rural Local: Total
Light Duty Gasoline Tracks 1 & 2 (M6) = LDGT1 (M5);Urban Interstate: Total
Light Duty Gasoline Tracks 1 & 2 (M6) = LDGT1 (M5);Urban Other Freeways and Expressways: Total
2201020270|Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Urban Other Principal Arterial: Total
220 1020290 (Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Urban Minor Arterial: Total
2201020310
2201020330
2201040110
2201040130
Light Duty Gasoline Tracks 1 & 2 (M6) = LDGT1 (M5);Urban Collector: Total
Light Duty Gasoline Tracks 1 & 2 (M6) = LDGT1 (M5);Urban Local: Total
Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5);Rural Interstate: Total
Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5);Rural Other Principal Arterial: Total
220 1040 150 (Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Rural Minor Arterial: Total
2201040170
2201040190
2201040210
2201040230
Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5);Rural Major Collector: Total
Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5);Rural Minor Collector: Total
Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5);Rural Local: Total
Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5);Urban Interstate: Total
220 1040250 (Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Urban Other Freeways and Expressways: Total
220 1040270 (Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Urban Other Principal Arterial: Total
2201040290
2201040310
Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5);Urban Minor Arterial: Total
Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5);Urban Collector: Total
2201040330|Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Urban Local: Total
Estimated Emissions Reduction from Measure (%):
With a 10% program penetration rate: 0.4%
With a 25% program penetration rate: 1%
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3a.2.5 Reduce Gasoline RVPfrom 7.8 to 7.0 in Remaining Nonattainment Areas
Volatility is the property of a liquid fuel that defines its evaporation characteristics. RVP
is an abbreviation for "Reid vapor pressure," a common measure of gasoline volatility, as
well as a generic term for gasoline volatility. EPA regulates the vapor pressure of all
gasoline during the summer months (June 1 to September 15 at retail stations). Lower
RVP helps to reduce VOCs, which are a precursor to ozone formation. This control
measure represents the use of gasoline with a RVP limit of 7.0 psi from May through
September in counties with an ozone season RVP value greater than 7.0 psi.
Under section 21 l(c)(4)(C) of the CAA, EPA may approve a non-identical state fuel
control as a SIP provision, if the state demonstrates that the measure is necessary to
achieve the national primary or secondary ambient air quality standard (NAAQS) that the
plan implements. EPA can approve a state fuel requirement as necessary only if no other
measures would bring about timely attainment, or if other measures exist but are
unreasonable or impracticable.
Pollutants and Source Categories Affected by Measure (SCC):
• All light-duty gasoline vehicles and trucks: Affected SCC:
• 2201001000 Light Duty Gasoline Vehicles (LDGV), Total: All Road Types
• 2201020000 Light Duty Gasoline Trucks 1 (LDGT1), Total: All Road Types
• 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Total: All Road Types
• 2201070000 Heavy Duty Gasoline Vehicles (HDGV), Total: All Road Types
• 2201080000 Motorcycles (MC), Total: All Road Types
3'a.2.6 Application order for Onroad and Nonroad Mobile Controls
Application order- 0.084 Mobile
• Eliminate Long Duration Idling
• ONRetrofit
• LOWRVP
• Best Workplaces for Commuters
Application order- 0.084 Nonroad
• Diesel C1&C2 Marine/Diesel C3 Marine - 90% Rule (adding controls for
SCCs for residual fuel)
• ICAO Engine NOx Standards for Commercial Aircraft
• NRRetrofit
• LOWRVP
Application order- 0.070 Mobile
• Eliminate Long Duration Idling
• Inspection and Maintenance
• ONRetrofit
3a-18
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• LOWRVP
• Best Workplaces for Commuters
Application order - 0.070 Nonroad
• Diesel C1&C2 Marine/Diesel C3 Marine - 90% Rule (adding controls for
SCCs for residual fuel)
• ICAO Engine NOx Standards for Commercial Aircraft
• NRRetrofit
• LOWRVP
3a.3 EGU Controls Used in the Control Strategy
CAIR
The data and projections presented in Section 3.2.2 cover the electric power sector, an
industry that will achieve significant emission reductions under the Clean Air Interstate
Rule (CAIR) over the next 10 to 15 years. Based on an assessment of the emissions
contributing to interstate transport of air pollution and available control measures, EPA
determined that achieving required reductions in the identified States by controlling
emissions from power plants is highly cost effective. CAIR will permanently cap
emissions of sulfur dioxide (802) and nitrogen oxides (NOX) in the eastern United States.
CAIR achieves large reductions of SCh and/or NOX emissions across 28 eastern states and
the District of Columbia.
Figure 3a.l CAIR Affected Region
States not covered by CAIR
H States controlled for fine particles (annual SO2 and NOx)
^ States controlled for both fine particles (annual SO2 and NOx) and ozone (ozone season NOx)
_J States controlled for ozone (ozone season NOx)
3a-19
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When fully implemented, CAIR will reduce SC>2 emissions in these states by over 70%
and NOX emissions by over 60% from 2003 levels (some of which are due to NOx SIP
Call). This will result in significant environmental and health benefits and will
substantially reduce premature mortality in the eastern United States. The benefits will
continue to grow each year with further implementation. CAIR was designed with current
air quality standard in mind, and requires significant emission reductions in the East,
where they are needed most and where transport of pollution is a major concern. CAIR
will bring most areas in the Eastern US into attainment with the current ozone and current
PM2.5 standards. Some areas will need to adopt additional local control measures beyond
CAIR. CAIR is a regional solution to address transport, not a solution to all local
nonattainment issues. The large reductions anticipated with CAIR, in conjunction with
reasonable additional local control measures for SC>2, NOX, and direct PM, will move
States towards attainment in a deliberate and logical manner.
Based on the final State rules that have been submitted and the proposed State rules that
EPA has reviewed, EPA believes that all States intend to use the CAIR trading programs
as their mechanism for meeting the emission reduction requirements of CAIR.
The analysis in this section reflects these realities and attempts to show, in an illustrative
fashion, the costs and impacts of meeting a proposed 8-hr ozone standard of 0.070 for the
power sector.
Integrated Planning Model and Background
CAIR was designed to achieve significant emissions reductions in a highly cost-effective
manner to reduce the transport of fine particles that have been found to contribute to
nonattainment. EPA analysis has found that the most efficient method to achieve the
emissions reduction targets is through a cap-and-trade system on the power sector that
States have the option of adopting. The modeling done with IPM assumes a region-wide
cap and trade system on the power sector for the States covered.
It is important to note that the analysis herein uses the Integrated Planning Model (IPM)
v2.1.9 to ensure consistency with the analysis presented in 2006 PM NAAQS RIA and
report incremental results. EPA's IPM v2.1.9 incorporates Federal and State rules and
regulations adopted before March 2004 and various NSR settlements. A detailed
discussion of uncertainties associated with the EGU sector can be found in 2006 PM
NAAQS RIA (pg. 3-50). A newer version of the model (IPM v3.0) is available which
includes input and model assumption updates in modeling power sector. IPM v3.0 will
be used in the Final Ozone NAAQS RIA as part of the updated modeling platform.
Additionally, other control strategies are being considered that may be applicable to the
EGU sector, which would be presented in the final Ozone RIA.
The economic modeling using IPM presented in this and other chapters has been
developed for specific analyses of the power sector. EPA's modeling is based on its best
judgment for various input assumptions that are uncertain, particularly assumptions for
future fuel prices and electricity demand growth. To some degree, EPA addresses the
uncertainty surrounding these two assumptions through sensitivity analyses. More detail
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on IPM can be found in the model documentation, which provides additional information
on the assumptions discussed here as well as all other assumptions and inputs to the
model (http://www.epa.gov/airmarkets/progsregs/epa-ipm/past-modeling.html).
EGU NOx Emission Control Technologies
The Integrated Planning Model v2.1.9 (IPM) includes SC>2, NOX, and mercury (Hg)
emission control technology options for meeting existing and future federal, regional, and
state, SCb, NOX and Hg emission limits. The NOx control technology options include
Selective Catalytic Reduction (SCR) system and Selective Non-Catalytic Reduction
(SNCR) systems. It is important to note that beyond these emission control options, IPM
offers other compliance options for meeting emission limits. These include fuel
switching, re-powering, and adjustments in the dispatching of electric generating units.
Table 3a.lO summarizes retro fit NOx emission control performance assumptions.
Table 3a.lO. Summary of Retrofit NOx Emission Control Performance
Assumptions
Selective Catalytic Reduction
(SCR)
Selective Non-Catalytic Reduction
(SNCR)
Unit Type
Percent Removal
Coal
90%
down to 0.06
Ib/mmBtu
Oil/Gas* Coal Oil/Gas*
80% 35% 50%
Units. 25 MW
and
Size Applicability Units. 100 MW Units. 25 MW Units < 200 MW Units. 25 MW
* Controls to oil- or gas-fired EGUs are not applied as part of the EGU control strategy
included in this RIA.
Existing coal-fired units that are retrofit with SCR have a NOx removal efficiency of
90%, with a minimum controlled NOx emission rate of 0.06 Ib/mmBtu in IPM v2.1.9..
Potential (new) coal-fired, combined cycle, and IGCC units are modeled to be
constructed with SCR systems and designed to have emission rates ranging between 0.02
and 0.06 Ib NOx/mmBtu.
Detailed cost and performance derivations for NOx controls are discussed in detail in the
EPA's documentation of IPM (http://www.epa.gov/airmarkets/progsregs/epa-ipm/past-
modeling.html).
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3a.4 Emissions Reductions by Sector
Figures 3a.2- 3a.6 show the NOx reductions for each sector under the 0.070 ppm control
strategy.
Figure 3a.2 Tons of Nitrogen Oxide (NOx) Emissions Reduced from Electrical
Generating Unit (EGU) Sources*
• -16,892- -2.500
_ .2,499 - -500
.499--100
-99 - +100 **
t +101 - +500
+501 - +2.500
+2.501 -+10,768
* Reductions are negative and increases are positive
**The -99 - +100 range is not shown because these are small county-level NOx
reductions or increases that likely had little to no impact on ozone estimates. Most
counties in this range had NOx differences of under 1 ton.
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Figure 3a.3 Tons of Nitrogen Oxide (NOx) Emissions Reduced from Non-EGU Point
Sources*
•I -25880 - -2500
• -2499- -500
J-499--100,
-99 - 0 **
* Reductions are negative and increases are positive
**The -99 - 0 range is not shown because these are small county-level NOx reductions or
increases that likely had little to no impact on ozone estimates. Most counties in this
range had NOx differences of under 1 ton.
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Figure 3a.4 Tons of Nitrogen Oxide (NOx) Emissions Reduced from Area Sources'*
• -2.285--500
-499--100
-99 -0**
* Reductions are negative and increases are positive
**The -99 - 0 range is not shown because these are small county-level NOx reductions or
increases that likely had little to no impact on ozone estimates. Most counties in this
range had NOx differences of under 1 ton.
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Figure 3a.5 Tons of Nitrogen Oxide (NOx) Emissions Reduced from Nonroad
Sources*
-65-+100*
H +101- +500
* Reductions are negative and increases are positive
**The -99 - 0 range is not shown because these are small county-level NOx reductions or
increases that likely had little to no impact on ozone estimates. Most counties in this
range had NOx differences of under 1 ton.
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Figure 3a.6 Tons of Nitrogen Oxide (NOx) Emissions Reduced from Onroad
Sources*
-3.890 - -2.500
-2,499 - -500
.499--100
-99 - 0 **
* Reductions are negative and increases are positive
**The -99 - 0 range is not shown because these are small county-level NOx reductions or
increases that likely had little to no impact on ozone estimates. Most counties in this
range had NOx differences of under 1 ton.
3a-26
-------
3a.5 Change in Ozone Concentrations Between Baseline and Post-0.070 ppm
Control Strategy Modeling
Table 3a.ll Changes in Ozone Concentrations between Baseline and Post-0.070
ppm Control Strategy Modeling
State
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arkansas
Arkansas
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
County
Baldwin
Clay
Elmore
Jefferson
Madison
Mobile
Montgomery
Morgan
Shelby
Tuscaloosa
Maricopa
Final
Crittenden
Pulaski
Alameda
Amador
Butte
Calaveras
Colusa
Contra Costa
El Dorado
Fresno
Glenn
Imperial
Kern
Kings
Lake
Los Angeles
Madera
Mariposa
Merced
Monterey
Baseline
8-hour ozone
DV (ppm)
0.067
0.060
0.062
0.064
0.063
0.068
0.061
0.066
0.066
0.057
0.078
0.072
0.075
0.069
0.067
0.068
0.069
0.073
0.059
0.070
0.080
0.092
0.060
0.072
0.096
0.079
0.053
0.105
0.075
0.073
0.080
0.054
Control Scenario
8-hour ozone
DV (ppm)
0.066
0.056
0.060
0.063
0.061
0.068
0.060
0.065
0.065
0.056
0.077
0.071
0.072
0.068
0.067
0.068
0.069
0.073
0.059
0.070
0.080
0.092
0.060
0.072
0.096
0.079
0.053
0.105
0.075
0.073
0.080
0.054
Change
(ppm)
0.001
0.004
0.002
0.001
0.002
0.000
0.001
0.001
0.001
0.001
0.001
0.001
0.003
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
3a-27
-------
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
D.C.
Napa
Nevada
Orange
Placer
Riverside
Sacramento
San Benito
San Bernardino
San Diego
San Joaquin
San Luis Obispo
Santa Barbara
Santa Clara
Santa Cruz
Shasta
Solano
Sonoma
Stanislaus
Sutter
Tehama
Tulare
Tuolumne
Ventura
Yolo
Adams
Arapahoe
Boulder
Denver
Douglas
El Paso
Jefferson
Larimer
Weld
Fairfield
Hartford
Litchfield
Middlesex
New Haven
New London
Tolland
Washington
0.051
0.076
0.066
0.076
0.102
0.076
0.067
0.129
0.077
0.067
0.053
0.065
0.065
0.054
0.058
0.057
0.049
0.076
0.065
0.066
0.088
0.073
0.079
0.064
0.061
0.073
0.066
0.068
0.076
0.064
0.078
0.069
0.067
0.088
0.069
0.063
0.081
0.084
0.072
0.071
0.076
0.051
0.076
0.065
0.076
0.102
0.076
0.067
0.129
0.077
0.067
0.053
0.065
0.065
0.055
0.058
0.057
0.049
0.076
0.065
0.066
0.088
0.073
0.080
0.064
0.060
0.072
0.064
0.067
0.076
0.063
0.076
0.067
0.065
0.087
0.067
0.061
0.080
0.083
0.070
0.069
0.073
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
-0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
-0.001
0.000
0.001
0.001
0.002
0.001
0.000
0.001
0.002
0.002
0.002
0.001
0.002
0.002
0.001
0.001
0.002
0.002
0.003
3a-28
-------
Delaware
Delaware
Delaware
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Kent
New Castle
Sussex
Bay
Brevard
Duval
Escambia
Hillsborough
Manatee
Pasco
Pinellas
Santa Rosa
Sarasota
Bibb
Chatham
Cherokee
Cobb
Coweta
Dawson
De Kalb
Douglas
Fayette
Fulton
Glynn
Gwinnett
Henry
Murray
Muscogee
Paulding
Richmond
Rockdale
Adams
Champaign
Clark
Cook
Du Page
Effingham
Hamilton
Jersey
Kane
Lake
0.072
0.075
0.070
0.067
0.055
0.058
0.069
0.072
0.067
0.061
0.064
0.065
0.063
0.073
0.057
0.055
0.072
0.072
0.058
0.076
0.071
0.069
0.080
0.058
0.067
0.072
0.062
0.065
0.068
0.067
0.071
0.062
0.064
0.057
0.083
0.065
0.062
0.066
0.074
0.067
0.074
0.070
0.073
0.068
0.066
0.053
0.057
0.069
0.071
0.065
0.060
0.063
0.064
0.061
0.069
0.056
0.052
0.068
0.064
0.055
0.072
0.067
0.066
0.076
0.057
0.064
0.068
0.059
0.061
0.065
0.063
0.067
0.057
0.062
0.056
0.083
0.064
0.061
0.064
0.069
0.066
0.073
0.002
0.002
0.002
0.001
0.002
0.001
0.000
0.001
0.002
0.001
0.001
0.001
0.002
0.004
0.001
0.003
0.004
0.008
0.003
0.004
0.004
0.003
0.004
0.001
0.003
0.004
0.003
0.004
0.003
0.004
0.004
0.005
0.002
0.001
0.000
0.001
0.001
0.002
0.005
0.001
0.001
3a-29
-------
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Iowa
Iowa
Macon
Macoupin
Madison
McHenry
McLean
Peoria
Randolph
Rock Island
Sangamon
St Clair
Will
Winnebago
Allen
Boone
Carroll
Clark
Delaware
Floyd
Gibson
Greene
Hamilton
Hancock
Hendricks
Huntington
Jackson
Johnson
La Porte
Lake
Madison
Marion
Morgan
Perry
Porter
Posey
Shelby
St Joseph
Vanderburgh
Vigo
Warrick
Clinton
Scott
0.060
0.064
0.071
0.070
0.063
0.066
0.065
0.058
0.060
0.072
0.068
0.061
0.070
0.072
0.066
0.076
0.069
0.071
0.056
0.069
0.076
0.074
0.071
0.067
0.068
0.070
0.075
0.084
0.071
0.075
0.070
0.071
0.078
0.071
0.077
0.069
0.068
0.070
0.068
0.063
0.066
0.059
0.060
0.066
0.068
0.061
0.064
0.062
0.057
0.058
0.069
0.067
0.060
0.068
0.069
0.064
0.074
0.067
0.070
0.054
0.067
0.073
0.071
0.069
0.065
0.065
0.068
0.073
0.083
0.068
0.072
0.066
0.071
0.077
0.070
0.074
0.067
0.066
0.065
0.067
0.062
0.065
0.001
0.004
0.005
0.002
0.002
0.002
0.003
0.001
0.002
0.003
0.001
0.001
0.002
0.003
0.002
0.002
0.002
0.001
0.002
0.002
0.003
0.003
0.002
0.002
0.003
0.002
0.002
0.001
0.003
0.003
0.004
0.000
0.001
0.001
0.003
0.002
0.002
0.005
0.001
0.001
0.001
3a-30
-------
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Wyandotte
Bell
Boone
Boyd
Bullitt
Campbell
Carter
Christian
Daviess
Edmonson
Fayette
Graves
Greenup
Hancock
Hardin
Henderson
Jefferson
Jessamine
Kenton
Livingston
McCracken
McLean
Oldham
Pulaski
Scott
Simpson
Trigg
Warren
Ascension
Bossier
Caddo
Calcasieu
East Baton Rouge
Grant
Iberville
Jefferson
Lafayette
Lafourche
Livingston
Orleans
Ouachita
0.070
0.063
0.067
0.072
0.067
0.077
0.064
0.066
0.062
0.067
0.063
0.068
0.068
0.067
0.068
0.066
0.072
0.062
0.073
0.071
0.069
0.065
0.072
0.065
0.056
0.066
0.060
0.066
0.071
0.073
0.068
0.072
0.077
0.063
0.076
0.072
0.070
0.072
0.072
0.060
0.068
0.069
0.062
0.066
0.067
0.064
0.073
0.061
0.065
0.062
0.066
0.061
0.066
0.064
0.067
0.065
0.065
0.070
0.063
0.069
0.069
0.067
0.064
0.070
0.064
0.055
0.065
0.058
0.065
0.066
0.070
0.065
0.067
0.074
0.059
0.072
0.069
0.064
0.068
0.068
0.058
0.064
0.001
0.001
0.001
0.005
0.003
0.004
0.003
0.001
0.000
0.001
0.002
0.002
0.004
0.000
0.003
0.001
0.002
-0.001
0.004
0.002
0.002
0.001
0.002
0.001
0.001
0.001
0.002
0.001
0.005
0.003
0.003
0.005
0.003
0.004
0.004
0.003
0.006
0.004
0.004
0.002
0.004
3a-31
-------
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Pointe Coupee
St Bernard
St Charles
St James
St John The
Baptist
StMary
West Baton
Rouge
Cumberland
Hancock
Kennebec
Knox
Penobscot
York
Anne Arundel
Baltimore
Calvert
Carroll
Cecil
Charles
Frederick
Harford
Kent
Montgomery
Prince Georges
Washington
Barnstable
Berkshire
Bristol
Essex
Hampden
Hampshire
Middlesex
Suffolk
Worcester
Allegan
Benzie
Berrien
Cass
Clinton
0.064
0.067
0.068
0.069
0.072
0.068
0.074
0.065
0.070
0.060
0.063
0.062
0.069
0.076
0.077
0.065
0.068
0.078
0.070
0.067
0.084
0.075
0.072
0.075
0.067
0.072
0.067
0.072
0.071
0.070
0.068
0.067
0.067
0.064
0.075
0.070
0.072
0.069
0.066
0.060
0.065
0.066
0.065
0.069
0.062
0.071
0.063
0.067
0.057
0.060
0.059
0.066
0.074
0.075
0.063
0.066
0.075
0.068
0.065
0.082
0.072
0.070
0.072
0.063
0.070
0.066
0.069
0.070
0.068
0.066
0.064
0.065
0.062
0.072
0.068
0.070
0.067
0.062
0.004
0.002
0.002
0.004
0.003
0.006
0.003
0.002
0.003
0.003
0.003
0.003
0.003
0.002
0.002
0.002
0.002
0.003
0.002
0.002
0.002
0.003
0.002
0.003
0.004
0.002
0.001
0.003
0.001
0.002
0.002
0.003
0.002
0.002
0.003
0.002
0.002
0.002
0.004
3a-32
-------
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Minnesota
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Nevada
Nevada
New
Hampshire
New
Hampshire
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
Genesee
Huron
Ingham
Kalamazoo
Kent
Lenawee
Macomb
Mason
Missaukee
Muskegon
Oakland
Ottawa
St Clair
Washtenaw
Wayne
Anoka
Washington
De Soto
Hancock
Harrison
Hinds
Jackson
Warren
Clay
Jefferson
Platte
St Charles
St Louis
St Louis City
Ste Genevieve
Clark
Washoe
Hillsborough
Rockingham
Atlantic
Bergen
Camden
Cumberland
Essex
0.067
0.070
0.066
0.065
0.067
0.069
0.080
0.072
0.064
0.074
0.077
0.070
0.073
0.076
0.076
0.058
0.059
0.069
0.070
0.064
0.055
0.069
0.054
0.070
0.076
0.069
0.076
0.079
0.078
0.068
0.072
0.063
0.063
0.063
0.071
0.077
0.082
0.073
0.056
0.064
0.067
0.062
0.062
0.064
0.063
0.078
0.070
0.061
0.071
0.075
0.067
0.071
0.072
0.073
0.057
0.059
0.067
0.068
0.067
0.053
0.070
0.051
0.068
0.072
0.068
0.072
0.075
0.075
0.064
0.072
0.063
0.060
0.061
0.069
0.075
0.080
0.071
0.055
0.003
0.003
0.004
0.003
0.003
0.006
0.002
0.002
0.003
0.003
0.002
0.003
0.002
0.004
0.003
0.001
0.000
0.002
0.002
-0.003
0.002
-0.001
0.003
0.002
0.004
0.001
0.004
0.004
0.003
0.004
0.000
0.000
0.003
0.002
0.002
0.002
0.002
0.002
0.001
3a-33
-------
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Mexico
New Mexico
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
Gloucester
Hudson
Hunterdon
Mercer
Middlesex
Monmouth
Morris
Ocean
Passaic
Dona Ana
San Juan
Albany
Bronx
Chautauqua
Dutchess
Erie
Jefferson
Monroe
Niagara
Orange
Putnam
Queens
Richmond
Saratoga
Suffolk
Ulster
Wayne
Westchester
Alexander
Buncombe
Camden
Caswell
Chatham
Cumberland
Davie
0.080
0.074
0.078
0.083
0.081
0.078
0.077
0.084
0.071
0.071
0.071
0.064
0.069
0.074
0.067
0.079
0.075
0.073
0.076
0.063
0.070
0.068
0.074
0.066
0.086
0.065
0.070
0.075
0.066
0.065
0.063
0.063
0.063
0.065
0.067
0.078
0.073
0.077
0.081
0.079
0.077
0.075
0.081
0.069
0.070
0.068
0.063
0.068
0.070
0.066
0.075
0.072
0.072
0.075
0.061
0.068
0.067
0.072
0.065
0.084
0.063
0.068
0.074
0.064
0.065
0.062
0.059
0.061
0.063
0.065
0.002
0.001
0.001
0.002
0.002
0.001
0.002
0.003
0.002
0.001
0.003
0.001
0.001
0.004
0.001
0.004
0.003
0.001
0.001
0.002
0.002
0.001
0.002
0.001
0.002
0.002
0.002
0.001
0.002
0.000
0.001
0.004
0.002
0.002
0.002
3a-34
-------
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
North
Carolina
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Durham
Edgecombe
Forsyth
Franklin
Granville
Guilford
Johnston
Lincoln
Mecklenburg
New Hanover
Northampton
Person
Randolph
Rockingham
Rowan
Union
Wake
Allen
Ashtabula
Butler
Clark
Clermont
Clinton
Cuyahoga
Delaware
Franklin
Geauga
Greene
0.063
0.066
0.068
0.063
0.067
0.064
0.062
0.069
0.074
0.062
0.067
0.071
0.063
0.064
0.073
0.065
0.066
0.071
0.077
0.073
0.068
0.071
0.074
0.071
0.071
0.076
0.080
0.068
0.060
0.064
0.065
0.060
0.065
0.061
0.059
0.067
0.072
0.061
0.064
0.068
0.060
0.061
0.071
0.063
0.064
0.067
0.073
0.070
0.063
0.068
0.070
0.068
0.068
0.073
0.076
0.062
0.003
0.002
0.003
0.003
0.002
0.003
0.003
0.002
0.002
0.001
0.003
0.003
0.003
0.003
0.002
0.002
0.002
0.004
0.004
0.003
0.005
0.003
0.004
0.003
0.003
0.003
0.004
0.006
3a-35
-------
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Hamilton
Jefferson
Knox
Lake
Lawrence
Licking
Lorain
Lucas
Madison
Mahoning
Medina
Miami
Montgomery
Portage
Preble
Stark
Summit
Trumbull
Warren
Washington
Wood
Cleveland
Marshall
Me Clain
Oklahoma
Tulsa
Allegheny
Armstrong
Beaver
Berks
Blair
Bucks
Cambria
Centre
Chester
Clearfield
Dauphin
Delaware
Erie
Franklin
Greene
0.074
0.067
0.069
0.076
0.069
0.069
0.071
0.072
0.068
0.071
0.069
0.065
0.068
0.074
0.061
0.071
0.075
0.073
0.071
0.064
0.070
0.065
0.069
0.067
0.067
0.073
0.079
0.072
0.076
0.071
0.065
0.084
0.071
0.066
0.075
0.068
0.070
0.074
0.069
0.070
0.069
0.070
0.064
0.065
0.073
0.065
0.066
0.068
0.069
0.063
0.068
0.066
0.061
0.062
0.070
0.058
0.068
0.071
0.070
0.068
0.061
0.067
0.064
0.067
0.065
0.065
0.070
0.076
0.069
0.073
0.068
0.063
0.082
0.068
0.064
0.073
0.065
0.068
0.073
0.067
0.068
0.066
0.004
0.003
0.004
0.003
0.004
0.003
0.003
0.003
0.005
0.003
0.003
0.004
0.006
0.004
0.003
0.003
0.004
0.003
0.003
0.003
0.003
0.001
0.002
0.002
0.002
0.003
0.003
0.003
0.003
0.003
0.002
0.002
0.003
0.002
0.002
0.003
0.002
0.001
0.002
0.002
0.003
3a-36
-------
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Rhode Island
Rhode Island
Rhode Island
South
Carolina
South
Carolina
South
Carolina
South
Carolina
South
Carolina
South
Carolina
South
Carolina
South
Carolina
South
Carolina
South
Carolina
South
Carolina
Tennessee
Tennessee
Tennessee
Tennessee
Lackawanna
Lancaster
Lawrence
Lehigh
Luzerne
Lycoming
Mercer
Montgomery
Northampton
Perry
Philadelphia
Washington
Westmoreland
York
Kent
Providence
Washington
Anderson
Berkeley
Charleston
Cherokee
Chester
Edgefield
Pickens
Richland
Spartanburg
Union
York
Anderson
Blount
Davidson
Hamilton
0.064
0.071
0.063
0.071
0.064
0.059
0.073
0.078
0.072
0.063
0.080
0.070
0.070
0.071
0.074
0.071
0.075
0.067
0.058
0.057
0.063
0.064
0.063
0.065
0.069
0.066
0.062
0.063
0.064
0.071
0.064
0.066
0.062
0.068
0.059
0.069
0.063
0.057
0.069
0.076
0.070
0.061
0.078
0.067
0.067
0.067
0.072
0.068
0.072
0.065
0.057
0.055
0.061
0.061
0.058
0.063
0.066
0.063
0.059
0.061
0.061
0.067
0.063
0.063
0.002
0.003
0.004
0.002
0.001
0.002
0.004
0.002
0.002
0.002
0.002
0.003
0.003
0.004
0.002
0.003
0.003
0.002
0.001
0.002
0.002
0.003
0.005
0.002
0.003
0.003
0.003
0.002
0.003
0.004
0.001
0.003
3a-37
-------
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Virginia
Virginia
Virginia
Virginia
Haywood
Jefferson
Knox
Meigs
Rutherford
Shelby
Sullivan
Sumner
Williamson
Wilson
Brazoria
Collin
Dallas
Denton
El Paso
Ellis
Galveston
Gregg
Harris
Harrison
Hood
Jefferson
Johnson
Marion
Montgomery
Orange
Parker
Rockwall
Smith
Tarrant
Box Elder
Cache
Davis
Salt Lake
Utah
Weber
Bennington
Alexandria City
Arlington
Caroline
Charles City
0.067
0.068
0.071
0.066
0.065
0.072
0.072
0.068
0.068
0.066
0.078
0.075
0.079
0.078
0.070
0.074
0.078
0.079
0.092
0.065
0.068
0.079
0.073
0.069
0.072
0.068
0.068
0.067
0.071
0.079
0.066
0.055
0.071
0.073
0.070
0.067
0.060
0.072
0.078
0.063
0.074
0.063
0.065
0.066
0.063
0.063
0.069
0.062
0.067
0.066
0.065
0.076
0.072
0.077
0.075
0.069
0.069
0.075
0.073
0.090
0.062
0.066
0.072
0.069
0.065
0.070
0.064
0.066
0.063
0.068
0.076
0.065
0.054
0.070
0.072
0.069
0.066
0.059
0.069
0.075
0.062
0.073
0.004
0.003
0.005
0.003
0.002
0.003
0.010
0.001
0.002
0.001
0.002
0.003
0.002
0.003
0.001
0.005
0.003
0.006
0.002
0.003
0.002
0.007
0.004
0.004
0.002
0.004
0.002
0.004
0.003
0.003
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.003
0.003
0.001
0.001
3a-38
-------
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Chesterfield
Fairfax
Fauquier
Frederick
Hampton City
Hanover
Henrico
Loudoun
Madison
Prince William
Roanoke
Stafford
Suffolk City
Berkeley
Cabell
Hancock
Kanawha
Monongalia
Ohio
Wood
Brown
Columbia
Dane
Dodge
Door
Fond Du Lac
Jefferson
Kenosha
Kewaunee
Manitowoc
Milwaukee
Outagamie
Ozaukee
Racine
Rock
Sheboygan
Walworth
Washington
Waukesha
Winnebago
0.071
0.077
0.062
0.067
0.077
0.074
0.074
0.070
0.067
0.066
0.069
0.064
0.080
0.068
0.073
0.068
0.069
0.064
0.067
0.065
0.065
0.062
0.062
0.063
0.074
0.061
0.066
0.086
0.074
0.074
0.075
0.059
0.079
0.079
0.069
0.082
0.066
0.065
0.067
0.063
0.070
0.074
0.061
0.064
0.076
0.072
0.073
0.068
0.065
0.064
0.067
0.062
0.080
0.063
0.069
0.065
0.064
0.063
0.064
0.062
0.063
0.061
0.060
0.062
0.072
0.060
0.065
0.085
0.072
0.072
0.073
0.057
0.077
0.077
0.068
0.080
0.065
0.063
0.065
0.062
0.001
0.003
0.001
0.003
0.001
0.002
0.001
0.002
0.002
0.002
0.002
0.002
0.000
0.005
0.004
0.003
0.005
0.001
0.003
0.003
0.002
0.001
0.002
0.001
0.002
0.001
0.001
0.001
0.002
0.002
0.002
0.002
0.002
0.002
0.001
0.002
0.001
0.002
0.002
0.001
3a-39
-------
Chapter 4: Approach for Estimating Reductions for Full Attainment Scenario
Synopsis
This chapter presents the methodology used to estimate emission reductions that may be
needed to reach national attainment of the proposed tighter alternate primary 8-hour
ozone standard of 0.070-0.075 ppm. After applying the hypothetical control strategy
described in Chapter 3, there were many areas that were still not projected to attain the
more stringent standard modeled of 0.070 ppm. This chapter presents the methodology
EPA developed to determine emissions reductions needed for national attainment of the
alternate standards on each end of the proposed range (e.g. 0.070 and 0.075 ppm). It also
presents estimated emission reductions needed to attain a more stringent option analyzed
of 0.065 ppm, and a less stringent option of 0.079 ppm.
4.1 Development of Air Quality Impact Ratios for Determination of
Extrapolated Costs
Table 3a. 11 lists the highest projected design value in each monitored county for the
2020 baseline (current standard - effectively 0.084 ppm) and after application of the
illustrative national control strategy designed to attain an alternate primary standard of
0.070 ppm. From this table one can determine the counties that did not meet the target
air quality levels after implementation of the national hypothetical 0.070 control scenario.
Because the goal of the RIA is to estimate the estimated incremental costs of full
attainment, some estimate of the remaining emissions needed to reach these targets is
required for each of these areas.
It was beyond the scope of this illustrative analysis to perform detailed area-specific
analyses of the predicted additional emissions reductions needed to meet various air
quality goals. Instead, based on existing air quality sensitivity modeling, EPA developed
several simple, generic relationships of the expected air quality improvement to be
achieved as a result of ozone precursor reductions. These relationships are referred to
here as "impact ratios" and have units of ppb of ozone improvement per thousand tons of
ozone precursor emissions reduction (ppb/kton). Two separate approaches were used to
develop the impact ratios. The following paragraphs describe the development of the
impact ratios used in the extrapolated cost analysis of this RIA. Based on data presented
later in this chapter and considering the uncertainties and limitations of both approaches,
we decided to use a single impact ratio for NOx and a single impact ratio for VOC for the
purposes of this illustrative analysis for all areas in the U.S. that are included in the
extrapolated cost analysis.
4.1.1 Approach A: Use of Sensitivity Modeling of Local Emissions Reductions
In this approach, the impact ratios were calculated based on modeling results from four
existing, 36 km CMAQ 2010 emissions sensitivity simulations and a 2010 base case
simulation also derived from previously completed modeling:
4-1
-------
1. 90% NOx reduction in all anthropogenic sectors in nine specific local areas,
2. 90% NOx reduction in all anthropogenic sectors over the rest of the U.S.,
3. 90% VOC reduction in all anthropogenic sectors in nine specific local areas,
4. 90% VOC reduction in all anthropogenic sectors over the rest of the U.S.
We calculated the ppb/kton ratios for five of the nine zones shown in Figure 4.1 that are
included in the extrapolated costs analysis: Dallas, Atlanta, the Lake Michigan area, the
Northeast Corridor, and central California. It is expected that these five zones would
provide a representative range of ratios, so the analysis was not done for Denver,
Phoenix, and Salt Lake City. Because we were not calculating extrapolated tons for
Seattle, the ratio determination was not done for that region. For monitoring sites in each
of these five geographic areas we compared the ozone improvement in the 90% control
cases (simulations 1 and 3) against the tons of NOx and VOC reduced within the
corresponding control area. The impact ratio for each site was calculated by dividing the
ozone improvement by the corresponding tons reduced. Impact ratios were calculated for
88 sites over the five analysis zones. The results from Approach A are summarized in
Table 4.2
Figure 4.1: Nine Local Control Areas in Existing 2010 Sensitivity Runs
SMOKE/CMAQ Utility Special Region (Grid Cells)
4-2
-------
A sample calculation for one of the sites in the five analysis zones (a monitoring site
located in Denton TX) is shown below:
• A 90% NOx reduction equals 130.4 ktons in the local Dallas area.
• The ozone improvement from this reduction was 17.6 ppb (87.9 to 70.3).
• This yields an impact ratio of 0.135 ppb/kton for this county.
The advantage to this approach is that it allows for all-sector, local-only controls without
consideration of transport effects. This approach is best-suited for areas in which ozone
transport is not a large contributor to the local ozone problem, relative to local emissions
(e.g., Atlanta, Dallas).
Table 4.1: Summary of site-specific impact ratios over the five analysis zones of
Approach A.
Atlanta
Central CA
Dallas
Lake Michigan Area
Northeast Corridor
Minimum
Impact Ratio
0.051
0.077
0.118
-0.0221
0.002
Maximum
Impact Ratio
0.187
0.106
0.138
0.052
0.035
Average
Impact Ratio
0.123
0.095
0.130
0.010
0.022
Controlling County
Impact Ratio
0.187
0.106
0.135
0.032
0.035
It is important to note that we are not able to factor in impacts of controls outside of the
local regions using this methodology and thus, the impact ratios are likely to be
conservative. Additionally, depending upon the source-receptor relationship at a
particular location, some impact ratios would be expected to be lower than others due to
prevailing transport direction. For instance, one would not expect a location in the
southern portion of the Northeast Corridor to show much local air quality improvement
when the majority of the controls were implemented upwind. Other limitations to this
approach include: the assumption that response to NOx and VOC reductions is linear
between 0% and 90% control, the assumption that ratios developed from 2010 base case
modeling are applicable to 2020 post-strategy ozone, and the fact that impact ratios
calculated from a single month of 36 km modeling may not be appropriate for an analysis
of urban scale ozone.
4.1.2 Approach B: Use of 2020 Baseline and RIA Control Scenario
In the second approach, we used the results from the 2020 baseline and the 2020
hypothetical control scenario to calculate impact ratios for Atlanta, Houston, the Lake
1 The negative value of minimum impact ratio in the Lake Michigan area indicates that
ozone levels at one monitoring site are projected to increase slightly with 90% local NOx
control. This 'ozone disbenefit' has been projected by the model to occur in a very few,
highly localized, areas with large amounts of NOx emissions. This lone negative value is
not representative of regional impact ratios."
4-3
-------
Michigan Area and the Northeast Corridor. We focused on these four analysis zones
because they were expected to require extensive extrapolated tons to attain the air quality
targets. We did not use this approach in the western U.S. because there was very little
difference in the controls between those two cases in California. We again calculated
impact ratios for all monitoring sites in each zone by dividing the ozone change at each
site by the NOx emissions reductions that led to that ozone reduction. For the specific
purpose of estimating impact ratios, we have made the unrealistic but simplifying
assumption that the air quality change can be fully ascribed to the total NOx emissions
changes within 200 km of the area. Different assumptions about which emissions are
responsible for the air quality change would yield different impact ratios.
A sample calculation for one of the counties (Kenosha WI) is shown below:
• The RIA control scenario resulted in a NOx reduction of 16.8 ktons in the
Chicago zone (including 200 km buffer).
• The ozone improvement from this reduction was 1.6 ppb (86.6 to 85.0).
• This yields an impact ratio of 0.095 ppb/kton for this county.
The advantage to Approach B is that it allows for an estimate of the impact of actual
controls applied regionally because controls in the hypothetical scenario cover nearly the
entire eastern US. Thus, this approach is best suited for areas in which ozone transport is
a large contributor to the local ozone problem (e.g., the Lake Michigan area and the
Northeast Corridor). The primary disadvantage to this approach is that the 2020 control
scenario is weighted toward non-EGU point source controls which may result in non-
homogeneous reductions and thereby affect individual county impact ratios. Impact
ratios were calculated for 47 counties over the four analysis zones. The results from
Approach B are summarized in Table 4.2.
Table 4.2: Summary of site-specific impact ratios over the four analysis zones of
Approach B.
Atlanta
Houston
Lake Michigan Area
Northeast Corridor
Minimum
Impact Ratio
0.041
0.050
-0.006
0.068
Maximum
Impact Ratio
0.129
0.057
0.095
0.155
Average
Impact Ratio
0.068
0.054
0.064
0.110
Controlling County
Impact Ratio
0.070
0.057
0.095
0.105
4-4
-------
4.2 Results from Impact Ratio Analyses
In general, both approaches indicate that impact ratios could range between 0.03 and 0.20
ppb/kton. However, the approaches did not yield consistent impact ratios for individual
analysis zones. Individual local impact ratios are likely influenced by: the importance of
transport, the local NOx/VOC ratio, the meteorology within the region, and the location
of monitors relative to specific source areas.
Table 4.3 shows the impact ratios for each of the controlling counties2 within the areas
considered. Figure 4.2 shows the range of county-specific impact ratios calculated over
the four areas included in the calculations for Approach B. Based on these data and
considering the uncertainties and limitations of both approaches, we decided to use a
single impact ratio for NOx and a single impact ratio for VOC for the purposes of this
illustrative analysis for all areas in the U.S. that are included in the extrapolated cost
analysis. These general impact ratios are:
• NOx impact ratio = 0.100 ppb/kton
• VOC impact ratio = 0.025 ppb/kton
Table 4.3. The NOx impact ratios at the controlling counties for each methodology
over the analysis zones.
Analysis Area
Atlanta
Central California
Dallas
Houston
Lake Michigan area
Northeast Corridor
Impact Ratio at controlling county
Approach A
0.187
0.106
0.135
0.032
0.035
Approach B
0.070
0.057
0.095
0.105
The controlling county is the county within an area whose design value is farthest away
from attaining the air quality target.
4-5
-------
Figure 4.2. The NOx impact ratios at each county (sorted from lowest to highest)
for the Approach B over the four analysis areas3
Range of Impact Ratios: 084-070 methodology
0.20
0.15
0.10
0.05
0.00
-0.05
Percentile (Counties in the Analysis)
The selection of a 0.100 ppb/kton ratio was based on a consideration of all estimated
impact ratios from the bounding exercise of both approaches and the technical limitations
of approaches. There were three specific reasons why we thought 0.100 ppb/kton
represented the best choice for extrapolating the tons needed to attain an air quality target
beyond the reductions from the RIA control scenario:
• 0.100 is within, and near the midpoint of, the 0.03 to 0.20 range
• 0.100 is close to the median value from Approach B (0.093 ppb/kton)
• 0.100 is close to the average value at the key sites (0.091 ppb/kton).
As noted above, the various methods did not generate consistent area-specific NOx
impact ratios. As the impact ratios are used to estimate extrapolated costs, one should
keep in mind that higher impact ratios would yield lower estimates of needed
extrapolated tons and lower impact ratios would yield higher estimates of extrapolated
tons/costs.
As an example, if in a given area X, our impact ratio of 0.1 ppb/kton for NOx is defined
as equivalent to 10 extrapolated ktons of emission reductions needed to achieve a
particular air quality target, then a doubling of the impact ratio (thus, this ratio becomes
3 The lone negative value of impact ratio occurs in the Lake Michigan area and indicates
that ozone levels at that site are projected to increase slightly in response to the RIA
control scenario. This 'ozone disbenefit' has previously been projected by the model to
occur in a very few, highly localized, areas with large amounts of NOx emissions. This
lone negative value is not representative of regional impact ratios.
4-6
-------
0.2 ppb/kton) means that 5 extrapolated ktons of NOx, or one-half the original tonnage
reduction needed, to achieve the same air quality target. As a further example, a
reduction in the NOx impact ratio by half (thus, the ratio becomes 0.05 ppb/kton) means
that 20 extrapolated ktons of NOx emission reductions, or twice the original tonnage
reduction needed, to achieve the same air quality target.
We intend to conduct additional sensitivity analysesfor the final RIA to improve the
estimates of extrapolated tons needed to meet various targets. While it is premature to
specify the exact nature of these analyses, we expect this will include modeling to
provide more information about the non-linear responsiveness of ozone, the geographic
variation in ozone responsiveness, the impacts of local versus upwind emissions
reductions, and the relationship between NOx and VOC controls in various areas. It will
also include an analysis of the geographic application of impact ratios.
4.3 Determination of Extrapolated Tons Control Areas
The extrapolated tons analysis varied slightly from the geographic areas in which controls
were applied for the illustrative 0.070 control strategy described in Chapter 3. In the
extrapolated tons analysis, we aggregated all counties that were above the air quality goal
into discrete control areas, that is, areas from which the tons would need to be extracted
in order to meet the target. These control areas were either regional, statewide, or local
depending upon the nature of the ozone problem within the area. Two regional areas
were identified: the Ozone Transport Region and the Lake Michigan region. Both of
these areas have traditionally employed multi-State control plans to lower ozone in those
regions. For states with multiple areas above the air quality target, we assumed that
statewide control programs would be developed to bring these areas into attainment. For
example, for the 0.065 ppm target, Ohio exceeds the air quality target in Cincinnati,
Cleveland, and Columbus. We assumed that extrapolated tons could be achieved
anywhere in Ohio to meet the targets in all three areas. All remaining counties were
treated as places where local controls would be effective. The only exceptions to the
statewide assumption were in Texas and California. We separated the El Paso area into
its own area due to its distance (i.e., far greater than 200 km, which was the distance used
for the 0.070 control strategy) from the Eastern Texas areas (Dallas, Houston). In
California, we combined the Sacramento and San Joaquin Valley counties into a single
control area, but created a separate control area for Southern California. Table 4.4 shows
how the monitoring counties were aggregated into the extrapolated tons control areas for
the 0.070 ppm target.
4-7
-------
Table 4.4 List of counties that did not reach 0.070 in the RIA control scenario and
how they were aggregated into extrapolated tons control areas.
Control Region
Atlanta, GA
Baton Rouge, LA
Central Califronia
Charlotte-Gastonia-Rock Hill, NC-SC
Cleveland-Columbus-Cincinnati
Denver-Boulder
Detroit-Ann Arbor, Ml
Houston-Dallas
Indianapolis, IN
Lake Michigan region
State
Georgia
Georgia
Louisiana
Louisiana
Louisiana
California
California
California
California
California
California
California
California
California
California
California
California
California
California
North Carolina
North Carolina
Ohio
Ohio
Kentucky
Ohio
Ohio
Ohio
Colorado
Colorado
Colorado
Michigan
Michigan
Michigan
Michigan
Michigan
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Indiana
Indiana
Indiana
Indiana
Wisconsin
Indiana
Illinois
Wisconsin
Indiana
Wisconsin
Wisconsin
Wisconsin
Indiana
Illinois
Wisconsin
Michigan
Wisconsin
Wisconsin
Michigan
County
Fulton
De Kalb
East Baton Rouge
Iberville
West Baton Rouge
Kern
Fresno
Tulare
Merced
El Dorado
Kings
Stanislaus
Nevada
Placer
Sacramento
Madera
Mariposa
Tuolumne
Calaveras
Mecklenburg
Rowan
Geauga
Ashtabula
Campbell
Franklin
Lake
Summit
Jefferson
Douglas
Arapahoe
Macomb
Oakland
Wayne
Washtenaw
St Clair
Harris
Dallas
Brazoria
Tarrant
Denton
Galveston
Gregg
Jefferson
Collin
Shelby
Hamilton
Marion
Hancock
Kenosha
Lake
Cook
Sheboygan
Porter
Ozaukee
Racine
Milwaukee
La Porte
Lake
Kewaunee
Allegan
Manitowoc
Door
Muskegon
Control Region
Las Vegas, NV
Los Angeles South Coast Air Basin, CA
Louisville, KY-IN
Memphis, TN-AR
Ozone Transport Region
Phoenix-Mesa, AZ
Richmond-Norfolk
Salt Lake City, UT
St Louis, MO-IL
Tampa Bay, FL
State
Nevada
California
California
California
California
California
California
Indiana
Indiana
Arkansas
Connecticut
New York
Connecticut
Pennsylvania
Maryland
New Jersey
New Jersey
New Jersey
Connecticut
New Jersey
New Jersey
Pennsylvania
New Jersey
New Jersey
Pennsylvania
Pennsylvania
Maryland
New Jersey
New York
Virginia
Maryland
New Jersey
New York
New York
Virginia
Maryland
D.C.
Delaware
Pennsylvania
Pennsylvania
Pennsylvania
New Jersey
Maryland
Maryland
New York
Rhode Island
New York
Rhode Island
New York
New Jersey
Arizona
Arizona
Virginia
Virginia
Virginia
Virginia
Virginia
Utah
Missouri
Missouri
Missouri
Missouri
Florida
County
Clark
San Bernardino
Los Angeles
Riverside
Ventura
San Diego
Imperial
Clark
Perry
Crittenden
Fairfield
Suffolk
New Haven
Bucks
Harford
Ocean
Mercer
Camden
Middlesex
Middlesex
Gloucester
Philadelphia
Hunterdon
Monmouth
Allegheny
Montgomery
Cecil
Bergen
Erie
Arlington
Baltimore
Morris
Niagara
Westchester
Fairfax
Anne Arundel
Washington
New Castle
Beaver
Chester
Delaware
Hudson
Prince Georges
Kent
Richmond
Washington
Jefferson
Kent
Monroe
Cumberland
Maricopa
Pinal
Suffolk City
Hampton City
Henri co
Charles City
Hanover
Salt Lake
St Louis City
St Louis
Jefferson
St Charles
Hillsborough
4-8
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4.4 Selection of Air Quality Goal for this analysis
Under the Clean Air Act, areas are required to reach the air quality standards as
expeditiously as practicable and within certain statutorily defined time periods. In
advance of formal designations and ozone pollution level classifications, which will
depend upon future air quality data, it is uncertain when areas would be required to attain
a new ozone standard. In addition, states may request, and EPA must grant, a higher
classification which under the law provides flexibility for a state to justify a later
attainment date. (The state implementation plan must show that the attainment date
selected for an area is as expeditious as practicable, and no later than the maximum
statutory date for the area's classification.) In view of these and other factors, it is beyond
our capability to simulate in advance the state implementation process to determine the
appropriate attainment date and required controls for each potential nonattainment area
for a new standard. Instead, we have constructed an illustrative analysis that provides a
level playing field for comparison of the impacts of potential new, alternative standards.
An important consideration in the determination of the amount of air quality
improvement needed to reach a tighter ozone standard is the dates by which each area
must come into attainment. As discussed earlier, for several analytical reasons we
selected the year 2020 (i.e., approximately 10 years from designations), as the analytical
target year for this analysis. Therefore, this analysis presents two sets of results. The
first reflects attainment of the alternative ozone standards in all locations of the U.S.
except two areas of California in 2020. These two areas of California are not planning to
meet the current standard by 2020 (see discussion below), so the estimated costs and
benefits for these areas are based on reaching an estimated progress point (their
"glidepath" targets) in 2020. The second set of results, for California only, estimate the
costs and benefits from California fully attaining the alternative standards in a year
beyond 2020 (glidepath estimates, plus the increment needed to reach full attainment
beyond 2020, added together for a California total). However, as noted above, we are not
attempting to prejudge the attainment dates and controls that ultimately will be
determined through the SIP process, and it may turn out that attainment occurs later than
2020 for additional areas, particularly in areas where the future SIP process shows that
very high-cost controls would be needed to attain by 2020. For reasons explained below,
assuming longer attainment dates would reduce costs and benefits of meeting the current
and alternative standards, and would reduce costs more sharply in areas assumed to
employ high-cost controls to meet an artificial deadline.
The South Coast (Los Angeles area) and San Joaquin Air Quality Management Districts
recently have proposed for comment state implementation plans with the statutory
maximum 20-year attainment dates (June 2024, with attainment-level reductions by
2023) for meeting the current 8-hour standard, which would involve a request to
reclassify those two areas to the "extreme" classification4. This presented an analytical
4 Proposed State Strategy for California's State Implementation Plan (SIP) for the New
PM2.5 and 8-Hour Ozone Standard, California Air Resources Board web page,
http://www.arb.ca.gov/planning/sip/2007sip/2007sip.html, update May 30, 2007.
4-9
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dilemma for this analysis because assuming that these areas would be classified severe or
extreme for purposes of a new standard, these areas would not be required to attain any
new standard until after the analytical year of 2020. If an area is initially classified
severe, the law still would allow the state to request reclassification to extreme and to
demonstrate that a 20-year attainment date (e.g., in 2030, if designations occurred in
2010) is as expeditious as practicable. Thus, an assumption that these areas would attain
a tighter standard by 2020, significantly earlier than would be required under the Clean
Air Act, would artificially inflate both the costs and benefits of the nation attaining the
new standard in 2020 on a national level.
A further reason that we believe it would be inappropriate for the analysis to assume
attainment by 2020 with new, more stringent alternative standards in the San Joaquin and
South Coast areas is that existing rules, especially for on-road and non-road mobile
sources, will achieve substantial additional reductions in NOx and VOC after 2020 before
reaching their full impact in 2030. If San Joaquin and South Coast received the
maximum statutory 20-year attainment dates for new alternative standards, for example,
they would have an attainment date in 2030, and would benefit from reductions in NOx
and VOC from existing rules between 2020 and 2030. By 2029, existing rules for onroad
and nonroad engines would achieve 62,000 tons5 of residual emissions reductions needed
for attainment at no cost beyond the baseline for this analysis. By contrast, assuming
2020 attainment for these areas would result in assuming additional high-cost reductions
from unknown control measures. Therefore, assuming 2020 attainment for these areas
could result in a significant overestimate of costs. Likewise, the benefits would be
overestimated because the tons are attributable to these existing rules that have reductions
occurring after 2020 and are not part of our hypothetical control strategy.
5 62,000 tons was estimated by subtracting the California county level Onroad and
Nonroad 2030 NOx and VOC emissions from their totals in 2020. These differences
were estimated for counties listed under the CA control regions (Los Angeles and Kern
County) detailed in Table 4.4. (Los Angeles accounted for 38,500 tons, while Kern
accounted for 23,300 tons, for a total of 61,800 rounded to 62,000). In order to estimate
total emissions for both VOC and NOx, VOC emission reductions for these counties were
adjusted using the adjustment factor detailed earlier in the chapter (4 VOC tons = 1 NOx
ton). Given that the San Joaquin and South Coast air quality management districts have
adopted plans allowing until June 2024 (20 years from designation) to meet the current
standard, EPA believes that it would be consistent for purposes of this analysis to assume
a 20-year period for attainment of a new more stringent standard. If designations
occurred in 2010, the 20-year attainment period would end in 2030. Significant
emissions reductions from implementation of mobile source rules are anticipated between
2020 and 2030. Consistent with the non-EGU growth assumptions for the rest of the
RIA, non-EGU emissions are assumed to stay constant. EGU emissions are a small
fraction of the California inventory and are assumed not to significantly affect the change
in the state's emissions between 2020 and 2030. Accordingly, we have used the
difference between 2020 and 2030 mobile emissions to estimate the post-2020 emissions
reductions that will assist the two California areas in reaching attainment.
4-10
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Thus, for this analysis we have chosen to present the 2020 costs and benefits in a way
that reflects partial attainment in certain California areas, an outcome consistent with the
Clean Air Act. This national estimate includes full attainment in all locations except two
areas of California, which do not plan to meet the current standard by 2020, and so have
estimates for a progress point in 2020 (their "glidepath" targets). The second set of results
presents a total for California only, which adds the 2020 progress point to the additional
tons of emissions that may be needed in California to fully attain the standards in a year
beyond 2020.
The following table shows the results of the calculation of the glidepath targets for these
two areas used in this analysis6. The glidepath targets reflect the more stringent of two
air quality targets: (1) the improvement assumed by 2020 to meet the current standard by
years specified below, or the improvement needed by 2020 to make linear air quality
progress between 2010 and a post-2020 attainment date for the more stringent, alternative
standards. For Los Angeles County, the glidepath air quality targets below for all four
alternative standards are set based on reductions needed to meet the current standard,
with the level of the current standard being achieved in 2021. For Kern County in the
San Joaquin area, the glidepath for the 0.075 ppm and 0.079 ppm alternative standards is
based on achieving the level of the current standard by 2020. For the other two
alternative standards, the 2020 glide path targets for San Joaquin are based on meeting
the level of the alternative, more stringent standards in 2025.
Table 4-5: 2020 Air Quality Glidepath Targets for LA and Kern County
Alternative Standard Level
0.079 ppm
0.075 ppm
0.070 ppm
0.065 ppm
LA County
86.9 ppb*
86.9 ppb
86.9 ppb
86.9 ppb
Kern County
84.9 ppb
84.9 ppb
82.9 ppb
79.9 ppb
* targets are expressed in ppb for clarity of presentation
As noted above, since our glidepath calculations and cost-benefit estimates were made,
the two California districts have proposed state implementation plans for the current
standard that allow the statutory maximum 20-year period for attainment. This in turn
suggests that that it would be reasonable solely for purposes of this analysis to assume a
20-year period for implementation of new standards. In part because decisions regarding
6 Assumptions made for the purposes of this analysis were made prior to two California
areas adopting SIPs which assumed attainment by June 2024. For purposes of this
analysis, San Joaquin (including Kern County) was assumed to meet the current standard
by 2020 (consistent with the analysis assumption for most areas), and South Coast
(including Los Angeles County) was assumed to meet the current standard in 2021 (the
maximum attainment date of a severe-17 area is in June 2021). Because the San Joaquin
and South Coast air districts have adopted SIPs with later attainment dates for the current
standard, we recognize that the assumptions used in this analysis are not likely to be the
actual years of attainment for these two areas.
4-11
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our analysis were made prior to the California proposals, the assumed time periods in this
analysis for attainment by the San Joaquin and Los Angeles areas are shorter — both for
the current standards, and for the potential alternative standards. This suggests that the
glidepath figures above are all more stringent than likely implementation of the Clean Air
Act, and that as a result our analysis applies more unknown controls than would be
needed in these areas for the current and alternative standards assuming 20-year
deadlines. Thus, our estimated 2020 costs and benefits for the two California areas,
which influence the total cost and benefit figures in this draft RIA, are higher than would
likely occur under the Clean Air Act for the current and the potential alternative
standards. We intend to consider this issue further in the final RIA.
4.5 National 2020 Estimates of Additional Emissions Reductions Needed to Meet
Four Potential Air Quality Targets
This analysis presents two sets of estimates: national 2020 estimates, and California-only
estimates. This section presents the national 2020 estimates.
The national 2020 estimates assume full attainment in all locations except two areas of
California, which are assumed to meet 2020 air quality glidepath targets on their way
toward full attainment after 2020. These 2020 national estimates present incremental
tons that may be needed to meet the four separate air quality targets were considered as
part of this analysis: a less stringent alternative standard of 0.079 ppm, 0.075 ppm and
0.070 ppm, which bound the range that is being proposed, and a more stringent
alternative of 0.065 ppm. After the RIA control scenario, there were 24, 50, 126, and 280
counties above these four thresholds, respectively. The aggregation technique discussed
above grouped these counties into 6, 11, 20, and 29 extrapolated ton control areas for the
four targets. The calculation of additional tons needed does not account for the ancillary
effects of ozone transport reductions (e.g. the impact of Lake Michigan region reductions
on the OTR). The national 2020 total estimated incremental tons that may be needed to
attain the four targets are summarized below and presented in Tables 4.6-4.9.
• 0.079 = 102,000 tons of additional NOx control
• 0.075 = 321,000 tons of additional NOx control
• 0.070 = 1,004,000 tons of additional NOx control
• 0.065 = 2,239,000 tons of additional NOx control7
7 While the proposed rule takes comment on a range of alternate standards from 0.060
ppm to 0.084 ppm, the RIA analysis focused on a more limited range
4-12
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Table 4.6 Estimated Annual Incremental Tons Needed for an 0.065 ppm Air
Quality Target in 2020 (29 areas)
Control Region
Lake Michigan region
Ozone Transport Region
Eastern TX areas (Houston/Dallas/Beaumont)
VA areas (Norfolk/Richmond/Roanoke)
Detroit, MI
Phoenix, AZ
Denver, CO
OH areas (Cleveland/Columbus/Cincinnati)
Atlanta, GA
St Louis, MO-IL
Indiana areas (Indianapolis / Evansville)
LA areas (Baton Rouge/New
Orleans/Shreveport)
KY areas (Louisville/Paducah/Bowling Green)
TN areas (Knoxville/Memphis/Nashville)
NC areas (Charlotte / Raleigh)
Salt Lake City, UT
Las Vegas, NV
FL areas (Tampa / Panama City / Pensacola)
Sacramento / San Joaquin Valley / S Fran
Jackson, MS
New Mexico areas (Farmington / Las Graces)
OK areas (Tulsa, Marshall)
Huntington, WV-KY
El Paso, TX
Kansas City, MO/KS
Little Rock, AR
Mobile AL
Columbia, SC
Los Angeles South Coast Air Basin, CA
Controlling
County
KenoshaWI
Fairfield CT
Harris TX
Suffolk City VA
Macomb MI
Maricopa AZ
Jefferson CO
Geauga OH
Fulton GA
St Louis City MO
Shelby IN
E Baton Rouge LA
Clark IN
Crittenden AR
Mecklenburg NC
Salt Lake UT
Clark NV
Hillsborough FL
Kem CA**
Jackson MS
Dona Ana NM
Tulsa OK
Cabell WV
El Paso TX
Wyandotte KS
Pulaski AR
Mobile AL
Richland SC
Los Angeles CA**
Post-scenario
design value
(ppb)
85.0
87.1
90.5
80.8
78.4
77.6
76.9
76.5
76.0
75.7
74.5
74.4
74.0
72.9
72.3
72.2
72.0
71.4
96.3
70.6
70.3
70.3
69.9
69.3
69.0
68.7
68.6
66.9
105.0
Incremental
Extrapolated
NOx Tons
174,000
173,000
166,000
149,000
125,000
117,000
110,000
106,000
101,000
98,000
86,000
85,000
81,000
70,000
64,000
63,000
61,000
55,000
50,000
47,000
44,000
44,000
40,000
34,000
31,000
28,000
27,000
10,000
0*
*In EPA's illustrative analysis for the PM NAAQS PJA, there were reductions of NOx in
California. The amount of reductions assumed there are sufficient for these counties to
achieve their glidepath targets in 2020.
** Los Angeles and Kern Counties have expected attainment dates after 2020. This
analysis counts the portion of reductions assumed by this analysis by 2020 or earlier.
4-13
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Table 4.7. Estimated Annual Incremental Tons Needed for an 0.070 ppm air
quality target in 2020 (20 areas)
Control Region
Lake Michigan region
Ozone Transport Region
Eastern TX areas (Houston/Dallas)
VA areas (Norfolk/Richmond)
Detroit, MI
Phoenix, AZ
Denver, CO
OH areas
(Cleveland/Columbus/Cincinnati)
Atlanta, GA
St Louis, MO-IL
Indiana areas (Indianapolis)
LA areas (Baton Rouge)
KY areas (Louisville)
Sacramento / San Joaquin Valley
TN areas (Memphis)
NC areas (Charlotte)
Salt Lake City, UT
Las Vegas, NV
FL areas (Tampa)
Los Angeles South Coast Air Basin, CA
Controlling
County
Kenosha WI
Fairiield CT
Harris TX
Suffolk City VA
Macomb MI
Maricopa AZ
Jefferson CO
Geauga OH
Fulton GA
St Louis City MO
Shelby IN
E Baton Rouge LA
Clark IN
Kern CA**
Crittenden AR
Mecklenburg NC
Salt Lake UT
Clark NV
Hillsborough FL
Los Angeles CA**
Post-scenario
design value (ppb)
85.0
87.1
90.5
80.8
78.4
77.6
76.9
76.5
76.0
75.7
74.5
74.4
74.0
96.3
72.9
72.3
72.2
72.0
71.4
105.0
Incremental
Extrapolated NOx
Tons
124,000
123,000
116,000
99,000
75,000
67,000
60,000
56,000
51,000
48,000
36,000
35,000
31,000
20,000
20,000
14,000
13,000
11,000
5,000
0*
* In EPA's illustrative analysis for the PM NAAQS RIA, there were reductions of NOx
in California. The amount of reductions assumed there are sufficient for these counties to
achieve their glidepath targets in 2020.
** Los Angeles and Kern Counties have expected attainment dates after 2020. This
analysis counts the portion of reductions assumed by this analysis by 2020 or earlier.
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Table 4.8. Estimated Annual Incremental Tons Needed for an 0.075 ppm air quality
target in 2020 (11 areas)
Control Region
Lake Michigan region
Ozone Transport Region
Eastern TX areas (Houston/Dallas)
VA areas (Norfolk)
Detroit, MI
Phoenix, AZ
Denver, CO
OH areas (Cleveland)
Atlanta, GA
Sacramento / San Joaquin Valley
Los Angeles South Coast Air Basin, CA
Controlling
County
Kenosha WI
Fairfield CT
Harris TX
Suffolk City VA
Macomb MI
Maricopa AZ
Jefferson CO
Geauga OH
Fulton GA
Kern CA**
Los Angeles CA**
Post-scenario
design value (ppb)
85.0
87.1
90.5
80.8
78.4
77.6
76.9
76.5
76.0
96.3
105.0
Incremental
Extrapolated NOx
Tons
74,000
73,000
66,000
49,000
25,000
17,000
10,000
6,000
1,000
0*
0*
*In EPA's illustrative analysis for the PM NAAQS RIA, there were reductions of NOx in
California. The amount of reductions assumed there are sufficient for these counties to
achieve their glidepath targets in 2020.
** Los Angeles and Kern Counties have expected attainment dates after 2020. This
analysis counts the portion of reductions assumed by this analysis by 2020 or earlier.
Table 4.9. Estimated Annual Incremental Tons Needed for an 0.079 ppm air quality
target in 2020 (6 areas)
Control Region
Lake Michigan region
Ozone Transport Region
Eastern TX areas (Houston/Dallas)
VA areas (Norfolk)
Sacramento / San Joaquin Valley
Los Angeles South Coast Air Basin, CA
Controlling
County
Kenosha, WI
Fairfield. CT
Harris, TX
Suffolk City, VA
Kern CA**
Los Angeles CA**
Post-scenario
design value (ppb)
85.0
87.1
90.5
80.8
96.3
105.0
Incremental
Extrapolated NOx
Tons
34,000
33,000
26,000
9,000
0*
0*
*In EPA's illustrative analysis for the PM NAAQS RIA, there were reductions of NOx in
California. The amount of reductions assumed there are sufficient for these counties to
achieve their glidepath targets in 2020.
** Los Angeles and Kern Counties have expected attainment dates after 2020. This
analysis counts the portion of reductions assumed by this analysis by 2020 or earlier.
4-15
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4.6 Estimates of Additional Tons Needed for Four Potential Air Quality Targets
(California only, post-2020 Attainment)
The second estimates presented are for California only. Tables 4.10-4.13 below,
present the estimated tons needed to attain California's glidepath targets in 2020 and the
additional increment of tons that may be needed for full attainment in a year beyond 2020
Table 4.10 California: Estimated Tons Needed For Attainment 0.065 ppm air
quality target (beyond 2020)
Control Region
Sacramento / San
Joaquin Valley / S
Fran
Los Angeles South
Coast Air Basin, CA
Controlling
County
KernCA
Los Angeles CA
Post-
Total Glidepath
scenario _ , ... ^ . , . .
, . Extrapolated Extrapolated
flaeitTTl * *
,7° NOxTons NOxTons
value (ppb)
96.3 190,000 50,000
105.0 188,000 0
Remaining
tons needed
(2020-
attainment)
140,000
188,000
"Credit" tons
from mobile
rules
2020-2030
23,300
38,500
Tons needed
after mobile
reductions
116,700
149,500
Table 4.11 California: Estimated Tons Needed for Attainment of 0.070 ppm air
quality target (beyond 2020)
Control Region
Sacramento / San
Joaquin Valley
Los Angeles South
Coast Air Basin, CA
Controlling
County
KernCA
Los Angeles CA
Post-
Total Glidepath
scenario _ , ... ^ . , . .
, . Extrapolated Extrapolated
rlaeitTTI
,7 , . NOx Tons NOx Tons
value (ppb)
96.3 140,000 20,000
105.0 138,000 0
Remaining
tons needed
(2020-
attainment)
120,000
138,000
"Credit" tons
from mobile
rules
2020-2030
23,300
38,500
Tons needed
after mobile
reductions
96,700
99,500
Table 4.12 California: Estimated Tons Needed for Attainment of 0.075 ppm air
quality target (beyond 2020)
Control Region
Sacramento / San
Joaquin Valley
Los Angeles South
Coast Air Basin, CA
Controlling
County
KernCA
Los Angeles CA
Post-
Total Glidepath
scenario _ , ... ^ . , . .
, . Extrapolated Extrapolated
rlaeitTTI
,7 , , NOx Tons NOx Tons
value (ppb)
96.3 90,000 0
105.0 88,000 0
Remaining
tons needed
(2020-
attainment)
90,000
88,000
"Credit" tons
from mobile
rules
2020-2030
23,300
38,500
Tons needed
after mobile
reductions
66,700
49,500
Table 4.13 California: Estimated Tons Needed for Attainment of 0.079 ppm air
quality target (beyond 2020)
Control Region
Sacramento / San
Joaquin Valley
Los Angeles South
Coast Air Basin, CA
Controlling
County
KernCA
Los Angeles CA
P°St". Total Glidepath
scenario „. ,., ^ . • x j
, . Extrapolated Extrapolated
design NQx Tons NQx Tons
value (ppb)
96.3 50,000 0
105.0 48,000 0
Remaining
tons needed
(2020-
attainment)
50,000
48,000
"Credit" tons
from mobile
rules
2020-2030
23,300
38,500
Tons needed
after mobile
reductions
26,700
9,500
4-16
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Chapter 5: Cost Estimates
Synopsis
This chapter summarizes the data sources and methodology used to estimate the costs of
attaining the alternative more stringent levels for the ozone primary standard analyzed in this
RIA. This chapter estimates the costs of the bounds of the proposed range, 0.075- 0.070 ppm, a
more stringent alternative of 0.065 ppm, as well as a less stringent option of 0.079 ppm. The
chapter presents cost estimates for the illustrative control strategy outlined in Chapter 3 (which
uses currently available known controls). The control strategy discussion is followed by a
presentation of estimates for the costs of the additional tons of emissions that are needed to move
to full attainment of the alternate standards analyzed (methodology and numbers discussed in
Chapter 4).
As noted in Chapter 3, EPA first modeled an illustrative control strategy aimed at attaining a
tighter standard of 0.070 ppm in 2020. These known controls were insufficient to bring all areas
into attainment with 0.070 ppm, and EPA then developed methodology to estimate additional
tons of emissions needed to attain the bounds of the proposed range, 0.075 and 0.070 ppm, the
tighter alternative of 0.065 ppm and the less stringent alternative option of 0.079 ppm. This
chapter presents the costs associated with each portion of the control analysis, clearly identifying
the relative costs of modeled versus extrapolated emissions reductions as well as providing an
estimate of the total cost of attainment nationwide in 2020. Section 5.1 summarizes the
methodology and the engineering costs associated with applying known and supplemental
controls to partially attain a 0.070 ppm alternative standard, incremental to reaching the current
baseline (effectively 0.084 ppm) in 2020.
Section 5.2 describes the methodology used to estimate the cost of extrapolated tons needed to
reach attainment of the bounds of the proposed alternative standard (0.070 and 0.075 ppm, the
less stringent alternative of 0.079ppm, as well as the more stringent alternative of 0.065 ppm)
and provides estimates of how much additional cost will be associated with moving from the
modeled partial attainment scenario to the nationwide attainment scenario (see Chapter 4 for
discussion of extrapolated tons needed to attain 0.079, 0.075, 0.070, and 0.065 ppm). In general,
EPA increased the tons required for each area using the same impact/ton estimate ( 5 ppb =
50,000 tons) and extrapolated cost approaches in order to estimate additional costs for reaching a
standard level of 0.065 ppm as well as estimate cost savings for the 0.075 and 0.079 ppm
standard levels (compared to the 0.070 ppm case).
Section 5.3 then combines the results from Sections 5.1 and 5.2 to describe the total estimated
cost of full attainment in 2020, including both the costs of modeled controls for reaching partial
attainment (engineering costs) and the additional costs of tons of extrapolated emissions
reductions needed to reach attainment. This section includes two sets of costs. The first reflects
full attainment in 2020 in all locations of the U.S. except two areas of California. These two
areas are not planning to meet the current standard by 2020, so the estimated costs for these areas
are based on reaching an estimated progress point in 2020 (their "glidepath" targets). The
second set of results for California only, estimate the costs from California fully attaining the
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alternative standards in a year beyond 2020 (glidepath estimates for 2020, plus further
increments needed to reach full attainment beyond 2020, added together for California total).
The costs described in this chapter generally include the costs of purchasing, installing, and
operating the referenced technologies. For a variety of reasons, actual control costs may vary
from the estimates EPA presents here. As discussed throughout this report, the technologies and
control strategies selected for analysis are illustrative of one way in which nonattainment areas
could meet a revised standard. There are numerous ways to construct and evaluate potential
control programs that would bring areas into attainment with alternative standards, and EPA
anticipates that state and local governments will consider programs that are best suited for local
conditions. Furthermore, based on past experience, EPA believes that it is reasonable to
anticipate that the marginal cost of control will decline over time due to technological
improvements and more widespread adoption of previously niche control technologies. Also,
EPA recognizes the extrapolated portion of the cost estimates reflects substantial uncertainty
about which sectors, and which technologies, might become available for cost-effective
application in the future. This is explained in further detail in Section 5.4.
It is also important to recognize that the cost estimates are limited in their scope. Because we are
not certain of the specific actions that states will take to design State Implementation Plans to
meet the revised standards, we do not present estimated costs that government agencies may
incur for managing the requirement and implementation of these control strategies or for offering
incentives that may be necessary to encourage or motivate the implementation of the
technologies, especially for technologies that are not necessarily market driven. This analysis
does not assume specific control measures that would be required in order to implement these
technologies on a regional or local level.
5.1 Modeled Controls
5.1.1 Sector methodology
5.1.1.1 Non-EGU Point and Area Sources: AirControlNET
After designing a national hypothetical control strategy to meet an alternative standard of 0.070
ppm using the methodology discussed in Chapter 3 (see sub-section 3.2.1), EPA used
AirControlNET to estimate engineering control costs. AirControlNET calculates costs using
three different methods: (1) by multiplying an average annualized cost-per-ton estimate against
the total tons of a pollutant reduced to derive a total cost estimate; (2) by calculating cost using
an equation that incorporates information regarding key plant information; or (3) by using both
cost per ton and cost equations. Most control cost information within AirControlNET has been
developed based on the cost-per-ton approach. This is because estimating cost using an equation
requires more data, and parameters used in other non-cost per ton methods may not be readily
available or broadly representative across sources within the emissions inventory. The costing
equations used in AirControlNET require either plant capacity or stack flow to determine annual,
capital and/or operating and maintenance (O&M) costs. Capital costs are converted to annual
5-2
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costs, in dollars per ton, using the capital recovery factor.1 Applied controls and their respective
costs are provided in Ozone NAAQS RIA docket.
The control strategy for Non-EGU Point and Area Sources incorporated cost-per-ton caps.
These caps were pollutant specific and applicable only in the eastern U.S. portion of the analysis.
For reductions of NOx emissions the cap was $16,000/ton. This was based upon the approximate
benefit per ton of reductions in NOx, as well as an examination of the marginal cost curve for
NOx reductions from these sectors. There were only two controls whose cost per ton were
greater than this cap, and subsequently not included in this analysis, due to the large capital
component of installing these controls. A similar process was followed for reductions from
VOCs. The marginal cost curve was analyzed and there was a clear break in the curve at
approximately $6,000/ton. At this cap, over sixty percent of the possible reductions are being
controlled at less than thirty percent of the total cost of the VOC reductions.
Supplemental controls were applied in this illustrative analysis in order to achieve the highest
possible emission reduction from Non-EGU point and area sources. Supplemental control
measures are those controls that are 1) applied in these analyses but are not found in
AirControlNET, and 2) are in AirControlNET but whose data have been modified to better
approximate their applicability to source categories in 2020. The controls and associated data
such as control cost estimates not found in AirControlNET are taken from technical reports
prepared to support preliminary 8-hour ozone State Implementation Plans (SIPs) prepared by
States and from various reports prepared by the staffs of various local air quality regulatory
agencies (e.g. Bay Area Air Quality Management District). The reports that are the sources of
additional controls data are included within footnotes in the Chapter 3 Appendix. Modification
of control data, including percent reduction levels and control cost data, in AirControlNET
occurred as a result of a review of the nonEGU point and area NOx control measures by
technical staff. The changes EPA supplied are provided later in the Chapter 3 Appendix.
5.1.1.2 EGU Sources: the Integrated Planning Model
Costs for the electric power sector are estimated using the Integrated Planning Model (IPM).
The model determines the least-cost means of meeting energy and peak demand requirements
over a specified period, while complying with specified constraints, including air pollution
regulations, transmission bottlenecks, fuel market restrictions, and plant-specific operational
constraints. IPM is unique in its ability to provide an assessment that integrates power,
environmental, and fuel markets. The model accounts for key operating or regulatory constraints
(e.g. emission limits, transmission capabilities, renewable generation requirements, fuel market
constraints) that are placed on the power, emissions, and fuel markets. IPM is particularly well-
suited to consider complex treatment of emission regulations involving trading and banking of
1 For more information on this cost methodology and the role of AirControlNext, see Section 6
of the 2006 PM RIA, AirControlNET 4.1 Control Measures Documentation (Pechan, 2006b), or
http ://www. epa. gov/ttn/catc/products .html#cccinfo
5-3
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emission allowances, as well as traditional command-and-control emission policies.2 Applied
controls and their respective costs are provided in the docket. IPM is described in further detail
in Appendix 3.
5.1.1.3 Onroad and Nonroad Mobile Sources: MOBILE model
Cost information for mobile source controls was taken from studies conducted by EPA for
previous rulemakings and studies conducted for development of voluntary and local measures
that could be used by state or local programs to assist in improving air quality. Applied controls
and their respective costs are provided in the docket.3
Cost analysis of the onroad and nonroad mobile sector was performed using the MOBILE6
model. MOBILE is an EPA model for estimating pollution from highway vehicles. MOBILE
calculates emissions of hydrocarbons (HC), oxides of nitrogen (NOx), and carbon monoxide
(CO) from passenger cars, motorcycles, light- and heavy-duty trucks. The model accounts for the
emission impacts of factors such as changes in vehicle emission standards, changes in vehicle
populations and activity, and variation in local conditions such as temperature, humidity and fuel
quality4.
5.1.2 Known Controls— Cost by Sector
In this section, we provide engineering cost estimates of the control strategies identified in
Chapter 3 that include control technologies on non-EGU stationary sources, area sources, EGUs,
and onroad and nonroad mobile sources. Engineering costs generally refer to the capital
equipment expense, the site preparation costs for the application, and annual operating and
maintenance costs.
The total annualized cost of control in each sector in the control scenario is provided in Table
5.1. These numbers reflect the engineering costs across sectors annualized at a discount rate of
7% and 3%, consistent with the guidance provided in the Office of Management and Budget's
(OMB) (2003) Circular A-4. However, it is important to note that it is not possible to estimate
2 The application of the 0.070 EGU control strategy results in NOx allowance price decreasing
from $1340/ton in the baseline to $715/ton. See Technical Support Document on EGU Control
Strategies for more details. Further detailed information on IPM is available in Section 6 of the
2006 PM RIA or at http://www.epa.gov/aimiarkets/epa-ipm
3 The expected emissions reductions from SCR retrofits are based on data derived from EPA
regulations (Control of Emissions of Air Pollution from 2004 and Later Model Year Heavy-duty
Highway Engines and Vehicles published October 2000), interviews with component
manufacturers, and EPA's Summary of Potential Retrofit Technologies available at
www.epa.gov/otaq/retrofit/retropotentialtech.htm.
For more information on mobile idle reduction technologies (MIRTs) see EPA's Idle Reduction
Technology page at http://www.epa.gov/otaq/smartway/idlingtechnologies.htm.
4 More information regarding the MOBILE6 model can be found at
http://www.epa.gov/otaq/mobile.htm
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both 7% and 3% discount rates for each source (see section 5.1.3). In Table 5.1, an annualized
control cost is provided to allow for comparison across sectors, and between costs and benefits.
A 7% discount rate was used for control measures applied to non-EGU point, area, and mobile
sources. Costs from EGU sources, which are calculated using the IPM model and variable
interest rates, are captured in this table at an annualized 7% discount rate5.
Total annualized costs were calculated using a 3% discount rate for controls which had a capital
component and where equipment life values were available. In this RIA, the non-EGU point
source sector was the only sector with available data to perform a sensitivity analysis of our
annualized control costs to the choice of interest rate. Sufficient information on annualized
capital calculations was not available for area source and mobile controls to provide a reliable 3
percent discount rate estimate. As such, the 3% value in figure 5.1 is representative of the sum of
the non-EGU Point Source sector at a 3% discount rate, and the EGU, mobile, and Area Source
sector at a 7% discount rate. It is expected that the 3% discount rate value is overestimated due
to the addition of cost sectors at a higher discount rate. With the exception of the 3 % Total
Annualized Cost estimate on Table 5.1, cost estimates presented throughout this and subsequent
chapters are based on 7% discount rate.
The total annualized engineering costs associated with the application of known and
supplemental controls to reach a revised 0.070 ppm standard, incremental to the current standard,
are approximately $3.9 billion.
5 A different plant-specific interest rate is applied in estimating control costs within IPM. See
PM RIA for details.
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Table 5.1 Comparison of Modeled Annual Control Costs Nationwide, by sector, for a 0.070
ppm control scenario ($1999)6
Source Category
A. Electric Generating Units (EGU) Sector
Controls for NOx Cap-and-Trade Program and Local
Measures in Projected Nonattainment Areas
Total
B. Onroad
C. Nonroad
Total
D. Non-EGU Sector
Point Sources (Ex: Pulp & Paper, Iron & Steel,
Cement, Chemical Manu.)
E. Area Sector
Area Sources (Ex: Res. Woodstoves, Agriculture)
Total
Total Annualized Costs
(using a 7% interest rate)
Total Annualized Costs
(using a 3% interest rate)
0.070 ppm Control Strategy
Total Cost
($B 1999)
East West
$0.20 $0
$0.20 $0
$0.51 $0.11
$0.09 $0.02
$0.60 $0.13
$2.30 $0.34
$0.31 $0.01
$2.6 $0.35
$3.90
$3.60
Average
Cost per Ton
($1999)
$2,000
$2,300
$4,400
$3,600
$2,000
5.1.3 Limitations and Uncertainties Associated with Engineering Cost Estimates
EPA bases its estimates of emissions control costs on the best available information from
engineering studies of air pollution controls and has developed a reliable modeling framework
for analyzing the cost, emissions changes, and other impacts of regulatory controls. The
annualized cost estimates of the private compliance costs are meant to show the increase in
production (engineering) costs to the various affected sectors in our control strategy analyses.
To estimate these annualized costs, EPA uses conventional and widely-accepted approaches that
are commonplace for estimating engineering costs in annual terms. However, our cost analysis
is subject to uncertainties and limitations.
There are some unqualified costs that are not adequately captured in this illustrative analysis.
These costs include the costs of federal and State administration of control programs, which we
believe are less than the alternative of States developing approvable SIPs, securing EPA approval
of those SIPs, and Federal/State enforcement. Additionally, control measure costs referred to as
All estimates provided reflect the cost of a control strategy for 0.070 pm, incremental to a
2020 baseline of compliance with the current standard of 0.084 ppm. Note, for the final RIA
we will be updating our estimates to $2006.
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"no cost" may require limited government agency resources for administration and oversight of
the program not included in this analysis; those costs are generally outweighed by the saving to
the industrial, commercial, or private sector. The Agency also did not consider transactional
costs and/or effects on labor supply in the illustrative analysis.
The economic impacts (i.e. social costs) of the cost of these modeled controls were not included
in this analysis. Incorporating the economic impact of the extrapolated portion of the costs was
too uncertain to be included as part of these estimates, and it was determined best to keep the
modeled and extrapolated costs on the same basis. However, incorporating any economic
impacts would increase the total cost of attainment in 2020 for a revised ozone standard.
The illustrative analysis does quantify the potential for advancements in the capabilities of
pollution control technologies as well as reductions in their costs over time. This is discussed in
Section 5.4.
5.2 Extrapolated Costs
This section presents the results and methodology behind the extrapolated cost calculations of
attainment of the alternate standards (the ends of the proposed range - 0.075 and 0.070 ppm, the
less stringent alternative of 0.079 ppm, and the more stringent alternative of 0.065 ppm).
Consistent with the rest of this RIA, this section presents two sets of results. The first reflects
full attainment in 2020 in all locations of the U.S. except two areas of California. These two
areas are not planning to meet the current standard by 2020, so the estimated costs for these areas
are based on reaching an estimated progress point in 2020 (their "glidepath" targets). The
second set of results for California only, estimate the costs from California fully attaining the
alternative standards in a year beyond 2020 (glidepath estimates for 2020, plus further
increments needed to reach full attainment beyond 2020, added together for California total).
As discussed in Chapter 3, the application of the 0.070 ppm control strategy was not successful
in reaching nationwide attainment of the alternate ozone standards. Many areas remained in
non-attainment for all three alternate standard scenarios; therefore, the engineering costs detailed
in Section 5.1 represent only the costs of partial attainment.
The estimation of the costs of unidentified controls needed to reach attainment is inherently a
difficult issue. The degree to which unspecified controls are needed to achieve attainment
depends upon other variables in the analysis, such as attainment date assumptions. We will
better understand the true scope of the issue in the future as states conduct detailed area-by-area
analyses to determine available controls and attainment dates that are appropriate under the
Clean Air Act. We do not attempt to determine specific attainment dates in this analysis.
This draft RIA used two different approaches to estimating the costs of unspecified control
measures. This reflects the difficulty in defining a "best" approach to this issue as well as the
uncertainty related to the extrapolated costs. One approach assumes that the marginal cost of
abatement increases at a constant rate. The other approach assumes a fixed cost for abatement
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tons and provides estimates for two fixed cost/ton values. Use of the fixed cost per ton approach
reduces the possibility of inflated extrapolated costs that would result from an infinitely
increasing marginal cost curve. However it does not take into account the probability that
abatement costs would increase as an industry or state reduces a higher portion of available NOx
or VOC tons. In turn, the marginal cost approach captures this increase in abatement cost, but its
lack of "cost caps" can result in overestimates of costs.
Our approaches have yet to be peer reviewed and reflect a range of views about the likely cost of
future techniques and strategies that reduce air pollutant emissions. (The higher-cost estimation
approaches are implicitly more pessimistic about prospects for technological advances that avoid
large increases in the cost per ton of emission reduction relative to controls employed in the
past.) Section 5.4 discusses historical experience which has shown numerous technological
advances in emission reduction technologies, and provides a few examples of today's emerging
technologies. EPA will continue to consider these issues between now and the publication of the
final RIA for the final ozone NAAQS rule.
This section provides the additional costs of reaching nationwide full attainment of the alternate
ozone standards utilizing three values: a lower fixed cost per ton estimate based on the majority
of the known cost/ton control values, an upper fixed cost per ton estimate based on the cost of
the last few known control measures used and an increasing marginal cost estimate similar to
that used in the PM NAAQS Final RIA. In addition to presenting the full attainment cost, this
section will provide the methodology behind each approach.
Prior to presenting the aforementioned full attainment costs, it is important to provide
information from EPA's Science Advisory Board Council Advisory7, dated June 8, 2007, on the
issue of estimating costs of unidentified control measures. In that letter, the Council advises
against any approach that deviates from using a fixed cost/ton estimate such as the increasing
marginal cost approach provided below. This increasing marginal cost approach 'grows'
extrapolated costs that have an unquantifiable level of uncertainty at a rate with an equivalent
level of uncertainty. This approach is presented in this Proposal RIA in order to maintain a
consistency with the PM NAAQS Final RIA cost extrapolation. EPA is going to reconsider its
approach to estimating the full attainment costs in the final RIA, in light of this advice.
Consideration of this advice will be balanced with the requirements of E.O. 12866 and OMB
circular A-4, which provides guidance on the estimation of benefits and costs of regulations.
812 Council Advisory, Direct Cost Report, Unidentified Measures (charge question 2.a)
"The Project Team has been unable to identify measures that yield sufficient emission
reductions to comply with the National Ambient Air Quality Standards (NAAQS) and
relies on unidentified pollution control measures to make up the difference. Emission
reductions attributed to unidentified measures appear to account for a large share of
7 U.S. Environmental Protection Agency. June 2007. Advisory Council on Clean Air
Compliance Analysis (COUNCIL), Council Advisory on OAR's Direct Cost Report and
Uncertainty Analysis Plan. Washington, DC
-------
emission reductions required for a few large metropolitan areas but a relatively small
share of emission reductions in other locations and nationwide.
"The Council agrees with the Project Team that there is little credibility and hence
limited value to assigning costs to these unidentified measures. It suggests taking great
care in reporting cost estimates in cases where unidentified measures account for a
significant share of emission reductions. At a minimum, the components of the total cost
associated with identified and unidentified measures should be clearly distinguished. In
some cases, it may be preferable to not quantify the costs of unidentified measures and to
simply report the quantity and share of emissions reductions attributed to these
measures.
"When assigning costs to unidentified measures, the Council suggests that a simple,
transparent method that is sensitive to the degree of uncertainty about these costs is best.
Of the three approaches outlined, assuming a fixed cost/ton appears to be the simplest
and most straightforward. Uncertainty might be represented using alternative fixed costs
per ton of emissions avoided. "
5.2.1 Increasing Marginal Cost Methodology
This approach stems from the assumption that each unit of incremental reduction in non-
attainment areas will result in an increase in cost per ton or marginal cost of abatement.
Therefore, similar to the approach used in the PM NAAQS RIA, EPA estimated constantly
increasing marginal cost curves for emission reductions using cost per ton values from control
strategy data in representative non-attainment areas. These curves were then used to estimate a
cost of full attainment using the emission reduction targets detailed in Chapter 4 of this report.
5.2.1.1 Marginal Cost Regions
EPA grouped the non-attainment areas described in Chapter 4 along with their emission
reduction targets into six regions of the country (Table 5.2) in order to acquire sufficient and
representative data for deriving the slopes of the marginal cost curves.
• Nonattainment areas in Virginia were grouped with the Northeast due to the fact that
Northern Virginia is part of the Ozone Transport Region (OTR) which makes up the
Northeast. Resources were not available to disaggregate states by counties.
• Nonattainment areas in Louisiana were grouped with Texas and Oklahoma (Plains
region) due to the similarity in industry mix among those states.
• California was separated from the rest of the west due to the severity of the ozone
problem in the state, the glide path targets unique to the state, and because EPA
determined the rest of the west was not an ideal representation of California.
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Fable 5.2 Regions and Slopes for Extrapolated Costs
Region
Northeast (OTR)
Midwest
Southeast
Plains (TX/LA)
West (Not CA)
CA
Marginal Cost Slope
0.035
0.045
0.036
0.033
0.152
0.211
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5.2.1.2 Derivation of the Marginal Cost Slopes
Due to the efficaciousness and efficiency of NOx controls compared to VOC controls, control
strategy cost per ton data was acquired for each region using a selection criteria defined in Table
5.3 and applied in Ordinary Least Squares regression equations. Results of these equations
provided the slope for the marginal cost curves. For each equation, the dependant variable (Y =
cost/ton)8 and was regressed conditional to (X = cumulative emissions reductions).9
Y= c + 0X+ e c = constant
ft = slope
e = residual
The regression equations are not intended to be used for statistical inference in regards to the
relation between cost/ton and cumulative emission reductions. They represent a rough
approximation of an increasing rate or slope of cost/ton which could be used to project
extrapolated costs. This slope would then provide an increasing cost/ton rate in the extrapolated
portion of the cost that was equivalent to the rate observed under the modeled costs. As can be
seen by the range of the goodness of fit estimates (R2 = 0.5 to 0.8), there is a high level of
uncertainty with these slope estimates which is propagated through the extrapolated cost
estimates.
Table 5.3 Data Selection Criteria for Extrapolated Costs
1) Determine if area has sufficient NOx emissions remaining to reach
attainment
2) If area has sufficient NOx remaining to reach attainment, then use NOx
cost/ton data due to their cost effectiveness compared VOC controls
3) If area does not have sufficient NOx emissions to reach attainment, then
include VOC controls in the data set if:
• VOC controls were part of the control strategy for the area in question
• VOC control cost/ton inclusions to the regression data set would
significantly alter the value of the slope for the marginal cost curve
derived using only NOx cost/ton data
Note: Data analysis demonstrated that VOC control data would only be needed for California.
Due to lack of available ozone data, NOx controls from California also include control cost from
the PM NAAQS RIA control strategies.
8 For the east regions, the full cost/ton data set was applied and had a maximum value of
$15,267/ton. For the west, the cost/ton data was truncated at $15,267/ton in order to maintain a
consistent comparison with the east regions and because the few remaining controls had costs
greater $35,000/ton and were therefore judged to be not economically feasible.
9 EPA recognizes that these regression equations may be misspecified. As stated above, the
objective was not to accurately capture the relation between control cost/ton and emission
reduction for statistical or economic inference purposes. These equations represent the most
statistically adequate models that could be specified given the data, time, and resource
constraints.
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5.2.1.3 Calculating Extrapolated Costs Using Marginal Cost Approach
Once the slope of the marginal cost curve was derived, the extrapolation was calculated by
multiplying that slope with the emission reduction target and adding that value to the highest of
the observed cost/ton value (Figure 5.1). For this illustrative analysis, the highest of the
observed cost/ton values was roughly $15,267/ton which represented the intercept of the
marginal cost equation. Total costs could then be estimated by adding the area under the
marginal cost curve in Figure 5.1 or by taking the integral of the marginal cost function and
inputting the emission reduction target into the equation for total cost.10
Figure 5.1 Extrapolated Cost Example (MC Approach)11
o
O
O
$15,267
Lower Fixed Cost/Ton
Estimate
0 50 100
Reductions Needed (thousands)
5.2.2 Fixed Cost per Ton Values
Similar to the 1997 Ozone NAAQS RIA, a fixed cost/ton value was also applied to estimate the
extrapolated costs of nationwide full attainment. Total costs for each non-attainment area was
calculated by multiplying the fixed cost/ton value with the emission reduction targets for each
region. For this particular illustrative analysis, a pair of fixed cost/ton values was used to
calculate costs.
NOx control strategy data for the East and West were examined for 'clustering' within their
individual distributions. Cost/ton data for the east and west were stratified into thousands (Ex.
$0-$1000, $1000-$2000) with individual source counts aggregated within each interval.
California was separated from the west so source cost/ton counts were conducted separately for
the state. This was the result of limited ozone NOx data availability for the state, the low number
10 Total Cost = $15,267x + (fl/2)x2, where x = emission reduction target
11 In the case of 0.075 ppm, negative reductions are needed in order to estimate extrapolated cost
savings.
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of NOx emissions remaining for CA, and the inclusion of ozone VOC controls as well as NOx
controls from PM NAAQS RIA control strategies which were required to resolve these data and
emissions issues.
For the East, 90% of the controls were below $6,000/ton. As a result, the control cost closest to
$6,000/ton ($6,012) was selected to represent the cost of the majority of the modeled controls as
well as the lower estimate of the Eastern fixed cost/ton approach. For the West, 94% of the
controls had a cost/ton value below $4,000/ton. Therefore, $4,213 was selected as the lower
estimate for the western fixed cost/ton approach. For California, the lower estimate was $9,035
using the same method but including VOC and PM NAAQS NOx control data from the PM
NAAQS RIA hypothetical control scenario. This lower estimate for CA captured 81% of the
cost/ton data below $15,267 and 65% of all cost/ton data for controls applied in the ozone and
PM control strategies after multiplying VOC controls by a 4 to 1 substitution factor.
In addition to a lower fixed cost/ton estimate, an upper fixed cost/ton value was used for
calculating extrapolated costs. This upper value as estimated at $15,267 for all regions for the
following reasons.
• This value represented the highest, in terms of cost/ton, of the controls applied in the
East. The East control strategy made up the majority of the modeled controls for the
ozone standard.
• In the case of the West, the next highest controls were roughly $35,000 and $39,000 per
ton. Controls with these costs were determined to be significantly less feasible to
implement compared other controls.
• This value provides a consistent platform from which to incorporate and compare
marginal cost values derived using the increasing marginal cost approach.
5.2.3 Results
Tables 5.4 to 5.7 provide the extrapolated cost values for 2020 attainment of the 0.079, 0.075,
0.070, 0.065 ppm standards in each area (including the California 2020 glidepath targets)
applying the increasing marginal cost value as well as the two fixed cost/ton values. The reader
should be aware of the following stipulations prior to making inferences from the extrapolated
costs presented in the following tables.
• The two extrapolated cost approaches provide three rough estimates of potential costs
with the marginal cost approach providing the highest value. Neither result includes a
probability or a link to sectors where reductions will be attained. Therefore, there are no
expected values within this range of outcomes and no assumptions made about the types
of controls that would be applied in 2020. Although the amount of reduction assumed to
occur using unknown controls increases, the uncertainty of the associated costs and
benefits calculations increases.
• 0.070 ppm extrapolated costs were estimated using data from the 0.070 ppm control
strategy. Therefore, although the degree of uncertainty is still significant, these results
can be expected to have a higher level of confidence than results for the 0.079, 0.075, and
0.065 ppm alternate standards.
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The use of the 0.070 ppm control strategy as a starting point for extrapolating the 0.079
and 0.075 ppm standard resulted in over attainment of these targets in some areas. For
over attaining areas, cost savings and emission increases were extrapolated using the
impact/ton estimates derived in Chapter 4 and their appropriate emission targets until
reaching the respective alternate standard.
Several new non-attainment counties were added to the analysis as a result of moving to
the 0.065 ppm alternate standard. Most of these counties were in states within the 0.070
ppm control strategy region described in Chapter 3. For the east, this region was made up
of counties with 0.070 ppm violating monitors and their 200 km buffers which made up
most of the eastern part of the US. In the west, this region was made up of six states (AZ,
CA, CO, NM, NV, UT). Due to the geographic scope of the 0.070 control strategy, no
additional controls were available and costs had to be extrapolated using the same
impact/ton estimates applied in the 0.070 ppm estimates. Two new states were added to
the non-attainment region (KS and AL). Since controls were available for these states,
AirControlNET was used to identify controls that would achieve the required emission
reduction targets.
Consistent with OMB Circular A-4, costs are presented at a 7% discount rate. It is more
consistent to present the extrapolated costs at the same discount rate as the modeled
control costs, for which a 7% rate was determined to be more representative of actual
costs (see section 5.1.3)
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Table 5.4 Extrapolated Costs of Meeting the 0.079 ppm Standard
Extrapolated Costs for 0.079
Standard
Extrapolated Costs
b
CA - Los Angeles
CA - Kern County
Houston / Dallas
Ozone Transport Region
MC Curve
Estimate
($M 1999)
$0
$0
$477
$568
Lower Fixed
Cost/Ton
Estimate
($M 1999)
$0
$0
$158
$200
Upper Fixed
Cost/Ton
Estimate9
($M 1999)
$0
$0
$400
$504
Cost/Ton Estimate of
Last Control Applied on
MC Curve Estimate
($1,999)
$18,765
$17,787
Lake Michigan region
Richmond / Norfolk
Total Cost
Extrapolated Cost Savings
Atlanta, GA
Cleveland, OH
Detroit, Ml
Control Deletions
Charlotte, Memphis, Las Vegas, Salt
Lake City, Tampa
Colorado, Arizona
Baton Rouge, Indianapolis, Louisville,
St. Louis
Total Cost Savings
Total Extrapolated Cost
Average cost per ton
$571
$139
$1,755
($568)
($493)
($224)
($119)
($676)
($241)
($2,321)
($566)
$2,1 13
$206
$55
$619
($236)
($206)
($91)
($119)
($676)
($241)
($1,569)
($950)
$3,549
$519
$137
C
C
C
C
C
c
$17,562
$15,582
"Due to the limited amount of controls in the modeled control strategy which had this value,
deducting this amount would likely result in an over estimate of the savings. For example,
estimating cost savings by multiplying changes in emission reduction by $15,267 would
result in an overestimate of cost savings since not all of the applied controls had a cost/ton as
high as $15,267.
bLos Angeles and Kern Counties have expected attainment dates after 2020. This analysis
counts the portion of reductions expected by 2020 or earlier.
c We did not calculate the cost savings which would result from using the upper bound
values. This would result in an unrealistic savings because none of these cities with less
significant air quality problems would be expected to be getting all their expected emission
reductions at such a high cost/ton. We therefore used the lower cost estimate which
included roughly 90% of the controls applied.
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Table 5.5 Extrapolated Costs of Meeting the 0.075 ppm Standard
Extrapolated Costs for 075
Standard
Extrapolated Costs
b
CA - Los Angeles
CA - Kern County
Houston / Dallas
Ozone Transport Region
Lake Michigan region
Richmond / Norfolk
Detroit
Phoenix
Denver
Cleveland/Columbus/Cincinnati
Atlanta
Total Cost
Extrapolated Cost Savings
Baton Rouge, LA
Indianapolis, IN
Louisville, KY-IN
St. Louis, MO-IL
Total Cost Savings
Total Extrapolated Cost
Average cost per ton
MC Curve
Estimate
($M 1999)
$0
$0
$1,254
$1,307
$1,310
$790
$396
$282
$160
$92
$15
$5,606
($225)
($209)
($284)
($30)
($748)
$4,858
$23,000
Lower Fixed
Cost/Ton
Estimate
($M 1999)
$0
$0
$400
$443
$449
$297
$152
$72
$42
$36
$6
$1,896
($91)
($85)
($115)
($12)
($303)
$1,593
$7,600
Upper Fixed
Cost/Ton
Estimate9
($M 1999)
$0
$0
$1,008
$1,114
$1,130
$748
$382
$260
$153
$92
$15
c
c
c
c
Cost/Ton Estimate of
Last Control Applied
on MC Curve Estimate
($ 1999)
$20,085
$19,187
$19,362
$16,982
$16,392
$17,851
$16,787
$15,537
$15,303
"Due to the limited amount of controls in the modeled control strategy which had this value,
deducting this amount would likely result in an over estimate of the savings. For example,
estimating cost savings by multiplying changes in emission reduction by $15,267 would
result in an overestimate of cost savings since not all of the applied controls had a cost/ton as
high as $15,267.
bLos Angeles and Kern Counties have expected attainment dates after 2020. This analysis
counts the portion of reductions expected by 2020 or earlier.
c We did not calculate the cost savings which would result from using the upper bound
values. This would result in an unrealistic savings because none of these cities with less
significant air quality problems would be expected to be getting all their expected emission
reductions at such a high cost/ton. We therefore used the lower cost estimate which
included roughly 90% of the controls applied.
5-16
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Table 5.6 Extrapolated Costs of Meeting the 0.070 ppm Standard9
Extrapolated Costs for 0.070
Standard
CA - Los Angeles"
CA - Kern County b
Houston / Dallas
Ozone Transport Region
Lake Michigan region
Richmond / Norfolk
Detroit
Phoenix
Denver
Cleveland/Columbus/Cincinnati
Atlanta
St. Louis
Indianapolis
Baton Rouge
Louisville
Memphis
Charlotte
Salt Lake City
Las Vegas
Tampa
Total Extrapolated Cost
Average cost per ton
MC Curve Estimate
($M 1999)
$0
$829
$2,299
$2,310
$2,334
$1,683
$1,272
$1,364
$1,190
$926
$825
$785
$579
$555
$491
$313
$217
$211
$177
$77
$18,441
$1 8,400
Lower Fixed
Cost/Ton
Estimate
($M 1999)
$0
$181
$703
$746
$752
$600
$455
$282
$253
$339
$309
$291
$218
$212
$188
$121
$85
$55
$46
$30
$5,867
$5,900
Upper Fixed
Cost/Ton
Estimate
($M 1999)
$0
$305
$1,771
$1,878
$1,893
$1,51 1
$1,145
$1,023
$916
$855
$779
$733
$550
$534
$473
$305
$214
$198
$168
$76
$15,328
$/5,300
Cost/Ton Estimate
of Last Control
Applied on MC
Curve Estimate
($ 1999)
$43,541
$21,735
$20,937
$21,612
$18,732
$18,642
$25,45 1
$24,387
$17,787
$17,103
$17,427
$16,887
$16,422
$16,383
$15,987
$15,771
$17,243
$16,939
$15,447
a EPA was not able to estimate benefit changes when moving the standard from 0.070 to
0.075 for Memphis, Charlotte, Salt Lake City, Las Vegas, and Tampa. Therefore, in order to
maintain a consistent comparison, cost savings were not estimated for these locations.
b Los Angeles and Kern Counties have expected attainment dates after 2020. This analysis
counts the portion of reductions expected by 2020 or earlier.
5-17
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Table 5.7 Extrapolated Costs of Meeting the 0.065 ppm Standard
Extrapolated Costs for 065
Standard
CA - Los Angelesa
CA - Kern County2
Houston / Dallas
Ozone Transport Region
Lake Michigan region
Richmond / Norfolk
Detroit
Phoenix
Denver
Cleveland/Columbus/Cincinnati
Atlanta
St. Louis
Indianapolis
Baton Rouge
Louisville
Memphis
Charlotte
Salt Lake City
Las Vegas
Tampa
Jackson, MS
New Mexico areas (Farmington / Las
Cruces)
OK areas (Tulsa, Marshall)
Huntington, VW-KY
El Paso, TX
Kansas City, MO/KS
Little Rock, AR
Mobile AL
Columbia, SC
Extrapolated Total
Average cost per ton
MC Curve
Estimate
($M 1999)
$0
$2,230
$3,427
$3,401
$3,471
$2,663
$2,260
$2,827
$2,599
$1,871
$1,726
$1,712
$1,479
$1,417
$1,355
$1,157
$1,051
$1,263
$1,214
$894
$757
$819
$704
$639
$538
$325
$442
$70
$154
$42,465
$1 9,300
Lower Fixed
Cost/Ton
Estimate
($M 1999)
$0
$452
$1,006
$1,049
$1,055
$903
$758
$493
$463
$643
$612
$594
$521
$515
$491
$424
$388
$265
$257
$333
$285
$185
$267
$242
$206
$142
$170
$70
$61
$12,851
$5,800
Upper
Fixed
Cost/Ton
Estimate
($M 1999)
$0
$763
$2,534
$2,641
$2,656
$2,275
$1,908
$1,786
$1,679
$1,618
$1,542
$1,496
$1,313
$1,298
$1,237
$1,069
$977
$962
$931
$840
$718
$672
$672
$61 1
$519
$317
$427
$70
$153
$33,684
$/5,300
Cost/Ton
Estimate of Last
Control Applied
on MC Curve
Estimate
($ 1999)
$15,267
$49,871
$23,385
$22,687
$23,862
$20,482
$20,892
$33,051
$3 1 ,987
$20,037
$18,903
$19,677
$19,137
$18,072
$18,183
$17,787
$17,571
$24,843
$24,539
$17,247
$16,959
$21,955
$16,719
$16,707
$16,389
$16,122
$16,275
$16,239
$15,627
a Los Angeles and Kern Counties have expected attainment dates after 2020. This analysis
counts the portion of reductions expected by 2020 or earlier.
5-18
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5.3 Summary of Costs
Table 5.8 presents a summary of the total national cost of attaining 0.079, 0.075, 0.070, and
0.065 ppm standards in 2020 (including the California glidepath). This summary includes the
costs presented above from the modeled controls and the extrapolated costs. The range
presented in the extrapolated costs and the total costs represent the upper and lower bound cost
estimates. Consistent with OMB Circular A-4, costs are presented at a 7% discount rate. It is
more consistent to present the extrapolated costs at the same discount rate as the modeled control
costs, for which a 7% rate was determined to be more representative of actual costs (see section
5.1.3). Although the amount of reduction assumed to occur using unknown controls increases,
the uncertainty of the associated costs and benefits calculations increases.
Table 5.8 Total Costs of Attainment in 2020 for Different Levels of the Ozone Standard
(National Attainment in 2020)
Level of Standard in 2020
0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
Modeled Costs ($B) $3.9 $3.9 $3.9 $3.9
Extrapolated Costs ($B) $13 to $42 $5.9 to $18 $1.6 to $4.9 ($0.95) to ($0.57)*
Total Costs ($B) $17 to $46 $10 to $22 $5.5 to $8.8 $3 to $3.3
* The use of the 0.070 ppm control strategy as a starting point for extrapolating the 0.079
standard resulted in over attainment in some areas. For over attaining areas, cost savings were
applied. For the 0.079 ppm standard the cost savings from over attaining areas was greater than
the costs for areas still needing extrapolated tons (see Table 5.4).
Table 5.9 presents an estimate of total costs of California only, for fully attaining the alternative
standards in a year beyond 2020 (glidepath estimates for 2020, plus further increments needed to
reach full attainment beyond 2020, added together for California total).
5-19
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Table 5.9 California Extrapolated Costs ($M)
CA (Glidepath)
CA Increment Needed for
Full Attainment
CA (Full Attainment-Later
Year)
0.079
Marginal Cost Approach $0
Lower Estimate (fixed cost) $0
Upper Estimate (fixed cost) *
Marginal Cost Approach $4,566
Lower Estimate (fixed cost) $1,187
Upper Estimate (fixed cost) *
Marginal Cost Approach $4,566
Lower Estimate (fixed cost) $1,187
Upper Estimate (fixed cost) *
0.075 0.070 0.065
$0 $829 $2,230
$0 $181 $452
* $305 $763
$6,227 $12,022 $18,301
$1,050 $1,773 $2,405
* $2,995 $4,064
$6,227 $12,851 $20,530
$1,050 $1,953 $2,857
* $3,301 $4,827
* Due to the limited amount of controls in the modeled control strategy which had this value,
deducting this amount would likely result in an over estimate of the savings.
It is not appropriate to add together the 2020 national attainment, California glidepath estimate
and the estimate of California full attainment as an estimate of national full attainment in 2020.
The extra increment of attainment that is estimated for California will not occur in 2020, so it is
not accurate to add it to our nationwide estimate of the "glidepath" benefits and costs to arrive at
a "full attainment" estimate for 202012. It is also not accurate to add the two estimates together to
arrive at an estimate of future, post-2020 full attainment benefits and costs, because EPA's
nationwide full attainment estimates do not allow other areas of the nation to take credit for the
reductions in NOx from the mobile source rules that will occur after 2020.13
5.4 Technology Innovation and Regulatory Cost Estimates
The history of the Clean Air Act provides many examples in which technological innovation and
"learning by doing" have made it possible to achieve greater emissions reductions than had been
feasible earlier, or have reduced the costs of emission control in relation to original estimates.
Innovative companies have successfully responded to the regulatory challenges and market
opportunities provided by the Act, producing breakthrough technologies for multiple sectors.
12
The California full attainment costs calculated using the offset in NOx emissions from mobile
programs would understate the costs of fully attaining in 2020, however, California will not be
required to attain in 2020.
13 This approach would be an overestimate of national full attainment costs in a future year after
2020 because it would not take into account that other states (not just California) could replace
more expensive NOx reductions from other sources with the post-2020 reductions obtained from
implementation of mobile source rules that are included in the regulatory baseline.
5-20
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Studies14 have suggested that costs of some EPA programs have been less than originally
estimated due in part to inadequate inability to predict and account for future technological
innovation in regulatory impact analyses.
Technological change will affect baseline conditions for our analysis. This change may lead to
potential improvements in the efficiency with which firms produce goods and services, for
example, firms may use less energy to produce the same quantities of output. In addition,
technological change may result in improvements in the quality of health care, which can have
impacts on the baseline health of the population, potentially reducing the susceptibility of the
population to the effects of air pollution. While our baseline mortality incidence rates account
for increasing life expectancy, and thus reflect projected improvements in health care, our
baseline incidence rates for other health endpoints such as hospital admissions do not reflect any
future advances in health care, and thus, our estimates of avoided health impacts for these
endpoints will potentially be overstated. For other endpoints, such as asthma, there has been an
observed upward trend in prevalence, which we have not captured in our incidence rates. For
these endpoints, our estimates will potentially be understated. In general, for non-mortality
endpoints, there is increased uncertainty in our estimates due to our use of current baseline
incidence and prevalence rates.
A constantly increasing marginal cost curve similar to the one utilized for estimating
extrapolated costs in this RIA is likely to induce the type of innovation that would result in lower
costs than estimated early in this chapter. Breakthrough technologies in control equipment could
by 2020 result in a rightward shift in the marginal cost curve (Figure 5.2)15 as well as perhaps a
decrease in its slope, reducing marginal costs per unit of abatement, and thus deviate from the
assumption of one constantly increasing marginal cost curve. In addition, elevated abatement
costs may result in significant increases in the cost of production and would likely induce
production efficiencies, in particular those related to energy inputs, which would lower emissions
from the production side.
14 Harrinton et al ,2000, and previous studies cited by Harrington
15 Figure 5.2 shows a linear marginal abatement cost curve. It is possible that the shape of the
marginal abatement cost curve is non-linear.
5-21
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Figure 5.2 Technological Innovation
Reflected by Marginal Cost Shift
c
o
-I—'
(/>
o
O
Induced Technology Shift
Cumulative NOx Reductions
5.4.1 Examples of Technological Advances in Pollution Control
There are numerous examples of low-emission technologies developed and/or commercialized
over the past 15 or 20 years, such as:
• Selective catalytic reduction (SCR) and ultra-low NOx burners for NOx emissions
• Scrubbers which achieve 95% and even greater SO2 control on boilers
• Sophisticated new valve seals and leak detection equipment for refineries and chemical
plans
• Low or zero VOC paints, consumer products and cleaning processes
• Chlorofluorocarbon (CFC) free air conditioners, refrigerators, and solvents
• Water and power-based coatings to replace petroleum-based formulations
• Vehicles far cleaner than believed possible in the late 1980s due to improvements in
evaporative controls, catalyst design and fuel control systems for light-duty vehicles; and
treatment devices and retrofit technologies for heavy-duty engines
• Continued development of activated carbon injection (ACI) technology for control of
mercury from electric generating units
• Development of integrated gasification combined cycle (IGCC) and ultra-super critical
pulverized coal technologies for electricity generation
• Idle-reduction technologies for engines, including truck stop electrification efforts
• Market penetration of gas-electric hybrid vehicles, biodiesel and other clean fuels
5-22
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These technologies were not commercially available two decades ago, and some were not even
in existence. Yet today, all of these technologies are on the market, and many are widely
employed. Several are key components of major pollution control programs,
5.4.2 Influence on Regulatory Cost Estimates
Studies indicate that it is not uncommon for pre-regulatory cost estimates to be higher than later
estimates, in part because of inability to predict technological advances. Over longer time
horizons, such as the time allowed for areas with high levels of ozone pollution to meet the
ozone NAAQS, the opportunity for technical advances is greater.
• Multi-rule study: Harrington et al. of Resources for the Future (2000) conducted an
analysis of the predicted and actual costs of 28 federal and state rules, including 21 issued
by EPA and the Occupational Safety and Health Administration (OSHA), and found a
tendency for predicted costs to overstate actual implementation costs. Costs were
considered accurate if they fell within the analysis error bounds or if they fall within 25
percent (greater or less than) the predicted amount. They found that predicted total costs
were overestimated for 14 of the 28 rules, while total costs were underestimated for only
three rules. Differences can result because of quantity differences (e.g., overestimate of
pollution reductions) or differences in per-unit costs (e.g., cost per unit of pollution
reduction). Per-unit costs of regulations were overestimated in 14 cases, while they were
underestimated in six cases. In the case of EPA rules, the agency overestimated per-unit
costs for five regulations, underestimated them for four regulations (three of these were
relatively small pesticide rules), and accurately estimated them for four. Based on
examination of eight economic incentive rules, "for those rules that employed economic
incentive mechanisms, overestimation of per-unit costs seems to be the norm," the study
said.
Based on the case study results and existing literature, the authors identified
technological innovation as one of five explanations of why predicted and actual
regulatory cost estimates differ: "Most regulatory cost estimates ignore the possibility of
technological innovation ... Technical change is, after all, notoriously difficult to forecast
... In numerous case studies actual compliance costs are lower than predicted because of
unanticipated use of new technology."16
It should be noted that many (though not all) of the EPA rules examined by Harrington
had compliance dates of several years, which allowed a limited period for technical
innovation. Much longer time periods (ranging up to 20 years) are allowed by the statute
for meeting the ozone NAAQS in areas with high ozone levels, where a substantial
fraction of the estimated cost in this analysis is incurred.
• Acid Rain SO2 Trading Program: Recent cost estimates of the Acid Rain SO2 trading
program by Resources for the Future (RFF) and MIT have been as much as 83 percent
16 Harrington et al., 2000.
5-23
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lower than originally projected by EPA.17 Note that the original EPA cost analysis also
relied on an optimization model like IPM to approximate the results of emissions trading.
As noted in the RIA for the Clean Air Interstate Rule, the ex ante numbers in 1989 were
an overestimate in part because of the limitation of economic modeling to predict
technological improvement of pollution controls and other compliance options such as
fuel switching. Harrington et al report that scrubbing turned out to be more efficient
(95% removal vs. 80-85% removal) and more reliable (95% vs. 85% reliability) than
expected, and that unanticipated opportunities arose to blend low and high sulfur coal in
older boilers up to a 40/60 mixture, compared with the 5/95 mixture originally estimated.
Phase 2 Cost Estimates
Ex ante estimates
Ex post estimates
$2.7 to $6.2 billion18
$1.0 to $1.4 billion
• EPA Fuel Control Rules: A 2002 study by two economists with EPA's Office of
Transportation and Air Quality19 examined EPA vehicle and fuels rules and found a
general pattern that "all ex ante estimates tended to exceed actual price impacts, with the
EPA estimates exceeding actual prices by the smallest amount." The paper notes that cost
is not the same as price, but suggests that a comparison nonetheless can be instructive.20
An example focusing on fuel rules is provided:
17 Carlson et al., 2000; Ellerman, 2003.
18
2010 Phase II cost estimate in $1995.
19 Anderson et al, 2002.
20 The paper notes: "Cost is not the same as price. This simple statement reflects the fact that a
lot happens between a producer's determination of manufacturing cost and its decisions about
what the market will bear in terms of price change."
5-24
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Table 5.10 Comparison of Inflation-Adjusted Estimated Costs and Actual Price Changes
for EPA Fuel Control Rules21
Inflation-adjusted Cost Estimates
(c/gal)
EPA
DOE
API
Other
Actual Price
Changes
(c/gal)
Gasoline
Phase 2 RVP Control (7.8 RVP -
Summer) (1995$)
Reformulated Gasoline Phase 1
(1997$)
Reformulated Gasoline Phase 2
(Summer) (2000$)
30 ppm sulfur gasoline (Tier 2)
1.1
3.1-
5.1
4.6-
6.8
1.7-
1.9
3.4-4.1
7.6-
10.2
2.9-3.4
1.8
8.2-
14.0
10.8-
19.4
2.6
7.4 (CRA)
12
5.7
(NPRA),
3.1
(AIAM)
0.5
2.2
7.2 (5.1, when
corrected to
Syr MTBE
price)
N/A
Diesel
500 ppm sulfur highway diesel
fuel (1997$)
15 ppm sulfur highway diesel
fuel
1.9-
2.4
4.5
4.2-6.0
6.2
3.3
(NPRA)
4.2-6.1
(NPRA)
2.2
N/A
• Chlorofluorocarbon (CFC) Phase-Out: EPA used a combination of regulatory, market
based (i.e., a cap-and-trade system among manufacturers), and voluntary approaches to
phase out the most harmful ozone depleting substances. This was done more efficiently
than either EPA or industry originally anticipated. The phaseout for Class I substances
was implemented 4-6 years faster, included 13 more chemicals, and cost 30 percent less
than was predicted at the time the 1990 Clean Air Act Amendments were enacted.22
The Harrington study states, "When the original cost analysis was performed for the CFC
phase-out it was not anticipated that the hydrofiuorocarbon HFC-134a could be
substituted for CFC-12 in refrigeration. However, as Hammit (1997) notes, 'since 1991
most new U.S. automobile air conditioners have contained HFC-134a (a compound for
which no commercial production technology was available in 1986) instead of CFC-12"
(p. 13). He cites a similar story for HCFRC-141b and 142b, which are currently
substituting for CFC-11 in important foam-blowing applications."
21
22
Anderson et al, 2002.
Holmstead, 2002.
5-25
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5.5 References
Anderson, J.F., and Sherwood, T., 2002. "Comparison of EPA and Other Estimates of Mobile
Source Rule Costs to Actual Price Changes," Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Technical Paper published by the Society of Automotive
Engineers. SAE 2002-01-1980.
Babiker, M.H., and T.F. Rutherford. 1997. "Input Output and General Equilibrium Estimates of
Embodied CO2: A Data Set and Static Framework for Assessment." University of Colorado at
Boulder, Working Paper 97 2. Available at http://debreu.colorado.edu/papers/gtaptext.html.
Babiker, M.H., J.M. Reilly, M. Mayer, R.S. Eckaus, I.S. Wing, and R.C. Hyman. 2001. "The
MIT Emissions Prediction and CO2 Policy Analysis (EPPA) Model: Revisions, Sensitivities,
and Comparisons of Results." MIT Joint Program on the Science and Policy of Global Change,
Report No. 71. Available at http://web.mit.edu/globalchange/www/eppa.html.
Bovenberg, L.A., and L.H. Goulder. 1996. "Optimal Environmental Taxation in the Presence of
Other Taxes: General Equilibrium Analysis." American Economic Review 86(4):985-1000.
Available at .
Brooke, A., D. Kendrick, A. Meeraus, and R. Raman. 1998. GAMS: A User's Guide. GAMS
Development Corporation. Available at http://www.gams.com.
Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000.
"SulfurDioxide Control by Electric Utilities: What Are the Gains from Trade?" Journal of
Political Economy 108(#6): 1292-1326.
Ellerman, Denny. January 2003. Ex Post Evaluation of Tradable Permits: The U.S. SO2
Cap-and-Trade Program. Massachusetts Institute of Technology Center for Energy and
Environmental Policy Research.
Feenberg, D., and E. Courts. 1993. "An Introduction to the TAXSIM Model." Journal of Policy
Analysis and Management 12(1):189 194. Available at http://www.nber.org/~taxsim/.
Fullerton, D., and D.Rogers. 1993. "Who Bears the Lifetime Tax Burden?" Washington, DC:
The Brookings Institute. Available at http://bookstore.brookings.edu/
book_details.asp?product%5Fid=10403.
Goulder, L.H., and R.C. Williams. 2003. "The Substantial Bias from Ignoring General
Equilibrium Effects in Estimating Excess Burden, and a Practical Solution." Journal of Political
Economy 111:898 927. Available at http://www.journals.uchicago.edu/JPE/home.html
Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. "On the Accuracy of Regulatory Cost
Estimates." Journal of Policy Analysis and Management 19(2):297-322.
Hammit, J.K. (1997). "Are the costs of proposed environmental regulations overestimated?
Evidence from the CFC phaseout." Unpublished paper, Center for Risk Analysis, Harvard
School of Public Health, Cambridge, MA.
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Holmstead, Jeffrey, 2002. "Testimony of Jeffrey Holmstead, Assistant Administrator, Office of
Air and Radiation, U.S. Environmental Protection Agency, Before the Subcommittee on Energy
and air Quality of the committee on Energy and Commerce, U.S. House of Representatives, May
1,2002, p. 10.
Minnesota IMPLAN Group. 2003. State Level Data for 2000. Available from
http://www.implan.com/index.html.
Nestor, D.V., and C.A. Pasurka. 1995. The U.S. Environmental Protection Industry: A
Proposed Framework for Assessment. U.S. Environmental Protection Agency, Office of Policy,
Planning, and Evaluation. EPA 230-R-95-001. Available at
http://yosemite.epa.gov/ee/epa/eermfile.nsf/llf680ff78df42f585256b45007e6235/41b8b642ab93
71df852564500004b543/$FILE/EE 0217A l.pdf.
U.S. Department of Energy, Energy Information Administration. Undated (b). State Energy
Price and Expenditure Report. Washington DC. Available at
http://www.eia.doe.gov/emeu/states/price_multistate.html.
U.S. Department of Energy, Energy Information Administration. 2001. Manufacturing Energy
Consumption Survey 1998. Washington DC. Available at http://www.eia.doe.gov/emeu/mecs/.
U.S. Department of Energy, Energy Information Administration. January 2003. Annual Energy
Outlook 2003. DOE/EIA 0383(2003). Washington DC. Available at
http://www.eia. doe.gov/oiaf/archive/aeo03/pdf/0383(2003).pdf.
U.S. Department of Energy, Energy Information Administration. January 2004. Annual Energy
Outlook 2004. DOE/EIA-0383(2003). Washington, DC. Available at
http://www.eia.doe.gov/oiaf/aeo/pdf/0383 (2001).
U.S. Environmental Protection Agency. June 2007. Advisory Council on Clean Air Compliance
Analysis (COUNCIL), Council Advisory on OAR's Direct Cost Report and Uncertainty
Analysis Plan. Washington, DC. http://www.epa.gov/sab/pdf/council-07-002.pdf
5-27
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Appendix- Chapter 5
5a.l Cost Information for Non-EGU and area sources
(Full details on controls can be found in Appendix Chapter 3)
Low Emission Combustion (LEC)
The average cost effectiveness for large 1C engines using LEC technology was estimated
to be $532/ton (ozone season).1 The EC/R report on 1C engines (Ec/R, September 1,
2000) estimates the average cost effectiveness for 1C engines using LEC technology to
range from $420-840/ton (ozone season) for engines in the 2,000-8,000 bhp range. The
key variables in determining average cost effectiveness for LEC technology are the
average uncontrolled emissions at the existing source, the projected level of controlled
emissions, annualized costs of the controls, and number of hours of operation in the
ozone season. The ACT document uses an average uncontrolled level of 16.8 g/bhp-hr, a
controlled level of 2.0 g/bhp-hr (87% decrease), and nearly continuous operation in the
ozone season. The EPA believes the ACT document provides a reasonable approach to
calculating cost effectiveness for LEC technology.
Leak Detection and Repair (LDAR) for Fugitive Leaks
The control efficiency is 80 percent reduction of VOC at an annualized cost of $4,800 per
ton. We do not include the costs of this control measure in our analyses in the Houston
nonattainment area since these controls are already included in the 8-hour Ozone SIP for
this area.
Enhanced LDAR for Fugitive Leaks
The control efficiency of this measure is estimated at 50 percent at a cost of $3,050/ton of
VOC reduced2.
Flare Gas Recovery
The control efficiency of this measure is 98 percent reduction of VOC emissions at a cost
of $2,700/ton. Costs may become negligible as the size of the flare increases due to
recovery credit.3
1 "NOx Emissions Control Costs for Stationary Reciprocating Internal Combustion Engines in
the NOx SIP Call States," E.H. Pechan and Associates, Inc., Springfield, VA, August 11, 2000.
Available on the Internet at http://www.epa.gov/ttn/ecas/regdata/cost/pechan8-l 1 .pdf
2 "Suggested Short List and Evaluation of Point and Area Source Emission Control Measures for
the Houston-Galveston-Brazoria 8-Hour Ozone Nonattainment Area," Texas Council on
Environmental Quality," Prepared by ENVIRON International Corp. for Lamar Univ. June 15,
2006. Available on the Internet at http://www.h-
gac.com/NR/rdonlvres/e4cgpdlu4wd3tiguvvxkg5ziefqy36adm2o5cz5ipm36c67ksxbtfurvvwvgdq
uy362skyhnsel5uli4rdkfz2rusphd/Final+Short+List+%26+Evaluations.pdf.
J MARAMA Multipollutant Rule Basis for Flares, part of "Assessment of Control Technology
Options for Petroleum Refineries in the mid-Atlantic Region." February 19, 2007. Found on the
Internet at http://www.marama.org/reports/021907 Refinery Control Options TSD Final.pdf.
5a-l
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Cooling Towers
There is not a general estimate of control efficiency for this measure; one is to apply a
continuous flow monitor until VOC emissions have reached a level of 1.7 tons/year for a
given cooling tower.4 The annualized cost for a continuous flow monitor is $63,000 -
this is constant over a variety of cooling tower sizes.
Wastewater Drains and Separators
The control efficiency is 65 percent reduction of VOC emissions at a cost of $3,050/ton.
This is based on actual sampling and cost data for 5 refineries in the Bay Area Air
Quality Management District (BAAQMD).5
5a.2 Cost Information for EGU sources
(Full details on controls can be found in Appendix Chapter 3)
Cost of Controls as a Result of Lower Sub-regional Caps within the MWRPO and OTC and
other Local Controls outside of these Regions within CAIR
As previously discussed, the power sector will achieve significant emission reductions under the
Clean Air Interstate Rule (CAIR) over the next 10 to 15 years. When fully implemented, CAIR
(in conjunction with NOx SIP Call) will reduce ozone season NOx emissions by over 60 percent
from 2003 levels within the CAIR states. These reductions will greatly improve air quality and
will lessen the challenges that some areas face when solving nonattainment issues significantly.
Power sector impacts analyzed in detail in the Final PM NAAQS RIA 15/35
(http://www.epa.gov/ttn/ecas/ria.html ) provides the baseline for this RIA. The analysis and
projections in this section attempt to show the potential impacts of the additional controls applied
(see section 3.3.3 of this RIA) to facilitate attainment of the more stringent 8-hr ozone standard
of 0.070 ppm. Generally, the incremental impacts of these controls on the power sector are
marginal.
Projected Costs. EPA projects that the annual incremental cost of the proposed new ozone
standard approach is $0.2billion in 2020. The additional annual costs reflect additional retrofits
(SCR and SNCR) and generation shifts,. Annualized cost of CAIR is projected to be $6.17
billion in 2020. The proposed approach applied in this RIA would add $0.2 billion incremental to
this cost.
Projected Generation Mix. Coal-fired generation and natural gas/oil-fired generation are
proj ected to remain almost unchanged. Installation of approximately 3.7 GWs of SCR and 1.1
GWs of SNCR incremental to the base case are projected as a result of the lower sub-regional
caps. There are very small changes in the generation mix. Coal-fired generation increases about
12 GWh (an increase of approximately 0.25% of the total generation) and gas-fired generation
4 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of Regulation 8,
Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004.
5 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of Regulation 8,
Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004.
5a-2
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decreases a similar amount. Hydo, nuclear, other, and renewable based generation projected to
remain the same. Projected retirements of coal units is marginal, accounting to about 0.4 GWs
compared to the base case approach.
Projected Nationwide Retail Electricity Prices. Retail electricity prices are projected to change
marginally, only about 1%. The extension of the cap-and-trade approach in the form of lower
sub-regional caps allows industry to meet the requirements of CAIR in the most cost-effective
manner, thereby minimizing the costs passed on to consumers. Retail electricity prices are
projected to increase less than 1% within the MWRPO and OTC regions, and decrease about 1%
in the rest of the CAIR region.
5a.3 Cost information for Onroad and Nonroad Mobile Sources
(Full details on controls can be found in Appendix Chapter 3)
Diesel Retrofits and Vehicle Replacement
To calculate costs for the use of selective catalytic reduction as a retrofit technology, the
assumption was made that all relevant vehicles would be affected by the control. Therefore, all
on-road heavy duty diesel vehicles that received a retrofit were assumed to employ selective
catalytic reduction as a retrofit technology. The average cost of a selective catalytic reduction
system ranges from $10,000 to $20,000 per vehicle depending on the size of the engine, the sales
volume, and other factors (Pechan, 2003). For AirControlNET analysis, the average estimated
cost of this system is $15,000 per heavy duty diesel vehicle. (Source: AirControlNET
Documentation, III-160). OTAQ conducted an additional assessment of current SCR costs and
calculated that for the year 2020, the cost of SCRs will be approximately $13,000 per unit. This
estimate reflects an economy of scale cost reduction of 33%, which is consistent with trends in
other mobile source control technologies that enter large scale production.
The rebuild/upgrade kit is applied to nonroad equipment. OTAQ estimates the cost of this kit to
be $2,000 to $4,000 per vehicle. For this analysis, the average estimated cost is $3,000 per
vehicle.
Table 5a.l: Summary of Cost Effectiveness for Rebuild/Upgrade Kit for Various Nonroad
Vehicles
Nonroad Vehicle
Tractors/Loaders/Backhoes
Excavators
Crawler Tractor/Dozers
Skid Steer Loaders
Agricultural Tractors
Retrofit
Technology
Rebuild/
Upgrade kit
Range of $/ton NOx
Emission Reduced
$1,300
$1,100
$1,100
$1,000
$1,200
$2,200
$4,200
$4,200
$1,600
$4,900
Range of $/ton HC
Emission Reduced
$9,600
$8,100
$8,300
$7,400
$9,300
$18,900
$43,400
$43,500
$14,800
$34,300
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Table 5a.2: Summary of Cost Effectiveness for SCR for Various Nonroad Vehicles
Nonroad Vehicle
Tractors/Loaders/Backhoes
Excavators
Crawler Tractor/Dozers
Skid Steer Loaders
Agricultural Tractors
Retrofit
Technology
SCR
Range of $/ton NOx
Emission Reduced
$2,900
$2,700
$2,800
$2,600
$3,000
$5,300
$10,400
$10,400
$4,000
$7,600
Range of $/ton HC
Emission Reduced
$32,200
$27,400
$27,900
$24,900
$31,200
$63,700
$146,200
$146,700
$52,100
$115,500
Table 5a.3: Summary of Cost Effectiveness for SCR for Various Highway Vehicles
Highway Vehicle
Class 6&7 Truck
Class 8b Truck
Retrofit
Technology
SCR
Range of $/ton NOx
Emission Reduced
$5,600
$1,100
$14,100
$2,500
Range of $/ton HC
Emission Reduced
$46,900
$14,900
$126,200
$44,600
Implement Continuous Inspection and Maintenance Using Remote Onboard Diagnostics (OBD)
Continuous I/M can significantly lower test costs and "convenience" costs of I/M programs.
Using radio frequency transmission, there is a one-time cost for the Continuous I/M device and
its installation. In the case of Oregon, this cost is $50. The unit is then good for the life of the
vehicle. Annual or biennial test fees are not required beyond this initial fee to operate the system
but there may be additional operational costs to cover data processing, reporting, and oversight.
For the proposal RIA, we present estimated cost savings of the Continuous I/M program, but do
not include the cost savings in the overall cost estimates. For the final RIA, we plan to include
the Continuous I/M cost savings in the overall costs. This will result in a significant reduction of
overall cost.
We can compare the costs of periodic testing to Continuous I/M. The cost of data processing,
reporting and oversight is estimated to be $2 per vehicle per year in the typical I/M area. If we
assume an average vehicle life span of 14 years, with the first test at 4 years of age, vehicles will
get 5 inspections in a biennial program and 10 in an annual program (not including additional
change of ownership inspections, which are required in some areas). Thus, in a Continuous I/M
program, an additional cost of $10-$20 will be incurred for each vehicle over its life, assuming
the same costs apply in a Continuous I/M program as in a tailpipe test program.
In addition to test costs, Continuous I/M avoids most of the convenience costs associated with
I/M - the time and fuel it takes to drive to the station, get a test, and return home. The one-time
installation of the transmitter requires a visit to the test station, but no further visits are required
after that. So, if we assume, conservatively, that the typical test cycle requires a total of two
hours of time at $20 per hour and a half-gallon of gas (10 miles round trip with an average fuel
economy of 20 mpg) at $3 per gallon gives us a cost of $41.50. Over the life of the vehicle that
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works out to $207.50 in a biennial program or $415 in an annual program. Compare this to the
one time trip for Continuous I/M OBD at a cost of $41.50 and substantial savings are realized.
Application of Continuous I/M resulted inNOx reductions of 4.2 to 6.5 percent (approximately
10,000 tons), depending on the geographic area, vehicle class, and type of existing I/M program.
Some areas have no I/M, some I/M programs require annual testing, some require biennial
testing, and some areas are piloting continuous I/M. We applied continuous I/M reductions only
to those areas that currently have annual or biennial programs.
Putting it all together, the table below shows the lifetime inspection and convenience costs of
Continuous I/M versus periodic I/M (assuming the current mix of annual and biennial testing
and current test costs). Periodic I/M testing costs about $20 billion over a 10 year lifecycle with
an additional $25 billion in convenience costs for a total of $45 billion. By contrast, Remote
OBD has a test and installation cost of $4.3 billion dollars over the same 10 year period, and a
convenience cost of $2.5 billion for a total of $6.8 billion. Thus, nationwide installation of
Remote OBD would save the nation's motorists about $38 billion in inspection and convenience
costs over a 10 year period.
Table 5a.4 Lifetime Inspection and Convenience Costs of I/M
Continuous
I/M
Remote I/M
Savings
Test/Install
Cost
$20 billion
$4.3 billion
$15.7 billion
Convenience
Cost
$25 billion
$2.5 billion
$22.5 billion
Total
Cost
$45 billion
$6.8 billion
$38.2 billion
Given that Continuous I/M will actually reduce the cost of I/M, implementation of this measure
is highly cost-effective. More information on I/M can be found at
http ://www. epa. gov/otaq/regs/im/im-tsd.pdf and www.epa.gov/obd/regtech/inspection.htm
Eliminating Long Duration Truck Idling
For purposes of this RIA, we identified this measure as a no cost strategy i.e. $0/ton NOx. Both
TSEs and MIRTs have upfront capital costs, but these costs can be fully recovered by the fuel
savings. The examples below illustrate the potential rate of return on investments in idle
reduction strategies.
TSE
The average price of TSE technology is $11,500 per parking space. The average service life of
this technology is 15 years. Truck engines at idle consume approximately 1 gallon per hour of
idle. Current TSE projects are operating in environments where trucks are idling, on average, for
8 hours per day per space for 365 days per year (or about 2,920 hours per year). Since TSE
technology can completely eliminate long duration idling at truck spaces (i.e. a 100% fuel
savings), this translates into 2,920 gallons of fuel saved per year per space. At current diesel
prices ($2.90/gallon), this fuel savings translates into $8,468. Therefore, an $11,500 capital
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investment should be recovered within about 17 months. In this scenario, TSE investments offer
over a 70% annual rate of return over the life of the technology.
While it is technically feasible to electrify all parking spaces that support long duration idling
trucks, we should note that TSE technology is generally deployed at a minimum of 25-50
parking spaces per location to maximize economies of scale. The financial attractiveness of
installing TSE technology will depend on the demonstrated truck idling behavior - the greater
the rates of idling, the greater the potential emissions reductions and associated fuel and cost
savings.
MRTs
The price of MIRT technologies ranges from $1,000-$10,000. The most popular of these
technologies is the auxiliary power unit (APU) because it provides air conditioning, heat, and
electrical power to operate appliances. The average price of an APU is $7,000. The average
service life of an APU is 10 years. An APU consumes two-tenths of a gallon per hour, so the net
fuel savings is 0.80 gallons per hour. EPA estimates that trucks idle for 7 hours per rest period,
on average, and about 300 days per year (or 2,100 hours per year). Since idling trucks consume
1 gallon of fuel per hour of idle, APUs can reduce fuel consumption for truck drivers/owners by
approximately 1,680 gallons per year. At current diesel prices ($2.90/gallon), truck
drivers/owners would save $4,872 on fuel if they used an APU. Therefore, a $7,000 capital
investment should be recovered within about 18 months. In this scenario, APU investments offer
almost a 70% annual rate of return over the life of the technology.
Cost-Effectiveness of Measure: $0/tonNOx
Commuter Programs
We used the Transportation Research Board's (TRB) cost-effectiveness analysis of Congestion
Mitigation and Air Quality Improvement Program (CMAQ) projects to estimate the cost-
effectiveness of this measure.6 TRB conducted an extensive literature review and then
synthesized the data to develop comparable estimates of cost-effectiveness of a wide range of
CMAQ-funded measures. We took the average of the median cost-effectiveness of a sampling
of CMAQ-funded measures and then applied this number to the overarching commuter reduction
measure. The CMAQ-funded measures we selected were:
• regional rideshares
• vanpool programs
• park-and-ride lots
• regional transportation demand management
• employer trip reduction programs
We felt that these measures were a representative sampling of commuter reduction incentive
programs. There is a great deal of variability, however, in the type of programs and the level of
incentives that employers offer which can impact both the amount of emissions reductions and
the cost of commuter reduction incentive programs.
6 Transportation Research Board, National Research Council, 2002. The Congestion Mitigation and Air Quality
Improvement Program: assessing 10 years of experience, Committee for the Evaluation of the Congestion
Mitigation and Air Quality Improvement Program.
5a-6
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We chose to apply the resulting average cost-effectiveness estimate to one pollutant - NOX - in
order to be able to compare commuter reduction programs to other NOX reduction strategies.
TRB reported the cost-effectiveness of each measure, however, as a $/ton reduction of both VOC
and NOx by applying the total cost of the program to a 1:4 weighted sum of VOC and NOx
[[total emissions reduction = (VOC * 1) + (NOX * 4)). There was not enough information in the
TRB study to isolate the $/ton cost-effectiveness for just NOX reductions, so we used the
combined NOX and VOC estimate. The results are presented in Table 5a.5.
Table 5a.5 Cost-Effectiveness of Best Workplaces for Commuters Type
2002 TRB Study,
$/ton (2000$) 1:4 VOC: NOx (reported in theRIA as $/ton NOx)
Low High
Regional Rideshare
Vanpool Programs
Park-and-ride lots
Regional TDM
Employer trip reduction programs
Average of All Measures
$1,200
$5,200
$8~600
$2,300
$5,800
$4,620
$16,000
$89,000
$70,700
$33,200
$175,500
$76,900
Measures from the
Median
$7,400
$10,500
$43,000
$12,500
$22,700
$19,200
Cost-Effectiveness of Measure: $19,200/tonNOx
Reduce Gasoline RVPfrom 7.8 to 7.0 in Remaining Nonattainment Areas
Cost-Effectiveness of Measure: Cost per ton will be $5,700 to $36,000 / ton VOC
For more information on RVP:
• Michigan Department of Environmental Quality and Southeast Michigan Council of
Governments. Proposed Revision to State of Michigan State Implementation Plan for 7.0
Low Vapor Pressure Gasoline Vapor Request for Southeast Michigan. May 24, 2006.
• U.S. EPA. Guide on Federal and State Summer R VP Standards for Conventional
Gasoline Onlv. EPA420-B-05-012. November 2005
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Chapter 6. Incremental Benefits of Attaining Alternative Ozone Standards Relative to the
Current 8-hour Standard (0.08 ppm)
Synopsis
Based on projected emissions and air quality modeling, in 2020, 203 counties in the U.S. with
ozone monitors are estimated to fail to meet an alternative ozone standard of 0.070 ppm for the
4th highest maximum 8-hour ozone concentration. This number falls to 82 for an alternative
standard of 0.075 ppm, and further to 29 for an ozone standard of 0.079 ppm and increases to
360 for an alternative standard of 0.065 ppm. We estimated the health benefits of attaining these
alternative ozone standards across the U.S. using the EPA Environmental Benefits Modeling and
Analysis Program (BenMAP). We performed a two-stage analysis.
In the first stage we estimated the benefits associated with changes in modeled air quality
following application of control technologies known to be currently available. These control
strategies were sufficient to bring some, but not all, areas into attainment with the various
standard levels. Thus, the benefits computed during this first stage were for partial attainment in
some areas. In the second stage, we estimated the benefits of fully attaining the standards in all
areas by using a "rollback" methodology to reduce ozone concentrations at residually
nonattaining monitors to a level that would just meet the standards. We deviated from this two-
stage approach when analyzing the 0.075 ppm standard alternative, where we applied an
interpolation technique that is detailed further in this chapter. To calculate the monetary value of
the adverse health outcomes potentially avoided due to these reductions in ambient ozone levels,
we used health impact functions based on published epidemiological studies, and valuation
functions derived from the economics literature.1 Key health endpoints analyzed included
premature mortality, hospital and emergency room visits, school absences, and minor restricted
activity days.
There is considerable uncertainty in the magnitude of the association between ozone and
premature mortality. This analysis presents four alternative estimates for the association based
upon different functions reported in the scientific literature.. We also note that there are
uncertainties within each study that are not fully captured by this range of estimates.
Recognizing that additional research is needed to more fully establish underlying mechanisms by
which such effects occur, we also consider the possibility that the observed associations between
ozone and mortality may not be causal in nature. Using the National Morbidity, Mortality and
Air Pollution Study (NMMAPS) that was used as the primary basis for the risk analysis
presented in our Staff Paper and reviewed by Clean Air Science Advisory Committee (CAS AC),
we estimated 280 avoided premature deaths annually in 2020 from reducing ozone levels to meet
a standard of 0.070 ppm, which, when added to the other projected benefits from reduced ozone,
including 5,600 hospital and emergency room admissions, 780,000 school absences, and over
2,100,000 minor restricted activity days, leads to an estimated total ozone-related benefit of $2
1 Health impact functions measure the change in a health endpoint of interest, such as hospital
admissions, for a given change in ambient ozone or PM concentration
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billion/yr (1999$). Using three studies that synthesize data across a large number of individual
studies, we estimate between 1,100 and 1,400 avoided premature deaths annually in 2020 from
reducing ozone to 0.070 ppm, leading to total monetized ozone-related benefits of between $7.4
and $9.1 billion/yr. Alternatively, if there is no causal relationship between ozone and mortality,
avoided premature deaths associated with reduced ozone exposure would be zero and total
monetized ozone-related morbidity benefits would be $190 million/yr.
For a less stringent standard of 0.075 ppm, using the NMMAPS ozone mortality study resulted in
200 premature deaths avoided and total monetized benefits of $ 1.6 billion/yr. Using the three
synthesis studies, estimated premature deaths avoided for the less stringent standard are between
880 and 1,100, with total monetized ozone benefits between $5.9 and $7.3 billion/yr.
Alternatively, if there is no causal relationship between ozone and mortality, avoided premature
deaths associated with reduced ozone exposure would be zero and total monetized ozone-related
morbidity benefits would be $150 million/yr.
For a less stringent standard of 0.079 ppm, using the NMMAPS ozone mortality study resulted in
19 premature deaths avoided and total monetized benefits of $140 million/yr. Using the three
synthesis studies, estimated premature deaths avoided for the less stringent standard are between
78 and 85, with total monetized ozone benefits between $510 and $560 million/yr. Alternatively,
if there is no causal relationship between ozone and mortality, avoided premature deaths
associated with reduced ozone exposure would be zero and total monetized ozone-related
morbidity benefits would be $12 million/yr.
For a more stringent standard of 0.065 ppm, using the NMMAPS ozone mortality study resulted
in 530 premature deaths avoided and total monetized benefits of $3.7 billion/yr. Using the three
synthesis studies, estimated premature deaths avoided for the more stringent standard are
between 2,100 and 2,400, with total monetized ozone benefits between $14 and $16 billion/yr.
Alternatively, if there is no causal relationship between ozone and mortality, avoided premature
deaths associated with reduced ozone exposure would be zero and total monetized ozone-related
morbidity benefits would be $330 million/yr. These estimates reflect EPA's interim approach to
characterizing the benefits of reducing premature mortality associated with ozone exposure.
EPA has requested advice from the National Academy of Sciences on how best to quantify
uncertainty in the relationship between ozone exposure and premature mortality in the context of
quantifying benefits associated with alternative ozone control strategies.
In addition to the direct benefits from reduced ozone concentrations, attainment of the standards
would likely result in health and welfare benefits from the reduction of PM2.5 that would occur as
ozone precursor emissions (NOx and VOC) are reduced. Using both modeled and extrapolated
reductions in these precursor emissions, we estimated PM-related co-benefits for the four
alternative standards. For each alternative standard, we provide a range of estimated benefits
based on several different PM mortality effect estimates. These effect estimates were derived
from two different sources: the published epidemiology literature and an expert elicitation study
conducted by EPA in 2006. For the partial attainment of the 0.070 ppm standard, we estimated
PM co-benefits including between 220 and 2,200 premature deaths avoided, with total monetized
PM co-benefits of between $1 and $9.9 billion/yr (3% discount rate, 1999$). For the 2020
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attainment of the 0.070 ppm alternative, incremental to attainment of the 0.08 ppm standard, we
estimate total ozone and PIVb.s-related co-benefits to be between $2.5 and $33 billion/yr; this
range encompasses the expert functions and the ozone mortality functions as well as the
possibility that there is no causal relationship between ozone and mortality. For the 2020
attainment of the 0.065 ppm alternative, incremental to attainment of the 0.08 ppm standard, we
estimate total benefits of between $4.3 and $57 billion/yr; this range encompasses the expert
functions and the ozone mortality functions as well as the possibility that there is no causal
relationship between ozone and mortality. For the 2020 attainment of the 0.075 ppm alternative,
incremental to attainment of the 0.08 ppm standard, we estimate total ozone and PIVk.s-related
co-benefits to be between $1.5 and $22 billion/yr; this range encompasses the expert functions
and the ozone mortality functions as well as the possibility that there is no causal relationship
between ozone and mortality. For the 2020 attainment of the 0.079 ppm alternative, incremental
to attainment of the 0.08 ppm standard, we estimate total ozone and PM2.s-related co-benefits to
be between $1.1 and $12 billion/yr; this range encompasses the expert functions and the ozone
mortality functions as well as the possibility that there is no causal relationship between ozone
and mortality.
6.1. Background
Our purpose for this analysis is to assess the human health benefits of attaining alternative 8-hour
ozone standards, including 0.075 ppm, 0.070 ppm, and 0.065 ppm, incremental to attainment of
the current 8-hour ozone standard of 0.08 ppm.2 We applied a damage function approach similar
to those used in several recent U.S. EPA regulatory impact analyses, including those for the 2006
Particulate Matter (PM) NAAQS (U.S. EPA, 2006) and the Clean Air Interstate Rule (U.S. EPA,
2005). This approach estimates changes in individual health and welfare endpoints (specific
effects that can be associated with changes in air quality) and assigns values to those changes
assuming independence of the individual values. Total benefits are calculated simply as the sum
of the values for all non-overlapping health and welfare endpoints. This analysis largely builds
off of both the analytical approach used in the 2006 PM NAAQS PJA and the analysis of ozone
health impacts reported in Hubbell et al. (2005) and the Clean Air Interstate Rule RIA (2005).
For a more detailed discussion of the principles of benefits analysis used here, we refer the
reader to those documents, as well as to the EPA Guidelines for Economic Analysis."'4'5
We applied a two-stage approach to estimate the benefits of fully attaining each alternative
standard. In the first stage, we estimated the benefits associated with changes in modeled air
2 This is effectively 0.084 ppm due to current rounding conventions. When calculating benefits
in this chapter we followed the rounding convention and rounded to 0.084 ppm.
3 U.S. EPA. 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards
for Particle Pollution, Chapter 5. Available at http://www.epa.gov/ttn/ecas/ria.html.
4 Hubbell, B., A. Hallberg, D.R. McCubbin, and E. Post. 2005. Health-Related Benefits of
Attaining the 8-Hr Ozone Standard. Environmental Health Perspectives 113:73-82.
U.S. EPA. 2000. Guidelines for Preparing Economic Analyses.
http://vosemitel.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/Guidelines.pdf
5 U.S. EPA. 2000. Guidelines for Preparing Economic Analyses.
http://yosemitel.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/Guidelines.pdf
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quality following application of control technologies known to be currently available. These
control strategies were sufficient to bring some, but not all, areas into attainment with the various
standard levels. Thus, the benefits computed during this first stage were for partial attainment in
some areas (see Chapter 3 for details on these control technologies and the results of the air
quality modeling). In the second stage, we estimated the benefits of fully attaining the standards
in all areas by using a "rollback" methodology to reduce ozone concentrations at residually
nonattaining monitors to a level that would just meet the standards (see Appendix 6 for details on
this methodology). We conducted analyses to examine the sensitivity of our results to a number
of different assumptions about the choice of health effects and effect estimates from published
epidemiological studies, as well as parameters that affect the economic valuation of health
effects. A quantitative assessment of non-health benefits, e.g. benefits from reduced ozone-
related crop damage, was outside of the scope of this analysis due to data and resource
limitations.
For this assessment, we estimated benefits of changes in ozone and PM co-benefits resulting
from application of illustrative control strategies on ozone precursor emissions to attain
alternative ozone NAAQS. With the exception of ozone-related premature mortality, we use
methods consistent with previous PM and ozone benefits assessments. Specifically, the analysis
of PM co-benefits uses an approach identical to that used in the 2006 PM NAAQS PJA (U.S.
EPA, 2006). The ozone benefits analysis for non-mortality endpoints uses an approach nearly
identical to that for the Clean Air Interstate Rule RIA (U.S. EPA, 2005).6
All ozone and PM2.5 co-benefits estimates in this chapter are incremental to a baseline of national
full attainment with 0.08 ppm.7 This baseline incorporates emission reductions projected to be
achieved as a result of an array of federal rules such as the Clean Air Interstate and Non-Road
Diesel Rule, as well as ozone and PM2.s state implementation plans. Moreover, the PM2.s co-
benefits are incremental to an assumption of full attainment of the 2006 PM2.s NAAQS. A
complete discussion of the baseline may be found in Chapter 3. The PM co-benefits presented in
this chapter are incremental to the PM benefits estimated in the 2006 PM NAAQS RIA and
reflect the PM benefits from NOx reductions associated with each ozone control strategy.
The remainder of this chapter describes the data and methods used in this analysis, along with
the results. Additional details of the analysis are provided in Appendix 6 of this RIA. Section
6.2 discusses the probabilistic framework for the benefits analysis and how key uncertainties are
addressed in the analysis. Section 6.3 discusses the literature on ozone- and PM-related health
effects and describes the specific set of health impact functions we used in the benefits analysis.
Section 6.4 describes the economic values selected to estimate the dollar value of ozone- and
PM- related health impacts. Finally, Section 6.5 presents the results and implications of the
analysis.
6 The one exception relates to the use of updated health impact functions for emergency
department visits. These new functions are detailed further in this chapter.
7 The PM2.5 benefits presented below reflect the NOx emission reductions from the ozone
control strategy. Reductions from Ocean-Going Vessels burning residual diesel fuel were
included both East and West in the baseline PM co-benefits, but not included in the ozone
baseline for the west. See chapter 3 for more details of this rule and its application.
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6.2. Characterizing Uncertainty: Moving Toward a Probabilistic Framework for
Benefits Assessment
The National Research Council (NRC) (2002) highlighted the need for EPA to conduct rigorous
quantitative analysis of uncertainty in its benefits estimates and to present these estimates to
decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. In
response to these comments, EPA's Office of Air and Radiation (OAR) is developing a
comprehensive strategy for characterizing the aggregate impact of uncertainty in key modeling
elements on both health incidence and benefits estimates. Components of that process include
emissions modeling, air quality modeling, health effects incidence estimation, and valuation.
Two aspects of OAR's approach that have been used in several recent RIAs are employed
here.8'9'10 First, we use Monte Carlo methods for estimating characterizing random sampling
error associated with the concentration response functions from epidemiological studies and
economic valuation functions. Monte Carlo simulation uses random sampling from distributions
of parameters to characterize the effects of uncertainty on output variables, such as incidence of
premature mortality. Specifically, we used Monte Carlo methods to generate confidence intervals
around the estimated health impact and dollar benefits. Distributions for individual effect
estimates are based on the reported standard errors in the epidemiological studies. Distributions
for unit values are described in Table 6-4.
Second, we use a recently completed expert elicitation of the concentration response function
describing the relationship between premature mortality and ambient PM2.5 concentration.11 We
note that incorporating only the uncertainty from random sampling error omits important sources
of uncertainty (e.g., in the functional form of the model—e..g., whether or not a threshold may
exist). Use of the expert elicitation and incorporation of the standard errors approaches provide
insights into the likelihood of different outcomes and about the state of knowledge regarding the
benefits estimates. Both approaches have different strengths and weaknesses, which are full
described in Chapter 5 of the PM NAAQS RIA.
In benefit analyses of air pollution regulations conducted to date, the estimated impact of
reductions in premature mortality has accounted for 85% to 95% of total benefits. Therefore, in
characterizing the uncertainty related to the estimates of total benefits it is particularly important
to attempt to characterize the uncertainties associated with this endpoint. The health impact
U.S. Environmental Protection Agency, 2004a. Final Regulatory Analysis: Control of
Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Prepared by Office of Air and
Radiation. Available at http://www.epa.gov/nonroad-diesel/2004fr/420r04007.pdf
9 U.S. Environmental Protection Agency, 2005. Regulatory Impact Analysis for the Clean Air
Interstate Rule. EPA 452/-03-001. Prepared by Office of Air and Radiation. Available at:
http ://www. epa. gov/interstateairquality/tsdO 175 .pdf
10 U.S. Environmental Protection Agency, 2006. Regulatory Impact Analysis for the PM
NAAQS. EPA Prepared by Office of Air and Radiation. Available at:
http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205—Benefits.pdf
11 Expert elicitation is a formal, highly structured and well documented process whereby expert
judgments, usually of multiple experts, are obtained (Ayyb, 2002).
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functions used to estimate avoided premature deaths associated with reductions in ozone have
associated standard errors that represent the statistical errors around the effect estimates in the
underlying epidemiological studies.12 In our results, we report credible intervals based on these
standard errors, reflecting the uncertainty in the estimated change in incidence of avoided
premature deaths. We also provide multiple estimates, to reflect model uncertainty between
alternative study designs. In addition, we characterize the uncertainty introduced by the inability
of existing empirical studies to discern whether the relationship between ozone and pre-mature
mortality is causal by providing an effect estimate preconditioned on an assumption that the
effect estimate for pre-mature mortality from ozone is zero.
For premature mortality associated with exposure to PM, we follow the same approach used in
the RIA for 2006 PM NAAQS (U.S. EPA, 2006), presenting several empirical estimates of
premature deaths avoided, and a set of twelve estimates based on results of the expert elicitation
study.13 Even these multiple characterizations, including confidence intervals, omit the
contribution to overall uncertainty of uncertainty in air quality changes, baseline incidence rates,
populations exposed and transferability of the effect estimate to diverse locations. Furthermore,
the approach presented here does not yet include methods for addressing correlation between
input parameters and the identification of reasonable upper and lower bounds for input
distributions characterizing uncertainty in additional model elements. As a result, the reported
confidence intervals and range of estimates give an incomplete picture about the overall
uncertainty in the estimates. This information should be interpreted within the context of the
larger uncertainty surrounding the entire analysis.
6.3. Health Impact Functions
Health impact functions measure the change in a health endpoint of interest, such as hospital
admissions, for a given change in ambient ozone or PM concentration. Health impact functions
are derived from primary epidemiology studies, meta-analyses of multiple epidemiology studies,
or expert elicitations. A standard health impact function has four components: 1) an effect
estimate from a particular study; 2) a baseline incidence rate for the health effect (obtained from
either the epidemiology study or a source of public health statistics such as the Centers for
Disease Control); 3) the size of the potentially affected population; and 4) the estimated change
in the relevant ozone or PM summary measures.
A typical health impact function might look like:
12 Health impact functions measure the change in a health endpoint of interest, such as hospital
admissions, for a given change in ambient ozone or PM concentration.
13 Industrial Economics, Inc. 2006. Expanded Expert Judgment Assessment of the
Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared for
EPA Office of Air Quality Planning and Standards, September. Available at:
http://www.epa.gov/ttn/ecas/regdata/Uncertaintv/pm ee report.pdf
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where yo is the baseline incidence (the product of the baseline incidence rate times the potentially
affected population), p is the effect estimate, and Ax is the estimated change in the summary
ozone measure. There are other functional forms, but the basic elements remain the same.
Chapter 3 described the ozone and PM air quality inputs to the health impact functions. The
following subsections describe the sources for each of the other elements: size of potentially
affected populations; effect estimates; and baseline incidence rates.
6.3.1 Potentially Affected Populations
The starting point for estimating the size of potentially affected populations is the 2000 U.S.
Census block level dataset (Geolytics 2002). Benefits Modeling and Analysis Program
(BenMAP) incorporates 250 age/gender/race categories to match specific populations potentially
affected by ozone and other air pollutants. The software constructs specific populations
matching the populations in each epidemiological study by accessing the appropriate age-
specific populations from the overall population database. BenMAP projects populations to
2020 using growth factors based on economic projections (Woods and Poole Inc. 2001).
6.3.2 Effect Estimate Sources
The most significant monetized benefits of reducing ambient concentrations of ozone and PM
are attributable to reductions in human health risks. EPA's Ozone and PM Criteria Documents
and the World Health Organization's 2003 and 2004 reports outline numerous health effects
known or suspected to be linked to exposure to ambient ozone and PM (US EPA, 2006; US
EPA, 2005; WHO, 2003; Anderson et al, 2004). EPA recently evaluated the PM literature for
use in the benefits analysis for the 2006 PM NAAQS PJA. Because we use the same literature
for the PM co-benefits analysis in this PJA, we do not provide a detailed discussion of individual
effect estimates for PM in this section. Instead, we refer the reader to the 2006 PM NAAQS RIA
for details.14
More than one thousand new ozone health and welfare studies have been published since EPA
issued the 8-hour ozone standard in 1997. Many of these studies investigated the impact of
ozone exposure on health effects such as: changes in lung structure and biochemistry; lung
inflammation; asthma exacerbation and causation; respiratory illness-related school absence;
hospital and emergency room visits for asthma and other respiratory causes; and premature
death.
We were not able to separately quantify all of the PM and ozone health effects that have been
reported in the ozone and PM criteria documents in this analysis for four reasons: (1) the
possibility of double counting (such as hospital admissions for specific respiratory diseases); (2)
uncertainties in applying effect relationships that are based on clinical studies to the potentially
14 U.S. Environmental Protection Agency, 2005. Regulatory Impact Analysis for the PM
NAAQS. EPA Prepared by Office of Air and Radiation. Available at:
http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205—Benefits.pdf pp. 5-29.
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affected population; (3) the lack of an established concentration-response relationship; or 4) the
inability to appropriately value the effect (for example, changes in forced expiratory volume) in
economic terms. Table 6-1 lists the human health and welfare effects of pollutants affected by
the alternate standards. Table 6-2 lists the health endpoints included in this analysis.
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Table 6-1 Human Health and Welfare Effects of Pollutants Affected by the Alternate
Standards
Pollutant/Effect
Quantified and Monetized in Base
Estimates
Unqualified Effects - Changes in:
Premature mortality based on both
cohort study estimates and on expert
elicitation '
Bronchitis: chronic and acute
Hospital admissions: respiratory
and cardiovascular
Emergency room visits for asthma
Nonfatal heart attacks (myocardial
PM/Health infarction)
Lower and upper respiratory illness
Minor restricted-activity days
Work loss days
Asthma exacerbations (asthmatic
population)
Respiratory symptoms (asthmatic
population)
Infant mortality
Subchronic bronchitis cases
Low birth weight
Pulmonary function
Chronic respiratory diseases other than chronic bronchitis
Nonasthma respiratory emergency room visits
UVb exposure (+/-)
PM/Welfare
Visibility in Southeastern Class I areas
Visibility in northeastern and Midwestern Class I areas
Household soiling
Visibility in western U.S. Class I areas
Visibility in residential and non-Class I areas
UVb exposure (+/-)
Ozone/Health
Premature mortality: short-term
exposures
Hospital admissions: respiratory
Emergency room visits for asthma
Minor restricted-activity days
School loss days
Asthma attacks
Acute respiratory symptoms
Cardiovascular emergency room visits
Chronic respiratory damage
Premature aging of the lungs
Nonasthma respiratory emergency room visits
UVb exposure (+/-)
Ozone/Welfare
Decreased outdoor worker productivity
Yields for commercial crops
Yields for commercial forests and noncommercial crops
Damage to urban ornamental plants
Recreational demand from damaged forest aesthetics
Ecosystem functions
UVb exposure (+/-)
Primary quantified and monetized effects are those included when determining the primary estimate of total
monetized benefits of the proposed standards.
In addition to primary economic endpoints, there are a number of biological responses that have been associated
with PM health effects including morphological changes and altered host defense mechanisms. The public health
impact of these biological responses may be partly represented by our quantified endpoints.
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Cohort estimates are designed to examine the effects of long term exposures to ambient pollution, but relative risk
estimates may also incorporate some effects due to shorter term exposures (see Kunzli, 2001 for a discussion of this
issue).
While some of the effects of short-term exposure are likely to be captured by the cohort estimates, there may be
additional premature mortality from short-term PM exposure not captured in the cohort estimates included in the
primary analysis.
May result in benefits or disbenefits.
In addition to primary economic endpoints, there are a number of biological responses that have been associated
with ozone health including increased airway responsiveness to stimuli, inflammation in the lung, acute
inflammation and respiratory cell damage, and increased susceptibility to respiratory infection. The public health
impact of these biological responses may be partly represented by our quantified endpoints.
g The categorization of unquantified toxic health and welfare effects is not exhaustive.
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Table 6-2. Ozone and PM Related Health Endpoints basis for the concentration-response
function associated with that endpoint, and sub-populations for which they were computed.
Endpoint
Pollutant
Study
Study Population
Premature Mortality
Premature mortality
- daily time series,
non-accidental
Premature mortality
— cohort study, all-
cause
Premature mortality,
total exposures
Premature mortality
— all-cause
O3 (24-hour avg)
O3 (24-hour avg)
O3 (1-hour max)
O3 (1-hour max)
PM2.5 (annual avg)
PM2.5 (annual avg)
PM2.5 (annual avg)
Bell et al (2004) (NMMAPS study)
Meta-analvses:
Bell et al (2005)
Ito et al (2005)
Levy et al (2005)
Pope et al. (2002)
Laden et al. (2006)
Expert Elicitation (lEc, 2006)
Woodruff etal. (1997)
All ages
>29 years
>25 years
>24 years
Infant (<1 year)
Chronic Illness
Chronic bronchitis
Nonfatal heart
attacks
PM2.5 (annual avg)
PM2.5 (24-hour avg)
Abbey etal. (1995)
Peters etal. (2001)
>26 years
Adults (> 18 years)
Hospital Admissions
Respiratory
O3 (24-hour avg)
PM2.5 (24-hour avg)
PM2.5 (24-hour avg)
PM2.5 (24-hour avg)
PM2.5 (24-hour avg)
Pooled estimate:
Schwartz (1995) - ICD 460-519 (all resp)
Schwartz (1994a; 1994b) - ICD 480-486
(pneumonia)
Moolgavkar et al. (1997) - ICD 480-487 (pneumonia)
Schwartz (1994b) - ICD 491-492, 494-496 (COPD)
Moolgavkar et al. (1997) - ICD 490-496 (COPD)
Burnett etal. (2001)
Pooled estimate:
Moolgavkar (2003)—ICD 490-496 (COPD)
Ito (2003)—ICD 490-496 (COPD)
Moolgavkar (2000)—ICD 490-496 (COPD)
Ito (2003)—ICD 480-486 (pneumonia)
Sheppard (2003)—ICD 493 (asthma)
>64 years
<2 years
>64 years
20-64 years
>64 years
<65 years
Cardiovascular
PM2.5 (24-hour avg)
Pooled estimate:
Moolgavkar (2003)—ICD 390-429 (all
cardiovascular)
Ito (2003)—ICD 410-414, 427-428 (ischemic heart
disease, dysrhythmia, heart failure)
>64 years
PM2.5 (24-hour avg)
Moolgavkar (2000)—ICD 390-429 (all
cardiovascular)
20-64 years
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Endpoint
Asthma-related ER
visits
Asthma-related ER
visits (con't)
Pollutant
O3 (8-hour max)
PM2.5 (24-hour avg)
Study
Pooled estimate:
Jaffe et al (2003)
Peel et al (2005)
Wilson et al (2005)
Morris etal. (1999)
Study Population
5-34 years
All ages
All ages
0-1 8 years
Other Health Endpoints
Acute bronchitis
Upper respiratory
symptoms
Lower respiratory
symptoms
Asthma
exacerbations
Work loss days
School absence
days
Minor Restricted
Activity Days
(MRADs)
PM2.5 (annual avg)
PM10 (24-hour avg)
PM2.5 (24-hour avg)
PM2.5 (24-hour avg)
PM2.5 (24-hour avg)
O3 (8-hour avg)
O3 (1-hour max)
O3 (24-hour avg)
PM2.5 (24-hour avg)
Dockery etal. (1996)
Pope etal. (1991)
Schwartz and Neas (2000)
Pooled estimate:
Ostro et al. (2001) (cough, wheeze and shortness of
breath)
Vedal etal. (1998) (cough)
Ostro (1987)
Pooled estimate:
Gilliland etal. (2001)
Chen etal. (2000)
Ostro and Rothschild (1989)
Ostro and Rothschild (1989)
8-1 2 years
Asthmatics, 9-1 1
years
7-14 years
6-1 8 years3
1 8-65 years
5-17yearsb
18-65 years
18-65 years
The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for
the Vedal et al. (1998) study. Based on advice from the Science Advisory Board Health
Effects Subcommittee (SAB-HES), we extended the applied population to 6 to 18, reflecting
the common biological basis for the effect in children in the broader age group. See: U.S.
Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the Analytical
Plan for EPA's Second Prospective Analysis -Benefits and Costs of the Clean Air Act,
1990—2020. EPA-SAB-COUNCIL-ADV-04-004. See also National Research Council
(NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations.
Washington, DC: The National Academies Press.
Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to
11. Based on recent advice from the National Research Council and the EPA SAB-HES, we
have calculated reductions in school absences for all school-aged children based on the
biological similarity between children aged 5 to 17.
In selecting epidemiological studies as sources of effect estimates, we applied several criteria to
develop a set of studies that is likely to provide the best estimates of impacts in the U.S. To
account for the potential impacts of different health care systems or underlying health status of
populations, we give preference to U.S. studies over non-U.S. studies. In addition, due to the
potential for confounding by co-pollutants, we give preference to effect estimates from models
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including both ozone and PM over effect estimates from single-pollutant models.15'16
A number of endpoints that are not health-related also may significantly contribute to monetized
benefits. Potential welfare benefits associated with ozone exposure include: increased outdoor
worker productivity; increased yields for commercial and non-commercial crops; increased
commercial forest productivity; reduced damage to urban ornamental plants; increased
recreational demand for undamaged forest aesthetics; and reduced damage to ecosystem
functions (U.S. EPA 1999, 2006). While we include estimates of the value of increased outdoor
worker productivity, estimation of other welfare impacts is beyond the scope of this analysis.
6.3.2.1 Premature Mortality Effects Estimates
While particulate matter is the criteria pollutant most clearly associated with premature
mortality, recent research suggests that short-term repeated ozone exposure likely contributes to
premature death. The 2006 Ozone Criteria Document states: "Consistent with observed ozone-
related increases in respiratory- and cardiovascular-related morbidity, several newer multi-city
studies, single-city studies, and several meta-analyses of these studies have provided relatively
strong epidemiologic evidence for associations between short-term ozone exposure and all-cause
mortality, even after adjustment for the influence of season and PM" (EPA, 2006: E-17). The
epidemiologic data are also supported by newly available experimental data from both animal
and human studies which provide evidence suggestive of plausible pathways by which risk of
respiratory or cardiovascular morbidity and mortality could be increased by ambient ozone.
With respect to short-term exposure, the ozone Criteria Document concludes: "This overall body
of evidence is highly suggestive that ozone directly or indirectly contributes to non-accidental
and cardiopulmonary-related mortality, but additional research is needed to more fully establish
underlying mechanisms by which such effects occur" (pg. E-18).
With respect to the time-series studies, the conclusion regarding the relationship between short-
term exposure and premature mortality is based, in part, upon recent city-specific time-series
studies such as the Schwartz (2004) analysis in Houston and the Huang et al. (2004) analysis in
Los Angeles.17 This conclusion is also based on recent meta-analyses by Bell et al. (2005), Ito et
al. (2005), and Levy et al. (2005), and a new analysis of the National Morbidity, Mortality, and
Air Pollution Study (NMMAPS) data set by Bell et al. (2004), which specifically sought to
disentangle the roles of ozone, PM, weather-related variables, and seasonality. The 2006 Criteria
Document states that "the results from these meta-analyses, as well as several single- and
15 U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the
Analytical Plan for EPA's Second Prospective Analysis -Benefits and Costs of the Clean Air
Act, 1990—2020. EPA-SAB-COUNCIL-ADV-04-004.
16 National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed
Air Pollution Regulations. Washington, DC: The National Academies Press.
17 For an exhaustive review of the city-specific time-series studies considered in the ozone staff
paper, see: U.S. Environmental Protection Agency, 2007. Review of the National Ambient Air
Quality Standards for Ozone: Policy Assessment of Scientific and Technical Information.
Prepared by the Office of Air and Radiation. Available at
http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_0l_ozone_staff_paper.pdf. pp. 5-36.
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multiple-city studies, indicate that co-pollutants generally do not appear to substantially
confound the association between ozone and mortality" (p. 7-103). However, CASAC raised
questions about the implications of these time-series results in a policy context. Specifically,
CASAC emphasized that ".. .while the time-series study design is a powerful tool to detect very
small effects that could not be detected using other designs, it is also a blunt tool" (Henderson,
2006: 3). They point to findings (e.g., Stieb et al., 2002, 2003) that indicated associations
between premature mortality and all of the criteria pollutants, indicating that "findings of time-
series studies do not seem to allow us to confidently attribute observed effects to individual
pollutants" (id.). They note that "not only is the interpretation of these associations complicated
by the fact that the day-to-day variation in concentrations of these pollutants is, to a varying
degree, determined by meteorology, the pollutants are often part of a large and highly correlated
mix of pollutants, only a very few of which are measured" (id.). Even with these uncertainties,
the CASAC Ozone Panel, in its review of EPA's Staff Paper, found ".. .premature total non-
accidental and cardiorespiratory mortality for inclusion in the quantitative risk assessment to be
appropriate."
Consistent with the methodology used in the ozone risk assessment found in the Characterization
of Health Risks found in the Review of the National Ambient Air Quality Standards for Ozone:
Policy Assessment of Scientific and Technical Information, we included ozone mortality in the
primary health effects analysis, with the recognition that the exact magnitude of the effects
estimate is subject to continuing uncertainty. We used effect estimates from the Bell et al.
(2004) NMMAPS analysis, as well as effect estimates from the three meta-analyses. In addition,
we include the possibility that there is not a causal association between ozone and mortality, i.e.,
that the effect estimate for premature mortality could be zero.
We estimate the change in mortality incidence and estimated credible interval18 resulting from
application of the effect estimate from each study and present them separately to reflect
differences in the study designs and assumptions about causality. However, it is important to
note that this procedure only captures the uncertainty in the underlying epidemiological work,
and does not capture other sources of uncertainty, such as uncertainty in the estimation of
changes in air pollution exposure (Levy et al., 2000).
6.3.2.2 Respiratory Hospital Admissions Effect Estimates
Detailed hospital admission and discharge records provide data for an extensive body of
literature examining the relationship between hospital admissions and air pollution. This is
especially true for the portion of the population aged 65 and older, because of the availability of
detailed Medicare records. In addition, there is one study (Burnett et al., 2001) providing an
effect estimate for respiratory hospital admissions in children under two.
Because the number of hospital admission studies we considered is so large, we used results
from a number of studies to pool some hospital admission endpoints. Pooling is the process by
which multiple study results may be combined in order to produce better estimates of the effect
18 A credible interval is a posterior probability interval used in Bayesian statistics, which is
similar to a confidence interval used in frequentist statistics.
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estimate, or p. For a complete discussion of the pooling process, see Abt (2005).19 To estimate
total respiratory hospital admissions associated with changes in ambient ozone concentrations for
adults over 65, we first estimated the change in hospital admissions for each of the different
effects categories that each study provided for each city. These cities included Minneapolis,
Detroit, Tacoma and New Haven. To estimate total respiratory hospital admissions for Detroit,
we added the pneumonia and COPD estimates, based on the effect estimates in the Schwartz
study (1994b). Similarly, we summed the estimated hospital admissions based on the effect
estimates the Moolgavkar study reported for Minneapolis (Moolgavkar et al, 1997). To estimate
total respiratory hospital admissions for Minneapolis using the Schwartz study (1994a), we
simply estimated pneumonia hospital admissions based on the effect estimate. Making this
assumption that pneumonia admissions represent the total impact of ozone on hospital
admissions in this city will give some weight to the possibility that there is no relationship
between ozone and COPD, reflecting the equivocal evidence represented by the different studies.
We then used a fixed-effects pooling procedure to combine the two total respiratory hospital
admission estimates for Minneapolis. Finally, we used random effects pooling to combine the
results for Minneapolis and Detroit with results from studies in Tacoma and New Haven from
Schwartz (1995). As noted above, this pooling approach incorporates both the precision of the
individual effect estimates and between-study variability characterizing differences across study
locations.
6.3.2.3 Asthma-Related Emergency Room Visits Effect Estimates
We used three studies as the source of the concentration-response functions we used to estimate
the effects of ozone exposure on asthma-related emergency room (ER) visits: Peel et al. (2005);
Wilson et al. (2005); and Jaffe et al. (2003). We estimated the change in ER visits using the
effect estimate(s) from each study and then pooled the results using the random effects pooling
technique (see Abt, 2005). The study by Jaffe et al. (2003) examined the relationship between
ER visits and air pollution for populations aged five to 34 in the Ohio cities of Cleveland,
Columbus and Cincinnati from 1991 through 1996. In single-pollutant Poisson regression
models, ozone was linked to asthma visits. We use the pooled estimate across all three cities as
reported in the study. The Peel et al. study (2005) estimated asthma-related ER visits for all ages
in Atlanta, using air quality data from 1993 to 2000. Using Poisson generalized estimating
equations, the authors found a marginal association between the maximum daily 8-hour average
ozone level and ER visits for asthma over a 3-day moving average (lags of 0, 1, and 2 days) in a
single pollutant model. Wilson et al. (2005) examined the relationship between ER visits for
respiratory illnesses and asthma and air pollution for all people residing in Portland, Maine from
1998-2000 and Manchester, New Hampshire from 1996-2000. For all models used in the
analysis, the authors restricted the ozone data incorporated into the model to the months ozone
levels are usually measured, the spring-summer months (April through September). Using the
generalized additive model, Wilson et al. (2005) found a significant association between the
maximum daily 8-hour average ozone level and ER visits for asthma in Portland, but found no
significant association for Manchester. Similar to the approach used to generate effect estimates
for hospital admissions, we used random effects pooling to combine the results across the
19 Abt Associates, Incorporated. Environmental Benefits Mapping and Analysis Program,
Technical Appendices. May 2005. pp. 1-3
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individual study estimates for ER visits for asthma. The Peel et al. (2005) and Wilson et al.
(2005) Manchester estimates were not significant at the 95 percent level, and thus, the
confidence interval for the pooled incidence estimate based on these studies includes negative
values. This is an artifact of the statistical power of the studies, and the negative values in the
tails of the estimated effect distributions do not represent improvements in health as ozone
concentrations are increased. Instead these should be viewed as a measure of uncertainty due to
limitations in the statistical power of the study. Note that we included both hospital admissions
and ER visits as separate endpoints associated with ozone exposure, because our estimates of
hospital admission costs do not include the costs of ER visits, and because most asthma ER visits
do not result in a hospital admission.
6.3.2A Minor Restricted Activity Days Effects Estimate
Minor restricted activity days (MRADs) occur when individuals reduce most usual daily
activities and replace them with less-strenuous activities or rest, but do not miss work or school.
We estimated the effect of ozone exposure on MRADs using a concentration-response function
derived from Ostro and Rothschild (1989). These researchers estimated the impact of ozone and
PM2.5 on MRAD incidence in a national sample of the adult working population (ages 18 to 65)
living in metropolitan areas. We developed separate coefficients for each year of the Ostro and
Rothschild analysis (1976-1981), which we then combined for use in EPA's analysis. The effect
estimate used in the impact function is a weighted average of the coefficients in Ostro and
Rothschild (1989, Table 4), using the inverse of the variance as the weight.
6.3.2.5 School Absences Effect Estimate
Children may be absent from school due to respiratory or other acute diseases caused, or
aggravated by, exposure to air pollution. Several studies have found a significant association
between ozone levels and school absence rates. We use two studies (Gilliland et al., 2001; Chen
et al., 2000) to estimate changes in school absences resulting from changes in ozone levels. The
Gilliland et al. study estimated the incidence of new periods of absence, while the Chen et al.
study examined daily absence rates. We converted the Gilliland et al. estimate to days of
absence by multiplying the absence periods by the average duration of an absence. We estimated
1.6 days as the average duration of a school absence, the result of dividing the average daily
school absence rate from Chen et al. (2000) and Ransom and Pope (1992) by the episodic
absence duration from Gilliland et al. (2001). Thus, each Gilliland et al. period of absence is
converted into 1.6 absence days.
Following recent advice from the National Research Council (2002), we calculated reductions in
school absences for the full population of school age children, ages five to 17. This is consistent
with recent peer-reviewed literature on estimating the impact of ozone exposure on school
absences (Hall et al. 2003). We estimated the change in school absences using both Chen et al.
(2000) and Gilliland et al. (2001) and then, similar to hospital admissions and ER visits, pooled
the results using the random effects pooling procedure.
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6.3.2.6 Worker Productivity
To monetize benefits associated with increased worker productivity resulting from improved
ozone air quality, we used information reported in Crocker and Horst (1981). Crocker and Horst
examined the impacts of ozone exposure on the productivity of outdoor citrus workers. The
study measured productivity impacts. Worker productivity is measuring the value of the loss in
productivity for a worker who is at work on a particular day, but due to ozone, cannot work as
hard. It only applies to outdoor workers, like fruit and vegetable pickers, or construction
workers. Here, productivity impacts are measured as the change in income associated with a
change in ozone exposure, given as the elasticity of income with respect to ozone concentration.
The reported elasticity translates a ten percent reduction in ozone to a 1.4 percent increase in
income. Given the national median daily income for outdoor workers engaged in strenuous
activity reported by the U.S. Census Bureau (2002), $68 per day (2000$), a ten percent reduction
in ozone yields about $0.97 in increased daily wages. We adjust the national median daily
income estimate to reflect regional variations in income using a factor based on the ratio of
county median household income to national median household income. No information was
available for quantifying the uncertainty associated with the central valuation estimate.
Therefore, no uncertainty analysis was conducted for this endpoint.
6.3.2.7 Visibility Benefits
Changes in the level of ambient PlV^.s caused by the reduction in emissions associated with the
proposed standards will change the level of visibility throughout the United States. Increases in
PM concentrations cause increases in light extinction, a measure of how much the components of
the atmosphere absorb light. Due to time limitations, this benefits assessment does not consider
the value of improvements in visibility associated with simulated attainment of alternate ozone
standards. We anticipate that the benefits assessment supporting the promulgated ozone standard
will consider this important benefits category.
6.3.2.8 Other Unquantified Effects
6.3.2.8.1 Direct Ozone Effects on Vegetation
The Ozone Criteria Document notes that "current ambient concentrations in many areas of the
country are sufficient to impair growth of numerous common and economically valuable plant
and tree species." (U.S. EPA, 2006, page 9-1). Changes in ground-level ozone resulting from the
implementation of alternative ozone standards are expected to affect crop and forest yields
throughout the affected area. Recent scientific studies have also found the ozone negatively
impacts the quality or nutritive value of crops (U.S. EPA, 2006, page 9-16).
Well-developed techniques exist to provide monetary estimates of these benefits to agricultural
producers and to consumers. These techniques use models of planting decisions, yield response
functions, and the supply of and demand for agricultural products. The resulting welfare
measures are based on predicted changes in market prices and production costs. Models also
exist to measure benefits to silvicultural producers and consumers. However, these models have
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not been adapted for use in analyzing ozone-related forest impacts. Because of resource
limitations, we are unable to provide agricultural or benefits estimates for the proposed rule.
An additional welfare benefit expected to accrue as a result of reductions in ambient ozone
concentrations in the United States is the economic value the public receives from reduced
aesthetic injury to forests. There is sufficient scientific information available to reliably establish
that ambient ozone levels cause visible injury to foliage and impair the growth of some sensitive
plant species (U.S. EPA, 2006, page 9-19). However, present analytic tools and resources
preclude EPA from quantifying the benefits of improved forest aesthetics.
Urban ornamentals (floriculture and nursery crops) represent an additional vegetation category
likely to experience some degree of negative effects associated with exposure to ambient ozone
levels and likely to affect large economic sectors. In the absence of adequate exposure-response
functions and economic damage functions for the potential range of effects relevant to these
types of vegetation, no direct quantitative economic benefits analysis has been conducted. The
farm production value of ornamental crops was estimated at over $14 billion in 2003 (USDA,
2004). This is therefore a potentially important welfare effects category. However, information
and valuation methods are not available to allow for plausible estimates of the percentage of
these expenditures that may be related to impacts associated with ozone exposure.
6.3.2.8.2 Nitrogen Deposition
Deposition to Estuarine and Coastal Waters
Excess nutrient loads, especially of nitrogen, cause a variety of adverse consequences to the
health of estuarine and coastal waters. These effects include toxic and/or noxious algal blooms
such as brown and red tides, low (hypoxic) or zero (anoxic) concentrations of dissolved oxygen
in bottom waters, the loss of submerged aquatic vegetation due to the light-filtering effect of
thick algal mats, and fundamental shifts in phytoplankton community structure (Bricker et al.,
1999). A recent study found that for the period 1990-2002, atmospheric deposition accounted
for 17 percent of nitrate loadings in the Gulf of Mexico, where severe hypoxic zones have been
existed over the last two decades (Booth and Campbell, 2007)20.
Reductions in atmospheric deposition of NOx are expected to reduce the adverse impacts
associated with nitrogen deposition to estuarine and coastal waters. However, direct functions
relating changes in nitrogen loadings to changes in estuarine benefits are not available. The
preferred WTP-based measure of benefits depends on the availability of these functions and on
estimates of the value of environmental responses. Because neither appropriate functions nor
sufficient information to estimate the marginal value of changes in water quality exist at present,
calculation of a WTP measure is not possible.
20 Booth, M.S., and C. Campbell. 2007. Spring Nitrate Flux in the Mississippi River Basin: A Landscape Model
with Conservation Applications. Environ. Sci. Technol.; 2007; ASAP Web Release Date: 20-Jun-2007; (Article)
DOI: 10.1021/es070179e
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Deposition to Agricultural and Forested Land
Implementation strategies for alternative standards which reduce NOx emissions, will also
reduce nitrogen deposition on agricultural land and forests. There is some evidence that nitrogen
deposition may have positive effects on agricultural output through passive fertilization.
Holding all other factors constant, farmers' use of purchased fertilizers or manure may increase
as deposited nitrogen is reduced. Estimates of the potential value of this possible increase in the
use of purchased fertilizers are not available, but it is likely that the overall value is very small
relative to other health and welfare effects. The share of nitrogen requirements provided by this
deposition is small, and the marginal cost of providing this nitrogen from alternative sources is
quite low. In some areas, agricultural lands suffer from nitrogen over-saturation due to an
abundance of on-farm nitrogen production, primarily from animal manure. In these areas,
reductions in atmospheric deposition of nitrogen from PM represent additional agricultural
benefits.
Information on the effects of changes in passive nitrogen deposition on forests and other
terrestrial ecosystems is very limited. The multiplicity of factors affecting forests, including other
potential stressors such as ozone, and limiting factors such as moisture and other nutrients,
confound assessments of marginal changes in any one stressor or nutrient in forest ecosystems.
However, reductions in deposition of nitrogen could have negative effects on forest and
vegetation growth in ecosystems where nitrogen is a limiting factor (US EPA, 1993). Moreover,
any positive effect that nitrogen deposition has on forest productivity would enhance the level of
carbon dioxide sequestration as well.21'22'23
On the other hand, there is evidence that forest ecosystems in some areas of the United States
(such as the western U.S.) are nitrogen saturated (US EPA, 1993). Once saturation is reached,
adverse effects of additional nitrogen begin to occur such as soil acidification which can lead to
leaching of nutrients needed for plant growth and mobilization of harmful elements such as
aluminum. Increased soil acidification is also linked to higher amounts of acidic runoff to
streams and lakes and leaching of harmful elements into aquatic ecosystems.
6.3.2.8.3 Ultraviolet Radiation
Atmospheric ozone absorbs a harmful band of ultraviolet radiation from the sun called UV-B,
providing a protective shield to the Earth's surface. The majority of this protection occurs in the
stratosphere where 90% of atmospheric ozone is located. The remaining 10% of the Earth's
ozone is present at ground level (referred to as tropospheric ozone) (NAS, 1991; NASA). Only a
portion of the tropospheric fraction of UV-B shielding is from anthropogenic sources (e.g.,
power plants, byproducts of combustion). The portion of ground level ozone associated with
21 Peter M. Vitousek et. al., "Human Alteration of the Global Nitrogen Cycle: Causes and Consequences" Issues in
Ecology No. 1 (Spring) 1997.
22 Knute J. Nadelhoffer et. al., "Nitrogen deposition makes a minor contribution to carbon
sequestration in temperate forests" Nature 398, 145-148 (11 March 1999)
23 Martin KQchy and Scott D. Wilson, "Nitrogen deposition and forest expansion in the northern Great Plains
Journal of Ecology Journal of Ecology 89 (5), 807-817
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anthropogenic sources varies by locality and over time. Even so, it is reasonable to assume that
reductions in ground level ozone would lead to increases in the same health effects linked to in
UV-B exposures. These effects include fatal and nonfatal melanoma and non-melanoma skin
cancers and cataracts. The values of $15,000 per case for non-fatal melanoma skin cancer,
$5,000 per case for non-fatal non-melanoma skin cancer, and $15,000 per case of cataracts have
been used in analyses of stratospheric ozone depletion (U.S. EPA, 1999). Fatal cancers are
valued using the standard VSL estimate, which for 2020 is $6.6 million (1999$). UV-B has also
been linked to ecological effects including damage to crops and forest. For a more complete
listing of quantified and unqualified UV-B radiation effects, see Table G-4 and G-7 in the
Benefits and Costs of the Clean Air Act, 1990-2010 (U.S. EPA, 1999. UV-B related health
effects are also discussed in the context of stratospheric ozone in a 2006 report by ICF
Consulting, prepared for the U.S. EPA.
There are many factors that influence UV-B radiation penetration to the earth's surface,
including latitude, altitude, cloud cover, surface albedo, PM concentration and composition, and
gas phase pollution. Of these, only latitude and altitude can be defined with small uncertainty in
any effort to assess the changes in UV-B flux that may be attributable to any changes in
tropospheric O3 as a result of any revision to the O3 NAAQS. Such an assessment of UV-B
related health effects would also need to take into account human habits, such as outdoor
activities (including age- and occupation-related exposure patterns), dress and skin care to
adequately estimate UV-B exposure levels. However, little is known about the impact of these
factors on individual exposure to UV-B.
Moreover, detailed information does not exist regarding other factors that are relevant to
assessing changes in disease incidence, including: type (e.g., peak or cumulative) and time
period (e.g., childhood, lifetime, current) of exposures related to various adverse health outcomes
(e.g., damage to the skin, including skin cancer; damage to the eye, such as cataracts; and
immune system suppression); wavelength dependency of biological responses; and
interindividual variability in UV-B resistance to such health outcomes. Beyond these well
recognized adverse health effects associated with various wavelengths of UV radiation, the
Criteria Document (section 10.2.3.6) also discusses protective effects of UV-B radiation. Recent
reports indicate the necessity of UV-B in producing vitamin D, and that vitamin D deficiency can
cause metabolic bone disease among children and adults, and may also increase the risk of many
common chronic diseases (e.g., type I diabetes and rheumatoid arthritis) as well as the risk of
various types of cancers. Thus, the Criteria Document concludes that any assessment that
attempts to quantify the consequences of increased UV-B exposure on humans due to reduced
ground-level O3 must include consideration of both negative and positive effects. However, as
with other impacts of UVB on human health, this beneficial effect of UVB radiation has not
previously been studied in sufficient detail. We will develop approaches for estimating the
effects of increased UVB exposures resulting from reductions in tropospheric ozone and will
work to present peer-reviewed quantified estimates for the final rule.
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6.3.2.8.4 Climate Implications of Tropospheric Ozone
Although climate and air quality are generally treated as separate issues, they are closely coupled
through atmospheric processes. Ozone, itself, is a major greenhouse gas and climate directly
influences ambient concentrations of ozone.
The concentration of tropospheric ozone has increased substantially since the pre-industrial era
and has contributed to warming. Tropospheric ozone is (after CO2 and CH4) the third most
important contributor to greenhouse gas warming. The National Academy of Sciences recently
stated24 that regulations targeting ozone precursors would have combined benefits for public
health and climate. As noted in the OAQPS Staff Paper, the overall body of scientific evidence
suggests that high concentrations of ozone on a regional scale could have a discernible influence
on climate. However, the Staff Paper concludes that insufficient information is available at this
time to quantitatively inform the secondary NAAQS process with regard to this aspect of the
ozone-climate interaction.
Climate change can affect tropospheric ozone by modifying emissions of precursors, chemistry,
transport and removal.25 Climate change affects the sources of ozone precursors through physical
response (lightning), biological response (soils, vegetation, and biomass burning) and human
response (energy generation, land use, and agriculture). Increases in regional ozone pollution are
expected due to higher temperatures and weaker circulation. Simulations with global climate
models for the 21st century indicate a decrease in the lifetime of tropospheric ozone due to
increasing water vapor which could decrease global background ozone concentrations.
The Intergovernmental Panel on Climate Change (IPCC) recently released a report26 which
projects, with "virtual certainty," declining air quality in cities due to warmer and fewer cold
days and nights and/or warmer/more frequent hot days and nights over most land areas. The
report states that projected climate change-related exposures are likely to affect the health status
of millions of people, in part, due to higher concentrations of ground level ozone related to
climate change.
24 National Academy of Sciences, "Radiative Forcing of Climate Change: Expanding the
Concept and Addressing Uncertainties," October 2005.
25Denman, K.L., G. Brasseur, A. Chidthaisong, P. Ciais, P.M. Cox, R.E. Dickinson, D.
Hauglustaine, C. Heinze, E. Holland, D. Jacob, U. Lohmann, S Ramachandran, P.L. da Silva
Bias, S.C. Wofsy and X. Zhang, 2007: Couplings Between Changes in the Climate System and
Biogeochemistry. In: Climate Change 2007: The Physical Science Basis. Contribution of
Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning,
Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA.
26 IPCC, Climate Change 2007: Climate Change Impacts, Adaptation and Vulnerability,
Summary for Policymakers
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The IPCC also reports27 that the current generation of tropospheric ozone models is generally
successful in describing the principal features of the present-day global ozone distribution.
However, there is much less confidence in the ability to reproduce the changes in ozone
associated with perturbations of emissions or climate. There are major discrepancies with
observed long-term trends in ozone concentrations over the 20th century, including after 1970
when the reliability of observed ozone trends is high. Resolving these discrepancies is needed to
establish confidence in the models.
The EPA is currently leading a research effort with the goal of identifying changes in regional
US air quality that may occur in a future (2050) climate, focusing on fine particles and ozone.
The research builds first on an assessment of changes in US air quality due to climate change,
which includes direct meteorological impacts on atmospheric chemistry and transport and the
effect of temperature changes on air pollution emissions. Further research will result in an
assessment that adds the emission impacts from technology, land use, demographic changes, and
air quality regulations to construct plausible scenarios of US air quality 50 years into the future.
As noted in the Staff Paper, results from these efforts are expected to be available for
consideration in the next review of the ozone NAAQS.
6.3.3 Baseline Incidence Rates
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 100 ppb decrease in daily ozone levels might, in turn, decrease
hospital admissions by 3 percent. The baseline incidence of the health effect is necessary to
convert this relative change into a number of cases. A baseline incidence rate 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 100,000 people, that number must be
multiplied by the number of 100,000s in the population.
Table 6-3 summarizes the sources of baseline incidence rates and provides 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. In most cases, we used a single national incidence rate, due to a lack of
more spatially disaggregated data. 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. Regional incidence rates are available for hospital admissions, and county-level
27 Denman, et al, 2007: Couplings Between Changes in the Climate System and
Biogeochemistry. In: Climate Change 2007: The Physical Science Basis.
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data are available for premature mortality. We have projected mortality rates such that future
mortality rates are consistent with our projections of population growth (Abt Associates, 2005).
Table 6-3. National Average Baseline Incidence Rates
Rate per 100 people per year D by Age Group
Endpoint
Source
Notes
<18 18-24 25-34 35-44 45-54 55-64 65+
Mortality
CDC Compressed Mortality File,
accessed through CDC Wonder (1996-
1998)
accidental
0.025 0.022 0.057 0.150 0.383 1.006 4.937
Respiratory
Hospital
Admissions.
1999 NHDS public use data files3
incidence 0.043 0.084 0.206 0.678 1.926 4.389 11.629
Asthma ER visits
2000 NHAMCS public use data filesc
1999 NHDS public use data files3
incidence 1.011 1.087 0.751 0.438 0.352 0.425 0.232
Minor Restricted
Activity Days
(MRADs)
Ostro and Rothschild (1989, p. 243) incidence
780 780 780 780 780
School Loss Days
National Center for Education
Statistics (1996) and 1996 HIS
(Adams et al., 1999, Table 47);
estimate of 180 school days per year
all-cause 990.0
The following abbreviations are used to describe the national surveys conducted by the National Center for Health Statistics:
HIS refers to the National Health Interview Survey; NHDS - National Hospital Discharge Survey; NHAMCS - National Hospital
Ambulatory Medical Care Survey.
BSeeftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/
cSeeftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/
All of the rates reported here are population-weighted incidence rates per 100 people per year. Additional details on the
incidence and prevalence rates, as well as the sources for these rates are available upon request.
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Table 6-3 National Average Baseline Incidence Rates (continued)
Rate per 100 people per
Endpoint
Asthma Exacerbations
Source Notes
Incidence (and
prevalence)
among
Ostro et al. (200 1 ) asthmatic
African-
American
children
Daily wheeze
Daily cough
Daily dyspnea
year
0.076 (0.173)
0.067 (0.145)
0.037 (0.074)
Incidence (and Daily wheeze
prevalence)
Vedal et al. (1998) among Daily cough
asthmatic
children Daily dyspnea
0.038
0.086
0.045
6.4 Economic Values for Health Outcomes
Reductions in ambient concentrations of air pollution generally lower the risk of future adverse
health effects for a large population. Therefore, the appropriate economic measure is
willingness-to-pay (WTP) for changes in risk of a health effect rather than WTP for a health
effect that would occur with certainty (Freeman, 1993). Epidemiological studies generally
provide estimates of the relative risks of a particular health effect that is avoided because of a
reduction in air pollution. We converted those to units of avoided statistical incidence for ease of
presentation. We calculated the value of avoided statistical incidences by dividing individual
WTP for a risk reduction by the related observed change in risk. For example, suppose a
pollution-reduction regulation is able to reduce 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 death is $1 million ($100/0.0001 change in risk).
WTP estimates generally are not available for some health effects, such as hospital admissions.
In these cases, we used the cost of treating or mitigating the effect as a primary estimate. These
cost-of-illness (COI) estimates generally understate the true value of reducing the risk of a health
effect, because they reflect the direct expenditures related to treatment, but not the value of
avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987). We provide unit
values for health endpoints (along with information on the distribution of the unit value) in Table
6-4. All values are in constant year 2000 dollars, adjusted for growth in real income out to 2020
using projections provided by Standard and Poor's. Economic theory argues that WTP for most
goods (such as environmental protection) will increase if real income increases. Many of the
valuation studies used in this analysis were conducted in the late 1980s and early 1990s.
Because real income has grown since the studies were conducted, people's willingness to pay for
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reductions in the risk of premature death and disease likely has grown as well. We did not adjust
cost of illness-based values because they are based on current costs. Similarly, we did not adjust
the value of school absences, because that value is based on current wage rates. Table 6.4
presents the values for individual endpoints adjusted to year 2020 income levels. The discussion
below provides additional details on ozone related endpoints. For details on valuation estimates
for PM related endpoints, see the 2006 PM NAAQS RIA.
6.4.1 Mortality Valuation
To estimate the monetary benefit of reducing the risk of premature death, we used the "value of
statistical lives" saved (VSL) approach, which is a summary measure for the value of small
changes in mortality risk for a large number of people. The VSL approach applies information
from several published value-of-life studies to determine a reasonable monetary value of
preventing premature mortality. The mean value of avoiding one statistical death is estimated to
be roughly $5.5 million at 1990 income levels (2000 $), and $6.6 million at 2020 income levels.
This represents an intermediate value from a variety of estimates in the economics literature (see
the 2006 PM NAAQS RIA for more details on the calculation of VSL).
6.4.2 Hospital Admissions Valuation
In the absence of estimates of societal WTP to avoid hospital visits/admissions for specific
illnesses, estimates of total cost of illness (total medical costs plus the value of lost productivity)
typically are used as conservative, or lower bound, estimates. These estimates are biased
downward, because they do not include the willingness-to-pay value of avoiding pain and
suffering.
The International Classification of Diseases (ICD-9, 1979) code-specific COI estimates used in
this analysis consist of estimated hospital charges and the estimated opportunity cost of time
spent in the hospital (based on the average length of a hospital stay for the illness). We based all
estimates of hospital charges and length of stays on statistics provided by the Agency for
Healthcare Research and Quality (AHRQ 2000). We estimated the opportunity cost of a day
spent in the hospital as the value of the lost daily wage, regardless of whether the hospitalized
individual is in the workforce. To estimate the lost daily wage, we divided the 1990 median
weekly wage by five and inflated the result to year 2000$ using the CPI-U "all items." The
resulting estimate is $109.35. The total cost-of-illness estimate for an ICD code-specific hospital
stay lasting n days, then, was the mean hospital charge plus $109 • n.
6.4.3 Asthma-Related Emergency Room Visits Valuation
To value asthma emergency room visits, we used a simple average of two estimates from the
health economics literature. The first estimate comes from Smith et al. (1997), who reported
approximately 1.2 million asthma-related emergency room visits in 1987, at a total cost of
$186.5 million (1987$). The average cost per visit that year was $155; in 2000$, that cost was
$311.55 (using the CPI-U for medical care to adjust to 2000$). The second estimate comes from
Stanford et al. (1999), who reported the cost of an average asthma-related emergency room visit
at $260.67, based on 1996-1997 data. A simple average of the two estimates yields a (rounded)
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unit value of $286.
6.4.4 Minor Restricted Activity Days Valuation
No studies are reported to have estimated WTP to avoid a minor restricted activity day.
However, one of EPA's contractors, lEc (1993) has derived an estimate of willingness to pay to
avoid a minor respiratory restricted activity day, using estimates from Tolley et al. (1986) of
WTP for avoiding a combination of coughing, throat congestion and sinusitis. The lEc estimate
of WTP to avoid a minor respiratory restricted activity day is $38.37 (1990$), or about $52
($2000).
Although Ostro and Rothschild (1989) statistically linked ozone and minor restricted activity
days, it is likely that most MRADs associated with ozone exposure are, in fact, minor respiratory
restricted activity days. For the purpose of valuing this health endpoint, we used the estimate of
mean WTP to avoid a minor respiratory restricted activity day.
6.4.5 School Absences
To value a school absence, we: (1) estimated the probability that if a school child stays home
from school, a parent will have to stay home from work to care for the child; and (2) valued the
lost productivity at the parent's wage. To do this, we estimated the number of families with
school-age children in which both parents work, and we valued a school-loss day as the
probability that such a day also would result in a work-loss day. We calculated this value by
multiplying the proportion of households with school-age children by a measure of lost wages.
We used this method in the absence of a preferable WTP method. However, this approach
suffers from several uncertainties. First, it omits willingness to pay to avoid the symptoms/illness
that resulted in the school absence; second, it effectively gives zero value to school absences that
do not result in work-loss days; and third, it uses conservative assumptions about the wages of
the parent staying home with the child. Finally, this method assumes that parents are unable to
work from home. If this is not a valid assumption, then there would be no lost wages.
For this valuation approach, we assumed that in a household with two working parents, the
female parent will stay home with a sick child. From the Statistical Abstract of the United States
(U.S. Census Bureau, 2001), we obtained: (1) the numbers of single, married and "other"
(widowed, divorced or separated) working women with children; and (2) the rates of
participation in the workforce of single, married and "other" women with children. From these
two sets of statistics, we calculated a weighted average participation rate of 72.85 percent.
Our estimate of daily lost wage (wages lost if a mother must stay at home with a sick child) is
based on the year 2000 median weekly wage among women ages 25 and older (U.S. Census
Bureau, 2001). This median weekly wage is $551. Dividing by five gives an estimated median
daily wage of $ 103. To estimate the expected lost wages on a day when a mother has to stay
home with a school-age child, we first estimated the probability that the mother is in the
workforce then multiplied that estimate by the daily wage she would lose by missing a work day:
72.85 percent times $103, for a total loss of $75. This valuation approach is similar to that used
by Hall et al. (2003).
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Table 6-4. Unit Values for Economic Valuation of Health Endpoints (2000$)
Health Endpoint
Central Estimate of Value Per
Statistical Incidence
1990 Income
Level
2020 Income
Level
Derivation of Distributions of Estimates
Premature Mortality (Value
of a Statistical Life)
$5,500,000
$6,600,000
Point estimate is the mean of a normal distribution with a 95% confidence interval between
$1 and $10 million. Confidence interval is based on two meta-analyses of the wage-risk
VSL literature: $1 million represents the lower end of the interquartile range from the
Mrozek and Taylor (2002) meta-analysis and $10 million represents the upper end of the
interquartile range from the Viscusi and Aldy (2003) meta-analysis. The mean of the
distribution is consistent with the mean estimate from a third meta-analysis (Kochi et al
2006). The VSL represents the value of a small change in mortality risk aggregated over the
affected population.
Chronic Bronchitis (CB)
$340,000
$420,000
The WTP to avoid a case of pollution-related CB is calculated as
WTPx = WTPU *e " " ^ where x is the severity of an average CB case, WTP13 is the WTP
for a severe case of CB, and is the parameter relating WTP to severity, based on the
regression results reported in Krupnick and Cropper (1992). The distribution of WTP for an
average severity-level case of CB was generated by Monte Carlo methods, drawing from
each of three distributions: (1) WTP to avoid a severe case of CB is assigned a 1/9
probability of being each of the first nine deciles of the distribution of WTP responses in
Viscusi et al. (1991); (2) the severity of a pollution-related case of CB (relative to the case
described in the Viscusi study) is assumed to have a triangular distribution, with the most
likely value at severity level 6.5 and endpoints at 1.0 and 12.0; and (3) the constant in the
elasticity of WTP with respect to severity is normally distributed with mean = 0.18 and
standard deviation = 0.0669 (from Krupnick and Cropper [1992]). This process and the
rationale for choosing it is described in detail in the Costs and Benefits of the Clean Air Act,
1990 to 2010 (EPA, 1999).
(continued)
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Table 6-4: Unit Values Used for Economic Valuation of Health Endpoints (2000$) (continued)
Health Endpoint
Central Estimate of Value Per
Statistical Incidence
1990 Income
Level
2020 Income
Level
Derivation of Distributions of Estimates
Nonfatal Myocardial
Infarction (heart attack)
3% discount rate
Age 0-24
Age 25^t4
Age 45-54
Age 55-65
Age 66 and over
7% discount rate
Age 0-24
Age 25^t4
Age 45-54
Age 55-65
Age 66 and over
$66,902
$74,676
$78,834
$140,649
$66,902
$65,293
$73,149
$76,871
$132,214
$65,293
$66,902
$74,676
$78,834
$140,649
$66,902
$65,293
$73,149
$76,871
$132,214
$65,293
No distributional information available. Age-specific cost-of-illness values reflect lost
earnings and direct medical costs over a 5-year period following a nonfatal Ml. Lost
earnings estimates are based on Cropper and Krupnick (1990). Direct medical costs are
based on simple average of estimates from Russell et al. (1998) and Wittels et al. (1990).
Lost earnings:
Cropper and Krupnick (1990). Present discounted value of 5 years of lost earnings:
age of onset: at 3% at 7%
25-44 $8,774 $7,855
45-54 $12,932 $11,578
55-65 $74,746 $66,920
Direct medical expenses: An average of:
1. Wittels et al. (1990) ($102,658—no discounting)
2. Russell etal. (1998), 5-year period ($22,331 at 3% discount rate; $21,113 at 7%
discount rate)
Hospital Admissions
Chronic Obstructive
Pulmonary Disease
(COPD)
Asthma Admissions
All Cardiovascular
All respiratory (ages 65+)
$12,378
$6,634
$18,387
$18,353
$12,378
$6,634
$18,387
$18,353
No distributional information available. The COI estimates (lost earnings plus direct medical
costs) are based on ICD-9 code-level information (e.g., average hospital care costs,
average length of hospital stay, and weighted share of total COPD category illnesses)
reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov).
No distributional information available. The COI estimates (lost earnings plus direct medical
costs) are based on ICD-9 code-level information (e.g., average hospital care costs,
average length of hospital stay, and weighted share of total asthma category illnesses)
reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov).
No distributional information available. The COI estimates (lost earnings plus direct medical
costs) are based on ICD-9 code-level information (e.g., average hospital care costs,
average length of hospital stay, and weighted share of total cardiovascular category
illnesses) reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov).
No distributions available. The COI point estimates (lost earnings plus direct medical costs)
are based on ICD-9 code level information (e.g., average hospital care costs, average
length of hospital stay, and weighted share of total COPD category illnesses) reported in
Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov).
6-28
-------
Table 6-4: Unit Values Used for Economic Valuation of Health Endpoints (2000$) (continued)
Health Endpoint
Central Estimate of Value Per
Statistical Incidence
1990 Income
Level
2020 Income
Level
Derivation of Distributions of Estimates
All respiratory (ages 0-2)
$7,741
$7,741
No distributions available. The COI point estimates (lost earnings plus direct medical costs)
are based on ICD-9 code level information (e.g., average hospital care costs, average
length of hospital stay, and weighted share of total COPD category illnesses) reported in
Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov).
Emergency Room Visits
for Asthma
$286
$286
No distributional information available. Simple average of two unit COI values:
(1) $311.55, from Smith et al. (1997) and
(2) $260.67, from Stanford et al. (1999).
Respiratory Ailments Not Requiring Hospitalization
Upper Respiratory
Symptoms (URS)
$25
$27
Combinations of the three symptoms for which WTP estimates are available that closely
match those listed by Pope et al. result in seven different "symptom clusters," each
describing a "type" of URS. A dollar value was derived for each type of URS, using mid-
range estimates of WTP (lEc, 1994) to avoid each symptom in the cluster and assuming
additivity of WTPs. In the absence of information surrounding the frequency with which
each of the seven types of URS occurs within the URS symptom complex, we assumed a
uniform distribution between $9.2 and $43.1.
Lower Respiratory
Symptoms (LRS)
$16
$18
Combinations of the four symptoms for which WTP estimates are available that closely
match those listed by Schwartz et al. result in 11 different "symptom clusters," each
describing a "type" of LRS. A dollar value was derived for each type of LRS, using mid-
range estimates of WTP (lEc, 1994) to avoid each symptom in the cluster and assuming
additivity of WTPs. The dollar value for LRS is the average of the dollar values for the 11
different types of LRS. In the absence of information surrounding the frequency with which
each of the 11 types of LRS occurs within the LRS symptom complex, we assumed a
uniform distribution between $6.9 and $24.46.
Asthma Exacerbations
$42
$45
Asthma exacerbations are valued at $45 per incidence, based on the mean of average
WTP estimates for the four severity definitions of a "bad asthma day," described in Rowe
and Chestnut (1986). This study surveyed asthmatics to estimate WTP for avoidance of a
"bad asthma day," as defined by the subjects. For purposes of valuation, an asthma
exacerbation is assumed to be equivalent to a day in which asthma is moderate or worse as
reported in the Rowe and Chestnut (1986) study. The value is assumed have a uniform
distribution between $15.6 and $70.8.
(continued)
6-29
-------
Table 6-4: Unit Values Used for Economic Valuation of Health Endpoints (2000$) (continued)
Health Endpoint
Acute Bronchitis
Work Loss Days (WLDs)
Minor Restricted Activity
Days (MRADs)
School Absence Days
Central Estimate of Value Per
Statistical Incidence
1990 Income
Level
$360
Variable (U.S.
median=$110)
$51
$75
2020 Income
Level
$380
$54
$75
Derivation of Distributions of Estimates
Assumes a 6-day episode, with the distribution of the daily value specified as uniform with
the low and high values based on those recommended for related respiratory symptoms in
Neumann et al. (1 994). The low daily estimate of $1 0 is the sum of the mid-range values
recommended by lEc (1994) for two symptoms believed to be associated with acute
bronchitis: coughing and chest tightness. The high daily estimate was taken to be twice the
value of a minor respiratory restricted-activity day, or $1 1 0.
No distribution available. Point estimate is based on county-specific median annual wages
divided by 50 (assuming 2 weeks of vacation) and then by 5 — to get median daily wage.
U.S. Year 2000 Census, compiled by Geolytics, Inc.
Median WTP estimate to avoid one MRAD from Tolley et al. (1986). Distribution is
assumed to be triangular with a minimum of $22 and a maximum of $83, with a most likely
value of $52. Range is based on assumption that value should exceed WTP for a single
mild symptom (the highest estimate for a single symptom — for eye irritation — is $16.00) and
be less than that for a WLD. The triangular distribution acknowledges that the actual value
is likely to be closer to the point estimate than either extreme.
No distribution available
6-30
-------
6.5 Results and Implications
Tables 6-5 through 6-28 summarize the reduction in incidence for ozone- and PM-related health
endpoints for each of the alternative ozone standards evaluated. Tables 6-29 through 6-44
summarize the ozone-related economic benefits for each of the alternative standards.28 Note that
incidence and valuation estimates for each standard alternative are broken into two sets of tables.
The first set of tables summarizes incidence and valuation for simulated national attainment with
the standard alternative in the East and areas outside of California, and "glidepath" attainment in
California. The second set of tables present incidence and valuation estimates for California post-
2020, to account for the additional emission reductions projected to occur as a result of full
implementation of a series of mobile source rules. In addition to the mean incidence estimates, we
have included 5th and 95th percentile estimates, except where noted, based on the Monte Carlo
simulations described above. In the tables presenting the 0.065 ppm and 0.070 ppm estimates, the
total change in ozone-related incidence from fully attaining the alternative standards is broken out
into the change in incidence associated with the modeled partial attainment scenario and the sum
of the change in incidence associated with achieving the partial attainment increment plus the
residual attainment increment. As described in Appendix 6, to calculate the change in ozone
concentrations to reach full attainment, we rolled back the ozone monitor data so that the 4th
highest daily maximum 8-hour average just met the level required to attain the alternative standard.
This approach will likely understate the benefits that would occur due to implementation of actual
controls to reduce ozone precursor emissions because controls implemented to reduce ozone
concentrations at the highest monitor would likely result in some reductions in ozone
concentrations at attaining monitors down-wind (i.e. the controls would lead to concentrations
below the standard in down-wind locations). Therefore, air quality improvements and resulting
health benefits from full attainment would be more widespread than we have estimated in our
rollback analyses. The incidence and valuation results for attainment of the 0.075 ppm alternative
are derived through an interpolation technique described in Appendix 6. As such, these estimates
are presented as full attainment only. The incidence and valuation estimates for attainment of the
0.079 ppm alternative are derived through monitor rollbacks alone and thus are presented as full
attainment only.
In addition to disaggregating ozone benefits between modeled and rollback for the 0.070 ppm and
0.065 ppm standard alternatives, we also provide disaggregation by region, with separate benefits
estimates for the Eastern U.S., California, and the Western U.S. outside of California. The
estimates of ozone-related mortality and morbidity for California are broken into glidepath and full
attainment. Certain California projected non-attainment counties are required to meet an ozone
target above the actual standard (that is, a "glidepath") by 2020 due to the severity of non-
attainment. The estimates in this column reflect the benefits of meeting this target.
6.5.1 Glidepath incidence and valuation estimates for 0.065 ppm and 0.075 ppm alternatives
This analysis includes an assessment of the benefits of reaching the glidepath targets for each of
the standard alternatives in 2020 in California. Due to time and resource limitations, we were able
28 Note that the valuation estimates for ozone benefits are not discounted. Because these are short
term benefits that occur the same year in which the alternate standard is met, discounting is not
necessary.
6-31
-------
to perform a full scale benefits analysis of the California glidepath targets for the 0.070 ppm
alternative only. Thus, we derived the glidepath benefits estimates for the 0.075 ppm and 0.065
ppm alternatives by applying a scaling factor. This scaling factor represents the ratio of the
California 0.070 ppm glidepath full attainment benefits to the California 0.070 ppm full attainment
benefits. This process entailed the following steps: (1) calculate the ratio of the California 0.070
ppm glidepath target benefits to the California 0.070 ppm full attainment benefits for each
incidence and valuation estimate; (2) multiply this ratio by the California full attainment 0.075
ppm and 0.065 ppm incidence and valuation estimate to derive glidepath estimates. Because these
results are scaled, it was not possible to generate confidence intervals.
While clearly the 2020 glidepath targets for the current and alternative standards vary among the
standard alternatives, the relative air quality increment between the glidepath base and control
cases in California is nearly identical among the standard alternatives. As such, we believe this
scaling approach is a valid technique to develop screening-level estimates of 0.065 ppm and 0.075
ppm California glidepath benefits.
6.5.2 PM2.5 co-benefit estimates
As discussed further below, tables 6-9, 6-10, 6-15, 6-16, 6-21, 6-22, 6-27 and 6-28 present the
PM2.5 co-benefits associated with full attainment of the 0.065 ppm, 0.070 ppm, 0.075 ppm and
0.079 ppm alternatives. To derive estimates of incidence and valuation for the PM2 5 related co-
benefits of full attainment of each ozone standard alternative, we applied two different scaling
techniques. To estimate total valuation estimates, we applied benefit per-ton metrics; this
procedure is detailed further below. Note that the valuation estimates of the PM2.5-related full
attainment benefits are presented at a 3% discount rate; due to time and resource limitations it was
not possible to calculate these benefits at a 7% discount rate. Had we performed this calculation,
we estimate that PM2.5-related full attainment co-benefits would be approximately 15% lower. All
PM2.5 co-benefit estimates are incremental to the 2006 PM NAAQS RIA.
To estimate total incidence estimates, we applied a simple scaling factor. To estimate PM2.5-related
incidence associated with the attainment of each ozone alternative, we calculated a separate scaling
factor as follows: (1) we calculated the ratio of the full attainment PM2.5 valuation estimate
(calculated using the benefit per ton metrics described below) to the partial attainment to the partial
attainment PM2 5 valuation estimate; (2) multiply this scaling ratio against each of the PM2 5 partial
attainment mortality and morbidity endpoints to generate a scaled estimate of mortality and
morbidity. While there are clearly substantial uncertainties inherent in this technique, it does
produce useful screening-level estimates of PM2.5-related incidence
6-32
-------
Table 6-5: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the Incidence of Premature Mortality
Associated with Ozone Exposure in 2020 (Incremental to Current Ozone Standard)0
Eastern U.S.
Model or
AssumptionA
NMMAPS
Meta-Analysis
Assumption that
is not causal
Reference
Bell et al. 2004
Bell etal. 2005
Levy et al. 2005
Ito etal. 2005
association
Modeled Partial
Attainment
130
(45--220)
540
(260--820)
780
(540-1,000)
590
(360--820)
0
Full Attainment
480
(160-790)
1,900
(930-2,900)
2,100
(1,500-2,800)
2,100
(1,300-2,900)
0
Western U.S.
Excluding California
Modeled
Partial Full
Attainment Attainment
Arithmetic Mean8
(95% Credible Intervals)0
0.23 43
(0.08--0.37) (15-72)
0.86 180
(0.42-1.3) (86-270)
31 190
(22--41) (130-250)
1 190
(0.6--1.4) (120-270)
0 0
California
GlidepathE
National 2020
Benefits
8.5
34
32
37
0
530
2,100
2,400
2,300
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number
of lives saved are based on an assumption of a normal distribution.
c A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics.
D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
E This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-33
-------
Table 6-6: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the Incidence of Morbidity Associated
with Ozone Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parentheses)8
Eastern U.S.
Morbidity Endpoint
Hospital Admissions
(ages 0-1)
Hospital Admissions
(ages 65-99)
Emergency Department
Visits, Asthma-RelatedA
School Absences
Minor Restricted Activity
Days
Modeled Partial
Attainment
960
(410-1,500)
1,100
(52—2,800)
830
(-230-2,500)
410,000
(100,000-1,000,000)
1,100,000
(460,000-1,800,000)
Full Attainment
2,700
(1,200-4,300)
3,900
(180—9,800)
2,500
(-680-7,700)
1,200,000
(290,000—3,000,000)
3,200,000
(1,300,000—5,000,000)
Western U.S. Excluding
California
Modeled Partial
Attainment
53
(23-83)
3.8
(0.17-9.4)
21
(-5.8-66)
20,000
(4,900-53,000)
49,000
(20,000-78,000)
Full Attainment
330
(150-520)
320
(16—790)
130
(-35-400)
120,000
(30,000—310,000)
310,000
(130,000—490,000)
California
Glidepath
Attainment0
48
57
19
19,000
50,000
National 2020
Benefits
3,100
4,300
2,600
1,300,000
3,500,000
AThe negative 5th percentile incidence estimates for this health endpoint are a result of the weak statistical power of the study and should not be inferred to
indicate that decreased ozone exposure may cause an increase in asthma-related emergency department visits.
B All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
GThis table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-34
-------
Table 6-7: Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual Reductions in
the Incidence of Premature Mortality Associated with Ozone Exposure
(Incremental to Current Ozone Standard)E
California
Model or Incremental Post-
AssumptionA Reference California GlidepathB 2020 Benefits0 California Total0
NMMAPS
Meta-Analysis
Bell et al.
Bell etal.
Levy et al
2004
2005
. 2005
Ito et al. 2005
Assumption that
is not causal
association
8.5
34
32
37
0
95
390
420
420
0
100
420
450
450
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B Two areas in California have high levels of ozone and are not planning to meet the current standard until after 2020. The estimates in the table reflect a
progress point in 2020 or "glidepath target" for the two California areas.
c Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
E All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-35
-------
Table 6-8: Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual
Reductions in the Incidence of Morbidity Associated with Ozone Exposure D
California California Incremental
Morbidity Endpoint GlidepathA Post-2020 Benefits8 California Totaf
Hospital Admissions .„ c^n ssn
(ages 0-1) 48 83° 88°
Hospital Admissions „ .,_.- .,-,.-
(ages 65-99) 57 62° 67°
Emergency Department Visits, 1Q 7Qn _,in
Asthma-RelatedA iy zyu J1U
School Absences 19,000 320,000 340,000
Minor Restricted Activity Days 50,000 780,000 830,000
A Two areas in California have high levels of ozone and are not planning to meet the current standard until after 2020. The estimates in the table
reflect a progress point in 2020 or "glidepath target" for the two California areas.
B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
c This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-36
-------
Table 6-9: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of PM Premature Mortality associate with PM co-benefit0
National + 2020
California Glidepath
Benefits
Mortality Impact Functions Derived from
ACS StudyA
Harvard Six-City Study5
Woodruff et al 1997 (infant mortality)
Mortality Impact Functions Derived from
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Eoidemioloav Literature
1,800
4,000
4
Expert Elicitation
5,500
4,200
4,100
2,900
6,800
3,800
2,400
3,100
4,100
3,300
660
3,000
California
Glidepath
33
75
0.1
100
78
77
55
130
71
45
58
77
62
12
57
Incremental
Post-2020
Benefits
160
360
0.34
490
370
370
260
610
340
220
280
370
300
59
270
Total
190
430
0.41
590
450
450
320
740
410
260
330
440
360
72
330
A The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which
has previously been reported as the primary estimate in recent RIAs
B Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has
previously been a sensitivity estimate.
Q
All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to
2006 PM NAAOS RIA. Estimates do not include confidence intervals because thev were derived through a scaling techniaue described above.
6-37
-------
Table 6-10: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of Morbidity Associated with PM Co-benefit*
National + 2020
California Glidepath
Benefits
Morbidity Impact Functions Derived from Epidemiology Literature
Chronic Bronchitis (age >25 and over)
Nonfatal myocardial infarction (age >17)
Hospital admissions—respiratory (all ages)
Hospital admissions-- cardiovascular
(age >17)
Emergency room visits for asthma
(age <19)
Acute bronchitis (age 8-12)
Lower respiratory symptoms (age 7-14)
Upper respiratory symptoms (asthmatic
children age 9-18)
Asthma exacerbation (asthmatic children
age 6-18)
Work loss days (age 18-65)
Minor restricted activity days (age 18-65)
1,300
4,000
460
930
2,000
3,500
29,000
22,000
27,000
190,000
1,100,000
California
Glidepath
25
74
9
17
35
65
550
400
500
3,500
21,000
Incremental
Post-2020
Benefits
120
350
41
83
180
310
2,600
1,900
2,400
17,000
100,000
Total
150
430
50
100
210
380
3,200
2,300
2,900
20,000
120,000
All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates
incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were derived through a scaling technique described above.
6-38
-------
Table 6-11: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in the Incidence of Premature Mortality
Associated with Ozone Exposure (Incremental to Current Ozone Standard)E
Eastern U.S.
Model or Assumption* Reference
NMMAPS Bell et al. 2004
Bell et al. 2005
Meta-Analysis Levy et al. 2005
Ito et al. 2005
Assumption that
association is not causal
Modeled
Partial
Attainment
Full
Attainment
Western U.S. Excluding
California
Modeled
Partial
Attainment
Full Attainment
Arithmetic Mean8
(95% Credible Intervals)
130
(45-220)
540
(260-820)
780
(540-1,000)
590
(360-820)
0
260
(88-440)
1,100
(510-1,600)
1,300
(900-1,700)
1,200
(700-1,600)
0
0.23
(0.08-0.37)
0.86
(0.42-1.3)
31
(22-41)
1
(0.6-1.4)
0
11
(3.8-19)
47
(23-71)
73
(50-95)
50
(30-70)
0
California
Glidepath
Attainment0
2020 National
Attainment
c
5.5
(1.8-9.1)
22
(11-34)
21
(14-27)
24
(15-34)
0
280
(93-470)
1,100
(540-1,700)
1,400
(960-1,800)
1,200
(740-1,700)
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number
of lives saved are based on an assumption of a normal distribution.
c A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics.
DThis table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
E All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-39
-------
Table 6-12: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in the Incidence of Morbidity Associated with Ozone
Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parentheses)0
Eastern U.S.
Morbidity
Endpoint
Hospital Admissions
(ages 0-1)
Hospital Admissions
(ages 65-99)
Emergency
Department Visits,
Asthma-Related*
School Absences
Minor Restricted
Activity Days
Modeled Partial
Attainment
960
(410-1,500)
1,100
(52—2,800)
830
(-230-2,500)
410,000
(100,000-1,000,000)
1,100,000
(460,000-1,800,000)
Full Attainment
1,700
(720-2,600)
2,100
(100-5,400)
1,500
(-400-4,300)
720,000
(170,000-1,800,000)
1,900,000
(790,000-3,000,000)
Western U.S. Excluding
California
Modeled Partial
Attainment
53
(23-83)
3.8
(0.17-9.4)
21
(-5.8-66)
20,000
(4,900-53,000)
49,000
(20,000-78,000)
Full Attainment
130
(55-200)
86
(4.2-210)
50
(-13-150)
47,000
(11,000-120,000)
120,000
(49,000-190,000)
California
Glidepath Attainment6
33
(14-51)
37
(1.8-92)
13
(-3.5-37)
13,000
(3,100-33,000)
34,000
(14,000-53,000)
2020 National
Benefits
1,800
(790—2,900)
2,300
(110—5,700)
1,500
(-420—4,500)
780,000
(190,000-1,900,000)
2,100,000
(850,000-3,300,000)
AThe negative 5 percentile incidence estimates for this health endpoint are a result of the weak statistical power of the study and should not be inferred to
indicate that decreased ozone exposure may cause an increase in asthma-related emergency department visits.
B This table reflects full attainment in all locations of the U. S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
c All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns.
6-40
-------
Table 6-13: Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual Reductions in
the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current
Ozone Standard)E
California
Model or Incremental Post-
AssumptionA Reference California Glidepath3 2020 Benefits0 California Total0
NMMAPS
Meta-Analysis
Bell et al.
Bell et al.
Levy et al
2004
2005
. 2005
Ito et al. 2005
Assumption that
is not causal
association
5.5
(1.8-9.1)
22
(11-34)
21
(14-27)
24
(15-34)
0
56
230
250
250
0
62
250
280
270
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B Two areas in California have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress
point in 2020 or "glidepath target" for the two California areas.
c Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
E All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-41
-------
Table 6-14: Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual
Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to
Current Ozone Standard, 95% Confidence Intervals in Parentheses)0
California California Incremental
Morbidity Endpoint GlidepathA Post-2020 Benefits8 California Totaf
Hospital Admissions
(ages 0-1)
Hospital Admissions
(ages 65-99)
Emergency Department Visits,
Asthma-RelatedA
33
(14-51)
37
(1.8-92)
13
(-3.5-37)
520
370
180
560
400
190
School Absences (3,100-™%) 200'000 210'000
Minor Restricted Activity Days Ooo'.°53 QOO) 480,000 520,000
A Two areas in California have high levels of ozone and not planning to meet the current standard until after 2020. The estimates in the table reflect a progress
point in 2020 or "glidepath target" for the two California areas.
B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
c This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-42
-------
Table 6-15: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of PM Premature Mortality associate with PM co-benefit0
National + 2020
California Glidepath
Benefits
Mortality Impact Functions Derived from
ACS StudyA
Harvard Six-City Study5
Woodruff et al 1997 (infant mortality)
Mortality Impact Functions Derived from
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Eoidemioloav Literature
1,000
2,300
2
Expert Elicitation
3,200
2,400
2,400
1,700
4,000
2,200
1,400
1,800
2,400
1,900
390
1,800
California
Glidepath
13
30
0
41
31
31
22
51
28
18
23
31
25
5
23
Incremental
Post-2020
Benefits
120
270
0.3
360
280
280
190
450
250
160
200
270
220
44
200
Total
130
300
0.3
410
310
310
220
500
280
180
230
300
250
49
220
The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which
has previously been reported as the primary estimate in recent RIAs
B Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has
previously been a sensitivity estimate.
All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to
2006 PM NAAOS RIA. Estimates do not include confidence intervals because thev were derived through a scaling techniaue described above.
6-43
-------
Table 6-16: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of Morbidity Associated with PM Co-benefit*
National + 2020
California Glidepath
Benefits
Morbidity Impact Functions Derived from Epidemiology Literature
Chronic Bronchitis (age >25 and over)
Nonfatal myocardial infarction (age >17)
Hospital admissions—respiratory (all ages)
Hospital admissions-- cardiovascular
(age >17)
Emergency room visits for asthma
(age <19)
Acute bronchitis (age 8-12)
Lower respiratory symptoms (age 7-14)
Upper respiratory symptoms (asthmatic
children age 9-18)
Asthma exacerbation (asthmatic children
age 6-18)
Work loss days (age 18-65)
Minor restricted activity days (age 18-65)
780
2,300
270
540
1,200
2,000
17,000
13,000
16,000
110,000
650,000
California
Glidepath
10
30
3
7
14
26
220
160
200
1,400
8,300
Incremental
Post-2020
Benefits
89
260
31
62
130
230
2,000
1,400
1,800
12,000
74,000
Total
99
290
34
69
150
260
2,200
1,600
2,000
14,000
82,000
All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates
incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were derived through a scaling technique described above.
6-44
-------
Table 6-17: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in the Incidence of Premature
Mortality Ozone Exposures (Incremental to Current Ozone Standard)0
Model or Assumption1* Reference
NMMAPS Bell et al. 2004
Bell et al. 2005
Meta-Analysis Levy et al. 2005
Ito et al. 2005
Assumption that association
is not causal
WesternU.S. California
Excluding
Eastern U.S. California Glidepath Attainment'
2020 National
Benefits
Arithmetic Mean8
(95% Credible Intervals)0
190 8.9 0
840 40 0
1,100 65 0
920 43 0
I
200
880
1,100
960
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number
of lives saved are based on an assumption of a normal distribution.
c A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible
intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that precluded us from
generating such estimates.
D All estimates rounded to two significant figures. As such, totals will not sum across columns
EThis table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-45
-------
Table 6-18: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in the Incidence of Morbidity Associated
with Ozone Exposure (Incremental to Current Ozone Standard)*'6
Morbidity End point
Hospital Admissions (ages 0-1)
Hospital Admissions (ages 65-99)
Emergency Department Visits, Asthma-Related
School Absences
Minor Restricted Activity Days
Western U.S. California
Excluding
Eastern U.S. California Glidepath Attainment0
1,300 110 0
1,700 76 0
1,200 44 0
570,000 42,000 0
1,500,000 110,000 0
2020 National
Benefits
1,400
1,800
1,200
610,000
1,600,000
A Confidence intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that precluded
us from generating such estimates.
B All estimates rounded to two significant figures. As such, totals will not sum across columns
c This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-46
-------
Table 6-19: Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual Reductions in
the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current
Ozone Standard)E
California
Model or Incremental Post-
AssumptionA Reference California Glidepath8 2020 Benefits0 California Total0
NMMAPS
Meta-Analysis
Bell etal.
Bell etal.
Levy et al
2004
2005
. 2005
Ito et al. 2005
Assumption that
is not causal
association
0
0
0
0
0
35
140
ISO
160
0
35
140
ISO
160
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B Two areas in California have high levels of ozone and not planning to meet the current standard until after 2020. The estimates in the table reflect a progress
point in 2020 or "glidepath target" for the two California areas.
c Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
E All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-47
-------
Table 6-20: Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual
Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to
Current Ozone Standard, 95% Confidence Intervals in Parentheses)0
California California Incremental
Morbidity Endpoint GlidepathA Post-2020 Benefits8 California Totaf
Hospital Admissions 32Q
(ages 0-1)
Hospital Admissions n »,n
(ages 65-99)
Emergency Department Visits, _ ... . .-
„ . I r, I . |A U I IU I IU
Asthma-Related
School Absences 0 120,000 120,000
Minor Restricted Activity Days 0 290,000 290,000
A Two areas in California have high levels of ozone and are not planning to meet the current standard until after 2020. The estimates in the table reflect a
progress point in 2020 or "glidepath target" for the two California areas.
B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
c This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-48
-------
Table 6-21: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of PM Premature Mortality associate with PM co-benefit0
National + 2020
California Glidepath
Benefits
Mortality Impact Functions Derived from
ACS StudyA
Harvard Six-City Study5
Woodruff et al 1997 (infant mortality)
Mortality Impact Functions Derived from
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Eoidemioloav Literature
620
1,400
1
Expert Elicitation
1,900
1,500
1,400
1,000
2,400
1,200
840
1,100
1,400
1,200
230
1,100
California
Glidepath
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Incremental
Post-2020
Benefits
70
160
0.2
220
170
160
120
270
150
96
120
160
130
26
120
Total
70
160
0.2
220
170
160
120
270
150
96
120
160
130
26
120
A The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has
previously been reported as the primary estimate in recent RIAs
B Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has
previously been a sensitivity estimate.
All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to 2006
PM NAAQS RIA. Estimates do not include confidence intervals because they were derived through a scaling technique described above.
6-49
-------
Table 6-22: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of Morbidity Associated with PM Co-benefit*
National + 2020
California Glidepath
Benefits
Morbidity Impact Functions Derived from Epidemiology Literature
Chronic Bronchitis (age >25 and over)
Nonfatal myocardial infarction (age >17)
Hospital admissions—respiratory (all ages)
Hospital admissions-- cardiovascular
(age >17)
Emergency room visits for asthma
(age <19)
Acute bronchitis (age 8-12)
Lower respiratory symptoms (age 7-14)
Upper respiratory symptoms (asthmatic
children age 9-18)
Asthma exacerbation (asthmatic children
age 6-18)
Work loss days (age 18-65)
Minor restricted activity days (age 18-65)
470
1,400
160
320
690
1,200
10,000
7,500
9,400
65,000
390,000
California
Glidepath
0
0
0
0
0
0
0
0
0
0
0
Incremental
Post-2020
Benefits
53
160
18
37
78
140
1,200
850
1,100
7,400
44,000
Total
53
160
18
37
78
140
1,200
850
1,100
7,400
44,000
All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates
incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were derived through a scaling technique described above.
6-50
-------
Table 6-23: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in the Incidence of Premature
Mortality Ozone Exposures (Incremental to Current Ozone Standard)0
Model or Assumption1* Reference
NMMAPS Bell et al. 2004
Bell et al. 2005
Meta-Analysis Levy et al. 2005
Ito et al. 2005
Assumption that association
is not causal
Eastern U.S.
19
(7.6-31)
78
(41-120)
78
(56-100)
85
(55-120)
0
WesternU.S. California
Excluding
California Glidepath Attainment'
2020 National
Benefits
Arithmetic Mean8
(95% Credible Intervals)0
0 0
0 0
0 0
0 0
0 0
19
(7.6-31)
78
(41-120)
78
(56-100)
85
(55-120)
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number
of lives saved are based on an assumption of a normal distribution.
c A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible
intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that precluded us from
generating such estimates.
D All estimates rounded to two significant figures. As such, totals will not sum across columns
EThis table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-51
-------
Table 6-24: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in the Incidence of Morbidity Associated
with Ozone Exposure (Incremental to Current Ozone Standard)8
Morbidity End point
Hospital Admissions (ages 0-1)
Hospital Admissions (ages 65-99)
Emergency Department Visits, Asthma-Related
School Absences
Minor Restricted Activity Days
Eastern U.S.
120
(56-180)
160
(7.4-310)
94
(-5.7-250)
50,000
(15,000-76,000)
130,000
(58,000-190,000)
Western U.S. California
Excluding
California Glidepath Attainment A'c
0 0
0 0
0 0
0 0
0 0
2020 National
Benefits
120
(56-180)
160
(7.4-310)
94
(-5.7-250)
50,000
(15,000-76,000)
130,000
(58,000-190,000)
A Confidence intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that precluded
us from generating such estimates.
B All estimates rounded to two significant figures. As such, totals will not sum across columns
c This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-52
-------
Table 6-25: Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual Reductions in
the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current
Ozone Standard)E
California
Model or Incremental Post-
AssumptionA Reference California Glidepath8 2020 Benefits0 California Total0
NMMAPS
Meta-Analysis
Bell etal.
Bell etal.
Levy et al
2004
2005
. 2005
Ito et al. 2005
Assumption that
is not causal
association
0
0
0
0
0
8.4
34
38
37
0
8.4
34
38
37
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B Two areas of California that have high levels of ozone are not planning to meet the current standard until after 2020. The estimates in the table reflect a
progress point in 2020 or "glidepath target" for the two California areas.
c Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
E All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-53
-------
Table 6-26: Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual
Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to
Current Ozone Standard, 95% Confidence Intervals in Parentheses)0
California California Incremental
Morbidity Endpoint GlidepathA Post-2020 Benefits8 California Totaf
Hospital Admissions
(ages 0-1) ° 80 80
Hospital Admissions n
(ages 65-99) U " "
Emergency Department Visits, n _7 _7
Asthma-RelatedA
School Absences 0 30,000 30,000
Minor Restricted Activity Days 0 73,000 73,000
A Two areas have high levels of ozone and are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in
2020 or "glidepath target" for the two California areas.
B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
c This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-54
-------
Table 6-27: Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of PM Premature Mortality associate with PM co-benefit0
National + 2020
California Glidepath
Benefits
Mortality Impact Functions Derived from
ACS StudyA
Harvard Six-City Study5
Woodruff et al 1997 (infant mortality)
Mortality Impact Functions Derived from
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Eoidemioloav Literature
480
1,100
1
Expert Elicitation
1,500
1,200
1,100
800
1,900
1,000
660
840
1,100
910
180
830
California
Glidepath
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Incremental
Post-2020
Benefits
22
50
0.05
68
52
51
36
84
47
30
38
51
41
8.2
37
Total
22
50
0.05
68
52
51
36
84
47
30
38
51
41
8.2
37
A The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has
previously been reported as the primary estimate in recent RIAs
B Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has
previously been a sensitivity estimate.
All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to 2006
PM NAAQS RIA. Estimates do not include confidence intervals because they were derived through a scaling technique described above.
6-55
-------
Table 6-28: Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of Morbidity Associated with PM Co-benefit (95th
percentile confidence intervals provided in parentheses)*
National + 2020
California Glidepath
Benefits
Morbidity Impact Functions Derived from Epidemiology Literature
Chronic Bronchitis (age >25 and over)
Nonfatal myocardial infarction (age >17)
Hospital admissions—respiratory (all ages)
Hospital admissions-- cardiovascular
(age >17)
Emergency room visits for asthma
(age <19)
Acute bronchitis (age 8-12)
Lower respiratory symptoms (age 7-14)
Upper respiratory symptoms (asthmatic
children age 9-18)
Asthma exacerbation (asthmatic children
age 6-18)
Work loss days (age 18-65)
Minor restricted activity days (age 18-65)
370
1,100
130
250
540
950
8,100
5,900
7,300
51,000
310,000
California
Glidepath
0
0
0
0
0
0
0
0
0
0
0
Incremental
Post-2020
Benefits
17
49
5.7
12
24
43
360
270
330
2,300
14,000
Total
17
49
5.7
12
24
43
360
270
330
2,300
14,000
A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates
incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were derived through a scaling technique described above.
6-56
-------
Table 6-29: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Valuation of Reductions in the Incidence of
Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard, Millions of 1999$)°
Eastern U.S.
Modeled Partial
Attainment Full Attainment
Model or
AssumptionA
NMMAPS
Meta-
Analysis
Assumption that
is not causal
Reference
Bell etal. 2004
Bell et al. 2005
Levy et al. 2005
Ito et al. 2005
association
Western U.S. Excluding
California California
Modeled
Partial Glidepath
Attainment Full Attainment AttainmentE
2020 National
Benefits
Arithmetic Mean8
(95% Credible Intervals)0
$850
($120-$!, 900)
$3,500
($550-$7,500)
$5,000
($890-$9,600)
$3,800
($650-$7,500)
0
$3,100
($430-$6,800)
$12,000
($2,000-$26,000)
$14,000
($2,400-$26,000)
$13,000
($2,300-$27,000)
0
$1.4
($0.2-$3.2)
$5.5
($0.9-$12)
$200
($36-$390)
$6.3
($1.1-$13)
0
$28° 454
($39-$620) *
$1'100 4220
($180-$2,400) *
$1'200 4200
($220-$2,400) ?2UU
$1,200
($210-$2,400) *
0 0
$3,400
$14,000
$15,000
$15,000
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number
of lives saved are based on an assumption of a normal distribution.
c A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics.
D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
EThis table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-57
-------
Table 6-30: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the Incidence of Morbidity Associated
with Ozone Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parentheses,
Millions of 1999$)A
Morbidity Endpoint
Hospital Admissions
(ages 0-1)
Hospital Admissions
(ages 65-99)
Emergency Department
Visits, Asthma-Related
School Absences
Worker Productivity
Minor Restricted Activity
Days
Eastern
Modeled Partial
Attainment
$7.1
($3.1~$11)
$19
($0.9--$49)
$0.23
($-0.06--$0.67)
$30
($7.2-$72)
$15
$27
($1.2-$63)
U.S.
Full
Attainment
$20
($8.8--$32)
$68
($3.2-$170)
$0.7
($-0.2-$2)
$87
($21-$210)
$38
$79
($3.4-$180)
Western U.S. Excluding
California California
Modeled Partial
Attainment
$0.39
($0.17-$0.62)
$0.67
($0.003-
$0.2)
--
$1.5
($0.35-$3.8)
$0.38
$1.2
($0.05-$2.8)
Full Attainment Glidepath Attainment6
$2.5 $Q
($1.1~$3.9) $0'4
$5.6 ,,
($0.28--$14) *
$0.04
($-0.009--$0.1)
$8'9 $14
($2.1--$22) *1'^
$3.9 $2.9
$7'6 SI 3
($0.3-$18) *1"3
2020 National
Benefits
$23
$75
$0.7
$97
$45
$87
A All estimates rounded to two significant figures. As such, totals will not sum across columns
BThis table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-58
-------
Table 6-31: Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual Valuation of
Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental
to Current Ozone Standard)E
California
Model or Incremental Post-
AssumptionA Reference California GlidepathB 2020 Benefits0 California Total0
NMMAPS
Meta-Analysis
Bell et al.
Bell etal.
Levy et al
2004
2005
. 2005
Ito et al. 2005
Assumption that
is not causal
association
$54
$220
$200
$240
0
$610
$2,500
$2,600
$2,700
0
$660
$2,700
$2,900
$2,900
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
c Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
E All estimates rounded to two significant figures. As such, totals will not sum across columns
6-59
-------
Table 6-32: Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual
Valuation of Reductions in the Incidence of Morbidity Associated with Ozone Exposure
(Incremental to Current Ozone Standard)0
California California Incremental
Morbidity End point GlidepathA Post-2020 Benefits8 California Totaf
$0.4 $6.2 $6.6
Hospital Admissions . ^ .
(ages 65-99) $1 $11 $12
Emergency Department Visits, . .
Asthma-RelatedA " $0'8 $0'9
School Absences $1.4 $23 $25
Worker Productivity $2.9 $26 $29
Minor Restricted Activity Days $1.3 $19 $21
AThis table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
c This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
D All estimates rounded to two significant figures. As such, totals will not sum across columns
6-60
-------
Table 6-33: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of Reductions in the Incidence of Premature
Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard, Millions of 1999$)E
Eastern U.S.
Model or
AssumptionA Reference
NMMAPS Bell et al. 2004
Bell et al. 2005
Meta-Analysis Levy et a 1. 2005
Ito et al. 2005
Assumption that
association is not
causal
Modeled Partial
Attainment
Full Attainment
Western U.S.
Excluding California
Modeled
Partial
Attainment
California
Full
Attainment Glidepath Attainment0
2020 National
Benefits
Arithmetic Mean8
(95% Credible Intervals)0
$850
($120-$!, 900)
$3,500
($550-$7,500)
$5,000
($890-$9,600)
$3,800
($1,300-$9,300)
0
$1,700
($240-$3,800)
$6,800
($1,100-$14,000)
$8,300
($1,500-$16,000)
$7,400
($1,300-$15,000)
0
$1.4
($0.2-$3.2)
$5.5
($0.9-$12)
$200
($36-$390)
$6.3
($1.1-$13)
0
$73
($10-$160)
$300
($48-$630)
$470
($83-$900)
$320
($56-$640)
0
$35
($5-$78)
$140
($23-$300)
$130
($24-$260)
$150
($27-$310)
0
$1,800
($250-$4,000)
$7,200
($1,200-$15,000)
$8,900
($1,600-$17,000)
$7,900
($1,400-$16,000)
0
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number
of lives saved are based on an assumption of a normal distribution.
c A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics.
DThis table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are notplanning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
E All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-61
-------
Table 6-34: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of Reductions in the Incidence of Morbidity
Associated with Ozone Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parentheses, Millions
of 1999$)B
Morbidity Endpoint
Hospital Admissions
(ages 0-1)
Hospital Admissions
(ages 65-99)
Emergency Department
Visits, Asthma-Related
School Absences
Worker Productivity
Minor Restricted Activity
Days
Eastern
Modeled Partial
Attainment
$7.1
($3.1~$11)
$19
($0.9--$49)
$0.23
($-0.06--$0.67)
$30
($7.2-$72)
$15
$27
($1.2-$63)
U.S.
Full
Attainment
$12
($5.3-$19)
$38
($1.8--$95)
$0.4
($-0.1--$!. 2)
$52
($13-$130)
$22
$47
($2-$110)
Western U.S. Excluding
California
Modeled Partial
Attainment
$0.39
($0.17-$0.62)
$0.67
($0.003-
$0.2)
--
$1.5
($0.35-$3.8)
$0.38
$1.2
($0.05-$2.8)
Full
Attainment
$1
($0.41--$!. 5)
$1.5
($0.074-
$3.8)
--
$3.4
($0.8-$8.4)
$1.4
$2.9
($0.13-$6.8)
California
Glidepath Attainment*
$0.24
($0.11-$0.38)
$0.65
($0.32--$!. 6)
--
$0.93
($0.2-$2.4)
$1.9
$0.83
($0.036--$!. 9)
2020 National
Benefits
$14
($5.9-$21)
$40
($1.9-$100)
$0.5
(-$0.1-$1.2)
$56
($14-$140)
$26
$51
($2.2-$120)
A This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
B
All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-62
-------
Table 6-35: Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual Valuation of
Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental
to Current Ozone Standard)E
California
Model or Incremental Post-
AssumptionA Reference California GlidepathB 2020 Benefits0 California Total0
NMMAPS
Meta-Analysis
Bell et al.
Bell etal.
Levy et al
2004
2005
. 2005
Ito et al. 2005
Assumption that
is not causal
association
$35
($5-$78)
$140
($23-$300)
$130
($24-$260)
$150
($27-$310)
0
$360
$1,500
$1,600
$1,600
0
$390
$1,600
$1,800
$1,700
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
c Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
E All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-63
-------
Table 6-36: Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual Valuation
of Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to
Current Ozone Standard, 95% Confidence Intervals in Parentheses)0
California California Incremental
Morbidity Endpoint GlidepathA Post-2020 Benefits8 California Totaf
Hospital Admissions $0.24 4_ q 4. _
(ages 0-1) ($0.ii~$0.38) ?J'y ?
Hospital Admissions $0.65 ^ £-, -,
(ages 65-99) ($0.32--$!.6) ?b'b ?/'2
i_iiidydiuy L-'tpaiLiiidiL v IOILO, mo LI NIK
Related*
School Absences
Worker Productivity
Minor Restricted Activity Days
$0.93
($0.2-$2.4)
$1.9
$0.83
($0.036--$!. 9)
$0.05
$15
$16
$12
$0.05
$15
$17
$13
AThis table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
c This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-64
-------
Table 6-37: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Monetary Value of Reductions in the Incidence of
Mortality Associated with Exposure to Ozone (Millions of 1999$, Incremental to Current Standard)8
Eastern U.S.
Model or Assumption^1 Reference
NMMAPS Bell et al. 2004 $1,400
Bell et al. 2005 $5,400
Meta-Analysis Levy et al. 2005 $6,700
Ito et al. 2005 $5,900
Assumption that association Q
is not causal
... *. . . «- California
western u.a.
Excluding California Glidepath Attainment
2020 National
Benefits
Arithmetic Mean8
(95% Credible Intervals)0
$66 0
$270 0
$430 0
$290 0
0 0
$1,400
$5,700
$7,100
$6,200
0
A Confidence intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that precluded
us from generating such estimates.
B All estimates rounded to two significant figures. As such, totals will not sum across columns
c This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-65
-------
Table 6-38: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Monetary Value of Reductions in the Incidence of
Morbidity Associated with Exposure to Ozone (Millions of 1999$, Incremental to Current Standard)*
Morbidity Endpoint
Hospital Admissions (ages 0-1)
Hospital Admissions (ages 65-99)
Emergency Department Visits, Asthma-Related
School Absences
Worker Productivity
Minor Restricted Activity Days
Eastern U.S.
$9.9
$31
$0.3
$41
$20
$38
Western U.S.
Excluding
California
$0.9
$1.4
$0.013
$3
$1.3
$2.6
California
Glidepath Attainment6
0
0
0
0
0
0
2020 National
Benefits
$11
$32
$0.3
$44
$21
$40
All estimates rounded to two significant figures. As such, totals will not sum across columns
B This table reflects full attainment in all locations of the U. S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-66
-------
Table 6-39: Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual Valuation of
Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental
to Current Ozone Standard)E
California
Model or Incremental Post-
AssumptionA Reference California GlidepathB 2020 Benefits0 California Total0
NMMAPS
Meta-Analysis
Bell et al.
Bell etal.
Levy et al
2004
2005
. 2005
Ito et al. 2005
Assumption that
is not causal
association
0
0
0
0
0
$220
$910
$990
$990
0
$220
$910
$990
$990
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
c Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
E All estimates rounded to two significant figures. As such, totals will not sum across columns
6-67
-------
Table 6-40: Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual
Valuation of Reductions in the Incidence of Morbidity Associated with Ozone Exposure
(Incremental to Current Ozone Standard)0
California California Incremental
Morbidity End point _ GlidepathA _ Post-2020 Benefits8 California Totaf
Hospital Admissions
(ages 0-1)
Hospital Admissions
(ages 65-99)
'4
^ .
$4
'4
^.
$4
Emergency Department Visits,
Asthma-RelatedA
.
$0'03
.
$0'03
School Absences
$8.7
$8.7
Worker Productivity
$9
$9
Minor Restricted Activity Days
$7.3
$7.3
AThis table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
c This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
D All estimates rounded to two significant figures. As such, totals will not sum across columns
6-68
-------
Table 6-41: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Monetary Value of Reductions in the Incidence of
Premature Mortality Associated with Exposure to Ozone (Millions of 1999$, Incremental to Current Standard)8
Model or AssumptionA Reference
NMMAPS Bell et al. 2004
Bell et al. 2005
Meta-Analysis Levy et al. 2005
Ito et al. 2005
Assumption that association
is not causal
..._ ^_ . . „ California
vvtjbLfc:! ii \j.a.
Eastern U.S. Excluding California Glidepath Attainment
$120
($18--$280)
$500
($82--$l,100)
$500
($89--$960)
$550
($94--$l,100)
0
2020 National
Benefits
Arithmetic Mean8
(95% Credible Intervals)0
0 0
0 0
0 0
0 0
0 0
$120
($18-$280)
$500
($82-$l,100)
$500
($89-$960)
$550
($94-$l,100)
0
Confidence intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that precluded
us from generating such estimates.
B
All estimates rounded to two significant figures. As such, totals will not sum across columns
c This table reflects full attainment in all locations of the U. S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-69
-------
Table 6-42: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Monetary Value of Reductions in the Incidence of
Morbidity Associated with Exposure to Ozone (Millions of 1999$, Incremental to Current Standard)*
Morbidity Endpoint
Hospital Admissions (ages 0-1)
Hospital Admissions (ages 65-99)
Emergency Department Visits, Asthma-Related
School Absences
Worker Productivity
Minor Restricted Activity Days
Eastern U.S.
$0.9
($0.5~$1.3)
$2.8
($0.4--$5.1)
$0.03
(0-$0.07)
$3.6
($1.3--$5.3)
$1.3
$3.1
($0.1--$7.6)
Western U.S.
Excluding
California
0
0
0
0
0
0
California
Glidepath Attainment6
0
0
0
0
0
0
2020 National
Benefits
$0.9
($0.5-$1.3)
$2.8
($0.4--$5.1)
$0.03
(0-$0.07)
$3.6
($1.3-$5.3)
$1.3
$3.1
($0.1-$7.6)
All estimates rounded to two significant figures. As such, totals will not sum across columns
B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels
of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or
"glidepath target" for the two California areas.
6-70
-------
Table 6-43: Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual Valuation of
Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental
to Current Ozone Standard)E
California
Model or Incremental Post-
AssumptionA Reference California GlidepathB 2020 Benefits0 California Total0
NMMAPS
Meta-Analysis
Bell et al.
Bell etal.
Levy et al
2004
2005
. 2005
Ito et al. 2005
Assumption that
is not causal
association
0
0
0
0
0
$46
$190
$180
$200
0
$46
$190
$180
$200
0
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
c Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described above.
E All estimates rounded to two significant figures. As such, totals will not sum across columns
6-71
-------
Table 6-44: Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual
Valuation of Reductions in the Incidence of Morbidity Associated with Ozone Exposure
(Incremental to Current Ozone Standard)0
California California Incremental
Morbidity End point GlidepathA Post-2020 Benefits8 California Totaf
Hospital Admissions . .
(ages 0-1) ° $0'3 $0'3
Hospital Admissions n d.1 d.1
(ages 65-99) ° $1 $1
Emergency Department Visits, . .
Asthma-RelatedA ° S0'1 ^^
School Absences 0 $1.3 $1.3
Worker Productivity 0 $0.5 $0.5
Minor Restricted Activity Days 0 $1.2 $1.2
AThis table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning
to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and
2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the
baseline, incremental to 2020 glidepath attainment.
c This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were
derived through a scaling technique described below.
D All estimates rounded to two significant figures. As such, totals will not sum across columns
6-72
-------
Estimated reductions in ozone mortality incidence provided in Tables 6-5, 6-7, 6-11, 6-13, 6-17,
6-19, 6-23 and 6-25 represent the number of premature deaths potentially avoided due to
reductions in ozone exposure in 2020 using warm season functions from the recent ozone-
mortality NMMAPS analysis of 95 U.S. communities (Bell et al., 2004) and three meta-analyses
of the available published literature on ozone-mortality effects (Bell et al., 2005; Ito et al., 2005;
Levy et al., 2005). These same tables also include the possibility that there is not a causal
association between ozone and mortality, i.e., that the estimate for premature mortality avoided
could be zero. As noted above, for each standard alternative we break out estimates between
2020 national glidepath and California post-2020. Model uncertainty, including whether or not
the relationship is assumed to be causal, is a key source of uncertainty. Although multiple
estimates are presented in these tables, no attempt was made to quantify the likelihood of a
causal relationship between short-term ozone exposure and increased mortality or to weigh the
results of the various models.
The estimate of central tendency for premature mortality is expressed as the arithmetic mean,
with the assumption of a normal distribution, and represents the central estimate of the number of
premature deaths avoided in association with the proposed standard based on each study.
Statistical uncertainty associated with the model estimate for each study is characterized by the
95% credible interval29 around the mean estimate (i.e., 2.5th and 97.5th percent interval). Of the
four available studies, the NMMAPS study by Bell et al. (2004) is considered to be the most
representative for evaluating potential mortality-related benefits associated with the proposed
standard due to its extensive coverage (examination of 95 large communities across the United
States over an extended period of time, from 1987 to 2000) and its specific focus on the ozone-
mortality relationship. Annual estimates of lives saved from this study are lower than those from
the three meta-analyses, possibly due to more stringent adjustment for meteorological factors (Ito
et al., 2005; Ostro et al., 2006), publication bias in the meta-analyses (Bell et al., 2005; Ito et al.,
2005) or other factors. Clearly, the ozone-mortality reduction estimates are conditional on a
causal relationship.
The Ozone Criteria Document (U.S. EPA, 2006) and Staff Paper (U.S. EPA, 2007) concluded
that the overall body of evidence is highly suggestive that (short-term exposure to) ozone directly
or indirectly contributes to non-accidental cardiopulmonary-related mortality. However, various
sources of uncertainty remain, including the possibility that there is no causal relationship
between ozone and mortality (i.e., zero effect). For instance, because results of time-series
studies implicate all of the criteria air pollutants, and those who would be expected to be
potentially more susceptible to ozone exposure are likely to have lower exposure to ozone due to
the amount of time that they spend indoors, CAS AC30 stated that it seems unlikely that the
observed associations between short-term ozone concentrations and daily mortality are due
29 A credible interval is a posterior probability interval used in Bayesian statistics, which is
similar to a confidence interval used in frequentist statistics.
^n
J Clean Air Scientific Advisory Committee's Peer Review of the Agency's 2nd Draft Ozone
Staff Paper, October 24, 2006. EPA-CASAC-07-001. Available at
http://www.epa.gov/sab/pdf/casac-07-001.pdf
6-73
-------
solely to ozone itself (i.e., ozone may be serving as a marker for other agents that are
contributing to the short-term exposure effects on mortality). Even so, CASAC concluded that
the evidence was strong enough to support a quantitative risk assessment of the relationship
between short-term exposure to ozone and premature mortality as part of the Staff Paper. EPA
has asked the National Academy of Sciences31 for their advice on how best to quantify the
uncertainty about the relationship between ambient ozone exposure and premature mortality
within the context of quantifying projected benefits of alternative control strategies.
Using the NMMAPS study that was used as the basis for the risk analysis presented in our Staff
Paper, we estimate 280 avoided premature deaths annually in 2020 from reducing ozone levels to
meet a proposed standard of 0.070 ppm, which, when added to the other projected ozone related
benefits, leads to an estimated total benefit of $1.8 billion/yr. Using three studies that synthesize
data across a large number of individual studies, we estimate between 1,100 and 1,400 avoided
premature deaths annually in 2020, leading to total monetized benefits of between $7.2 and $8.9
billion/yr. Alternatively, if there is no causal relationship between ozone and mortality, avoided
premature deaths would be zero. For a proposed standard of 0.075 ppm, using the NMMAPS
ozone mortality study, we estimate 200 premature deaths avoided and total monetized benefits of
$1.4 billion/yr. Using the three synthesis studies, we estimate premature deaths avoided for the
less stringent standard to be between 880 and 1,100, with total monetized ozone benefits to be
between $5.7 and $7.1 billion/yr. For a proposed standard of 0.079 ppm, using the NMMAPS
ozone mortality study, we estimate 19 premature deaths avoided and total monetized benefits of
$140 million/yr. Using the three synthesis studies, we estimate premature deaths avoided for the
less stringent standard to be between 78 and 85, with total monetized ozone benefits to be
between $510 and $560 million/yr. Because EPA is taking comment on alternatives as low as
0.065 ppm, we show that a more stringent standard of 0.065 ppm, using the NMMAPS ozone
mortality study is estimated to result in 530 premature deaths avoided and total monetized
benefits of $3.4 billion/yr. Using the three synthesis studies, estimated premature deaths avoided
for the more stringent standard are between 2,100 and 2,400, with total monetized ozone benefits
between $14 and $15 billion/yr. Including premature mortality in our estimates had the largest
impact on the overall magnitude of benefits: Premature mortality benefits account for more than
95 percent of the total benefits we can monetize. We note that these estimates reflect EPA's
interim approach to characterizing the benefits of reducing premature mortality associated with
ozone exposure. EPA has requested advice from the NAS on how best to quantify uncertainty
in the relationship between ozone exposure and premature mortality in the context of quantifying
benefits associated with alternative ozone control strategies.
6.5.3 PM2.5 Co-Benefits Resulting from Attainment of 0.070 ppm incremental to 0.08 ppm
The summary of PM2.5 related co-benefits in the tables above represent the benefits of partially
attaining 0.070 ppm incremental to a partial attainment of 0.08 ppm. Thus, these estimates
overstate the benefits of 0.070 ppm partial attainment relative to the actual incremental benefits
31 National Academy of Sciences (2007) Project Scope. Estimating Mortality Risk Reduction Benefits from
Decreasing Tropospheric Ozone Exposure. Division on Earth and Life Studies, Board on Environmental Studies
and Toxicology. Available at http://www8.nationalacademies.org/cp/projectview.aspx?key=48768
6-74
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of this scenario; this is due to the fact that the benefits estimates in these tables include the
benefits of NOx reductions that would be required to attain a baseline of 0.08 ppm. Of greater
analytical value would be an estimate of the PM2.5 co-benefits associated with fully attaining
0.070 ppm incremental to full attainment of the 0.08 ppm standard.
To generate such an estimate, we calculated a new PM2 5 baseline that established the PM2 5 air
quality associated with full attainment of 0.08 ppm. To create such a baseline, EPA utilized
benefit PM2 5 per-ton estimates. These PM2 5 benefit per-ton estimates provide the total
monetized human health benefits (the sum of premature mortality and premature morbidity) of
reducing one ton of PM2.s from a specified source. EPA has used a similar technique in previous
Regulatory Impact Analyses.32 These estimates are based on the sum of the valuation of the Pope
(2002) estimates of mortality (3% discount rate, 1999$) and valuation of the morbidity
incidence. Readers interested in reviewing the complete methodology for creating the benefit
per-ton estimates used in this analysis can consult the Technical Support Document
accompanying this RIA.
Estimating the PM2 5 benefits that represented the full attainment of both 0.070 ppm incremental
to full attainment of 0.08 ppm entailed the following four steps:
1. Estimate the number of tons of NOx necessary to attain a baseline of 0.08 ppm. Chapter 3
described the method used to estimate the extrapolated NOx emissions reductions
necessary to attain a baseline of 0.08 ppm full attainment.
2. Calculate the benefits of attaining 0.08 ppm. To estimate the benefits of fully attaining
0.08 ppm incremental to partial attainment of 0.08 ppm, the relevant benefit per ton is
simply multiplied by the total number of extrapolated NOx tons abated.
3. Calculate the benefits of partially attaining 0.070 ppm incremental to full attainment of
0.08ppm. Subtract the benefits of fully attaining 0.080 ppm incremental to the partial
attainment of 0.08 ppm to create a new estimate of incremental 0.070 ppm partial
attainment.
4. Calculate the PM2.s benefits of fully attaining 0.070 ppm. Multiplying the estimate of the
extrapolated NOx tons necessary to attain 0.070 ppm fully (found in chapter 3) produces
an estimate of the incremental benefits of fully attaining 0.070 ppm incremental to partial
attainment of 0.070 ppm. By adding this incremental benefit estimate to the benefits
generated in step 3, we derived a total benefit estimate of attaining 0.070 ppm
incremental to 0.08 ppm.
The process for estimating the PM2 5 co-benefits of fully attaining 0.065 ppm and 0.075 ppm is
identical to the steps above, with the following exception; in step four we substituted the number
of extrapolated tons necessary to attain 0.065 ppm and 0.075 ppm, respectively. Table 5-21
below provides the inputs to the calculation steps described above. In the example below we
Final Regulatory Impact Analysis: Industrial Boilers and Process Heaters. Prepared by Office
of Air and Radiation. Available: http://www.epa.gov/ttn/ecas/regdata/EIAs/chapterlO.pdf
[accessed 18 May 2007].
6-75
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calculate total benefits using the Pope et al. (2002) mortality estimate. However, in subsequent
tables we present benefits using Laden et al. (2006) as well as the twelve expert functions
described previously in this document. Note that while our benefit per ton estimates are
associated with broad source categories (in this case, NOx Electrical Generating Units, Other
NOx point sources and Mobile NOx sources) the extrapolated tons were not. For this reason we
simply assumed that the total number of extrapolated NOx tons were evenly distributed between
these three source types.
Table 6-45: Estimated PM2.s Co-Benefits Associated with Full Attainment of 0.070
ppm incremental to 0.08 ppmA
Calculation
Benefit per ton
Extrapolated NOx Tons estimate
Valuation of PM2.5
Benefits
(Billions 1999$)B
Benefits of attaining 0.08 ppm
partially and 0.070 ppm partially:
Benefits of attaining 0.08 ppm
from a baseline of 0.08 ppm
partial attainment:
Benefits of attaining 0.070 ppm
partially, incremental to
attainment of 0.08 ppm
Benefits of attaining 0.070 ppm in
2020 incremental to partial
attainment of 0.070 ppm
Benefits of attaining 0.070 ppm
incremental to attainment of 0.08
ppm
NOx ECU: 45,000
NOx Point: 45,000
NOx Mobile: 45,000
$3,100
$2,800
$4,600
NOx ECU: 340,000
NOx Point: 340,000
NOx Mobile: 340,000
$3,100
$2,800
$4,600
$2.9B
$0.48B
= $2.9B-$0.48B
=$2.5 B
$3.5B
=$2.5B + 3.5B
=$6.OB
Numbers have been rounded to two significant figures and therefore summation may not match table estimates.
PM2.s benefit estimates do not include confidence intervals because they are derived using benefit per-ton estimates.
B All estimates derived using the Pope et al. (2002) mortality estimate at a 3% discount rate, in 1999$.
The procedure for calculating the PIVb.s benefits resulting from full attainment of 0.079 ppm,
0.075 ppm and 0.065 ppm is identical to this example, with the exception of step 4; the PM2.5
benefits of attaining 0.065 ppm, 0.075 ppm and 0.079 ppm incremental to partial attainment of
0.070 ppm are $7.8B, $1.1B and $0.4B respectively. Thus, the total PIVb.s benefits of attaining
0.065 ppm, 0.075 ppm and 0.079 ppm are $10.2B, $3.6B and $2.5B respectively. The full
attainment PlV^.s benefits do not include confidence intervals. Because this full attainment
estimate was derived by summing the modeled PM2.5 benefits and the benefits derived using the
benefit per-ton metrics—and these benefit per ton metrics do not include confidence intervals—
the resulting sum of total PM2.5 benefits do not include confidence intervals.
6-76
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6.5.4 Estimate of Full Attainment Benefits
Tables 6-36 through 6-43 below summarize the estimates of full attainment and 2020 California
glidepath attainment ozone benefits and PIVb.s co-benefit estimate for each standard alternative.
The presentation of ozone benefits and PIVb.s co-benefits for each standard alternative is broken
into two tables. The first table presents the national glidepath ozone benefits and PIVb.s co-
benefits. The second table presents California-only glidepath and post-2020 ozone benefits and
PM2.5 co-benefits. Tables 6-44 through 6-53 summarize the combined ozone and PIVb.s co-
benefits. The presentation of combined ozone and PIVb.s co-benefit tables is broken into four
components for each standard alternative. The first table presents national glidepath benefits. The
second table presents the California-only glidepath benefits. The third table presents the
incremental benefits that accrue in California from full attainment of the alternative standard
after 2020. The last table presents total California benefits—the sum of glidepath benefits and
post-2020 benefits.
6-77
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Table 6-46: Estimate of Total Annual Ozone and PM2.5 Benefits (95%
Confidence Intervals, Millions of $1999) for the 0.065 ppm Standard
Alternative: National Glidepath Attainment
Ozone Mortality and Morbidity Benefits of Attaining 0.065 ppm
Standard Alternative and
Model or AssumptionA Ozone Benefits, Arithmetic Mean8
NMMAPS Bell (2004) $3,700
Bell (2005) $14,000
Analysis Ito (2005) *15'000
Levy (2005) $16,000
No Causality $330
PM^g Mortality and Morbidity Benefits of Attaining 0.065 ppm
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study0 $10,000
Harvard Six-City StudyD $23,000
Mortality Impact Functions Derived from Expert Elicitation
Expert A $33,000
Expert B $25,000
Expert C $25,000
Expert D $17,000
Expert E $41,000
Expert F $23,000
Expert G $15,000
Expert H $19,000
Expert I $25,000
Expert J $20,000
Expert K $4,000
Expert L $18,000
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist
statistics. Credible intervals for ozone estimates and confidence intervals for PM2.s estimates not provided due to the fact that the valuation
estimates were derived through a scaling technique (see above) that precluded us from generating such estimates.
c The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported
in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs
D Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method
for incorporating what has previously been a sensitivity estimate.
E All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived
using a 7% discount rate would be approximately 15% lower.
6-78
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Table 6-47: Estimate of Total Annual Ozone and PM2.5 Benefits (95%
Confidence Intervals, Millions of $1999) for the 0.065 ppm Standard
Alternative: California Attainment
Ozone Mortality and Morbidity Benefits of Attaining 0.065 ppm
Ozone Benefits, Arithmetic MeanE
Standard Alternative and
Model or Assumption*
NMMAPS Bell (2004)
Bell (2005)
Analysis It0 (2005>
Levy (2005)
No Causality
Glidepath
$61
$230
$210
$240
$6.8
Incremental Post-
2020 Benefits
$690
$2,600
$2,800
$2,700
$93
Total
$750
$2,800
$3,000
$3,000
$100
PM? g Mortality and Morbidity Benefits of Attaining 0.065 ppm
_.. , ., Incremental Post- ^ . ,
GUdepath Total
Mortality Impact Functions Derived from Epidemiology Literature
ACS Studyc $180 $930 $1,100
Harvard Six-City StudyD $380 $2,000 $2,400
Mortality Impact Functions Derived from Expert Elicitation
Ex pert A $570 $3,000 $3,600
Expert B $430 $2,300 $2,700
Expert C $430 $2,300 $2,700
Expert D $300 $1,600 $1,900
Expert E $710 $3,800 $4,500
Expert F $390 $2,100 $2,500
Expert G $250 $1,300 $1,600
Expert H $320 $1,700 $2,000
Expert I $420 $2,200 $2,700
ExpertJ $340 $1,800 $2,200
Expert K $68 $360 $430
Expert L $310 $1,700 $2,000
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist
statistics. Credible intervals for ozone estimates and confidence intervals for PM2.s estimates not provided due to the fact that the valuation
estimates were derived through a scaling technique (see above) that precluded us from generating such estimates.
c The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported
in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs
D Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method
for incorporating what has previously been a sensitivity estimate.
E All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived
using a 7% discount rate would be approximately 15% lower.
6-79
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Table 6-48: Estimate of Total Annual Ozone and PM2.5 Benefits (95%
Confidence Intervals, Millions of $1999) for the 0.070 ppm Standard
Alternative: National Glidepath Attainment
Ozone Mortality and Morbidity Benefits of Attaining 0.070 ppm
Standard Alternative and Ozone Benefits, Arithmetic Mean8
Model or AssumptionA (95% Credible Intervals)0
NMMAPS Bell f20041 $2,000
INI II IMKS D6II (.ZUU^J ($300--$4,400)
Bell (2005)
A^y'sis "° C2005)
Levy (2005)
No Causality
$7,400
($1,200--$16,000)
$8,000
($1,400-$16,000)
$9,100
($1,600--$18,000)
$190
($49-$400)
PMj^ Mortality and Morbidity Benefits of Attaining 0.070 ppm
Mortality Impact Functions Derived from Epidemiology Literature
ACS StudyD $6,000
Harvard Six-City StudyE $14,000
Mortality Impact Functions Derived from Expert Elicitation
Expert A $19,000
Expert B $15,000
Expert C $15,000
Expert D $10,000
Expert E $24,000
Expert F $13,000
Expert G $8,500
Expert H $11,000
Expert I $14,000
Expert J $12,000
Expert K $2,300
Expert L $11,000
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the
annual number of lives saved are based on an assumption of a normal distribution. Confidence intervals not available for PM2 5 valuation
estimates due to the fact that they were derived through a scaling technique (see above) that precluded us from generating such estimates.
c A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist
statistics.
D The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported
in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs
E Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method
for incorporating what has previously been a sensitivity estimate.
F All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived
using a 7% discount rate would be approximately 15% lower.
6-80
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Table 6-49: Estimate of Total Annual Ozone and PM2.5 Benefits (95%
Confidence Intervals, Millions of $1999) for the 0.070 ppm Standard
Alternative: California Attainment
Ozone Mortality and Morbidity Benefits of Attaining 0.070 ppm
Ozone Benefits, Arithmetic Mean6
Standard Alternative and Incremental Post- _ . .
Model or Assumption* Glidepath 2Q2Q Benefits Total
NMMAPS Bell (2004)
Bell (2005)
Anty'sis *> (^05)
Levy (2005)
No Causality
$40
($7.2-$86)
$150
($25-$310)
$160
($29-$320)
$140
($26-$270)
$4.5
($2.3-$8.2)
$410
$1,500
$1,600
$1,700
$57
$450
$1,700
$1,800
$1,800
$61
PM^g Mortality and Morbidity Benefits of Attaining 0.070 ppm
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study0
Harvard Six-City StudyD
Mortality Impact Functions
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Glidepath
$70
$150
Derived from
$230
$170
$170
$120
$280
$160
$100
$130
$170
$140
$27
$120
2020 Benefits
$690
$1,500
Expert Elicitation
$2,200
$1,700
$1,700
$1,200
$2,800
$1,500
$980
$1,200
$1,700
$1,300
$270
$ 1 ,200
Total
$760
$1,600
$2,400
$ 1 ,900
$1,900
$1,300
$3,100
$ 1 ,700
$ 1 , 1 00
$1,400
$1,800
$1,500
$300
$1,300
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist
statistics. Credible intervals for ozone estimates and confidence intervals for PM2 5 estimates not provided due to the fact that the valuation
estimates were derived through a scaling technique (see above) that precluded us from generating such estimates.
c The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported
in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs
D Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method
for incorporating what has previously been a sensitivity estimate.
E All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived
using a 7% discount rate would be approximately 15% lower.
6-81
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Table 6-50: Estimate of Total Annual Ozone and PM2.5 Benefits (95%
Confidence Intervals, Millions of $1999) for the 0.075 ppm Standard
Alternative: National Glidepath Attainment
Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm
Standard Alternative and
Model or Assumption* Ozone Benefits, Arithmetic Mean8
NMMAPS Bell (2004) $1,600
Bell (2005)
Analysis It0 (2005)
Levy (2005)
No Causality
$5,900
$6,400
$7,300
$150
PM^g Mortality and Morbidity Benefits of Attaining 0.075 ppm
Mortality Impact Functions Derived from Epidemiology Literature
ACS Studyc $3,600
Harvard Six-City StudyD $8,600
Mortality Impact Functions Derived from Expert Elicitation
Ex pert A $12,000
Expert B $8,800
Expert C $8,700
Expert D $6,100
Expert E $14,000
Expert F $7,900
Expert G $5,100
Expert H $6,500
Expert I $8,600
Expert J $7,000
Expert K $1,400
Expert L $6,300
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B With the exception of the assumption of no causal relationship, the arithmetic mean estimates of the annual number of lives saved are based on
an assumption of a normal distribution. Credible intervals for ozone estimates and confidence intervals for PM2.s estimates not provided due to
the fact that the valuation estimates were derived through a scaling technique (see above) that precluded us from generating such estimates..
c The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported
in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs
D Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method
for incorporating what has previously been a sensitivity estimate.
F All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived
using a 7% discount rate would be approximately 15% lower.
6-82
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Table 6-51: Estimate of Total Annual Ozone and PM2.5 Benefits (95%
Confidence Intervals, Millions of $1999) for the 0.075 ppm Standard
Alternative: California Attainment
Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm
Ozone Benefits, Arithmetic MeanE
Standard Alternative and
Model or Assumption*
NMMAPS Bell (2004)
Bell (2005)
Analysis It0 (2005>
Levy (2005)
No Causality
Glidepath
0
0
0
0
0
Incremental Post-
2020 Benefits
$260
$940
$1,000
$1,000
$33
Total
$260
$940
$1,000
$1,000
$33
PM? g Mortality and Morbidity Benefits of Attaining 0.075 ppm
Mortality Impact Functions Derived from Epidemiology Literature
_.. , ., Incremental Post- ^ . ,
GUdepath 202Q Benefjts Total
ACS Studyc 0 $410 $410
Harvard Six-City StudyD 0 $870 $870
Mortality Impact Functions Derived from Expert Elicitation
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
0
0
0
0
0
0
0
0
0
0
0
0
$1,300
$1,000
$990
$690
$1,600
$900
$580
$740
$980
$790
$160
$720
$1,300
$1,000
$990
$690
$1,600
$900
$580
$740
$980
$790
$160
$720
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist
statistics. Credible intervals for ozone estimates and confidence intervals for PM2.s estimates not provided due to the fact that the valuation
estimates were derived through a scaling technique (see above) that precluded us from generating such estimates.
c The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported
in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs
D Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method
for incorporating what has previously been a sensitivity estimate.
E All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived
using a 7% discount rate would be approximately 15% lower.
6-83
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Table 6-52: Estimate of Total Annual Ozone and PM2.5 Benefits (95%
Confidence Intervals, Millions of $1999) for the 0.079 ppm Standard
Alternative: National Glidepath Attainment
Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm
Standard Alternative and
Model or Assumption* Ozone Benefits, Arithmetic Mean8
$140
NMMAPS Bell (2004) ($22-$300)
Bell (2005)
Meta- _. ,___.-,
Ito i 2005)
Analysis v '
Levy (2005)
No Causality
$510
($86-$l,100)
$560
($98-$l,100)
$510
($93-$980)
$12
($3.5-$21)
PM^g Mortality and Morbidity Benefits of Attaining 0.075 ppm
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study0 $2,800
Harvard Six-City StudyD $7,000
Mortality Impact Functions Derived from Expert Elicitation
Expert A $9,100
Expert B $6,900
Expert C $6,800
Expert D $4,800
Expert E $11,000
Expert F $6,200
Expert G $4,000
Expert H $5,100
Expert I $6,800
Expert J $5,500
Expert K $1,100
Expert L $5,000
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B With the exception of the assumption of no causal relationship, the arithmetic mean estimates of the annual number of lives saved are based on
an assumption of a normal distribution. Credible intervals for ozone estimates and confidence intervals for PM2.5 estimates not provided due to
the fact that the valuation estimates were derived through a scaling technique (see above) that precluded us from generating such estimates..
c The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported
in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs
D Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method
for incorporating what has previously been a sensitivity estimate.
F All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived
using a 7% discount rate would be approximately 15% lower.
6-84
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Table 6-53: Estimate of Total Annual Ozone and PM2.5 Benefits (95%
Confidence Intervals, Millions of $1999) for the 0.079 ppm Standard
Alternative: California Attainment
Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm
Ozone Benefits, Arithmetic Mean6
Standard Alternative and
Model or Assumption1*
NMMAPS Bell (2004)
Bell (2005)
A^y'sis "° C2005)
Levy (2005)
No Causality
Glidepath
0
0
0
0
0
Incremental Post-
2020 Benefits
$50
$190
$210
$190
$4.3
Total
$50
$190
$210
$190
$4.3
PM^g Mortality and Morbidity Benefits of Attaining 0.075 ppm
Mortality Impact Functions Derived from Epidemiology Literature
_,. , ., Incremental Post- _ . .
GUdepath Total
ACS Study0 0 $130 $130
Harvard Six-City StudyD 0 $270 $270
Mortality Impact Functions Derived from Expert Elicitation
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
0
0
0
0
0
0
0
0
0
0
0
0
$410
$310
$310
$220
$510
$280
$180
$230
$310
$250
$49
$220
$410
$310
$310
$220
$510
$280
$180
$230
$310
$250
$49
$220
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist
statistics. Credible intervals for ozone estimates and confidence intervals for PM2.s estimates not provided due to the fact that the valuation
estimates were derived through a scaling technique (see above) that precluded us from generating such estimates.
c The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported
in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs
D Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method
for incorporating what has previously been a sensitivity estimate.
E All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived
using a 7% discount rate would be approximately 15% lower.
6-85
-------
Table 6-54: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: National Glidepath
Attainment
Alternative Standard and Model or Assumption1"1
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study5
Harvard Six-City Study0
Mortality Impact Functions
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Derived
$14,000
$27,000
Derived
$37,000
$29,000
$29,000
$21,000
$45,000
$26,000
$18,000
$22,000
$28,000
$24,000
$7,700
$22,000
from Epidemiology Literature
$24,000
$37,000
from Expert
$47,000
$39,000
$39,000
$32,000
$55,000
$37,000
$29,000
$33,000
$39,000
$34,000
$18,000
$32,000
$25,000
$38,000
Elicitation
$48,000
$40,000
$40,000
$33,000
$56,000
$38,000
$30,000
$34,000
$40,000
$35,000
$19,000
$33,000
$26,000
$38,000
$49,000
$41,000
$40,000
$33,000
$57,000
$38,000
$30,000
$34,000
$40,000
$35,000
$20,000
$34,000
$11,000
$23,000
$33,000
$26,000
$25,000
$18,000
$42,000
$23,000
$15,000
$19,000
$25,000
$20,000
$4,300
$19,000
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
D All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.s estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.
6-86
-------
Table 6-55: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: California Glidepath
Attainment
Alternative Standard and Model or Assumption1"1
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study5 $240 $400 $390 $420 $180
Harvard Six-City Study0 $440 $600 $590 $620 $380
Mortality Impact Functions Derived from Expert Elicitation
Expert A $630 $790 $780 $810 $570
Expert B $490 $660 $650 $670 $440
Expert C $490 $650 $640 $670 $430
Expert D $360 $520 $510 $540 $300
Expert E $770 $930 $920 $950 $710
Expert F $450 $620 $600 $630 $400
Expert G $310 $480 $460 $490 $260
Expert H $380 $540 $530 $560 $320
Expert I $480 $650 $630 $660 $430
Expert J $400 $570 $550 $580 $350
Expert K $130 $300 $280 $310 $75
Expert L $370 $540 $520 $550 $320
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
D All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2 5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.
6-87
-------
Table 6-56: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: Incremental Benefits
of California Post 2020 Attainment
Alternative Standard and Model or Assumption1"1
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study5
Harvard Six-City Study0
Mortality Impact Functions
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Derived
$1,600
$2,600
Derived
$3,600
$2,900
$2,900
$2,200
$4,400
$2,700
$2,000
$2,300
$2,900
$2,500
$1,000
$2,300
from Epidemiology Literature
$3,400
$4,500
from Expert
$5,500
$4,800
$4,800
$4,100
$6,200
$4,600
$3,800
$4,200
$4,700
$4,300
$2,900
$4,100
$3,600
$4,700
Elicitation
$5,700
$5,000
$5,000
$4,300
$6,500
$4,800
$4,000
$4,400
$5,000
$4,500
$3,100
$4,400
$3,600
$4,700
$5,700
$5,000
$4,900
$4,300
$6,400
$4,700
$4,000
$4,400
$4,900
$4,500
$3,000
$4,300
$1,000
$2,100
$3,100
$2,400
$2,400
$1,700
$3,800
$2,200
$1,400
$1,800
$2,300
$1,900
$450
$1,700
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
D All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.s estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.
6-S
-------
Table 6-57: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: Total California
Benefits of Post 2020 Attainment
Alternative Standard and Model or Assumption1"1
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study5
Harvard Six-City Study0
Mortality Impact Functions
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Derived
$1,800
$3,100
Derived
$4,300
$3,400
$3,400
$2,600
$5,200
$3,200
$2,300
$2,700
$3,400
$2,900
$1,100
$2,700
from Epidemiology Literature
$3,800
$5,100
from Expert
$6,300
$5,400
$5,400
$4,600
$7,200
$5,200
$4,300
$4,700
$5,400
$4,900
$3,100
$4,700
$4,000
$5,300
Elicitation
$6,500
$5,700
$5,600
$4,800
$7,400
$5,400
$4,500
$4,900
$5,600
$5,100
$3,400
$4,900
$4,000
$5,300
$6,500
$5,600
$5,600
$4,800
$7,400
$5,400
$4,500
$4,900
$5,600
$5,100
$3,300
$4,900
$1,200
$2,500
$3,700
$2,800
$2,800
$2,000
$4,600
$2,600
$1,700
$2,100
$2,800
$2,300
$520
$2,100
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
D All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.s estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.
6-89
-------
Table 6-58: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: National Glidepath
Attainment
Alternative Standard and Model or Assumption1"1
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study5
Harvard Six-City Study0
Mortality Impact Functions
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Derived
$7,900
$16,000
Derived
$21,000
$17,000
$ 1 7,000
$12,000
$26,000
$15,000
$11,000
$13,000
$ 1 6,000
$14,000
$4,300
$13,000
from Epidemiology Literature
$13,000
$21,000
from Expert
$27,000
$22,000
$22,000
$18,000
$31,000
$21,000
$16,000
$18,000
$22,000
$19,000
$9,700
$18,000
$14,000
$22,000
Elicitation
$27,000
$23,000
$23,000
$18,000
$32,000
$21,000
$17,000
$19,000
$22,000
$20,000
$10,000
$19,000
$15,000
$23,000
$28,000
$24,000
$24,000
$19,000
$33,000
$22,000
$18,000
$20,000
$23,000
$21,000
$11,000
$20,000
$6,200
$14,000
$19,000
$15,000
$ 1 5,000
$10,000
$24,000
$13,000
$8,700
$ 1 1 ,000
$ 1 5,000
$12,000
$2,500
$11,000
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
D All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.s estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.
6-90
-------
Table 6-59: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: California Glidepath
Attainment
Alternative Standard and Model or Assumption1"1
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study5 $110 $220 $230 $210 $75
Harvard Six-City Study0 $190 $300 $310 $290 $150
Mortality Impact Functions Derived from Expert Elicitation
Expert A $270 $370 $380 $360 $230
Expert B $210 $320 $330 $310 $180
Expert C $210 $320 $330 $310 $180
Expert D $160 $270 $280 $260 $120
Expert E $320 $430 $440 $420 $290
Expert F $200 $300 $310 $290 $160
Expert G $140 $250 $260 $240 $100
Expert H $170 $270 $290 $260 $130
Expert I $210 $320 $330 $310 $170
Expert J $180 $280 $300 $270 $140
Expert K $67 $170 $190 $170 $32
Expert L $160 $270 $280 $260 $130
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
D All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2 5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.
6-91
-------
Table 6-60: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: Incremental Benefits
of California Post 2020 Attainment
Alternative Standard and Model or Assumption1"1
Bell (2004)
Mortality Impact Functions
ACS Study5
Harvard Six-City Study0
Mortality Impact Functions
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Derived
$1,100
$1,900
Derived
$2,600
$2,100
$2,100
$1,600
$3,200
$1,900
$1,400
$1,600
$2,000
$1,700
$650
$1,600
Bell (2005)
Ito (2005)
Levy (2005)
No Causality
from Epidemiology Literature
$2,200
$3,000
from Expert
$3,700
$3,200
$3,200
$2,700
$4,300
$3,000
$2,500
$2,700
$3,200
$2,800
$1,800
$2,700
$2,300
$3,100
Elicitation
$3,800
$3,300
$3,300
$2,800
$4,400
$3,100
$2,600
$2,900
$3,300
$3,000
$1,900
$2,800
$2,300
$3,100
$3,900
$3,300
$3,300
$2,800
$4,400
$3,200
$2,600
$2,900
$3,300
$3,000
$1,900
$2,900
$740
$1,500
$2,300
$1,800
$1,700
$1,200
$2,800
$1,600
$1,000
$1,300
$1,700
$1,400
$320
$1,300
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
D All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.s estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.
6-92
-------
Table 6-61: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: California Post 2020
Attainment
Alternative Standard and Model or Assumption1"1
Bell (2004)
Mortality Impact Functions
ACS Study5
Harvard Six-City Study0
Mortality Impact Functions
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Derived
$1,200
$2,100
Derived
$2,900
$2,300
$2,300
$1,700
$3,500
$2,100
$1,500
$1,800
$2,300
$1,900
$720
$1,800
Bell (2005)
Ito (2005)
Levy (2005)
No Causality
from Epidemiology Literature
$2,400
$3,300
from Expert
$4,100
$3,500
$3,500
$2,900
$4,700
$3,300
$2,700
$3,000
$3,500
$3,100
$1,900
$3,000
$2,500
$3,400
Elicitation
$4,200
$3,600
$3,600
$3,100
$4,800
$3,400
$2,800
$3,100
$3,600
$3,200
$2,100
$3,100
$2,300
$3,400
$4,200
$3,700
$3,600
$3,100
$4,800
$3,500
$2,900
$3,200
$3,600
$3,300
$2,100
$3,100
$810
$1,700
$2,500
$1,900
$1,900
$1,300
$3,100
$1,700
$1,100
$1,400
$1,900
$1,500
$350
$1,400
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
D All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2 5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.
6-93
-------
Table 6-62: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.075 ppm Alternative Standard: National Glidepath
Attainment
Alternative Standard and Model or Assumption1"1
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study5
Harvard Six-City Study0
Mortality Impact Functions
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Derived
$5,100
$10,000
Derived
$13,000
$10,000
$10,000
$7,600
$16,000
$9,500
$6,700
$8,000
$10,000
$8,500
$3,000
$7,900
from Epidemiology Literature
$9,400
$ 1 5,000
from Expert
$17,000
$15,000
$ 1 5,000
$12,000
$20,000
$14,000
$11,000
$12,000
$ 1 5,000
$13,000
$7,300
$12,000
$10,000
$15,000
Elicitation
$18,000
$15,000
$15,000
$13,000
$21,000
$14,000
$12,000
$13,000
$15,000
$13,000
$7,800
$13,000
$11,000
$16,000
$19,000
$16,000
$16,000
$13,000
$22,000
$15,000
$12,000
$14,000
$16,000
$14,000
$8,700
$14,000
$3,700
$8,800
$12,000
$9,000
$8,900
$6,200
$15,000
$8,100
$5,200
$6,600
$8,800
$7,100
$1,500
$6,500
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
D All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.s estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.
6-94
-------
Table 6-63: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.075 ppm Alternative Standard: California Post 2020
Attainment
Alternative Standard and Model or Assumption1"1
Bell (2004)
Mortality Impact Functions
ACS Study5
Harvard Six-City Study0
Mortality Impact Functions
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Derived
$660
$1,100
Derived
$1,600
$1,300
$1,300
$950
$1,900
$1,200
$830
$990
$1,200
$1,100
$410
$980
Bell (2005)
Ito (2005)
Levy (2005)
No Causality
from Epidemiology Literature
$1,400
$1,800
from Expert
$2,300
$2,000
$1,900
$ 1 ,600
$2,600
$1,900
$1,500
$1,700
$1,900
$1,700
$1,100
$1,700
$1,400
$1,900
Elicitation
$2,300
$2,000
$2,000
$1,700
$2,700
$1,900
$1,600
$1,800
$2,000
$1,800
$1,200
$1,700
$1,400
$1,900
$2,300
$2,000
$2,000
$1,700
$2,700
$1,900
$1,600
$1,800
$2,000
$1,800
$1,200
$1,700
$440
$900
$1,400
$1,000
$1,300
$950
$1,900
$1,200
$830
$990
$1,200
$1,100
$410
$980
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
D All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2 5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.
6-95
-------
Table 6-64: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.079 ppm Alternative Standard: National Glidepath
Attainment
Alternative Standard and Model or Assumption1"1
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study5
Harvard Six-City Study0
Mortality Impact Functions
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Derived
$3,100
$7,300
Derived
$9,200
$7,100
$7,000
$4,900
$11,000
$6,400
$4,100
$5,200
$6,900
$5,600
$1,200
$5,100
from Epidemiology Literature
$4,000
$8,100
from Expert
$9,600
$7,400
$7,400
$5,300
$12,000
$6,700
$4,500
$5,600
$7,300
$6,000
$1,600
$5,500
$4,000
$8,200
Elicitation
$9,600
$7,500
$7,400
$5,300
$12,000
$6,800
$4,600
$5,600
$7,300
$6,000
$1,700
$5,500
$4,000
$8,200
$9,600
$7,400
$7,400
$5,300
$12,000
$6,700
$4,500
$5,600
$7,300
$6,000
$ 1 ,600
$5,500
$3,100
$7,000
$9,100
$6,900
$6,900
$4,800
$ 1 1 ,000
$6,200
$4,000
$5,100
$6,800
$5,500
$1,100
$5,000
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
D All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.s estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.
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Table 6-65: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for the 0.079 ppm Alternative Standard: California Post 2020
Attainment
Alternative Standard and Model or Assumption1"1
Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study5 $180 $320 $330 $320 $130
Harvard Six-City Study0 $320 $460 $480 $460 $280
Mortality Impact Functions Derived from Expert Elicitation
Ex pert A $460 $600 $620 $600 $410
Expert B $360 $500 $520 $500 $320
Expert C $360 $500 $520 $500 $310
Expert D $270 $400 $420 $400 $220
Expert E $560 $700 $720 $700 $520
Expert F $330 $470 $490 $470 $290
Expert G $230 $370 $390 $370 $190
Expert H $280 $420 $440 $420 $230
Expert I $360 $500 $510 $490 $310
Expert J $300 $440 $450 $440 $250
Expert K $100 $240 $260 $240 $54
Expert L $270 $410 $430 $410 $230
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63).
B The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
c Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
D All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.s estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower.
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6.5.5 Discussion of Results and Uncertainties
This analysis has estimated the health and welfare benefits of reductions in ambient
concentrations of ozone and particulate matter resulting from a set of illustrative control
strategies to reduce emissions of ozone. The results suggest there will be significant additional
health and welfare benefits arising from reducing emissions from a variety of sources in and
around projected nonattaining counties in 2020. While 2020 is the expected date that states
would need to demonstrate attainment with the revised standard, it is expected that benefits (and
costs) will begin occurring much earlier, as states begin implementing control measures to show
reasonable progress towards attainment. Using the full range of benefits (including the results of
the expert elicitation), we estimate that total ozone and PIVb.s benefits would be between and
$2.5 and $33 billion annually for the 0.070 ppm alternative when the emissions reductions from
implementing the new standard is fully realized provides additional evidence of the important
role that implementation of the standards plays in reducing the health risks associated with
exceeding the standard.
There are several important factors to consider when evaluating the relative benefits of the
attainment strategies for each of the alternative ozone standards.
1. California accounts for a substantial share of the total benefits for each of the evaluated
standards. Benefits are most uncertain for California given the unique challenge of
modeling attainment with the standards due to the high levels of ozone, difficulties of
modeling the impacts of emissions controls on air quality, and the very large proportion
of California benefits that were derived through extrapolation are very large relative to
other areas of the U.S. for each standard alternative. On the one hand, these California
benefits are likely to understate the actual benefits of attainment strategies, because we
applied an estimation approach that reduced concentrations only at the specific violating
monitors and not surrounding monitors that did not violate the standards. The magnitude
of this underestimate is unknown. On the other hand, it is possible that new technologies
might not meet the specifications, development timelines, or cost estimates provided in
this analysis, thereby increasing the uncertainty in when and if such benefits would be
truly achieved.
2. There are substantial uncertainties associated with the estimated benefits of the 0.065
ppm, 0.075 ppm, and 0.079 ppm alternatives, which were derived through extrapolation
and interpolation, respectively. The great majority of benefits estimated for the 0.065
ppm standard alternative were derived through extrapolation. As noted above, these
benefits are likely to be more uncertain than the modeled benefits. The 0.075 ppm
benefits were derived through an interpolation technique (described in Appendix 6)
which scaled-down the benefits of the 0.070 ppm benefits analysis. A key assumption in
this approach is that the control strategy to attain 0.075 ppm would share the same
characteristics of the 0.070 ppm strategy—namely, regional emission controls on
electrical generating units and emission controls applied to counties within 200km of
projected non-attainment monitors. To the extent that states utilized fewer regional
emission controls, total benefits for the 0.075 ppm strategy may be smaller.
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EPA employed a monitor rollback approach to estimate the benefits of attaining an
alternative standard of 0.079 ppm nationwide. This approach likely understates the
benefits that would occur due to implementation of actual controls because controls
implemented to reduce ozone concentrations at the highest monitor would likely result in
some reductions in ozone concentrations at attaining monitors down-wind (i.e. the
controls would lead to concentrations below the standard in down-wind locations).
Therefore, air quality improvements and resulting health benefits from full attainment
would be more widespread than we have estimated in our rollback analysis.
EPA calculated 0.075 ppm benefits by interpolating the 0.070 ppm benefits estimates.33
This interpolation approach may overestimate benefits relative to a modeled control
scenario developed specifically to attain the 0.075 ppm alternative. The interpolation
method scales down benefits only at the monitors we project to exceed 0.075 ppm—but it
still captures the benefits achieved by the 0.070 ppm regional control strategy that occur
outside of these projected non-attainment areas. To the extent that a modeled emission
control strategy to attain 0.075 ppm does not include these broader regional emission
reductions, total benefits would be lower than those we have estimated in this RIA.
Interpolation and monitor rollback methods of benefits estimation are inherently
different. As described above, for the purposes of reviewing this analysis, the reader
should understand that the benefits described for attaining a standard of 0.079 ppm are
likely understated, whereas the estimated benefits of attaining a standard of 0.075 ppm
are likely overstated. We will develop and present consistent approaches for the
alternative standards for the final RIA.
3. There are a variety of uncertainties associated with the health impact functions used in
this modeling effort. These include: within study variability, which is the precision with
which a given study estimates the relationship between air quality changes and health
effects; across study variation, which refers to the fact that different published studies of
the same pollutant/health effect relationship typically do not report identical findings and
in some instances the differences are substantial.; the application of C-R functions
nationwide, which does not account for any relationship between region and health effect,
to the extent that such a relationship exists; extrapolation of impact functions across
population, in which we assumed that certain health impact functions applied to age
ranges broader than that considered in the original epidemiological study; and, finally,
there are various uncertainties in the C-R function, including causality, the correlation
among multiple pollutants, the shape of the C-R function and the relative toxicity of PM
component species, and the lag between exposure and the onset of the health effect.
4. There are a variety of uncertainties associated with the economic valuation of the health
endpoints estimated in this analysis. Uncertainties specific to the valuation of premature
mortality include across study variation; the assumption that WTP for mortality risk
33 This procedure is detailed in Appendix 6A.
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reduction is linear; assuming that voluntary and involuntary mortality risk will be valued
equally; assuming that premature mortality from air pollution risk, which tend to involve
longer periods of time, will be valued the same as short catastrophic events; the
possibility for self-selection in avoiding risk, which may bias WTP estimates upward.
5. This analysis includes estimates of PM2.5 co-benefits that were derived through benefit
per-ton estimates derived from the Pope et. al (2002) mortality estimate. These benefit
per-ton estimates represent regional averages. As such, they do not reflect any local
variability in the incremental PlV^.s benefits per ton of NOx abated. As discussed in the
PM NAAQS RIA (Table 5.5), there are a large number of uncertainties associated with
these PM benefits.
6. For the 0.070 ppm alternative, we estimate co-benefits from PM to be between 20% and
99% of total benefits, depending on the PM2.5 and ozone mortality functions used. In our
calculation of PM2.5 co-benefits we assume that states will pursue an ozone strategy that
reduces NOx emissions. As such, these estimates are strongly influenced by the
assumption that all PM components are equally toxic. We also acknowledge that when
implementing any new standard, states may elect to pursue a different ozone strategy,
which would in turn affect the level of PM2.5 co-benefits.
7. Inherent in any analysis of future regulatory programs are uncertainties in projecting
atmospheric conditions and source-level emissions, as well as population, health
baselines, incomes, technology, and other factors. In addition, data limitations prevent an
overall quantitative estimate of the uncertainty associated with estimates of total
economic benefits. If one is mindful of these limitations, the magnitude of the benefits
estimates presented here can be useful information in expanding the understanding of the
public health impacts of reducing ozone precursor emissions.
8. There are certain unqualified effects not considered in this benefits analysis due to lack
of data, time and resources. These unqualified endpoints include the direct effects of of
ozone on vegetation, the deposition of nitrogen to estuarine and coastal waters and
agricultural and forested land, and the changes in the level of exposure to ultraviolet
radiation from ground level ozone.
EPA will continue to evaluate new methods and models and select those most appropriate for
estimating the health benefits of reductions in air pollution. It is important to continue improving
benefits transfer methods in terms of transferring economic values and transferring estimated
impact functions. The development of both better models of current health outcomes and new
models for additional health effects such as asthma, high blood pressure, and adverse birth
outcomes (such as low birth weight) will be essential to future improvements in the accuracy and
reliability of benefits analyses (Guo et al., 1999; Ibald-Mulli et al., 2001). Enhanced
collaboration between air quality modelers, epidemiologists, toxicologists, and economists
should result in a more tightly integrated analytical framework for measuring health benefits of
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air pollution policies. Readers interested in a more extensive discussion of the sources of
uncertainty in human health benefits analyses should consult the PM NAAQS RIA.34
6.5.6 Summary of Total Benefits
Tables 6-54 presents the total number of estimated ozone and PlV^.s-related premature mortalities
and morbidities avoided nationwide in 2020. Table 6-55 presents these estimates for California,
post 2020.
34 U.S. Environmental Protection Agency, 2006. Regulatory Impact Analysis for the PM
NAAQS. EPA Prepared by Office of Air and Radiation. Available at:
http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf
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Table 6-66: Summary of Total Number of Annual Ozone and PM2.5-Related Premature
Mortalities and Premature Morbidity Avoided:
2020 National Benefits
Combined Estimate of Mortality
Standard Alternative and
Model or AssumptionA
NMMAPS Bell (2004)
Bell (2005)
Meta-Analysis Ito (2005)
Levy (2005)
No Causality
Combined Estimate of Morbidity
Acute Myocardial Infarction
Hospital and ER Visits
Chronic Bronchitis
Acute Bronchitis
Asthma Exacerbation
Lower Respiratory Symptoms
Upper Respiratory Symptoms
School Loss Days
Work Loss Days
Minor Restricted Activity Days
Combined Range
0.079 ppm
200 to 1,900
260 to 2,000
270 to 2,000
260 to 2,000
180 to 1,900
1,100
1,300
370
950
7,300
8,100
5,900
50,000
5 1 ,000
430,000
PM2.5
0.075 ppm
430 to 2,600
1,100 to 3,300
1,200 to 3,300
1,300 to 3,500
230 to 2,400
1,400
5,600
470
1,200
9,400
10,000
7,500
610,000
65,000
2,000,000
of Ozone Benefits and
Co-Benefits
0.070 ppm
670 to 4,300
1,500 to 5,100
1,600 to 5,200
1,800 to 5,400
390 to 4,000
2,300
7,600
780
2,000
1 6,000
1 7,000
13,000
780,000
110,000
2,700,000
0.065 ppm
1,200 to 7,400
2,800 to 9,000
3,000 to 9,200
3,000 to 9,200
660 to 6,900
4,000
13,000
1,300
3,500
27,000
29,000
22,000
1 ,300,000
190,000
4,700,000
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Table 6-67: Summary of Total Number of Annual Ozone and PM2.5-Related Premature
Mortalities and Premature Morbidity Avoided: California Post 2020 Attainment
Combined Estimate of Mortality
Standard Alternative and
Model or AssumptionA
NMMAPS Bell (2004)
Bell (2005)
Meta-Analysis Ito (2005)
Levy (2005)
No Causality
Combined Estimate of Morbidity
Acute Myocardial Infarction
Hospital and ER Visits
Chronic Bronchitis
Acute Bronchitis
Asthma Exacerbation
Lower Respiratory Symptoms
Upper Respiratory Symptoms
School Loss Days
Work Loss Days
Minor Restricted Activity Days
Combined Range of Ozone Benefits and
PM2.5 Co-Benefits
0.079 ppm
17 to 93
42 to 120
45 to 120
46 to 120
8.2 to 84
49
200
17
43
330
360
270
30,000
2,300
87,000
0.075 ppm
61 to 310
170 to 410
180 to 430
180 to 430
26 to 270
160
790
53
140
1,100
1,200
850
120,000
7,400
340,000
0.070 ppm
110 to 570
300 to 760
320 to 780
320 to 780
49 to 500
290
1,400
99
260
2,000
2,200
1,600
210,000
14,000
600,000
0.065 ppm
180 to 840
490 to 1,200
530 to 1,200
520 to 1,200
72 to 740
430
2,200
ISO
380
2,900
3,200
2,300
340,000
20,000
960,000
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Appendix Chapter 6a: Additional Benefits Information
Summary
This appendix provides additional information regarding the benefits analysis, including (1)
methods for developing estimate of full attainment air quality; (2) the process for interpolating
the 0.075 ppm benefits estimate; (3) the partial attainment PM2.5 incidence and valuation
estimates.
6a.l Developing an air quality estimate of full attainment with the alternative ozone
standards
As discussed in chapter 3, the modeled attainment scenarios were not sufficient to simulate full
attainment with each of the three alternative ozone standards analyzed. To meet our analytical
goal of estimating the human health benefits of full simulated attainment with each of these
standard alternatives, it became necessary to derive an estimate of the full attainment air quality
increment through a simple monitor rollback approach.
We rolled back the values at each monitor such that no monitor in the U.S. exceeded the
alternative standard in question. This approach makes the bounding assumption that ozone
concentrations can be reduced only at monitors projected to exceed the alternative standards.
From a benefits perspective, this approach leads to a downward bias in the estimates because
populations are assumed to be exposed at a distance weighted average of surrounding monitors.
Thus, any individual's reduction in exposure from a change at a given monitor will be weighted
less if there are other attaining monitors in close proximity.
We determined projected attainment status of each monitor by calculating design values.
However, to estimate changes in ozone-related health effects resulting from improvement in air
quality, the BenMAP model requires a series of metrics. When performing a benefits assessment
with air quality modeling data, BenMAP calculates these metrics based on the distribution of
CMAQ-modeled hourly ozone concentrations for the ozone season. However, because we were
performing a benefits assessment based on monitor values that have been rolled-back, it was
necessary to derive each of these metrics outside of the BenMAP model. Thus, we first
developed a scaling ratio that related the calculated design value to each of the ozone metrics.
A summary of this procedure is as follows:
1. Import partial attainment 0.08 ppm calculated design values into the BenMAP model
2. Perform a spatial interpolation of these design values using the Voronoi Neighborhood
Averaging algorithm. Design values are then interpolated to the CMAQ grid cell.
3. Import distribution of air quality modeled daily and hourly ozone concentrations into
BenMAP. Create air quality grid in BenMAP using spatial and temporal scaling
6a-l
-------
technique.1 This procedure creates grid cell level summer season ozone metrics (1 hour
maximum, 5 hour average, 8 hour maximum, 8 hour average and 24 hour average).
4. Calculate grid cell-level ratio of each ozone metric to calculated design value. The result
of this calculation is a grid cell-level ratio of metric to design value that can then be
subsequently used to scale the calculated design value and thus derive each of the
metrics.
After having calculated these scaling ratios we then performed the monitor rollback as follows:
1. Roll back the calculated 0.08 ppm partial attainment design value to just equal the 0.08
ppm standard. This process creates a new baseline design value grid.
2. Scale the design value grid cell values to ozone metric grid cell values by using ratios
described above.
3. Create new 0.084 ppm baseline air quality grid from grid cell-level ozone metrics.
4. Roll back the calculate calculated 0.070 ppm and 0.065 ppm partial attainment design
values at each monitor to just each the 0.070 ppm and 0.065 ppm standards, respectively.
5. Scale the calculated full attainment design value to grid cell-level ozone metric using
ratios described above.
6. Create new 0.070 ppm and 0.065 ppm air quality grids from grid cell-level ozone metrics.
7. Perform benefits analysis with baseline and control grids.
To develop a 0.075 ppm full attainment air quality grid we performed an interpolation of the
0.070 ppm full attainment air quality grid, rather than a monitor rollback. This interpolation
entailed the following steps:
1. We identified any monitors that were projected to not attain 0.075 ppm alternative in the
0.084 ppm base case air quality grid.
2. For these monitors we calculated an adjustment factor that would scale down the air
quality improvement at that monitor. The purpose of this adjustment was to ensure that
the improvement in air quality at that monitor reflected the attainment of the 0.075 ppm
standard. This ratio was calculated by dividing the improvement in the design value
necessary to attain 0.075 ppm by the improvement in the design value necessary to attain
0.070 ppm. For example, a monitor whose baseline is 0.084 would receive 2/3 of the air
quality improvement from attaining 0.075 ppm than they would from attaining 0.070
ppm.
3. We then interpolated these monitor-specific ratios to the grid cell-level in BenMAP.
4. Finally, we used these grid cell-level ratios as the basis for scaling down the grid cell-
level estimates of incidence and valuation from the 0.070 ppm analysis.
6a.2 Partial Attainment PM2.s Incidence and Valuation Estimates
Tables 6a.l through 6a.5 below summarize the estimates of PM2.5 incidence and valuation
resulting from the 0.070 ppm partial attainment scenario. These estimates provided the basis for
the full attainment PM2.5 co-benefit estimates found in Chapter 6 of this RIA.
1 BenMAP Technical Appendices, Abt Associates: May 2005. Page C-12.
6a-2
-------
Table 6a-l: Illustrative 0.070 ppm Partial Attainment Scenario: Estimated Reductions in PM Premature
Mortality associate with PM co-benefit (95th percentile confidence intervals provided in parentheses)
Mortality Impact Functions Derived from
ACS StudyA
Harvard Six-City Study5
Woodruff et al 1997 (infant mortality)
Mortality Impact Functions Derived from
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Eastern U.S.
Epidemiology Literature
510
(170--840)
1,100
(570-1,700)
1 i
(0.5-1.7)
Expert Elicitation
1,600
(170-3,000)
1,200
(140-2,600)
1,200
(140-2,700)
820
(85-1,400)
2,000
(890-3,000)
1,100
(740-1,600)
690
(0-1,300)
880
(-46-2,100)
1,200
(60-2,200)
950
(230-2,200)
190
(0-970)
860
(120-1,600)
Western U.S. Excluding
California
0.17
(0.06-0.27)
0.4
(0.18-0.6)
n D4
\J • \Ji
(0.02-0.06)
77
(8.2-150)
56
(4-130)
58
(6.6-130)
40
(4.1-68)
95
(44-150)
51
(33-74)
34
(0-65)
43
(-2.2-100)
57
(3-110)
46
(11-110)
8.8
(0-47)
33
(0.04-81)
California
47
(16-77)
110
(53-160)
0 14
(0.07-0.2)
140
(15-270)
110
(13-240)
110
(12-250)
76
(7.8-130)
180
(82-280)
99
(67-150)
63
(0-120)
80
(-4.2-200)
110
(5.5-200)
87
(21-200)
19
(0-95)
79
(11-150)
National PM co-benefits
550
(190-920)
1,300
(630-1,900)
1 3
(0.6-2)
1,800
(190-3,400)
1,400
(160-3,000)
1,400
(150-3,000)
940
(96-1,600)
2,200
(1,000-3,500)
1,200
(840-1,800)
790
(0-1,500)
1,000
(-52-2,400)
1,300
(69-2,500)
1,100
(260-2,500)
220
(0-1,100)
970
(130-1,900)
A The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs
B Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
Q
All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA
6a-3
-------
Table 6a-2: Illustrative 0.070 ppm Partial Attainment Scenario: Estimated Reductions in Morbidity
Associated with PM Co-benefit (95th percentile confidence intervals provided in parentheses)
Eastern U.S.
Western U.S. Excluding
California
California
Morbidity Impact Functions Derived from Epidemiology Literature
Chronic Bronchitis (age >25 and over)
Nonfatal myocardial infarction (age >17)
Hospital admissions—respiratory (all ages)
Hospital admissions-- cardiovascular
(age >17)
Emergency room visits for asthma
(age <19)
Acute bronchitis (age 8-12)
Lower respiratory symptoms (age 7-14)
Upper respiratory symptoms (asthmatic
children age 9-18)
Asthma exacerbation (asthmatic children
age 6-18)
Work loss days (age 18-65)
Minor restricted activity days (age 18-65)
380
(42-720)
1,100
(560-1,700)
130
(59-200)
270
(160-370)
560
(310-820)
990
(-130-2,100)
8,400
(3,600-13,000)
6,100
(1,500-11,000)
7,700
(550-24,000)
53,000
(46,000-61,000)
320,000
(260,000-370,000)
12
(1.3-21)
0.4
(0.2-0.6)
--
--
--
32
(-4.1-67)
3.6
(1.6-5.5)
2.6
(0.7-4.6)
3.4
(0.24-11)
20
(17-22)
120
(100-140)
43
(4.6-81)
94
(47—140)
10
(4.4-15)
20
(12-28)
22
(12-32)
130
(-17-270)
1,200
(520-1,900)
870
(220-1,500)
1,100
(77-3,400)
7,200
(6,100-8,200)
42,000
(35,000-49,000)
National PM co-benefits
440
(47-820)
1,200
(610-1,800)
140
(63-220)
290
(170-400)
590
(320-850)
1,200
(-150-2,400)
9,600
(4,200-15,000)
7,000
(1,800-12,000)
8,700
(620-28,000)
61,000
(52,000-69,000)
360,000
(300,000-420,000)
A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA
6a-4
-------
Table 6a-3: Illustrative Strategy to Partially Attain 0.070 ppm: Estimated Partial Attainment Value of Reductions in PM2.5-Related Premature
Mortality Associated with PM co-benefit (3 percent discount rate, in millions of 1999$) 95th Percentile Confidence Intervals Provided in Parentheses
Mortality Impact Functions Derived from
ACS StudyA
Harvard Six-City Study5
Woodruff et al 1997 (infant mortality)
Mortality Impact Functions Derived from
F v n o ft" A
CXptir L M
F v r» o r t" R
CXptrrL D
Fvn^rl" C*
I^ApCI L V-.
F Y n ^ rl" Pi
I^ApCI L Ly
Expert E
Expert F
Fvi-iort" f~*
CXpclL o
Cvi-iQ^t- U-l
expert n
Expert I
Expert J
F v n o r"t" \{
CXptrrL IS.
F v n o ft" 1
CXpfcJF L L
Eastern U.S.
Eoidemioloav Literature
$2,900
($410--$6,700)
$6,600
($1,100--$14,000)
$6.3
($1--$14)
Expert Elicitation
$9,100
($810-$23,000)
$6,900
($470--$21,000)
$6,800
($620-$21,000)
$4,800
($500~$11, 000)
$11,000
($1,800-$25,000)
$6,200
($1,100--$14,000)
$4,000
(0--$11,000)
$5,100
($11-$16,000)
$6,800
($570--$17,000)
$5,500
($700-$17,000)
$1,100
(0--$6,500)
$5,000
($440--$13,000)
Western U.S. Excluding
California
$1
($0.14-$2.2)
$2.1
($0.35-$4.6)
$0.2
($0.03--$0.5)
$440
($51-$1,100)
$320
($16--$1,000)
$340
($38-$990)
$230
($30--$550)
$550
($120--$!, 200)
$290
($67-$630)
$200
(0--$530)
$250
(0.7-$770)
$330
($35--$840)
$270
($44--$810)
$51
(0-$320)
$190
($0.2--$640)
California
$270
($38-$610)
$610
($98--$l,300)
$0.8
($0.12--$!. 7)
$830
($75-$2,100)
$630
($42--$2,000)
$630
($57-$l,900)
$440
($46--$l,000)
$1,000
($160-$2,300)
$570
(100--$!, 200)
$370
(0--$990)
$470
($1-$1,500)
$620
($53--$l,600)
$500
($64-$l,500)
$110
(0--$650)
$460
($39--$l,200)
National PM co-
benefits
$3,200
($450-$7,300)
$7,200
($1,200-$15,000)
$7.3
($1.1--$16)
$10,000
($930-$26,000)
$7,900
($520--$24,000)
$7,800
($710-$23,000)
$5,400
($570--$13,000)
$13,000
($2,000--$28,000)
$7,100
($1,300-$15,000)
$4,600
(0--$12,000)
$5,800
($12-$18,000)
$7,700
($650--$19,000)
$6,200
($790--$19,000)
$1,300
(0-$7,500)
$5,700
($480--$15,000)
A The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been
reported as the primary estimate in recent RIAs
B Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity
estimate.
c All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RLA
6a-5
-------
Table 6a-4: Illustrative Strategy to Partially Attain 0.070 ppm: Estimated Partial Attainment Value of Reductions in PM2.5-Related Premature
Mortality Associated with PM co-benefit (7 percent discount rate, in millions of 1999$) 95th Percentile Confidence Intervals Provided in Parentheses
Mortality Impact Functions Derived from
ACS StudyA
Harvard Six-City Study5
Woodruff et al 1997 (infant mortality)
Mortality Impact Functions Derived from
Expert A
Expert B
Expert C
Expert D
Expert E
Expert F
Expert G
Expert H
Expert I
Expert J
Expert K
Expert L
Eastern U.S.
Eoidemioloav Literature
$2,500
($350--$5,600)
$5,600
($890--$12,000)
$5.3
($0.81--$12)
Expert Elicitation
$7,700
($880--$19,000)
$5,800
($510-$18,000)
$5,800
($660-$17,000)
$4,000
($520-$9,500)
$9,500
($2,000-$21,000)
$5,300
($1,300--$!!, 000)
$3,400
(0-$9,100)
$4,300
($12-$13,000)
$5,900
($600-$14,000)
$4,600
($750-$14,000)
$920
(0-$5,500)
$4,200
($460-$11,000)
Western U.S. Excluding
California
$0.8
(0. !--$!. 9)
$1.8
($0.3-$3.9)
$0.2
($0.03-$0.38)
$370
($43-$940)
$270
($13-$870)
$280
($32-$830)
$200
($26-$470)
$470
($98-$l,000)
$250
($57-$530)
$160
(0-$440)
$210
($0.6-$650)
$290
($30-$700)
$230
($37-$680)
$43
(0-$270)
$160
($0.16-$540)
California
$230
($32-$520)
$510
($82-$l,100)
$0.7
($0.1-$1.4)
$700
($81-$1,800)
$530
($46-$l,700)
$530
($61-$1,600)
$370
($48-$880)
$870
($180-$1,900)
$480
($120-$!, 000)
$310
(0-$830)
$390
($1.1-$1,200)
$540
($55-$l,300)
$420
($69-$l,300)
$93
(0-$550)
$380
($42-$l,000)
National PM co-
benefits
$2,700
($380-$6,100)
$6,100
($980-$13,000)
$6.2
($0.9-$14)
$8,700
($780-$22,000)
$6,600
($440-$21,000)
$6,600
($600-$19,400)
$4,600
($480-$11,000)
$11,000
($1,700-$24,000)
$6,000
($1,100-$13,000)
$3,800
(0-$10,00)
$4,900
($10-$15,000)
$6,700
($550-$16,000)
$5,300
($670-$16,000)
$1,100
(0-$6,300)
$4,700
($400-$13,000)
6a-6
-------
Table 6a-5: Illustrative Strategy to Partially Attain 0.070 ppm: Estimated Partial Attainment Monetary Value of Reductions in Risk of PM2.5-
Related Morbidity Reductions Associated with PM co-benefit (in millions of 1999$) 95th Percentile Confidence Intervals Provided in
Parentheses
Eastern U.S.
Western U.S. Excluding
California California
Morbidity Impact Functions Derived from Epidemiology Literature
Chronic Bronchitis (age >25 and over)
Nonfatal myocardial infarction (age >17)
Hospital admissions—respiratory (all ages)
Hospital admissions-- cardiovascular
(age >17)
Emergency room visits for asthma
(age <19)
Acute bronchitis (age 8-12)
Lower respiratory symptoms (age 7-14)
Upper respiratory symptoms (asthmatic
children age 9-18)
Asthma exacerbation (asthmatic children
age 6-18)
Work loss days (age 18-65)
Minor restricted activity days (age 18-65)
$160
($8.7-$720)
$93
($25-$200)
$90
($23-$200)
$2.1
$5.5
($3.4-$7.5)
$0.2
($0.04-$0.3)
$0.4
($-0.02-$!)
$0.1
($0.04-$0.27)
$0.2
($0.03-0.38)
$0.34
($0.03-$1.3)
$5.3
$4.7 $17
($0.3-$22) ($1-$80)
$0.03 $8
($0.01-$0.7) ($2.1-$17)
$0.03 $7.7
($0.01-$0.7) ($2-$17)
$0.2
($0.08-$0.2)
$0.4
($0.3-$0.57)
...
$0.05
($-0.002-$0.1)
$0.02
($0.006-$0.04)
$0.022
($0.005-$0.05)
$0.05
($0.004-$0.18)
$0.9
($0.7-$1)
National PM co-benefits
$180
($10-$820)
$100
($27--$220)
$98
($25--$220)
$2.3
($l.l-$3.4)
$5.9
($3.7-$8.1)
$0.2
($0.09-$0.26)
$0.4
($-0.02-$!. 2)
$0.15
($0.05-$0.3)
$0.2
($0.04-$0.44)
$0.4
($0.03-$1.5)
$7.4
($6.4-$8.3)
All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across
columns. All estimates incremental to 2006 PM NAAQS
6a-7
-------
Appendix Chapter 6b: Health-Based Cost-Effectiveness of Reductions in Ambient
PM2.s Associated with Illustrative Ozone NAAQS 0.070ppm Attainment Strategy
6b.l Summary
Health-based cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) have been used
to analyze numerous health interventions but have not been widely adopted as tools to analyze
environmental policies. The Office of Management and Budget (OMB) recently issued Circular
A-4 guidance on regulatory analyses, requiring federal agencies to "prepare a CEA for all major
rulemakings for which the primary benefits are improved public health and safety to the extent
that a valid effectiveness measure can be developed to represent expected health and safety
outcomes." Environmental quality improvements may have multiple health and ecological
benefits, making application of CEA more difficult and less straightforward. For the Ozone
NAAQS, CEA may provide a useful framework for evaluation: non-health benefits are
substantial, but the majority of quantified benefits come from health effects. Therefore, EPA is
including in the Ozone NAAQS RIA a preliminary and experimental application of one type of
CEA — a modified quality-adjusted life -years (QALYs) approach.
This cost effectiveness analysis considers the PMa.s benefits resulting from the illustrative ozone
control strategies only. Estimation of QALY or Morbidity Inclusive Life Year (MILY, discussed
below) impacts associated with reducing ozone concentrations is difficult for several reasons.
First, with the exception of premature death, the set of ozone -related health endpoints includes
only acute diseases and impacts. As discussed below, there are a number of reasons that the
QALY method is not appropriate for valuing acute health effects. Second, calculation of QALY
or MILY impacts for premature mortality is complicated by a lack of information, including the
change in life expectancy associated with the risk reduction (for MILYs and QALYs) and the
baseline quality of life for individuals experiencing the risk reduction (for QALY calculations).
The EPA has recently asked the National Academies of Sciences1 for advice on characterizing
the mortality risk reduction benefits of reducing ozone concentrations. In their evaluation, the
NAS Committee on Estimating Mortality Risk Reduction Benefits from Decreasing
Tropospheric Ozone Exposure will provide advice on, among other topics, the adequacy of a
basis for estimating the likely impact on life expectancy from reductions in short-term daily
exposures to ozone. If there is an adequate basis, they will, to the extent practicable, estimate the
magnitude and associated uncertainties of this impact. While awaiting the recommendations of
the NAS committee, EPA is electing to not calculate QALY or MILY impacts for ozone related
health effects for this proposal RIA. EPA will investigate the feasibility of performing such an
analysis for the final RIA. As a result, the overall $/MILY estimates for attainment of alternative
ozone NAAQS reported in this appendix will overstate the expected $/MILY incorporating
ozone effects.
National Academy of Sciences (2007) Project Scope. Estimating Mortality Risk
Reduction Benefits from Decreasing Tropospheric Ozone Exposure. Division on Earth and Life
Studies, Board on Environmental Studies and Toxicology. Available at:
http://www8.nati onalacademies.org/cp/proiectview.aspx?kev=48768
6b-l
-------
QALYs were developed to evaluate the effectiveness of individual medical treatments, and EPA
is still evaluating the appropriate methods for CEA for environmental regulations. Agency
concerns with the standard QALY methodology include the treatment of people with fewer years
to live (the elderly); fairness to people with preexisting conditions that may lead to reduced life
expectancy and reduced quality of life; and how the analysis should best account for non-health
benefits, such as improved visibility.
The Institute of Medicine (a member institution of the National Academies of Science)
established the Committee to Evaluate Measures of Health Benefits for Environmental, Health,
and Safety Regulation to assess the scientific validity, ethical implications, and practical utility
of a wide range of effectiveness measures used or proposed in CEA. This committee prepared a
report titled "Valuing Health for Regulatory Cost-Effectiveness Analysis" which concluded that
CEA is a useful tool for assessing regulatory interventions to promote human health and safety,
although not sufficient for informed regulatory decisions (Miller, Robinson, and Lawrence,
2006). They emphasized the need for additional data and methodological improvements for
CEA analyses, and urged greater consistency in the reporting of assumptions, data elements, and
analytic methods. They also provided a number of recommendations for the conduct of
regulatory CEA analyses. EPA is evaluating these recommendations and will determine a
response for upcoming analyses. For this analysis, we use the same approach that was applied in
the CEA that accompanied the RIA's for the Clean Air Interstate Rule and the PM NAAQS.
The methodology presented in this appendix is not intended to stand as precedent either for
future air pollution regulations or for other EPA regulations where it may be inappropriate. It is
intended solely to demonstrate one particular approach to estimating the cost-effectiveness of
reductions in ambient PM2.5 in achieving improvements in public health. Reductions in ambient
PM2.5 likely will have other health and environmental benefits that will not be reflected in this
CEA. Other EPA regulations affecting other aspects of environmental quality and public health
may require additional data and models that may preclude the development of similar health-
based CEAs. A number of additional methodological issues must be considered when
conducting CEAs for environmental policies, including treatment of nonhealth effects,
aggregation of acute and long-term health impacts, and aggregation of life extensions and
quality-of-life improvements in different populations. The appropriateness of health-based CEA
should be evaluated on a case-by-case basis subject to the availability of appropriate data and
models, among other factors.
Attainment of the revised Ozone NAAQS is expected to result in substantial reductions in
potential population exposure to ambient concentrations of PM by 2020. The benefit-cost
analysis presented in the RIA shows that partial attainment of the revised 0.070 ppm ozone
standard achieves substantial health benefits whose monetized value is roughly equal to costs
(net benefits are between -$6B and $4. IB). Despite the risk of oversimplifying benefits,
cautiously-interpreted cost-effectiveness calculations may provide further evidence of whether
the costs associated with attainment strategies for the Ozone NAAQS are a reasonable health
investment for the nation.
This analysis provides estimates of commonly used health-based effectiveness measures,
including lives saved, life years saved (from reductions in mortality risk), and QALYs saved
(from reductions in morbidity risk) associated with the reduction of ambient PMa.s due to
6b-2
-------
illustrative attainment strategies for a more stringent annual ozone standard. In addition, we use
an alternative aggregate effectiveness metric, Morbidity Inclusive Life Years (MILY) to address
some of the concerns about aggregation of life extension and quality-of-life impacts. It
represents the sum of life years gained due to reductions in premature mortality and the QALY
gained due to reductions in chronic morbidity. This measure may be preferred to existing QALY
aggregation approaches because it does not devalue life extensions in individuals with
preexisting illnesses that reduce quality of life. However, the MILY measure is still based on life
years and thus still inherently gives more weight to interventions that reduce mortality and
morbidity impacts for younger populations with higher remaining life expectancy. This analysis
focuses on life extensions and improvements in quality of life through reductions in two diseases
with chronic impacts: chronic bronchitis (CB) and nonfatal acute myocardial infarctions. Monte
Carlo simulations are used to propagate uncertainty in several analytical parameters and
characterize the distribution of estimated impacts. While the benefit-cost analysis presented in
the RIA characterizes mortality impacts using a number of different sources for the PM mortality
effect estimate, for this analysis, we focus on the mortality results generated using the effect
estimates derived from the Pope et al. (2002) and Laden et al. (2005) studies.
Presented in three different metrics, the analysis suggests the following:
• In 2020 the illustrative attainment strategy for the revised 0.070 ppm standards will result
in:
- Between 550 (95% CI: 215 - 890) and 1,300 (95% CI: 680 - 1,800) premature
deaths avoided using the Pope (2002) and Laden (2006) studies, respectively, or
- Between 6,100 (95% CI: 2,400 - 9,800) and 14,000 (95% CI: 7,500 - 20,000)
life years gained (discounted at 3 percent) using the Pope (2002) and Laden
(2006) studies, respectively, or
- Between 9,100 (95% CI: 3,100 - 16,000) and 17,000 (95% CI: 8,200-27,000)
MILYs gained (discounted at 3 percent) using the Pope (2002) and Laden (2006)
studies, respectively.
• Using a 7 percent discount rate, mean discounted life years gained are between 4,600 and
10,400 using the Pope (2002) and Laden (2006) studies, respectively; mean MILYs
gained are 6,800 and 13,000 using the two studies (The estimates of premature deaths
avoided are not affected by the discount rate.)
• The associated reductions in CB and nonfatal acute myocardial infarctions will reduce
medical costs by approximately $140 million based on a 3 or 7 percent discount rate.
Direct private compliance costs for the 0.070 ppm partial attainment strategy, are $3.9 billion in
2020. Based on these costs, the incremental cost effectiveness (net of cost of illness and other
health and visibility benefits) of the 0.070 ppm partial attainment strategy is $430,000/MILY
using a 3 percent discount rate and $580,000/MILY using a 7 percent discount rate if one
calculates MILY's using the Pope (2002) mortality estimate. The incremental cost effectiveness
(again, net of cost of illness and other health and visibility benefits) of the 0.070 ppm partial
attainment strategy is $230,000/MILY using a 3 percent discount rate and $310,000/MILY using
6b-3
-------
a 7 percent discount rate if one calculates MILY's using the Laden (2006) mortality estimate.
See Chapters 3 and 5 of this RIA for more discussion of the control strategies and cost estimates.
6b.2 Introduction
Analyses of environmental regulations have typically used benefit-cost analysis to characterize
impacts on social welfare. Benefit-cost analyses allow for aggregation of the benefits of
reducing mortality risks with other monetized benefits of reducing air pollution, including acute
and chronic morbidity, and nonhealth benefits such as improved visibility. One of the great
advantages of the benefit-cost paradigm is that a wide range of quantifiable benefits can be
compared to costs to evaluate the economic efficiency of particular actions. However,
alternative paradigms such as CEA and CUA analyses may also provide useful insights. CEA
involves estimation of the costs per unit of benefit (e.g., lives or life years saved). CUA is a
special type of CEA using preference-based measures of effectiveness, such as QALYs.
CEA and CUA are most useful for comparing programs that have similar goals, for example,
alternative medical interventions or treatments that can save a life or cure a disease. They are
less readily applicable to programs with multiple categories of benefits, such as those reducing
ambient air pollution, because the cost-effectiveness calculation is based on the quantity of a
single benefit category. In other words, we cannot readily convert improvements in nonhealth
benefits such as visibility to a health metric such as life years saved. For these reasons,
environmental economists prefer to present results in terms of monetary benefits and net
benefits.
However, QALY-based CUA has been widely adopted within the health economics literature
(Neumann, 2003; Gold et al., 1996) and in the analysis of public health interventions (US FDA,
2004). QALY-based analyses have not been as accepted in the environmental economics
literature because of concerns about the theoretical consistency of QALYs with individual
preferences (Hammitt, 2002), treatment of nonhuman health benefits, and a number of other
factors (Freeman, Hammitt, and De Civita, 2002). For environmental regulations, benefit-cost
analysis has been the preferred method of choosing among regulatory alternatives in terms of
economic efficiency. Recently several academic analyses have proposed the use of life years-
based benefit-cost or CEAs of air pollution regulations (Cohen, Hammitt, and Levy, 2003; Coyle
et al., 2003; Rabl, 2003; Carrothers, Evans, and Graham, 2002). In addition, the World Health
Organization has adopted the use of disability-adjusted life years, a variant on QALYs, to assess
the global burden of disease due to different causes, including environmental pollution (Murray
et al., 2002; de Hollander et al., 1999).
Recently, the U.S. OMB (Circular A-4, 2003) issued new guidance requiring federal agencies to
provide both CEA and benefit-cost analyses for major regulations. The OMB Circular A-4
directs agencies to "prepare a CEA for all major rulemakings for which the primary benefits are
improved public health and safety to the extent that a valid effectiveness measure can be
developed to represent expected health and safety outcomes." We are including a CEA for the
illustrative PM NAAQS attainment strategies to illustrate one potential approach for conducting
a CEA. EPA is still evaluating the appropriate methods for CEA for environmental regulations
with multiple outcomes.
6b-4
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The methodology presented in this appendix is not intended to stand as precedent either for
future air pollution regulations or for other EPA regulations governing water, solid waste, or
other regulatory objectives. It is intended solely to demonstrate one particular approach to
estimating the effectiveness of reductions in ambient PMa.s in achieving improvements in public
health. This analysis focuses on effectiveness measured by improvements in life expectancy and
reductions in the incidence of two diseases with chronic impacts on quality of life: CB and
nonfatal acute myocardial infarctions. Other EPA regulations affecting other aspects of
environmental quality and public health may require additional data and models that may
preclude the development of similar QALY-based analyses. The appropriateness of QALY-
based CEA should be evaluated on a case-by-case basis subject to the availability of appropriate
data and models.
Preparation of a CEA requires identification of an appropriate measure of rule effectiveness.
Given the significant impact of reductions in ambient PMa.s on reductions in the risk of
mortality, lives saved is an important measure of effectiveness. However, one of the ongoing
controversies in health impact assessment regards whether reductions in mortality risk should be
reported and valued in terms of statistical lives saved or in terms of statistical life years saved.
Life years saved measures differentiate among premature mortalities based on the remaining life
expectancy of affected individuals. In general, under the life years approach, older individuals
will gain fewer life years than younger individuals for the same reduction in mortality risk during
a given time period, making interventions that benefit older individuals seem less beneficial
relative to similar interventions benefiting younger individuals. A further complication in the
debate is whether to apply quality adjustments to life years lost. Under this approach,
individuals with preexisting health conditions would have fewer QALYs lost relative to healthy
individuals for the same loss in life expectancy, making interventions that primarily benefit
individuals with poor health seem less beneficial to similar interventions affecting primarily
healthy individuals.
In addition to substantial mortality risk reduction benefits, strategies for attaining the revised PM
NAAQS will also result in significant reductions in chronic and acute morbidity. Several
approaches have been developed to incorporate both morbidity and mortality into a single
effectiveness metric. The most common of these is the QALY approach, which expresses all
morbidity and mortality impacts in terms of quality of life multiplied by the duration of time with
that quality of life. The QALY approach has some appealing characteristics. For example, it can
account for morbidity effects as well as losses in life expectancy without requiring the
assignment of dollar values to calculate total benefits. By doing so it provides an alternative
framework to benefit-cost analysis for aggregating quantitative measures of health impacts.
While used extensively in the economic evaluation of medical interventions (Gold et al., 1996),
QALYs have not been widely used in evaluating environmental health regulations. A number of
specific issues arise with the use of QALYs in evaluating environmental programs that affect a
broad and heterogeneous population and that provide both health and nonhealth benefits. The
U.S. Public Health Service report on cost-effectiveness in health and medicine notes the
following:
For decisions that involve greater diversity in interventions and the people to whom
they apply, cost-effectiveness ratios continue to provide essential information, but
6b-5
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that information must, to a greater degree, be evaluated in light of circumstances and
values that cannot be included in the analysis. Individuals in the population will
differ widely in their health and disability before the intervention, or in age, wealth,
or other characteristics, raising questions about how society values gains for the
more and less health, for young and old, for rich and poor, and so on. The
assumption that all QALYs are of equal value is less likely to be reasonable in this
context. (Goldetal.,1996,p.ll)
Use of QALYs as a measure of effectiveness for environmental regulations is still developing,
and while this analysis provides one framework for using QALYs to evaluate environmental
regulations, there are clearly many issues, both scientific and ethical, that need to be addressed
with additional research. The Institute of Medicine panel evaluating QALYs and other
effectiveness measures prepared a report titled "Valuing Health for Regulatory Cost-
Effectiveness Analysis" which concluded that "the QALY is the best measure at present on
which to standardize Health Adjusted Life Year estimation because of its widespread use,
flexibility, and relative simplicity" (Miller, Robinson, and Lawrence, 2006). EPA is evaluating
this recommendation and will determine a response for upcoming analyses. For this analysis, for
reasons discussed in the text, we use the same MILY approach that was applied in the CEA that
accompanied the RIA for the Clean Air Interstate Rule.
This appendix presents cost-effectiveness methodologies for evaluating programs such as
attainment strategies for the revised Ozone NAAQS that are intended to reduce both ozone and
PMa.s precursors, such as NOx and VOCs, starting from the standard QALY literature and
seeking a parallel structure to benefit-cost analysis in the use of air quality and health inputs (see
Hubbell [2004a] for a discussion of some of the issues that arise in comparing QALY and
benefit-cost frameworks in analyzing air pollution impacts). For the purposes of this analysis,
we calculate effectiveness using several different metrics, including lives prolonged, life years
gained, and modified QALYs. For the life years and QALY-type approaches, we use life table
methods to calculate the change in life expectancy expected to result from changes in mortality
risk from PM. We use existing estimates of preferences for different health states to obtain
QALY weights for morbidity endpoints associated with air pollution. In general, consistent with
the Gold et al. (1996) recommendations, we use weights obtained from a societal perspective
when available. We explore several different sources for these weights to characterize some of
the potential uncertainty in the QALY estimates. We follow many of the principles of the
reference case analysis as defined in Gold et al. (1996), although in some cases we depart from
the reference case approach when data limitations require us to do so (primarily in the selection
of quality-of-life weights for morbidity endpoints). We also depart from the reference case (and
the recommendations of the IOM report) in the method of combining life expectancy and
quality-of-life gains.
Results in most tables are presented only at a discount rate of 3 percent, rather than at both 3
percent and 7 percent as recommended in EPA and OMB guidance. This is strictly for ease of
presentation. Aggregate results at 7 percent are presented in the summary, and the impact of
using a 7 percent discount rate instead of 3 percent rate is summarized in a sensitivity analysis.
Monte Carlo simulation methods are used to propagate uncertainty in several of the model
parameters throughout the analysis. We characterize overall uncertainty in the results with 95
6b-6
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percent confidence intervals based on the Monte Carlo simulations. In addition, we examine the
impacts of changing key parameters, such as the discount rate, on the effectiveness measures and
the cost-effectiveness metrics.
The remainder of this appendix provides an overview of the key issues involved in life year- and
QALY-based approaches for evaluating the health impacts of air pollution regulations, provides
detailed discussions of the steps required for each type of effectiveness calculation, and presents
the CEA for the PM NAAQS illustrative attainment strategies. Section G.3 introduces the
various effectiveness measures and discusses some of the assumptions required for each. Section
G.4 details the methodology used to calculate changes in life years and quality adjustments for
mortality and morbidity endpoints. Section G.5 provides the results for the illustrative
attainment strategies for the revised and more stringent alternative PM NAAQS and discusses
their implications for cost-effectiveness of these attainment strategies.
6b.3 Effectiveness Measures
Three major classes of benefits are associated with reductions in air pollution: mortality,
morbidity, and nonhealth (welfare). For the purposes of benefit-cost analysis, EPA has presented
mortality-related benefits using estimates of avoided premature mortalities, representing the
cumulative result of reducing the risk of premature mortality from long-term exposure to PMa.s
for a large portion of the U.S. population. Morbidity benefits have been characterized by
numbers of new incidences avoided for chronic diseases such as CB, avoided admissions for
hospitalizations associated with acute and chronic conditions, and avoided days with symptoms
for minor illnesses. Nonhealth benefits are characterized by the monetary value of reducing the
impact (e.g., the dollar value of improvements in visibility at national parks).
For the purposes of CEA, we focus the effectiveness measure on the quantifiable health impacts
of the reduction in PM2.5. Treatment of nonhealth benefits is important and is discussed in some
detail later in this section. If the main impact of interest is reductions in mortality risk from air
pollution, the effectiveness measures are relatively straightforward to develop. Mortality
impacts can be characterized similar to the benefits analysis, by counting the number of
premature mortalities avoided, or can be characterized in terms of increases in life expectancy or
life years.2 Estimates of premature mortality have the benefit of being relatively simple to
calculate, are consistent with the benefit-cost analysis, and do not impose additional assumptions
on the degree of life shortening. However, some have argued that counts of premature
mortalities avoided are problematic because a gain in life of only a few months would be
Life expectancy is an ex ante concept, indicating the impact on an entire population's
expectation of the number of life years they have remaining, before knowing which individuals
will be affected. Life expectancy thus incorporates both the probability of an effect and the
impact of the effect if realized. Life years is an ex post concept, indicating the impact on
individuals who actually die from exposure to air pollution. Changes in population life
expectancy will always be substantially smaller than changes in life years per premature
mortality avoided, although the total life years gained in the population will be the same. This is
because life expectancy gains average expected life years gained over the entire population,
while life years gained measures life years gained only for those experiencing the life extension.
6b-7
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considered equivalent to a gain of a many life years, and the true effectiveness of an intervention
is the gain in life expectancy or life years (Rabl, 2003; Miller and Hurley, 2003).
Calculations of changes in life years and life expectancy can be accomplished using standard life
table methods (Miller and Hurley, 2003). However, the calculations require assumptions about
the baseline mortality risks for each age cohort affected by air pollution. A general assumption
may be that air pollution mortality risks affect the general mortality risk of the population in a
proportional manner. However, some concerns have been raised that air pollution affects mainly
those individuals with preexisting cardiovascular and respiratory disease, who may have reduced
life expectancy relative to the general population. This issue is explored in more detail below.
Air pollution is also associated with a number of significant chronic and acute morbidity
endpoints. Failure to consider these morbidity effects may understate the cost-effectiveness of
air pollution regulations or give too little weight to reductions in particular pollutants that have
large morbidity impacts but no effect on life expectancy. The QALY approach explicitly
incorporates morbidity impacts into measures of life years gained and is often used in health
economics to assess the cost-effectiveness of medical spending programs (Gold et al., 1996).
Using a QALY rating system, health quality ranges from 0 to 1, where 1 may represent full
health, 0 death, and some number in between (e.g., 0.8) an impaired condition. QALYs thus
measure morbidity as a reduction in quality of life over a period of life. QALYs assume that
duration and quality of life are equivalent, so that 1 year spent in perfect health is equivalent to 2
years spent with quality of life half that of perfect health. QALYs can be used to evaluate
environmental rules under certain circumstances, although some very strong assumptions
(detailed below) are associated with QALYs. The U.S. Public Health Service Panel on Cost
Effectiveness in Health and Medicine recommended using QALYs when evaluating medical and
public health programs that primarily reduce both mortality and morbidity (Gold et al., 1996).
Although there are significant nonhealth benefits associated with air pollution regulations, over
90 percent of quantifiable monetized benefits are health-related, as is the case with the
attainment strategies for the PM NAAQS. Thus, it can be argued that QALYs are more
applicable for these types of regulations than for other environmental policies. However, the
value of nonhealth benefits should not be ignored. As discussed below, we have chosen to
subtract the value of nonhealth benefits from the costs in the numerator of the cost-effectiveness
ratio.
In the following sections, we lay out a phased approach to describing effectiveness. We begin by
discussing how the life-extending benefits of air pollution reductions are calculated, and then we
incorporate morbidity effects using the QALY approach. We also introduce an alternative
aggregated health metric, Morbidity Inclusive Life Years (MILY) to address some of the ethical
concerns about aggregating life extension impacts in populations with preexisting disabling
conditions.
The use of QALYs is predicated on the assumptions embedded in the QALY analytical
framework. As noted in the QALY literature, QALYs are consistent with the utility theory that
underlies most of economics only if one imposes several restrictive assumptions, including
independence between longevity and quality of life in the utility function, risk neutrality with
respect to years of life (which implies that the utility function is linear), and constant
proportionality in trade-offs between quality and quantity of life (Pliskin, Shepard, and
6b-8
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Weinstein, 1980; Bleichrodt, Wakker, and Johannesson, 1996). To the extent that these
assumptions do not represent actual preferences, the QALY approach will not provide results
that are consistent with a benefit-cost analysis based on the Kaldor-Hicks criterion.3 Even if the
assumptions are reasonably consistent with reality, because QALYs represent an average
valuation of health states rather than the sum of societal WTP, there are no guarantees that the
option with the highest QALY per dollar of cost will satisfy the Kaldor-Hicks criterion (i.e.,
generate a potential Pareto improvement [Garber and Phelps, 1997]).
Benefit-cost analysis based on WTP is not without potentially troubling underlying structures as
well, incorporating ability to pay (and thus the potential for equity concerns) and the notion of
consumer sovereignty (which emphasizes wealth effects). Table G-l compares the two
approaches across a number of parameters. For the most part, WTP allows parameters to be
determined empirically, while the QALY approach imposes some conditions a priori.
Table 6b-1: Comparison of QALY and WTP Approaches
Parameter
QALY
WTP
Risk aversion Risk neutral
Relation of duration and quality Independent
Proportionality of duration/ quality trade-off Constant
Treatment of time/age in utility function Utility linear in time
Preferences Community/Individual
Source of preference data Stated
Treatment of income and prices Not explicitly considered
Empirically determined
Empirically determined
Variable
Empirically determined
Individual
Revealed and stated
Constrains choices
6b.4 Changes in Premature Death, Life Years, and Quality of Life
To generate health outcomes, we used the same framework as for the benefit-cost analysis
described in Chapter 6. For convenience, we summarize the basic methodologies here. For
more details, see Chapter 6 and the BenMAP user's manual
(http://www.epa.gov/ttn/ecas/benmodels.html).
BenMAP uses health impact functions to generate changes in the incidence of health effects.
Health impact functions are derived from the epidemiology literature. A standard health impact
function has four components: an effect estimate from a particular epidemiological study, a
baseline incidence rate for the health effect (obtained from either the epidemiology study or a
The Kaldor-Hicks efficiency criterion requires that the "winners" in a particular case be
potentially able to compensate the "losers" such that total societal welfare improves. In this
case, it is sufficient that total benefits exceed total costs of the regulation. This is also known as
a potential Pareto improvement, because gains could be allocated such that at least one person in
society would be better off while no one would be worse off.
6b-9
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source of public health statistics like CDC), the affected population, and the estimated change in
the relevant PM summary measure.
A typical health impact function might look like this:
where yo is the baseline incidence, equal to the baseline incidence rate times the potentially
affected population; P is the effect estimate; and Ax is the estimated change in PMa.s. There are
other functional forms, but the basic elements remain the same.
6b.4. 1 Calculating Reductions in Premature Deaths
As in several recent air pollution health impact assessments (e.g., Kunzli et al., 2000; EPA,
2004), we focus on the prospective cohort long-term exposure studies in deriving the health
impact function for the estimate of premature mortality. Cohort analyses are better able to
capture the full public health impact of exposure to air pollution over time (Kunzli et al., 200 1 ;
NRC, 2002). We selected effects estimate from the extended analysis of the ACS cohort (Pope
et al., 2002) as well as from the Harvard Six City Study (Laden et al., 2006). Given the focus in
this analysis on developing a broader expression of uncertainties in the benefits estimates, and
the weight that was placed on both the ACS and Harvard Six-city studies by experts participating
in the PM2.5 mortality expert elicitation, we have elected to provide estimates derived from both
Pope et al. (2002) and Laden et al. (2006).
This latest re-analysis of the ACS cohort data (Pope et al, 2002) provides additional refinements
to the analysis of PM-related mortality by (a) extending the follow-up period for the ACS study
subjects to 16 years, which triples the size of the mortality data set; (b) substantially increasing
exposure data, including consideration for cohort exposure to PMa 5 following implementation of
PMa.s standard in 1999; (c) controlling for a variety of personal risk factors including
occupational exposure and diet; and (d) using advanced statistical methods to evaluate specific
issues that can adversely affect risk estimates, including the possibility of spatial autocorrelation
of survival times in communities located near each other. The effect estimate from Pope et al.
(2002) quantifies the relationship between annual mean PMa.s levels and all-cause mortality in
adults 30 and older. We selected the effect estimate estimated using the measure of PM
representing average exposure over the follow-up period, calculated as the average of 1979-1984
and 1999-2000 PMa.s levels. The effect estimate from this study is 0.0058, which is equivalent
to a relative risk of 1.06 for a 10 |_ig change in
Very recently, a follow up to the Harvard 6-city study was published (Laden et al., 2006), that
both confirmed the effect size from the first study and provided additional confirmation that
reductions in PMa.s directly result in reductions in the risk of premature death. This additional
evidence stems from the observed reductions in PMa.s in each city during the extended follow-up
period. Laden et al. (2006) found that mortality rates consistently went down at a rate
proportionate to the observed reductions in PMa.s. The effect estimate obtained from the Laden
et al is 0.0148, which is equivalent to a relative risk of 1.16 fora 10 p.g/m3 change in PMa.s.
6b-10
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Age, cause, and county-specific mortality rates were obtained from CDC for the years 1996
through 1998. CDC maintains an online data repository of health statistics, CDC Wonder,
accessible at http://wonder.cdc.gov/. The mortality rates provided are derived from U.S. death
records and U.S. Census Bureau postcensal population estimates. Mortality rates were averaged
across 3 years (1996 through 1998) to provide more stable estimates. When estimating rates for
age groups that differed from the CDC Wonder groupings, we assumed that rates were uniform
across all ages in the reported age group. For example, to estimate mortality rates for individuals
ages 30 and up, we scaled the 25- to 34-year old death count and population by one-half and then
generated a population-weighted mortality rate using data for the older age groups.
The reductions in incidence of premature mortality within each age group associated with the
illustrative attainment strategies for the revised and more stringent alternative Ozone NAAQS in
2020 are summarized in Table G-2.
6b.4.2 Calculating Changes in Life Years from Direct Reductions in PM2.s-Related Mortality
Risk
To calculate changes in life years associated with a given change in air pollution, we used a life
table approach coupled with age-specific estimates of reductions in premature mortality. We
began with the complete unabridged life table for the United States in 2000, obtained from CDC
(CDC, 2002). For each 1-year age interval (e.g., zero to one, one to two) the life table provides
estimates of the baseline probability of dying during the interval, person years lived in the
interval, and remaining life expectancy. From this unabridged life table, we constructed an
abridged life table to match the age intervals for which we have predictions of changes in
incidence of premature mortality. We used the abridgement method described in CDC (2002).
Table G-3 presents the abridged life table for 10-year age intervals for adults over 30 (to match
the Pope et al. [2002] study population). Note that the abridgement actually includes one 5-year
interval, covering adults 30 to 34, with the remaining age intervals covering 10 years each. This
is to provide conformity with the age intervals available for mortality rates.
6b-ll
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Table 6b-2: Estimated Reduction in Incidence of All-cause Premature Mortality Associated
with Illustrative Attainment Strategies for the Revised and More Stringent
Alternative Ozone NAAQS in 2020
Reduction in All-Cause Premature Mortality
(95% Cl)
Age Interval
30-
35-
45-
55-
65-
75-
85+
Total
34
44
54
64
74
84
Pope (2002)
4
(2-6)
12
(5 - 20)
26
(10-42)
70
(27-110)
120
(48 - 200)
140
(56 - 230)
170
(68 - 280)
550
(220 - 890)
Laden (2006)
9
(5-13)
28
(15-40)
60
(32 - 87)
160
(86 - 230)
280
(150-400)
320
(180-470)
390
(210-570)
1,300
(680 - 1 ,800)
From the abridged life table (Table 6b-3), we obtained the remaining life expectancy for each
age cohort, conditional on surviving to that age. This is then the number of life years lost for an
individual in the general population dying during that age interval. This information can then be
combined with the estimated number of premature deaths in each age interval calculated with
BenMAP (see previous subsection). Total life years gained will then be the sum of life years
gained in each age interval:
Total Life Years = ^LE, x M.
. ,
where LE; is the remaining life expectancy for age interval i, M; is the change in incidence of
mortality in age interval i, and N is the number of age intervals.
For the purposes of determining cost-effectiveness, it is also necessary to consider the time-
dependent nature of the gains in life years. Standard economic theory suggests that benefits
occurring in future years should be discounted relative to benefits occurring in the present. OMB
and EPA guidance suggest discount rates of three and seven percent. As noted earlier, we
present gains in future life years discounted at 3 percent. Results based on 7 percent are included
in the summary and the overall impact of a 7 percent rate is summarized in Table 6b-16.
Selection of a 3 percent discount rate is also consistent with recommendations from the U.S.
Public Health Service Panel on Cost Effectiveness in Health and Medicine (Gold et al., 1996).
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Table 6b-3: Abridged Life Table for the Total Population, United States, 2000
Age Interval
Probability of
Dying
Between
Ages x to
x+1
Number
Dying
Number Between
Surviving to Ages x to
Agex x+1
Person Total
Years Lived Number of
Between Person Expectation
Ages x to Years Lived of Life at
x+1 Above Agex Agex
Start
Age
30
35
45
55
65
75
85
95
100+
End
Age
35
45
55
65
75
85
95
100
Qx
0.00577
0.01979
0.04303
0.09858
0.21779
0.45584
0.79256
0.75441
1 .00000
/x
97,696
97,132
95,210
91,113
82,131
64,244
34,959
7,252
1,781
*
564
1,922
4,097
8,982
17,887
29,285
27,707
5,471
1,781
/-x
487,130
962,882
934,026
872,003
740,927
505,278
196,269
20,388
4,636
Tx
4,723,539
4,236,409
3,273,527
2,339,501
1 ,467,498
726,571
221,293
25,024
4,636
6x
48.3
43.6
34.4
25.7
17.9
11.3
6.3
3.5
2.6
Discounted total life years gained is calculated as follows:
Discounted LY = { e~rtdt,
where r is the discount rate, equal to 0.03 in this case, t indicates time, and LE is the life
expectancy at the time when the premature death would have occurred. Life years are further
discounted to account for the lag between the reduction in ambient PM2.5 and the reduction in
mortality risk. We use the same 20-year segmented lag structure that is used in the benefit-cost
analysis (see Chapter 6).
The most complete estimate of the impacts of PMa.s on life years is calculated using the Pope et
al. (2002) C-R function relating all-cause mortality in adults 30 and over with ambient PMa.s
concentrations averaged over the periods 1979-1983 and 1999-2000. Use of all-cause mortality
is appropriate if there are no differences in the life expectancy of individuals dying from air
pollution-related causes and those dying from other causes. The argument that long-term
exposure to PM2.5 may affect mainly individuals with serious preexisting illnesses is not
supported by current empirical studies. For example, the Krewski et al. (2000) ACS reanalysis
suggests that the mortality risk is no greater for those with preexisting illness at time of
6b-13
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enrollment in the study. Life expectancy for the general population in fact includes individuals
with serious chronic illness. Mortality rates for the general population then reflect prevalence of
chronic disease, and as populations age the prevalence of chronic disease increases.
The only reason one might use a lower life expectancy is if the population at risk from air
pollution was limited solely to those with preexisting disease. Also, note that the OMB Circular
A-4 notes that "if QALYs are used to evaluate a lifesaving rule aimed at a population that
happens to experience a high rate of disability (i.e., where the rule is not designed to affect the
disability), the number of life years saved should not necessarily be diminished simply because
the rule saves lives of people with life-shortening disabilities. Both analytic simplicity and
fairness suggest that the estimate number of life years saved for the disabled population should
be based on average life expectancy information for the relevant age cohorts." As such, use of a
general population life expectancy is preferred over disability-specific life expectancies. Our
primary life years calculations are thus consistent with the concept of not penalizing individuals
with disabling chronic health conditions by assessing them reduced benefits of mortality risk
reductions.
For this analysis, direct impacts on life expectancy are measured only through the estimated
change in mortality risk based on the Pope et al. (2002) C-R function. The SAB-HES has
advised against including additional gains in life expectancy due to reductions in incidence of
chronic disease or nonfatal heart attacks (EPA-SAB-COUNCIL-ADV-04-002). Although
reductions in these endpoints are likely to result in increased life expectancy, the HES has
suggested that the cohort design and relatively long follow-up period in the Pope et al. study
should capture any life-prolonging impacts associated with those endpoints. Impacts of CB and
nonfatal heart attacks on quality of life will be captured separately in the QALY calculation as
years lived with improved quality of life. The methods for calculating this benefit are discussed
below.
6b.4.2.1 Should Life Years Gained Be Adjusted for Initial Health Status?
The methods outlined above provide estimates of the total number of life years gained in a
population, regardless of the quality of those life years, or equivalently, assuming that all life
years gained are in perfect health. In some CEAs (Cohen, Hammitt, and Levy, 2003; Coyle et
al., 2003), analysts have adjusted the number of life years gained to reflect the fact that 1) the
general public is not in perfect health and thus "healthy" life years are less than total life years
gained and 2) those affected by air pollution may be in a worse health state than the general
population and therefore will not gain as many "healthy" life years adjusted for quality, from an
air pollution reduction. This adjustment, which converts life years gained into QALYs, raises a
number of serious ethical issues. Proponents of QALYs have promoted the nondiscriminatory
nature of QALYs in evaluating improvements in quality of life (e.g., an improvement from a
score of 0.2 to 0.4 is equivalent to an improvement from 0.8 to 1.0), so the starting health status
does not affect the evaluation of interventions that improve quality of life. However, for life-
extending interventions, the gains in QALY will be directly proportional to the baseline health
state (e.g., an individual with a 30-year life expectancy and a starting health status of 0.5 will
gain exactly half the QALYs of an individual with the same life expectancy and a starting health
status of 1.0 for a similar life-extending intervention). This is troubling because it imposes an
additional penalty for those already suffering from disabling conditions. Brock (2002) notes that
6b-14
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"the problem of disability discrimination represents a deep and unresolved problem for resource
prioritization."
OMB (2003) has recognized this issue in their Circular A-4 guidance, which includes the
following statement:
When CEA is performed in specific rulemaking contexts, you should be prepared to
make appropriate adjustments to ensure fair treatment of all segments of the
population. Fairness is important in the choice and execution of effectiveness
measures. For example, ifQALYs are used to evaluate a lifesaving rule aimed at a
population that happens to experience a high rate of disability (i.e., where the rule is
not designed to affect the disability), the number of life years saved should not
necessarily be diminished simply because the rule saves the lives of people with life-
shortening disabilities. Both analytic simplicity and fairness suggest that the
estimated number of life years saved for the disabled population should be based on
average life expectancy information for the relevant age cohorts. More generally,
when numeric adjustments are made for life expectancy or quality of life, analysts
should prefer use of population averages rather than information derived from
subgroups dominated by a particular demographic or income group, (p. 13)
This suggests two adjustments to the standard QALY methodology: one adjusting the relevant
life expectancy of the affected population, and the other affecting the baseline quality of life for
the affected population.
In addition to the issue of fairness, potential measurement issues are specific to the air pollution
context that might argue for caution in applying quality-of-life adjustments to life years gained
due to air pollution reductions. A number of epidemiological and toxicological studies link
exposure to air pollution with chronic diseases, such as CB and atherosclerosis (Abbey et al.,
1995; Schwartz, 1993; Suwa et al., 2002). If these same individuals with chronic disease caused
by exposure to air pollution are then at increased risk of premature death from air pollution, there
is an important dimension of "double jeopardy" involved in determining the correct baseline for
assessing QALYs lost to air pollution (see Singer et al. [1995] for a broader discussion of the
double-jeopardy argument).
Analyses estimating mortality from acute exposures that ignore the effects of long-term exposure
on morbidity may understate the health impacts of reducing air pollution. Individuals exposed to
chronically elevated levels of air pollution may realize an increased risk of death and chronic
disease throughout life. If at some age they contract heart (or some other chronic) disease as a
result of the exposure to air pollution, they will from that point forward have both reduced life
expectancy and reduced quality of life. The benefit to that individual from reducing lifetime
exposure to air pollution would be the increase in life expectancy plus the increase in quality of
life over the full period of increased life expectancy. If the QALY loss is determined based on
the underlying chronic condition and life expectancy without regard to the fact that the person
would never have been in that state without long-term exposure to elevated air pollution, then the
person is placed in double jeopardy. In other words, air pollution has placed more people in the
susceptible pool, but then we penalize those people in evaluating policies by treating their
subsequent deaths as less valuable, adding insult to injury, and potentially downplaying the
importance of life expectancy losses due to air pollution. If the risk of chronic disease and risk
6b-15
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of death are considered together, then there is no conceptual problem with measuring QALYs,
but this has not been the case in recent applications of QALYs to air pollution (Carrothers,
Evans, and Graham, 2002; Coyle et al., 2003). The use of QALYs thus highlights the need for a
better understanding of the relationship between chronic disease and long-term exposure and
suggests that analyses need to consider morbidity and mortality jointly, rather than treating each
as a separate endpoint (this is an issue for current benefit-cost approaches as well).
Because of the fairness and measurement concerns discussed above, for the purposes of this
analysis, we do not reduce the number of life years gained to reflect any differences in
underlying health status that might reduce quality of life in remaining years. Thus, we maintain
the assumption that all direct gains in life years resulting from mortality risk reductions will be
assigned a weight of 1.0. The U.S. Public Health Service Panel on Cost Effectiveness in Health
and Medicine recommends that "since lives saved or extended by an intervention will not be in
perfect health, a saved life year will count as less than 1 full QALY" (Gold et al., 1996).
However, for the purposes of this analysis, we propose an alternative to the traditional aggregate
QALY metric that keeps separate quality adjustments to life expectancy and gains in life
expectancy. As such, we do not make any adjustments to life years gained to reflect the less than
perfect health of the general population. Gains in quality of life will be addressed as they accrue
because of reductions in the incidence of chronic diseases. This is an explicit equity choice in
the treatment of issues associated with quality-of-life adjustments for increases in life expectancy
that still capitalizes on the ability of QALYs to capture both morbidity and mortality impacts in a
single effectiveness measure.
6b.5 Calculating Changes in the Quality of Life Years (Morbidity)
In addition to directly measuring the quantity of life gained, measured by life years, it may also
be informative to measure gains in the quality of life. Reducing air pollution also leads to
reductions in serious illnesses that affect quality of life. These include CB and cardiovascular
disease, for which we are able to quantify changes in the incidence of nonfatal heart attacks. To
capture these important benefits in the measure of effectiveness, they must first be converted into
a life-year equivalent so that they can be combined with the direct gains in life expectancy.
For this analysis, we developed estimates of the QALYs gained from reductions in the incidence
of CB and nonfatal heart attacks associated with reductions in ambient PMa.s. In general, QALY
calculations require four elements:
1. the estimated change in incidence of the health condition,
2. the duration of the health condition,
3. the quality-of-life weight with the health condition, and
4. the quality-of-life weight without the health condition (i.e., the baseline health state).
The first element is derived using the health impact function approach. The second element is
based on the medical literature for each health condition. The third and fourth elements are
derived from the medical cost-effectiveness and cost-utility literature. In the following two
subsections, we discuss the choices of elements for CB and nonfatal heart attacks.
6b-16
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The preferred source of quality-of-life weights are those based on community preferences, rather
than patient or clinician ratings (Gold et al., 1996). Several methods are used to estimate quality-
of-life weights. These include rating scale, standard gamble, time trade-off, and person trade-off
approaches (Gold, Stevenson, and Fryback, 2002). Only the standard gamble approach is
completely consistent with utility theory. However, the time trade-off method has also been
widely applied in eliciting community preferences (Gold, Stevenson, and Fryback, 2002).
Quality-of-life weights can be directly elicited for individual specific health states or for a more
general set of activity restrictions and health states that can then be used to construct QALY
weights for specific conditions (Horsman et al., 2003; Kind, 1996). For this analysis, we used
weights based on community-based preferences, using time trade-off or standard gamble when
available. In some cases, we used patient or clinician ratings when no community preference-
based weights were available. Sources for weights are discussed in more detail below. Table G-4
summarizes the key inputs for calculating QALYs associated with chronic health endpoints.
6b.5.1 Calculating QALYs Associated with Reductions in the Incidence of Chronic Bronchitis
CB is characterized by mucus in the lungs and a persistent wet cough for at least 3 months a year
for several years in a row. CB affects an estimated 5 percent of the U.S. population (American
Lung Association, 1999). For gains in quality of life resulting from reduced incidences of PM-
induced CB, discounted QALYs are calculated as
DISCOUNTED QALY GAINED = ^JACBi x D" x (w. - w,
where ACB; is the number of incidences of CB avoided in age interval i, w; is the average QALY
f-in j-i *
weight for age interval i, w, is the QALY weight associated with CB, ^; is the discounted
duration of life with CB for individuals with onset of disease in age interval i, equal to
I e ~n dl , where D; is the duration of life with CB for individuals with onset of disease in age
interval i.
A limited number of studies have estimated the impact of air pollution on new incidences of CB.
Schwartz (1993) and Abbey et al. (1995) provide evidence that long-term PM exposure gives
rise to the development of CB in the United States. Because this analysis focuses on the impacts
of reducing ambient PMa.s, only the Abbey et al. (1995) study is used, because it is the only
study focusing on the relationship between PMa.s and new incidences of CB. The number of
cases of CB in each age interval is derived from applying the impact function from Abbey et al.
(1995), to the population in each age interval with the appropriate baseline incidence rate.4 The
effect estimate from the Abbey et al. (1995) study is 0.0137, which, based on the logistic
4 Prevalence rates for CB were obtained from the 1999 National Health Interview Survey
(American Lung Association, 2002). Prevalence rates were available for three age groups: 18-
44, 45-64, and 65 and older. Prevalence rates per person for these groups were 0.0367 for 18-
44, 0.0505 for 45-64, and 0.0587 for 65 and older. The incidence rate for new cases of CB
(0.00378 per person) was taken directly from Abbey et al. (1995).
6b-17
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specification of the model, is equivalent to a relative risk of 1.15 for a 10 |_ig change in
Table G-5 presents the estimated reduction in new incidences of CB associated with the
illustrative PM NAAQS attainment strategies.
Table 6b-4: Summary of Key Parameters Used in QALY Calculations for Chronic Disease
Endpoints
Parameter
Value(s)
Source(s)
Discount rate
Quality of life preference
score for chronic
bronchitis
Duration of acute phase
of acute myocardial
infarction (AMI)
Probability of CHF post
AMI
Probability of angina post
AMI
Quality-of-life preference
score for post-AMI with
CHF (no angina)
Quality-of-life preference
score for post-AMI with
CHF and angina
Quality-of-life preference
score for post-AMI with
angina (no CHF)
Quality-of-life preference
score for post-AMI (no
angina, no CHF)
0.03 (0.07
sensitivity
analysis)
0.5-0.7
5.5 days - 22
days
0.2
0.51
0.80 - 0.89
0.76-0.85
0.7-0.89
0.93
Gold et al. (1996), U.S. EPA (2000), U.S. OMB (2003)
Triangular distribution centered at 0.7 with upper bound at
0.9 (Vos, 1999a) (slightly better than a mild/moderate case)
and a lower bound at 0.5 (average weight for a severe case
based on Vos [1999a] and Smith and Peske [1994])
Uniform distribution with lower bound based on average
length of stay for an AMI (AHRQ, 2000) and upper bound
based on Vos (1999b).
Vos, 1999a (WHO Burden of Disease Study, based on
Cowieetal., 1997)
American Heart Association, 2003
(Calculated as the population with angina divided by the
total population with heart disease)
Uniform distribution with lower bound at 0.80 (Stinnett et
al., 1996) and upper bound at 0.89 (Kuntz et al., 1996).
Both studies used the time trade-off elicitation method.
Uniform distribution with lower bound at 0.76 (Stinnett et
al., 1996, adjusted for severity) and upper bound at 0.85
(Kuntz et al., 1996). Both studies used the time trade-off
elicitation method.
Uniform distribution with lower bound at 0.7, based on the
standard gamble elicitation method (Pliskin, Stason, and
Weinstein, 1981) and upper bound at 0.89, based on the
time trade-off method (Kuntz et al., 1996).
Only one value available from the literature. Thus, no
distribution is specified. Source of value is Kuntz et al.
(1996).
CB is assumed to persist for the remainder of an affected individual's lifespan. Duration of CB
will thus equal life expectancy conditioned on having CB. CDC has estimated that COPD (of
which CB is one element) results in an average loss of life years equal to 4.26 per COPD death,
relative to a reference life expectancy of 75 years (CDC, 2003). Thus, we subtract 4.26 from the
remaining life expectancy for each age group, up to age 75. For age groups over 75, we apply
the ratio of 4.26 to the life expectancy for the 65 to 74 year group (0.237) to the life expectancy
6b-18
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for the 75 to 84 and 85 and up age groups to estimate potential life years lost and then subtract
that value from the base life expectancy.
Table 6b-5: Estimated Reduction in Incidence of Chronic Bronchitis Associated with Illustrative
Attainment Strategies for the Revised and More Stringent Alternative PM NAAQS in
2020
Reduction in Incidence (95% Confidence Interval)
Age Interval 070 ppm Partial Attainment Strategy
25 - 34 74
(14-140)
35 - 44 85
(16-160)
45 - 54 82
(15-150)
55 - 64 88
(16-160)
65 - 74 62
(12-640)
75 - 84 31
(6 - 56)
85+ 14
(3 - 25)
Total 440
(80 - 790)
Quality of life with chronic lung diseases has been examined in several studies. In an analysis of
the impacts of environmental exposures to contaminants, de Hollander et al. (1999) assigned a
weight of 0.69 to years lived with CB. This weight was based on physicians' evaluations of
health states similar to CB. Salomon and Murray (2003) estimated a pooled weight of 0.77
based on visual analogue scale, time trade-off, standard gamble, and person trade-off techniques
applied to a convenience sample of health professionals. The Harvard Center for Risk Analysis
catalog of preference scores reports a weight of 0.40 for severe COPD, with a range from 0.2 to
0.8, based on the judgments of the study's authors (Bell et al., 2001). The Victoria Burden of
Disease (BoD) study used a weight of 0.47 for severe COPD and 0.83 for mild to moderate
COPD, based on an analysis by Stouthard et al. (1997) of chronic diseases in Dutch populations
(Vos, 1999a). Based on the recommendations of Gold et al. (1996), quality-of-life weights based
on community preferences are preferred for CEA of interventions affecting broad populations.
Use of weights based on health professionals is not recommended. It is not clear from the
Victoria BoD study whether the weights used for COPD are based on community preferences or
judgments of health professionals. The Harvard catalog score is clearly identified as based on
author judgment. Given the lack of a clear preferred weight, we select a triangular distribution
centered at 0.7 with an upper bound at 0.9 (slightly better than a mild/moderate case defined by
the Victoria BoD study) and a lower bound at 0.5 based on the Victoria BoD study. We will
need additional empirical data on quality of life with chronic respiratory diseases based on
community preferences to improve our estimates.
6b-19
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Selection of a reference weight for the general population without CB is somewhat uncertain. It
is clear that the general population is not in perfect health; however, there is some uncertainty as
to whether individuals' ratings of health states are in reference to a perfect health state or to a
generally achievable "normal" health state given age and general health status. The U.S. Public
Health Service Panel on Cost Effectiveness in Health and Medicine recommends that "since
lives saved or extended by an intervention will not be in perfect health, a saved life year will
count as less than 1 full QALY" (Gold et al., 1996). Following Carrothers, Evans, and Graham
(2002), we assumed that the reference weight for the general population without CB is 0.95. To
allow for uncertainty in this parameter, we assigned a triangular distribution around this weight,
bounded by 0.9 and 1.0. Note that the reference weight for the general population is used solely
to determine the incremental quality-of-life improvement applied to the duration of life that
would have been lived with the chronic disease. For example, if CB has a quality-of-life weight
of 0.7 relative to a reference quality-of-life weight of 0.9, then the incremental quality-of-life
improvement in 0.2. If the reference quality-of-life weight is 0.95, then the incremental quality-
of-life improvement is 0.25. As noted above, the population is assumed to have a reference
weight of 1.0 for all life years gained due to mortality risk reductions.
We present discounted QALYs over the duration of the lifespan with CB using a 3 percent
discount rate. Based on the assumptions defined above, we used Monte Carlo simulation
methods as implemented in the Crystal Ball™ software program to develop the distribution of
QALYs gained per incidence of CB for each age interval.5 Based on the assumptions defined
above, the mean 3 percent discounted QALY gained per incidence of CB for each age interval
along with the 95 percent confidence interval resulting from the Monte Carlo simulation is
presented in Table G-6. Table G-6 presents both the undiscounted and discounted QALYs
gained per incidence.
Monte Carlo simulation uses random sampling from distributions of parameters to characterize
the effects of uncertainty on output variables. For more details, see Gentile (1998).
6b-20
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Table 6b-6: QALYs Gained per Avoided Incidence of CB
Age Interval
Start Age End Age
25 34
35 44
45 54
55 64
65 74
75 84
85+
QALYs Gained per Incidence
Undiscounted
12.15
(4.40-19.95)
9.91
(3.54-16.10)
7.49
(2.71-12.34)
5.36
(1.95-8.80)
3.40
(1.22-5.64)
2.15
(0.77-3.49)
0.79
(0.27-1.29)
Discounted (3%)
6.52
(2.36-10.71)
5.94
(2.12-9.66)
5.03
(1 .82-8.29)
4.03
(1.47-6.61)
2.84
(1.02-4.71)
1.92
(0.69-3.13)
0.77
(0.26-1.25)
6b.5.2 Calculating QALYs Associated with Reductions in the Incidence ofNonfatal Myocardial
Infarctions
Nonfatal heart attacks, or acute myocardial infarctions, require more complicated calculations to
derive estimates of QALY impacts. The actual heart attack, which results when an area of the
heart muscle dies or is permanently damaged because of oxygen deprivation, and subsequent
emergency care are of relatively short duration. Many heart attacks result in sudden death.
However, for survivors, the long-term impacts of advanced CHD are potentially of long duration
and can result in significant losses in quality of life and life expectancy.
In this phase of the analysis, we did not independently estimate the gains in life expectancy
associated with reductions in nonfatal heart attacks. Based on recommendations from the SAB-
HES, we assumed that all gains in life expectancy are captured in the estimates of reduced
mortality risk provided by the Pope et al. (2002) analysis. We only estimate the change in
quality of life over the period of life affected by the occurrence of a heart attack. This may
understate the QALY impacts of nonfatal heart attacks but ensures that the overall QALY impact
estimates across endpoints do not double-count potential life-year gains.
Our approach adapts a CHD model developed for the Victoria Burden of Disease study (Vos,
1999b). This model accounts for the lost quality of life during the heart attack and the possible
health states following the heart attack. Figure G-l shows the heart attack QALY model in
diagrammatic form.
The total gain in QALYs is calculated as:
6b-21
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DISCOUNTED AM QALY GAINED =
x w -
where AAMI; is the number of nonfatal acute myocardial infarctions avoided in age interval /,
wfm is the QALY weight associated with the acute phase of the AMI, PJ is the probability of
being in theyth post-AMI status, w^OSIAM |s the QALY weight associated with post-AMI health
(DAM1
e dt ; the discounted
Dpo,UM
value of Z);JM^the duration of the acute phase of the AMI, and A = Jr=1 e "?, is the
discounted value of Dtj M1 , the duration of post-AMI health statusj.
^\ Deleted:
Acute Treatment Stage
Chronic Post-AMI Follow up Stage
Post AMI QALY with Angina and CHF
Post AMI QALY with CHF without Angina
Post AMI QALY with Angina without CHF
Post AMI QALY without Angina or CHF
Figure 6b-l. Decision Tree Used in Modeling Gains in QALYs from Reduced Incidence of
Nonfatal Acute Myocardial Infarctions
6b-22
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Nonfatal heart attacks have been linked with short-term exposures to PM2.5 in the United States
(Peters et al., 2001) and other countries (Poloniecki et al., 1997). We used a recent study by
Peters et al. (2001) as the basis for the impact function estimating the relationship between PM2.5
and nonfatal heart attacks. Peters et al. is the only available U.S. study to provide a specific
estimate for heart attacks. Other studies, such as Samet et al. (2000) and Moolgavkar (2000),
show a consistent relationship between all cardiovascular hospital admissions, including for
nonfatal heart attacks, and PM. Given the lasting impact of a heart attack on longer-term health
costs and earnings, we chose to provide a separate estimate for nonfatal heart attacks based on
the single available U.S. effect estimate. The finding of a specific impact on heart attacks is
consistent with hospital admission and other studies showing relationships between fine particles
and cardiovascular effects both within and outside the United States. These studies provide a
weight of evidence for this type of effect. Several epidemiologic studies (Liao et al., 1999; Gold
et al., 2000; Magari et al., 2001) have shown that heart rate variability (an indicator of how much
the heart is able to speed up or slow down in response to momentary stresses) is negatively
related to PM levels. Heart rate variability is a risk factor for heart attacks and other CHDs
(Carthenon et al., 2002; Dekker et al., 2000; Liao et al., 1997, Tsuji et al., 1996). As such,
significant impacts of PM on heart rate variability are consistent with an increased risk of heart
attacks.
The number of avoided nonfatal AMI in each age interval is derived from applying the impact
function from Peters et al. (2001) to the population in each age interval with the appropriate
baseline incidence rate.6 The effect estimate from the Peters et al. (2001) study is 0.0241, which,
based on the logistic specification of the model, is equivalent to a relative risk of 1.27 for a 10 |_ig
change in PMa.s. Table 6b-7 presents the estimated reduction in nonfatal AMI associated with
the illustrative Ozone NAAQS attainment strategies.
6 Daily nonfatal myocardial infarction incidence rates per person were obtained from the 1999
National Hospital Discharge Survey (assuming all diagnosed nonfatal AMI visit the hospital).
Age-specific rates for four regions are used in the analysis. Regional averages for populations 18
and older are 0.0000159 for the Northeast, 0.0000135 for the Midwest, 0.0000111 for the South,
and 0.0000100 for the West.
6b-23
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Table 6b-7: Estimated Reduction in Nonfatal Acute Myocardial Infarctions Associated with
Illustrative Attainment Strategies for the Revised and More Stringent Alternative PM
NAAQS in 2020
Reduction in lncidence*(95% Confidence Interval)
Age Interval
070 ppm Attainment Strategy
18-24
1
(1-2)
25-34
4
(3-6)
35-44
37
(20 - 53)
45-54
110
(61 -170)
55-64
290
(160-430)
65-74
350
(190-500)
75-84
280
(150-410)
85+
150
(80 - 220)
Total
1,200
(660 - 1,800)
Acute myocardial infarction results in significant loss of quality of life for a relatively short
duration. The WHO Global Burden of Disease study, as reported in Vos (1999b), assumes that
the acute phase of an acute myocardial infarction lasts for 0.06 years, or around 22 days. An
alternative assumption is the acute phase is characterized by the average length of hospital stay
for an AMI in the United States, which is 5.5 days, based on data from the Agency for
Healthcare Research and Quality's Healthcare Cost and Utilization Project (HCUP).7 We
assumed a distribution of acute phase duration characterized by a uniform distribution between
5.5 and 22 days, noting that due to earlier discharges and in-home therapy available in the United
States, duration of reduced quality of life may continue after discharge from the hospital. In the
period during and directly following an AMI (the acute phase), we assigned a quality of life
weight equal to 0.605, consistent with the weight for the period in treatment during and
immediately after an attack (Vos, 1999b).
During the post-AMI period, a number of different health states can determine the loss in quality
of life. We chose to classify post-AMI health status into four states defined by the presence or
absence of angina and congestive heart failure (CHF). This makes a very explicit assumption
that without the occurrence of an AMI, individuals would not experience either angina or CHF.
7 Average length of stay estimated from the HCUP data includes all discharges, including those
due to death. As such, the 5.5-day average length of stay is likely an underestimate of the
average length of stay for AMI admissions where the patient is discharged alive.
6b-24
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If in fact individuals already have CHF or angina, then the quality of life gained will be
overstated. We do not have information about the percentage of the population have been
diagnosed with angina or CHF with no occurrence of an AMI. Nor do we have information on
what proportion of the heart attacks occurring due to PM exposure are first heart attacks versus
repeat attacks. Probabilities for the four post-AMI health states sum to one.
Given the occurrence of a nonfatal AMI, the probability of congestive heart failure is set at 0.2,
following the heart disease model developed by Vos (1999b). The probability is based on a
study by Cowie et al. (1997), which estimated that 20 percent of those surviving AMI develop
heart failure, based on an analysis of the results of the Framingham Heart Study.
The probability of angina is based on the prevalence rate of angina in the U.S. population. Using
data from the American Heart Association, we calculated the prevalence rate for angina by
dividing the estimated number of people with angina (6.6 million) by the estimated number of
people with CHD of all types (12.9 million). We then assumed that the prevalence of angina in
the population surviving an AMI is similar to the prevalence of angina in the total population
with CHD. The estimated prevalence rate is 51 percent, so the probability of angina is 0.51.
Combining these factors leads to the probabilities for each of the four health states as follows:
I. Post AMI with CHF and angina = 0.102
II. Post AMI with CHF without angina = 0.098
III. Post AMI with angina without CHF = 0.408
IV. Post AMI without angina or CHF = 0.392
Duration of post-AMI health states varies, based in part on assumptions regarding life
expectancy with post-AMI complicating health conditions. Based on the model used for
established market economies (EME) in the WHO Global Burden of Disease study, as reported
in Vos (1999b), we assumed that individuals with CHF have a relatively short remaining life
expectancy and thus a relatively short period with reduced quality of life (recall that gains in life
expectancy are assumed to be captured by the cohort estimates of reduced mortality risk).
Table 6b-8 provides the duration (both discounted and undiscounted) of CHF assumed for post-
AMI cases by age interval.
6b-25
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Table 6b-8: Assumed Duration of Congestive Heart Failure
Age Interval
Start Age
18
25
35
45
55
65
75
85+
End Age
24
34
44
54
64
74
84
Duration of Heart Failure (years)
Undiscounted
7.11
6.98
6.49
5.31
1.96
1.71
1.52
1.52
Discounted (3%)
6.51
6.40
6.00
4.99
1.93
1.69
1.50
1.50
Duration of health states without CHF is assumed to be equal to the life expectancy of
individuals conditional on surviving an AMI. Ganz et al. (2000) note that "Because patients with
a history of myocardial infarction have a higher chance of dying of CHD that is unrelated to
recurrent myocardial infarction (for example, arrhythmia), this cohort has a higher risk for death
from causes other than myocardial infarction or stroke than does an unselected population."
They go on to specify a mortality risk ratio of 1.52 for mortality from other causes for the cohort
of individuals with a previous (nonfatal) AMI. The risk ratio is relative to all-cause mortality for
an age-matched unselected population (i.e., general population). We adopted the same ratios and
applied them to each age-specific all-cause mortality rate to derive life expectancies (both
discounted and undiscounted) for each age group after an AMI, presented in Table 6b-9. These
life expectancies are then used to represent the duration of non-CHF post-AMI health states (III
and IV).
Table 6b-9: Assumed Duration of Non-CHF Post-AMI Health States
Age Interval
Post-AMI Years of Life Expectancy (non-CHF)
Start Age
18
25
35
45
55
65
75
85+
End Age
24
34
44
54
64
74
84
Undiscounted
55.5
46.1
36.8
27.9
19.8
12.8
7.4
3.6
Discounted (3%)
27.68
25.54
22.76
19.28
15.21
10.82
6.75
3.47
For the four post-AMI health states, we used QALY weights based on preferences for the
combined conditions characterizing each health state. A number of estimates of QALY weights
are available for post-AMI health conditions.
6b-26
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The first two health states are characterized by the presence of CHF, with or without angina.
The Harvard Center for Risk Analysis catalog of preference scores provides several specific
weights for CHF with and without mild or severe angina and one set specific to post-AMI CHF.
Following the Victoria Burden of Disease model, we assumed that most cases of angina will be
treated and thus kept at a mild to moderate state. We thus focused our selection on QALY
weights for mild to moderate angina. The Harvard database includes two sets of community
preference-based scores for CHF (Stinnett et al., 1996; Kuntz et al., 1996). The scores for CHF
with angina range from 0.736 to 0.85. The lower of the two scores is based on angina in general
with no delineation by severity. Based on the range of the scores for mild to severe cases of
angina in the second study, one can infer that an average case of angina has a score around 0.96
of the score for a mild case. Applying this adjustment raises the lower end of the range of
preference scores for a mild case of angina to 0.76. We selected a uniform distribution over the
range 0.76 to 0.85 for CHF with mild angina, with a midpoint of 0.81. The same two studies in
the Harvard catalog also provide weights for CHF without angina. These scores range from
0.801 to 0.89. We selected a uniform distribution over this range, with a midpoint of 0.85.
The third health state is characterized by angina, without the presence of CHF. The Harvard
catalog includes five sets of community preference-based scores for angina, one that specifies
scores for both mild and severe angina (Kuntz et al., 1996), one that specifies mild angina only
(Pliskin, Stason, and Weinstein, 1981), one that specifies severe angina only (Cohen, Breall, and
Ho, 1994), and two that specify angina with no severity classification (Salkeld, Phongsavan, and
Oldenburg, 1997; Stinnett et al., 1996). With the exception of the Pliskin, Stason, and Weinstein
score, all of the angina scores are based on the time trade-off method of elicitation. The Pliskin,
Stason, and Weinstein score is based on the standard gamble elicitation method. The scores for
the nonspecific severity angina fall within the range of the two scores for mild angina
specifically. Thus, we used the range of mild angina scores as the endpoints of a uniform
distribution. The range of mild angina scores is from 0.7 to 0.89, with a midpoint of 0.80.
For the fourth health state, characterized by the absence of CHF and/or angina, there is only one
relevant community preference score available from the Harvard catalog. This score is 0.93,
derived from a time trade-off elicitation (Kuntz et al., 1996). Insufficient information is
available to provide a distribution for this weight; therefore, it is treated as a fixed value.
Similar to CB, we assumed that the reference weight for the general population without AMI is
0.95. To allow for uncertainty in this parameter, we assigned a triangular distribution around this
weight, bounded by 0.9 and 1.0.
Based on the assumptions defined above, we used Monte Carlo simulation methods as
implemented in the Crystal Ball™ software program to develop the distribution of QALYs
gained per incidence of nonfatal AMI for each age interval. For the Monte Carlo simulation, all
distributions were assumed to be independent. The mean QALYs gained per incidence of
nonfatal AMI for each age interval is presented in Table 6b-10, along with the 95 percent
confidence interval resulting from the Monte Carlo simulation. Table 6b-10 presents both the
undiscounted and discounted QALYs gained per incidence.
6b-27
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Table 6b-10: QALYs Gained per Avoided Nonfatal Myocardial Infarction
Age Interval
QALYs Gained per Incidence3
Start Age
18
25
35
45
55
65
75
85+
End Age
24
34
44
54
64
74
84
Undiscounted
4.18
(1.24-7.09)
3.48
(1.09-5.87)
2.81
(0.88^.74)
2.14
(0.67-3.61)
1.49
(0.42-2.52)
0.97
(0.30-1.64)
0.59
(0.20-0.97)
0.32
(0.13-0.50)
Discounted (3%)
2.17
(0.70-3.62)
2.00
(0.68-3.33)
1.79
(0.60-2.99)
1.52
(0.51-2.53)
1.16
(0.34-1.95)
0.83
(0.26-1.39)
0.54
(0.19-0.89)
0.31
(0.13-0.49)
Mean of Monte Carlo generated distribution; 95% confidence interval presented in parentheses.
6b.6 Cost-Effectiveness Analysis
Given the estimates of changes in life expectancy and quality of life, the next step is to aggregate
life expectancy and quality-of-life gains to form an effectiveness measure that can be compared
to costs to develop cost-effectiveness ratios. This section discusses the proper characterization of
the combined effectiveness measure and the appropriate calculation of the numerator of the cost-
effectiveness ratio.
6b. 6.1 Aggregating Life Expectancy and Quality-of-Life Gains
To develop an integrated measure of changes in health, we simply sum together the gains in life
years from reduced mortality risk in each age interval with the gains in QALYs from reductions
in incidence of CB and acute myocardial infarctions. The resulting measure of effectiveness
then forms the denominator in the cost-effectiveness ratio. What is this combined measure of
effectiveness? It is not a QALY measure in a strict sense, because we have not adjusted life-
expectancy gains for preexisting health status (quality of life). It is however, an effectiveness
measure that adds to the standard life years calculation a scaled morbidity equivalent. Thus, we
term the aggregate measure morbidity inclusive life years, or MILYs. Alternatively, the
combined measure could be considered as QALYs with an assumption that the community
preference weight for all life-expectancy gains is 1.0. If one considers that this weight might be
considered to be a "fair" treatment of those with preexisting disabilities, the effectiveness
measure might be termed "fair QALY" gained. However, this implies that all aspects of fairness
have been addressed, and there are clearly other issues with the fairness of QALYs (or other
effectiveness measures) that are not addressed in this simple adjustment. The MILY measure
6b-28
-------
violates some of the properties used in deriving QALY weights, such as linear substitution
between quality of life and quantity of life. However, in aggregating life expectancy and quality -
of-life gains, it merely represents an alternative social weighting that is consistent with the spirit
of the recent OMB guidance on CEA. The guidance notes that "fairness is important in the
choice and execution of effectiveness measures" (OMB, 2003). The resulting aggregate measure
of effectiveness will not be consistent with a strict utility interpretation of QALYs; however, it
may still be a useful index of effectiveness.
Applying the life expectancies and distributions of QALYs per incidence for CB and AMI to
estimated distributions of incidences yields distributions of life expectancy and QALYs gained
due to the Ozone NAAQS illustrative attainment strategies. These distributions reflect both the
quantified uncertainty in incidence estimates and the quantified uncertainty in QALYs gained per
incidence.
For the attainment strategy for the revised 070 ppm standards, Table 6b-l 1 presents the mean 3
percent discounted MILYs gained for each age interval, broken out by life expectancy and
quality-of-life categories. Note that quality-of-life gains occur from age 18 and up, while life
expectancy gains accrue only after age 29. This is based on the ages of the study populations in
the underlying epidemiological studies. It is unlikely that such discontinuities exist in reality, but
to avoid overstating effectiveness, we chose to limit the life-expectancy gains to those occurring
in the population 30 and over and the morbidity gains to the specific adult populations examined
in the studies.
It is worth noting that around a third of mortality-related benefits are due to reductions in
premature deaths among those 75 and older, while only 7 percent of morbidity benefits occur in
this age group. This is due to two factors: (1) the relatively low baseline mortality rates in
populations under 75, and (2) the relatively constant baseline rates of chronic disease coupled
with the relatively long period of life that is lived with increased quality of life without CB and
advanced heart disease.
The relationship between age and the distribution of MILYs gained from mortality and morbidity
is shown for the 070 ppm attainment strategy in Figure _-2. Because the baseline mortality rate
is increasing in age at a much faster rate than the prevalence rate for CB, the share of MILYs
gained accounted for by mortality is proportional to age. At the oldest age interval, avoiding
incidences of CB leads to only a few MILYs gained, due to the lower number of years lived with
CB. MILYs gained from avoided premature mortality is low in the youngest age intervals
because of the low overall mortality rates in these intervals, although the number of MILYs per
incidence is high. In later years, even though the MILYs gained per incidence avoided is low,
the number of cases is very high due to higher baseline mortality rates.
6b-29
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Table 6b-11. Estimated Gains in 3 Percent Discounted MILYs Associated with Illustrative
Attainment Strategies for the Revised Ozone NAAQS (0.070 ppm) in 2020: Pope
(2002) Estimate of Mortality3
Age
Life Years Gained
from Mortality Risk
Reductions
(95% Cl)
QALY Gained from
Reductions in
Chronic Bronchitis
(95% Cl)
QALY Gained from
Reductions in Acute
Myocardial Infarctions
(95% Cl)
Total Gain in
MILYs
(95% Cl)
18-24
25-34
35^4
45-54
55-64
65-74
75-84
85+
Total
—
100
(41 -170)
310
(120-490)
580
(230 - 930)
1,300
(500-2,100)
1,700
(680 - 2,800)
1,400
(540 - 2,200)
690
(270-1,100)
6,100
(2,400 - 9,800)
—
490
(91 -1,100)
500
(93-1,100)
420
(77 - 890)
350
(64 - 760)
180
(33 - 380)
60
(11 -130)
10
(2 - 22)
2,000
(370 - 4,300)
3
(1-5)
8
(3-15)
70
(23-120)
170
(60 - 320)
330
(110-620)
280
(100-530)
150
(54 - 280)
43
(16-80)
1,100
(370 - 2,000)
3
(1-5)
600
(130-1,200)
870
(240 - 1 ,700)
1,200
(360-2,100)
2,000
(680 - 3,400)
2,200
(810-3,700)
1,600
(610-2,600)
740
(290 - 1 ,200)
9,100
(3,100-16,000)
Note that all estimates have been rounded to two significant digits.
6b-30
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Table 6b-12. Estimated Gains in 3 Percent Discounted MILYs Associated with Illustrative
Attainment Strategies for the Revised Ozone NAAQS (0.070 ppm) in 2020: Laden
(2006) Estimate of Mortality3
Age
Life Years Gained
from Mortality Risk
Reductions
(95% Cl)
QALY Gained from
Reductions in
Chronic Bronchitis
(95% Cl)
QALY Gained from
Reductions in Acute
Myocardial Infarctions
(95% Cl)
Total Gain in
MILYs
(95% Cl)
18-24
25-34
35^4
45-54
55-64
65-74
75-84
85+
Total
—
240
(130-340)
690
(380 - 1 ,000)
1,400
(710-1,900)
2,900
(1,600-4,200)
3,900
(2,100-5,700)
3,100
(1,700-4,600)
1,600
(840 - 2,300)
14,000
(7,500 - 20,000)
—
490
(91 -1,100)
500
(93-1,100)
420
(77 - 890)
350
(64 - 760)
180
(33 - 380)
60
(11 -130)
10
(2 - 22)
2,000
(370 - 4,300)
3
(1-5)
8
(3-15)
70
(23-120)
170
(60 - 320)
330
(110-620)
280
(100-530)
150
(54 - 280)
43
(16-80)
1,100
(370 - 2,000)
3
(1-5)
730
(220 - 1 ,400)
1,300
(490 - 2,200)
1,900
(850-3,100)
3,600
(1 ,800 - 5,600)
4,400
(2,300 - 6,600)
3,300
(1 ,800 - 5,000)
1,600
(860 - 2,400)
17,000
(8,200 - 26,000)
Note that all estimates have been rounded to two significant digits.
Summing over the age intervals provides estimates of total MILYs gained for the Ozone
NAAQS illustrative attainment strategies. The total number of discounted (3 percent) MILYs
gained for the 070 ppm attainment strategy using the Pope (2002) estimate is 9,100 (95% Cl:
3,100 - 16,000). Using the Laden (2006) estimate, the total number of discounted (3 percent)
MILYs is 17,000 (95% Cl: 8,200 - 26,000).
6b.6.2 Dealing with Acute Health Effects and Non-health Effects
Health effects from exposure to particulate air pollution encompass a wide array of chronic and
acute conditions in addition to premature mortality (EPA, 1996). Although chronic conditions
and premature mortality generally account for the majority of monetized benefits, acute
symptoms can affect a broad population or sensitive populations (e.g., asthma exacerbations in
asthmatic children. In addition, reductions in air pollution may result in a broad set of nonhealth
environmental benefits, including improved visibility in national parks, increased agricultural
and forestry yields, reduced acid damage to buildings, and a host of other impacts. QALYs
address only health impacts, and the OMB guidance notes that "where regulation may yield
several different beneficial outcomes, a cost-effectiveness comparison becomes more difficult to
interpret because there is more than one measure of effectiveness to incorporate in the analysis."
6b-31
-------
With regard to acute health impacts, Bala and Zarkin (2000) suggest that QALYs are not
appropriate for valuing acute symptoms, because of problems with both measuring utility for
acute health states and applying QALYs in a linear fashion to very short duration health states.
Johnson and Lievense (2000) suggest using conjoint analysis to get healthy-utility time
equivalences that can be compared across acute effects, but it is not clear how these can be
combined with QALYs for chronic effects and loss of life expectancy. There is also a class of
effects that EPA has traditionally treated as acute, such as hospital admissions, which may also
result in a loss of quality of life for a period of time following the effect. For example, life after
asthma hospitalization has been estimated with a utility weight of 0.93 (Bell et al., 2001;
Kerridge, Glasziou, and Hillman, 1995).
How should these effects be combined with QALYs for chronic and mortality effects? One
method would be to convert the acute effects to QALYs; however, as noted above, there are
problems with the linearity assumption (i.e., if a year with asthma symptoms is equivalent to 0.7
year without asthma symptoms, then 1 day without asthma symptoms is equivalent to 0.0019
QALY gained). This is troubling from both a conceptual basis and a presentation basis. An
alternative approach is simply to treat acute health effects like nonhealth benefits and subtract the
dollar value (based on WTP or COI) from compliance costs in the CEA.
25-34
35-44
Figure 6b-2. Distribution of Mortality and Morbidity Related MILY Across Age Groups
for Illustrative Attainment Strategy for the Revised PM NAAQS (3 percent
Discount Rate)
To address the issues of incorporating acute morbidity and nonhealth benefits, OMB suggests
that agencies "subtract the monetary estimate of the ancillary benefits from the gross cost
estimate to yield an estimated net cost." As with benefit-cost analysis, any unquantified benefits
and/or costs should be noted and an indication of how they might affect the cost-effectiveness
6b-32
-------
ratio should be described. We will follow this recommended "net cost" approach in the
illustrative exercise, specifically in netting out the benefits of health improvements other than
reduced mortality and chronic morbidity, and the benefits of improvements in visibility at
national parks (see Chapter 5 for more details on these benefit categories).
6b. 6.3 Cost-Effectiveness Ratios
Construction of cost-effectiveness ratios requires estimates of effectiveness (in this case
measured by lives saved, life years gained, or MILYs gained) in the denominator and estimates
of costs in the numerator. The estimate of costs in the numerator should include both the direct
costs of the controls necessary to achieve the reduction in ambient PMa.s and the avoided costs
(cost savings) associated with the reductions in morbidity (Gold et al., 1996). In general,
because reductions in air pollution do not require direct actions by the affected populations, there
are no specific costs to affected individuals (aside from the overall increases in prices that might
be expected to occur as control costs are passed on by affected industries). Likewise, because
individuals do not engage in any specific actions to realize the health benefit of the pollution
reduction, there are no decreases in utility (as might occur from a medical intervention) that need
to be adjusted for in the denominator. Thus, the elements of the numerator are direct costs of
controls minus the avoided COI associated with CB and nonfatal AMI. In addition, to account
for the value of reductions in acute health impacts and nonhealth benefits, we net out the
monetized value of these benefits from the numerator to yield a "net cost" estimate. For the
MILY aggregate effectiveness measure, the denominator is simply the sum of life years gained
from increased life expectancy and the sum of QALYs gained from the reductions in CB and
nonfatal AMI.
Avoided costs for CB and nonfatal AMI are based on estimates of lost earnings and medical
costs.8 Using age-specific annual lost earnings and medical costs estimated by Cropper and
Krupnick (1990) and a 3 percent discount rate, we estimated a lifetime present discounted value
(in 2000$) due to CB of $150,542 for someone between the ages of 27 and 44; $97,610 for
someone between the ages of 45 and 64; and $11,088 for someone over 65. The corresponding
age-specific estimates of lifetime present discounted value (in 2000$) using a 7 percent discount
rate are $86,026, $72,261, and $9,030, respectively. These estimates assumed that 1) lost
earnings continue only until age 65, 2) medical expenditures are incurred until death, and 3) life
expectancy is unchanged by CB.
Because the costs associated with a myocardial infarction extend beyond the initial event itself,
we consider costs incurred over several years. Using age-specific annual lost earnings estimated
by Cropper and Krupnick (1990) and a 3 percent discount rate, we estimated a present
Gold et al. (1996) recommend not including lost earnings in the cost-of-illness estimates,
suggesting that in some cases, they may be already be counted in the effectiveness measures.
However, this requires that individuals fully incorporate the value of lost earnings and reduced
labor force participation opportunities into their responses to time-tradeoff or standard-gamble
questions. For the purposes of this analysis and for consistency with the way costs-of-illness are
calculated for the benefit-cost analysis, we have assumed that individuals do not incorporate lost
earnings in responses to these questions. This assumption can be relaxed in future analyses with
improved understanding of how lost earnings are treated in preference elicitations.
6b-33
-------
discounted value in lost earnings (in 2000$) over 5 years due to a myocardial infarction of
$8,774 for someone between the ages of 25 and 44, $12,932 for someone between the ages of 45
and 54, and $74,746 for someone between the ages of 55 and 65. The corresponding age-
specific estimates of lost earnings (in 2000$) using a 7 percent discount rate are $7,855, $11,578,
and $66,920, respectively. Cropper and Krupnick (1990) do not provide lost earnings estimates
for populations under 25 or over 65. Thus, we do not include lost earnings in the cost estimates
for these age groups.
Two estimates of the direct medical costs of myocardial infarction are used. The first estimate is
from Wittels, Hay, and Gotto (1990), which estimated expected total medical costs of MI over 5
years to be $51,211 (in 1986$) for people who were admitted to the hospital and survived
hospitalization (there does not appear to be any discounting used). Using the CPI-U for medical
care, the Wittels estimate is $109,474 in year 2000$. This estimated cost is based on a medical
cost model, which incorporated therapeutic options, projected outcomes, and prices (using
"knowledgeable cardiologists" as consultants). The model used medical data and medical
decision algorithms to estimate the probabilities of certain events and/or medical procedures
being used. The second estimate is from Russell et al. (1998), which estimated first-year direct
medical costs of treating nonfatal myocardial infarction of $15,540 (in 1995$), and $1,051
annually thereafter. Converting to year 2000$, that would be $23,353 for a 5-year period
(without discounting).
The two estimates from these studies are substantially different, and we have not adequately
resolved the sources of differences in the estimates. Because the wage-related opportunity cost
estimates from Cropper and Krupnick (1990) cover a 5-year period, we used estimates for
medical costs that similarly cover a 5-year period. We used a simple average of the two 5-year
estimates, or $65,902, and add it to the 5-year opportunity cost estimate. The resulting estimates
are given in Table 6b-13.
Table 6b-13: Estimated Costs Over a 5-Year Period (in 2000$) of a Nonfatal Myocardial Infarction
Age Group
Opportunity Cost
Medical Cosf
Total Cost
0-24
25^4
45-54
55-65
>65
$0
$8,774b
$12,253"
$70,619"
$0
$65,902
$65,902
$65,902
$65,902
$65,902
$65,902
$74,676
$78,834
$140,649
$65,902
An average of the 5-year costs estimated by Wittels, Hay, and Gotto (1990) and Russell et al. (1998).
b From Cropper and Krupnick (1990), using a 3 percent discount rate.
The total avoided COI by age group associated with the reductions in CB and nonfatal acute
myocardial infarctions is provided in Table 6b-14. Note that the total avoided COI associated
with the revised PM NAAQS is $520 million and is $1,200 million for the more stringent
alternative. Note that this does not include any direct avoided medical costs associated with
premature mortality. Nor does it include any medical costs that occur more than 5 years from the
onset of a nonfatal AMI. Therefore, this is likely an underestimate of the true avoided COI
associated with strategies for attainment of the PM NAAQS.
6b-34
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Table 6b-14: Avoided Costs of Illness Associated with Reductions In Chronic Bronchitis and
Nonfatal Acute Myocardlal Infarctions Associated with Attainment Strategies for the
0.070 ppm alternative Ozone NAAQS In 2020
Avoided Cost of Illness
(in millions of 1999$)
Age
Range
18-24
25-34
35^4
45-54
55-64
65-74
75-84
85+
Total
Chronic Bronchitis
—
$11
$13
$7
$8
$0.7
$0.3
$0.1
$41
Nonfatal Acute Myocardial Infarction
$0.07
$0.3
$2.6
$8.6
$40
$22
$18
$9.4
$100
6b.7 Discount Rate Sensitivity Analysis
A large number of parameters and assumptions are necessary in conducting a CEA. Where
appropriate and supported by data, we have included distributions of parameter values that were
used in generating the reported confidence intervals. For the assumed discount rate, we felt it
more appropriate to examine the impact of the assumption using a sensitivity analysis rather than
through the integrated probabilistic uncertainty analysis.
The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing
discussion within the academic community. OMB and EPA guidance require using both a 7
percent rate and a 3 percent rate. In the most recent benefit-cost analyses of air pollution
regulations, a 3 and 7 percent discount rate have been adopted in the primary analysis. A 3
percent discount rate reflects a "social rate of time preference" discounting concept. A 3 percent
discount rate is also consistent with the recommendations of the NAS panel on CEA (Gold et al.,
1996), which suggests that "a real annual (riskless) rate of 3 percent should be used in the
Reference Case analysis." We have also calculated MILYs and the implicit cost thresholds using
a 7 percent rate consistent with an "opportunity cost of capital" concept to reflect the time value
of resources directed to meet regulatory requirements. Further discussion of this topic appears in
Chapter 7 of Gold et al. (1996), in Chapter 6 of the EPA Guidelines for Economic Analysis, and
in OMB Circular A-4.
6b-35
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Table 6b-15: Summary of Results for the Illustrative Partial Attainment Strategies for the
Alternative Ozone Standard of 0.070 ppm In 2020a
Result Using 3% Discount Rate (95% Confidence Interval)
Life years gained from mortality
risk reductions
Pope et al. (2002)
Laden et al. (2006)
QALY gained from reductions in
chronic bronchitis
QALY gained from reductions in
acute myocardial infarctions
Total gain in MILYs
Pope et al. (2002)
Laden et al. (2006)
Avoided cost of illness
Chronic bronchitis
Nonfatal AMI
Implementation strategy costsb
Net cost per Ml LY
Pope et al. (2002)
Laden et al. (2006)
070 ppm Partial Attainment Strategy
6,100
(2,400 - 9,800)
14,000
(7,500 - 20,000
2,000
(370 - 4,300)
1,100
(370 - 2,000)
9,100
(3,100-16,000)
17,000
(8,200 - 26,000)
$41 million
($7.6 million -$75 million)
$100 million
($63 million - $220 million)
$3.9 billion
$410,000
($220,000 - $1 ,300,000)
$220,000
($140,000 -$470,000)
* Consistent with recommendations of Gold et al. (1996), all summary results are reported at a precision level of
two significant digits to reflect limits in the precision of the underlying elements.
b Costs are the private firm costs of control, as discussed in Chapter 6, and reflect discounting using firm
specific costs of capital.
Table 6b-16 presents a summary of results using the 7 percent discount rate and the percentage
difference between the 7 percent results and the base case 3 percent results. Adoption of a 7
percent discount rate decreases the estimated life years and QALYs gained from implementing
the PM NAAQS. Adopting a discount rate of 7 percent results in a 35 percent reduction in the
estimated total MILYs gained in each year, while the cost per MILY increases by approximately
60 percent.
6b-36
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Table 6b-16: Impacts of Using a 7 Percent Discount Rate on Cost Effectiveness Analysis for the
Illustrative Attainment Strategies for the Revised and More Stringent PM NAAQS in
2020
Result Using 7 Percent
Discount Rate
Percentage Change Relative to
Result Using 3 Percent
Discount Rate
Life years gained from
mortality risk reductions
QALY gained from reductions
in chronic bronchitis
QALY gained from reductions
in acute myocardial infarctions
Total gain in MILYs
Pope et al. (2002)
Laden et al. (2006)
Avoided cost of illness
Chronic bronchitis
Nonfatal AMI
Net cost per MILY
Pope et al. (2002)
Laden et al. (2006)
4,600
10,000
1,300
830
6,500
12,000
$27 million
$111 million
$550,000
$300,000
-24%
-24%
-35%
-21%
-26%
-25%
-36%
+ 10%
+36%
+33%
6b.8 Conclusions
We calculated the effectiveness of PM NAAQS attainment strategies based on reductions in
premature deaths and incidence of chronic disease. We measured effectiveness using several
different metrics, including lives saved, life years saved, and QALYs (for improvements in
quality of life due to reductions in incidence of chronic disease). We suggested a new metric for
aggregating life years saved and improvements in quality of life, morbidity inclusive life years
(MILY) which assumes that society assigns a weight of one to years of life extended regardless
of preexisting disabilities or chronic health conditions.
CEA of environmental regulations that have substantial public health impacts may be
informative in identifying programs that have achieved cost-effective reductions in health
impacts and can suggest areas where additional controls may be justified. However, the overall
efficiency of a regulatory action can only be judged through a complete benefit-cost analysis that
takes into account all benefits and costs, including both health and nonhealth effects. The
benefit-cost analysis for the PM NAAQS attainment strategies, provided in Chapter 9, shows that
the attainment strategies we modeled have potentially large net benefits, indicating that
implementation of the revised PM NAAQS will likely result in improvements in overall public
welfare.
6b-37
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6b-43
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Appendix Chapter 6-c: Additional Sensitivity Analyses Related To the Benefits Analysis
The analysis presented in Chapter 6 is based on our current interpretation of the scientific and
economic literature. That interpretation requires judgments regarding the best available data,
models, and modeling methodologies and the assumptions that are most appropriate to adopt
in the face of important uncertainties. The majority of the analytical assumptions used to
develop the primary estimates of benefits have been reviewed and approved by EPA's SAB.
Both EPA and the SAB recognize that data and modeling limitations as well as simplifying
assumptions can introduce significant uncertainty into the benefit results and that alternative
choices exist for some inputs to the analysis, such as the mortality C-R functions.
This appendix supplements our primary analysis of benefits with three additional sensitivity
calculations. These supplemental estimates examine sensitivity to both valuation issues (e.g.,
the appropriate income elasticity) and for physical effects issues (e.g., the structure of the
cessation lag and the sensitivity of the premature mortality estimate to the presence of a
presumed threshold). These supplemental estimates are not meant to be comprehensive.
Rather, they reflect some of the key issues identified by EPA or commentors as likely to have
a significant impact on total benefits. The individual adjustments in the tables should not
simply be added together because 1) there may be overlap among the alternative assumptions
and 2) the joint probability among certain sets of alternative assumptions may be low.
6c.l Premature Mortality Cessation Lag Structure
Over the last ten years, there has been a continuing discussion and evolving advice regarding
the timing of changes in health effects following changes in ambient air pollution. It has
been hypothesized that some reductions in premature mortality from exposure to ambient
PM2.5 will occur over short periods of time in individuals with compromised health status,
but other effects are likely to occur among individuals who, at baseline, have reasonably
good health that will deteriorate because of continued exposure. No animal models have yet
been developed to quantify these cumulative effects, nor are there epidemiologic studies
bearing on this question. The SAB-HES has recognized this lack of direct evidence.
However, in early advice, they also note that "although there is substantial evidence that a
portion of the mortality effect of PM is manifest within a short period of time, i.e., less than
one year, it can be argued that, if no lag assumption is made, the entire mortality excess
observed in the cohort studies will be analyzed as immediate effects, and this will result in an
overestimate of the health benefits of improved air quality. Thus some time lag is
appropriate for distributing the cumulative mortality effect of PM in the population" (EPA-
SAB-COUNCIL-ADV-00-001, 1999, p. 9). In recent advice, the SAB-HES suggests that
appropriate lag structures may be developed based on the distribution of cause-specific
deaths within the overall all-cause estimate (EPA-SAB-COUNCIL-ADV-04-002, 2004).
They suggest that diseases with longer progressions should be characterized by longer-term
lag structures, while air pollution impacts occurring in populations with existing disease may
be characterized by shorter-term lags.
6c-l
-------
A key question is the distribution of causes of death within the relatively broad categories
analyzed in the long-term cohort studies. Although it may be reasonable to assume the
cessation lag for lung cancer deaths mirrors the long latency of the disease, it is not at all
clear what the appropriate lag structure should be for cardiopulmonary deaths, which include
both respiratory and cardiovascular causes. Some respiratory diseases may have a long
period of progression, while others, such as pneumonia, have a very short duration. In the
case of cardiovascular disease, there is an important question of whether air pollution is
causing the disease, which would imply a relatively long cessation lag, or whether air
pollution is causing premature death in individuals with preexisting heart disease, which
would imply very short cessation lags. The SAB-HES provides several recommendations for
future research that could support the development of defensible lag structures, including
using disease-specific lag models and constructing a segmented lag distribution to combine
differential lags across causes of death (EPA-SAB-COUNCIL-ADV-04-002, 2004). The
SAB-HES indicated support for using "a Weibull distribution or a simpler distributional form
made up of several segments to cover the response mechanisms outlined above, given our
lack of knowledge on the specific form of the distributions" (EPA-SAB-COUNCIL-ADV-
04-002, 2004, p. 24). However, they noted that "an important question to be resolved is what
the relative magnitudes of these segments should be, and how many of the acute effects are
assumed to be included in the cohort effect estimate" (EPA-SAB-COUNCIL-ADV-04-002,
2004, p. 24-25). Since the publication of that report in March 2004, EPA has sought
additional clarification from this committee. In its followup advice provided in December
2004, this SAB suggested that until additional research has been completed, EPA should
assume a segmented lag structure characterized by 30 percent of mortality reductions
occurring in the first year, 50 percent occurring evenly over years 2 to 5 after the reduction in
PM2.5, and 20 percent occurring evenly over the years 6 to 20 after the reduction in PM2.5
(EPA-COUNCIL-LTR-05-001, 2004). The distribution of deaths over the latency period is
intended to reflect the contribution of short-term exposures in the first year, cardiopulmonary
deaths in the 2- to 5-year period, and long-term lung disease and lung cancer in the 6- to 20-
year period. Furthermore, in their advisory letter, the SAB-HES recommended that EPA
include sensitivity analyses on other possible lag structures. In this appendix, we investigate
the sensitivity of premature mortality-reduction related benefits to alternative cessation lag
structures, noting that ongoing and future research may result in changes to the lag structure
used for the primary analysis.
In previous advice from the SAB-HES, they recommended an analysis of 0-, 8-, and 15-year
lags, as well as variations on the proportions of mortality allocated to each segment in the
segmented lag structure (EPA-SAB-COUNCIL-ADV-00-001, 1999, (EPA-COUNCIL-LTR-
05-001, 2004). The 0-year lag is representative of EPA's assumption in previous RIAs. The
8- and 15-year lags are based on the study periods from the Pope et al. (1995) and Dockery et
al. (1993) studies, respectively.1 However, neither the Pope et al. nor Dockery et al. studies
assumed any lag structure when estimating the relative risks from PM exposure. In fact, the
1 Although these studies were conducted for 8 and 15 years, respectively, the choice of the duration of the
study by the authors was not likely due to observations of a lag in effects but is more likely due to the
expense of conducting long-term exposure studies or the amount of satisfactory data that could be collected
during this time period.
6c-2
-------
Pope et al. and Dockery et al. analyses do not supporting or refute the existence of a lag.
Therefore, any lag structure applied to the avoided incidences estimated from either of these
studies will be an assumed structure. The 8- and 15-year lags implicitly assume that all
premature mortalities occur at the end of the study periods (i.e., at 8 and 15 years).
In addition to the simple 8- and 15-year lags, we have added three additional sensitivity
analyses examining the impact of assuming different allocations of mortality to the
segmented lag of the type suggested by the SAB-HES. The first sensitivity analysis assumes
that more of the mortality impact is associated with chronic lung diseases or lung cancer and
less with acute cardiopulmonary causes. This illustrative lag structure is characterized by 20
percent of mortality reductions occurring in the first year, 50 percent occurring evenly over
years 2 to 5 after the reduction in PIVb.s, and 30 percent occurring evenly over the years 6 to
20 after the reduction in PIVb.s. The second sensitivity analysis assumes the 5-year
distributed lag structure used in previous analyses, which is equivalent to a three-segment lag
structure with 50 percent in the first 2-year segment, 50 percent in the second 3-year
segment, and 0 percent in the 6- to 20-year segment. The third sensitivity analysis assumes a
negative exponential relationship between reduction in exposure and reduction in mortality
risk. This structure is based on an analysis by Roosli et al. (2004), which estimates the
percentage of total mortality impact in each period t as
% Mortality Reduction(t) =
The Roosli et al. (2004) analysis derives the lag structure by calculating the rate constant
(-0.5) for the exponential lag structure that is consistent with both the relative risk from the
cohort studies and the change in mortality observed in intervention type studies (e.g., Pope et
al. [1992] and Clancy et al. [2002]). This is the only lag structure examined that is based on
empirical data on the relationship between changes in exposure and changes in mortality.
The estimated impacts of alternative lag structures on the monetary benefits associated with
reductions in PM-related premature mortality (estimated with the Pope et al. ACS impact
function) are presented in Table J-l. These estimates are based on the value of statistical
lives saved approach (i.e., $5.5 million per incidence) and are presented for both a 3 and 7
percent discount rate over the lag period.
6c-3
-------
Table 6c-l. Sensitivity of Benefits of Premature Mortality Reductions to Alternative Cessation Lag Structures, Using
Pope et al (2002) Effect Estimate
Alternative Lag Structures for PM-Related Premature
Mortality
None
8-year
15 -year
Alternative
Segmented
5-Year
Distributed
Exponential
Incidences all occur in the first year
3% discount rate
7% discount rate
Incidences all occur in the 8 year
3% discount rate
7% discount rate
Incidences all occur in the 1 5 year
3% discount rate
7% discount rate
20 percent of incidences occur in 1st year, 50
percent in years 2 to 5, and 30 percent in
years 6 to 20
3% discount rate
7% discount rate
50 percent of incidences occur in years 1
and 2 and 50 percent in years 2 to 5
3% discount rate
7% discount rate
Incidences occur at an exponentially
declining rate following year of change in
exposure
3% discount rate
7% discount rate
Value
(billion 1999$)" b
$3.5
$3.5
$2.9
$2.2
$2.3
$1.4
$3.1
$2.5
$3.4
$3.1
$3.4
$3.1
Percent
Difference from
Base Estimate
10.4%
31.2%
-10.3%
-18.3%
-27.0%
-49.1%
-3.2%
-8.7%
4.9%
17.1%
5.6%
14.8%
Dollar values rounded to two significant digits.
6c-4
-------
The results of this sensitivity analyses demonstrate that because of discounting of delayed
benefits, the lag structure may also have a large impact on monetized benefits, reducing
benefits by 30 percent if an extreme assumption that no effects occur until after 15 years is
applied. However, for most reasonable distributed lag structures, differences in the specific
shape of the lag function have relatively small impacts on overall benefits. For example, the
overall impact of moving from the previous 5-year distributed lag to the segmented lag
recommended by the SAB-HES in 2004 in the primary estimate is relatively modest,
reducing benefits by approximately 5 percent when a 3 percent discount rate is used and 15
percent when a 7 percent discount rate is used. If no lag is assumed, benefits are increased
by around 10 percent relative to the segmented lag with a 3 percent discount rate and 30
percent with a 7 percent discount rate.
6c. 2 Threshold Sensitivity Analysis
Chapter 6 presents the results of the PM2.5 premature mortality benefits analysis based on an
assumed cutpoint in the long-term mortality concentration-response function at 10 ug/m3,
and an assumed cutpoint in the short-term morbidity concentration-response functions at 10
ug/m3. There is ongoing debate as to whether there exists a threshold below which there
would be no benefit to further reductions in PIVb.s. Some researchers have hypothesized the
presence of a threshold relationship. The nature of the hypothesized relationship is the
possibility that there exists a PM concentration level below which further reductions no
longer yield premature mortality reduction benefits. EPA's most recent PM2.5 Criteria
Document concludes that "the available evidence does not either support or refute the
existence of thresholds for the effects of PM on mortality across the range of concentrations
in the studies" (U.S. EPA, 2004b, p. 9-44). EPA's Science Advisory Board (SAB) that
provides advice on benefits analysis methods2 has been to model premature mortality
associated with PM exposure as a non-threshold effect, that is, with harmful effects to
exposed populations regardless of the absolute level of ambient PM concentrations.
For these reasons we provide the results of a sensitivity analysis in which we estimate the
change in reduction in incidence of PM2.5-related premature mortality resulting from
changes in the presumed threshold. We also provide a corresponding estimate of the
valuation of these changes in incidence.
2 The advice from the 2004 SAB-HES (U.S. EPA-SAB, 2004b) is characterized by the following: "For the
studies of long-term exposure, the HES notes that Krewski et al. (2000) have conducted the most careful work
on this issue. They report that the associations between PM2.s and both all-cause and cardiopulmonary mortality
were near linear within the relevant ranges, with no apparent threshold. Graphical analyses of these studies
(Dockery et al., 1993, Figure 3, and Krewski et al., 2000, page 162) also suggest a continuum of effects down to
lower levels. Therefore, it is reasonable for EPA to assume a no threshold model down to, at least, the low end
of the concentrations reported in the studies."
6c-5
-------
Table 6c-2: Mortality Threshold Sensitivity Analysis for 0.070 ppm Ozone Scenario (Using
Pope et al., 2002 Effect Estimate with Slope Adjustment for Thresholds Above 7.5 ug) 90th
Percentile Confidence Intervals Provided in Parentheses a
Less Certainty
That Benefits Are
at Least as Large
0
More Certainty
That Benefits are
at Least as Large
No
Threshold
Threshold
at 7.5 |jg
Threshold
at 10 pg
Threshold
at 12 |jg
Threshold
at 14 pg
East
570
(230—920)
580
(230—930)
510
(200—810)
100
(40—160)
—
Western U.S.
Excluding CA
28
(11-45)
15
(6-24)
0.2
(0.07—0.3)
0.03
(0.01—0.04)
—
California Total
53
(21—85)
51
(20—85)
47
(18—75)
42
(16—67)
36
(14—59)
650
(260—1,100)
650
(250—1,000)
550
(220—890)
140
(56—230)
36
(14—59)
a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not
sum across columns
6c-6
-------
Table 6c-3: Sensitivity of Monetized Benefits of Reductions in Mortality Risk to Assumed
Thresholds for 0.070 ppm Partial Attainment Scenario (Using Pope et al., 2002 Effect Estimate
with Slope Adjustment for Thresholds Above 7.5 ug) 90th Percentile Confidence Intervals
Provided in Parentheses3
Less Certain
that Benefits
Are at Least
as Large
More Certain
that Benefits
Are at Least
as Large
No
Threshold
Threshold at
7.5 ug
Threshold at
10 ug
Threshold at
12 ug
Threshold at
14 ug
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
Eastern U.S.
$3,300
($830-
$6,900)
$2,800
($700-
$5,800)
$3,400
($840-
$7,000)
$2,800
($710-
$5,900)
$2,900
($730-
$6,100)
$2,500
($620-
$5,100)
$590
($150-
$1 ,200)
$490
($120-
$1,000)
—
__
Western U.S.
Excluding CA
$160
($41 -$340)
$140
($34-$280)
$86
($22-$$ 180)
$72
($18~$150)
$1
($0.2-$2)
$0.8
($0.2~$1.7)
$0.2
($0.04-$0.3)
$0.1
($0.03-$0.3)
_
__
Total Nationwide
California Attainment
$310
($77-$630)
$260
($64-$530)
$300
($74-$620)
$250
($63-$520)
$270
($67-$560)
$230
($57-$470)
$240
($61 -$500)
$200
($51 -$420)
$210
($53-$440)
$180
($44-$370)
$3,800
($950-$7,900)
$3,200
($800-$6,200)
$3,700
($940-$7,800)
$3,200
($790-$6,600)
$3,200
($800-$6,600)
$2,700
($670-$5,600)
$830
($2 10-$ 1,700)
$700
($180-$1,500)
$210
($53-$440)
$180
($44-$370)
a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will
not sum across columns
6c-7
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6c.3 Income Elasticity of Willingness to Pay
As discussed in Chapter 6, our estimates of monetized benefits account for growth in real
GDP per capita by adjusting the WTP for individual endpoints based on the central estimate
of the adjustment factor for each of the categories (minor health effects, severe and chronic
health effects, premature mortality, and visibility). We examined how sensitive the estimate
of total benefits is to alternative estimates of the income elasticities. Table 6c-3 lists the
ranges of elasticity values used to calculate the income adjustment factors, while Table 6c-4
lists the ranges of corresponding adjustment factors. The results of this sensitivity analysis,
giving the monetized benefit subtotals for the four benefit categories, are presented in Table
6c-5.
Table 6c-4. Ranges of Elasticity Values Used to Account for Projected Real Income
Growth3
Benefit Category
Minor Health Effect
Severe and Chronic Health Effects
Premature Mortality
Visibility13
Lower Sensitivity Bound
0.04
0.25
0.08
—
Upper Sensitivity Bound
0.30
0.60
1.00
—
Derivation of these ranges can be found in Kleckner and Neumann (1999) and Chestnut (1997). COI estimates are
assigned an adjustment factor of 1.0.
No range was applied for visibility because no ranges were available in the current published literature.
Table 6c-5. Ranges of Adjustment Factors Used to Account for Projected Real Income
Growth3
Benefit Category
Minor Health Effect
Severe and Chronic Health Effects
Premature Mortality
Visibility13
Lower Sensitivity Bound
1.018
1.121
1.037
—
Upper Sensitivity Bound
1.147
1.317
1.591
—
a Based on elasticity values reported in Table C-4, U.S. Census population projections, and projections of real GDP per
capita.
b No range was applied for visibility because no ranges were available in the current published literature.
6c-8
-------
Table 6c-6. Sensitivity of Monetized Benefits to Alternative Income Elasticities3
Benefits Incremental to 080 ppm Partial Attainment Strategy
(Millions of 1999$)
Benefit Category
Minor Health Effect
Severe and Chronic Health Effects
Premature Mortality
Total Benefits'3
Ozone Analysis
Lower
Sensitivity
Bound
$64
--
$2,000
$2,000
Upper
Sensitivity
Bound
$72
--
$3,000
$3,100
PM Analysis
Lower
Sensitivity
Bound
$9.7
$160
$2,800
$2,900
Upper
Sensitivity
Bound
$11
$190
$3,600
$3,800
a All estimates rounded to two significant digits.
b Using mortality effect estimate from Pope et al (2002) to estimate PM2.s mortality and a 3 percent discount
rate and mortality effect estimate from Bell (2004).
c No range was applied for visibility because no ranges were available in the current published literature.
Consistent with the impact of mortality on total benefits, the adjustment factor for mortality
has the largest impact on total benefits. The value of mortality in 2020 ranges from 90
percent to 130 percent of the primary estimate based on the lower and upper sensitivity
bounds on the income adjustment factor. The effect on the value of minor and chronic health
effects is much less pronounced, ranging from 98 percent to 105 percent of the primary
estimate for minor effects and from 93 percent to 106 percent for chronic effects.
6c.4 References
Chestnut, L.G. 1997. "Draft Memorandum: Methodology for Estimating Values for
Changes in Visibility at National Parks." April 15.
Chestnut, L.G., and R.D. Rowe. 1990a. Preservation Values for Visibility Protection at the
National Parks: Draft Final Report. Prepared for Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC and Air
Quality Management Division, National Park Service, Denver, CO.
Chestnut, L.G., and R.D. Rowe. 1990b. "A New National Park Visibility Value Estimates."
In Visibility and Fine Particles, Transactions of an AWMA/EPA International Specialty
Conference, C.V. Mathai, ed. Air and Waste Management Association, Pittsburgh.
6c-9
-------
Clancy, L., P. Goodman, H. Sinclair, and D.W. Dockery. 2002. "Effect of Air-pollution
Control on Death Rates in Dublin, Ireland: An Intervention Study." Lancet Oct
19;360(9341):1210-4.
Desvousges, W.H., F.R. Johnson, and H.S. Banzhaf. 1998. Environmental Policy Analysis
With Limited Information: Principles and Applications of the Transfer Method (New
Horizons in Environmental Economics.) Edward Elgar Pub: London.
EPA-SAB-COUNCIL-ADV-00-001. October 1999. The Clean Air Act Amendments
(CAAA) Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Health
and Ecological Effects Subcommittee on Initial Assessments of Health and Ecological
Effects. Part 2.
EPA-SAB-COUNCIL-ADV-99-012. July 1999. The Clean Air Act Amendments (CAAA)
Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Health and
Ecological Effects Subcommittee on Initial Assessments of Health and Ecological Effects.
Part 1.
EPA-SAB-COUNCIL-ADV-01 -004. September 2001. Review of the Draft Analytical Plan
for EPA's Second Prospective Analysis—Benefits and Costs of the Clean Air Act 1990-2020:
An Advisory by a Special Panel of the Advisory Council on Clean Air Compliance Analysis.
EPA-SAB-COUNCIL-ADV-04-002. March 2004. Advisory on Plans for Health Effects
Analysis in the Analytical Plan for EPA 's Second Prospective Analysis—Benefits and Costs
of the Clean Air Act, 1990-2020: Advisory by the Health Effects Subcommittee of the
Advisory Council on Clean Air Compliance Analysis.
Kleckner, N., and J. Neumann. June 3, 1999. "Recommended Approach to Adjusting WTP
Estimates to Reflect Changes in Real Income." Memorandum to Jim Democker, US
EPA/OPAR.
Roosli M, Kunzli N, Braun-Fahrlander C, Egger M. 2005. "Years of life lost attributable to
air pollution in Switzerland: dynamic exposure-response model." InternationalJournal of
Epidemiology 34(5): 1029-35.
U.S. Environmental Protection Agency (EPA). 2004. Air Quality Criteria for Particulate
Matter, Volume II. Office of Research and Development. EPA/600/P-99/002bF, October
2004.
6c-10
-------
Chapter 7: Discussion of Ozone Secondary Standard
Exposures to ozone have been associated with a wide array of vegetation and ecosystem
effects in the published literature. These effects include those that damage or impair the
intended use of the plant or ecosystem. Such effects are considered adverse to the public
welfare and can include: reduced plant growth, visible foliar (leaf) injury, reduced plant
vigor (e.g., increased susceptibility to harsh weather, disease, insect pest infestation, and
competition), reduced crop yields, and changes in ecosystems and associated ecosystem
services.
Vegetation effects research has shown that seasonal air quality indices that cumulate
peak-weighted hourly ozone concentrations are the best candidates for relating exposure
to plant growth effects. On the basis of this research, as well as other information
considered in this review (e.g., policy-relevant background (PRB) levels), the Staff Paper
concluded that the cumulative, seasonal index referred to as "W126" is the most
appropriate index for relating vegetation response to ambient ozone exposures. Based on
additional conclusions regarding appropriate diurnal and seasonal exposure windows, the
Staff Paper concluded that it was appropriate for the Administrator to consider a
cumulative seasonal secondary standard, expressed as an index of the annual sum of
weighted hourly concentrations (using the W126 form), set at a level in the range of 7 to
21 ppm-hours. The index would be cumulated over the 12-hour daylight window (8:00
a.m. to 8:00 p.m.) during the consecutive 3 month period during the ozone season with
the maximum index value (hereafter referred to as the 12-hour, maximum 3-month
W126).
The Staff Paper also considered the extent to which there is overlap between county-level
air quality measured in terms of the 8-hour average form of the current secondary
standard and that measured in terms of the 12-hour W126, alternative cumulative,
seasonal form. These comparisons were done using 3-year averages for both forms, as
well as using the 3-year average current 8-hour form and the annual W126 county-level
air quality values. This Staff Paper assessment used 2002-2004 county-level air quality
data from the AQS sites and the subset of CASTNET sites having the highest ozone
levels for the counties in which they are located. Since the completion of the Staff Paper,
this analysis has been updated using the more recent 3-year period of 2003 to 2005.
Results from the more recent (2003-2005) 3-year average comparisons (see Table 1
below) showed that after meeting the current 3-year average form of the 0.08-ppm, 8-
hour average standard, the number of counties not meeting a 3-year average W126 form
ranged from 11 at the upper level of the proposed W126 range (21 ppm-hours) to 76
counties (W126 of 15 ppm-hours- representing the upper bound of the CAS AC
recommended range), to 221 counties at the lower end of the proposed W126 range (7
ppm-hours). The degree of overlap is greater when levels within the proposed range
(0.070-0.075 ppm) for a revised 8-hour average standard are met, specifically the number
of counties still exceeding a W126 form of a standard range from 0 at a Wl26 level of 21
ppm-hours to 25 at a W126 level of 7 ppm-hours. The Staff Paper notes that when
individual years are compared (e.g., using the annual W126 level) significant variability
occurs between years in the degree of overlap between the numbers of counties meeting
7-1
-------
various levels of the 8-hour and W126 forms and, therefore, cautions that the degree of
protection for vegetation provided by an 8-hour average form in terms of cumulative,
seasonal exposures would not be expected to be consistent on a year to year basis.
The Staff Paper also identified additional aspects of this analysis that would suggest
caution should be used in interpreting these results. First, due to the lack of more
complete monitor coverage in many rural areas, the Staff Paper concluded that this
analysis may not be an accurate reflection of the situation in non-monitored, rural
counties. Because of the lack of monitoring in rural areas where important vegetation
and ecosystems are located, it remains uncertain as to the extent to which air quality
improvements designed to reduce 8-hour ozone average concentrations would reduce
ozone exposures measured by a seasonal, cumulative W126 index. The Staff Paper
indicated this to be an important consideration because: (1) the biological database
stresses the importance of cumulative, seasonal exposures in determining plant response;
(2) plants have not been specifically tested for the importance of daily maximum 8-hour
ozone concentrations in relation to plant response; and (3) the effects of attainment of a
8-hour standard in upwind urban areas on rural air quality distributions cannot be
characterized with confidence due to the lack of monitoring data in rural and remote
areas.
In addition, though within the range of 8-hour average levels being proposed the numbers
of counties exceeding mid- to low levels of W126 are greatly reduced, many of these
counties contain areas of national public interest. For example, at the 8-hour level of
0.075 ppm, 12 counties would still exceed the W126 level of 15 ppm-hours. Most of
these counties contain high elevation, rural or remote sites where ozone air quality
distributions tend to be flatter and the potential for disconnect between 8-hour average
and cumulative, seasonal forms, greater. Therefore, the Staff Paper notes that additional
rural high elevation areas important for vegetation that are not currently monitored likely
experience similar ozone exposure patterns. These factors are important considerations
in determining whether the current 8-hour form can appropriately provide requisite
protection for vegetation.
Due to time and resource limitations, EPA did not calculate the costs and monetized
benefits of a separate secondary standard. Consideration to these costs and benefits will
be provided in the final RIA.
7-2
-------
Table 7.1 Comparison of number of counties exceeding various W126 levels when
meeting various levels of the 8-hr standard for the 3-year period 2003-20051
8-hr level met
0.084 ppm
0.075 ppm
0.070 ppm
Levels of 12-hr W126 (ppm-hrs)
>21
11
(7-27)
0
(0-7)
0
(0-1)
>15
76
(23-173)
11
(3-33)
2
(1-6)
>7
221
(244-382)
114
(49-134)
25
(13-36)
1 The top value in each box represents the number of counties meeting the 8-hour level based on 2003-2005
data but exceeding the W126 level based on a 3-year W126 average for the 2003-2005 period. The
numbers in parentheses indicate the range in the number of counties that exceed the W126 level on an
annual basis in one of the three years—2003, 2004, 2005- based on 1-year W126 values. The range
indicates significant interannual variability.
7-3
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Chapter 8: Conclusions and Implications of the Illustrative Benefit-Cost Analysis
Synopsis
EPA has performed an illustrative analysis to estimate the costs and human health benefits of
nationally attaining alternative ozone standards. We have considered 4 alternative standards
incremental to attaining the current ozone standard: 0.079 ppm, 0.075 ppm, 0.070 ppm, and
0.065 ppm. This chapter summarizes these results and discusses the implications of the analysis.
This analysis serves both to satisfy the requirements of E.O. 12866 and to provide the public
with an estimate of the potential costs and benefits of attaining alternative ozone standards. The
benefit and cost estimates below are calculated incremental to a 2020 baseline that incorporates
air quality improvements achieved through the projected implementation of existing regulations
and full attainment of the current standards for ozone and PM NAAQS (including the
hypothetical control strategy developed in the RIA for full attainment of the PM NAAQS 15/35
promulgated in September, 2006). This RIA presents two sets of results: The first reflects full
attainment in all locations except two areas of California, which are planning to meet the current
standards after 2020, and so have estimated costs and benefits for the analyzed standards for
partial attainment in 2020 (their "glidepath" targets).1 The second estimate, for California only,
presents the additional costs and benefits that might result from California fully attaining the
standards in a year beyond 2020. Finally, this chapter provides additional context for the RIA
analysis and a discussion of limitations and uncertainties. In addition, given the technological
limitations associated with reducing ozone precursors, we provide estimated cost and benefit
numbers based on both partial attainment (manageable with current technologies) and full
attainment (manageable in some locations only with hypothetical technologies).
8.1 Results
Presentation of Results
There are two sets of results presented below. The first set of results is for 2020. For analytical
purposes explained previously, we assume that almost all areas of the country will meet each
alternative standard in 2020 through the development of technologies at least as effective as the
hypothetical strategies used in this illustration. It is expected that benefits and costs will begin
occurring earlier, as states begin implementing control measures to attain earlier or to show
progress towards attainment. Some areas with very high levels of ozone do not plan to meet even
the current standard until after 2020; specifically, two California areas have adopted plans for
post-2020 attainment as noted above. In these locations, we provide estimates of the costs and
benefits of attaining a "glidepath" target in our 2020 analysis year.2 The 2020 results thus do
1 Because these two areas adopted 8-hour ozone implementation plans calling for post-2020
attainment after we had completed much of our analysis, these areas are assumed to meet the
current standard in 2020 and 2021 respectively, somewhat earlier than the date in their plans,
which results in a steeper glide path, and higher costs and benefits, for all the standards analyzed.
See chapter 4.
2 See footnote 1.
8-1
-------
not represent a complete "full attainment" scenario for the entire nation, particularly for more
stringent alternative new standards examined. In order to gain an understanding of the possible
additional costs and benefits of fully attaining in California, we provide an additional set of
results focusing on California.
By the year 2030, various mobile source rules, such as the onroad and nonroad diesel rules,
among others, would be expected to be fully implemented. Because California will likely not
have to attain until closer to 2030, it is important to reflect the impact those rules might have on
the emissions that affect ozone nonattainment. To reflect the emission reductions that are
expected from these rules, we subtract those tons from our estimates of the emissions reductions
that might be needed for California to fully attain in 2020, thus making our analysis more
consistent with full attainment later than 2020. EPA did the analysis this way because to force
full attainment in California in an earlier year would not be consistent with the CAA, and would
likely lead to an overstatement of costs because those areas might benefit from these existing
federal or state programs that would be implemented between 2020 and the attainment year (see
detail in Chapter 4); because additional new technologies may become available between 2020
and the attainment year; and/or the cost of existing technologies might fall over time due to
economic factors such as economies of scale or improvements in the efficiency of installing and
operating controls ('learning by doing'). On the other hand, it is also possible that new
technologies might not meet the specifications, development time lines, or cost estimates
provided in this analysis.
It is not appropriate to add together the 2020 national attainment, California glidepath estimate
and the estimate of California full attainment as an estimate of national full attainment in 2020 It
is not appropriate to do this because each estimate is based on different baseline conditions for
emissions and air quality. In addition, both estimates include estimates of California glidepath
results, leading to the potential for double counting if added together.
The following set of tables summarizes the costs and benefits of the scenarios analyzed, and
shows the net benefits for each of the scenarios across a range of modeling assumptions
concerning the calculation of costs and benefits. Tables 8.1a-c present benefits and costs of
national attainment in 2020, including the "glidepath" targets for California. Companion Table
8.2 provides the estimated reductions in premature mortality and morbidity for national
attainment in 2020, including the "glidepath" targets for California. Tables 8.3a-c present the
additional costs and benefits of full attainment for California ("glidepath" plus future year
attainment added together into one total); Table 8.4 is the companion table showing estimated
reductions in premature mortality and morbidity.
The individual row estimates for benefits reflect the variability in the functions available for
estimating the largest source of benefits - avoided ozone premature mortality. Ranges within the
total benefits column reflect variability in the estimates of PM premature mortality co-benefits
across the available effect estimates. Ranges in the total costs column reflect different
assumptions about the extrapolation of costs. The low end of the range of net benefits is
constructed by subtracting the highest cost from the lowest benefit, while the high end of the
range is constructed by subtracting the lowest cost from the highest benefit. Following these
8-2
-------
tables is a discussion of the implications of these estimates, as well as the uncertainties and
limitations that should be considered in interpreting the estimates.
8-3
-------
Table 8.1a National Annual Costs and Benefits: 0.079 ppm Standard in 2020
(including California glidepath )
Premature
Mortality
Function or
Assumption
NMMAPS
Meta-analysis
Assumption that
causal***
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits*
$1.2 to $11
$1.6 to $12
$1.7 to $12
$1.6 to $12
$l.lto$ll
Total Costs**
$3 to $3.3
$3 to $3.3
$3 to $3.3
$3 to $3.3
$3 to $3.3
Net Benefits
-$2.1 to $8.5
-$1.7 to $8.9
-$1.7 to $8.9
-$1.7 to $8.9
-$2.2 to $8.4
Table 8.1b National Annual Costs and Benefits: 0.075 ppm Standard in 2020
(including California glidepath )
Premature
Mortality
Function or
Assumption
NMMAPS
Meta-analysis
Assumption that
causal***
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits*
$3 to $16
$7.3 to $20
$7.8 to $21
$8.7 to $22
$1.5 to $15
Total Costs**
$5.5 to $8. 8
$5.5 to $8. 8
$5.5 to $8. 8
$5.5 to $8. 8
$5.5 to $8. 8
Net Benefits
-$5.8 to $10.5
-$1.5 to $15
-$l.to$15
-$0.1 to $16
-$7.3 to $9
Table 8.1c National Annual Costs and Benefits: 0.070 ppm Standard in 2020
(including California glidepath)
Premature
Mortality
Function or
Assumption
NMMAPS
Meta-analysis
Assumption that
causal***
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits*
$4.3 to $26
$9.7 to $31
$10 to $32
$11 to $33
$2.5 to $24
Total Costs**
$10 to $22
$10 to $22
$10 to $22
$10 to $22
$10 to $22
Net Benefits
-$17 to $16
-$12 to $21
-$11 to $22
-$10 to $23
-$20 to $14
8-4
-------
Table 8.1d National Annual Costs and Benefits : 0.065 ppm Standard in 2020
(including California glidepath)
Premature
Mortality
Function or
Assumption
NMMAPS
Meta-analysis
Assumption that
causal***
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits*
$7.7 to $45
$18 to $55
$19 to $56
$20 to $57
$4.3 to $42
Total Costs**
$17 to $46
$17 to $46
$17 to $46
$17 to $46
$17 to $46
Net Benefits
-$38 to $28
-$28 to $38
-$27 to $39
-$27 to $40
-$42 to $25
*Includes ozone benefits, and PM 2.5 co-benefits. Range was developed by adding the estimate
from the ozone premature mortality function to both the lower and upper ends of the range of the
PM2.5 premature mortality functions characterized in the expert elicitation
**Range reflects lower and upper bound cost estimates
***Total includes ozone morbidity benefits only
8-5
-------
Table 8.2: Summary of Total Number of Annual Ozone and PM2.5-Related Premature
Mortalities and Premature Morbidity Avoided: 2020 National Benefits
Combined Estimate of Mortality
Standard Alternative and Combined Range of Ozone Benefits and
Model or Assumption PM2.s Co-Benefits
0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm
NMMAPS
Meta-Analysis
No Causality
Bell (2004)
Bell (2005)
Ito (2005)
Levy (2005)
200 to 1,900
260 to 2,000
270 to 2,000
260 to 2,000
180 to 1,900
430 to 2,600
1,100 to 3,300
1,200 to 3,300
1,300 to 3,500
230 to 2,400
670 to 4,300
1,500 to 5,100
1,600 to 5,200
1,800 to 5,400
390 to 4,000
1,200 to 7,400
2,800 to 9,000
3,000 to 9,200
3,000 to 9,200
660 to 6,900
Combined Estimate of Morbidity
Acute Myocardial Infarction
Hospital and ER Visits
Chronic Bronchitis
Acute Bronchitis
Asthma Exacerbation
Lower Respiratory Symptoms
Upper Respiratory Symptoms
School Loss Days
Work Loss Days
Minor Restricted Activity Days
1,100
1,300
370
950
7,300
8,100
5,900
50,000
51,000
430,000
1,400
5,600
470
1,200
9,400
10,000
7,500
610,000
65,000
2,000,000
2,300
7,600
780
2,000
16,000
17,000
13,000
780,000
110,000
2,700,000
4,000
13,000
1,300
3,500
27,000
29,000
22,000
1,300,000
190,000
4,700,000
8-6
-------
Table 8.3a California: Annual Costs and Benefits of Attaining 0.079 ppm Standard
(beyond 2020)*
Premature
Mortality
Function
or Assumption
NMMAPS
Meta-analysis
Assumption that
causal
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is not
Mean Total Benefits, in Billions of 1999$
Total Benefits**
$0.1 to $0.6
$0.2 to $0.7
$0.3 to $0.7
$0.2 to $0.7
$0.05 to $0.5
Total Costs***
$0.3 to $1.7
$0.3 to $1.7
$0.3 to $1.7
$0.3 to $1.7
$0.3 to $1.7
Net Benefits
-$1-6 to $0.2
-$1.5 to $0.4
-$1.4 to $0.4
-$1.5 to $0.4
-$1.6 to $0.2
Table 8.3b California: Annual Costs and Benefits of Attaining 0.070 ppm Standard
(beyond 2020)*
Premature
Mortality Function
or Assumption
NMMAPS
Meta-analysis
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
Assumption that association is not
causal****
Mean Total Benefits, in Billions of 1999$
Total Benefits**
$0.7 to $3.5
$1.9 to $4.7
$2. Ito $4.8
$2.1 to $4.8
$0.4 to $3.1
Total Costs***
$2 to $13
$2 to $13
$2 to $13
$2 to $13
$2 to $13
Net Benefits
-$12 to $1.5
-$11 to $2.7
-$11 to $2.9
-$11 to $2.9
-$13 to $1.2
Table 8.3c California: Annual Costs and Benefits of Attaining 0.075 ppm Standard
(beyond 2020)*
Premature
Mortality Function
or Assumption
NMMAPS
Meta-analysis
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
Assumption that association is not
causal****
Mean Total Benefits, in Billions of 1999$
Total Benefits**
$0.4 to $1.9
$1.1 to $2.6
$1.2 to $2.7
$1.2 to $2.7
$0.2 to $1.7
Total Costs***
$1.1 to $6.2
$1.1 to $6.2
$1.1 to $6.2
$1.1 to $6.2
$1.1 to $6.2
Net Benefits
-$5.8 to $0.8
-$5.1 to $1.5
-$5.1 to $1.6
-$5to$1.6
-$6to$0.6
8-7
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Table 8.3d California: Annual Costs and Benefits of Attaining 0.065 ppm Standard
(beyond 2020)*
Premature
Mortality Function
or Assumption
Reference
NMMAPS Bell et al. 2004
Bell et al. 2005
Meta-analysis Ito et al. 2005
Levy et al. 2005
Assumption that association is not
causal
Mean Total Benefits, in Billions of 1999$
Total Benefits**
$1.1 to $5.2
$3. Ito $7.2
$3.4 to $7.4
$3.3 to $7.4
$0.5 to $4.6
Total Costs***
$2.9 to $21
$2.9 to $21
$2.9 to $21
$2.9 to $21
$2.9 to $21
Net Benefits
-$19 to $2.3
-$17 to $4.3
-$17 to $4.5
-$17 to $4.5
-$20 to $1.7
* Tables present the total of CA glidepath in 2020, plus the additional increment needed to reach
full attainment in a year beyond 2020
** Includes ozone benefits and PM 2.5 co-benefits. Range was developed by adding the estimate
from the ozone premature mortality function to both the lower and upper ends of the range of the
PM2.5 premature mortality functions characterized in the expert elicitation
***Range reflects lower and upper bound cost estimates
****Total includes ozone morbidity benefits only
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Table 8.4: Summary of Total Number of Annual Ozone and PM2.5-Related Premature
Mortalities and Premature Morbidity Avoided: California Post 2020 Attainment
Combined Estimate of Mortality
Standard Alternative and Combined Range of Ozone Benefits and
Model or Assumption PM2.s Co-Benefits
0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm
NMMAPS
Meta-Analysis
No Causality
Bell (2004)
Bell (2005)
Ito (2005)
Levy (2005)
17 to 93
42 to 120
45 to 120
46 to 120
8.2 to 84
61 to 310
170 to 410
180 to 430
180 to 430
26 to 270
110 to 570
300 to 760
320 to 780
320 to 780
49 to 500
180 to 840
490 to 1,200
530 to 1,200
520 to 1,200
72 to 740
Combined Estimate of Morbidity
Acute Myocardial Infarction
Hospital and ER Visits
Chronic Bronchitis
Acute Bronchitis
Asthma Exacerbation
Lower Respiratory Symptoms
Upper Respiratory Symptoms
School Loss Days
Work Loss Days
Minor Restricted Activity Days
49
200
17
43
330
360
270
30,000
2,300
87,000
160
790
53
140
1,100
1,200
850
120,000
7,400
340,000
290
1,400
99
260
2,000
2,200
1,600
210,000
14,000
600,000
430
2,200
ISO
380
2,900
3,200
2,300
340,000
20,000
960,000
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8.2 Discussion of Results
Relative Contribution of PM benefits to total benefits
Because of the relatively strong relationship between PM2.5 concentrations and premature
mortality, PM co-benefits resulting from reductions in NOx emissions can make up a large
fraction of total montetized benefits, depending on the specific PM mortality impact function
used, and on the relative magnitude of ozone benefits, which is dependent on the specific ozone
mortality function assumed. PM co-benefits based on daily average concentrations are
calculated over the entire year, while ozone related benefits are calculated only during the
summer ozone season. Because the control strategies evaluated in this RIA are assumed to
operate year round rather than only during the ozone season, this means that PM benefits will
accumulate during both the ozone season and the rest of the year.
PM co-benefits account for between 13 and 99 percent of co-benefits, depending on the standard
analyzed and on the choice of ozone and PM mortality functions used. The estimate with the
lowest fraction from PM co-benefits occurs when ozone mortality is based on the Levy et al
(2005) study and when PM2.5 mortality is based on the function provided by "Expert K"3 from
the expert elicitation. The estimate with the highest fraction from PM co-benefits occurs when
no ozone mortality reductions are included (following the assumption of no causal relationship
between ozone and mortality) and when PM2.5 mortality is based on the function provided by
"Expert E"4 from the expert elicitation.
Impact of Uncertainty in the Magnitude of ozone benefits.
The degree to which net benefits are positive depends largely on the size of the effect estimate
used for the relationship between premature mortality and ozone and to a lesser extent on the
cost extrapolation methodology. In the cases where net benefits are negative, the magnitude of
the economic loss depends largely on the extrapolation method used to calculate the costs of full
attainment. Because of the high degree of uncertainty in these calculations, overall conclusions
about the magnitude of net benefits and the likelihood they will be positive or negative for any of
our evaluated scenarios cannot be drawn with any degree of confidence. As such, we cannot
conclude that strategies for attainment of a tighter ozone NAAQS would either pass or fail a
cost-benefit test. In other words, we cannot make an estimate of whether costs will outweigh
benefits (or vice versa). As we improve our databases of control technologies and refine our
understanding of the magnitude of the relationships between air pollution and premature
mortality, our confidence in estimates of costs and benefits will likely improve.
3 As discussed in Chapter 6, one way in which we characterize the model uncertainty associated
with the relationship between particulate matter and premature mortality was to conduct an
expert elicitation. The elicitation yielded twelve different functions, generated by asking
12 experts a structured set of questions, leading each to articulate a functional form for the
relationship, in a probabilistic estimate of uncertainty. Among the twelve experts, Expert K's
function characterizes the weakest relationship between PM and premature mortality, whereas
Expert E's function characterizes the strongest relationship.
4 See above.
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Challenges to Modeling Full Attainment in All Areas
Because of relatively higher ozone levels in several large urban areas (Southern California,
Chicago, Houston, and the Northeastern urban corridor, including New York and Philadelphia)
and because of limitations on the available database of currently known emissions control
technologies, EPA recognized from the outset that known and reasonably anticipated emissions
controls would likely be insufficient to bring many areas into attainment with either the current
or alternative, more stringent ozone standards. Therefore, we designed this analysis in two
stages: the first stage focused on analyzing the air quality improvements that could be achieved
through application of documented, well-characterized emissions controls, and the costs and
benefits associated with those controls. The second stage utilized extrapolation methods to
estimate the costs and benefits of additional emissions reductions needed to bring all areas into
full attainment with the standards. Clearly, the second stage analysis is a highly speculative
exercise, as it is based on estimating emission reductions and air quality improvements without
any information about the specific controls that would be available to do so.
The structure of the RIA reflects this 2-stage analytical approach. Separate chapters are provided
for the cost, emissions and air quality impacts of modeled controls and for extrapolated costs and
air quality impacts. We have used the information currently available to develop reasonable
approximations of the costs and benefits of the extrapolated portion of the emissions reductions
necessary to reach attainment. However, due to the high level of uncertainty in all aspects of the
extrapolation, we judged it appropriate to provide separate estimates of the costs and benefits for
the modeled stage and the extrapolated stage, as well as an overall estimate for reaching full
attainment. There is a single chapter on benefits, because the methodology for estimating
benefits does not change between stages. However, in that chapter, we again provide separate
estimates of the benefits associated with the modeled and extrapolated portions of the analysis.
In both stages of the analysis, it should be recognized that all estimates of future costs and
benefits are not intended to be forecasts of the actual costs and benefits of implementing revised
standards. Ultimately, states and urban areas will be responsible for developing and
implementing emissions control programs to reach attainment with the ozone NAAQS, with the
timing of attainment being determined by future decisions by states and EPA. Our estimates are
intended to provide information on the general magnitude of the costs and benefits of alternative
standards, rather than precise predictions of control measures, costs, or benefits. With these
caveats, we expect that this analysis can provide a reasonable picture of the types of emissions
controls that are currently available, the direct costs of those controls, the levels of emissions
reductions that may be achieved with these controls, the air quality impact that can be expected
to result from reducing emissions, and the public health benefits of reductions in ambinent ozone
levels. This analysis identifies those areas of the U.S. where our existing knowledge of control
strategies is not sufficient to allow us to model attainment, and where additional data or research
may be needed to develop strategies for attainment. EPA plans to address some of these areas in
the RIA analysis for the final rule through additional research on control technologies, sensitivity
analyses using air quality models, and refinement of methods for extrapolating the costs and
benefits of reaching full attainment.
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In many ways, regulatory impact analyses for proposed actions are a learning process that can
yield valuable information about the technical and policy issues that are associated with a
particular regulatory action. This is especially true for RIAs for proposed NAAQS, where we
are required to stretch our understanding of both science and technology to develop scenarios
that illustrate how certain we are about how economically feasible the attainment of these
standards might be regionally. The proposed ozone NAAQS RIA provided great challenges
when compared to previous RIAs. Why was this so? Primarily because as we tighten standards
across multiple pollutants with overlapping precursors (e.g. the recent tightening of the PIVb.s
standards), we move further down the list of cost-effective known and available controls. As we
deplete our database of available choices of known controls, we are left with background
emissions and remaining anthropogenic emissions for which we do not have enough knowledge
to determine how and at what cost reductions can be achieved in the future when attainment
would be required. With the more stringent NAAQS, more areas will need to find ways of
reducing emissions, and as existing technologies are either inadequate to achieve desired
reductions, or as the stock of low-cost existing technologies is depleted (causing the cost per ton
of pollution reduced to increase), there will be pressure to develop new technologies to fill these
needs. While we can speculate on what some of these technologies might look like based on
current research and development and model programs being evaluated by states and localities,
the actual technological path is highly uncertain.
Because of the lack of knowledge regarding the development of future emissions control
technologies, a significant portion of our analysis is based on extrapolating from available data to
generate the emissions reductions necessary to reach full attainment of an alternative ozone
NAAQS and the resulting costs and benefits. Studies indicate that it is not uncommon for pre-
regulatory cost estimates to be higher than later estimates, in part because of inability to predict
technological advances. Over longer time horizons, such as the time allowed for areas with high
levels of ozone pollution to meet the ozone NAAQS, the opportunity for technical advances is
greater (See Chapter 5 for detail). Also, due to the nature of the extrapolation method for
benefits (which focuses on reductions in ozone only at monitors that exceed the NAAQS), we
generally understate the total benefits that would result from implementing additional emissions
controls to fully attain the ozone NAAQS (i.e., assuming that the application of control strategies
would result in ozone reductions both at nonattaining and attaining monitors). On the other
hand, the possibility also exists that benefits are overestimated, both because it is possible that
new technologies might not meet the specifications, development time lines, or cost estimates
provided in this analysis and because the analysis assumes there are quantifiable benefits to
reducing ambient ozone below each of the alternative standards.
Estimated benefits and costs may reflect both bias and uncertainty. While we strive to avoid bias
and characterize uncertainty to the extent possible, we note that in some cases, biased estimates
were used due to data and/or methodological limitations. In these cases we have tried to identify
the direction and potential magnitude of the bias." These extrapolated benefits are uncertain, but
the relative uncertainty compared to the modeled benefits is similar, once the underestimation
bias has been taken into account. The emissions and cost extrapolations do not have a clear
directional bias, however, they are much more uncertain relative to the modeled emissions and
cost estimates, because of the lack of refined information about the relationship between
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emissions reductions and ozone changes in specific locations, and because of the difficulties in
extrapolating costs along a marginal cost curve well beyond the observed data without
accounting for shifts in the cost-curve due to improvements in technology or use of technologies
over time. Of course, these benefits and costs will only be realized if the emission reductions
projected in this extrapolated approach actually occur in the future.
8.3 What did we learn through this analysis?
1) As in our analysis for the PMNAAQS RIA, in selecting controls, we focused more
on the ozone cost-effectiveness (measured as $/ppb) than on the NOx or VOC
cost-effectiveness (measured as $/ton). When compared on a $/ton basis, many
VOC controls (average $/ton of $4,100) appear cost-effective relative to NOx
reductions (average $/ton of $3,600). However, when compared on a $/ppb basis,
NOx reductions (average $/ppb of $36 million) are almost always more cost-
effective than VOC controls (average $/ppb of $164 million) because of the much
lower conversion of VOC to ozone. The one exception to this is in urban areas
which are VOC limited. In those locations, NOx reductions can actually result in
increases in ozone, and as such, VOC reductions can be cost-effective relative to
NOx on a $/ppb basis.
2) Our knowledge of technologies that might achieve NOx and VOC reductions to attain
alternative ozone NAAQS is insufficient. In some areas of the U.S., our existing
controls database was insufficient to meet even the current ozone standard. After
applying existing rules and the hypothetical controls applied in the PM NAAQS
RIA across the nation (excluding California), we were able to identify controls for
35 states and DC that reduced overall NOx emissions by 17 percent and VOC by
4 percent. For California, the percentages were 8 percent for NOx and 10 percent
for VOC. After these reductions, remaining emissions were still substantial, with
over 7 million tons of NOx and 9 million tons of VOCs remaining. The large
remaining inventories of NOx and VOC emissions suggests that additional control
measures need to be developed, with appropriate consideration of the relative
effectiveness of NOx and VOC in achieving ozone reductions.
3) Most of the overall reductions in NOx achieved in our illustrative control strategy
were from non-EGU point sources. This was due to the fact that: 1) EGUs have
been heavily controlled under the recent NOx SIP call and Clean Air Interstate
Rules. The EGU program we included in our strategy for meeting the alternative
ozone standards was not intended to achieve overall reductions in NOx beyond
the CAIR caps, but instead to obtain NOx emission reductions in areas where they
would more effectively reduce ozone concentrations in downwind nonattainment
areas; and 2) mobile sources are already subject to ongoing emission reduction
programs through the Tier 2 highway, onroad diesel and nonroad diesel rules.
Thus, the opportunities for controlling NOx emissions were much greater in the
non-EGU sector than in the mobile or EGU sectors. However, the remaining
NOx emissions from EGU and mobile sectors are still greater than non-EGU
sources, and additional reductions from these sectors may need to be considered
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in developing strategies to achieve full attainment. We are evaluating
technologies and programs that might be applied in these sectors in the future.
Exploratory analyses indicate that there are opportunities to achieve emission
reductions from EGU peaking units on High Energy Demand Days (HEDD) with
targeted strategies. Another area under analysis is the energy efficiency/clean
distributed generation based emission reductions. Potential changes in the
generation mix as a result of increase in the use of renewables and Renewable
Portfolio Standards (RPS) are also likely to create changes in emission behavior.
However, overall regional or national emissions levels stay constant under a given
cap.
4) Some EPA existing mobile source programs will help areas reach attainment.
These programs promise to continue to help areas reduce ozone concentrations
between 2020 and 2030. In California, continued implementation of mobile
source rules including the onroad and nonroad diesel rules and the locomotive and
marine engines rule are projected to reduce NOx emissions by an additional 25
percent and VOC emissions by an additional 11 percent during this time period.
These additional reductions will significantly reduce the overall cost of attainment
relative to what California might have needed to reduce from other sectors if
attainment were to be required in 2020. However, delaying attainment by 10
years will result in delayed health benefits as well. Based on a simple scaling
exercise, we estimate that between $0.3 and $1 billion in benefits could have been
realized from full attainment with the 0.070 ppm alternative each year between
2020 and 2030. However, the potential for extra costs of up to between $0.3 and
$4 billion per year suggests that allowing for delayed attainment until 2030 for
these severe nonattainment areas may make economic sense. We are unable at
this time to identify controls that would achieve the full attainment in California
by 2020.
5) Tightening the ozone standards can provide significant, but not uniform, health
benefits. The magnitude of the benefits is highly uncertain, and is not expected to
be uniform throughout the nation. While our illustrative analyses showed that the
benefits of implementing a tighter standard will likely result in reduced health
impacts for the nation as a whole, the particular scenarios that we modeled show
that some areas of the U.S. will see ozone (and PIVb.s) levels increase. This is due
to two reasons. The first reason is that the complexities involved in the
atmospheric processes which govern the transformation of emissions into ozone
result in some locations and times when reducing NOx emissions can actually
increase ozone levels on some days (see Chapter 2 for more discussion). For
most locations, these days are few relative to the days when ozone levels are
decreased. However, in some urban areas the net effect of implementing NOx
controls is to increase overall ozone levels and increase the health effects
associated with ozone. This same phenomenon results in some areas also seeing
increases in PIVb.s formation. The second reason is that the particular control
strategy that we modeled for EGU sources is a modification to controls on sources
within the overall cap and trade program in the Eastern U.S, established under the
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CAIR. As with any cap and trade program, changes in requirements at particular
sources will result in shifts in power generation and emissions at other sources.
Because under our chosen EGU control scenario the overall emissions cap for the
CAIR region remains the same, some areas of the country will see a decrease in
emissions, while others will see an increase. This is not unexpected, and is an
essential element of the cap and trade program. Our goal in selecting the EGU
control strategy was to focus the emissions reductions in areas likely to benefit the
most from EGU NOx emissions reductions, with emissions increases largely
occurring in areas in attainment with the ozone NAAQS. However, this
necessarily means that in those areas where emissions increases occurred, ozone
levels would also be expected to increase, with commensurate increases in health
impacts. On a national level, however, we expected overall health benefits of the
modeled EGU strategy to be positive. In addition, our air quality modeling
analysis showed that while ozone levels did increase in some areas, none of these
increases resulted in an attaining area moving into nonattainment. Adjustments to
our control scenario might achieve a pattern of reductions that achieves further air
quality improvement.
6) There is uncertainty in Estimating the Benefits of 0.079ppm and 0.075ppm
EPA employed a monitor rollback approach to estimate the benefits of attaining
an alternative standard of 0.079 ppm nationwide. This approach likely understates
the benefits that would occur due to implementation of actual controls because
controls implemented to reduce ozone concentrations at the highest monitor
would likely result in some reductions in ozone concentrations at attaining
monitors down-wind (i.e. the controls would lead to concentrations below the
standard in down-wind locations). Therefore, air quality improvements and
resulting health benefits from full attainment would be more widespread than we
have estimated in our rollback analysis.
EPA calculated 0.075 ppm benefits by interpolating the 0.070 ppm benefits
estimates.5 This interpolation approach may overestimate benefits relative to a
modeled control scenario developed specifically to attain the 0.075 ppm
alternative. The interpolation method scales down benefits only at the monitors
we project to exceed 0.075 ppm—but it still captures the benefits achieved by the
0.070 ppm regional control strategy that occur outside of these projected non-
attainment areas. To the extent that a modeled emission control strategy to attain
0.075 ppm does not include these broader regional emission reductions, total
benefits would be lower than those we have estimated in this RIA.
Interpolation and monitor rollback methods of benefits estimation are inherently
different. As described above, for the purposes of reviewing this analysis, the
reader should understand that the benefits described for attaining a standard of
0.079 ppm are likely understated, whereas the estimated benefits of attaining a
standard of 0.075 ppm are likely overstated. EPA will develop and present
consistent approaches for the alternative standards for the final RIA.
1 This procedure is detailed in Appendix 6A.
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7) Tightening the ozone standards can incur significant, but uncertain, costs
An engineering cost comparison demonstrates that the cost of the 0.070 ppm
Ozone NAAQS control strategy ($3.9 billion per year) is only slightly higher than
the Clean Air Interstate Rule ($3.6 billion per year) and roughly one and half to
just over four times higher than the PM NAAQS 15/35 control strategy with
annual engineering costs of $850 million. It should be noted that for the Ozone
NAAQS $3.9 billion represent the cost of partial attainment. Full attainment
using extrapolation methods are expected to increase total costs significantly. For
example, total costs for the 0.070 ppm standard are significant at $13 to $26
billion. Yet, the magnitude and distribution of costs across sectors and areas is
highly uncertain. Our estimates of costs for a set of modeled NOx and VOC
controls comprise only a small part of the estimated costs of full attainment.
These estimated costs for the modeled set of controls are still uncertain, but they
are based on the best available information on control technologies, and have their
basis in real, tested technologies. Estimating costs of full attainment required
significant extrapolation of the cost curve for known technologies, and was based
on generalized relationships between emissions and ozone levels. Based on air
quality modeling sensitivity analyses, there is clearly significant spatial variability
in the relationship between local and regional NOx emission reductions and ozone
levels across urban areas. However, because we were unable to analyze all of the
urban areas that are expected to need reductions, we used the same ratio of ozone
to emissions throughout the U.S. This introduces significant uncertainty into the
calculation of the emissions reductions that might be needed to reach full
attainment. In addition, because VOCs are generally much less effective than
NOx in achieving ozone reductions at key monitors (with the exception of
California), we did not use any VOC control data in the extrapolation to full
attainment. This meant that in some areas, we assumed the need for more
expensive NOx controls than might be required if a specific area chose to use a
combination of NOx and VOC controls. However, VOC controls would have to
be very inexpensive relative to NOx controls on a per ton basis in order for VOC
controls to be a cost-effective substitute for NOx reductions. Extrapolating costs
by applying a cost-curve based on known technologies also introduces
uncertainties. For some locations, the extrapolation requires only a modest
reduction beyond known controls. In these cases, the extrapolation is likely
reasonable and not as prone to uncertainties. However, for areas where the bulk
of air quality improvements were derived from extrapolated emissions reductions
that go well beyond the area of the known controls, the increasing marginal costs
can suggest a cost per ton which stretches credibility. For example, in California,
extrapolation to full attainment results in a marginal cost for the last ton of NOx
of $89,645 in Los Angeles and $74,495 in Kern County, which are five to six
times larger than the marginal cost at the last known cost effective control.
Economic theory would suggest that as marginal costs rise, research and
development to produce new, more cost effective technologies will also increase,
leading to a downward shift in the overall cost curve. We did not assume any
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shift in the cost curve to reflect technological innovation, instead we provide a
sensitivity analysis by showing estimates assuming a high and low fixed cost per
ton. We are likely overstating costs in the future when using the marginal cost
and high fixed estimates.
8) Non-EGUpoint source controls dominate the estimated costs. These costs
account for about 70 percent of modeled costs. The average cost per ton for these
reductions is approximately $3,400, and the highest marginal cost for the last cost
effective control applied is $15,267. Mobile source controls were also significant
contributors to overall costs, accounting for over 25 percent of total modeled
costs.
9) The economic impacts (i.e. social costs) of the cost of these modeled controls
were not included in this analysis. Incorporating the economic impact of the
extrapolated portion of the costs was too uncertain to be included as part of these
estimates, and it was determined best to keep the modeled and extrapolated costs
on the same basis. However, incorporating any economic impacts would increase
the total cost of attainment in 2020 for a revised ozone standard.
10) California costs and benefits are highly uncertain.. California faces large
challenges in meeting any alternative standard, but their largest challenges may be
in attaining the existing standard. Because our analysis suggested that all
available controls would be exhausted in attempting (unsuccessfully) to meet the
current 0.08 ppm standard (effectively 0.084 ppm) all of the benefits and costs in
California are based on extrapolation. Both the benefits and the costs associated
with the assumed NOx and VOC reductions in California are particularly
uncertain. The costs are uncertain to the point where we have little confidence
that they represent a meaningful characterization of possible future costs of
implementation in California. As such, we recommend comparison of costs and
benefits for the rest of the U.S. as a basis for judging the relative merits of
implementation. Costs for full attainment in California will clearly be substantial,
but the level of uncertainty about those costs is simply too great to provide any
useful conclusions. This is also true for many other areas of the U.S., but the
uncertainties are magnified in the case of California.
11) Costs and benefits will depend on implementation timeframes. States will
ultimately select the specific timelines for implementation as part of their State
Implementation Plans. To the extent that states seek classification as extreme
nonattainment areas, the timeline for implementation may be extended beyond
2020, meaning that the amount of emissions reductions that will be required in
2020 will be less, and costs and benefits in 2020 will also be lowered.
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Chapter 9: Statutory and Executive Order Impact Analyses
Synopsis
This chapter summarizes the Statutory and Executive Order (EO) impact analyses relevant for
the ozone NAAQS RIA. In general, because this RIA analyzes an illustrative attainment strategy
to meet the revised NAAQS, and because States will ultimately implement the new NAAQS, the
Statutory and Executive Orders below did not require additional analysis. For each EO and
Statutory requirement we describe both the requirements and the way in which the RIA
addresses these requirements. Further analyses of the NAAQS proposal and its impact on these
statutory and executive orders are found in section VII of the NAAQS preamble.
9.1 Executive Order 12866: Regulatory Planning and Review
Under section 3(f)(l) of Executive Order (EO) 12866 (58 FR 51735, October 4, 1993), the ozone
NAAQS action is an "economically significant regulatory action" because it is likely to have an
annual effect on the economy of $100 million or more. Accordingly, EPA prepared this
regulatory impact analysis (RIA) of the potential costs and benefits associated with this action.
The RIA estimates the costs and monetized human health benefits of attaining three alternative
ozone NAAQS nationwide. Specifically, the RIA examines the alternatives of 0.075 ppm, 0.070
ppm, and 0.065 ppm. The RIA contains illustrative analyses that consider a limited number of
emissions control scenarios that States and Regional Planning Organizations might implement to
achieve these alternative ozone NAAQS. However, the Clean Air Act (CAA) and judicial
decisions make clear that the economic and technical feasibility of attaining ambient standards
are not to be considered in setting or revising NAAQS, although such factors may be considered
in the development of State plans to implement the standards. Accordingly, although an RIA has
been prepared, the results of the RIA have not been considered in issuing this rule.
9.2 Paperwork Reduction Act
This RIA does not impose an information collection burden under the provisions of the
Paperwork Reduction Act, 44 U.S.C. 3501 et seq. There are no information collection
requirements directly associated with revisions to a NAAQS under section 109 of the CAA.
Burden is defined as the total time, effort, or financial resources expended by persons to
generate, maintain, retain, or disclose or provide information to or for a Federal agency. This
includes the time needed to review instructions; develop, acquire, install, and utilize technology
and systems for the purposes of collecting, validating, and verifying information, processing and
maintaining information, and disclosing and providing information; adjust the existing ways to
comply with any previously applicable instructions and requirements; train personnel to be able
to respond to a collection of information; search data sources; complete and review the collection
of information; and transmit or otherwise disclose the information.
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An agency may not conduct or sponsor information collection, and a person is not required to
respond to a collection of information unless it displays a currently valid OMB control number.
The OMB control numbers for EPA's regulations in 40 CFR are listed in 40 CFR part 9.
9.3 Regulatory Flexibility Act
The EPA has determined that it is not necessary to prepare a regulatory flexibility analysis in
connection with this RIA. For purposes of assessing the impacts of today's rule on small
entities, small entity is defined as: (1) a small business that is a small industrial entity as defined
by the Small Business Administration's (SBA) regulations at 13 CFR 121.201; (2) a small
governmental jurisdiction that is a government of a city, county, town, school district or special
district with a population of less than 50,000; and (3) a small organization that is any not-for-
profit enterprise which is independently owned and operated and is not dominant in its field.
After considering the economic impacts of today's rule on small entities, EPA has concluded that
this action will not have a significant economic impact on a substantial number of small entities.
This rule will not impose any requirements on small entities. This rule establishes national
standards for allowable concentrations of ozone in ambient air, as required by section 109 of the
CAA. See also ATA I at 1044-45 (NAAQS do not have significant impacts upon small entities
because NAAQS themselves impose no regulations upon small entities).
9.4 Unfunded Mandates Reform Act
Title II of the Unfunded Mandates Reform Act of 1995 (UMRA), Public Law 104-4, establishes
requirements for Federal agencies to assess the effects of their regulatory actions on State, local,
and Tribal governments and the private sector. Under section 202 of the UMRA, EPA generally
must prepare a written statement, including a cost-benefit analysis, for proposed and final rules
with "Federal mandates" that may result in expenditures to State, local, and Tribal governments,
in the aggregate, or to the private sector, of $100 million or more in any 1 year. Before
promulgating an EPA rule for which a written statement is needed, section 205 of the UMRA
generally requires EPA to identify and consider a reasonable number of regulatory alternatives
and adopt the least costly, most cost-effective or least burdensome alternative that achieves the
objectives of the rule. The provisions of section 205 do not apply when they are inconsistent
with applicable law. Moreover, section 205 allows EPA to adopt an alternative other than the
least costly, most cost-effective or least burdensome alternative if the Administrator publishes
with the final rule an explanation why that alternative was not adopted. Before EPA establishes
any regulatory requirements that may significantly or uniquely affect small governments,
including Tribal governments, it must have developed under section 203 of the UMRA a small
government agency plan. The plan must provide for notifying potentially affected small
governments, enabling officials of affected small governments to have meaningful and timely
input in the development of EPA regulatory proposals with significant Federal intergovernmental
mandates, and informing, educating, and advising small governments on compliance with the
regulatory requirements.
This proposal contains no Federal mandates (under the regulatory provisions of Title II of the
UMRA) for State, local, or Tribal governments or the private sector. The rule imposes no new
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expenditure or enforceable duty on any State, local or Tribal governments or the private sector,
and EPA has determined that this rule contains no regulatory requirements that might
significantly or uniquely affect small governments. Furthermore, as indicated previously, in
setting a NAAQS, EPA cannot consider the economic or technological feasibility of attaining
ambient air quality standards, although such factors may be considered to a degree in the
development of State plans to implement the standards. See also ATA I at 1043 (noting that
because EPA is precluded from considering costs of implementation in establishing NAAQS,
preparation of a Regulatory Impact Analysis pursuant to the Unfunded Mandates Reform Act
would not furnish any information which the court could consider in reviewing the NAAQS).
Accordingly, EPA has determined that the provisions of sections 202, 203, and 205 of the
UMRA do not apply to this final decision. The EPA acknowledges, however, that any
corresponding revisions to associated SIP requirements and air quality surveillance requirements,
40 CFR part 51 and 40 CFR part 58, respectively, might result in such effects. Accordingly,
EPA has addressed unfunded mandates in the notice that announces the revisions to 40 CFR part
58, and will, as appropriate, address unfunded mandates when it proposes any revisions to 40
CFR part 51.
9.5 Executive Order 13132: Federalism
Executive Order 13132, entitled "Federalism" (64 FR 43255, August 10, 1999), requires EPA to
develop an accountable process to ensure "meaningful and timely input by State and local
officials in the development of regulatory policies that have federalism implications." "Policies
that have federalism implications" is defined in the Executive Order to include regulations that
have "substantial direct effects on the States, on the relationship between the national
government and the States, or on the distribution of power and responsibilities among the various
levels of government."
At the time of this proposal, EPA concludes that the proposed rule would not have substantial
direct effects on the States, on the relationship between the national government and the States,
or on the distribution of power and responsibilities among the various levels of government, as
specified in Executive Order 13132. However, EPA recognized that States would have a
substantial interest in this rule and any corresponding revisions to associated SIP requirements
and air quality surveillance requirements, 40 CFR part 51 and 40 CFR part 58, respectively.
Therefore, in the spirit of Executive Order 13132, and consistent with EPA policy to promote
communications between EPA and State and local governments, EPA specifically solicits
comment on the rule from State and local officials at this time.
9.6 Executive Order 13175: Consultation and Coordination with Indian Tribal
Governments
Executive Order 13175, entitled "Consultation and Coordination with Indian Tribal
Governments" (65 FR 67249, November 9, 2000), requires EPA to develop an accountable
process to ensure "meaningful and timely input by tribal officials in the development of
regulatory policies that have tribal implications." This rule concerns the establishment of ozone
NAAQS. The Tribal Authority Rule gives Tribes the opportunity to develop and implement
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CAA programs such as the ozone NAAQS, but it leaves to the discretion of the Tribe whether to
develop these programs and which programs, or appropriate elements of a program, they will
adopt.
This proposed rule does not have Tribal implications, as specified in Executive Order 13175. It
does not have a substantial direct effect on one or more Indian Tribes, since Tribes are not
obligated to adopt or implement any NAAQS. Thus, Executive Order 13175 does not apply to
this rule. However, in the spirit of efficaciousness, EPA staff participated in the regularly
scheduled Tribal Air call sponsored by the National Tribal Air Association during the spring of
2007 as this proposal was under development. EPA specifically solicits additional comment on
the proposed NAAQS rule from Tribal officials.
9.7 Executive Order 13045: Protection of Children from Environmental Health &
Safety Risks
Executive Order 13045, "Protection of Children from Environmental Health Risks and Safety
Risks" (62 FR 19885, April 23, 1997) applies to any rule that: (1) is determined to be
"economically significant" as defined under Executive Order 12866, and (2) concerns an
environmental health or safety risk that EPA has reason to believe may have a disproportionate
effect on children. If the regulatory action meets both criteria, the Agency must evaluate the
environmental health or safety effects of the rule on children, and explain why the regulation is
preferable to other potentially effective and reasonably feasible alternatives considered by the
Agency. This rule is subject to Executive Order 13045 because it is an economically significant
regulatory action as defined by Executive Order 12866, and we believe that the environmental
health risk addressed by this action may have a disproportionate effect on children.
The NAAQS constitute uniform, national standards for ozone pollution; these standards are
designed to protect public health with an adequate margin of safety, as required by CAA section
109. However, the protection offered by these standards may be especially important for
children because children, along with other sensitive population subgroups such as the elderly
and people with existing heart or lung disease, are potentially susceptible to health effects
resulting from ozone exposure. Because children are considered a potentially susceptible
population, we have carefully evaluated the environmental health effects of exposure to ozone
pollution to this sub-population. These effects and the size of the population affected are
summarized in section 8.7 of the Criteria Document and section 3.6 of the Staff Paper, and the
results of our evaluation of the effects of ozone pollution on children are discussed in sections
II.A-C of the NAAQS proposal preamble.
9.8 Executive Order 13211: Actions that Significantly Affect Energy Supply,
Distribution or Use
This proposed rule is not a "significant energy action" as defined in Executive Order 13211,
"Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or Use"
(66 FR 28355 (May 22, 2001)) because in the Agency's judgment it is not likely to have a
significant adverse effect on the supply, distribution, or use of energy. The purpose of this rule is
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to establish revised NAAQS for ozone. The rule does not prescribe specific pollution control
strategies by which these ambient standards will be met. Such strategies will be developed by
States on a case-by-case basis, and EPA cannot predict whether the control options selected by
States will include regulations on energy suppliers, distributors, or users. Thus, EPA concludes
that this rule is not likely to have any adverse energy effects and does not constitute a significant
energy action as defined in Executive Order 13211.
9.9 National Technology Transfer Advancement Act
Section 12(d) of the National Technology Transfer Advancement Act of 1995 (NTTAA), Public
Law No. 104-113, § 12(d) (15 U.S.C. 272 note), directs EPA to use voluntary consensus
standards in its regulatory activities unless to do so would be inconsistent with applicable law or
otherwise impractical. Voluntary consensus standards are technical standards (e.g., materials
specifications, test methods, sampling procedures, and business practices) that are developed or
adopted by voluntary consensus standards bodies. The NTTAA directs EPA to provide
Congress, through OMB, explanations when the Agency decides not to use available and
applicable voluntary consensus standards. Since EPA is not changing any of the monitoring
requirements as part of this proposal, there are no impacts associated with the NTTAA.
9.10 Executive Order 12898: Federal Actions to Address Environmental Justice in
Minority Populations and Low-Income Populations
Executive Order 12898, "Federal Actions to Address Environmental Justice in Minority
Populations and Low-Income Populations," requires Federal agencies to consider the impact of
programs, policies, and activities on minority populations and low-income populations.
According to EPA guidance, agencies are to assess whether minority or low-income populations
face a risk or a rate of exposure to hazards that are significant and that "appreciably exceeds or is
likely to appreciably exceed the risk or rate to the general population or to the appropriate
comparison group" (EPA, 1998).
In accordance with Executive Order 12898, the Agency has considered whether these decisions
may have disproportionate negative impacts on minority or low-income populations. This rule
establishes uniform, national ambient air quality standards for ozone, and is not expected to have
disproportionate negative impacts on minority or low income populations. In this NAAQS
proposal, the Administrator considered the available information regarding health effects among
vulnerable and susceptible populations, such as those with preexisting conditions. Thus it
remains EPA's conclusion that this rule is not expected to have disproportionate negative
impacts on minority or low income populations.
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United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-07-008
Environmental Protection Air Quality Strategies and Standards Division July 2007
Agency Research Triangle Park, NC
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