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Final Ozone NAAQS Regulatory Impact
Analysis
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EPA-452/R-08-003
March 2008
Final Ozone NAAQS Regulatory Impact Analysis
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Air Benefit and Cost Group (C439-02)
Research Triangle Park, North Carolina
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Table of Contents
Number
Executive Summary ES-1
Chapter 1: Introduction and Background 1-1
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 1-2
1.2.4 Illustrative Nature of the Analysis 1-4
1.3 Overview and Design of the RIA 1-4
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 1-7
Chapter 2: Characterizing Ozone and Modeling Tools Used in This Analysis 2-1
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
2.2 Sources of Ozone 2-3
2.3 Modeling Ozone Levels in the Future 2-3
2.3.1 CMAQ Model and Inputs 2-4
2.3.2 Emissions Inventory 2-6
n
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2.4 References 2-10
Appendix: Chapter 2 Characterizing Ozone and Modeling Tools Used in This
Analysis 2a-l
Chapter 3: Modeled Control Strategy: Design and Analytical Results 3-1
Synopsis 3-1
3.1 Establishing the Baseline 3-2
3.1.1 Control Measures Applied in the Baseline for Ozone Precursors 3-4
3.1.2 Ozone Levels for Baseline 3-7
3.1.3 National Baseline Sensitivity Analysis 3-9
3.2 Developing the Modeled Control Strategy Analysis 3-10
3.2.1 Controls Applied for the Modeled Control Strategy: NonEGU
Point and Area Sectors 3-12
3.2.2 Controls Applied for the Modeled Control Strategy: EGU Sector 3-13
3.2.3 Controls Applied for the Modeled Control Strategy: Onroad and
Nonroad Mobile Sectors 3-16
3.2.4 Data Quality for this Analysis 3-17
3.3 Geographic Distribution of Emissions Reductions 3-18
3.4 Ozone Design Values for Partial Attainment 3-22
3.5 References 3-25
Chapter 4: Approach for Estimating Reductions for Full Attainment Scenario 4-1
Synopsis 4-1
4.1 Development of Full Attainment Targets for Estimate of Extrapolated
Costs 4-1
4.1.1 Design of Supplemental Modeling Scenarios 4-1
4.1.2 Results of Supplemental Modeling for Phase 1 Areas 4-3
4.1.3 Estimating Attainment of the 0.070 and 0.065 ppm Standards in
Phase 2 Areas 4-7
4.1.4 Estimating Attainment of the 0.065 ppm Standard outside of Phase
1 and 2 Areas 4-9
4.1.5 Aggregate Results / Verification Modeling of Extrapolated Targets .... 4-11
in
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4.2 Conversion of Full Attainment Percentage Targets into Extrapolated Tons 4-15
4.3 Methodology Used to Estimate the Amount of "Overcontrolled"
Emissions in the Modeled Control Strategy 4-17
4.4 Conversion of Estimated Percentages of Unnecessary Emission
Reductions into "Overcontrolled" Tons 4-19
Chapter 5: Engineering Cost Estimates 5-1
Synopsis 5-1
5.1 Modeled Controls 5-2
5.1.1 Sector Methodology 5-2
5.1.2 Modeled Controls—Engineering Cost by Sector 5-5
5.1.3 Limitations and Uncertainties Associated with Engineering Cost
Estimates 5-7
5.2 Extrapolated Engineering Costs 5-10
5.2.1 Methodology 5-10
5.2.2 Results 5-18
5.3 Summary of Costs 5-22
5.4 Technology Innovation and Regulatory Cost Estimates 5-25
5.4.1 Examples of Technological Advances in Pollution Control 5-26
5.4.2 Influence on Regulatory Cost Estimates 5-29
5.5 References 5-31
Appendix: Chapter 5 Additional Benefits Information 5a-l
Appendix: Chapter 5b Economic Impact of Modeled Controls 5b-l
Chapter 6: Incremental Benefits of Attaining Alternative Ozone Standards Relative to
the Current 8-hour Standard (0.08 ppm) 6-1
Synopsis 6-1
6.1 Background 6-3
6.2 Characterizing Uncertainty: Moving Toward a Probabilistic Framework
for Benefits Assessment 6-5
IV
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6.3 Health Impact Functions 6-7
6.3.1 Potentially Affected Populations 6-7
6.3.2 Effect Estimate Sources 6-7
6.3.3 Baseline Incidence Rates 6-21
6.4 Economic Values for Health Outcomes 6-21
6.4.1 Mortality Valuation 6-23
6.4.2 Hospital Admissions Valuation 6-23
6.4.3 Asthma-Related Emergency Room Visits Valuation 6-27
6.4.4 Minor Restricted Activity Days Valuation 6-27
6.4.5 School Absences 6-27
6.5 Results and Implications 6-28
6.5.1 Ozone Benefit Estimates 6-28
6.5.2 PM2.5 Co-Benefit Estimation Methodology 6-29
6.5.3 Estimate of Full Attainment Benefits 6-67
6.5.5 Estimates of Visibility Benefits 6-84
6.5.6 Discussion of Results and Uncertainties 6-85
6.5.7 Summary of Total Benefits 6-87
6.6 References: 6-95
Appendix Chapter 6a: Additional Benefits Information 6a-l
Appendix Chapter 6b: Cost Effectiveness Analysis 6b-l
Appendix Chapter 6c: Additional Sensitivity Analyses Related To the Benefits
Analysis 6c-l
Appendix Chapter 6d: Exploring the Effects of Changes in Tropospheric Ozone
onUVB 6d-l
Chapter 7: Conclusions and Implications of the Illustrative Benefit-Cost Analysis 7-1
7.1 Synopsis 7-1
7.2 Results 7-1
7.2.1 Presentation of Results 7-1
7.3 Discussion of Results 7-9
7.3.1 Sensitivity of Changes to Costs and Benefits Under an Alternate
Baseline Scenario 7-9
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7.3.2 Relative Contribution of PM Benefits to Total Benefits 7-11
7.3.3 Challenges to Modeling Full Attainment in All Areas 7-11
7.4 What Did We Learn through this Analysis? 7-13
7.5 References 7-16
Appendix Chapter 7a: National Baseline Sensitivity Analysis 7a-1
Appendix Chapter 7b Post 2020 Attainment Analysis 7b-l
Chapter 8: Statutory and Executive Order Impact Analyses 8-1
Synopsis 8-1
8.1 Executive Order 12866: Regulatory Planning and Review 8-1
8.2 Paperwork Reduction Act 8-1
8.3 Regulatory Flexibility Act 8-2
8.4 Unfunded Mandates Reform Act 8-2
8.5 Executive Order 13132: Federalism 8-3
8.6 Executive Order 13175: Consultation and Coordination with Indian
Tribal Governments 8-3
8.7 Executive Order 13045: Protection of Children from Environmental
Health & Safety Risks 8-4
8.8 Executive Order 13211: Actions that Significantly Affect Energy Supply,
Distribution or Use 8-4
8.9 National Technology Transfer Advancement Act 8-5
8.10 Executive Order 12898: Federal Actions to Address Environmental
Justice in Minority Populations and Low-Income Populations 8-6
VI
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LIST OF TABLES
Number Page
2.1 Geographic Specifications of Modeling Domains 2-6
2.2 Control Strategies and Projection Assumptions in the 2020 Emissions Inventory 2-7
3.1 Controls for Current Ozone Standard by Sector Applied in the Baseline
Determination for 2020 3-5
3.2 Controls Applied, by Sector, for the 0.070 ppm Control Strategy (Incremental to
Baseline) 3-11
3.3 Emissions and Reductions (2020) From Applying the Modeled Control Strategy
(Incremental to the Baseline) 3-24
4.1 Estimated Percentage Reductions of NOx and VOC beyond the RIA Control
Scenario Necessary to Meet Various Alternate Ozone Standards in the Phase I
Areas 4-7
4.2 Estimated Percentage Reductions of NOx beyond the RIA Control Scenario
Necessary to Meet Various Alternate Ozone Standards in the Phase I Areas 4-7
4.3 Estimated Percentage Reductions of NOx beyond the RIA Control Scenario
Necessary to Meet the 0.070 ppm Ozone Standard in Phase 2 Areas 4-9
4.4 Estimated Percentage Reductions of NOx beyond the RIA Control Case
Necessary to Meet the 0.065 ppm Ozone Standard in Phase 3 Areas 4-10
4.5a Complete Set of Estimated Percentage Reductions of NOx beyond the RIA
Control Scenario Necessary to Meet the Various Ozone Standards in 2020 4-11
4.5b Estimated Percentage Reductions of NOx + VOC beyond the RIA Control
Scenario Necessary to Meet the Various Ozone Standards in 2020 4-12
4.6 Summary of the Verification Modeling Results 4-15
4.7a Complete Set of Estimated Extrapolated Emissions Reductions of NOx Beyond
the RIA Control Scenario Necessary to Meet the Various Ozone Standards in
2020 4-16
4.7b Estimated Extrapolated Emissions Reductions of NOx + VOC Beyond the RIA
Control Scenario Necessary to Meet the Various Ozone Standards in 2020 4-17
4.8 Estimated Percentages of Modeled Control Strategy Emissions Reductions not
needed to Meet the Various Ozone Standards in 2020 4-18
4.9 Estimated 2020 Control Case Emission Reductions not needed to Meet the
Various Ozone Standards in 2020 4-20
VII
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LIST OF TABLES (CONTINUED)
Number Page
5.1 Annual Control Costs by Sector and Region, for the Modeled Control Strategy
(2006$) 5-6
5.2 Marginal Cost and Average Cost Values Used in Calculating M 5-16
5.3 Extrapolated Emission Reductions Needed (Post Application of Supplemental
Controls) to Meet Various Alternate Standards in 2020 5-18
5.4 Extrapolated Cost by Region to Meet Various Alternate Standards Using Fixed
Cost Approach ($15,000/ton) 5-20
5.5 Extrapolated Cost by Region to Meet Various Alternate Standards Using Hybrid
Approach (Mid) 5-21
5.6 Total Costs of Attainment in 2020 for Alternate Levels of the Ozone Standard 5-22
5.7 Comparison of Inflation-Adjusted Estimated Costs and Actual Price Changes
for EPA Fuel Control Rules 5-30
6.1 Human Health and Welfare Effects of Ozone and PM2.5 6-9
6.2 Ozone and PM Related Health Endpoints Basis for the Concentration-Response
Function Associated with that Endpoint, and Sub-Populations for which They
Were Computed 6-10
6.3 National Average Baseline Incidence Rates 6-22
6.4 Unit Values for Economic Valuation of Health Endpoints (2006$) 6-24
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, Arithmetic Mean, 95% Confidence
Intervals in Parentheses) 6-34
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) 6-35
6-7 Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Reductions
in the Incidence of PM Premature Mortality associated with PM co-benefit 6-36
6-8 Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Reductions
in the Incidence of Morbidity Associated with PM Co-benefit 6-37
6-9 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in the
Incidence of Premature Mortality Associated with Ozone Exposure in 2020
(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence
Intervals in Parentheses) 6-38
vin
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LIST OF TABLES (CONTINUED)
Number Page
6-10 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) 6-39
6-11 Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Reductions
in the Incidence of PM Premature Mortality associated with PM co-benefit 6-40
6-12 Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Reductions
in the Incidence of Morbidity Associated with PM Co-benefit 6-41
6-13 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in the
Incidence of Premature Mortality Associated with Ozone Exposure in 2020
(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence
Intervals in Parentheses) 6-42
6-14 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in the
Incidence of Morbidity Associated with Ozone Exposure (Incremental to
Current Ozone Standard, 95% Confidence Intervals in Parentheses) 6-43
6-15 Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Reductions
in the Incidence of PM Premature Mortality associated with PM co-benefit 6-44
6-16 Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Reductions
in the Incidence of Morbidity Associated with PM Co-benefit 6-45
6-17 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in the
Incidence of Premature Mortality Associated with Ozone Exposure in 2020
(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence
Intervals in Parentheses) 6-46
6-18 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in the
Incidence of Morbidity Associated with Ozone Exposure (Incremental to
Current Ozone Standard, 95% Confidence Intervals in Parentheses) 6-47
6-19 Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Reductions
in the Incidence of PM Premature Mortality associated with PM co-benefit 6-48
6-20 Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Reductions
in the Incidence of Morbidity Associated with PM Co-benefit 6-49
6-21 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, Arithmetic Mean, 95%
Confidence Intervals in Parentheses, Millions of 2006$) 6-50
6-22 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with Ozone Exposure
(Incremental to Current Ozone Standard, 95% Confidence Intervals in
Parentheses, Millions of 2006$) 6-51
6-23 Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Valuation of
Reductions in the Incidence of PM Premature Mortality associated with PM co-
benefit (Millions of 2006$) 6-52
IX
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LIST OF TABLES (CONTINUED)
Number Page
6-24 Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with PM Co-benefit
(Millions of 2006$) 6-53
6-25 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, Arithmetic Mean, 95%
Confidence Intervals in Parenthes, Millions of 2006$) 6-54
6-26 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with Ozone Exposure
(Incremental to Current Ozone Standard, 95% Confidence Intervals in
Parentheses, Millions of 2006$) 6-55
6-27 Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Valuation of
Reductions in the Incidence of PM Premature Mortality associated with PM co-
benefit (Millions of 2006$) 6-56
6-28 Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with PM Co-benefit
(Millions of 2006$) 6-57
6-29 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Premature Mortality Associated with Ozone
Exposure (Incremental to Current Ozone Standard, Arithmetic Mean, 95%
Confidence Intervals in Parenthes, Millions of 2006$) 6-58
6-30 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with Ozone Exposure
(Incremental to Current Ozone Standard, 95% Confidence Intervals in
Parentheses, Millions of 2006$) 6-59
6-31 Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Valuation of
Reductions in the Incidence of PM Premature Mortality associated with PM co-
benefit (Millions of 2006$) 6-60
6-32 Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with PM Co-benefit
(Millions of 2006$) 6-61
6-33 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Premature Mortality Associated with Ozone
Exposure (Incremental to Current Ozone Standard, Arithmetic Mean, 95%
Confidence Intervals in Parenthes, Millions of 2006$) 6-62
6-34 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with Ozone Exposure
(Incremental to Current Ozone Standard, 95% Confidence Intervals in
Parentheses, Millions of 2006$) 7-63
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LIST OF TABLES (CONTINUED)
Number Page
6-35 Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Valuation of
Reductions in the Incidence of PM Premature Mortality associated with PM co-
benefit (Millions of 2006$) 6-64
6-36 Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with PM Co-benefit
(Millions of 2006$) 6-65
6-38 Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality
(Millions of 2006$) for the 0.065 ppm Full Attainment 6-68
6-39 Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality
(Millions of 2006$) for the 0.070 ppm Full Attainment 6-69
6-40 Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality
(Millions of 2006$) for the 0.075 ppm Full Attainment 6-70
6-41 Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality
(Millions of 2006$) for the 0.079 ppm Full Attainment 6-71
6-42 Combined Estimate of Annual Ozone and PM2.s Benefits (Millions of $2006,
3% Discount Rate) for the 0.065 ppm Alternative Standard 6-72
6-43 Combined Estimate of Annual Ozone and PM2.s Benefits (Millions of $2006,
7% Discount Rate) for the 0.065 ppm Alternative Standard 6-73
6-44 Combined Estimate of Annual Ozone and PM2.s Benefits (Millions of $2006,
3% Discount Rate) for the 0.070 ppm Alternative Standard 6-74
6-45 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
7% Discount Rate) for the 0.070 ppm Alternative Standard 6-75
6-46 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
3% Discount Rate) for the 0.075 ppm Alternative Standard 6-76
6-47 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
7% Discount Rate) for the 0.075 ppm Alternative Standard 6-77
6-48 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
3% Discount Rate) for the 0.079 ppm Alternative Standard 6-78
6-49 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
7% Discount Rate) for the 0.079 ppm Alternative Standard 6-79
6-50 Monetary Benefits Associated with Visibility Improvements from the 0.070
Simulated Ozone Attainment Strategy in Selected Federal Class I Areas in 2020
(in millions of 2006$) 6-84
6.51 Summary of Total Number of Annual Ozone and PM2.5 -Related Premature
Mortalities and Premature Morbidity Avoided in 2020 6-88
6.52 Regional Breakdown of Annual Ozone Benefit Results by Health Endpoint in
2020 (thousands of 2006$) 6-90
6.53 Regional Breakdown of Annual PM Benefit Results by Health Endpoint in 2020
(thousands of 2006$) at 3% 6-91
XI
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LIST OF TABLES (CONTINUED)
Number Page
6.54 Regional Breakdown of Annual PM Benefit Results by Health Endpoint in 2020
(thousands of 2006$) at 7% 6-92
6.55 Regional Breakdown of Annual Ozone and PM Benefit Results by Health
Endpoint in 2020 (3% discount rate, thousands of 2006$) 6-93
6.56 Regional Breakdown of Annual Ozone and PM Benefit Results by Health
Endpoint in 2020 (7% discount rate, thousands of 2006$) 6-94
7. la Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5 Co-
Benefits: 0.075 ppm Standard in 2020 in Billions of 2006$ 7-3
7.1b Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5 Co-
Benefits: 0.079 ppm Standard in 2020 in Billions of 2006$ 7-3
7.1c Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5 Co-
Benefits: 0.070 ppm Standard in 2020 in Billions of 2006$ 7-3
7. Id Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5 Co-
Benefits: 0.065 ppm Standard in 2020 in Billions of 2006$ 7-4
7.2 Summary of Total Number of Annual Ozone and PM2.s-Related Premature
Mortalities and Premature Morbidity Avoided: 2020 National Benefits 7-9
XII
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LIST OF FIGURES
Number Page
1.1 The 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
NonEGU Point and Area Sources, for the Current Ozone Standard in the
Baseline 3-6
3.2 Counties Where Controls for Volatile Organic Chemicals (VOCs) Were
Applied to NonEGU Point and Area Sources for the Current Ozone Standard in
the Baseline 3-7
3.3 Areas Where NOx and VOC Controls Were Included for Mobile Onroad and
Nonroad Sources in Addition to National Mobile Controls for the Current
Ozone Standard in the Baseline 3-8
3.4 Baseline Projected 8-Hour Ozone Air Quality in 2020 3-9
3.5 Counties Where Controls for Nitrogen Oxides (NOx) Were Applied to
NonEGU Point and Areas Sources for the RIA Modeled Control Strategy
(Incremental to Baseline) 3-13
3.6 Counties Where VOC Controls Were Applied to NonEGU Point and Areas
Sources for the Modeled Control (Incremental to Baseline) 3-14
3.7 Geographic Areas where NOx Controls were Applied to Electrical Generating
Units (EGUs) for the Modeled Control Strategy (Incremental to Baseline) 3-15
3.8 Areas Where NOx and VOC Controls Were Applied to Mobile Onroad and
Nonroad Sources in Addition to National Mobile Controls for the Modeled
Control Strategy (incremental to Baseline) 3-17
3.9 Annual Tons of NOx Emission Reductions for the Modeled Control Strategy
(Incremental to the Baseline) 3-19
3.10 Percentage of 2020 Annual NOx Emissions Reduced by Sector Incremental to
the Baseline 3-20
3.11 Annual Tons of VOC Emission Reductions for the Modeled Control Strategy
(Incremental to the Baseline) 3-21
3.12 Percentage of 2020 Annual VOC Emissions Reduced by Sector 3-22
3.13 Projected 8-Hour Ozone Air Quality in 2020 From Applying the Modeled
Control Strategy 3-23
3.14 National Annual Emissions Remaining (2020) after Application of Controls for
the Baseline and Modeled Control Strategy 3-24
xin
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LIST OF FIGURES (CONTINUED)
Number Page
4.1 Counties within which Across-the-Board Emissions Reductions were Applied in
the Supplemental Modeling Analyses 4-2
4.2a Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and
Each of the Six Supplemental Modeling Scenarios for the Highest Three
Counties within the Houston Area 4-4
4.2b Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and
Each of the Six Supplemental Modeling Scenarios for the Highest Counties
within the Eastern Lake Michigan Area 4-5
4.2c Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and
Each of the Six Supplemental Modeling Scenarios for the Highest Counties
within the Northeast Corridor 4-5
4.2d Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and
Each of the Six Supplemental Modeling Scenarios for Three Specific Areas in
California 4-6
4.3a Map of Extrapolated Cost Counties for the 0.065 ppm Alternate Standard and
the Estimated Percent NOx Controls Needed to Meet that Standard 4-12
4.3b Map of Extrapolated Cost Counties for the 0.070 ppm Alternate Standard and
the Estimated Percent NOx Controls Needed to Meet that Standard 4-13
4.3c Map of Extrapolated Cost Counties for the 0.075 ppm Alternate Standard and
the Estimated Percent NOx Controls Needed to Meet that Standard 4-13
4.3d Map of Extrapolated Cost Counties for the 0.079 ppm Alternate Standard and
the Estimated Percent NOx Controls Needed to Meet that Standard 4-14
5.1 Marginal Cost Curve for Modeled Control Strategy Geographic Areas (NOX
nonEGU Point and Area Source Controls Prior to Cut Points) 5-4
5.2 Marginal Cost Curve for Modeled Control Strategy Geographic Areas (VOC
nonEGU Point and Area Source Controls Prior to Cut Points) 5-4
5.3 Total Annualized Costs by Emissions Sector and Region for Modeled Control
Strategy in 2020 5-8
5.4 Ratio of Unspecified Emission Reductions to Known Emission Reductions
Across Various Standards for Phase 1 Areas 5-16
5.5 Ranges of Hybrid (Mid) Average Cost/Ton Values across Geographic Areas
and Standards 5-17
5.6 Extrapolated Cost by Region to Meet Various Alternate Standards Using Fixed
Cost Approach ($15,000/ton) 5-20
5.7 Extrapolated Cost by Region to Meet Various Alternate Standards Using Hybrid
Approach (Mid) 5-21
5.8 Annual Total Costs by Region 5-23
xiv
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LIST OF FIGURES (CONTINUED)
Number Page
5.9 National Known Control Costs and Extrapolated Costs for Various Standards 5-24
5.10 Total Cost Ranges for Various Standards 5-25
5.11 Technological Innovation Reflected by Marginal Cost Shift 5-26
6.1 Valuation of Ozone Morbidity and Mortality Benefits Results by Standard
Alternative 6-29
6.2 Valuation of PM Co-Benefits by Standard Alternative at 3% and 7% 6-30
6.3 Ozone and PM2.5 Benefits by Standard Alternative (3% and 7% Discount Rates).... 6-81
6.4 Example Combined Ozone and PM2.5 Monetized Benefits Estimates by
Standard Alternative (3% and 7% Discount Rates) 6-82
6.5 Ozone and PM Total Benefits including all combinations of Mortality Estimates
(3% discount rate) 6-83
6.6 Ozone and PM Total Benefits including all combinations of Mortality Estimates
(7% discount rate) 6-84
6.7 Total Annual Ozone and PM2.5-Related Premature Mortalities Avoided in 2020
by Standard Alternative 6-89
7.1 Range of Net Benefits (2006$) for All Standard Alternatives (7% discount) 7-6
7.2 Range of Net Benefits (2006$) for Selected Standard 7-7
7.3 Range of Net Benefits for Select Combinations at 3% and 7% 7-8
xv
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Executive Summary
Overview
EPA has performed an illustrative analysis of the potential costs and human health and visibility
benefits of nationally attaining a new ozone standard of 0.075 ppm. Per Executive Order 12866
and the guidelines of OMB Circular A-4, this Regulatory Impact Analysis (RIA) also presents
analyses of three alternative standards, a less stringent 0.079 ppm and two more stringent options
(0.065 and 0.070 ppm). 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 also includes the
Clean Air Interstate Rule and mobile source programs, which will help many areas move toward
attainment of the current ozone standard.
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 extensions. For purposes of this analysis, though, we assume
attainment by 2020 for all areas except for two areas (San Joaquin Valley and South Coast air
basins) in California. The state has submitted to EPA plans for implementing the current ozone
standard which propose that these two areas of California meet that standard by 2024. We have
assumed for analytical purposes that the San Joaquin Valley and South Coast air basin would
attain a new standard in 2030. The actual attainment year for all areas will be determined through
the State Implementation Plan process. A separate analysis for the San Joaquin Valley and South
Coast air basins in California is provided in Appendix 7b.
EPA designed a two-stage approach to estimating costs and benefits, because we recognized that
some areas with significant ozone problems would need emission controls beyond those
currently available to meet either the 1997 ozone standards, or alternative, more stringent
standards. However, as documented in Chapter 5, there are numerous examples of how
technological innovation has led to the development of new and improved ways of reducing air
pollution, often at lower cost than estimated at the time a new NAAQS is established. 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 (which in
some locations will require new or innovative approaches and technology).
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
ES-1
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is an essential decision making tool for the efficient implementation of these standards. The
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.
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 control 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 new standard. After existing
control technologies have been applied, additional unspecified emission reductions are applied to
establish attainment. The cost of these unknown controls was extrapolated and is included in the
total cost numbers.
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 PM2.s 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. In moving further down the list of cost-effective known
and available controls, we deplete our database of available choices of known controls, and 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.
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
ES-2
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underlying mechanisms by which such effects occur, such ranges are illustrative of the extent of
uncertainly associated with some different modeling assumptions.
ES.2 Results of Benefit-Cost Analysis
The following is a presentation of the benefits and costs of attaining various Ozone National
Ambient Air Quality Standards in the year 2020. These estimates only include areas assumed to
meet the current standard by 2020. As mentioned earlier, they do not include the costs or benefits
of attaining the alternate standards in San Joaquin Valley and South Coast air basins. Due to the
differences in attainment year and other assumptions underlying the 2020 analysis presented
here, and the 2030 analysis in Appendix 7b, it is not appropriate to add the results together to get
a national "full attainment" scenario.
In Tables ES.l through ES.4, 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. For the 0.075ppm alternative, PIVb.s co-benefits account for between 42 and 99
percent of total benefits depending upon the study used. Details about these studies are in
Chapter 6.
Ranges in the total costs column reflect different assumptions about the extrapolation of costs as
discussed in Chapter 5. 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. These tables do not include visibility benefits,
which are estimated at $160 million/yr.
Table ES.l: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.s Co-
Benefits: 0.075 ppm Standard in 2020 in Billions of 2006$*
Ozone
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****
Total Benefits**
3% 7%
2.6-17
3.8-18
4.4-19
4.5-19
2.0-17
2.4-16
3.6-17
4.3-17
4.4-17
1.8-15
Total
Costs***
7%
7.6-8.8
7.6-8.8
7.6-8.8
7.6-8.8
7.6-8.8
Net Benefits
3% 7%
-6.3
-5.0
-4.4
-4.3
-6.1
-9.5
-11
-11
-11
1-9
-6.4-
-5.2-
-4.5-
-4.5-
-7.0-
7.9
9.1
9.8
9.9
7.4
ES-3
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Table ES.2: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.s Co-
Benefits: 0.079 ppm Standard in 2020 in Billions of 2006$*
Ozone
Mortality
Function or
Assumption Reference
NMMAPS Bell et al. 2004
Bell et al. 2005
Mea'. Itoetal.2005
analysis LevyetaL2005
Assumption that association is
not causal****
Total Benefits**
3% 7%
1.4-11
1.9-11
2.1-12
2.1-12
1.2-11
1.3-9.9
1.8-10
2.0-11
2.0-11
1.1-9.7
Total
Costs***
7%
2.4-2.9
2.4-2.9
2.4-2.9
2.4-2.9
2.4-2.9
Net Benefits
3% 7%
-1.5-8.5
-1.1-8.9
-0.83-9.2
-0.80-9.2
-1.7-8.3
-1.6-7.5
-1.2-7.9
-0.9-8.1
-0.9-8.2
-1.8-7.3
Table ES.3: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.s Co-
Benefits: 0.070 ppm Standard in 2020 in Billions of 2006$*
Ozone
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****
Total Benefits**
3% 7%
5.4-29
9.7-34
12-36
12-36
3.5-27
5.1-27
9.5-31
12-33
12-33
3.2-25
Total
Costs***
7%
19-25
19-25
19-25
19-25
19-25
Net Benefits
3% 7%
-20-10
-15-15
-13-17
-13-17
-22-8
-20 - 7.6
-16-12
-13-14
-13-14
-22 - 5.7
ES-4
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Table ES.4: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.s Co-
Benefits: 0.065 ppm Standard in 2020 in Billions of 2006$*
Ozone
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
Total Benefits**
3% 7%
9.0-46
17-54
21-58
21-58
5.5-42
8.6-42
16-50
21-54
21-54
5.1-38
Total
Costs***
7%
32-44
32-44
32-44
32-44
32-44
Net Benefits
3% 7%
-35 - 14
-27 - 22
-23 - 26
-23 - 26
-39-10
-35 - 9.7
-28-18
-23 - 22
-23 - 22
-39 - 6.2
*A11 estimates rounded to two significant figures. As such, they may not sum across columns. These estimates do
not include visibility benefits. Only includes areas required to meet the current standard by 2020, does not include
San Joaquin and South Coast areas in California. Appendix 7b shows the costs and benefits of attaining alternate
standards in San Joaquin and South Coast California.
**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. Tables exclude unquantified and nonmonetized benefits.
***Range reflects lower and upper bound cost estimates. Data for calculating costs at a 3% discount rate was not
available for all sectors, and therefore total annualized costs at 3% are not presented here. Additionally, these
estimates assume a particular trajectory of aggressive technological change. An alternative storyline might
hypothesize a much less optimistic technological trajectory, with increased costs, or with decreased benefits in
2020 due to a later attainment date.
****Total includes ozone morbidity benefits and total PM co-benefits only.
Table ES.5 presents the total number of estimated ozone and PlV^.s-related premature mortalities
and morbidities avoided nationwide in 2020.
ES-5
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Table ES.5: Summary of Total Number of Annual Ozone and PMi.s-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 5 Co-Benefits**
NMMAPS Bell (2004)
Bell (2005)
Meta-Analysis Ito (2005)
Levy (2005)
Assumption that association is not
causal***
Combined Estimate of Morbidity
Acute Myocardial Infarction
Upper Respiratory Symptoms
Lower Respiratory Symptoms
Chronic Bronchitis
Acute Bronchitis
Asthma Exacerbation
Work Loss Days
School Loss Days
Hospital and ER Visits
Minor Restricted Activity Days
0.079 ppm
140-1,300
200-1,300
230-1,300
230-1,400
120-1,200
570
3,100
4,200
240
640
3,900
28,000
72,000
890
340,000
0.075 ppm
260-2,000
420 - 2,200
500-2,300
510-2,300
190-2,000
890
4,900
6,700
380
1,000
6,100
43,000
200,000
1,900
750,000
0.070 ppm
560-3,500
560-4,100
1,100-4,300
1,400-4,400
310-3,200
1,500
8,100
11,000
630
1,700
10,000
72,000
640,000
5,100
2,100,000
0.065 ppm
940-5,500
2,000-6,500
2,500-7,000
2,500-7,100
490-5,000
2,300
13,000
17,000
970
2,600
16,000
110,000
1,100,000
9,400
3,500,000
*Only includes areas required to meet the current standard by 2020, does not include San Joaquin Valley
and South Coast air basins in California. Appendix 7b shows the costs and benefits of attaining
alternate standards in San Joaquin and South Coast California.
"""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 described in Chapter 6.
***Estimated reduction in premature mortality due to PM2.s reductions only
The following set of graphs is included to provide the reader with a richer presentation of the
range of costs and benefits of the alternative standards. The graphs supplement the tables by
displaying all possible combinations of net benefits, utilizing the five different ozone functions,
the fourteen different PM functions, and the two cost methods. Each of the 140 bars in each
graph represents an independent and equally probably point estimate of net benefits under a
certain combination of cost and benefit estimation methods. Thus it is not possible to infer the
likelihood of any single net benefit estimate. The blue bars indicate combinations where the net
benefits are negative, whereas the green bars indicate combinations where net benefits are
positive. Figure ES.l shows all of these combinations for all standards analyzed. Figure ES.2
shows a close-up of the range of net benefits for the selected standard of 0.075 ppm.
ES-6
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Figure ES.I:Range of Net Benefits Across Standard Alternatives'
cl H« Be-rMfits. fot 0,079 ppffl (A3 €«birattoiw of Cast*
and Benefts al 7X Discount SateJ
at Mat gtntftls. far CJ Vf$ pptn .|WI Comtnnagionf of Costs
BsrteR'ts- «i 7'Ki OlKCtuiYt Rats')
Benefits exceed
Costs
......rinitiiiiiiiiiuiiiiiiiiiimiiiillll Hill HUH Hill HUH HllilhM!'!-
f -*IO
I
Costs exceed
Benefits
••sw i
•wo
• i
Costs exceed
| -lie- • Benefits
3
Bcneilis exceed
Costs
R,an»3« iv t •!
n' "r , li i o 375, ppm (All Combnaliar* flf
• 1 f.'i ^'H at 7% C*4Csy.fit' Raw?
Mel Benefits for '3X65 pjim |AII Cisrifcii-albna ol Orals
ana bcnimisar "K, OiKounf Ftatoi
iio -
•120
.S3.fl
• Ml
5™ns 11 >•]»
pr
Costs exceed
Benefits
Benefits exceed
Costs
IIIIJPI
'""
: This graph shows all 140 combinations of the 5 different ozone mortality functions and assumptions, the 14 different PM mortality functions, and
the 2 cost methods. All combinations are treated as independent and equally probable.
ES-7
-------
Figure ES.iRange of Net Benefits for 0.075 ppm (AH Combinations of
Costs and Benefits at 7% Discount Rate) *
to
o
o
CM
c
g
QQ
$8 -
$6 -
$4-
$2-
$0 -
42-
44 -
46 -
Pill Ililllll
II"
I"1
Costs exceed Benefits
Benefits exceed Costs
... ......Hllillllllllll
410 J
' This graph shows all 140 combinations of the 5 different ozone mortality functions and assumptions, the 14 different PM mortality functions, and
the 2 cost methods. All combinations are treated as independent and equally probable.
For the selected standard of 0.075 ppm, the median value of all of the independent point estimates is $0.8 billion, and the majority (64%) of the
combinations indicate positive net benefits for this standard.
ES-8
-------
ES.3 Caveats and Conclusions
Of critical importance to understanding these estimates of future costs and benefits is that they
are 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 Chapters 5, 6, and 7. Analytically, the
characterization of mortality benefits and the estimation of the costs to the nation of fully
attaining a tighter standard will be subject to further review by EPA 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 accurately 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 the statistical model
chosen for each health benefit.
• 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 within the context of quantifying benefits. We expect to receive this advice
in the spring of 2008
• As shown in figure ES.l above, there is a considerable range of costs and benefits
associated with attainment of a tighter ozone standard, especially in the range of PM
2.5 benefits. EPA has plans to ask its Science Advisory Board for advice about how
to best characterize the PM mortality benefits in future analyses.
• 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).
• There are several nonqualified benefits (e.g., effects of reduced ozone on forest
health and agricultural crop production) and disbenefits (e.g., decreases in
ES-9
-------
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 our modeling of known controls does not get all areas into
attainment, we needed to make assumptions about the costs of control
technologies that might be developed in the future and used to meet the tighter
alternative. For the 21 counties (in four geographic areas) that are not expected to
attain 0.075 ppm1 in 20202, assumed costs of unspecified controls represent a
substantial fraction, of the costs estimated in this analysis ranging from 50% to
89% of total costs depending on the standard being analyzed.
• As discussed in Chapter 5, recent advice from EPA's Science Advisory Board has
questioned the appropriateness of an approach similar to one of those used here
for estimating extrapolated costs. For balance, EPA also applied a methodology
recommended by the Science Advisory Board in an effort to best approximate the
costs of control technologies that might be developed in the future.
• 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. Our estimates of costs of attainment in 2020
assume a particular trajectory of aggressive technological change. This trajectory
leads to a particular level of emissions reductions and costs which we have
estimated based on two different approaches, the fixed cost and hybrid
approaches. An alternative storyline might hypothesize a much less optimistic
technological change path, such that emissions reductions technologies for
industrial sources would be more expensive or would be unavailable, so that
emissions reductions from many smaller sources might be required for 2020
attainment, at a potentially greater cost per ton. Under this alternative storyline,
two outcomes are hypothetically possible: Under one scenario, total costs
associated with full attainment might be substantially higher. Under the second
scenario, states may choose to take advantage of flexibility in the Clean Air Act to
1 Areas that do not meet 0.075 ppm are Chicago, Houston, the Northeastern Corridor, and
Sacramento. For more information see chapter 4 section 4.1.1.
This list of areas does not include the San Joaquin and South Coast air basins who are not
expected to attain the current 0.08 ppm standard until 2024.
ES-10
-------
adopt plan with later attainment dates to allow for additional technologies to be
developed and for existing programs like EPA's Onroad Diesel, Nonroad Diesel,
and Locomotive and Marine rules to be fully implemented. If states were to
submit plans with attainment dates beyond our 2020 analysis year, benefits would
clearly be lower than we have estimated under our analytical storyline. However,
in this case, state decision makers seeking to maximize economic efficiency
would not impose costs, including potential opportunity costs of not meeting their
attainment date, when they exceed the expected health benefits that states would
realize from meeting their modeled 2020 attainment date. In this case, upper
bound costs are difficult to estimate because we do not have an estimate of the
point where marginal costs are equal to marginal benefits plus the costs of
nonattainment. Clearly, the second stage analysis is a highly speculative exercise,
because 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.
This analysis shows the costs and benefits of a standard of 0.075 ppm and other
alternate standards of 0.079, 0.070, and 0.065. The costs and benefits are
incremental to a baseline that assumes some additional technology changes in the
onroad technology sector. If these changes do not occur, then cost for all
standards would increase by $1.8 billion and benefits for all standards would
increase by $360 million to $3.1 billion using 2006$ and a 3% discount rate, and
$330 million to $2.8 billion when using a 7% discount rate.3 Details about costs
and benefits using an alternate baseline can be found in Appendix 7a.
3 These estimates are highly uncertain and are purely illustrative estimates of the potential costs
and benefits of these mobile control strategies. We present them only as screening-level
estimates to provide a bounding estimate of the costs and benefits of including these emissions
controls in the ozone NAAQS control case for all standards. As such, it would be inappropriate
to apply these benefit per-ton estimates to other policy contexts, including other regulatory
impact analyses. Furthermore, the benefits only reflect a partial accounting of the total benefits
associated with emission reductions related to the mobile controls included in this sensitivity
analysis.
ES-11
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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 a revised primary ozone National Ambient Air Quality Standard (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 retain or revise the
NAAQS. After promulgation or revision of the NAAQS, the standards are implemented by the
States.
1-1
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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. The analysis required by these statutes and executive orders is presented in
Chapter 8.
EPA presents this PJA pursuant to Executive Order 12866 and the guidelines of OMB Circular
A-4.1 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 which will be jointly presented in Appendix 6b.
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
1 U.S. Office of Management and Budget. Circular A-4, September 17, 2003. Found on the
Internet at .
1-2
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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 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. 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.
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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.
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.
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Figure 1.1: The Process Used to Create this RIA
Estimate Modeled
Control Costs
Estimate 2020 Emissions
Model 2020 Baseline Air Quality
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
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 which also used
2020 as its analysis year. Many areas will reach attainment of the current ozone standard or any
alternative ozone 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 Appendix
7b. 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 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
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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. We performed air quality sensitivity modeling by reducing the remaining
NOx and NOx + VOC emissions by 30, 60, and 90% beyond the percentage inventory reductions
that were achieved by the modeled known control strategy. This enabled us to determine, for an
extrapolation analysis, the approximate number of tons of additional reductions, beyond those
achieved by known controls that would be required to meet the alternate standards.
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 primary standard included both
mortality and morbidity calculations 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. It is important to note that this analysis did not
estimate any separate costs or benefits of attaining a secondary NAAQS standard due to resource
and time constraints. Since the secondary is being set to be equivalent to the primary standard,
few additional costs and benefits are expected.
To streamline this RIA, this document 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 effect, 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
1-6
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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
EPA has performed an illustrative analysis of the potential costs and human health and visibility
benefits of nationally attaining a new ozone standard of 0.075 ppm. Per Executive Order 12866
and the guidelines of OMB Circular A-4, this Regulatory Impact Analysis (RIA) also presents
analyses of three alternative standards, a less stringent 0.079 ppm and two more stringent options
(0.065 and 0.070 ppm). 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 also includes the
Clean Air Interstate Rule and mobile source programs, which will help many areas move toward
attainment of the current ozone standard.
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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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. Additional information on ozone formation can be found in
"Atmospheric Chemistry and Physics" (Seinfeld et. al., 1998).
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 of NOx, ozone
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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.2 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 from Nonroad Diesel
Engines. EPA420-R-04-007. May 2004.
2 http ://www. epa. gov/interstateairquality/pdfs/finaltech02 .pdf Table VI-2.
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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
emissions.3 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. 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, 2007a, 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 representing 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 PM2.s. The
air quality model used in this RIA is the Community Multi-Scale Air Quality (CMAQ) model4.
The modeling for ozone and PIVb.s was performed for a one year time period. All controls in the
illustrative 0.070 scenario were applied similarly to all months. There were no controls applied
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.
4 See CMAQ references listed at end of this chapter.
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specifically for PlV^.s co-benefits because the controls developed to reduce summer ozone were
applied to all months (see Chapter 3).
2.3.1 CMA Q Model and Inputs
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 PIVb.s were used as inputs to the calculation of expected
incremental benefits from the alternative ozone standards considered in this assessment. The
2002-based modeling platform (EPA, 2008) was used as the basis for air quality modeling of the
future baseline emissions and illustrative control scenario. This modeling platform includes a
number of updates and improvements to data and tools compared to the 2001-based platform that
was used for the proposal modeling. For the final rule modeling we used the new 2002 National
Emissions Inventory along with updated versions of the models used to project future emissions
from electric generating units (EGUs) and onroad and nonroad vehicles. The proposal modeling
was based on the 2001 National Emissions Inventory. The new platform also includes 2002
meteorology and more recent ambient design values which were used as the starting point for
projecting future air quality. For proposal, we used meteorology for 2001 for modeling the East
and 2002 for modeling the West. The updates5 to CMAQ between proposal and final include
(1) an in-cloud sulfate chemistry module that accounts for the nonlinear sensitivity of sulfate
formation to varying pH; (2) improved vertical convective mixing; (3) heterogeneous reaction
involving nitrate formation; (4) an updated gas-phase chemistry mechanism, Carbon Bond 2005
(CB05); and (5) an aqueous chemistry mechanism that provides a comprehensive simulation of
aerosol precursor oxidants.
The key non-emissions inputs to the CMAQ model include meteorological data, and initial and
boundary concentrations. 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 for the 36 km continental scale modeling domain, described below, were
obtained from a three-dimensional global atmospheric chemistry model, the GEOSChem model
(Yantosca, 2004). The global GEOSChem model simulates atmospheric chemical and physical
processes driven by assimilated meteorological observations from the NASA's Goddard Earth
Observing System (GEOS). We used GEOSChem results for 2002 to provide initial and
boundary concentrations for our final rule air quality modeling. For proposal we used
GEOSChem results for 2001.
EPA performed an extensive evaluation of CMAQ using the 2002 inputs for emissions,
meteorology, and boundary conditions. Details of the model performance methodology and
results are described in the 2002-Based Modeling Platform Report (EPA, 2008). 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
5 Additional documentation on the updates in CMAQ version 4.6 can be found at the following
web site: http://www.cmascenter.org/.
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ozone model applications for other EPA and non-EPA studies (see Appendix B of EPA, 2007b).
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 modeling the 2002 base year, 2020 baseline and 2020 control
strategy scenarios. These domains are labeled as the East and West 12 km domains in Figure 2.1.
Simulations for the 36 km domain were only used to provide initial and boundary concentrations
for the 12 km domains. As indicated above, the model produces spatial fields of gridded air
quality concentrations on an hourly basis for the entire modeling domain. These gridded
concentrations can be processed 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 air
quality modeling results are used in a relative sense to project concentrations for the future year
scenarios using procedures consistent with EPA guidance (EPA, 2007b). For the final rule
projections we used ambient design values for the period 2000 through 2004 as the starting point
for projections. For the proposal, design values from 1999 through 2003 were used. The change
between proposal and final in terms of the period of design values was made, in accordance with
EPA guidance, in order to align the central year of design values with the base year of the
emissions (i.e., 2001 for the proposed rule and 2002 for the final rule).
For this analysis, predictions from the East domain were used to provide data for all areas that
are east of approximately 104 degrees longitude. Model predictions from the West domain we
used for all areas west of this longitude.
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Figure 2.1: Map of the CMAQ Modeling Domains Used for Ozone NAAQS RIA
| 12km West Domain Boundary L\ <- ,,
> NT '? ^
t^j | 12km East Domain Boundary
—/A.
Table 2.1; Geographic Specifications of Modeling Domains
36 km Domain
(148 x 112 Grid Cells)
Lon lat
SW -121.77 18.17
NE -58.54 52.41
12 km East Domain
(279 x 240 Grid Cells)
Ion lat
SW -106.79 24.99
NE -65.32 47.63
12 km West Domain
(213 x 192 Grid Cells)
Ion lat
SW -121.65 28.29
NE -94.94 51.91
2.3.2 Emissions Inventory
The 2020 inventory, projected from the 2002 Version 3 emissions modeling platform (EPA,
2008), is the starting point for the baseline and control strategy for the Final Ozone NAAQS
emissions inventory. The 2002 documentation describes the 2002 base year inventory as well as
the projection methodology and controls applied to create year 2020 emissions. The 2020
inventory includes activity growth for some sectors, and controls 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, known
plant closures, and consent decrees and settlements. Table 2.2 provides a comprehensive list of
the rules/control strategies and projection assumptions in the 2020 inventory; full discussion of
the 2020 inventory is provided in the 2002 Version 3 emissions modeling platform (EPA,
2008a). The data for the controls and projection strategies can be found in the Loco-Marine
docket (EPA, 2008b).
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Table 2.2: Control Strategies and Projection Assumptions in the 2020 Emissions Inventory
Control Strategies
(Grouped by Affected Pollutants or Standard and Approach Used to Pollutants Approach or
Apply to the Inventory) Affected Reference
Non-EGU Point Controls
NOx SIP Call (Phase II):
Cement Manufacturing ,,„ 1
Large Boiler/Turbine Units
Large 1C Engines
DOJ Settlements: plant SCC controls
Alcoa, TX NOx, SO2 2
MOTIVA, DE
Refinery Consent Decrees: plant/SCC controls NOx, PM, SO2 3
Closures, pre-2007: plant control of 100%
Auto plants
Pulp and Paper all 4
Municipal Waste Combustors
Plants closed in preparation for 2005 inventory
Industrial Boiler/Process Heater plant/SCC controls for PM PM 5
MACT rules, national, VOC: national applied by SCC, MACT
Boat Manufacturing
Polymers and Resins III (Phenolic Resins)
Polymers and Resins IV (Phenolic Resins)
Wood Building Products Surface Coating
Generic MACT II: Spandex Production, Ethylene manufacture
Large Appliances
Miscellaneous Organic NESHAP (MON): Alkyd Resins, Chelating Agents,
Explosives, Phthalate Plasicizers, Polyester Resins, Polymerized Vinylidene
Chloride
Manufacturing Nutritional Yeast
Oil and Natural Gas
Petroleum Refineries—Catalytic Cracking, Catalytic Reforming, & Sulfur
Plant Units
Pesticide Active Ingredient Production
Publicly Owned Treatment Works
Reinforced Plastics ir/-,^ m* inn^c
T, UU T- A/T f *, ' VOC EPA> 2007f
Rubber Tire Manufacturing
Asphalt Processing & Roofing
Combustion Sources at Kraft, Soda, and Sulfite Paper Mills
Fabric Printing, Coating and Dyeing
Iron & Steel Foundries
Metal: Can, Coil
Metal Furniture
Miscellaneous Metal Parts & Products
Municipal Solid Waste Landfills
Paper and Other Web
Plastic Parts
Plywood and Composite Wood Products
Wet Formed Fiberglass Production
Wood Building Products Surface Coating
Carbon Black Production
Cellulose Products Manufacturing
Cyanide Chemical Manufacturing
(continued)
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Table 2.2: Control Strategies and Projection Assumptions in the 2020 Emissions Inventory
(continued)
Control Strategies
(Grouped by Affected Pollutants or Standard and Approach Used to
Apply to the Inventory)
Pollutants Approach or
Affected Reference
Friction Products Manufacturing
Leather Finishing Operations
Miscellaneous Coating Manufacturing
Organic Liquids Distribution (Non-Gasoline)
Refractory Products Manufacturing
Sites Remediation
Solid Waste Rules (Section 129d/llld)
Hospital/Medical/Infectious Waste Incinerator Regulations
NOx, PM, SO2 EPA, 2005
MACT rules, national, PM:
Portland Cement Manufacturing
Secondary Aluminum
MACT rules, plant-level, VOC:
Auto Plants
MACT rules, plant-level, PM & SO2:
Lime Manufacturing
MACT rules, plant-level, PM:
Taconite Ore
PM
VOC
PM, SO2
PM
6
7
8
9
Stationary Non-point (Area) Assumptions
Municipal Waste Landfills: projection factor of 0.25 applied
Livestock Emissions Growth
Residential Wood Combustion Growth
reflects increase in use of lower polluting wood stoves, and decrease in use
of higher polluting stoves
Gasoline Stage II growth and control
(also impacts non-EGU point sources in a couple of states)
Portable Fuel Container growth and control
VOC
NH3, PM
all
VOC
VOC
EPA, 2007f
10
11
12
13
ECU Point Controls
CAIR/CAMR/CAVR
IPM Model 3.0
NOx, SO2, PM
14
Onroad Mobile and Nonroad Mobile Growth and Controls
Onroad and Nonroad Growth:
Onroad growth is based on VMT growth from Annual Energy Outlook
(AEO) 2006 estimates of growth by vehicle type. Nonroad growth is based
on activity increases from NONROAD model default growth estimates
all
National Onroad Rules:
Tier 2 Rule
2007 Onroad Heavy-Duty Rule
Final Mobile Source Air Toxics Rule (MSAT2)
Renewable Fuel Standard
all
Local Onroad Programs:
National Low Emission Vehicle Program (NLEV)
Ozone Transport Commission (OTC) LEV Program
VOC
15
(continued)
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Table 2.2: Control Strategies and Projection Assumptions in the 2020 Emissions Inventory
(continued)
Control Strategies
(Grouped by Affected Pollutants or Standard and Approach Used to Apply Pollutants Approach or
to the Inventory) Affected Reference
National Nonroad Controls:
Clean Air Nonroad Diesel Final Rule—Tier 4 1116
Control of Emissions from Nonroad Large-Spark Ignition Engines and
Recreational Engines (Marine and Land Based): "Pentathalon Rule"
Aircraft, Locomotives, and Commercial Marine Assumptions
Aircraft:
Itinerant (ITN) operations at airports
Locomotives:
Energy Information Administration (EIA) fuel consumption projections for „„ . Onn7
., . , ., ,, EJ! A, .zuu /e,
freight rail all
Clean Air Nonroad Diesel Final Rule—Tier 4
Locomotive Final Rulemaking, December 17, 1997
Commercial Marine:
EIA fuel consumption projections for diesel-fueled vessels
Freight-tonnage growth estimates fro residual-fueled vessels ,, 18, (EPA,
Clean Air Nonroad Diesel Final Rule—Tier 4 2007e)
Emissions Standards for Commercial Marine Diesel Engines, December 29, 1999
Tier 1 Marine Diesel Engines, February 28, 2003
APPROACHES:
1. Used Emission Budget Inventories report (EPA, 1999) for list of SCCs for application of controls, and for
percent reductions (except 1C Engines). Used Federal Register on Response to Court decisions (Federal
Register, 2004) for 1C Engine percent reductions and geographic applicability
2. For ALCOA consent decree, used http:// cfpub.epa.gov/compliance/cases/index.cfm; for MOTIVA: used
information sent by State of Delaware
3. Used data provided by Brenda Shine, EPA, OAQP S
4. Closures obtained from EPA sector leads; most verified using the world wide web.
5. Used data list of plants provided by project lead from 2001-based platform; required mapping the 2001 plants
to 2002 NEI plants due to plant id changes across inventory years
6. Same as used in CAIR, except added SCCs appeared to be covered by the rule: both reductions based on
preamble to final rule. (Portland Cement used a weighted average across two processes )
7. Percent reductions recommended and plants to apply to reduction to were based on recommendations by rule
lead engineer, and are consistent with the reference: EPA, 2007e
8. Percent reductions recommended are determined from the existing plant estimated baselines and estimated
reductions as shown in the Federal Register Notice for the rule. SO2 % reduction will therefore be
6147/30,783 = 20% and PM10 and PM2.5 reductions will both be 3786/13588 = 28%
9. Same approach used in CAIR: FR notice estimates reductions of "PM emissions by 10,538 tpy, a reduction of
about 62%." Used same list of plants as were identified based on tonnage and SCC from CAIR.
10. Except for dairy cows and turkeys (no growth), based in animal population growth estimates from USDA and
Food and Agriculture Policy and Research Institute.
11. Expected benefits of woodstoves change-out program: http://www.epa.gov/woodstoves/index.html
12. VOC emission ratios of year 2020 to year 2002 from the National Mobile Inventory Model (NMIM) results
for onroad refueling including activity growth from VMT, Stage II control programs at gasoline stations, and
phase in of newer vehicles with onboard Stage II vehicle controls.
13. VOC emission ratios of year 2020 to year 2002 from MSAT rule (EPA, 2007c, EPA, 2007d)
14. http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/summary2006.pdf
15. Only for states submitting these inputs: http://www.epa.gov/otaq/lev-nlev.htm
16. http ://www. epa. gov/nonroad-diesel/2004fr.htm
17. Federal Aviation Administration (FAA) Terminal Area Forecast (TAF) System, February 2006:
http ://www. apo .data, faa. gov/main/taf.asp
18. http://www.epa.gov/nonroad-diesel/2004fr.htm
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Differences between the 2020 emissions modeling platforms—particularly the inventories—used
in the Ozone NAAQS Proposal and here in the Ozone NAAQS Final are discussed in the
Appendix for Chapter 2.
The development of the 2020 baseline inventory and the modeled control scenarios are discussed
in Chapter 3. The 2020 baseline inventory includes the same year 2020 Canada and year 1999
Mexico emissions as the Final PM NAAQS (EPA, 2006b).
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."/. Applied Mechanics Reviews 59(2):51-77.
Dennis, R.L., D.W. Byun, J.H. Novak, K.J. Galluppi, C.J. 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.
Seinfeld, J.H. and S. N. Pandis, 1998: Atmospheric Chemistry and Physics. John Wiley & Sons,
New York.
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). 2005. Clean Air Interstate Rule Emissions
Inventory Technical Support Document, U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards, March 2005. Available at
http://www.epa.gov/cair/pdfs/finaltechO 1 .pdf.
U.S. Environmental Protection Agency (EPA). 2006a. 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). 2006b. "Regulatory Impact Analysis for the
Review of the Particulate Matter National Ambient Air Quality Standards (Chapter 2: Defining
the PM2.5 Air Quality Problem)." EPA-HQ-OAR-2006-0834-0048.3.
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U.S. Environmental Protection Agency (EPA). 2007a. 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). 2007b. "Guidance on the Use of Models and
Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PIVb.s, and
Regional Haze." EPA-454/B-07-002, http://www.epa.gov/scramOO 1 /guidance/guide/final-03-
pm-rh-guidance.pdf.
U.S. Environmental Protection Agency (EPA). 2007c. Regulatory Impact Analysis for Final
Rule: Control of Hazardous Air Pollutants from Mobile Sources, U.S. Environmental Protection
Agency, Office of Transportation and Air Quality, Assessment and Standards Division, Ann
Arbor, MI 48105, EPA420-R-07-002, February 2007. Available at
http://www.epa.gov/otaq/regs/toxics/420r07002.pdf.
U.S. Environmental Protection Agency (EPA). 2007d. National Scale Modeling for the Final
Mobile Source Air Toxics Rule, Office of Air Quality Planning and Standards, Emissions
Analysis and Monitoring Division, Research Triangle Park, NC 27711, EPA 454/R-07-002,
February 2007. Available at http://www.epa.gov/otaq/regs/toxics/454r07002.pdf.
U.S. Environmental Protection Agency (EPA). 2007e. Draft Regulatory Impact Analysis:
Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression-
Ignition Engines Less than 30 Liters per Cylinder, Chapter 3: Emission Inventory, U.S.
Environmental Protection Agency, Office of Transportation and Air Quality, Assessment and
Standards Division, Ann Arbor, MI 48105. EPA420-D-07-001, March 2007. Available at
http://www.epa.gov/otaq/regs/nonroad/420d07001 chp3 .pdf.
U.S. Environmental Protection Agency (EPA). 2007f. Guidance for Estimating VOC and NOx
Emission Changes from MACT Rules, U.S. Environmental Protection Agency Office of Air
Quality Planning and Standards, Air Quality Policy Division, Research Triangle Park, NC
27711, EPA-457/B-07-001, May 2007. Available at
http://www.epa.gov/ttn/naaqs/ozone/o3imp8hr/documents/guidance/200705 epa457 b-07-
001 emission changes mact rules.pdf.
U.S. Environmental Projections Agency (EPA). 2008a. Air Quality Modeling Platform for the
Ozone National Ambient Air Quality Standard Final Rule Regulatory Impact Assessment.
U.S. Environmental Projections Agency (EPA). 2008b. Technical Support Document:
Preparation of Emissions Inventories For the 2002-based Platform, Version 3, Criteria Air
Pollutants, USEPA, January, 2008.
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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Appendix 2: Additional Emissions Modeling Platform Information
2a.l Discussion of Similarities and Differences Between Emissions Modeling Platforms
Used in Ozone NAAQS Proposal and Final
All emissions modeling in the Ozone NAAQS Proposal was based off the 2001 emissions
modeling platform. Version 3 of the 2002 emissions modeling platform (EPA, 2008) is used for
the Final Ozone NAAQS. In both platforms, emissions are first projected to a year 2020 Base
case. The following discusses similarities and differences in the 2001 and 2002 emission
platforms, as well as assumptions used to project emissions to the year 2020.
2a.l.l Similarities in the 2001 and2002 Emissions Modeling Platforms
The 2001 and 2002 emissions platforms share the same Canada, Mexico, and offshore oil
production emissions. Both platforms also share the same wildfire and prescribed burning
emissions. Most input ancillary files used in the emissions processor are also unchanged;
specifically, almost all cross-reference factors used in speciation profile assignments and temporal
and spatial allocations are the same. The land use data for biogenic emissions (BELD3) is the
same. The projection approach for stationary non-EGU emissions is also unchanged; however, for
a couple of source categories, activity growth was slightly modified to account for the change in
starting year -2002, rather than 2001. This effect on year 2020 activity (growth) factors is very
small. Plant closures, consent decrees and settlements, and most national programs for stationary
non-EGUs are applied as consistently as possible in 2002 as in 2001, by which, we used a cross-
reference file to match controls for plants in the 2001 to the 2002 inventories.
2a.l.2 Key Changes to the Emissions Modeling Platform
As discussed in Chapter 2, the Final Ozone NAAQS utilizes the 2020 inventory, projected from
the 2002 Version 3 emissions modeling platform. The Proposal utilized the 2001-based,
projected to year 2020, "PM NAAQS" platform (EPA, 2006). The most significant change in the
emissions modeling platform is the improvements to emissions estimates over multiple inventory
sectors. See the 2002, Version 3 documentation for detailed information on these improvements.
The SMOKE input ancillary data was updated to account for new source categories appearing in
different inventory sectors; examples include farms and airports in the point source inventory
and the new inclusion of portable fuel container emissions resulting from the Mobile Source Air
Toxics (MSAT2) Rule (EPA, 2007a and 2007b). Another significant change in the emissions
modeling platforms is the use of a new chemical mechanism -CB05 (Yarwood, 2005) versus CB-
IV in the proposal platform.
Emissions by geographic area and by model platform in the base and future years are shown in
Figure 2a.l and Figure 2a.2, for NOx and VOC, respectively. "Northeast" in all figures
represents the full OTC (Ozone Transport Commission) member states: Maine, New Hampshire,
Vermont, Massachusetts, Connecticut, Rhode Island, New York, New Jersey, Pennsylvania,
Maryland, Delaware, and the District of Columbia. Emissions summaries from the northern
counties of Virginia, while part of the OTC, are included in the "rest of US" geographic area.
The "Midwest" geographic area includes Illinois, Indiana, Ohio, Michigan, and Wisconsin.
2a-l
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Figure 2a.l: Total Anthropogenic NOx Emissions [tons/year] by Year and Platform
2001
2002
2020 from
2001
2020 from
2002
2001 minus
2020 from
2001
2002 minus
2020 from
2002
Figure 2a.2: Total Anthropogenic VOC Emissions [tons/year] by Year and Platform
2001
2002
2020 from
2001
2020 from
2002
2001 minus
2020 from
2001
2002 minus
2020 from
2002
Figure 2a.l and Figure 2a.2 demonstrate that total NOx and VOC emissions do not differ
significantly by geographic area when comparing the inventories used in the proposal (2001) and
final (2002). Small decreases in NOx and VOC are evident in the Northeast and Midwest, and
small decreases in NOx are also seen in the rest of the US. In contrast, slight overall increases of
NOx in Texas and VOC in the rest of the US can be seen.
Year 2020 emissions, projected from the 2001 and 2002 emission platforms show slightly less
NOx in 2020 in the 2002-based platform in the Northeast, Midwest, and rest of the US. Perhaps
most significant from an air quality modeling aspect is the relative change in emissions in 2020
when migrating from the 2001 to the 2002 emission platforms, represented by the last 2 sets of
2a-2
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columns in Figure 2a. 1 and Figure 2a.2. These show slightly less raw reductions in NOx and
VOC for all regions with the exception of a very slight increase in NOx reductions in 2020-
based-off-2002 in the Northeast and California. The net effect of these emission summaries is
that large changes in air quality modeling ozone estimates are unlikely to be explained by
significant changes in the overall emission changes by migrating from the 2001-based emissions
platform in the proposal to the 2002-based emissions platform used in the final rulemaking.
Emissions inventory summaries broken down by sectors (e.g., EGU, non-EGU Point, Onroad
Mobile, Nonroad Mobile...) also do not show any significant differences by geographic area for
year 2020 between the 2001-based and 2002-based emission modeling platforms.
2a.2 References
U.S. Environmental Protection Agency (EPA). 2006. "Regulatory Impact Analysis for the
Review of the Particulate Matter National Ambient Air Quality Standards (Chapter 2: Defining
the PM 2.5 Air Quality Problem)." EPA-HQ-OAR-2006-0834-0048.3.
U.S. Environmental Protection Agency (EPA). 2007a. Regulatory Impact Analysis for Final
Rule: Control of Hazardous Air Pollutants from Mobile Sources, U.S. Environmental Protection
Agency, Office of Transportation and Air Quality, Assessment and Standards Division, Ann
Arbor, MI 48105, EPA420-R-07-002, February 2007. Available at
http://www.epa.gov/otaq/regs/toxics/420r07002.pdf.
U.S. Environmental Protection Agency (EPA). 2007b. National Scale Modeling for the Final
Mobile Source Air Toxics Rule, Office of Air Quality Planning and Standards, Emissions
Analysis and Monitoring Division, Research Triangle Park, NC 27711, EPA 454/R-07-002,
February 2007. Available at http://www.epa.gov/otaq/regs/toxics/454r07002.pdf.
U.S. Environmental Projections Agency (EPA). 2008. Air Quality Modeling Platform for the
Ozone National Ambient Air Quality Standard Final Rule Regulatory Impact Assessment.
Yarwood, G., S. Rao, M. Yocke, and G. Written, 2005. Updates to the Carbon Bond Chemical
Mechanism: CB05. Final Report to the U.S. EPA, RT-0400675. Available at
http://www.camx.com/publ/pdfs/CB05 Final Report 120805.pdf.
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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. We
modeled the lower end of the range to capture a larger number of geographic areas that may be
affected by a new ozone standard. 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 PM2.s and ozone standards (baseline). Thus,
the modeled analysis for a revised standard focuses specifically on incremental improvements
beyond the current standards, and uses control options that might be available to states for
application by 2020. The hypothetical modeled control strategy presented in this PJA 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 incremental to attainment of the current
standard, EPA approached the analysis in stages. First, EPA identified controls to be included in
the baseline. These included current state and federal programs (see) plus controls to attain the
current ozone standard (Table 3.1) and PIVb.s standards (see http://www.epa.gov/ttnecas 1 /ria.html
for a complete list of controls). 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 modeled 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 for air quality changes by EPA. EPA did not expect the modeled control strategy to
result in attainment at 0.070 ppm everywhere, and the modeled control strategy did yield only
partial attainment. Chapter 4 will explain how EPA used additional air quality modeling to
estimate total annual tons/year of emissions reductions needed to achieve ozone concentrations
for 0.075 ppm as well as the less stringent option of 0.079 ppm the and the more stringent
options of 0.070 ppm and 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)—(Figure 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 Figure 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 an alternate primary ozone
standard (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 nonattainment 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 extrapolated tons necessary 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 either fully or partially attain the standards in 2020, 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 NAAQS standards 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 alternate primary
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 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
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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 California1 are not planning to meet the current standard by 2020.
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, in addition to the
controls applied as part of the PIVb.s NAAQS RIA analysis. Second, since these reductions 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 five sectors to establish the baseline:2 Non-Electricity Generating Unit Point
Sources (NonEGUs), 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.
• NonPoint Area Sources3 (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.
• Nonroad Mobile Sources4 are any combustion engine that travels by other means than
roadways. These sources include railroad locomotives; marine vessels; aircraft; off-road
:At the time of this analysis the South Coast and San Joaquin Valley air basins are expected to
request a redesignation to extreme status for the current ozone standard.
2 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.
3 Areas Sources include the nonpoint emissions sector only.
4 For the purposes of presentation nonroad mobile sources incorporates both the nonroad
emissions sector and the aircraft, locomotive, and marine vessels emissions sector.
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motorcycles; snowmobiles; pleasure craft; and farm, construction, industrial and
lawn/garden equipment.
• Electricity Generating Unit Point Sources (EGUs) are stationary sources of 25 megawatts
(MW) capacity or greater producing and selling electricity to the grid, such as fossil-fuel-
fired boilers and combustion turbines.
3.1.1 Control Measures Applied in the Baseline for Ozone Precursors
The purpose of identifying and modeling baseline controls for ozone precursors, NOx and VOC,
is to reduce ambient ozone concentrations to meet the current ozone standard in this analysis.
Control measures were applied in the baseline to reduce ozone concentrations in addition to the
control set developed for the hypothetical national attainment strategy presented in the PIVb.s
NAAQS RIA (for more information, see http://www.epa.gov/ttn/ecas/ria.html).
The additional known controls included in the baseline to simulate attainment with current ozone
NAAQS are listed in Table 3.1 and are 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 for the current ozone standard.
Controls included in the baseline for NonEGU point and Area sources came from a variety of
geographic areas and scales. Almost all available controls in Chicago, Houston, and California
were included in the baseline because these areas contain counties that were projected to be
nonattainment of the current ozone NAAQS in 2020.
NOx controls from NonEGU point/Area sources were included in two ways. First, controls were
included in counties with monitors that were projected to violate the current standard in 2020.
Controls were then applied to all surrounding counties within the same state that were
completely contained within 200 km5 of the county containing the projected violating monitor
(Figure 3.1). Second, controls were applied to large nonEGU point sources6 outside the 200km
buffer zones. The criteria for control was as follows: the plant level emissions exceeded 1,000
tons of NOx in 2020, the plant was in a county that touches the 200km buffer, and the plant was
close to a nonattainment county that had difficulty attaining the baseline in the ozone NAAQS
proposal RIA. VOC controls were applied to select counties where: VOC emissions were high
(>5,000 tpy or >25tpy/sq. mi), the county design value was projected to be > 0.08 ppm in the
2020 basecase, and the area had some historical evidence that VOC controls would appreciably
lower ozone in the local region (Figure 3.2). This evidence came from internal EPA modeling or
State-submitted modeling.
5 It is a generic approximation used in this analysis for the sphere of possible emissions influence
on air quality at the violating monitors. The actual area of emissions control is determined by
states during attainment planning.
6 Large point sources, due to the relative magnitude of emissions and high emissions stack
heights, theoretically may impact air quality at a downwind violating monitor at distances
beyond 200km.
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Table 3.1: Controls for Current Ozone Standard by Sector Applied in the Baseline
Determination for 2020
Sector
Control Measures
NOx
VOC
NonEGU Biosolid Injection Technology
Point LNB (Low NOx Burner)
LNB + FOR (Flu Gas Recirculation)
LNB + SCR (Selective Catalytic Reduction)
NSCR (Non-selective Catalytic Reduction)
OXY-Firing
SCR
SCR + Steam Injection
SCR + Water Injection
SNCR (Selective Non-catalytic Reduction)
SNCR—Urea
SNCR—Urea Based
Permanent Total Enclosure (PTE)
Work Practices, Use of Low VOC Coatings
(NonEGU Point Sources)
Area RACT to 25 tpy (LNB)
Switch to Low Sulfur Fuel
Water Heater + LNB Space Heaters
Nonroad
Mobile
EGU
CARB Long-Term Limits
Catalytic Oxidizer
Equipment and Maintenance
Gas Collection (SCAQMD/BAAQMD)
Incineration >100,000 Ibs bread
Low Pressure/Vacuum Relief Valve
OTC Mobile Equipment Repair and Refinishing
Rule
OTC Solvent Cleaning Rule
SCAQMD—Low VOC
SCAQMD Limits
SCAQMD Rule 1168
Work Practices, Use of Low VOC Coatings (Area
Sources)
Switch to Emulsified Asphalts
Onroad Diesel Retrofits
Mobile Reduce Gasoline Reid Vapor Pressure (RVP) to 7.0 (EPA, 2005a)
Elimination of Long Duration Idling
Continuous Inspection and Maintenance
Commuter Programs
Additional Technology Changes in the Onroad Transportation Sector
Diesel Retrofits and Engine Rebuilds
Reduce Gasoline Reid Vapor Pressure (RVP) to 7.0 (EPA, 2005a)
Aircraft NOx International Standard
None
None
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Figure 3.1: Counties Where Controls for Nitrogen Oxides (NOx) Were Included for
NonEGU Point and Area Sources, for the Current Ozone Standard in the Baseline
f \
V 1 V
I 1 Nitrogen oxide (NOx) controls applied to NonEGU point and Area sources
For the Onroad and Nonroad Mobile source sectors, some controls were applied nationwide for
the current ozone standard in the baseline, while others were applied statewide in certain states or
locally in a limited number of counties (see Figure 3.3). Counties were identified for locally
applied Mobile source controls as follows: 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 generally
included first. Both onroad and nonroad diesel retrofits and idling elimination were included in
California with an assumed 75% market penetration, and in baseline reduction areas outside of
California with an assumed 25% market penetration. EPA determined that 25% would have a
significant impact, but was feasible to achieve and was applied for reduction areas outside of
California. EPA further determined that for southern California a 75% level of reduction could
be achieved, which was the highest cost-effective penetration rate that EPA felt could be
reasonably accomplished.
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Figure 3.2: Counties Where Controls for Volatile Organic Chemicals (VOCs) Were
Applied to NonEGU Point and Area Sources for the Current Ozone Standard in the
Baseline
VOC Controls applied to NonEGU Point and Area Sources
3.1.2 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 661 counties with ozone
monitors. Projections of ozone design values were developed according to procedures outlined in
EPA modeling guidance.7'8
7 Available online at: http ://www. epa. gov/scramOO 1 /guidance/guide/final-03 -pm-rh-guidance .pdf
8 As part of the procedure for projecting future ozone design values, the guidance recommends
using a criterion that there be a minimum of 5 modeled days with predicted base year ozone at or
above 0.070 ppm. This criterion was relaxed to a minimum of 1 day at or above 0.060 ppm for
the 82 counties with fewer than 5 days with predicted 2002 concentrations at or above 0.070
ppm.
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Figure 3.3: Areas Where NOx and VOC Controls Were Included for Mobile Onroad and
Nonroad Sources in Addition to National Mobile Controls* for the Current Ozone
Standard in the Baseline
I Statewide controls**
I Statewide + Local controls***
* International Aircraft NOx Standard, national control measures applied as part of the PM NAAQS RIA,
and Additional Technology Changes in the Onroad Transportation Sector.
**Onroad retrofits, elimination of long duration idling, and lower Reid Vapor Pressure (RVP) gasoline.
***Nonroad retrofits, continuous inspection and maintenance, and commuter programs.
The baseline shows that 6 counties would not meet the current ozone standard in 2020, even after
inclusion of all known controls. Of these 6 counties, 5 of them are in portions of California that
have current state implementation plans that reflect an attainment date of 2024. After including
known controls as described above, the analysis predicted that the remaining 655 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.
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Figure 3.4: Baseline Projected 8-Hour Ozone Air Quality in 2020
a, b, c, d
Legend
6 counties that exceed 0.084
5 counties that exceed 0.079 ppm for a total of 11
17 additional counties that exceed 0.075 ppm for a total of 28
61 additional counties that exceed 0.070 ppm for a total of 89
142 additional counties that exceed 0.065 ppm for a total of 231
430 counties meet 0.065 ppm
a Modeled emissions reflect the expected reductions from federal programs including the Clean Air
Interstate Rule (EPA, 2005b), the Clean Air Mercury Rule (EPA, 2005c), the Clean Air Visibility Rule
(EPA, 2005d), the Clean Air Nonroad Diesel Rule (EPA, 2004), the Light-Duty Vehicle Tier 2 Rule
(EPA, 1999), the Heavy Duty Diesel Rule (EPA, 2000), proposed rules for Locomotive and Marine
Vessels (EPA, 2007a) and for Small Spark-Ignition Engines (EPA, 2007b), 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.
3.1.3 National Baseline Sensitivity Analysis
Circular A-4 of the Office of Management and Budget's (OMB) guidance under Executive Order
12866 defines a no-action baseline as "what the world will be like if the proposed rule is not
adopted." The illustrative analysis in this RIA assesses the costs and benefits of moving from this
"no-action" baseline to a suite of possible new standards. Circular A-4 states that the choice of
an appropriate baseline may require consideration of a wide range of potential factors, including:
• evolution of the market,
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• changes in external factors affecting expected benefits and costs,
• changes in regulations promulgated by the agency or other government entities, and
• the degree of compliance by regulated entities with other regulations (OMB, 2003).
Circular A-4 also recommends that:
"When more than one baseline is reasonable and the choice of baseline will significantly
affect estimated benefits and costs, you should consider measuring benefits and costs
against alternative baselines. In doing so you can analyze the effects on benefits and costs
of making different assumptions about other agencies' regulations, or the degree of
compliance with your own existing rules." (OMB 2003)
In Appendix la, we describe a sensitivity analysis that we conducted to provide information
about how the no-action baseline would differ under different assumptions about mobile
technologies. It also assesses nationally what the change would be to costs and benefits of a new
standard of 0.075 ppm and alternate primary standards of 0.079, 0.070, and 0.065 ppm. See
Appendix 7a for more details.
3.2 Developing the Modeled 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
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. The 2020 baseline air quality modeling for proposal resulted in 203 counties with
projected design values exceeding 0.070 ppm. In the final rule modeling of the 2020 baseline
there are 89 counties projected to exceed 0.070 ppm. The reduction in the number of counties
projected to exceed 0.070 between proposal and final reflects the net effect of the updates to the
air quality modeling platform, as described in Chapter 2, and the additional emissions controls in
the final rule baseline modeling compared to proposal.
Controls for five sectors were used in developing the control analysis, as discussed previously:
nonEGU point, Area, onroad mobile and nonroad mobile, along with EGUs. Reductions in both
NOx and VOC ozone precursors were needed in all 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 known controls to reach attainment. For the control strategy, controls for five sectors
were used in developing the control analysis, as discussed previously: nonEGU point, Area,
onroad mobile and nonroad mobile, along with EGUs. Reductions in both NOx and VOC ozone
precursors were needed in all sectors to meet a tighter standard. The emissions for this control
strategy were input to the CMAQ model as part of the process to project ozone design values for
the 2020 control strategy. The results of modeling the control strategy indicate that there were
some areas projected not to attain 0.070 ppm in 2020 using all known control measures. To
complete the analysis, EPA was then required to extrapolate the additional emission reductions
required to reach attainment. The methodology used to develop those estimates and those
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calculations are presented in Chapter 4. Appendix 7a presents a sensitivity analysis of three
mobile source control measures that could be included in the control strategy to illustrate
attainment.
Table 3.2: Controls Applied, by Sector, for the 0.070 ppm Control Strategy (Incremental to
Baseline)
Sector
Control Measures
NOx
VOC
NonEGU Biosolid Injection Technology
Point LNB (Low NOx Burner)
LNB + FOR (Flu Gas Recirculation)
LNB + SCR (Selective Catalytic Reduction)
NSCR (Non-selective Catalytic Reduction)
OXY-Firing
SCR
SCR + Steam Injection
SCR + Water Injection
SNCR (Selective Non-catalytic Reduction)
SNCR—Urea
SNCR—Urea Based
Permanent Total Enclosure (PTE)
Work Practices, Use of Low VOC Coatings
(NonEGU Point Sources)
Area
Nonroad
Mobile3
RACT to 25 tpy (LNB)
Switch to Low Sulfur Fuel
Water Heater + LNB Space Heaters
CARB Long-Term Limits
Catalytic Oxidizer
Equipment and Maintenance
Gas Collection (SCAQMD/BAAQMD)
Incineration >100,000 Ibs bread
Low Pressure/Vacuum Relief Valve
OTC Mobile Equipment Repair and
Refinishing Rule
OTC Solvent Cleaning Rule
SCAQMD—Low VOC
SCAQMD Limits
SCAQMD Rule 1168
Work Practices, Use of Low VOC Coatings
(Area Sources)
Switch to Emulsified Asphalts
Onroad Increased Penetration of Onroad SCR and DPF from 25% to 75%
Mobile" Continuous Inspection and Maintenance (OBD)
Increased Penetration of Nonroad SCR and DPF from 25% to 75%
EGU -Lower ozone season nested caps in OTC and
MWRPO states while retaining the current
CAIR cap and a new cap for Eastern Texas.
-Application of local controls (SCR and
SNCR) nationally to coal fired units in and
around NA counties covering the combination
of CBSA (Core based Statistical Areas) and
CSA (Combined Statistical Areas)B outside of
OTC and, MWRPO, and East Texas.
None
a Onroad and Nonroad Mobile Source control measures applied for the Baseline analysis were applied to
additional geographic areas in the 0.070 ppm analysis. SCR and DPF retrofits market penetration was
increased from 25% to 75% for all areas outside of California.
bFor the definition and current lists of CBSA and CSAs, see
http://www.census.gov/population/www/estimates/metrodef.html
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3.2.1 Controls Applied for the Modeled Control Strategy: NonEGU Point and Area Sectors
NonEGU point and Area control measures were identified using AirControlNET 4.1.9'10 To
reduce NOx and VOC emissions, all known control measures, below a cost cap, were applied,
allowing for the largest emission reduction per source over the widest geographic area. Because
all available controls up to the cost cap were used in counties needing emission reductions,
ordering of which controls were applied first was not relevant. In areas where residual
nonattainment remained after the modeled control strategy, some known controls above the cost
cap were analyzed and applied to achieve additional emissions reductions as a portion of the
extrapolated cost analysis. See Chapter 5 for more information on how we selected our cost cap
and the extrapolated cost analysis.
Supplemental controls, which estimated additional emissions control based on similar
technology for NonEGU point and Areas sources were included in the analysis prior to the
extrapolating costs of unknown controls. Supplemental controls are described in further detail in
Appendix 3.
NOx nonEGU point and Area controls were applied to 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 addition, controls were applied to large nonEGU
point sources outside the 200km buffer zones. The criteria for control of these large nonEGU
point sources was as follows: the plant level emissions exceeded 1,000 tons of NOx in 2020, the
plant was in a county that touches the 200km buffer, and the plant was close to a nonattainment
county that had difficulty attaining 0.070 ppm in the ozone NAAQS proposal RIA.
9 See http://www.epa.gov/ttnecas 1 /AirControlNET.htm for a description of how AirControlNET
operates and what data is included in this tool.
10 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. See
http://www.epa.gov/particles/measures/pm_control_measures_tables_verl.pdf for more details
on this control measures database. 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 Association 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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Figure 3.5: Counties Where Controls for Nitrogen Oxides (NOx) Were Applied to NonEGU
Point and Areas Sources for the RIA Modeled Control Strategy (Incremental to Baseline)
I 1 Nitrogen oxide (NOx) controls applied to NonEGU Point and area sources
VOC controls were applied in select counties where the following criteria were met (including
the counties which included VOC controls in their baselines): VOC emissions were high (>5,000
tpy or >25tpy/sq. mi), the county design value was projected to be > 0.070 ppm in the 2020 (See
Figure 3.6), and the area had some historical evidence that VOC controls would appreciably
lower ozone in the local region. This evidence came from internal EPA modeling or State-
submitted modeling.
3.2.2 Controls Applied for the Modeled Control Strategy: EGU Sector
In the Proposal RIA, a control strategy was applied for the EGU sector for the East only, (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.) In the proposed RIA, emissions
reductions were targeted in the OTC and MWRPO states through lower "nested caps" and
"command and control" application in the non-attainment counties outside of the OTC and
MWRPO within CAIR.
For the Final RIA, we have employed an enhanced strategy, both in terms of the quantity of
reductions and the geographic extent of the areas covered. Figure 3.7 depicts the areas covered
for the EGU sector emission reduction strategy.
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Figure 3.6: Counties Where VOC Controls Were Applied to NonEGU Point and Areas
Sources for the Modeled Control (Incremental to Baseline)
] VOC controls applied to NonEGU Point and Area Sources
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 eastern U.S. by 2020.n 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 Rule.12
11 See http://www.epa.gov/airmarkets/progress/progress-reports.html for more information
12 See http://www.epa.gov/CAIR/technical.html
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Figure 3.7: Geographic Areas where NOx Controls were Applied to Electrical Generating
Units (EGUs) for the Modeled Control Strategy (Incremental to Baseline)
OTC NrjiBdC.jp
kWPPO M?*lrd Cap 1Xfk)
Ej-J Ta tii L 4f, ^5
-------
post-combustion controls, such as Selective Catalytic Reduction (SCR) and Selective Non-
Catalytic Reduction (SNCR), 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
reducti14on that could be obtained from these units was subtracted from the sum of State level
ozone control season NOx caps for the OTC and East Texas regions, and 90% for the MWRPO
states in CAIR.15 The CAIR cap for the entire region was kept unchanged.
In order to address nonattainment elsewhere in the West and CAIR region outside of the
MWRPO, and OTC, and East Texas a "command and control" type strategy for coal-fired units
has been designed. Annual and ozone season CAIR caps remained unchanged in the East, 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 on coal units that are projected to remain without controls under the
CAIR/CAMR/CAVR cap-and-trade scheme. All non-attainment areas nationwide, outside of the
OTC, MWRPO, and East Texas were subject to this local command-and-control strategy,
covering the CBSA and CSA counties in and around nonattainment counties.
At this time, we are in the process of improving our ability to achieve additional reductions
available in NOx emissions from EGUs and corresponding air quality benefits,
especially on high energy demand days (HEDDs) through energy efficiency measures.
We were not able to apply such control strategies as part of this RIA. A Technical
Support Document (TSD) is available summarizing the previous and ongoing work in
this area.
3.2.3 Controls Applied for the Modeled Control Strategy: 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 an alternate primary standard (See Figure 3.8).
Information on mobile source control measures for the modeled control strategy analysis were
derived from various EPA studies and from running EPA's National Mobile Inventory Model
(NMIM), which includes the MOBILE6 Onroad model and the NONROAD model. See
www.epa.gov/otaq/nmim.htm for more information on NMIM and see Appendix 3.3 for more
information on mobile source controls included in the modeled control strategy analysis.
All of the local mobile source controls included in the ozone baseline were expanded for the
hypothetical national control strategy to attain an alternate primary standard. In the case of
onroad and nonroad Selective Catalytic Reduction (SCR) and Diesel Particulate Filters (DPF),
14 Potential for Reducing NOx Emissions from EGU Sources on High Energy Demand Days with
Energy Efficiency Measures. Technical Support Document for the Final Ozone NAAQS
Regulatory Impact Analysis. U.S. Environmental Protection Agency, Office of Air and
Radiation. March 2008.
15 Detailed analysis showed that 75%-90% 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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the measure was applied at a greater penetration rate—to 75% of the modeled equipment
population. 75% was the highest cost-effective penetration rate that EPA felt could be reasonably
accomplished. All local and statewide measures were applied to sources in additional geographic
areas beyond the areas controlled in the baseline. Descriptions of the mobile source rules and
measures can be found in Appendix 3.3.
Figure 3.8: Areas Where NOx and VOC Controls Were Applied to Mobile Onroad and
Nonroad Sources in Addition to National Mobile Controls for the Modeled Control
Strategy (incremental to Baseline)
J Statewide controls*
I Statewide + Local controls**
*Onroad retrofits, elimination of long duration idling, and lower Reid Vapor Pressure (RVP) gasoline.
**Nonroad retrofits, continuous inspection and maintenance, and commuter programs.
As in the baseline, some mobile source controls were applied statewide for all states with a
county projected to exceed 0.070 ppm. 'Local' controls were applied to counties within a 200 km
buffer from counties projected to exceed 0.070 ppm 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
As stated at the beginning of this section, additional reductions were needed to complete the
analysis of the alternate standard. In addition to the emission reductions accounted for in the
extrapolation approach described in Chapter 4, Appendix 7a presents a sensitivity analysis of
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three mobile source control measures that could be included in the control strategy to illustrate
attainment of the alternate standard.
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 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 VOC 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.
With respect to the baseline (CAIR/CAMR/CAVR), total emissions of NOx is lower. At the
same time emissions shift geographically and hence do not decrease everywhere within the cap-
and-trade regions. However, EGU NOx emissions do decrease substantially everywhere
compared to the pre-CAIR levels. Substantial EGU NOx emission reductions are already being
achieved through CAIR/CAMR/CAVR. This strategy focuses reductions under trading programs
where they are needed most, with the result that some areas get less reductions than might have
been otherwise expected 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, and East Texas) regions and local controls (SCR and
SNCR) on the uncontrolled coal units in the non-attainment counties (and surrounding CBSA
and CSA) outside of the trading regions OTC and MWRPO within CAIR region result in
emission shifts increase of emissions elsewhere within or outside of CAIR region compared to
the base line (CAIR/CAMR/CAVR). While there are substantial total NOx emission reductions
(roughly 53,000 tons within the OTC, and MWRPO, and East Texas; and roughly 16,000 tons
nationwide) expected for the 2020 ozone season (roughly 55,500 tons) compared to the base line
(CAIR/CAMR/CAVR) 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 are emission shifts geographically and there is the possibility of increases in emission
from the remainder of sources within and outside of the CAIR region. This approach provides a
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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, and the states
or other authorities may take additional steps to minimize these increases if warranted.
Figure 3.9: Annual Tons of NOx Emission Reductions for the Modeled Control Strategy
(Incremental to the Baseline)*
^-17,430 --2,500
[—1-2,499--500
; |-499--100
| 1-99-+100**
| 1+101- +500
^+501-+2,500
•+2,501 -+17,658
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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Figure 3.10: Percentage of 2020 Annual NOx Emissions Reduced by Sector Incremental to
the Baseline
21%
64%
Area ECU Point . NonEGU Point _ Onroad _ Nonroad
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Figure 3.11: Annual Tons of VOC Emission Reductions for the Modeled Control Strategy
(Incremental to the Baseline)*
Reductions are negative and increases are positive
* The -99-+S3 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.
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Figure 3.12: Percentage of 2020 Annual VOC Emissions Reduced by Sector
24%
70%
Area _ NonEGU Point _ Onroad _ Nonroad
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 the modeled control strategy. 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, and 0.079 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.
Table 3.3 shows the tons of emissions reduced from the modeled control strategy, incremental to
the baseline. Figure 3.14 shows the tons of emissions remaining after application of the
hypothetical modeled control strategy, by sector.
Using this strategy, it is possible to reach attainment in 600 counties. However, there are still 61
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
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additional reductions in NOx and VOC. Chapter 4 will address the methodology for determining
the additional tons that were needed to reach full attainment.
Figure 3.13: Projected 8-Hour Ozone Air Quality in 2020 From Applying the Modeled
Control Strategy"'b'c'd'e'
Legend
J 6 counties that exceed 0.084 ppm
I 4 counties that exceed 0.079 ppm for a total of 10
J 11 additional counties that exceed 0.075 ppm fora total of 21
J 40 additional counties that exceed 0.070 ppm fora total of 61
Jl05 additional counties that exceed 0.065 ppm for a total of 166
J 495 counties meet 0.065 ppm standard
' Modeled emissions reflect the expected reductions from federal programs including the Clean Air
Interstate Rule (EPA, 2005b), the Clean Air Mercury Rule (EPA, 2005c), the Clean Air Visibility Rule
(EPA, 2005d), the Clean Air Nonroad Diesel Rule (EPA, 2004), the Light-Duty Vehicle Tier 2 Rule
(EPA, 1999), the Heavy Duty Diesel Rule (EPA, 2000), Locomotive and Marine Vessels (EPA, 2007a)
and for Small Spark-Ignition Engines (EPA, 2007b), 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.
' Controls applied are illustrative. States may choose to apply different control strategies for
implementation.
: The current standard of 0.08 ppm is effectively expressed as 0.084 ppm when rounding conventions are
applied.
1 Modeled design values in ppm are only interpreted up to 3 decimal places.
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Table 3.3: Emissions and Reductions (2020) From Applying the Modeled Control Strategy
by Region (Incremental to the Baseline)
Emissions
Sector
Baseline Annual
Emissions
(annual tons/year)
Modeled Control Strategy Emission Reductions (annual
tons/year)
East West California"
VOC
NOX
VOC
NOX
VOC
NOX
VOC
NOX
Area 1,700,000 7,900,000 140,000 20,000 15,000 1,100 10,000
35
NonEGU
Point
1,900,000
49,000 4,000 350,000 280 19,000 260
1,600
EGU Point 2,000,000 1,100,000
7,500
19,000
1,400
Onroad 1,700,000 1,800,000 50,000 110,000 10,000 15,000
45
71
Nonroad 2,600,000 1,500,000 10,000 32,000
1,500
3,300
19
140
a A majority of the control measures were applied for the baseline in California.
Figure 3.14: National Annual Emissions Remaining (2020) after Application of Controls for
the Baseline and Modeled Control Strategy
Modeled Control Strategy 2.5
X
o
Baseline
Modeled Control Strategy
O
O
1.9
5.0
10.0 15.0
Emissions (M Tons)
Z Nonroad = Onroad Z Area • NonEGU EGU
20.0
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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).
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).
U.S. Environmental Protection Agency (EPA). 1999. Regulatory Impact Analysis - Control of
Air Pollution from New Motor Vehicles: Tier 2 Motor Vehicle Emissions Standards and
Gasoline Sulfur Control, U.S. Environmental Protection Agency, Office of Transportation and
Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105, EPA420-R-99-023,
December 1999. Available at http://www.epa.gov/tier2/frm/ria/r99023.pdf.
U.S. Environmental Protection Agency (EPA). 2000. Regulatory Impact Analysis: Heavy-Duty
Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements, U.S.
Environmental Protection Agency, Office of Transportation and Air Quality, Assessment and
Standards Division, Ann Arbor, MI 48105, EPA420-R-00-026, December 2000. Available at
http ://www. epa.gov/otaq/highway-diesel/regs/exec-sum.pdf
U.S. Environmental Protection Agency (EPA). 2004. Final Regulatory Analysis: Control of
Emissions from Nonroad Diesel Engines, U.S. Environmental Protection Agency, Office of
Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105,
EPA420-R-04-007, May 2004. Available at http://www.epa.gov/nonroad-
diesel/2004fr/420r04007.pdf.
U.S. Environmental Protection Agency (EPA), 2005a. Guide on Federal and State Summer RVP
Standards for Conventional Gasoline Only. EPA420-B-05-012. November 2005.
3-25
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U.S. Environmental Protection Agency (EPA). 2005b. Clean Air Interstate Rule Emissions
Inventory Technical Support Document, U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards, March 2005. Available at
http://www.epa.gov/cair/pdfs/finaltechO 1 .pdf.
U.S. Environmental Protection Agency (EPA). 2005c. Emissions Inventory and Emissions
Processing for the Clean Air Mercury Rule (CAMR), U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, March 2005. Available at
http ://www. epa. gov/ttn/atw/utility/emiss_inv_oar-2002-0056-6129 .pdf
U.S. Environmental Protection Agency (EPA). 2005d. Regulatory Impact Analysis for the Final
Clean Air Visibility Rule or the Guidelines for Best Available Retrofit Technology (BART)
Determinations Under the Regional Haze Regulations, U.S. Environmental Protection Agency,
Office of Air and Radiation, June 2005. EPA-452-R-05-004. Available at
http ://www. epa.gov/visibility/pdfs/bart_ria_2005_6_ 15 .pdf
U.S. Environmental Protection Agency (EPA). 2007a, Regulatory Announcement: EPA Proposal
for More Stringent Emissions Standards for Locomotives and Marine Compression-Ignition
Engines. EPA420-F-07-015.
U.S. Environmental Protection Agency (EPA). 2007b, Proposed Emission Standards for New
Nonroad Spark-Ignition Engines, Equipment, and Vessels. EPA420-F-07-032.
U.S. Environmental Protection Agency (EPA). 2008a. Air Quality Modeling Platform for the
Ozone National Ambient Air Quality Standard Final Rule Regulatory Impact Assessment.
U.S. Environmental Protection Agency (EPA). 2008b. Technical Support Document: Preparation
of Emissions Inventories For the 2002-based Platform, Version 3, Criteria Air Pollutants,
USEPA, January, 2008.
U.S. Office of Management and Budget. September 2003. Circular A-4, Regulatory Analysis
Guidance sent to the Heads of Executive Agencies and Establishments. Washington, DC.
http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf.
3-26
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Appendix 3: Additional Control Strategy Information
3a.l NonEGU Point and Area Source Controls
Ba.1.1 NonEGU Point and Area Source Control Strategies for Ozone NAAQS Final
In the NonEGU point 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 nonEGU point
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 70 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
nonEGU point NOx sources. We also looked into sources of controls for highly reactive VOC
nonEGU point sources. Four additional controls were applied for highly reactive VOC nonEGU
point sources not in AirControlNET.
3a.l.2 NOx Control Measures for NonEGU Point Sources.
Several types of NOx control technologies exist for nonEGU point 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 nonEGU point 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. 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
3a-l
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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. Table 3a.l contains a complete list of the NOx
nonEGU point control measures applied and their associated emission reductions obtained in the
modeled control strategy for the alternate primary standard. For more information on these
measures, please refer to the AirControlNET 4.1 control measures documentation report.
Table 3a.l: NOx NonEGU Point Emission Reductions by Control Measure
Control Measure
Biosolid Injection
Technology
LNB
LNB + FOR
LNB + SCR
NSCR
OXY-Firing
SCR
Source Type
Cement Kilns
Asphaltic Cone; Rotary Dryer; Conv Plant
Ceramic Clay Mfg; Drying
Conv Coating of Prod; Acid Cleaning Bath
Fuel Fired Equip; Furnaces; Natural Gas
In-Process Fuel Use; Natural Gas
In-Process Fuel Use; Residual Oil
In-Process; Process Gas; Coke Oven Gas
Lime Kilns
Sec Alum Prod; Smelting Furn
Steel Foundries; Heat Treating
Surf Coat Oper; Coating Oven Htr; Nat Gas
Fluid Cat Cracking Units
Fuel Fired Equip; Process Htrs; Process Gas
In-Process; Process Gas; Coke Oven Gas
Iron & Steel Mills — Galvanizing
Iron & Steel Mills — Reheating
Iron Prod; Blast Furn; Blast Htg Stoves
Sand/Gravel; Dryer
Steel Prod; Soaking Pits
Iron & Steel Mills — Annealing
Process Heaters — Distillate Oil
Process Heaters — Natural Gas
Process Heaters — Other Fuel
Process Heaters — Process Gas
Process Heaters — Residual Oil
Rich Burn 1C Engines — Gas
Rich Burn 1C Engines — Gas, Diesel, LPG
Rich Burn Internal Combustion Engines — Oil
Glass Manufacturing — Containers
Glass Manufacturing — Flat
Glass Manufacturing — Pressed
Ammonia — NG-Fired Reformers
Cement Manufacturing — Dry
Cement Manufacturing — Wet
1C Engines — Gas
ICI Boilers— Coal/Cyclone
ICI Boilers— Coal/Wall
ICI Boilers— Coke
ICI Boilers— Distillate Oil
Modeled Control
Strategy Reductions
(annual tons/year)
1,200
120
370
440
170
1,300
39
190
5,900
62
13
30
3,600
700
880
35
1,100
1,000
11
100
270
2,300
27,000
14
4,200
37
22,000
3,700
11,000
7,600
18,000
3,900
5,800
25,000
22,000
54,000
2,200
22,000
490
4,800
3a-2
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Control Measure
SCR + Steam Injection
SCR + Water Injection
SNCR
SNCR— Urea
SNCR— Urea Based
Source Type
ICI Boilers — Liquid Waste
ICI Boilers— LPG
ICI Boilers — Natural Gas
ICI Boilers — Process Gas
ICI Boilers— Residual Oil
Natural Gas Prod; Compressors
Space Heaters — Distillate Oil
Space Heaters — Natural Gas
Sulfate Pulping — Recovery Furnaces
Combustion Turbines — Natural Gas
Combustion Turbines — Jet Fuel
Combustion Turbines — Natural Gas
Combustion Turbines — Oil
By-Product Coke Mfg; Oven Underfiring
Comm./Inst. Incinerators
ICI Boilers— Coal/Stoker
Indust. Incinerators
Medical Waste Incinerators
In-Process Fuel Use; Bituminous Coal
Municipal Waste Combustors
Nitric Acid Manufacturing
Solid Waste Disp; Gov; Other Inc
ICI Boilers— MSW/Stoker
ICI Boilers— Coal/FBC
ICI Boilers— Wood/Bark/Stoker— Large
In-Process; Bituminous Coal; Cement Kilns
In-Process; Bituminous Coal; Lime Kilns
Modeled Control
Strategy Reductions
(annual tons/year)
730
280
36,000
8,600
17,000
810
22
640
9,900
18,000
—
—
210
4,300
1,400
7,000
250
—
32
4,400
3,100
95
120
100
5,500
300
31
3a.l.3 VOC Control Measures for NonEGU Point Sources.
VOC controls were applied to a variety of nonEGU point sources as defined in the emissions
inventory in this RIA. The first control is: 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). The second control applied is petroleum
and solvent evaporation applied to printing and publishing sources as well as to surface coating
operations. Table 3a.2 contains the emissions reductions for these measures in the modeled
control strategy for the alternate primary standard. For more information on these measures, refer
to the AirControlNET 4.1 control measures documentation report.
3a-3
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Table 3a.2: VOC NonEGU Point Emission Reductions by Control Measure
Modeled Control
Strategy Reductions
Control Measure Source Type (annual tons/year)
Permanent Total Enclosure (PTE) Fabric Printing, Coating and Dyeing 43
Paper and Other Web Coating 490
Petroleum and Solvent Evaporation Printing and Publishing 3,600
Surface Coating 400
3a.l.4 NOx Control Measures for Area Sources
There were three control measures 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. The third control was switching to
low sulfur fuel for residential home heating. This control is primarily designed to reduce sulfur
dioxide, but has a co-benefit of reducing NOx. Table 3a.3 contains the listing of control
measures and associated reductions for the modeled control strategy. For additional information
regarding these controls please refer to the AirControlNET 4.1 control measures documentation
report.
Table 3a.3: NOx Area Source Emission Reductions by Control Measure
Modeled Control Strategy
Reductions
Control Measure Source Type (annual tons/year)
RACT to 25 tpy (LNB) Industrial Coal Combustion 5,400
Industrial NG Combustion 3,000
Industrial Oil Combustion 570
Switch to Low Sulfur Fuel Residential Home Heating 970
Water Heater + LNB Space Heaters Commercial/Institutional—NG 4,300
Residential NG 6,700
3a.l.5 VOC Control Measures for Area Source.
The most frequently applied control to reduce VOC emissions from area sources was CARB
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 control 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 service
3a-4
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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. Table 3a.4 contains the control measures and
associated emission reductions described above for the modeled control strategy. For additional
information regarding these controls please refer to the AirControlNET 4.1 control measures
documentation report.
Table 3a.4: VOC Area Source Emission Reductions by Control Measure
Modeled Control
Strategy Reductions
Control Measure
CARB Long-Term Limits
Catalytic Oxidizer
Equipment and Maintenance
Gas Collection (SCAQMD/BAAQMD)
Incineration >100,000 Ibs bread
Low Pressure /Vacuum Relief Valve
OTC Mobile Equipment Repair and
Refinishing Rule
OTC Solvent Cleaning Rule
SCAQMD— Low VOC
SCAQMD Limits
SCAQMD Rule 11 68
Solvent Utilization
Switch to Emulsified Asphalts
Source Type
Consumer Solvents
Conveyorized Charbroilers
Oil and Natural Gas Production
Municipal Solid Waste Landfill
Bakery Products
Stage II Service Stations
Stage II Service Stations — Underground
Tanks
Aircraft Surface Coating
Machn, Electric, Railroad Ctng
Cold Cleaning
Rubber and Plastics Mfg
Metal Furniture, Appliances, Parts
Adhe sive s — Industrial
Large Appliances
Metal Furniture
Surface Coating
Cutback Asphalt
(annual tons/year)
78,000
250
450
1,100
2,700
9,900
9,800
720
4,400
10,000
1,700
6,300
22,000
8,200
7,600
2,900
3,300
3a. 1.6 Supplemental Controls
Table 3a.5 below summarizes the supplemental control measures added to our control measures
database by providing the pollutant it controls and its control efficiency (CE). These controls
were applied not as part of the modeled control strategy, but as supplemental measures prior to
extrapolating unknown control costs. However, these controls are not currently located in
AirControlNET. These measures are primarily found in draft SIP technical documents and have
not been fully assessed for inclusion in AirControlNET.
3a-5
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Table 3a.5: Supplemental Emissions Control Measures Added to the Control Measures
Database
Control
Poll Technology
NOx LEC
VOC Enhanced LDAR
LDAR
Monitoring Program
Inspection and
Maintenance Program
(Separators)
Water Seals (Drains)
Work Practices,
Use of Low VOC
Coatings
(Area Sources)
Work Practices,
Use of Low VOC
Coatings
(NonEGU Point)
SCC
20200252
20200254
3018001-
30600701
30600999 -
3018001 -
30600702-
30600503-
2401025000
2401030000
2401060000
2425010000
2425030000
2425040000
2461050000
307001199
Surface Coating
Operations
within SCC
4020000000,
Printing/Publis
hing processes
within SCC
4050000000
SCC
Description
Internal Comb. Engines/Industrial/
Natural Gas/2-cycle Lean Burn
Internal Comb. Engines/Industrial/
Natural Gas/4-cycle Lean Burn
Fugitive Leaks
Flares
Fugitive Leaks
Cooling towers
Wastewater Drains and Separators
Solvent Utilization
Petroleum and Solvent Evaporation
Percent
Reduction
87
87
50
98
80
No general
estimate
65
90
90
Low Emission Combustion (LEC)
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 CE, 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
3a-6
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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
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 CE 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
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.
2"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.
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-7
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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 CE 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
Work Practices or Use of Low VOC Coatings
Overview: The control measure is either application of work practices (e.g., storing VOC-
containing cleaning materials in closed containers, minimizing spills) or using coatings that have
much lower VOC content. These measures, which are of relatively low cost compared to other
VOC area source controls, can apply to a variety of processes, both for non-EGU point and area
sources, in different industries and is defined in the proposed control techniques guidelines
(CTG) for paper, film and foil coatings, metal furniture coatings, and large appliance coatings
published by the US EPA in July 2007.7
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.
7 U.S. Environmental Protection Agency. Consumer and Commercial Products: Control
Techniques Guidelines in Lieu of Regulations for Paper, Film, and Foil Coatings; Metal
3a-8
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The estimated CE expected to be achieved by either of these control measures is 90 percent.
Major Uncertainties: The greatest uncertainty is in how many potentially affected processes are
implementing or already implemented these control measures. This may be particularly true in
California. Also, there are nine States that have many of the above work practices in effect for
paper, film and foil coatings processes, but the work practices are not meant to achieve a specific
emissions limit.8 Hence, it is uncertain how much VOC reduction is occurring from this control
measure in this case.
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 nonEGU point 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 70 SCCs. The changes apply to the
control strategies performed for the Eastern US only. These changes are listed in Table 3a.6.
Table 3a.6: Supplemental Emission Control Measures—Changes to Control Technologies
Currently in our Control Measures Database For Application in 2020
Poll
NOX
NOX
NOX
sec
10200104
10200204
10200205
10300207
10300209
10200217
10300216
10200901
10200902
10200903
10200907
10300902
10300903
10200401
10200402
10200404
10200405
10300401
AirControlNE New Old
AirControlNET Source T Control New Control CE CE
Description Technology Technology (%) (%)
ICI Boilers— Coal-Stoker SNCR SCR 90 40
ICI Boilers— Wood/Bark/ SNCR SCR 90 55
Waste
ICI Boilers— Residual Oil SCR SCR 90 80
Furniture Coatings; and Large Appliance Coatings. 40 CFR 59. July 10, 2007. Available on the
Intenet at http://www.epa.gov/ttncaaa 1 /t 1 /fr notices/ctg ccp092807.pdf. It should be noted that
this CTG became final in October 2007.
8 U.S. Environmental Protection Agency. Consumer and Commercial Products: Control
Techniques Guidelines in Lieu of Regulations for Paper, Film, and Foil Coatings; Metal
Furniture Coatings; and Large Appliance Coatings. 40 CFR 59. July 10, 2007, p. 37597.
Available on the Intenet at http://www.epa.gov/ttncaaal/tl/fr notices/ctg ccp092807.pdf.
3a-9
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Poll
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
sec
10200501
10200502
10200504
10200601
10200602
10200603
10200604
10300601
10300602
10300603
10500106
10500206
30500606
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
30600106
30600199
30600102
30600105
AirControlNET Source
Description
ICI Boilers— Distillate Oil
ICI Boilers — Natural Gas
Cement Manufacturing — Dry
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
Process Heaters — Residual
Oil
Process Heaters — Natural
Gas
AirControlNE
T Control
Technology
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
Biosolid
Injection
SNCR
SNCR
LNB + FGR
LNB + SCR
LNB + SCR
LNB + SCR
LNB + SCR
New Control
Technology
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
Biosolid
Injection
SCR
SCR
SCR
LNB + SCR
LNB + SCR
LNB + SCR
LNB + SCR
New
CE
(%)
90
90
90
90
90
90
90
90
90
90
90
40
90
90
90
90
90
90
90
Old
CE
(%)
80
80
80
80
85
80
70
80
80
80
80
23
45
60 to
98
55
88
90
80
80
3a-10
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Poll
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
sec
30700104
30790013
39000201
39000203
39000289
39000489
39000689
39000701
39000789
50100101
50100506
50200506
50300101
50300102
50300104
50300506
50100102
AirControlNET Source
Description
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
AirControlNE
T Control
Technology
SCR
SNCR
SNCR— urea
based
SNCR— urea
based
SNCR
LNB
LNB
LNB + FGR
LNB
SNCR
New Control
Technology
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
New
CE
(%)
90
90
90
90
90
90
90
90
90
90
Old
CE
(%)
80
45
50
50
40
37
50
55
50
45
The last category of supplemental controls is control technologies currently in our control
measures database being applied to SCCs not controlled currently in AirControlNET.
Table 3a.7: Supplemental Emission Control Technologies Currently in our Control
Measures Database Applied to New Source Types
Pollutant
NOX
NOX
NOX
NOX
NOX
NOX
NOX
sec
39000602
30501401
30302351
30302352
30302359
10100101
10100202
10100204
10100212
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)
Control
Technology
SCR
OXY-Firing
SCR
SNCR
SNCR
SNCR
SNCR
CE
90
85
90
40
40
40
40
3a-ll
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Pollutant
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
sec
10100401
10100404
10100501
10100601
10100602
10100604
10101202
20200253
SCC Description
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
External Combustion Boilers; Electric Generation;
Solid Waste; Refuse Derived Fuel
Internal Comb. Engines/Industrial/Natural Gas/4-cycle
Rich Burn
Control
Technology
SNCR
SNCR
SNCR
NGR
NGR
NGR
SNCR
NSCR
CE
50
50
50
50
50
50
50
90
3a.2 Mobile Control Measures Used in Control Scenarios
Tables 3a.8 and 3a.9 summarize the emission reductions for the mobile source control measures
discussed in this section.
Table 3a.8: NOx Mobile Emission Reductions by Control Measure
Modeled Control Strategy Reductions
(annual tons/year)
Sector
Control Measure
Onroad
Eliminate Long Duration Truck Idling
5,800
Reduce Gasoline RVP
880
Diesel Retrofits
91,000
Continuous Inspection and Maintenance
20,000
Commuter Programs
4,100
Nonroad
Diesel Retrofits and Engine Rebuilds
35,000
Table 3a.9: VOC Mobile Emission Reductions by Control Measure
Sector
Onroad
Nonroad
Control Measure
Reduce Gasoline RVP
Diesel Retrofits
Continuous Inspection and Maintenance
Commuter Programs
Reduce Gasoline RVP
Diesel Retrofits and Engine Rebuilds
Modeled Control Strategy Reductions
(annual tons/year)
17,000
8,400
28,000
7,000
6,300
5,200
3a-12
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3a.2.1 Diesel Retrofits and Engine Rebuilds
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 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
3a-13
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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.lO and Table 3a.l 1, respectively. The groupings
of model years for highway vehicles reflect changes in EPA's published emissions standards for
new engines.
Table 3a.lO: 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.ll: 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.l2 and Table 3a.l3, respectively.
Table 3a.l2: Percentage Emissions Reduction by Highway Vehicle Retrofit Strategy
PM CO HC NOx
SCR (+DPF) 90% 90% 90% 70%
Table 3a.l3: Percentage Emissions Reduction by Nonroad Equipment Retrofit Strategy
Strategy PM CO HC NOx
SCR (+DPF) 90% 90% 90% 70%
Rebuild/Upgrade Kit 30% 15% 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
3a-14
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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 3 a. 12 and 3 a. 13 to the specified model years in Tables 3 a. 10
and 3a. 11 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.
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.
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.
Source Categories Affected by Measure:
3a-15
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• All 1996 and newer light-duty gasoline vehicles and trucks:
• All 1996 and newer (SCC 2201001000) Light Duty Gasoline Vehicles (LDGV), Total:
All Road Types
• All 1996 and newer (SCC 2201020000) Light Duty Gasoline Trucks 1 (LDGT1), Total:
All Road Types
• All 1996 and newer (SCC 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.
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/smartway/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
3a-16
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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/smartwav/idle-guid.htm.
Pollutants and Source Categories Affected by Measure: NOX
Table 3a.l4: 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"
223 0074110 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
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; Urban Local: Total
Estimated Emissions Reduction from Measure (%): 3.4 % decrease in NOx 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.9 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.
9 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.
3a-17
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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
3a-18
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Table 3a.l5: All Light-Duty Gasoline Vehicles and Trucks
Note: All SCC Descriptions below begin with "Mobile Sources; Highway Vehicles—
SCC Gasoline"
2201001110 Light Duty Gasoline Vehicles (LDGV); Rural Interstate: Total
2201001130 Light Duty Gasoline Vehicles (LDGV); Rural Other Principal Arterial: Total
2201001150 Light Duty Gasoline Vehicles (LDGV); Rural Minor Arterial: Total
2201001170 Light Duty Gasoline Vehicles (LDGV); Rural Major Collector: Total
2201001190 Light Duty Gasoline Vehicles (LDGV); Rural Minor Collector: Total
2201001210 Light Duty Gasoline Vehicles (LDGV); Rural Local: Total
2201001230 Light Duty Gasoline Vehicles (LDGV); Urban Interstate: Total
2201001250 Light Duty Gasoline Vehicles (LDGV); Urban Other Freeways and Expressways: Total
2201001270 Light Duty Gasoline Vehicles (LDGV); Urban Other Principal Arterial: Total
2201001290 Light Duty Gasoline Vehicles (LDGV); Urban Minor Arterial: Total
2201001310 Light Duty Gasoline Vehicles (LDGV); Urban Collector: Total
2201001330 Light Duty Gasoline Vehicles (LDGV); Urban Local: Total
2201020110
2201020130
2201020150
2201020170
2201020190
2201020210
2201020230
2201020250
2201020270
2201020290
2201020310
2201020330
Light Duty
Light Duty
Light Duty
Light Duty
Light Duty
Light Duty
Light Duty
Gasoline Tracks 1
Gasoline Tracks 1
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Light Duty Gasoline
Expressways: Total
Light Duty
Light Duty
Light Duty
Light Duty
Gasoline
Gasoline
Gasoline
Gasoline
Tracks 1
Tracks 1
Tracks 1
Tracks 1
Tracks 1
Tracks 1
Tracks 1
Tracks 1
Tracks 1
Tracks 1
&
&
&
&
&
&
&
&
&
&
&
&
2
2
2
2
2
2
2
2
2
2
2
2
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
Rural Interstate: Total
Rural Other Principal Arterial:
Total
Rural Minor Arterial: Total
Rural Major Collector:
Rural Minor Collector:
Rural Local: Total
Urban Interstate: Total
Urban Other Freeways
Urban Other Principal
Urban Minor Arterial:
Urban Collector: Total
Urban Local: Total
Total
: Total
and
Arterial:
Total
Total
2201040110 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Rural Interstate: Total
2201040130 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Rural Other Principal Arterial: Total
2201040150 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Rural Minor Arterial: Total
2201040170 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Rural Major Collector: Total
2201040190 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Rural Minor Collector: Total
2201040210 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Rural Local: Total
2201040230 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Urban Interstate: Total
2201040250 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Urban Other Freeways and
Expressways: Total
2201040270 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Urban Other Principal Arterial: Total
2201040290 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Urban Minor Arterial: Total
2201040310 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Urban Collector: Total
2201040330 Light Duty Gasoline Tracks 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 %
3a.2.5 Reduce Gasoline RVPfrom 7.8 to 7.0 in RemainingNonattainment 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,
3a-19
-------
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.
Source Categories Affected by Measure:
• 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
3a.3 ECU Controls Used in the Control Strategy
Table 3a.21 contains the ozone season emissions from all fossil EGU sources (greater than 25
megawatts) for the baseline and the control strategy.
Table 3a.l6: NOx EGU Ozone Season Emissions (All Fossil Units >25MW) (1,000 Tons)a
Baseline
(CAIR/CAMR/CAVR)
Control Strategy
OTC
73
65
(-11%)
MWRPO
154
113
(-26%)
East TX
43
33
(-23%)
National
828
812
(-2%)
CAIR
Region
463
470
CAIR
Cap
485
482
"Numbers in parentheses are the percentage change in emissions.
3a.3.1 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 SO2 and/or NOX emissions
across 28 eastern states and the District of Columbia.
3a-20
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Figure 3a.l: CAIR Affected Region
States not covered by CAIR
| States controlled for fine particles (annual SO2 and NOx)
5 States controlled for both fine particles (annual SO2 and NOx) and ozone (ozone season NOx)
States controlled for ozone (ozone season NOx)
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 PlV^.s 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 ppm for the power
sector.
3a.3.2 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
3a-21
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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 proposal RIA analysis used 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 incorporated Federal and State rules and regulations
adopted before March 2004 and various NSR settlements.
Final RIA analysis uses the latest version of IPM (v3.0) as part of the updated modeling
platform. IPM v3.0 includes input and model assumption updates in modeling the power sector
and incorporates Federal and State rules and regulations adopted before September 2006 and
various NSR settlements. A detailed discussion of uncertainties associated with the EGU sector
modeling can be found in 2006 PM NAAQS RIA (pg. 3-50)
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 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.html).
3a.3.3 EGUNOx Emission Control Technologies
IPM v3.0 includes SC>2, NOX, and mercury (Hg) emission control technology options for meeting
existing and future federal, regional, and state, SC>2, NOxand 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.22 summarizes retrofit NOx emission control performance assumptions.
Table 3a.l7: Summary of Retrofit NOx Emission Control Performance Assumptions
Unit Type
Percent Removal
Size Applicability
Selective Catalytic Reduction
(SCR)
Coal Oil/Gasa
90% down to 0.06 80%
Ib/mmBtu
Units. 100 MW Units. 25 MW
Selective Non-Catalytic Reduction
(SNCR)
Coal Oil/Gasa
35% 50%
Units. 25 MW Units. 25 MW
and
Units < 200 MW
a 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-
3a-22
-------
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).
3a.4 Emissions Reductions by Sector
Figures 3a.2-3a.6 show the NOx reductions for each sector and Figures 3a.7-3a.10 show the
VOC reductions for each sector under the modeled control strategy.
Figure 3a.2: Annual Tons of NOx Emissions Reduced from EGU Sources*
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.
3a-23
-------
Figure 3a.3: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from NonEGU
Point Sources*
* 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-24
-------
Figure 3a.4: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from Area
Sources*
*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-25
-------
Figure 3a.5: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from Nonroad
Sources*
-969---if'
! -499- -100
*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
-------
Figure 3a.6: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from Onroad
Sources*
*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-27
-------
Figure 3a.7: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced
from NonEGU Point Sources*
*Reductions are negative and increases are positive
**The -99-0 range is not shown because these are small county-level VOC reductions or increases that
likely had little to no impact on ozone estimates
3a-28
-------
Figure 3a.8: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced
from Area Sources*
I—I-499--100
=1-99-0
*Reductions are negative and increases are positive
**The -99-0 range is not shown because these are small county-level VOC reductions or increases that
likely had little to no impact on ozone estimates.
3a-29
-------
Figure 3a.9: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced
from Nonroad Mobile Sources*
.211 -.100
*Reductions are negative and increases are positive
**The -99-0 range is not shown because these are small county-level VOC reductions or increases that
likely had little to no impact on ozone estimates.
3a-30
-------
Figure 3a.lO: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced
from Onroad Mobile Sources*
-1.352--500
*Reductions are negative and increases are positive
**The -99-0 range is not shown because these are small county-level VOC reductions or increases that
likely had little to no impact on ozone estimates.
3a.5 Change in Ozone Concentrations Between Baseline and Modeled Control Strategy
Table 3a.23 provides the projected 8-hour ozone design values for the 2020 baseline and 2020
control strategy scenarios for each monitored county. The changes in ozone in 2020 between the
baseline and the control strategy are also provided in this table.
3a-31
-------
Table 3a.l8: Changes in Ozone Concentrations between Baseline and Modeled Control
Strategy
State
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arkansas
Arkansas
Arkansas
Arkansas
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
County
Baldwin
Clay
Elmore
Etowah
Jefferson
Lawrence
Madison
Mobile
Montgomery
Morgan
Shelby
Sumter
Tuscaloosa
Cochise
Coconino
Maricopa
Navajo
Pima
Final
Yavapai
Crittenden
Montgomery
Newton
Pulaski
Alameda
Amador
Butte
Calaveras
Colusa
Contra Costa
El Dorado
Fresno
Glenn
Imperial
Inyo
Kern
Kings
Lake
Los Angeles
Madera
Marin
Mariposa
Mendocino
Merced
Monterey
Napa
Nevada
Orange
Baseline 8-hour
Ozone Design Value
(ppm)
0.064
0.057
0.055
0.054
0.059
0.055
0.057
0.064
0.055
0.060
0.061
0.051
0.052
0.065
0.067
0.070
0.058
0.064
0.065
0.065
0.068
0.051
0.060
0.061
0.069
0.067
0.069
0.072
0.058
0.070
0.081
0.091
0.058
0.071
0.068
0.097
0.076
0.054
0.105
0.076
0.041
0.072
0.046
0.079
0.055
0.051
0.075
0.081
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.064
0.056
0.055
0.053
0.061
0.056
0.058
0.064
0.055
0.061
0.063
0.051
0.052
0.065
0.067
0.068
0.058
0.063
0.063
0.065
0.069
0.051
0.060
0.062
0.069
0.067
0.068
0.072
0.058
0.069
0.081
0.091
0.058
0.071
0.068
0.096
0.076
0.054
0.104
0.076
0.041
0.072
0.046
0.079
0.055
0.051
0.075
0.081
Change
(ppm)
0.000
-0.001
0.001
-0.001
0.001
0.001
0.001
0.000
0.000
0.001
0.002
0.000
0.000
0.000
0.000
-0.002
-0.001
-0.001
-0.002
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
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
3a-32
-------
State
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
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Delaware
Delaware
Delaware
D.C.
Florida
Florida
Florida
Florida
Florida
County
Placer
Riverside
Sacramento
San Benito
San Bernardino
San Diego
San Francisco
San Joaquin
San Luis Obispo
San Mateo
Santa Barbara
Santa Clara
Santa Cruz
Shasta
Solano
Sonoma
Stanislaus
Sutler
Tehama
Tulare
Tuolumne
Ventura
Yolo
Adams
Arapahoe
Boulder
Denver
Douglas
El Paso
Jefferson
La Plata
Larimer
Montezuma
Weld
Fairfield
Hartford
Litchfield
Middlesex
New Haven
New London
Tolland
Kent
New Castle
Sussex
Washington
Alachua
Baker
Bay
Brevard
Broward
Baseline 8-hour
Ozone Design Value
(ppm)
0.076
0.102
0.077
0.066
0.123
0.077
0.046
0.067
0.060
0.051
0.068
0.066
0.055
0.058
0.057
0.048
0.077
0.068
0.066
0.083
0.073
0.077
0.065
0.057
0.069
0.063
0.064
0.072
0.062
0.073
0.052
0.067
0.062
0.064
0.079
0.066
0.064
0.073
0.076
0.068
0.068
0.069
0.071
0.070
0.069
0.056
0.055
0.061
0.051
0.054
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.076
0.102
0.077
0.066
0.123
0.077
0.046
0.067
0.060
0.051
0.068
0.066
0.055
0.058
0.057
0.048
0.077
0.068
0.065
0.083
0.073
0.077
0.064
0.053
0.065
0.058
0.060
0.068
0.060
0.068
0.051
0.062
0.062
0.060
0.077
0.063
0.062
0.071
0.074
0.066
0.065
0.067
0.068
0.068
0.065
0.057
0.054
0.063
0.052
0.054
Change
(ppm)
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
-0.001
0.000
0.000
0.000
0.000
-0.004
-0.005
-0.004
-0.004
-0.005
-0.003
-0.005
0.000
-0.005
0.000
-0.004
-0.002
-0.003
-0.003
-0.003
-0.003
-0.002
-0.003
-0.002
-0.003
-0.002
-0.004
0.000
-0.001
0.002
0.001
0.000
3a-33
-------
State
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
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
Georgia
Georgia
Idaho
Idaho
Idaho
Idaho
Illinois
County
Collier
Columbia
Duval
Escambia
Highlands
Hillsborough
Holmes
Lake
Lee
Leon
Manatee
Marion
Miami-Bade
Orange
Osceola
Palm Beach
Pasco
Pinellas
Polk
St Lucie
Santa Rosa
Sarasota
Seminole
Volusia
Wakulla
Bibb
Chatham
Cherokee
Clarke
Cobb
Coweta
Dawson
De Kalb
Douglas
Fayette
Fulton
Glynn
Gwinnett
Henry
Murray
Muscogee
Paulding
Richmond
Rockdale
Sumter
Ada
Butte
Canyon
Elmore
Adams
Baseline 8-hour
Ozone Design Value
(ppm)
0.057
0.053
0.053
0.065
0.054
0.065
0.055
0.055
0.056
0.055
0.061
0.058
0.053
0.056
0.053
0.055
0.058
0.061
0.058
0.052
0.063
0.060
0.057
0.051
0.059
0.065
0.053
0.053
0.054
0.063
0.065
0.056
0.067
0.064
0.062
0.070
0.054
0.061
0.064
0.059
0.054
0.060
0.064
0.064
0.054
0.069
0.065
0.059
0.060
0.060
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.056
0.052
0.052
0.065
0.054
0.065
0.055
0.056
0.056
0.055
0.061
0.058
0.053
0.058
0.054
0.054
0.058
0.061
0.059
0.052
0.064
0.061
0.058
0.051
0.059
0.063
0.052
0.051
0.052
0.061
0.060
0.054
0.065
0.062
0.060
0.068
0.054
0.059
0.062
0.058
0.052
0.058
0.059
0.062
0.053
0.069
0.065
0.059
0.060
0.056
Change
(ppm)
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
0.002
0.001
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.001
0.000
0.000
-0.001
0.000
-0.002
-0.002
-0.002
-0.006
-0.002
-0.002
-0.002
-0.002
-0.002
-0.001
-0.002
-0.002
-0.001
-0.002
-0.002
-0.005
-0.002
-0.001
0.000
0.000
0.000
0.000
-0.004
3a-34
-------
State
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
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
County
Champaign
Clark
Cook
Du Page
Effingham
Hamilton
Jersey
Kane
Lake
McHenry
McLean
Macon
Macoupin
Madison
Peoria
Randolph
Rock Island
St Clair
Sangamon
Will
Winnebago
Allen
Boone
Carroll
Clark
Delaware
Elkhart
Floyd
Gibson
Greene
Hamilton
Hancock
Hendricks
Huntington
Jackson
Johnson
Lake
La Porte
Madison
Marion
Morgan
Porter
Posey
St Joseph
Shelby
Vanderburgh
Vigo
Warrick
Bremer
Clinton
Baseline 8-hour
Ozone Design Value
(ppm)
0.058
0.053
0.074
0.061
0.057
0.059
0.067
0.062
0.071
0.067
0.057
0.056
0.057
0.066
0.063
0.059
0.055
0.066
0.054
0.062
0.058
0.067
0.067
0.062
0.068
0.064
0.066
0.066
0.051
0.063
0.070
0.067
0.065
0.064
0.062
0.064
0.078
0.074
0.067
0.069
0.066
0.075
0.061
0.068
0.069
0.060
0.066
0.064
0.059
0.063
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.057
0.053
0.073
0.059
0.056
0.057
0.065
0.061
0.070
0.065
0.056
0.055
0.055
0.064
0.062
0.058
0.054
0.064
0.053
0.060
0.057
0.065
0.066
0.061
0.067
0.063
0.064
0.065
0.050
0.061
0.068
0.066
0.063
0.062
0.060
0.063
0.077
0.073
0.066
0.067
0.064
0.074
0.060
0.067
0.067
0.058
0.065
0.061
0.059
0.062
Change
(ppm)
-0.001
-0.001
-0.001
-0.001
-0.001
-0.002
-0.002
-0.001
-0.001
-0.001
-0.001
-0.001
-0.002
-0.003
-0.001
-0.001
-0.001
-0.002
-0.001
-0.001
-0.001
-0.002
-0.002
-0.001
-0.002
-0.002
-0.002
-0.002
-0.001
-0.001
-0.002
-0.002
-0.002
-0.002
-0.002
-0.002
-0.001
-0.001
-0.002
-0.002
-0.002
-0.001
-0.002
-0.002
-0.002
-0.002
-0.002
-0.003
0.000
-0.001
3a-35
-------
State
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
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
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
County
Harrison
Linn
Montgomery
Palo Alto
Polk
Scott
Story
Van Buren
Warren
Linn
Sedgwick
Sumner
Trego
Wyandotte
Bell
Boone
Boyd
Bullitt
Campbell
Carter
Christian
Daviess
Edmonson
Fayette
Graves
Greenup
Hancock
Hardin
Henderson
Jefferson
Jessamine
Kenton
Livingston
McCracken
McLean
Oldham
Perry
Pike
Pulaski
Scott
Simpson
Trigg
Warren
Ascension
Beauregard
Bossier
Caddo
Calcasieu
East Baton Rouge
Grant
Baseline 8-hour
Ozone Design Value
(ppm)
0.062
0.058
0.056
0.054
0.047
0.061
0.049
0.059
0.049
0.060
0.064
0.063
0.055
0.063
0.056
0.063
0.071
0.062
0.070
0.058
0.058
0.059
0.059
0.057
0.060
0.065
0.063
0.058
0.060
0.065
0.057
0.066
0.061
0.064
0.059
0.063
0.055
0.055
0.059
0.050
0.057
0.052
0.060
0.069
0.062
0.061
0.059
0.066
0.077
0.060
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.062
0.057
0.056
0.054
0.046
0.060
0.048
0.058
0.049
0.060
0.064
0.062
0.055
0.062
0.056
0.061
0.069
0.060
0.068
0.057
0.058
0.058
0.058
0.056
0.059
0.063
0.064
0.056
0.058
0.063
0.056
0.063
0.061
0.063
0.058
0.061
0.055
0.053
0.061
0.049
0.056
0.053
0.059
0.065
0.059
0.060
0.057
0.064
0.074
0.058
Change
(ppm)
0.000
-0.001
0.000
0.000
0.000
-0.001
0.000
-0.001
0.000
0.000
0.000
0.000
0.000
0.000
-0.001
-0.002
-0.002
-0.002
-0.003
-0.001
0.000
-0.001
-0.001
-0.002
-0.001
-0.001
0.001
-0.001
-0.003
-0.002
-0.001
-0.003
-0.001
-0.001
-0.001
-0.002
-0.001
-0.001
0.002
-0.001
0.000
0.000
-0.001
-0.004
-0.003
-0.001
-0.001
-0.002
-0.003
-0.002
3a-36
-------
State
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
County
Iberville
Jefferson
Lafayette
Lafourche
Livingston
Orleans
Ouachita
Pointe Coupee
St Bernard
St Charles
St James
St John The Baptist
StMary
West Baton Rouge
Cumberland
Hancock
Kennebec
Knox
Oxford
Penobscot
Sagadahoc
York
Anne Arundel
Baltimore
Carroll
Cecil
Charles
Frederick
Harford
Kent
Montgomery
Prince Georges
Washington
Barnstable
Berkshire
Bristol
Essex
Hampden
Hampshire
Middlesex
Norfolk
Suffolk
Worcester
Allegan
Benzie
Berrien
Cass
Clinton
Genesee
Huron
Baseline 8-hour
Ozone Design Value
(ppm)
0.073
0.069
0.066
0.065
0.069
0.058
0.061
0.064
0.063
0.066
0.064
0.069
0.061
0.074
0.063
0.071
0.060
0.063
0.050
0.064
0.060
0.067
0.072
0.071
0.065
0.071
0.065
0.066
0.077
0.070
0.064
0.069
0.064
0.071
0.069
0.069
0.070
0.068
0.066
0.065
0.074
0.069
0.065
0.073
0.067
0.071
0.068
0.065
0.066
0.069
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.069
0.067
0.061
0.062
0.064
0.056
0.060
0.057
0.062
0.064
0.061
0.066
0.058
0.070
0.061
0.069
0.058
0.061
0.049
0.062
0.057
0.064
0.069
0.068
0.062
0.068
0.062
0.061
0.074
0.067
0.061
0.066
0.061
0.068
0.067
0.067
0.068
0.066
0.064
0.062
0.072
0.067
0.063
0.072
0.065
0.069
0.067
0.063
0.065
0.067
Change
(ppm)
-0.004
-0.002
-0.005
-0.003
-0.004
-0.001
-0.001
-0.007
-0.001
-0.002
-0.003
-0.003
-0.004
-0.004
-0.002
-0.003
-0.002
-0.002
-0.001
-0.002
-0.002
-0.002
-0.003
-0.003
-0.003
-0.003
-0.003
-0.004
-0.003
-0.003
-0.003
-0.003
-0.003
-0.002
-0.002
-0.003
-0.002
-0.003
-0.002
-0.003
-0.002
-0.002
-0.002
-0.001
-0.001
-0.001
-0.002
-0.002
-0.002
-0.002
3a-37
-------
State
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Montana
Nebraska
Nebraska
Nevada
Nevada
Nevada
Nevada
Nevada
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
County
Ingham
Kalamazoo
Kent
Lenawee
Macomb
Mason
Missaukee
Muskegon
Oakland
Ottawa
St Clair
Schoolcraft
Washtenaw
Wayne
St Louis
Adams
Bolivar
De Soto
Hancock
Harrison
Hinds
Jackson
Lauderdale
Lee
Madison
Warren
Cass
Cedar
Clay
Greene
Jefferson
Monroe
Platte
St Charles
Ste Genevieve
St Louis
St Louis City
Flathead
Douglas
Lancaster
Clark
Douglas
Washoe
White Pine
Carson City
Belknap
Carroll
Cheshire
Grafton
Hillsborough
Baseline 8-hour
Ozone Design Value
(ppm)
0.064
0.063
0.065
0.067
0.075
0.066
0.062
0.070
0.072
0.067
0.070
0.063
0.069
0.071
0.059
0.060
0.057
0.062
0.063
0.063
0.051
0.067
0.051
0.056
0.054
0.052
0.061
0.064
0.065
0.059
0.067
0.060
0.063
0.071
0.065
0.070
0.071
0.053
0.056
0.046
0.072
0.059
0.064
0.066
0.063
0.060
0.055
0.057
0.058
0.065
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.062
0.061
0.063
0.065
0.073
0.064
0.061
0.069
0.071
0.065
0.068
0.062
0.067
0.069
0.059
0.060
0.057
0.062
0.062
0.065
0.050
0.068
0.051
0.058
0.054
0.052
0.061
0.063
0.064
0.058
0.064
0.059
0.063
0.069
0.063
0.068
0.068
0.053
0.056
0.046
0.071
0.059
0.063
0.065
0.063
0.058
0.054
0.055
0.057
0.063
Change
(ppm)
-0.002
-0.002
-0.002
-0.002
-0.002
-0.001
-0.001
-0.001
-0.001
-0.002
-0.002
-0.001
-0.002
-0.002
0.000
-0.001
0.000
0.000
-0.001
0.003
0.000
0.000
0.000
0.002
0.000
0.000
0.000
-0.001
-0.001
-0.001
-0.003
-0.001
-0.001
-0.002
-0.002
-0.003
-0.002
0.000
0.000
0.000
-0.001
0.000
0.000
0.000
0.000
-0.002
-0.001
-0.002
-0.001
-0.002
3a-38
-------
State
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Mexico
New Mexico
New Mexico
New Mexico
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
New York
New York
New York
New York
New York
New York
New York
New York
New York
County
Merrimack
Rockingham
Stafford
Sullivan
Atlantic
Bergen
Camden
Cumberland
Essex
Gloucester
Hudson
Hunterdon
Mercer
Middlesex
Monmouth
Morris
Ocean
Passaic
Bernalillo
Dona Ana
Eddy
Sandoval
San Juan
Valencia
Albany
Bronx
Chautauqua
Chemung
Dutchess
Erie
Essex
Hamilton
Herkimer
Jefferson
Madison
Monroe
Niagara
Oneida
Onondaga
Orange
Oswego
Putnam
Queens
Rensselaer
Richmond
Saratoga
Schenectady
Suffolk
Ulster
Wayne
Baseline 8-hour
Ozone Design Value
(ppm)
0.058
0.064
0.060
0.061
0.067
0.074
0.077
0.072
0.053
0.076
0.066
0.071
0.076
0.073
0.073
0.071
0.080
0.067
0.065
0.069
0.064
0.064
0.070
0.057
0.065
0.069
0.073
0.062
0.069
0.075
0.069
0.063
0.059
0.073
0.062
0.067
0.075
0.063
0.068
0.064
0.054
0.071
0.070
0.067
0.074
0.067
0.062
0.080
0.064
0.066
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.056
0.061
0.058
0.060
0.065
0.072
0.075
0.069
0.051
0.073
0.064
0.068
0.073
0.070
0.071
0.068
0.077
0.065
0.065
0.068
0.063
0.063
0.069
0.057
0.061
0.067
0.070
0.060
0.066
0.072
0.067
0.062
0.058
0.072
0.061
0.065
0.074
0.061
0.066
0.061
0.052
0.068
0.068
0.064
0.071
0.064
0.059
0.078
0.062
0.064
Change
(ppm)
-0.002
-0.002
-0.002
-0.001
-0.002
-0.002
-0.003
-0.003
-0.002
-0.003
-0.002
-0.003
-0.003
-0.003
-0.002
-0.003
-0.003
-0.003
0.000
-0.001
0.000
0.000
0.000
0.000
-0.003
-0.002
-0.003
-0.002
-0.003
-0.003
-0.002
-0.001
-0.001
-0.002
-0.002
-0.002
-0.002
-0.002
-0.002
-0.003
-0.002
-0.003
-0.002
-0.003
-0.002
-0.003
-0.002
-0.002
-0.002
-0.002
3a-39
-------
State
New York
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
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 Dakota
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
County
Westchester
Alexander
Avery
Buncombe
Caldwell
Caswell
Chatham
Cumberland
Davie
Duplin
Durham
Edgecombe
Forsyth
Franklin
Granville
Guilford
Haywood
Jackson
Johnston
Lenoir
Lincoln
Martin
Mecklenburg
New Hanover
Northampton
Person
Pitt
Randolph
Rockingham
Rowan
Swain
Union
Wake
Yancey
Billings
Cass
Dunn
McKenzie
Mercer
Oliver
Allen
Ashtabula
Butler
Clark
Clermont
Clinton
Cuyahoga
Delaware
Franklin
Geauga
Baseline 8-hour
Ozone Design Value
(ppm)
0.074
0.062
0.059
0.061
0.061
0.061
0.059
0.062
0.064
0.060
0.062
0.063
0.064
0.063
0.065
0.060
0.065
0.064
0.060
0.060
0.065
0.060
0.072
0.057
0.062
0.063
0.059
0.058
0.062
0.069
0.053
0.062
0.064
0.063
0.054
0.056
0.054
0.058
0.055
0.051
0.068
0.076
0.068
0.067
0.069
0.069
0.068
0.067
0.069
0.077
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.071
0.062
0.058
0.060
0.060
0.060
0.058
0.060
0.062
0.059
0.060
0.062
0.062
0.062
0.063
0.059
0.064
0.063
0.059
0.060
0.065
0.059
0.071
0.057
0.061
0.062
0.058
0.057
0.061
0.067
0.053
0.061
0.063
0.062
0.054
0.055
0.054
0.058
0.055
0.051
0.066
0.073
0.065
0.063
0.066
0.067
0.066
0.064
0.066
0.074
Change
(ppm)
-0.003
0.000
-0.001
-0.001
0.000
-0.001
-0.001
-0.001
-0.002
-0.001
-0.001
-0.001
-0.002
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
0.001
-0.001
-0.001
0.001
-0.002
-0.001
-0.001
-0.001
-0.001
-0.002
-0.001
-0.001
-0.001
-0.001
0.000
0.000
0.000
0.000
0.000
0.000
-0.003
-0.003
-0.003
-0.004
-0.003
-0.003
-0.002
-0.002
-0.002
-0.002
3a-40
-------
State
Ohio
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
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Oregon
Oregon
Oregon
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
County
Greene
Hamilton
Jefferson
Knox
Lake
Lawrence
Licking
Lorain
Lucas
Madison
Mahoning
Medina
Miami
Montgomery
Portage
Preble
Stark
Summit
Trumbull
Warren
Washington
Wood
Canadian
Cleveland
Comanche
Dewey
Kay
Me Clain
Oklahoma
Ottawa
Pittsburg
Tulsa
Clackamas
Columbia
Jackson
Lane
Marion
Adams
Allegheny
Armstrong
Beaver
Berks
Blair
Bucks
Cambria
Centre
Chester
Clearfield
Dauphin
Delaware
Baseline 8-hour
Ozone Design Value
(ppm)
0.066
0.069
0.064
0.065
0.073
0.065
0.065
0.067
0.070
0.065
0.065
0.067
0.065
0.066
0.069
0.060
0.066
0.071
0.069
0.069
0.061
0.068
0.057
0.060
0.061
0.058
0.061
0.062
0.061
0.063
0.061
0.066
0.063
0.056
0.061
0.060
0.055
0.060
0.072
0.068
0.071
0.066
0.061
0.078
0.064
0.062
0.071
0.065
0.065
0.071
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.062
0.066
0.062
0.062
0.070
0.064
0.063
0.065
0.068
0.062
0.063
0.065
0.062
0.063
0.066
0.058
0.063
0.069
0.066
0.065
0.061
0.065
0.056
0.059
0.060
0.057
0.060
0.060
0.060
0.062
0.060
0.066
0.063
0.056
0.061
0.060
0.055
0.056
0.069
0.066
0.069
0.063
0.058
0.075
0.061
0.060
0.068
0.062
0.061
0.068
Change
(ppm)
-0.004
-0.003
-0.002
-0.002
-0.002
-0.001
-0.002
-0.002
-0.002
-0.003
-0.002
-0.002
-0.003
-0.003
-0.002
-0.003
-0.003
-0.003
-0.003
-0.003
-0.001
-0.003
-0.001
-0.001
-0.002
-0.002
-0.001
-0.001
-0.001
-0.001
0.000
-0.001
0.000
0.000
0.000
0.000
0.000
-0.003
-0.003
-0.003
-0.003
-0.003
-0.002
-0.003
-0.003
-0.002
-0.003
-0.003
-0.005
-0.003
3a-41
-------
State
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
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
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
County
Erie
Franklin
Greene
Lackawanna
Lancaster
Lawrence
Lehigh
Luzerne
Lycoming
Mercer
Montgomery
Northampton
Perry
Philadelphia
Tioga
Washington
Westmoreland
York
Kent
Providence
Washington
Abbeville
Aiken
Anderson
Barnwell
Berkeley
Charleston
Cherokee
Chester
Chesterfield
Colleton
Darlington
Edgefield
Oconee
Pickens
Richland
Spartanburg
Union
Williamsburg
York
Pennington
Anderson
Blount
Davidson
Hamilton
Haywood
Jefferson
Knox
Lawrence
Meigs
Baseline 8-hour
Ozone Design Value
(ppm)
0.070
0.067
0.064
0.062
0.068
0.058
0.067
0.062
0.061
0.068
0.071
0.067
0.062
0.077
0.065
0.067
0.069
0.067
0.070
0.069
0.071
0.060
0.062
0.064
0.059
0.053
0.055
0.061
0.059
0.059
0.058
0.061
0.059
0.061
0.060
0.066
0.063
0.059
0.052
0.060
0.062
0.059
0.065
0.057
0.062
0.060
0.062
0.062
0.056
0.061
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.068
0.064
0.062
0.060
0.063
0.055
0.064
0.060
0.059
0.065
0.069
0.063
0.059
0.075
0.063
0.064
0.066
0.062
0.067
0.067
0.068
0.059
0.058
0.062
0.057
0.053
0.054
0.060
0.058
0.058
0.057
0.060
0.057
0.059
0.059
0.065
0.061
0.057
0.052
0.059
0.062
0.058
0.064
0.057
0.062
0.063
0.061
0.061
0.059
0.061
Change
(ppm)
-0.003
-0.003
-0.002
-0.002
-0.005
-0.002
-0.003
-0.002
-0.002
-0.003
-0.003
-0.004
-0.003
-0.003
-0.002
-0.003
-0.003
-0.005
-0.003
-0.003
-0.003
-0.001
-0.003
-0.001
-0.002
0.000
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.002
-0.001
-0.001
-0.002
-0.002
-0.001
-0.001
-0.001
0.000
0.000
-0.001
0.000
0.000
0.003
0.000
0.000
0.002
-0.001
3a-42
-------
State
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
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
Virginia
County
Putnam
Rutherford
Sevier
Shelby
Sullivan
Sumner
Williamson
Wilson
Bexar
Brazoria
Brewster
Cameron
Collin
Dallas
Denton
Ellis
El Paso
Galveston
Gregg
Harris
Harrison
Hidalgo
Hood
Jefferson
Johnson
Kaufman
Montgomery
Nueces
Orange
Parker
Rockwall
Smith
Tarrant
Travis
Victoria
Webb
Box Elder
Cache
Davis
Salt Lake
San Juan
Utah
Weber
Bennington
Chittenden
Arlington
Caroline
Charles City
Chesterfield
Fairfax
Baseline 8-hour
Ozone Design Value
(ppm)
0.062
0.058
0.066
0.066
0.066
0.062
0.061
0.060
0.068
0.074
0.054
0.053
0.070
0.069
0.075
0.063
0.069
0.074
0.068
0.089
0.061
0.062
0.058
0.074
0.066
0.055
0.074
0.065
0.066
0.063
0.062
0.064
0.075
0.063
0.061
0.054
0.064
0.056
0.070
0.070
0.064
0.067
0.065
0.061
0.063
0.072
0.059
0.069
0.066
0.071
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.061
0.058
0.065
0.066
0.066
0.062
0.060
0.060
0.067
0.073
0.054
0.052
0.068
0.067
0.072
0.059
0.068
0.073
0.064
0.088
0.059
0.062
0.057
0.071
0.063
0.053
0.073
0.064
0.064
0.062
0.060
0.062
0.073
0.062
0.060
0.053
0.062
0.055
0.068
0.067
0.064
0.065
0.063
0.058
0.062
0.069
0.057
0.067
0.064
0.068
Change
(ppm)
-0.001
0.000
-0.001
0.000
0.000
0.000
0.000
0.000
-0.001
-0.001
-0.001
-0.001
-0.002
-0.002
-0.002
-0.004
-0.001
-0.002
-0.004
-0.001
-0.003
-0.001
-0.002
-0.003
-0.003
-0.002
-0.001
-0.001
-0.003
-0.002
-0.002
-0.002
-0.002
-0.001
-0.001
-0.001
-0.002
-0.002
-0.003
-0.002
0.000
-0.002
-0.002
-0.003
-0.001
-0.004
-0.002
-0.002
-0.002
-0.004
3a-43
-------
State
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
West 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
County
Fauquier
Frederick
Hanover
Henrico
Loudoun
Madison
Page
Prince William
Roanoke
Rockbridge
Stafford
Wythe
Alexandria City
Hampton City
Suffolk City
Clallam
Clark
King
Klickitat
Mason
Pierce
Skagit
Spokane
Thurston
Whatcom
Berkeley
Cabell
Greenbrier
Hancock
Kanawha
Monongalia
Ohio
Wood
Brown
Columbia
Dane
Dodge
Door
Florence
Fond Du Lac
Green
Jefferson
Kenosha
Kewaunee
Manitowoc
Marathon
Milwaukee
Oneida
Outagamie
Ozaukee
Baseline 8-hour
Ozone Design Value
(ppm)
0.058
0.062
0.070
0.068
0.067
0.063
0.058
0.063
0.062
0.057
0.063
0.060
0.067
0.071
0.070
0.041
0.062
0.064
0.062
0.050
0.066
0.045
0.060
0.059
0.052
0.062
0.069
0.060
0.064
0.062
0.056
0.063
0.062
0.065
0.060
0.060
0.063
0.072
0.058
0.061
0.059
0.063
0.081
0.071
0.069
0.058
0.074
0.057
0.061
0.075
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.057
0.060
0.068
0.066
0.063
0.061
0.057
0.060
0.061
0.056
0.060
0.060
0.063
0.070
0.069
0.041
0.062
0.064
0.060
0.050
0.066
0.045
0.060
0.059
0.052
0.060
0.067
0.060
0.062
0.062
0.055
0.061
0.061
0.064
0.059
0.059
0.062
0.071
0.057
0.060
0.059
0.061
0.080
0.070
0.068
0.057
0.073
0.056
0.060
0.073
Change
(ppm)
-0.002
-0.002
-0.002
-0.002
-0.004
-0.002
-0.002
-0.003
-0.001
-0.001
-0.002
0.000
-0.003
-0.001
-0.001
0.000
0.000
0.000
-0.002
0.000
0.000
0.000
0.000
0.000
0.000
-0.002
-0.001
-0.001
-0.003
0.000
-0.001
-0.002
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
3a-44
-------
State
County
Baseline 8-hour
Ozone Design Value
(ppm)
Control Strategy 8-
hour Ozone Design Change
Value (ppm) (ppm)
Wisconsin
Racine
0.075
0.074
-0.001
Wisconsin
Rock
0.064
0.063
-0.001
Wisconsin
St Croix
0.060
0.060
0.000
Wisconsin
Sauk
0.057
0.057
-0.001
Wisconsin
Sheboygan
0.077
0.076
-0.001
Wisconsin
Vernon
0.060
0.059
-0.001
Wisconsin
Vilas
0.057
0.056
-0.001
Wisconsin
Walworth
0.064
0.063
-0.001
Wisconsin
Washington
0.065
0.064
-0.001
Wisconsin
Waukesha
0.063
0.062
-0.001
Wisconsin
Winnebago
0.066
0.064
-0.001
Wyoming
Wyoming
Campbell
Teton
0.067
0.063
0.067
0.063
3a-45
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Appendix 3: Additional Control Strategy Information
3a.l NonEGU Point and Area Source Controls
Ba.1.1 NonEGU Point and Area Source Control Strategies for Ozone NAAQS Final
In the NonEGU point 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 nonEGU point
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 70 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
nonEGU point NOx sources. We also looked into sources of controls for highly reactive VOC
nonEGU point sources. Four additional controls were applied for highly reactive VOC nonEGU
point sources not in AirControlNET.
3a.l.2 NOx Control Measures for NonEGU Point Sources.
Several types of NOx control technologies exist for nonEGU point 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 nonEGU point 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. 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
3a-l
-------
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. Table 3a.l contains a complete list of the NOx
nonEGU point control measures applied and their associated emission reductions obtained in the
modeled control strategy for the alternate primary standard. For more information on these
measures, please refer to the AirControlNET 4.1 control measures documentation report.
Table 3a.l: NOx NonEGU Point Emission Reductions by Control Measure
Control Measure
Biosolid Injection
Technology
LNB
LNB + FOR
LNB + SCR
NSCR
OXY-Firing
SCR
Source Type
Cement Kilns
Asphaltic Cone; Rotary Dryer; Conv Plant
Ceramic Clay Mfg; Drying
Conv Coating of Prod; Acid Cleaning Bath
Fuel Fired Equip; Furnaces; Natural Gas
In-Process Fuel Use; Natural Gas
In-Process Fuel Use; Residual Oil
In-Process; Process Gas; Coke Oven Gas
Lime Kilns
Sec Alum Prod; Smelting Furn
Steel Foundries; Heat Treating
Surf Coat Oper; Coating Oven Htr; Nat Gas
Fluid Cat Cracking Units
Fuel Fired Equip; Process Htrs; Process Gas
In-Process; Process Gas; Coke Oven Gas
Iron & Steel Mills — Galvanizing
Iron & Steel Mills — Reheating
Iron Prod; Blast Furn; Blast Htg Stoves
Sand/Gravel; Dryer
Steel Prod; Soaking Pits
Iron & Steel Mills — Annealing
Process Heaters — Distillate Oil
Process Heaters — Natural Gas
Process Heaters — Other Fuel
Process Heaters — Process Gas
Process Heaters — Residual Oil
Rich Burn 1C Engines — Gas
Rich Burn 1C Engines — Gas, Diesel, LPG
Rich Burn Internal Combustion Engines — Oil
Glass Manufacturing — Containers
Glass Manufacturing — Flat
Glass Manufacturing — Pressed
Ammonia — NG-Fired Reformers
Cement Manufacturing — Dry
Cement Manufacturing — Wet
1C Engines — Gas
ICI Boilers— Coal/Cyclone
ICI Boilers— Coal/Wall
ICI Boilers— Coke
ICI Boilers— Distillate Oil
Modeled Control
Strategy Reductions
(annual tons/year)
1,200
120
370
440
170
1,300
39
190
5,900
62
13
30
3,600
700
880
35
1,100
1,000
11
100
270
2,300
27,000
14
4,200
37
22,000
3,700
11,000
7,600
18,000
3,900
5,800
25,000
22,000
54,000
2,200
22,000
490
4,800
3a-2
-------
Control Measure
SCR + Steam Injection
SCR + Water Injection
SNCR
SNCR— Urea
SNCR— Urea Based
Source Type
ICI Boilers — Liquid Waste
ICI Boilers— LPG
ICI Boilers — Natural Gas
ICI Boilers — Process Gas
ICI Boilers— Residual Oil
Natural Gas Prod; Compressors
Space Heaters — Distillate Oil
Space Heaters — Natural Gas
Sulfate Pulping — Recovery Furnaces
Combustion Turbines — Natural Gas
Combustion Turbines — Jet Fuel
Combustion Turbines — Natural Gas
Combustion Turbines — Oil
By-Product Coke Mfg; Oven Underfiring
Comm./Inst. Incinerators
ICI Boilers— Coal/Stoker
Indust. Incinerators
Medical Waste Incinerators
In-Process Fuel Use; Bituminous Coal
Municipal Waste Combustors
Nitric Acid Manufacturing
Solid Waste Disp; Gov; Other Inc
ICI Boilers— MSW/Stoker
ICI Boilers— Coal/FBC
ICI Boilers— Wood/Bark/Stoker— Large
In-Process; Bituminous Coal; Cement Kilns
In-Process; Bituminous Coal; Lime Kilns
Modeled Control
Strategy Reductions
(annual tons/year)
730
280
36,000
8,600
17,000
810
22
640
9,900
18,000
—
—
210
4,300
1,400
7,000
250
—
32
4,400
3,100
95
120
100
5,500
300
31
3a.l.3 VOC Control Measures for NonEGU Point Sources.
VOC controls were applied to a variety of nonEGU point sources as defined in the emissions
inventory in this RIA. The first control is: 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). The second control applied is petroleum
and solvent evaporation applied to printing and publishing sources as well as to surface coating
operations. Table 3a.2 contains the emissions reductions for these measures in the modeled
control strategy for the alternate primary standard. For more information on these measures, refer
to the AirControlNET 4.1 control measures documentation report.
3a-3
-------
Table 3a.2: VOC NonEGU Point Emission Reductions by Control Measure
Modeled Control
Strategy Reductions
Control Measure Source Type (annual tons/year)
Permanent Total Enclosure (PTE) Fabric Printing, Coating and Dyeing 43
Paper and Other Web Coating 490
Petroleum and Solvent Evaporation Printing and Publishing 3,600
Surface Coating 400
3a.l.4 NOx Control Measures for Area Sources
There were three control measures 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. The third control was switching to
low sulfur fuel for residential home heating. This control is primarily designed to reduce sulfur
dioxide, but has a co-benefit of reducing NOx. Table 3a.3 contains the listing of control
measures and associated reductions for the modeled control strategy. For additional information
regarding these controls please refer to the AirControlNET 4.1 control measures documentation
report.
Table 3a.3: NOx Area Source Emission Reductions by Control Measure
Modeled Control Strategy
Reductions
Control Measure Source Type (annual tons/year)
RACT to 25 tpy (LNB) Industrial Coal Combustion 5,400
Industrial NG Combustion 3,000
Industrial Oil Combustion 570
Switch to Low Sulfur Fuel Residential Home Heating 970
Water Heater + LNB Space Heaters Commercial/Institutional—NG 4,300
Residential NG 6,700
3a.l.5 VOC Control Measures for Area Source.
The most frequently applied control to reduce VOC emissions from area sources was CARB
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 control 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 service
3a-4
-------
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. Table 3a.4 contains the control measures and
associated emission reductions described above for the modeled control strategy. For additional
information regarding these controls please refer to the AirControlNET 4.1 control measures
documentation report.
Table 3a.4: VOC Area Source Emission Reductions by Control Measure
Modeled Control
Strategy Reductions
Control Measure
CARB Long-Term Limits
Catalytic Oxidizer
Equipment and Maintenance
Gas Collection (SCAQMD/BAAQMD)
Incineration >100,000 Ibs bread
Low Pressure /Vacuum Relief Valve
OTC Mobile Equipment Repair and
Refinishing Rule
OTC Solvent Cleaning Rule
SCAQMD— Low VOC
SCAQMD Limits
SCAQMD Rule 11 68
Solvent Utilization
Switch to Emulsified Asphalts
Source Type
Consumer Solvents
Conveyorized Charbroilers
Oil and Natural Gas Production
Municipal Solid Waste Landfill
Bakery Products
Stage II Service Stations
Stage II Service Stations — Underground
Tanks
Aircraft Surface Coating
Machn, Electric, Railroad Ctng
Cold Cleaning
Rubber and Plastics Mfg
Metal Furniture, Appliances, Parts
Adhe sive s — Industrial
Large Appliances
Metal Furniture
Surface Coating
Cutback Asphalt
(annual tons/year)
78,000
250
450
1,100
2,700
9,900
9,800
720
4,400
10,000
1,700
6,300
22,000
8,200
7,600
2,900
3,300
3a. 1.6 Supplemental Controls
Table 3a.5 below summarizes the supplemental control measures added to our control measures
database by providing the pollutant it controls and its control efficiency (CE). These controls
were applied not as part of the modeled control strategy, but as supplemental measures prior to
extrapolating unknown control costs. However, these controls are not currently located in
AirControlNET. These measures are primarily found in draft SIP technical documents and have
not been fully assessed for inclusion in AirControlNET.
3a-5
-------
Table 3a.5: Supplemental Emissions Control Measures Added to the Control Measures
Database
Control
Poll Technology
NOx LEC
VOC Enhanced LDAR
LDAR
Monitoring Program
Inspection and
Maintenance Program
(Separators)
Water Seals (Drains)
Work Practices,
Use of Low VOC
Coatings
(Area Sources)
Work Practices,
Use of Low VOC
Coatings
(NonEGU Point)
SCC
20200252
20200254
3018001-
30600701
30600999 -
3018001 -
30600702-
30600503-
2401025000
2401030000
2401060000
2425010000
2425030000
2425040000
2461050000
307001199
Surface Coating
Operations
within SCC
4020000000,
Printing/Publis
hing processes
within SCC
4050000000
SCC
Description
Internal Comb. Engines/Industrial/
Natural Gas/2-cycle Lean Burn
Internal Comb. Engines/Industrial/
Natural Gas/4-cycle Lean Burn
Fugitive Leaks
Flares
Fugitive Leaks
Cooling towers
Wastewater Drains and Separators
Solvent Utilization
Petroleum and Solvent Evaporation
Percent
Reduction
87
87
50
98
80
No general
estimate
65
90
90
Low Emission Combustion (LEC)
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 CE, 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
3a-6
-------
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
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 CE 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
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.
2"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.
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-7
-------
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 CE 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
Work Practices or Use of Low VOC Coatings
Overview: The control measure is either application of work practices (e.g., storing VOC-
containing cleaning materials in closed containers, minimizing spills) or using coatings that have
much lower VOC content. These measures, which are of relatively low cost compared to other
VOC area source controls, can apply to a variety of processes, both for non-EGU point and area
sources, in different industries and is defined in the proposed control techniques guidelines
(CTG) for paper, film and foil coatings, metal furniture coatings, and large appliance coatings
published by the US EPA in July 2007.7
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.
7 U.S. Environmental Protection Agency. Consumer and Commercial Products: Control
Techniques Guidelines in Lieu of Regulations for Paper, Film, and Foil Coatings; Metal
3a-8
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The estimated CE expected to be achieved by either of these control measures is 90 percent.
Major Uncertainties: The greatest uncertainty is in how many potentially affected processes are
implementing or already implemented these control measures. This may be particularly true in
California. Also, there are nine States that have many of the above work practices in effect for
paper, film and foil coatings processes, but the work practices are not meant to achieve a specific
emissions limit.8 Hence, it is uncertain how much VOC reduction is occurring from this control
measure in this case.
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 nonEGU point 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 70 SCCs. The changes apply to the
control strategies performed for the Eastern US only. These changes are listed in Table 3a.6.
Table 3a.6: Supplemental Emission Control Measures—Changes to Control Technologies
Currently in our Control Measures Database For Application in 2020
Poll
NOX
NOX
NOX
sec
10200104
10200204
10200205
10300207
10300209
10200217
10300216
10200901
10200902
10200903
10200907
10300902
10300903
10200401
10200402
10200404
10200405
10300401
AirControlNE New Old
AirControlNET Source T Control New Control CE CE
Description Technology Technology (%) (%)
ICI Boilers— Coal-Stoker SNCR SCR 90 40
ICI Boilers— Wood/Bark/ SNCR SCR 90 55
Waste
ICI Boilers— Residual Oil SCR SCR 90 80
Furniture Coatings; and Large Appliance Coatings. 40 CFR 59. July 10, 2007. Available on the
Intenet at http://www.epa.gov/ttncaaa 1 /t 1 /fr notices/ctg ccp092807.pdf. It should be noted that
this CTG became final in October 2007.
8 U.S. Environmental Protection Agency. Consumer and Commercial Products: Control
Techniques Guidelines in Lieu of Regulations for Paper, Film, and Foil Coatings; Metal
Furniture Coatings; and Large Appliance Coatings. 40 CFR 59. July 10, 2007, p. 37597.
Available on the Intenet at http://www.epa.gov/ttncaaal/tl/fr notices/ctg ccp092807.pdf.
3a-9
-------
Poll
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
sec
10200501
10200502
10200504
10200601
10200602
10200603
10200604
10300601
10300602
10300603
10500106
10500206
30500606
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
30600106
30600199
30600102
30600105
AirControlNET Source
Description
ICI Boilers— Distillate Oil
ICI Boilers — Natural Gas
Cement Manufacturing — Dry
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
Process Heaters — Residual
Oil
Process Heaters — Natural
Gas
AirControlNE
T Control
Technology
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
Biosolid
Injection
SNCR
SNCR
LNB + FGR
LNB + SCR
LNB + SCR
LNB + SCR
LNB + SCR
New Control
Technology
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
Biosolid
Injection
SCR
SCR
SCR
LNB + SCR
LNB + SCR
LNB + SCR
LNB + SCR
New
CE
(%)
90
90
90
90
90
90
90
90
90
90
90
40
90
90
90
90
90
90
90
Old
CE
(%)
80
80
80
80
85
80
70
80
80
80
80
23
45
60 to
98
55
88
90
80
80
3a-10
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Poll
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
sec
30700104
30790013
39000201
39000203
39000289
39000489
39000689
39000701
39000789
50100101
50100506
50200506
50300101
50300102
50300104
50300506
50100102
AirControlNET Source
Description
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
AirControlNE
T Control
Technology
SCR
SNCR
SNCR— urea
based
SNCR— urea
based
SNCR
LNB
LNB
LNB + FGR
LNB
SNCR
New Control
Technology
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
SCR
New
CE
(%)
90
90
90
90
90
90
90
90
90
90
Old
CE
(%)
80
45
50
50
40
37
50
55
50
45
The last category of supplemental controls is control technologies currently in our control
measures database being applied to SCCs not controlled currently in AirControlNET.
Table 3a.7: Supplemental Emission Control Technologies Currently in our Control
Measures Database Applied to New Source Types
Pollutant
NOX
NOX
NOX
NOX
NOX
NOX
NOX
sec
39000602
30501401
30302351
30302352
30302359
10100101
10100202
10100204
10100212
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)
Control
Technology
SCR
OXY-Firing
SCR
SNCR
SNCR
SNCR
SNCR
CE
90
85
90
40
40
40
40
3a-ll
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Pollutant
NOX
NOX
NOX
NOX
NOX
NOX
NOX
NOX
sec
10100401
10100404
10100501
10100601
10100602
10100604
10101202
20200253
SCC Description
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
External Combustion Boilers; Electric Generation;
Solid Waste; Refuse Derived Fuel
Internal Comb. Engines/Industrial/Natural Gas/4-cycle
Rich Burn
Control
Technology
SNCR
SNCR
SNCR
NGR
NGR
NGR
SNCR
NSCR
CE
50
50
50
50
50
50
50
90
3a.2 Mobile Control Measures Used in Control Scenarios
Tables 3a.8 and 3a.9 summarize the emission reductions for the mobile source control measures
discussed in this section.
Table 3a.8: NOx Mobile Emission Reductions by Control Measure
Modeled Control Strategy Reductions
(annual tons/year)
Sector
Control Measure
Onroad
Eliminate Long Duration Truck Idling
5,800
Reduce Gasoline RVP
880
Diesel Retrofits
91,000
Continuous Inspection and Maintenance
20,000
Commuter Programs
4,100
Nonroad
Diesel Retrofits and Engine Rebuilds
35,000
Table 3a.9: VOC Mobile Emission Reductions by Control Measure
Sector
Onroad
Nonroad
Control Measure
Reduce Gasoline RVP
Diesel Retrofits
Continuous Inspection and Maintenance
Commuter Programs
Reduce Gasoline RVP
Diesel Retrofits and Engine Rebuilds
Modeled Control Strategy Reductions
(annual tons/year)
17,000
8,400
28,000
7,000
6,300
5,200
3a-12
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3a.2.1 Diesel Retrofits and Engine Rebuilds
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 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
3a-13
-------
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.lO and Table 3a.l 1, respectively. The groupings
of model years for highway vehicles reflect changes in EPA's published emissions standards for
new engines.
Table 3a.lO: 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.ll: 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.l2 and Table 3a.l3, respectively.
Table 3a.l2: Percentage Emissions Reduction by Highway Vehicle Retrofit Strategy
PM CO HC NOx
SCR (+DPF) 90% 90% 90% 70%
Table 3a.l3: Percentage Emissions Reduction by Nonroad Equipment Retrofit Strategy
Strategy PM CO HC NOx
SCR (+DPF) 90% 90% 90% 70%
Rebuild/Upgrade Kit 30% 15% 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
3a-14
-------
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 3 a. 12 and 3 a. 13 to the specified model years in Tables 3 a. 10
and 3a. 11 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.
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.
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.
Source Categories Affected by Measure:
3a-15
-------
• All 1996 and newer light-duty gasoline vehicles and trucks:
• All 1996 and newer (SCC 2201001000) Light Duty Gasoline Vehicles (LDGV), Total:
All Road Types
• All 1996 and newer (SCC 2201020000) Light Duty Gasoline Trucks 1 (LDGT1), Total:
All Road Types
• All 1996 and newer (SCC 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.
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/smartway/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
3a-16
-------
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/smartwav/idle-guid.htm.
Pollutants and Source Categories Affected by Measure: NOX
Table 3a.l4: 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"
223 0074110 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
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; Urban Local: Total
Estimated Emissions Reduction from Measure (%): 3.4 % decrease in NOx 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.9 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.
9 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.
3a-17
-------
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
3a-18
-------
Table 3a.l5: All Light-Duty Gasoline Vehicles and Trucks
Note: All SCC Descriptions below begin with "Mobile Sources; Highway Vehicles—
SCC Gasoline"
2201001110 Light Duty Gasoline Vehicles (LDGV); Rural Interstate: Total
2201001130 Light Duty Gasoline Vehicles (LDGV); Rural Other Principal Arterial: Total
2201001150 Light Duty Gasoline Vehicles (LDGV); Rural Minor Arterial: Total
2201001170 Light Duty Gasoline Vehicles (LDGV); Rural Major Collector: Total
2201001190 Light Duty Gasoline Vehicles (LDGV); Rural Minor Collector: Total
2201001210 Light Duty Gasoline Vehicles (LDGV); Rural Local: Total
2201001230 Light Duty Gasoline Vehicles (LDGV); Urban Interstate: Total
2201001250 Light Duty Gasoline Vehicles (LDGV); Urban Other Freeways and Expressways: Total
2201001270 Light Duty Gasoline Vehicles (LDGV); Urban Other Principal Arterial: Total
2201001290 Light Duty Gasoline Vehicles (LDGV); Urban Minor Arterial: Total
2201001310 Light Duty Gasoline Vehicles (LDGV); Urban Collector: Total
2201001330 Light Duty Gasoline Vehicles (LDGV); Urban Local: Total
2201020110
2201020130
2201020150
2201020170
2201020190
2201020210
2201020230
2201020250
2201020270
2201020290
2201020310
2201020330
Light Duty
Light Duty
Light Duty
Light Duty
Light Duty
Light Duty
Light Duty
Gasoline Tracks 1
Gasoline Tracks 1
Gasoline
Gasoline
Gasoline
Gasoline
Gasoline
Light Duty Gasoline
Expressways: Total
Light Duty
Light Duty
Light Duty
Light Duty
Gasoline
Gasoline
Gasoline
Gasoline
Tracks 1
Tracks 1
Tracks 1
Tracks 1
Tracks 1
Tracks 1
Tracks 1
Tracks 1
Tracks 1
Tracks 1
&
&
&
&
&
&
&
&
&
&
&
&
2
2
2
2
2
2
2
2
2
2
2
2
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
(M6)
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
= LDGT1
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
(M5);
Rural Interstate: Total
Rural Other Principal Arterial:
Total
Rural Minor Arterial: Total
Rural Major Collector:
Rural Minor Collector:
Rural Local: Total
Urban Interstate: Total
Urban Other Freeways
Urban Other Principal
Urban Minor Arterial:
Urban Collector: Total
Urban Local: Total
Total
: Total
and
Arterial:
Total
Total
2201040110 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Rural Interstate: Total
2201040130 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Rural Other Principal Arterial: Total
2201040150 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Rural Minor Arterial: Total
2201040170 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Rural Major Collector: Total
2201040190 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Rural Minor Collector: Total
2201040210 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Rural Local: Total
2201040230 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Urban Interstate: Total
2201040250 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Urban Other Freeways and
Expressways: Total
2201040270 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Urban Other Principal Arterial: Total
2201040290 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Urban Minor Arterial: Total
2201040310 Light Duty Gasoline Tracks 3 & 4 (M6) = LDGT2 (M5); Urban Collector: Total
2201040330 Light Duty Gasoline Tracks 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 %
3a.2.5 Reduce Gasoline RVPfrom 7.8 to 7.0 in RemainingNonattainment 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,
3a-19
-------
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.
Source Categories Affected by Measure:
• 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
3a.3 ECU Controls Used in the Control Strategy
Table 3a.l6 contains the ozone season emissions from all fossil EGU sources (greater than 25
megawatts) for the baseline and the control strategy.
Table 3a.l6: NOx EGU Ozone Season Emissions (All Fossil Units >25MW) (1,000 Tons)a
Baseline
(CAIR/CAMR/CAVR)
Control Strategy
OTC
73
65
(-11%)
MWRPO
154
113
(-26%)
East TX
43
33
(-23%)
National
828
812
(-2%)
CAIR
Region
463
470
CAIR
Cap
485
482
"Numbers in parentheses are the percentage change in emissions.
3a.3.1 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 SO2 and/or NOX emissions
across 28 eastern states and the District of Columbia.
3a-20
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Figure 3a.l: CAIR Affected Region
States not covered by CAIR
| States controlled for fine particles (annual SO2 and NOx)
5 States controlled for both fine particles (annual SO2 and NOx) and ozone (ozone season NOx)
States controlled for ozone (ozone season NOx)
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 PlV^.s 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 ppm for the power
sector.
3a.3.2 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
3a-21
-------
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 proposal RIA analysis used 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 incorporated Federal and State rules and regulations
adopted before March 2004 and various NSR settlements.
Final RIA analysis uses the latest version of IPM (v3.0) as part of the updated modeling
platform. IPM v3.0 includes input and model assumption updates in modeling the power sector
and incorporates Federal and State rules and regulations adopted before September 2006 and
various NSR settlements. A detailed discussion of uncertainties associated with the EGU sector
modeling can be found in 2006 PM NAAQS RIA (pg. 3-50)
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 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.html).
3a.3.3 EGUNOx Emission Control Technologies
IPM v3.0 includes SC>2, NOX, and mercury (Hg) emission control technology options for meeting
existing and future federal, regional, and state, SC>2, NOxand 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. 17 summarizes retrofit NOx emission control performance assumptions.
Table 3a.l7: Summary of Retrofit NOx Emission Control Performance Assumptions
Unit Type
Percent Removal
Size Applicability
Selective Catalytic Reduction
(SCR)
Coal Oil/Gasa
90% down to 0.06 80%
Ib/mmBtu
Units. 100 MW Units. 25 MW
Selective Non-Catalytic Reduction
(SNCR)
Coal Oil/Gasa
35% 50%
Units. 25 MW Units. 25 MW
and
Units < 200 MW
a 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-
3a-22
-------
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).
3a.4 Emissions Reductions by Sector
Figures 3a.2-3a.6 show the NOx reductions for each sector and Figures 3a.7-3a.10 show the
VOC reductions for each sector under the modeled control strategy.
Figure 3a.2: Annual Tons of NOx Emissions Reduced from EGU Sources*
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.
3a-23
-------
Figure 3a.3: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from NonEGU
Point Sources*
* 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-24
-------
Figure 3a.4: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from Area
Sources*
*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-25
-------
Figure 3a.5: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from Nonroad
Sources*
*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
-------
Figure 3a.6: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from Onroad
Sources*
*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-27
-------
Figure 3a.7: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced
from NonEGU Point Sources*
*Reductions are negative and increases are positive
**The -99-0 range is not shown because these are small county-level VOC reductions or increases that
likely had little to no impact on ozone estimates
3a-28
-------
Figure 3a.8: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced
from Area Sources*
I—I-499--100
=1-99-0
*Reductions are negative and increases are positive
**The -99-0 range is not shown because these are small county-level VOC reductions or increases that
likely had little to no impact on ozone estimates.
3a-29
-------
Figure 3a.9: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced
from Nonroad Mobile Sources*
.211 -.100
*Reductions are negative and increases are positive
**The -99-0 range is not shown because these are small county-level VOC reductions or increases that
likely had little to no impact on ozone estimates.
3a-30
-------
Figure 3a.lO: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced
from Onroad Mobile Sources*
-1.352--500
*Reductions are negative and increases are positive
**The -99-0 range is not shown because these are small county-level VOC reductions or increases that
likely had little to no impact on ozone estimates.
3a.5 Change in Ozone Concentrations Between Baseline and Modeled Control Strategy
Table 3a.l8 provides the projected 8-hour ozone design values for the 2020 baseline and 2020
control strategy scenarios for each monitored county. The changes in ozone in 2020 between the
baseline and the control strategy are also provided in this table.
Table 3a.l8: Changes in Ozone Concentrations between Baseline and Modeled Control
Strategy
State
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
County
Baldwin
Clay
Elmore
Etowah
Jefferson
Lawrence
Baseline 8-hour
Ozone Design Value
(ppm)
0.063
0.056
0.054
0.054
0.059
0.054
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.063
0.055
0.055
0.052
0.060
0.055
Change
(ppm)
0.000
-0.001
0.001
-0.002
0.001
0.001
3a-31
-------
State
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arkansas
Arkansas
Arkansas
Arkansas
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
County
Madison
Mobile
Montgomery
Morgan
Shelby
Sumter
Tuscaloosa
Cochise
Coconino
Maricopa
Navajo
Pima
Final
Yavapai
Crittenden
Montgomery
Newton
Pulaski
Alameda
Amador
Butte
Calaveras
Colusa
Contra Costa
El Dorado
Fresno
Glenn
Imperial
Inyo
Kern
Kings
Lake
Los Angeles
Madera
Marin
Mariposa
Mendocino
Merced
Monterey
Napa
Nevada
Orange
Placer
Riverside
Sacramento
San Benito
San Bernardino
San Diego
San Francisco
San Joaquin
Baseline 8-hour
Ozone Design Value
(ppm)
0.057
0.063
0.054
0.060
0.061
0.051
0.052
0.065
0.067
0.069
0.058
0.063
0.064
0.064
0.068
0.051
0.060
0.061
0.068
0.067
0.068
0.071
0.058
0.069
0.080
0.091
0.057
0.071
0.068
0.096
0.076
0.054
0.104
0.075
0.041
0.071
0.045
0.079
0.054
0.050
0.075
0.080
0.075
0.101
0.077
0.066
0.122
0.077
0.045
0.067
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.057
0.064
0.054
0.061
0.063
0.051
0.052
0.064
0.067
0.068
0.057
0.062
0.063
0.064
0.068
0.051
0.060
0.061
0.068
0.067
0.068
0.071
0.058
0.069
0.080
0.091
0.057
0.071
0.068
0.096
0.076
0.054
0.104
0.075
0.040
0.071
0.045
0.079
0.054
0.050
0.075
0.080
0.075
0.101
0.077
0.066
0.122
0.076
0.045
0.066
Change
(ppm)
0.000
0.001
0.000
0.001
0.002
0.000
0.000
-0.001
0.000
-0.001
-0.001
-0.001
-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
0.000
0.000
0.000
-0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
-0.001
0.000
-0.001
3a-32
-------
State
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Delaware
Delaware
Delaware
D.C.
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
County
San Luis Obispo
San Mateo
Santa Barbara
Santa Clara
Santa Cruz
Shasta
Solano
Sonoma
Stanislaus
Sutler
Tehama
Tulare
Tuolumne
Ventura
Yolo
Adams
Arapahoe
Boulder
Denver
Douglas
El Paso
Jefferson
La Plata
Larimer
Montezuma
Weld
Fairfield
Hartford
Litchfield
Middlesex
New Haven
New London
Tolland
Kent
New Castle
Sussex
Washington
Alachua
Baker
Bay
Brevard
Broward
Collier
Columbia
Duval
Escambia
Highlands
Hillsborough
Holmes
Lake
Baseline 8-hour
Ozone Design Value
(ppm)
0.060
0.051
0.068
0.066
0.054
0.057
0.057
0.048
0.076
0.067
0.065
0.083
0.072
0.077
0.064
0.056
0.069
0.062
0.064
0.072
0.062
0.072
0.051
0.066
0.062
0.063
0.079
0.065
0.064
0.073
0.076
0.067
0.068
0.069
0.070
0.070
0.068
0.056
0.054
0.061
0.050
0.054
0.056
0.052
0.052
0.064
0.053
0.065
0.054
0.054
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.060
0.050
0.068
0.066
0.054
0.057
0.057
0.048
0.076
0.067
0.065
0.083
0.072
0.077
0.064
0.053
0.064
0.058
0.060
0.067
0.059
0.067
0.051
0.061
0.062
0.059
0.076
0.062
0.061
0.070
0.073
0.065
0.065
0.067
0.067
0.067
0.065
0.056
0.054
0.063
0.051
0.054
0.056
0.052
0.052
0.064
0.053
0.065
0.054
0.056
Change
(ppm)
0.000
-0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
-0.003
-0.005
-0.004
-0.004
-0.005
-0.003
-0.005
0.000
-0.005
0.000
-0.004
-0.003
-0.003
-0.003
-0.003
-0.003
-0.002
-0.003
-0.002
-0.003
-0.003
-0.003
0.000
0.000
0.002
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.002
3a-33
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State
Florida
Florida
Florida
Florida
Florida
Florida
Florida
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
Georgia
Georgia
Idaho
Idaho
Idaho
Idaho
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
County
Lee
Leon
Manatee
Marion
Miami-Bade
Orange
Osceola
Palm Beach
Pasco
Pinellas
Polk
St Lucie
Santa Rosa
Sarasota
Seminole
Volusia
Wakulla
Bibb
Chatham
Cherokee
Clarke
Cobb
Coweta
Dawson
De Kalb
Douglas
Fayette
Fulton
Glynn
Gwinnett
Henry
Murray
Muscogee
Paulding
Richmond
Rockdale
Sumter
Ada
Butte
Canyon
Elmore
Adams
Champaign
Clark
Cook
Du Page
Efiingham
Hamilton
Jersey
Kane
Baseline 8-hour
Ozone Design Value
(ppm)
0.055
0.054
0.060
0.058
0.052
0.055
0.053
0.054
0.057
0.060
0.057
0.051
0.063
0.060
0.056
0.051
0.059
0.064
0.052
0.053
0.053
0.063
0.065
0.056
0.066
0.063
0.061
0.070
0.054
0.061
0.064
0.059
0.053
0.060
0.064
0.063
0.054
0.069
0.065
0.059
0.060
0.059
0.057
0.053
0.073
0.060
0.057
0.058
0.067
0.062
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.056
0.054
0.060
0.058
0.052
0.057
0.054
0.054
0.057
0.060
0.058
0.051
0.063
0.060
0.058
0.051
0.059
0.063
0.052
0.051
0.051
0.061
0.059
0.054
0.064
0.061
0.059
0.068
0.053
0.059
0.062
0.058
0.052
0.058
0.059
0.061
0.053
0.069
0.065
0.059
0.060
0.055
0.056
0.052
0.072
0.059
0.056
0.057
0.065
0.060
Change
(ppm)
0.001
0.000
0.000
0.000
0.000
0.002
0.001
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.002
0.000
0.000
-0.001
0.000
-0.002
-0.002
-0.002
-0.006
-0.002
-0.002
-0.002
-0.002
-0.002
-0.001
-0.002
-0.002
-0.001
-0.001
-0.002
-0.005
-0.002
-0.001
0.000
0.000
0.000
0.000
-0.004
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.002
-0.002
3a-34
-------
State
Illinois
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
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
County
Lake
McHenry
McLean
Macon
Macoupin
Madison
Peoria
Randolph
Rock Island
St Clair
Sangamon
Will
Winnebago
Allen
Boone
Carroll
Clark
Delaware
Elkhart
Floyd
Gibson
Greene
Hamilton
Hancock
Hendricks
Huntington
Jackson
Johnson
Lake
La Porte
Madison
Marion
Morgan
Porter
Posey
St Joseph
Shelby
Vanderburgh
Vigo
Warrick
Bremer
Clinton
Harrison
Linn
Montgomery
Palo Alto
Polk
Scott
Story
Van Buren
Baseline 8-hour
Ozone Design Value
(ppm)
0.070
0.066
0.057
0.055
0.057
0.066
0.062
0.059
0.054
0.065
0.053
0.061
0.058
0.066
0.067
0.062
0.068
0.064
0.065
0.066
0.051
0.062
0.069
0.067
0.064
0.063
0.062
0.064
0.077
0.074
0.067
0.068
0.065
0.075
0.061
0.068
0.068
0.060
0.066
0.064
0.058
0.062
0.062
0.057
0.056
0.054
0.046
0.061
0.048
0.059
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.069
0.065
0.055
0.054
0.055
0.063
0.061
0.058
0.053
0.063
0.052
0.060
0.056
0.065
0.065
0.061
0.066
0.062
0.064
0.064
0.050
0.061
0.068
0.065
0.063
0.062
0.060
0.062
0.077
0.072
0.065
0.066
0.063
0.074
0.059
0.066
0.067
0.058
0.064
0.061
0.058
0.061
0.062
0.057
0.056
0.053
0.046
0.060
0.048
0.057
Change
(ppm)
-0.001
-0.001
-0.002
-0.001
-0.002
-0.003
-0.001
-0.001
-0.001
-0.002
-0.001
-0.001
-0.002
-0.001
-0.002
-0.001
-0.002
-0.002
-0.001
-0.002
-0.001
-0.001
-0.001
-0.002
-0.001
-0.001
-0.002
-0.002
0.000
-0.002
-0.002
-0.002
-0.002
-0.001
-0.002
-0.002
-0.001
-0.002
-0.002
-0.003
0.000
-0.001
0.000
0.000
0.000
-0.001
0.000
-0.001
0.000
-0.002
3a-35
-------
State
Iowa
Kansas
Kansas
Kansas
Kansas
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
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
County
Warren
Linn
Sedgwick
Sumner
Trego
Wyandotte
Bell
Boone
Boyd
Bullitt
Campbell
Carter
Christian
Daviess
Edmonson
Fayette
Graves
Greenup
Hancock
Hardin
Henderson
Jefferson
Jessamine
Kenton
Livingston
McCracken
McLean
Oldham
Perry
Pike
Pulaski
Scott
Simpson
Trigg
Warren
Ascension
Beauregard
Bossier
Caddo
Calcasieu
East Baton Rouge
Grant
Iberville
Jefferson
Lafayette
Lafourche
Livingston
Orleans
Ouachita
Pointe Coupee
Baseline 8-hour
Ozone Design Value
(ppm)
0.049
0.060
0.063
0.062
0.055
0.062
0.056
0.063
0.070
0.061
0.070
0.057
0.057
0.058
0.059
0.057
0.059
0.064
0.063
0.057
0.060
0.064
0.057
0.065
0.061
0.063
0.059
0.063
0.055
0.054
0.058
0.050
0.056
0.052
0.060
0.068
0.061
0.060
0.058
0.066
0.076
0.060
0.072
0.069
0.065
0.065
0.068
0.057
0.061
0.063
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.048
0.059
0.063
0.062
0.055
0.062
0.055
0.060
0.069
0.059
0.067
0.056
0.057
0.058
0.057
0.055
0.058
0.063
0.064
0.056
0.057
0.063
0.056
0.062
0.060
0.062
0.058
0.061
0.054
0.053
0.060
0.049
0.056
0.052
0.058
0.065
0.058
0.060
0.057
0.063
0.073
0.058
0.068
0.066
0.061
0.062
0.064
0.056
0.060
0.057
Change
(ppm)
-0.001
-0.001
0.000
0.000
0.000
0.000
-0.001
-0.003
-0.001
-0.002
-0.003
-0.001
0.000
0.000
-0.002
-0.002
-0.001
-0.001
0.001
-0.001
-0.003
-0.001
-0.001
-0.003
-0.001
-0.001
-0.001
-0.002
-0.001
-0.001
0.002
-0.001
0.000
0.000
-0.002
-0.003
-0.003
0.000
-0.001
-0.003
-0.003
-0.002
-0.004
-0.003
-0.004
-0.003
-0.004
-0.001
-0.001
-0.006
3a-36
-------
State
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
County
St Bernard
St Charles
St James
St John The Baptis
StMary
West Baton Rouge
Cumberland
Hancock
Kennebec
Knox
Oxford
Penobscot
Sagadahoc
York
Anne Arundel
Baltimore
Carroll
Cecil
Charles
Frederick
Harford
Kent
Montgomery
Prince Georges
Washington
Barnstable
Berkshire
Bristol
Essex
Hampden
Hampshire
Middlesex
Norfolk
Suffolk
Worcester
Allegan
Benzie
Berrien
Cass
Clinton
Genesee
Huron
Ingham
Kalamazoo
Kent
Lenawee
Macomb
Mason
Missaukee
Muskegon
Baseline 8-hour
Ozone Design Value
(ppm)
0.063
0.066
0.064
0.068
0.061
0.073
0.063
0.071
0.060
0.063
0.050
0.064
0.059
0.066
0.072
0.070
0.065
0.071
0.065
0.065
0.076
0.069
0.064
0.069
0.063
0.070
0.068
0.069
0.070
0.068
0.066
0.064
0.073
0.068
0.065
0.073
0.066
0.070
0.068
0.064
0.066
0.068
0.063
0.062
0.065
0.067
0.075
0.065
0.061
0.069
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.061
0.063
0.061
0.066
0.057
0.070
0.061
0.068
0.058
0.061
0.048
0.062
0.057
0.064
0.069
0.067
0.061
0.068
0.062
0.061
0.073
0.067
0.061
0.066
0.061
0.068
0.066
0.066
0.068
0.065
0.063
0.062
0.071
0.067
0.062
0.072
0.065
0.069
0.066
0.062
0.064
0.067
0.062
0.061
0.063
0.065
0.073
0.064
0.060
0.068
Change
(ppm)
-0.002
-0.003
-0.003
-0.002
-0.004
-0.003
-0.002
-0.003
-0.002
-0.002
-0.002
-0.002
-0.002
-0.002
-0.003
-0.003
-0.004
-0.003
-0.003
-0.004
-0.003
-0.002
-0.003
-0.003
-0.002
-0.002
-0.002
-0.003
-0.002
-0.003
-0.003
-0.002
-0.002
-0.001
-0.003
-0.001
-0.001
-0.001
-0.002
-0.002
-0.002
-0.001
-0.001
-0.001
-0.002
-0.002
-0.002
-0.001
-0.001
-0.001
3a-37
-------
State
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Montana
Nebraska
Nebraska
Nevada
Nevada
Nevada
Nevada
Nevada
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Jersey
New Jersey
New Jersey
New Jersey
County
Oakland
Ottawa
St Clair
Schoolcraft
Washtenaw
Wayne
St Louis
Adams
Bolivar
De Soto
Hancock
Harrison
Hinds
Jackson
Lauderdale
Lee
Madison
Warren
Cass
Cedar
Clay
Greene
Jefferson
Monroe
Platte
St Charles
Ste Genevieve
St Louis
St Louis City
Flathead
Douglas
Lancaster
Clark
Douglas
Washoe
White Pine
Carson City
Belknap
Carroll
Cheshire
Grafton
Hillsborough
Merrimack
Rockingham
Stafford
Sullivan
Atlantic
Bergen
Camden
Cumberland
Baseline 8-hour
Ozone Design Value
(ppm)
0.072
0.066
0.070
0.062
0.069
0.071
0.059
0.060
0.057
0.062
0.063
0.062
0.050
0.067
0.051
0.056
0.053
0.052
0.060
0.063
0.064
0.058
0.066
0.060
0.063
0.071
0.065
0.070
0.070
0.052
0.056
0.045
0.072
0.059
0.063
0.065
0.062
0.059
0.055
0.056
0.057
0.065
0.057
0.063
0.059
0.061
0.067
0.074
0.077
0.071
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.071
0.064
0.067
0.061
0.067
0.069
0.059
0.059
0.057
0.062
0.062
0.065
0.050
0.067
0.050
0.058
0.053
0.052
0.060
0.062
0.064
0.057
0.064
0.058
0.062
0.068
0.062
0.067
0.068
0.052
0.056
0.045
0.071
0.059
0.063
0.065
0.062
0.058
0.054
0.054
0.056
0.063
0.056
0.061
0.057
0.059
0.065
0.071
0.074
0.068
Change
(ppm)
-0.001
-0.002
-0.003
-0.001
-0.002
-0.002
0.000
-0.001
0.000
0.000
-0.001
0.003
0.000
0.000
-0.001
0.002
0.000
0.000
0.000
-0.001
0.000
-0.001
-0.002
-0.002
-0.001
-0.003
-0.003
-0.003
-0.002
0.000
0.000
0.000
-0.001
0.000
0.000
0.000
0.000
-0.001
-0.001
-0.002
-0.001
-0.002
-0.001
-0.002
-0.002
-0.002
-0.002
-0.003
-0.003
-0.003
3a-38
-------
State
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Mexico
New Mexico
New Mexico
New Mexico
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
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
County
Essex
Gloucester
Hudson
Hunterdon
Mercer
Middlesex
Monmouth
Morris
Ocean
Passaic
Bernalillo
Dona Ana
Eddy
Sandoval
San Juan
Valencia
Albany
Bronx
Chautauqua
Chemung
Dutchess
Erie
Essex
Hamilton
Herkimer
Jefferson
Madison
Monroe
Niagara
Oneida
Onondaga
Orange
Oswego
Putnam
Queens
Rensselaer
Richmond
Saratoga
Schenectady
Suffolk
Ulster
Wayne
Westchester
Alexander
Avery
Buncombe
Caldwell
Caswell
Chatham
Cumberland
Baseline 8-hour
Ozone Design Value
(ppm)
0.052
0.075
0.066
0.071
0.075
0.073
0.073
0.071
0.079
0.067
0.065
0.069
0.063
0.063
0.069
0.056
0.064
0.069
0.072
0.061
0.068
0.075
0.069
0.063
0.059
0.073
0.062
0.067
0.075
0.063
0.067
0.063
0.053
0.070
0.069
0.066
0.073
0.067
0.061
0.080
0.063
0.065
0.074
0.062
0.059
0.060
0.060
0.060
0.058
0.061
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.051
0.073
0.064
0.068
0.073
0.070
0.070
0.068
0.076
0.064
0.064
0.068
0.063
0.063
0.069
0.056
0.061
0.067
0.069
0.059
0.065
0.072
0.067
0.062
0.057
0.071
0.060
0.064
0.073
0.061
0.065
0.061
0.052
0.068
0.067
0.063
0.071
0.063
0.059
0.077
0.061
0.063
0.071
0.061
0.057
0.059
0.060
0.059
0.057
0.060
Change
(ppm)
-0.001
-0.002
-0.002
-0.003
-0.002
-0.003
-0.003
-0.003
-0.003
-0.003
-0.001
-0.001
0.000
0.000
0.000
0.000
-0.003
-0.002
-0.003
-0.002
-0.003
-0.003
-0.002
-0.001
-0.002
-0.002
-0.002
-0.003
-0.002
-0.002
-0.002
-0.002
-0.001
-0.002
-0.002
-0.003
-0.002
-0.004
-0.002
-0.003
-0.002
-0.002
-0.003
-0.001
-0.002
-0.001
0.000
-0.001
-0.001
-0.001
3a-39
-------
State
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
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
County
Davie
Duplin
Durham
Edgecombe
Forsyth
Franklin
Granville
Guilford
Haywood
Jackson
Johnston
Lenoir
Lincoln
Martin
Mecklenburg
New Hanover
Northampton
Person
Pitt
Randolph
Rockingham
Rowan
Swain
Union
Wake
Yancey
Billings
Cass
Dunn
McKenzie
Mercer
Oliver
Allen
Ashtabula
Butler
Clark
Clermont
Clinton
Cuyahoga
Delaware
Franklin
Geauga
Greene
Hamilton
Jefferson
Knox
Lake
Lawrence
Licking
Lorain
Baseline 8-hour
Ozone Design Value
(ppm)
0.064
0.059
0.061
0.063
0.063
0.063
0.064
0.060
0.064
0.063
0.060
0.060
0.064
0.060
0.071
0.056
0.062
0.063
0.059
0.057
0.062
0.068
0.053
0.062
0.064
0.063
0.054
0.055
0.054
0.058
0.055
0.051
0.068
0.075
0.068
0.066
0.068
0.069
0.067
0.066
0.068
0.076
0.066
0.069
0.063
0.064
0.072
0.065
0.065
0.067
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.062
0.058
0.060
0.062
0.062
0.062
0.063
0.058
0.064
0.062
0.059
0.059
0.065
0.059
0.070
0.057
0.060
0.061
0.058
0.056
0.061
0.067
0.052
0.061
0.063
0.061
0.054
0.055
0.054
0.058
0.055
0.050
0.065
0.073
0.064
0.062
0.066
0.066
0.065
0.064
0.066
0.074
0.062
0.066
0.061
0.062
0.070
0.063
0.062
0.065
Change
(ppm)
-0.002
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.002
0.000
-0.001
-0.001
-0.001
0.001
-0.001
-0.001
0.001
-0.002
-0.002
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.002
0.000
0.000
0.000
0.000
0.000
-0.001
-0.003
-0.002
-0.004
-0.004
-0.002
-0.003
-0.002
-0.002
-0.002
-0.002
-0.004
-0.003
-0.002
-0.002
-0.002
-0.002
-0.003
-0.002
3a-40
-------
State
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Oregon
Oregon
Oregon
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
County
Lucas
Madison
Mahoning
Medina
Miami
Montgomery
Portage
Preble
Stark
Summit
Trumbull
Warren
Washington
Wood
Canadian
Cleveland
Comanche
Dewey
Kay
Me Clain
Oklahoma
Ottawa
Pittsburg
Tulsa
Clackamas
Columbia
Jackson
Lane
Marion
Adams
Allegheny
Armstrong
Beaver
Berks
Blair
Bucks
Cambria
Centre
Chester
Clearfield
Dauphin
Delaware
Erie
Franklin
Greene
Lackawanna
Lancaster
Lawrence
Lehigh
Luzerne
Baseline 8-hour
Ozone Design Value
(ppm)
0.070
0.065
0.065
0.067
0.065
0.065
0.068
0.060
0.065
0.071
0.068
0.068
0.061
0.068
0.056
0.060
0.061
0.058
0.060
0.061
0.061
0.062
0.060
0.066
0.062
0.055
0.061
0.059
0.054
0.059
0.072
0.068
0.071
0.066
0.060
0.078
0.063
0.062
0.071
0.065
0.065
0.070
0.070
0.067
0.063
0.061
0.067
0.057
0.067
0.062
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.067
0.062
0.062
0.065
0.062
0.062
0.066
0.057
0.063
0.068
0.066
0.065
0.060
0.065
0.056
0.058
0.059
0.056
0.060
0.060
0.060
0.062
0.060
0.065
0.062
0.055
0.061
0.059
0.054
0.056
0.069
0.065
0.068
0.063
0.058
0.075
0.061
0.059
0.068
0.062
0.060
0.068
0.067
0.064
0.061
0.059
0.062
0.055
0.063
0.059
Change
(ppm)
-0.003
-0.003
-0.003
-0.002
-0.003
-0.003
-0.002
-0.003
-0.002
-0.003
-0.002
-0.003
-0.001
-0.003
0.000
-0.002
-0.002
-0.002
0.000
-0.001
-0.001
0.000
0.000
-0.001
0.000
0.000
0.000
0.000
0.000
-0.003
-0.003
-0.003
-0.003
-0.003
-0.002
-0.003
-0.002
-0.003
-0.003
-0.003
-0.005
-0.002
-0.003
-0.003
-0.002
-0.002
-0.005
-0.002
-0.004
-0.003
3a-41
-------
State
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
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
County
Lycoming
Mercer
Montgomery
Northampton
Perry
Philadelphia
Tioga
Washington
Westmoreland
York
Kent
Providence
Washington
Abbeville
Aiken
Anderson
Barnwell
Berkeley
Charleston
Cherokee
Chester
Chesterfield
Colleton
Darlington
Edgefield
Oconee
Pickens
Richland
Spartanburg
Union
Williamsburg
York
Pennington
Anderson
Blount
Davidson
Hamilton
Haywood
Jefferson
Knox
Lawrence
Meigs
Putnam
Rutherford
Sevier
Shelby
Sullivan
Sumner
Williamson
Wilson
Baseline 8-hour
Ozone Design Value
(ppm)
0.061
0.068
0.071
0.066
0.061
0.077
0.064
0.066
0.068
0.067
0.069
0.069
0.070
0.060
0.061
0.063
0.058
0.052
0.054
0.061
0.059
0.058
0.058
0.061
0.059
0.060
0.059
0.066
0.062
0.058
0.052
0.059
0.062
0.058
0.064
0.056
0.061
0.060
0.061
0.061
0.056
0.061
0.061
0.058
0.066
0.065
0.066
0.061
0.060
0.060
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.059
0.065
0.068
0.062
0.058
0.074
0.062
0.063
0.065
0.062
0.067
0.066
0.068
0.058
0.058
0.062
0.056
0.052
0.054
0.059
0.058
0.058
0.057
0.060
0.056
0.059
0.058
0.064
0.061
0.057
0.051
0.058
0.061
0.058
0.064
0.056
0.062
0.062
0.061
0.061
0.058
0.060
0.061
0.057
0.065
0.065
0.066
0.061
0.060
0.060
Change
(ppm)
-0.002
-0.003
-0.003
-0.004
-0.003
-0.003
-0.002
-0.003
-0.003
-0.005
-0.002
-0.003
-0.002
-0.002
-0.003
-0.001
-0.002
0.000
0.000
-0.002
-0.001
0.000
-0.001
-0.001
-0.003
-0.001
-0.001
-0.002
-0.001
-0.001
-0.001
-0.001
-0.001
0.000
0.000
0.000
0.001
0.002
0.000
0.000
0.002
-0.001
0.000
-0.001
-0.001
0.000
0.000
0.000
0.000
0.000
3a-42
-------
State
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
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
Utah
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
County
Bexar
Brazoria
Brewster
Cameron
Collin
Dallas
Denton
Ellis
El Paso
Galveston
Gregg
Harris
Harrison
Hidalgo
Hood
Jefferson
Johnson
Kaufman
Montgomery
Nueces
Orange
Parker
Rockwall
Smith
Tarrant
Travis
Victoria
Webb
Box Elder
Cache
Davis
Salt Lake
San Juan
Utah
Weber
Bennington
Chittenden
Arlington
Caroline
Charles City
Chesterfield
Fairfax
Fauquier
Frederick
Hanover
Henrico
Loudoun
Madison
Page
Prince William
Baseline 8-hour
Ozone Design Value
(ppm)
0.068
0.073
0.054
0.052
0.069
0.068
0.074
0.063
0.069
0.074
0.067
0.089
0.061
0.062
0.058
0.074
0.065
0.054
0.073
0.065
0.066
0.063
0.061
0.064
0.075
0.063
0.060
0.053
0.064
0.056
0.070
0.069
0.064
0.067
0.065
0.061
0.063
0.072
0.059
0.069
0.066
0.071
0.058
0.061
0.069
0.067
0.066
0.062
0.058
0.063
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.067
0.072
0.053
0.051
0.067
0.066
0.072
0.059
0.068
0.072
0.064
0.087
0.058
0.061
0.056
0.071
0.062
0.052
0.072
0.063
0.063
0.061
0.060
0.061
0.073
0.062
0.059
0.053
0.062
0.054
0.067
0.067
0.063
0.065
0.062
0.058
0.062
0.068
0.057
0.066
0.064
0.067
0.056
0.060
0.067
0.065
0.063
0.061
0.056
0.060
Change
(ppm)
-0.001
-0.001
-0.001
-0.001
-0.002
-0.002
-0.002
-0.004
-0.001
-0.002
-0.003
-0.002
-0.003
-0.001
-0.002
-0.003
-0.003
-0.002
-0.001
-0.002
-0.003
-0.002
-0.001
-0.003
-0.002
-0.001
-0.001
0.000
-0.002
-0.002
-0.003
-0.002
-0.001
-0.002
-0.003
-0.003
-0.001
-0.004
-0.002
-0.003
-0.002
-0.004
-0.002
-0.001
-0.002
-0.002
-0.003
-0.001
-0.002
-0.003
3a-43
-------
State
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
West 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
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
County
Roanoke
Rockbridge
Stafford
Wythe
Alexandria City
Hampton City
Suffolk City
Clallam
Clark
King
Klickitat
Mason
Pierce
Skagit
Spokane
Thurston
Whatcom
Berkeley
Cabell
Greenbrier
Hancock
Kanawha
Monongalia
Ohio
Wood
Brown
Columbia
Dane
Dodge
Door
Florence
Fond Du Lac
Green
Jefferson
Kenosha
Kewaunee
Manitowoc
Marathon
Milwaukee
Oneida
Outagamie
Ozaukee
Racine
Rock
St Croix
Sauk
Sheboygan
Vernon
Vilas
Walworth
Baseline 8-hour
Ozone Design Value
(ppm)
0.061
0.057
0.062
0.060
0.066
0.071
0.070
0.041
0.061
0.063
0.061
0.049
0.065
0.044
0.060
0.059
0.051
0.062
0.068
0.060
0.064
0.062
0.055
0.063
0.062
0.065
0.059
0.060
0.063
0.071
0.058
0.061
0.059
0.062
0.081
0.071
0.068
0.058
0.074
0.056
0.060
0.074
0.074
0.063
0.059
0.057
0.077
0.060
0.057
0.063
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.060
0.055
0.060
0.059
0.063
0.070
0.069
0.041
0.061
0.063
0.059
0.049
0.065
0.044
0.060
0.059
0.051
0.060
0.067
0.059
0.061
0.061
0.054
0.061
0.061
0.064
0.058
0.059
0.061
0.070
0.057
0.060
0.058
0.061
0.080
0.069
0.067
0.057
0.072
0.055
0.059
0.073
0.073
0.062
0.059
0.056
0.076
0.059
0.055
0.062
Change
(ppm)
-0.001
-0.002
-0.002
-0.001
-0.003
-0.001
-0.001
0.000
0.000
0.000
-0.002
0.000
0.000
0.000
0.000
0.000
0.000
-0.002
-0.001
-0.001
-0.003
-0.001
-0.001
-0.002
-0.001
-0.001
-0.001
-0.001
-0.002
-0.001
-0.001
-0.001
-0.001
-0.001
-0.001
-0.002
-0.001
-0.001
-0.002
-0.001
-0.001
-0.001
-0.001
-0.001
0.000
-0.001
-0.001
-0.001
-0.002
-0.001
3a-44
-------
State
Wisconsin
Wisconsin
Wisconsin
Wyoming
Wyoming
County
Washington
Waukesha
Winnebago
Campbell
Teton
Baseline 8-hour
Ozone Design Value
(ppm)
0.064
0.063
0.065
0.067
0.062
Control Strategy 8-
hour Ozone Design
Value (ppm)
0.063
0.062
0.064
0.067
0.062
Change
(ppm)
-0.001
-0.001
-0.001
0.000
0.000
3a-45
-------
Chapter 4: Approach for Estimating Reductions for Full Attainment Scenario
Synopsis
After applying the hypothetical modeled control strategy described in Chapter 3, there were
multiple counties that were still not projected to attain potential new ozone standards. Because it
was impossible in some areas to meet a tighter ozone standard nationwide using only known
controls, EPA conducted a second step in the analysis and estimated the amount of further
emission reductions needed to attain an alternate primary ozone standard. The term "extrapolated
tons" will be used to refer to these additionally needed emissions reductions. Sections 4.1 and 4.2
of this chapter present the methodology EPA developed to determine the emissions reductions
needed for full attainment of the four alternate standards analyzed in the RIA (i.e., 0.065, 0.070,
0.075, and 0.079 ppm) and the results of that analysis. Additionally, in other areas, the known
controls in the hypothetical strategy resulted in ozone levels lower than one or more of the four
alternate standards. Sections 4.3 and 4.4 of this chapter discuss the methodology and present the
results of the "overcontrolled" analyses.
4.1 Development of Full Attainment Targets for Estimate of Extrapolated Costs
As previewed in the draft RIA, we conducted additional supplemental air quality modeling
analyses for the final RIA. This was intended to improve the estimates of extrapolated tons
needed to meet various potential standards. These additional modeling scenarios were designed
to provide more information about the response of ozone to emissions changes in terms of non-
linearities, geographic variations, the impacts of local versus upwind emissions reductions, and
the relationship between NOx and VOC emissions changes. As a result of this additional
information, the methodology to estimate the emissions reduction targets in the "extrapolated
cost areas" has been improved.
4.1.1 Design of Supplemental Modeling Scenarios
There were 61 counties that did not meet the 0.070 ppm standard even after application of the
controls in the hypothetical RIA modeled control scenario. There were 21 counties that did not
meet the 0.075 ppm standard.1 All 21 of these counties are in four broad geographic regions:
Houston, eastern Lake Michigan,2 the Northeast Corridor,3 and a large part of California.
Because these four areas will require the largest emissions reductions beyond the RIA control
1 10 counties did not meet the 0.079 ppm standard. 166 counties did not meet the 0.065 ppm
standard.
2 This geographic area is an aggregate of five existing nonattainment or maintenance areas: a)
Chicago-Gary-Lake County, IL-IN; b) Milwaukee-Racine, WI; c) Sheboygan WI; d) La Porte
IN; and e) South Bend-Elkhart IN.
3 This geographic area is an aggregate of six existing nonattainment or maintenance areas: a)
Philadelphia-Wilmington-Atlantic City, PA-NJ-MD-DE; b) New York-Northern New Jersey-
Long Island, NY-NJ-CT; c) Greater Connecticut, CT; d) Baltimore MD; e) Kent and Queen
Anne counties MD; and f) Poughkeepsie NY.
4-1
-------
scenario, and therefore likely the largest extrapolated costs, we focused on these areas within the
supplemental modeling analyses. We will refer to these four areas as "Phase 1" areas. Later, we
will define a second and third set of areas that also require extrapolated emissions reductions
which we will refer to as "Phase 2" and "Phase 3"areas. The primary distinction between these
three sets of areas is that the supplemental modeling was done only for the Phase 1 areas.
A map of the four Phase 1 areas is shown in Figure 4.1. An approach similar to that used to
define the geographic control areas for non-EGU point controls in the RIA control scenario
(discussed in Chapter 3) was also used to define the supplemental modeling control zones for
each of the four areas.
Figure 4.1: Counties within which Across-the-Board Emissions Reductions were Applied in
the Supplemental Modeling Analyses
| Starting Setfor Sensitivity Analyses (n=£
| | 1 00km buffer counties for VO C (n=186)
| | 200km buffer counties for NOx (n=317)
Six supplemental modeling runs were performed as part of this analysis. In the first three runs
anthropogenic NOx emissions within the appropriate Phase 1 areas (i.e., the red, pink, and
orange counties in Figure 4.1) were reduced across-the-board by 30, 60, and 90 percent. The
second set of runs included 30, 60, and 90 percent across-the-board reductions to anthropogenic
NOx and VOC emissions within the appropriate Phase 1 areas (i.e., the red, pink, and orange
counties for NOx; only the red and pink counties for VOC). An estimate of the effects of VOC
controls can be determined by comparing results from the NOx and VOC control run to the NOx
4-2
-------
only control run. In the two sets of across-the-board supplemental modeling runs the emissions
reductions were applied on top of the controls in the hypothetical RIA control case. As in the
modeled control strategy, NOx controls were applied to counties within a 200 km buffer and
VOC controls were applied to counties within a 100 km buffer of the starting set of counties.
In the draft RIA, we used the concept of "impact ratios"4 to calculate the additional tons needed
to meet the air quality standard. The updated approach uses the supplemental modeling to
determine what levels of ozone precursor reductions (NOx only or NOx plus VOC) are expected
to be sufficient to bring an area into attainment of one of the various alternate ozone standards
that were analyzed. After the development of emission targets for the 0.070 ppm alternative
standard, we conducted a "verification" model run to assess whether our estimated emissions
reductions actually resulted in attainment of 0.070 ppm in each area. The new estimates of
extrapolated tons represent a considerable improvement from what was done for the draft RIA.
For purposes of this analysis, we assume attainment by 2020 for all areas except San Joaquin
Valley and South Coast air basins in California. The state has submitted plans to EPA for
implementing the current ozone standard which propose that these two areas of California meet
that standard by 2024. We have assumed for analytical purposes that the San Joaquin Valley and
South Coast air basin would attain a new standard in 2030. There are many uncertainties
associated with the year 2030 analysis. Between 2020 and 2030 several federal air quality rules
are likely to further reduce emissions of NOx and VOC, such as, but not limited to National rules
for Diesel Locomotives, Diesel Marine Vessels, and Small Nonroad Gasoline Engines. These
emission reductions should lower ambient levels of ozone in California between 2020 and 2030.
Complete emissions inventories as well as air quality modeling were not available for this year
2030 analysis. Due to these limitations, it is not possible to adequately model 2030 air quality
changes that are required to develop robust controls strategies with associated costs and benefits.
In order to provide a rough approximation of the costs and benefits of attaining 0.075 ppm and
the alternate standards in San Joaquin and South Coast air basins, we've relied on the available
data. Available data includes emission inventories, which do not include any changes in
stationary source emissions beyond 2020, and 2020 supplemental air quality modeling. This
data was used to develop extrapolated costs and benefits of 2030 attainment. To view the
complete analysis for the San Joaquin Valley and South Coast air basins see Appendix 7b.
4.1.2 Results of Supplemental Modeling for Phase 1 Areas
Figures 4.2a through 4.2d show the projected design values for individual counties within each
of the Phase 1 areas for seven modeling cases (i.e., the RIA control scenario and each of the six
supplemental modeling runs). These figures are instructive in describing how the extrapolated
control targets were determined for these areas. For each area, the three counties that need the
most extrapolated controls were chosen for the graphs.
Figure 4.2a indicates that the highest ozone levels in the Houston area are projected to occur in
Harris, Galveston, and Brazoria counties with Harris being the controlling county. After
application of the RIA scenario controls, our modeling projects that the highest 2020 8-hour
4 The units for impact ratios are ppb/kton. In the draft RIA we used a single, national impact ratio
that assumed that 10,000 tons of NOx control would yield 0.001 ppm of ozone improvement.
4-3
-------
ozone design value in this area will be 0.087 ppm. Thus, additional precursor reductions are
needed to reach the current standard as well as all four of the alternate standards we are
considering. Based on the NOx plus VOC control modeling scenarios, we can see that increasing
the level of emissions reductions beyond the RIA case yields decreasing design values. At a 30%
NOx + VOC reduction, the projected design value is 0.084 ppm. At a 60% NOx + VOC
reduction, the projected design value is 0.079 ppm. Finally at a 90% NOx + VOC reduction, the
projected design value is 0.067 ppm.
Based on these results, it is concluded that it is possible to meet the current ozone standard with
additional NOx plus VOC emissions reductions between 0 and 30 percent. To meet an alternate
NAAQS of 0.079 ppm, the Houston area will require additional NOx plus VOC emissions of
approximately 60 percent. The 0.075 and 0.070 ppm standards will require between an additional
60-90% NOx plus VOC reduction beyond the RIA control case. The supplemental modeling
indicates that it will take more than 90% NOx plus VOC control (above and beyond the RIA
control case) to meet a 0.065 ppm standard. Based on these figures, one can also estimate the
levels of NOx-only controls needed to meet a particular standard. We used linear interpolation to
determine the specific percentage reduction in cases where attainment is expected to be achieved
Figure 4.2a: Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and
Each of the Six Supplemental Modeling Scenarios for the Highest Three Counties within
the Houston Area
0.090
• RIA Control Scenario
• 30% NOx + VOC control
• 60% NOx + VOC control
D 90% NOx + VOC control
B 30% NOx control
D 60% NOx control
D 90% NOx control
0.045
Harris TX
Galveston TX
Brazoria TX
between the supplemental scenarios of 0, 30, 60, and 90 percent.5 The specific percentage
reductions for Phase 1 areas are shown in Table 4.1.
Figure 4.2b shows two other aspects of the analysis. First, in some cases, the controlling county
within an area can vary as the precursor emissions are reduced. In the eastern Lake Michigan
area, the modeling indicates that an additional 60% NOx reduction will be sufficient to bring two
5 To add precision to this process, we based these calculations on projected design values that
contained data four places to the right of the decimal (e.g., 0.0755 ppm). In the last step of the
process however, EPA truncates all decimal places beyond the third decimal. This is consistent
with past policy on ozone design values.
4-4
-------
counties with high design values (Kenosha and Sheboygan WI) into attainment of an 0.070 ppm
standard. However, another county in that area does not reach 0.070 ppm with the 60% NOx
reduction. Lake IN is still 0.077 ppm. The full attainment, extrapolated target analysis is done on
a county by county basis, and the final area target is based on the county that requires the most
additional reductions. Second, it should be noted that in this area the addition of VOC controls
can have a significant impact on the projected design value. The 0.077 ppm value in Lake IN is
reduced to 0.073 ppm when 60% VOC controls are added to the 60% NOx controls. Figure 4.2c
is included for completeness sake and to show the supplemental modeling results in the
Northeast Corridor.
Figure 4.2b: Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and
Each of the Six Supplemental Modeling Scenarios for the Highest Counties within the
Eastern Lake Michigan Area
0.090
0.085 -
• RIA Control Scenario
• 30% NOx + VOC control
• 60% NOx + VOC control
D 90% NOx + VOC control
0 30% NOx control
D 60% NOx control
D 90% NOx control
KenoshaWI
Lake I
Sheboygan WI
Figure 4.2c: Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and
Each of the Six Supplemental Modeling Scenarios for the Highest Counties within the
Northeast Corridor
0.045
Suffolk NY
Fail-field CT
Ocean NJ
• RIA Control Scenario
• 30% NOx + VOC control
• 60% NOx + VOC control
D 90% NOx + VOC control
Q 30% NOx control
D 60% NOx control
D 90% NOx control
4-5
-------
As discussed in Chapter 3 and Chapter 5, there are two areas in Southern California that are not
planning to meet the current standard by 2020 (i.e., the Los Angeles South Coast Air Basin and
the San Joaquin Valley nonattainment areas). As a result, we have not estimated extrapolated
targets that will be necessary to bring these two nonattainment areas into attainment of the
alternate standards by 2020. However, due to the effects of ozone transport within California, we
are assuming that some extrapolated controls (beyond the RIA control case) will be needed in
these two areas to help other California nonattainment areas with earlier attainment dates meet
the standards by 2020. These additional reductions in Los Angeles and San Joaquin Valley are
considered to be part of the controls needed to meet the current NAAQS and are therefore not
considered as part of the cost of any new alternate standard. Figure 4-2d shows the results of the
supplemental modeling runs for three areas in California.
Figure 4.2d: Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and
Each of the Six Supplemental Modeling Scenarios for Three Specific Areas in California
0.105
0.045
I
i
I
1
^
• RIA Control Scenario
• 30% NOx + VOC control
• 60% NOx + VOC control
D 90% NOx + VOC control
0 30% NOx control
B 60% NOx control
D 90% NOx control
Los Angeles CA
San Joaquin ValleyCA
Sacramento CA
Extrapolated control targets were estimated for each Phase 1 area for: a) NOx only emissions
reductions and b) NOx plus VOC emissions reductions. The results of the analysis to estimate
emissions reductions for attainment in the Phase 1 areas are shown in Table 4.1 and Table 4.2.
The amount of additional emissions reductions necessary for full attainment ranges from zero to
over 90 percent depending upon the area and the standard.
4-6
-------
Table 4.1: Estimated Percentage Reductions of NOx and VOC beyond the RIA Control
Scenario Necessary to Meet Various Alternate Ozone Standards in the Phase I Areas
Phase 1 Area (NOx only)
Amador and Calaveras Cos., CA
Chico, CA
Imperial Co., CA
Inyo Co., CA
Los Angeles South Coast Air Basin, CA
Mariposa and Tuolumne Cos., CA
Nevada Co., CA
Sacramento Metro, CA
San Benito Co., CA,
San Diego, CA
San Francisco Bay Area CA
San Joaquin Valley, CA
Santa Barbara Co., CA
Sutler Co., CA
Ventura Co, CA
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Eastern Lake Michigan, IL-IN-WI
Houston, TX
2020 Design Value after RIA
Control Scenario (ppm)
0.071
0.068
0.071
0.06S
0.122
0.072
0.075
0.080
0.066
0.076
0.069
0.096
0.068
0.067
0.077
0.077
0.080
0.087
Additional local control needed to meet
various standards
0.065
28%
13%
29%
18%
>90%
32%
39%
55%
1%
52%
21%
76%
12%
9%
44%
57%
82%
> 90%
0.070
4%
1%
88%
8%
19%
38%
33%
67%
28%
39%
72%
83%
0.075
83%
20%
6%
59%
5%
13%
62%
71%
0.079
79%
3%
49%
3%
62%
0.08-1
75%
37%
36%
Table 4.2: Estimated Percentage Reductions of NOx beyond the RIA Control Scenario
Necessary to Meet Various Alternate Ozone Standards in the Phase I Areas
Phase 1 Area (NOx + VOC)
Arnador and Calaveras Cos., CA
Chico, CA
Imperial Co., CA.
Inyo Co., CA
Los Angeles / South Coast Air Basin, CA
Mariposa and Tuolumne Cos., CA
Nevada Co., CA
Sacramento Metro, CA
San Benito Co., CA.
San Diego, CA
San Francisco Bay Area CA
San Joaquin Valley, CA
Santa Barbara Co., CA
S utter Co., CA
Ventura Co, CA
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Eastern Lake Michigan, IL-IN-WI
Houston, TX
2020 Design Value after RIA
Control Scenario (ppm)
0.071
0.06B
0.071
0.068
0.122
0.072
0.075
0.080
0.066
0.076
0.069
0.096
0.068
0.067
0.077
0.077
0.080
0.087
Additional local control needed to meet
0.065
28%
13%
28%
18%
> 90%
32%
40%
55%
1%
49%
20%
76%
12%
9%
42%
54%
78%
> 90%
0.070
4%
1%
89%
8%
19%
38%
30%
67%
26%
35%
66%
82%
0.075
83%
20%
5%
58%
5%
10%
25%
69%
0.079
79%
3%
48%
2%
57%
0.084
74%
36%
29%
4.1.3 Estimating Attainment of the 0.070 and 0.065 ppm Standards in Phase 2 Areas
As discussed above, there were 61 counties that did not reach attainment of the 0.070 ppm
standard with the controls in the hypothetical RIA scenario. The majority of these counties are in
one of the Phase 1 areas. However, there were 12 counties (9 areas) outside of the Phase 1 areas
that were also not projected to meet the 0.070 NAAQS. (All counties outside the Phase 1 areas
met the 0.075 and 0.079 ppm air quality standards.) For convenience, these nine areas will be
referred to Phase 2 areas. A two-step process was used to estimate the additional emissions
reductions necessary for full attainment in the Phase 2 areas. Based on the Phase 1 modeling
4-7
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results, targets for these areas were only generated for NOx-only control given the
preponderance of cases where the additional VOC emissions reductions did not reduce ozone
enough to consider from a cost perspective.
For the Phase 2 areas, the first step in estimating attainment was to consider whether the
emissions reductions needed to bring the Phase 1 areas into attainment of 0.070 ppm would also
reduce ozone transport enough to bring these additional areas into attainment as well. For an
example of how this determination was made consider two counties: Norfolk County, MA
(Boston area) and Geauga County, OH (Cleveland area).
In Norfolk MA, the projected design value after the PJA control scenario is 0.071 ppm. This
county is downwind of the Northeast Corridor. The supplemental modeling showed that if the
Phase 1 areas reduced NOx emissions by at least 30% the 2020 design value in Norfolk MA
would be reduced to 0.069 ppm (i.e., does not exceed the 0.070 standard). As part of the Phase I
analysis, we estimated that the Northeast Corridor region would need an additional 39% NOx
reduction to meet the 0.070 ppm standard within this area. The supplemental modeling shows
that the same 39% NOx reduction would enable this standard to be met in Norfolk County as
well, without any additional local controls in the Boston area.
In Geauga OH, the projected design value after the RIA control scenario is 0.074 ppm. Thus,
Cleveland will need additional local emissions reductions to meet a revised ozone standard of
0.070 ppm. However, in the supplemental modeling, which did not include emissions reductions
in Cleveland, the Geauga design value declined by 0.001, 0.002, and 0.003 ppm, in the 30, 60,
and 90% NOx reduction runs, respectively. Given that the Lake Michigan region is the nearest
upwind Phase 1 area to Geauga County, we believe these ozone reductions in Geauga County are
associated with the emissions reductions modeled in the Lake Michigan region. The Lake
Michigan region is estimated to need 72% additional NOx control. Considering the projected
design values with an additional digit of precision, it is estimated that a 72% reduction in the
eastern Lake Michigan area will yield a Geauga OH design value of 0.0718 ppm.6
In the second step of the process, we estimate what level of local control is required to reach
0.070 ppm after consideration of the impact of Phase 1 emissions reductions. For each of the
Phase 2 areas that is still nonattainment after step 1 above, we developed a site-specific
relationship between the ozone improvement in the RIA control case and the percent reduction in
local NOx emissions in the RIA control case as compared to the baseline. This site-specific
relationship was then used to determine how much additional NOx reduction was needed to meet
the 0.070 ppm goal. Continuing with the Geauga County example helps illustrate this
calculation. In this county there was a 0.0023 ppm reduction due to the hypothetical RIA
controls. The RIA scenario represented a 17% reduction in NOx emissions within the 200 km
buffer around the Cleveland area. With the existing information it is not possible to distinguish
6 The full step 1 calculation for the Geauga OH example is as follows. A 60 percent reduction
yields a design value of 0.0722 ppm. A 90 percent reduction yields a design value of 0.0710
ppm. The estimated Phase 1 target for eastern Lake Michigan is 72%, or four-tenths of the
"distance" between 60 and 90% control. Forty percent of the 0.0012 ppm difference between the
two runs is 0.00048 ppm. Subtracting that from 0.0722 ppm, yields the transport-considered
design value of 0.0717 ppm which would be truncated to 0.071 ppm.
4-8
-------
how much of the ozone improvement is due to local controls (i.e., within 200 km) versus upwind
controls, so we made a simplifying assumption that all local air quality improvement for such
areas can be attributed to the controls within 200 km. Converting to units of ppb for simplicity,
dividing 2.3 ppb improvement by a 17% NOx emissions reduction yields a Geauga-specific
relationship of 0.135 ppb / percent NOx controlled. This ratio is applied to the 71.8 ppb value
from step 1 and it is determined that an additional 7 % reduction (0.9 ppb) would be sufficient to
lower the 2020 design value in Geauga County to 70.9 ppb or 0.070 ppm, thereby attaining the
standard.
The same two step methodology described above was used to estimate the extrapolated targets
for the 0.065 ppm standard in the Phase 2 areas. Table 4.3 shows the full set of results for each of
the nine Phase 2 areas. The amount of additional NOx control needed to meet the 0.070 ppm
standard in Phase 2 areas ranges from zero to 25 percent. The amount of additional NOx control
needed to meet the 0.065 ppm standard in Phase 2 areas ranges from zero to 74 percent.
Table 4.3: Estimated Percentage Reductions of NOx beyond the RIA Control Scenario
Necessary to Meet the 0.070 ppm Ozone Standard in Phase 2 Areas7
Phase 2 Area
(NOx only)
Allegan Co, Ml
Baton Rouge, LA
Boston-Lawre nee-Worcester, MA
Buffalo-Niagara Falls, NY
Cleveland-Akron-Lorain, OH
Dal las-Fort Wo rth,TX
Detroit-Ann Arbor, Ml
Jefferson Co, NY
Las Vegas, NV
2020 Design Value after RIA
Control Scenario (ppm)
0.072
0.073
0.071
0.073
0.074
0.073
0.073
0.071
0.071
Additional local control needed to meet
various standards
0.065
will attain
74%
14%
34%
40%
34%
57%
23%
14%
0.070
will attain
25%
will attain
8%
7%
2%
6%
will attain
will attain
4.1.4 Estimating Attainment of the 0.065 ppm Standard outside of Phase 1 and 2 Areas
The last set of reduction targets generated are for those areas that require additional ozone
precursor controls to meet the 0.065 ppm standard but are outside Phase 1 and 2 areas. There
were 166 counties that did not reach attainment of the 0.065 ppm standard with the emissions
reductions in the hypothetical RIA scenario. The majority of these counties are in one of the
Phase 1 or Phase 2 areas. However, there were 46 counties (36 areas) outside of the Phase 1 and
Phase 2 areas that were not projected to meet the 0.065 NAAQS. For convenience, these areas
will be referred to Phase 3 areas.
A similar methodology as described in Section 4.1.3 was used to estimate the additional
emissions reductions needed for the 0.065 ppm standard for the Phase 3 areas, but two
simplifying assumptions were made to expedite the analysis. First, instead of explicitly
accounting for the impacts of the Phase 1 and Phase 2 upwind emissions reductions on Phase 3
areas, we assumed that the design values from the 60% NOx reduction run were the appropriate
starting point for estimating the additional emissions reductions in the Phase 3 areas. Since the
7 The entry "will attain" in Tables 4.3 and 4.4 signifies that this area will come into attainment of
the standard due to reduced ozone transport resulting from upwind controls.
4-9
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targets for the Phase 1 areas are generally greater than 60% and since we have not accounted for
the Phase 2 reductions, these estimates should provide a conservative estimate of the percentage
emissions reductions needed for full attainment. Secondly, we did not develop site-specific
impact ratios for the 36 Phase 3 areas. Instead, we used a standard relationship of 0.150 ppb 71%
NOx reduction for calculating the emissions reductions needed to attain 0.065 ppm in these
areas. This value was the average site-specific relationship calculated for the Phase 2 areas, as
described above. These assumptions are reasonable given the available data and the relatively
small role that Phase 3 areas will play in determining the full costs of meeting a 0.065 ozone
standard. However, the estimated emissions reductions needed to attain 0.065 in the Phase 3
areas are considered to be more uncertain than the emissions reductions calculated for attaining
0.070, 0.075, and/or 0.079. The results of the Phase 3 analysis are shown in Table 4.4. The
amount of additional NOx control needed to meet the 0.065 ppm standard in Phase 3 areas
ranges from zero to 29 percent.
Table 4.4: Estimated Percentage Reductions of NOx beyond the RIA Control Case
Necessary to Meet the 0.065 ppm Ozone Standard in Phase 3 Areas
Phase 3 Area
(NOx only)
Ada Co, ID
Atlanta, GA
Berrtcm Harbor, Ml
Campbell Co., WY
Cass Co, Ml
Charlotte-Gastonia-Rock Hill, NC-SC
Cincinnati-Hamilton, OH-KY-IN
Coconino Co, AZ
Columbus, OH
Denver-Boulder-Greeley-R Collins-Love,
Dona Ana Co, NM
El Paso Co, TX
Erie, PA
Essex Co (Whiteface Mtn), NY
Hancock Knox, Lincoln 8, Waldo Cos, ME
Huntington-Ashland, VvV-KY
Huron Co, Ml
Indianapolis, IN
Jackson Co., MS
Jamestown, NY
Johnson CHy-Kinqsport-Bristol, TN
Louisville, KY-IN
Memphis, TN-AR
Muskegon, Ml
Norfolk-Virginia Beach-Newport News, VA
Phoenix-Mesa, AZ
Pittsburgh-Beaver Valley, PA
Providence (All Rl), Rl
Richmond-Petersburg, VA
Salt Lake City, UT
San Antonio, TX
San Juan Co, NM
Springfield (Western MA), MA.
St Louis, MO-IL
Toledo, OH
Washington, DC-MD-VA
2020 Design Value after RIA
Control Scenario (ppm)
0.069
O.OEB
0.069
0.067
0.066
0.070
0.067
0.067
0.066
0.067
0.068
0.068
0.067
0.067
0.068
0.069
0.067
0.068
0.067
0.069
0.066
0.066
0.068
0.068
0.070
0.068
0.069
0.068
0.067
0.067
0.067
0.069
0.066
0.068
0.067
0.068
Additional local control needed to meet
various standards
0.065
21%
12%
will attain
9%
will attain
29%
5%
will attain
will attain
11%
13%
14%
3%
will attain
will attain
15%
will attain
will attain
10%
16%
will attain
will attain
15%
will attain
20%
7%
18%
will attain
1%
10%
will attain
20%
will attain
16%
3%
will attain
4-10
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4.1.5 Aggregate Results / Verification Modeling of Extrapolated Targets
The complete set of NOx targets are provided in Table 4.5a. As noted earlier, a single 2020
target was determined for all of California. This target was based on the Sacramento area which
had the highest 2020 design values outside the Los Angeles and San Joaquin Valley areas. The
assumption is that if all of California reduces at that level then all areas aside from Los Angeles
and the San Joaquin Valley air basins will attain by 2020. Areas from which reductions would be
required include the Los Angeles and San Joaquin Valley air basins, but would not necessarily
bring them into attainment. Additional reductions may be required. Because of their later
attainment date, the costs and benefits of additional reductions for Los Angeles and San Joaquin
air basins are shown in Appendix 7b.
Table 4.5a: Complete Set of Estimated Percentage Reductions of NOx beyond the RIA
Control Scenario Necessary to Meet the Various Ozone Standards in 2020
All 2020 Extrapolated Cost Areas
(NOx only)
Ada Co., ID
Atlanta GA
Baton Rouge, LA
Boston-Lawrence-Worcester, MA
Buffalo-Niagara Falls, NY
Campbell Co., WY
Charlotte-Gastonia-Rock Hill, NC-SC
Cincinnati-Hamilton, OH-KY-IN
Cleveland-Akron-Lorain, OH
Dallas-Fort Worth, TX
Denver-Boulder-Greeley-Ft Collins, CO
Detroit-Ann Arbor, Ml
Dona Ana Co., NM
Eastern Lake Michigan, IL-IN-WI
El Paso Co., TX
Erie, PA
Houston, TX
Huntington-Ashland, WV-KY
Jackson Co., MS
Jamestown, NY
Jefferson Co, NY
Las Vegas, NV
Memphis, TN-AR
Norfolk-Virginia Beach-Newport News, VA
Northeast Corridor, CT-DE-MD-N J-NY-PA
Phoenix-Mesa, AZ
Pittsburgh-Beaver Valley, PA
Richmond-Petersburg, VA
Sacramento/CA
Salt Lake City, UT
San Juan Co, NM
St Louis, MO-IL
Toledo, OH
2020 Design Value after RIA
Control Scenario (ppm)
0.069
0.068
0.073
0.071
0.073
0.067
0.070
0.067
0.07-1
0.073
0.067
0.073
0.068
0.080
0.068
0.067
0.087
0.069
0.067
0.069
0.071
0.071
0.068
0.070
0.077
0.068
0.069
0.067
0.080
0.067
0.069
0.068
0.067
Additional local control needed to meet
0.065
21%
12%
74%
14%
34%
9%
29%
5%
40%
34%
11%
57%
13%
82%
14%
3%
> 90%
15%
10%
16%
23%
14%
15%
20%
57%
7%
18%
1%
55%
10%
20%
16%
3%
0.070
25%
8%
7%
2%
6%
72%
83%
39%
38%
0.075
62%
71%
13%
20%
0.079
3%
62%
3%
0.08-1
36%
In total, 33 areas were determined to need additional emissions reductions for one or more of the
alternate standards. The eastern Lake Michigan region was the only one in which NOx plus VOC
control targets could be substantially lower than NOx only control targets. Table 4.5b shows the
NOx + VOC targets for that area.
4-11
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Table 4.5b: Estimated Percentage Reductions of NOx + VOC beyond the RIA Control
Scenario Necessary to Meet the Various Ozone Standards in 2020
All 2020 Extrapolated Cost Areas
(NOx + VOC)
Eastern Lake Michigan, IL-IN-WI
2020 Design Value after RIA
Control Scenario (ppm)
0.080
Additional local control needed to meet
various standards
0065
78%
0.070
66%
0.075
25%
0.079
2%
0.084
Figures 4.3a through 4.3d show: 1) which counties are part of the 33 extrapolated cost areas and
2) the estimated percent reduction needed beyond the RIA control case to meet each of the four
alternate standards within each of those areas. The conversion of these additional percentage
reductions to actual extrapolated tons is described in Chapter 4.2. The calculation of the costs of
these extrapolated tons is described in Chapter 5.
Figure 4.3a: Map of Extrapolated Cost Counties for the 0.065 ppm Alternate Standard and
the Estimated Percent NOx Controls Needed to Meet that Standard
Extrapolated Cost Counties for 065 Standard
CA CtuttR* reded SBcianttulo Msho AraS 14?jel
4-12
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Figure 4.3b: Map of Extrapolated Cost Counties for the 0.070 ppm Alternate Standard and
the Estimated Percent NOx Controls Needed to Meet that Standard
Extrapolated Cost Counter for 070 Standard
Sac4»n«iilJ5M»lrtifVaa I4ra«.
Figure 4.3c: Map of Extrapolated Cost Counties for the 0.075 ppm Alternate Standard and
the Estimated Percent NOx Controls Needed to Meet that Standard
Extrapolated Cost Counties for 075 Standard
l SiiciSnt-Mlrj. Mslrtn Araa l
4-13
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Figure 4.3d: Map of Extrapolated Cost Counties for the 0.079 ppm Alternate Standard and
the Estimated Percent NOx Controls Needed to Meet that Standard
Extrapolated Cost Counties for 079 Standard
SaaanrSiiL&Meira Araa I4ra«.
As noted earlier in this section, an additional CMAQ air quality simulation, called a "verification
run," was completed after the extrapolated percent emissions reductions were estimated. The
purpose of this run was to determine the ozone design values that would be expected from the
additional extrapolated reductions shown in Table 4.5a and Table 4.5b. These are the reductions
that were estimated to be needed for full attainment of the 0.070 ppm standard for areas outside
of Los Angeles and San Joaquin Valley. The results of the verification modeling were
encouraging and confirmed our approach for estimating the extrapolated reductions. For the four
areas where we projected that no additional local controls were needed and that the additional
upwind reductions would be sufficient for attainment of 0.070 (see Table 4.3), the verification
modeling indicated that all four areas had ozone design values less than 0.070 ppm after the
extrapolated reductions were applied. Of the remaining nine areas that did not reach the 0.070
ppm standard in the RIA control case, eight of the nine were within plus or minus 0.002 ppm
after application of the extrapolated emissions reductions. The proximity of the verification
design values to the 0.070 ppm target provides confidence that the estimates of extrapolated tons
are reasonable. Table 4.6 shows the results of the verification modeling for the 13 areas that were
included in the (0.070 ppm) extrapolated cost analysis.
4-14
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Table 4.6: Summary of the Verification Modeling Results
Extrapolated Control Area
Boston, MA
Holland, Ml
Las Vegas, NV
Watertown, NY
Dallas-Fort Worth, TX
Detroit Ml
Cleveland, OH
Buffalo, NY
Baton Rouge, LA
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Sacramento, CA
Eastern Lake Michigan, IL-IN-WI
Houston, TX
2020 Design Value after RIA
Control Scenario (ppm)
0.071
0.072
0.071
0.071
0.073
0.073
0.074
0.073
0.073
0.077
0.071
0.080
0.087
% reduction estimated
for full attainment
attain due to upwind controls
attain due to upwind controls
attain due to upwind controls
attain due to upwind controls
2% NOx
6% NOx
7% NOx
8% NOx
25% NOx
37% NOx
38% NOx
66% NOX WOC
83% NOx
2020 Design Value after
Verification Scenario (ppm)
0.069
0.060
0.069
0.070
0.071
0.071
0.071
0.072
0.069
0.071
0.070
0.073
0.069
4.2 Conversion of Full Attainment Percentage Targets into Extrapolated Tons
Table 4.7a provides the complete set of extrapolated tons of NOx emissions reduction needed to
satisfy the various ozone standards. These extrapolated tons are obtained by multiplying the NOx
targets in Table 4.5a by the remaining emissions for each area after the RIA control scenario. It
is important to note that the extrapolated cost areas are potentially standard-specific because the
location of counties in an extrapolated area depends on whether the particular standard is being
violated. For example, as seen in Figures 4.3a and 4.3b, the Eastern Lake Michigan area extends
further north into Wisconsin for the 0.065 ppm standard where areas like Green Bay attained the
0.070 standard but not 0.065 ppm standard.
4-15
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Table 4.7a: Complete Set of Estimated Extrapolated Emissions Reductions of NOx Beyond
the RIA Control Scenario Necessary to Meet the Various Ozone Standards in 2020
All 2020 Extrapolated Cost Areas
(NOx only)
Ada Co., ID
Atlanta, GA
Baton Rouge, LA
Boston-Lawrence-Worcester, MA
Buffalo-Niagara Falls- Jamestown, NYA
Campbell Co., WY
Charlotte-Gastonia-Rock Hill, NC-SC
Cincinnati-Hamilton, OH-KY-IN
Cleveland-Akron-Lorain, OH
Dallas-Fort Worth, XX
Denver-Boulder-Greeley-Ft Collins-Love, CO
Detroit-Ann Arbor, MI
Dona Ana CO., NM
El Paso Co., XX
Houston, XX
Huntington-Ashland, WV-KY
Jackson Co., MS
Jefferson Co, NY
Las Vegas, NV
Memphis, XN-AR
Norfolk- Virginia Beach-Newport News, VA
Northeast Corridor, CX-DE-MD-NJ-NY-PA
Phoenix-Mesa, AZ
Pittsburgh-Beaver Valley-Erie, PA"
Richmond-Petersburg, VA
Sacramento Metro, CA
Salt Lake City, UX
San Juan Co., NM
St Louis, MO-IL
Xoledo, OH
Additional local emissions reductions [annual tons/year] needed to meet
various standards (ppm)
0.065
5,300
21,000
170,000
14,000
19,000
2,600
62,000
9,400
83,000
53,000
8,600
100,000
980
1,700
290,000
22,000
7,600
7,300
5,000
15,000
30,000
350,000
4,900
17,000
270
310,000
4,000
17,000
35,000
85
0.070
57,000
3,900
13,000
3,100
11,000
270,000
230,000
210,000
0.075
220,000
73,000
110,000
0.079
190,000
17,000
0.084
110,000
a Jamestown is included in the Buffalo-Niagara Falls, NY cost area because it falls within the 200km
Buffalo-Niagara Falls buffer and has a lower design value.
b Erie is included in the Pittsburgh-Beaver Valley, PA cost area because it falls within the 200km
Pittsburgh-Beaver Valley buffer and has a lower design value.
In total, additional emissions reductions are provided for 31 areas. As footnoted, Jamestown NY
is included in the Buffalo-Niagara Falls NY area. There are three reasons for this: 1) Jamestown
is within the 200km buffer for Buffalo-Niagara Falls, 2) as seen in Table 4-5a, the NOx target is
greater in Buffalo-Niagara than Jamestown for each standard, and 3) Jamestown is in the same
state. Erie is included in the Pittsburgh-Beaver Valley PA area for the same three reasons.
As noted in Table 4.5b in Section 4.1.5, the eastern Lake Michigan area was the only one in
which NOx plus VOC additional emission reductions could be substantially lower than NOx-
only emissions reductions. Table 4.7b shows the additional NOx + VOC emission reductions for
this area.
4-16
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Table 4.7b: Estimated Extrapolated Emissions Reductions of NOx + VOC Beyond the RIA
Control Scenario Necessary to Meet the Various Ozone Standards in 2020
All 2020 Extrapolated Cost Areas
(NOx + VOC)
Eastern Lake Michigan, IL-IN-WI
Additional local emissions reductions [annual tons/year] needed to meet various standards
(ppm)
0.065
NOx
350,000
VOC
400,000
0.070
NOx
280,000
VOC
330,000
0.075
NOx
100,000
VOC
120,000
0.079
NOx
8,100
VOC
9,800
4.3 Methodology Used to Estimate the Amount of "Overcontrolled" Emissions in the
Modeled Control Strategy
The corollary to extrapolated tons (needed tons above and beyond the modeled control strategy)
is "overcontrolled" tons. These are emissions reductions within the hypothetical control case that
were subsequently determined not to be needed to meet particular alternate standards. That is,
once we modeled the baseline and control strategy scenarios we found that we had reduced
ozone beyond the particular alternate standard. In order to better estimate the costs and benefits
of full attainment of the standards, EPA has estimated the "overcontrolled" emissions
percentages within the modeled control strategy for the four alternate standards: 079, 075, 070 &
065. These percentages are to be applied to the tons reduced between the baseline and the control
case.
The methodology for calculating the "overcontrol" percentages is based on simple linear
interpolation between the baseline scenario and the model control strategy. These two model
runs were used to estimate what level of control was just needed to bring an area into attainment
of a standard. A caveat to this approach is that it assumes that all air quality impacts are due to
local controls; there is no consideration of the potential impacts of ozone transport.
The details of the methodology are as follows. The first step was to identify all counties with
ozone concentrations greater than 0.070 ppm in the base case. These 142 counties were the
starting point for designing the modeled control strategy described in Chapter 3. Because the
majority of the California controls are in the baseline and because several CA areas continue to
be nonattainment of all four alternate standards in 2020 and beyond, we did not assess
"overcontrol" in California. The remaining counties were aggregated into 32 distinct areas for an
assessment of whether that area overcontrolled to meet an alternate standard. Each area included
the original nonattainment county or counties, plus all counties within 200 km of that county or
counties. The "overcontrolled" analysis was done for the county with the highest ozone levels in
the control case modeling. These 32 areas comprised 1,199 counties. These are the same 1,199
non-California counties over which NonEGU point and Area sources were controlled in the
hypothetical strategy.
A simple three-step process was used to determine the amount of overcontrol in the hypothetical
control case for each of the 32 areas. The results are summarized in Table 4.8.
4-17
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Table 4.8: Estimated Percentages of Modeled Control Strategy Emissions Reductions not
needed to Meet the Various Ozone Standards in 2020
Area controlled within the Modeled
Control Strategy
Houston, TX
Eastern Lake Michigan. IL-IN-WI
Northeast Corridor
Baton Rouge, LA
Cleveland-Akron-Lorain, OH
Detroit-Ann Arbor, Ml
D alias-Fort Wo rth, TX
Buffalo-Niagara Falls, NY
Allegan Co, Ml
Boston-Lawrence-Worcester, MA
Jefferson Co, NY
Las Vegas, NV
Jamestown, NY
Denver-Boulder-Greeley-R Collins-Love.,
Pitts burgh-Be aver Valley, PA
Charlotte-Gastonia-Rock Hill, NC-SC
Hancock, Knox Lincoln & Waldo Cos, ME
Norfolk-Virginia Beach-Newport News (HR)
St Louis, MO-IL
Providence (All Rl), Rl
Huntington-Ashland, Wv'-KY
Benton Harbor, Ml
Erie, PA
Cincinnati-Hamilton, OH-KY-IN
Atlanta, GA
Toledo, OH
Salt Lake City, UT
Muskegon, Ml
Phoenix-Mesa, AZ
Richmond-Petersburg, VA
Indianapolis, IN
Cass Co, Ml
Model projected 8-hour ozone
design values (ppm)
2020 Base
Case
0.0924
0.0850
0.0821
0.0781
0.0795
0.0766
0.0770
0.0777
0.0772
0.0762
0.0749
0.0749
0.0754
0.0742
0.0739
0.0730
0.0731
0.0729
0.0730
0.0737
0.0731
0.0740
0.0732
0.0723
0.0718
0.0728
0.0728
0.0734
0.0718
0.0712
0.0720
0.0717
2020 Base 2020 Control
Line Case
0.0890
0.0814
0.0796
0.0768
0.0765
0.0752
0.0754
0.0754
0.0734
0.0737
0.0734
0.0724
0.0728
0.0728
0.0721
0.0716
0.0713
0.0712
0.0710
0.0708
0.0707
0.0705
0.0704
0.0703
0.0701
0.0701
0.0701
0.0699
0.0699
0.0699
0.0697
0.0683
0.0877
0.0803
0.0767
0.0737
0.0742
0.0734
0.0732
0.0722
0.0721
0.0719
0.0715
0.0710
0.0697
0.0677
0.0693
0.0707
0.0688
0.0703
0.0686
0.0683
0.0690
0.0692
0.0675
0.0676
0.0680
0.0677
0.0676
0.0685
0.0682
0.0677
0.0681
0.0666
Percent of control emissions not needed for
alternate standards
0.079
NONE
NONE
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
0.075
NONE
NONE
NONE
71%
74%
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
0.070
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
39%
63%
57%
22%
84%
67%
96%
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
0.065
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
NONE
a) For each standard, we first determined if the area was below that standard in the baseline
modeled scenario. If so, then all of the hypothetical controls should be returned from the
control scenario. For example, the highest projected design value in the Cincinnati area
was 0.072 ppm in the basecase and 0.070 ppm in the baseline. Thus, that area did not
actually need any of the hypothetical controls above and beyond the baseline to meet the
0.079, 0.075, or 0.070 standards locally. Therefore, all of the controls in that area should
be returned for those standards.
b) For each standard, we then determined if the area was above that standard in the modeled
control case. If so, then none of the hypothetical controls should be given back. As an
example, the Houston area had a projected design value of 0.087 ppm in the control case.
Therefore, all of the emissions in the modeled control strategy (and some extrapolated
tons) are needed in that area.
c) For each standard, and for all other areas that were above the standard in the baseline and
below in the control case, we used linear interpolation to estimate what percentage of the
emissions reductions in the modeled control strategy could be returned and still allow the
standard to be met. For example, the maximum projected design value in the Cleveland
area was 0.0795 ppm in the basecase, 0.0765 ppm in the baseline, and 0.0742 ppm in the
4-18
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control case. Linear interpolation8 between the baseline and the control case indicates that
74% of the controls in the Cleveland area, including counties within a 200km buffer,
could be given back and still just meet the 0.075 ppm target. All of the control strategy
reductions would be given back for the less-stringent 0.079 ppm standard and none of the
reductions would be given back for the more-stringent 0.070 ppm standard.
4.4 Conversion of Estimated Percentages of Unnecessary Emission Reductions into
"Overcontrolled" Tons
The percentages of modeled control strategy emissions reductions not needed to meet the various
ozone standards in 2020 shown in Table 4.8 were applied to the control case reductions in
Table 4.9. In areas and targets where the percentages in Table 4.8 were "ALL," the unnecessary
emissions reductions in Table 4.9 are equal to the baseline minus control case emissions seen in
the same table. Similarly, in areas and targets where there was no "over-control" ("NONE" in
Table 4.8), emission reductions not needed for alternative standards in Table 4.9 are zero; that is,
the control scenario did not "over-control" emissions for that area and target. As seen in
Table 4.8, ozone concentration estimates are greater than 0.0795 ppm in both Houston and
Eastern Lake Michigan; therefore there was no over-control and no unnecessary emission
reductions.
8 The calculation used to determine the 74% target for the 0.075 ppm targets is as follows:
1.0-[(0.0765-0.0759)/(0.0765-0.0742)], where 0.0759 ppm represents the highest ozone level that
still attains a 0.075 ppm standard, due to the usual truncation of the fourth decimal place.
4-19
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Table 4.9: Estimated 2020 Control Case Emission Reductions not needed to Meet the
Various Ozone Standards in 2020
Area controlled within the modeled control
Strategy
Eastern Lake Michigan, IL-IN-WI-MI
Houston-Galveston-Brazoria, TX
Northeast Corridor, CT-DE-DC-NY-NJ-PA-VA
Jefferson Co., NY
AlleganCo.,MI
Buffalo-Niagara Falls, NY
Las Vegas, NV
Boston-Lawrence-Worcester-Portsmouth, MA-NH
Cleveland-Akron-Lorain, OH
Dallas-Fort Worth, TX
Detroit-Ann Arbor, Ml
Baton Rouge, LA
Richmond-Petersburg, VA
Muskegon Co., Ml
Norfolk-Virginia Beach-Newport News, VA
Huntington-Ashland, WV-KY
Providence (All Rl), Rl
Toledo, OH
Charlotte-Gastonia-Rock Hill, NC-SC
Indianapolis, IN
Salt Lake City, UT
Phoenix, AZ
Hancock, Knox, Lincoln & Waldo Cos, ME
Denver, CO
Pittsburgh-Beaver Valley, PA
St Louis, MO-IL
Atlanta, GA
Cincinnati-Hamilton, OH-KY-IN
Annual Emissions [tons/year]
2020 Base
Case
600,000
460,000
910,000
36,000
20,000
66,000
45,000
150,000
270,000
210,000
260,000
400,000
12,000
5,100
9,600
5,800
13,000
4,700
240,000
44,000
53,000
89,000
41,000
110,000
160,000
290,000
220,000
320,000
2020
Baseline
500,000
340,000
840,000
34,000
18,000
62,000
43,000
140,000
250,000
200,000
240,000
350,000
1 1 ,000
4,400
9,100
5,400
12,000
4,400
230,000
43,000
49,000
83,000
39,000
110,000
150,000
270,000
210,000
290,000
2020
Control
Case
460,000
320,000
750,000
32,000
15,000
55,000
36,000
130,000
210,000
160,000
190,000
230,000
11,000
4,000
8,300
4,200
10,000
2,800
220,000
36,000
42,000
75,000
30,000
81,000
120,000
240,000
180,000
250,000
Baseline
minus
Control
Case
36,000
12,000
98,000
2,000
3,100
7,000
7,800
14,000
44,000
43,000
50,000
110,000
310
420
780
1,200
1,500
1,600
14,000
6,600
7,400
7,500
9,300
25,000
30,000
30,000
31,000
41,000
2020 Control Case Emission Reductions
not needed for alternate standards
0.079
0
0
98,000
2,000
3,100
7,000
7,800
14,000
44,000
43,000
50,000
110,000
310
420
780
1,200
1,500
1,600
14,000
6,600
7,400
7,500
9,300
25,000
30,000
30,000
31,000
41,000
0.075
0
0
0
2,000
3,100
7,000
7,800
14,000
32,000
43,000
50,000
81,000
310
420
780
1,200
1,500
1,600
14,000
6,600
7,400
7,500
9,300
25,000
30,000
30,000
31,000
41,000
0.070
0
0
0
0
0
0
0
0
0
0
0
0
310
420
520
1,200
1,500
1,600
3,200
6,600
7,400
7,500
7,800
16,000
17,000
29,000
31,000
41,000
0.065
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4-20
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Chapter 5: Engineering Cost Estimates
Synopsis
This chapter summarizes the data sources and methodology used to estimate the engineering
costs of attaining the alternative more stringent levels for the ozone primary standard analyzed in
this RIA. This chapter estimates the engineering costs of 0.065 ppm, 0.070 ppm, 0.075 ppm, and
0.079 ppm. The chapter presents engineering cost estimates for the illustrative modeled control
strategy outlined in Chapter 3 (which uses currently available known controls). The modeled
control strategy discussion is followed by a presentation of estimates for the engineering costs of
the additional tons of emissions that are needed to move to full attainment of the alternate
standards analyzed, referred to as Extrapolated Costs (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. EPA modeled the lower end of the proposed range to
capture a larger number of geographic areas that may be affected by a new ozone standard.
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 0.079
ppm, 0.075 ppm, 0.070 ppm, and 0.065 ppm. This chapter presents the engineering costs
associated with each portion of the control analysis, clearly identifying the relative engineering
costs of modeled versus extrapolated emissions reductions as well as providing an estimate of the
total engineering cost of attainment nationwide in 2020. Nationwide attainment refers to all areas
of the nation that are required to attain the current ozone standard by the year 2020. It does not
reflect full attainment for the two areas of California, which have attainment dates for the current
standard post 2020. For a complete discussion attainment for these two areas of California see
Appendix 7b. 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 engineering costs of extrapolated
tons needed to reach attainment of the final 0.075 ppm standard as well as the three alternatives
and provides estimates of how much additional engineering costs will be associated with moving
from the modeled partial attainment scenario (i.e. modeled control strategy) 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).
The engineering 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
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technological improvements and more widespread adoption of previously niche control
technologies. Also, EPA recognizes the extrapolated portion of the engineering 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 5.3. Appendix 5a includes detailed cost and control efficiency information on different
control measures applied as part of our modeled control strategy, and also includes summary
results from applications of specific control measures.
It is also important to recognize that the engineering cost estimates are limited in their scope.
Because we is 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.
We use EMPAX-CGE to estimate the economic impacts and the social costs associated with the
modeled control strategy. BMP AX uses as input the engineering costs estimated for the
modeled control strategy to calculate its economic impacts and social costs. Economic impacts
are estimates of changes in price and output for those industries and consumers of their output
affected by the modeled control strategy. Social costs are costs from changes in household
welfare due to impacts from the costs of the controls in the modeled control strategy. For more
details on the economic impacts and social costs, please refer to Appendix 5b.
5.1 Modeled Controls
5.1.1 Sector Methodology
5.1.1.1 NonEGU Point and Area Sources: AirControlNET
After designing a national hypothetical control strategy using the methodology discussed in
Chapter 3 (see sub-section 3.2.1), EPA used AirControlNET to estimate engineering control
costs. AirControlNET calculates engineering 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 engineering costs 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 costs, in dollars
5-2
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per ton, using the capital recovery factor.1 Where possible cost calculations are used to calculate
total annual control cost (TACC) which is a function of the capital (CC) and O&M costs. Capital
costs are converted to annual costs, in dollars per ton, using the capital recovery factor (CRF).
The capital recovery factor incorporates the interest rate and equipment life (in years) of the
control equipment. Operating costs are calculated as a function of annual O&M and other
variable costs. The resulting TACC equation is TACC = (CRF * CC) + O&M.
Engineering costs will differ based upon quantity of emissions reduced, plant capacity, or stack
flow which can vary by emissions inventory year. Engineering costs will also differ by the year
the costs are calculated for (i.e., 1999$ versus 2006$). For capital investment, we do not assume
early capital investment in order to attain standards by 2020. For 2020, our estimate of
annualized costs represents a "snapshot" of the annualized costs, which include annualized
capital and O&M costs, for those controls included in our modeled control strategy. Our
engineering cost analysis uses the equivalent uniform annual costs (EUAC) method, in which
annualized costs are calculated based on the equipment life for the control measure along with
the interest rate by use of the CRF as mentioned previously in this chapter. Annualized costs are
estimated as equal for each year the control is expected to operate. Hence, our annualized costs
for nonEGU point and area sources estimated for 2020 are the same whether the control measure
is installed in 2019 or in 2010. We make no presumption of additional capital investment in
years beyond 2020. The EUAC method is discussed in detail in the EPA Air Pollution Control
Cost Manual (found at http://epa.gov/ttn/catc/products.htmltfcccinfo). Applied controls and their
respective engineering costs are provided in the Ozone NAAQS RIA docket.
The modeled control strategy for nonEGU Point and Area sources incorporated annualized
engineering cost per ton caps. These caps were defined as the upper cost per ton for controls of
nonEGU point and area sources. The caps were calculated by examining the marginal cost
curves for each pollutant for the geographic areas (approximately 1,300 counties for NOx
controls, see Figure 3.5 and approximately 120 counties for VOC controls, see Figure 3.6) being
analyzed for this analysis. For reductions of NOx emissions the cap (see Figure 5.1) was set at
$23,000/ton (2006$). At this cap, ninety-eight percent of the possible reductions from known
measures are achieved at eighty-two percent of the total annualized engineering cost. 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 relative air quality effectiveness of reductions in
VOC was considered, and the marginal cost curve (Figure 5.2) was analyzed. Subsequently, the
cap was set at approximately $5,000/ton (2006$). At this cap, forty-six percent of the possible
reductions are achieved at fifteen percent of the total engineering cost. It is important to note that
as part of the extrapolated cost analysis the VOC cap was raised to $15,000/ton (for geographic
areas where the supplemental air quality modeling showed VOC control to be beneficial). At this
cap (2006$) ninety-eight percent of the possible reductions could be achieved.
1 For more information on this cost methodology and the role of AirControlNET, see Section 6
of the 2006 PM RIA, AirControlNET 4.1 Control Measures Documentation (Pechan, 2006b), or
the EPA Air Pollution Control Cost Manual, Section 1, Chapter 2, found at
http://www. epa. gov/ttn/catc/products.html#cccinfo.
5-3
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Figure 5.1: Marginal Cost Curve for Modeled Control Strategy Geographic Areas
(NOX nonEGU Point and Area Source Controls Prior to Cut Points)
gf $160,000 -i
o $140,000 -
^r $120,000 -
£ $100,000 -
^ $80,000 -
0 $60,000 -
w $40,000 -
E $20,000 -
< $0 t
(
) 100,000 200,000 300,000 400,000 500,000 600
000
Cumulative Emission Reductions (annual tons/year)
Figure 5.2: Marginal Cost Curve for Modeled Control Strategy Geographic Areas
(VOC nonEGU Point and Area Source Controls Prior to Cut Points)
~ $35,000 -,
o $30,000 -
^ $25,000 -
$20,000 -
$15,000 -
$10,000 -
$5,000 -
$0
|
-
03
D
C
100,000 200,000 300,000 400,000 500,000
Cumulative Emission Reductions (annual tons/year)
600,000
5.1.1.2 EGU Sources: the Integrated Planning Model
Engineering 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 emission allowances, as well as traditional command-and-control emission
5-4
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policies.2 Applied controls and their respective engineering costs are provided in the docket. IPM
is described in further detail in Appendix 3.
5.1.1.3 Onroad andNonroad Mobile Sources: National Mobile Inventory Model (NMIM) and
Various Studies
Engineering 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 engineering costs are provided in the docket.3
Engineering costs, in terms of dollars per ton emissions reduced, were applied to emission
reductions calculated for the onroad and nonroad mobile sectors that were generated using the
NMIM. NMIM is an EPA model for estimating pollution from highway vehicles and nonroad
mobile equipment. NMIM uses current versions of EPA's model for onroad mobile sources,
MOBILE6. and nonroad mobile sources, NONROAD. to calculate emission inventories 4.
5.1.2 Modeled Controls—Engineering Cost by Sector
In this section, we provide engineering cost estimates of the control strategies identified in
Chapter 3 that include control technologies on nonEGU 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 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 nonEGU point, area,
2 The application of the 0.070 EGU control strategy results in annual NOx allowance price
decreasing from $1618/ton in the baseline to $64I/ton. See Technical Support Document on
EGU Control Strategies for more details. Further detailed information on IPM is available in
Section 6 of the 2006 PM RIA or at http://www.epa.gov/airmarkets/epa-ipm
3 The expected emissions reductions from SCR retrofits are based on data derived from EPA
regulations (Control of Emissions of Air Pollution from 2004 and Later Model Year Heavy-duty
Highway Engines and Vehicles published October 2000), interviews with component
manufacturers, and EPA's Summary of Potential Retrofit Technologies available at
www.epa.gov/otaq/retrofit/retropotentialtech.htm.
For more information on mobile idle reduction technologies (MIRTs) see EPA's Idle Reduction
Technology page at http://www.epa.gov/otaq/smartway/idlingtechnologies.htm.
4 More information regarding the National Mobile Inventory Model (NMIM) can be found at
http ://www. epa.gov/otaq/nmim.htm
5-5
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and mobile sources. Engineering costs from EGU sources, which are calculated using the IPM
model and variable interest rates, are captured in this table at an annualized 7% discount rate.5
5 A different plant-specific interest rate is applied in estimating control costs within IPM. See PM
RIA for details.
5-6
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Table 5.1: Annual Control Costs by Sector and Region, for the
Modeled Control Strategy (2006$) a'b'e
Source Category
Modeled Control Strategy .
Averase
Engineering Cost by Total Cost rn<.t/T!L
Region (M 2006$) (M 2006$) poOfisT
East West CA (~ }
Electric Generating Units (ECU) Sector
Controls for NOx cap and trade program and
local measures in projected nonattainment
areas for coal units.
$170 $(70)c
$160
$1,900f
Total
$170 $(70) $66 $160
Mobile Source Sector
Onroad Sources (Ex: automobiles, buses,
trucks, and motorcycles traveling on roads
and highways)
Nonroad Sources (Ex: railroad locomotives;
marine vessels, aircraft, and farm,
construction, industrial and lawn/garden
equipment)
$360 $55 $45 $460 $2,100
$150 $21 $16 $190 $3,400
Total
$510
$75
$61
$650
NonEGU Sector
Point Sources (Ex: chemical manufacturing,
cement manufacturing, petroleum refineries,
and iron and steel mills)
$1,400
$57
$4.7
$1,500
$3,800
Area Sector
Area Sources (Ex: residential woodstoves,
agriculture)
Total
$480
$44
$20
$550
$2,000
$1,900
Total Annualized Costs
(using a 7% interest rate)
$2,600 $170 $160 $2,800
Total Annualized Costs
(using a 3% interest rate)
$2,400 $160
$160
$2,600
"All estimates rounded to two significant figures. As such, totals will not sum down columns. The
modeled control strategy is that strategy applied to reach attainment of the 0.070 alternate primary
standard, and is described in detail in Chapter 3.
b All estimates provided reflect the engineering cost of the modeled control strategy, incremental to a
2020 baseline of compliance with the current standard of 0.084 ppm.
c The total cost is negative in the west for the modeled control strategy due to an electricity generation
shift. The west generates less electricity and exports from the east.
d Total annualized costs were calculated using a 3% discount rate for controls which had a capital
component and where equipment life values were available. For this modeled control strategy, data for
calculating annualized costs at a 3% discount was only available for NonEGU point sources. Therefore,
the total annualized cost value presented in this referenced cell is an aggregation of engineering costs at
3% and 7% discount rate.
e These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
5-7
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f This average cost/ton estimate is based on ozone season NOx reductions from EGUs from controls that
operate year-round as explained in Chapter 3. By counting NOx reductions in the ozone season while
operation of NOx controls is modeled as year-round, our cost/ton estimate may spread out reductions
and thus affect the average cost/ton estimate. It should be noted that the resulting cost/ton of the
controls applied within EGU control strategy is practically the same as that in 2020 for the final CAIR
rule ($1,900 in 2006 dollars).
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 nonEGU 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 Table 5.1 is representative of the sum of
the nonEGU 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, engineering cost estimates presented throughout this and
subsequent chapters are based on a 7% discount rate.
The total annualized engineering costs associated with the application of known and
supplemental controls, incremental to the baseline, are approximately $2.8 billion using a 7%
discount rate.
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 engineering cost
analysis is subject to uncertainties and limitations.
One of these limitations is that we do not have sufficient information for all of our known control
measures to calculate cost estimates that vary with an interest rate. We are able to calculate
annualized costs at an interest rate other than 7% (e.g., 3% interest rate) where there is sufficient
information—available capital cost data, and equipment life—to annualize the costs for
individual control measures. For the vast majority of nonEGU point source control measures, we
do have sufficient capital cost and equipment life data for individual control measures to prepare
annualized capital costs using the standard capital recovery factor. Hence, we are able to provide
annualized cost estimates at different interest rates for these point source control measures as we
have done for the proposed ozone RIA and the PM2.5 RIA last year.
For area source control measures, the engineering cost information is available only in
annualized cost/ton terms. We have extremely limited capital cost and equipment life data for
area source control measures. We know that these annualized cost/ton estimates reflect an
5-S
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interest rate of 7% because these estimates are typically products of technical memos and reports
prepared as part of rules issued by our office (OAQPS) over the last 10 years or so, and the costs
estimated in these reports have followed the policy provided in OMB Circular A-4 that
recommends the use of 7% as the interest rate for annualizing regulatory costs. Capital cost
Figure 5.3: Total Annualized Costs by Emissions Sector and Region for Modeled Control
Strategy in 2020a'b'c'd
$1.50-,
-$0.10
East
West
Area -nonEGU Point EGU -Onroad _ Nonroad
a Total costs presented above are for a seven percent discount rate.
b All estimates provided reflect the engineering cost of the modeled control strategy, incremental to a
2020 baseline of compliance with the current standard of 0.084 ppm.
c The total cost is negative in the west for the modeled control strategy due to an electricity generation
shift. The west generates less electricity and exports from the east.
d These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
information for these area source controls, however, is often limited since these measures are
often not the traditional add-on controls where the capital cost is well known and convenient to
estimate. Such area source controls can include reformulation of coatings to reduce VOC, as one
example. The limited availability of useful capital cost data for such control measures has led to
our use of annualized cost/ton estimates to represent the engineering costs of these controls in
our cost tools and hence in the PM2.5 and ozone RIAs.
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For mobile source measures, the situation is very much like that for our area source measures.
We do not have sufficient capital cost information from what our mobile source office (OTAQ)
has sent us to compute annualized costs for different interest rates other than 7%. Finally, It
should be noted that the annualized capital costs for EGUs are prepared at an interest rate other
than 7%. Information on the annualization of EGU control costs is presented later in this chapter.
There are some unquantified 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
"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 of the cost of these modeled control strategy is included in Appendix 5b
of this analysis. The illustrative analysis does quantify the potential for advancements in the
capabilities of pollution control technologies as well as reductions in their engineering costs over
time. This is discussed in Section 5.4.
For purposes of this analysis, we assume attainment by 2020 for all areas except San Joaquin
Valley and South Coast air basins in California. The state has submitted plans to EPA for
implementing the current ozone standard which propose that these two areas of California meet
that standard by 2024. We have assumed for analytical purposes that the San Joaquin Valley and
South Coast air basin would attain a new standard in 2030. There are many uncertainties
associated with the year 2030 analysis. Between 2020 and 2030 several federal air quality rules
are likely to further reduce emissions of NOx and VOC, such as, but not limited to National rules
for Diesel Locomotives, Diesel Marine Vessels, and Small Nonroad Gasoline Engines. These
emission reductions should lower ambient levels of ozone in California between 2020 and 2030.
Complete emissions inventories as well as air quality modeling were not available for this year
2030 analysis. Due to these limitations, it is not possible to adequately model 2030 air quality
changes that are required to develop robust controls strategies with associated costs and benefits.
In order to provide a rough approximation of the costs and benefits of attaining 0.075 ppm and
the alternate standards in San Joaquin and South Coast air basins, we've relied on the available
data. Available data includes emission inventories, which do not include any changes in
stationary source emissions beyond 2020, and 2020 supplemental air quality modeling. This
data was used to develop extrapolated costs and benefits of 2030 attainment. To view the
complete analysis for the San Joaquin Valley and South Coast air basins see Appendix 7b.3
5.2 Extrapolated Engineering Costs
5.2.1 Methodology
This section presents the methodology and results of the extrapolated engineering cost
calculations of attainment of a new ozone standard of 0.075 ppm and analyses of three
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alternative standards, a less stringent 0.079 ppm and two more stringent options (.065 and 0.070
ppm).
As discussed in Chapter 3, the application of the modeled control strategy was not successful in
reaching nationwide attainment of the alternate ozone standards. Many areas remained in
nonattainment for all four alternate standard scenarios; therefore, the engineering costs detailed
in Section 5.1 represent only the costs of partial attainment.
The estimation of engineering costs for unspecified emission reductions needed to reach
attainment many years in the future is inherently a difficult issue. As described later in this
chapter, our experience with Clean Air Act implementation shows that technological advances
and development of innovative strategies can make possible emissions reductions that are
unforeseen today, and to reduce costs of emerging technologies over time. But we cannot
quantitatively predict the amount of technology advance in the future. For areas needing
significant additional emission reductions, much of the control must be for sources that
historically haven't been controlled. The relationship of the cost of such control to the cost of
control options available today is not at all clear. Available, current known control measures
increase in cost beyond the range of what has ever been implemented and would still not provide
the needed additional control for full attainment in the analysis year 2020. In the absence of
technological change, the needed control for full attainment in 2020 would not be available.
The degree to which unknown controls are needed to achieve attainment depends significantly
upon 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. The Clean Air Act
provides flexibility for a nonattainment area to receive an attainment date up to 20 years after
designation if earlier attainment is not practical based on controls that are reasonably available
considering cost. Although we assume attainment in 2020 (except for two California areas),
areas that face difficulty attaining could qualify under the Clean Air Act for an attainment date as
late as 2030 (assuming designations in 2010). This would give such areas additional time to take
advantage for national standards to reduce emissions from onroad and nonroad mobile sources
through fleet turnover, and to take advantage of technological innovation in cleaner technologies
after 2020.
Prior to presenting the methodology for estimating costs for unspecified emission reductions, it
is important to provide information from EPA 's Science Advisory Board Council Advisory,6
dated June 8, 2007, on the issue of estimating costs of unidentified control measures.
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
6 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.
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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
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. "
EPA has considered this advice and the requirements of E.O. 12866 and OMB circular A-4,
which provides guidance on the estimation of benefits and costs of regulations.
To generate estimates of the costs and benefits of meeting alternative standards, EPA has
assumed the application of unspecified future controls that make possible the emissions
reductions needed for attainment in 2020 (excluding two California areas). By definition, there is
no cost data in existence for unidentified future technologies or innovative strategies.
EPA used two methodologies for estimating the costs of unspecified future controls: a new
hybrid methodology and a fixed-cost methodology. Both approaches assume that innovative
strategies and new control options make possible the emissions reductions needed for attainment
by 2020. The fixed cost methodology was preferred by EPA's Science Advisory Board over two
other options, including a marginal-cost-based approach. The hybrid approach has not yet been
reviewed by the SAB.
The hybrid approach creates a marginal cost curve and an average cost curve representing the
cost of unknown future controls needed for 2020 attainment. This approach explicitly estimates
the average per-ton cost of unspecified emissions reductions assumed for each area, with a
higher average cost-per-ton in areas needing a higher proportion of unknown controls relative to
known modeled controls. This requires assumptions about the average cost of the least expensive
unspecified future controls, and the rate at which the average cost of these controls rises as more
extrapolated tons are needed for attainment (relative to the amount of reductions from known,
modeled controls). These factors in turn depend on implicit assumptions about future
technological progress and innovation in emission reduction strategies.
The fixed cost methodology utilizes a national average cost per ton of future unspecified controls
needed for attainment, as well as two sensitivity values (presented in Appendix 5a.4.3). The
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range of estimates reflects different assumptions about the cost of additional emissions
reductions beyond those in the modeled control strategy. The alternative estimates implicitly
reflect different assumptions about the amount of technological progress and innovation in
emission reduction strategies.
The hybrid methodology has the advantage of using the information about how significant the
needed reductions from unspecified control technology are relative to the known control
measures and matching that with expected increasing per unit cost for going beyond the modeled
technology. Under this approach, the relative costs of unspecified controls in different
geographic areas reflect the expectation that average per-ton control costs are likely to be higher
in areas needing a higher ratio of emission reductions from unspecified and known controls.
The fixed cost methodology reflects a view that because no cost data exists for unspecified future
strategies, it is unclear whether approaches using hypothetical cost curves will be more accurate
or less accurate in forecasting total national costs of unspecified controls than a fixed-cost
approach that uses a range of national cost per ton values.
Technological change will provide new control possibilities that can be employed to provide the
additional unspecified control needed to reach attainment. These new technologies will make
control possible where control has not been available for estimating our known control. An
example might be the development of a new control technology for a type of emissions that have
never been controlled. Technological change is also expected to reduce the cost of known
controls that currently have prohibitive costs. For example, suppose a source that was not chosen
for control because the estimated cost was $60,000 per ton but technological change reduces the
cost to $16,000 per ton. Finally, control technologies may change so that higher control
efficiencies may be obtained without a significant increase in per unit costs of control.
Both approaches (the hybrid and the fixed) estimate costs using national level parameters and
local area information about needed emission reductions. Because cost changes due to
technological change will be available on a national level, it makes sense to use national level
estimates of these parameters. Local areas have different levels of needed emission reductions
and different inventories of uncontrolled emissions and estimates of needed emission reductions
are used in both models. The hybrid model also uses information about the amount of modeled
control estimated for the local area.
The hybrid approach has yet to be peer reviewed and reflects a range of views about the likely
cost of future techniques and strategies that reduce air pollutant emissions. 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.
5.2.1.1 Initial Steps
The first step involved identifying supplemental known controls not included in the modeled
control strategy. These controls include the controls discussed in Appendix 3a.l.6, as well as
additional controls applied to select EGU sources, and VOC controls up to $15,000/ton for select
geographic areas. For the more stringent alternative of 0.065 ppm additional geographic areas
were included, and therefore additional known measures were available to be applied as well.
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For the other three alternatives, there were geographic areas that were "over controlled" and
controls were removed from the analysis. For a complete discussion of the supplemental and
"over control" emission reductions and costs see Appendix 5a.4.1 and 5a4.2 respectively. After
the supplemental controls are applied, any remaining emission reductions needed are classified
as additional tons from unknown control measures.
Supplemental controls were applied in addition to the known controls in this illustrative analysis
in order to achieve the highest possible known emission reduction from NonEGU 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.
Next, we classified the areas needing additional controls by attainment date. Because two areas
in California require no incremental additional progress towards attainment by 2020 for a more
stringent standard (their requirements to reach attainment of the current standard by 2024 will be
the requirement that is binding) we separated the requirements to attain more stringent standards
for those two areas from the analysis for the rest of the nation. A highly uncertain estimate of the
extrapolated engineering cost in 2030 is provided in Appendix 5a.5.
5.2.1.2 Theoretical Model for Hybrid Approach
A simple model of how marginal costs increase with increasing control requirements was
developed. The model relies on emission estimates of unspecified emissions (Ei) needed to reach
attainment and the modeled control emission estimates. These unspecified emissions vary both
with the area and standard being analyzed. The modeled emissions vary by area. The ratio (R) of
unspecified emissions (Ei) to controlled emissions estimates (Eo) is thus unique to each area and
standard being analyzed. The model of cost also includes two parameters developed for use that
don't vary across analyses of areas and standards. One is a national projected dollar per ton cost
for the last ton controlled for the controlled emissions (N or jumping off price). The other is a
constant multiplier (M) to determine an average cost per ton that increases as size of the needed
unknown controls (Ei) increase relative to the modeled controls (R). The following equations
show how Average cost (AC), Total Cost (TC), and marginal (MC) are modeled in the hybrid
approach. See the appendix for a more detailed explanation.
AC = N(1+RM)
TC = AC(Ei)
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MC = N(1+2RM)
For the controlled emissions estimated in the modeled control, costs increase at an increasing
rate as more control is applied. The shape of the control cost curve for 2020 after technological
change is unknown but would also be expected to increase at an increasing rate. With all of the
uncertainty and as part of the trade-off between simplicity/transparency and model richness we
chose a proportional per unit cost increase. This model assumes per unit costs increase at a
constant rate proportional to R.
5.2.1.3 Parameter Estimation for Hybrid Approach
The jumping off price (N) used is $15,000/ton (2006$). To determine this number we calculated
the marginal costs for the last control applied in all geographic areas for nonEGU and Area
known controls7 and averaged them for both the modeled control strategy and an alternate
primary standard of 0.065 ppm, this allowed for consistency with the modeled control strategy
marginal costs. These calculations showed a range of $14,500 to $16,000 per ton (2006$), with
$15,000 falling in the middle. The February 2007 report, "Direct Cost Estimates for the Clean
Air Act Second Section 812," uses $10,000 (1999$) per ton. For simplicity and comparability we
used the $15,000/ton. In addition the marginal cost curve for the modeled control strategy NOx
nonEGU and Area, 90% of the controls applied are below $15,000/ton. The jumping off price
(N) should be interpreted as the cost of the very first ton needed from the unknown control8. We
chose the value $15,000/ton and not the $23,000/ton applied for NOx nonEGU point and Area
source controls because the $23,000/ton was calculated as an extreme upper limit for NOx
nonEGU controls and is not representative of the upper limit of controls applied across all
emissions sectors. It is important to note that the cost/ton numbers calculated above are specific
to this scenario. In an ideal world, we would have more complete information about the available
control options in each area and we would be able to estimate what the next control to be
employed (the "jumping off control) would be for each area needing control beyond the
modeled known control.
We have to estimate R and E information for each area and each standard. Figure 5.4 shows how
for phase 1 supplemental air quality modeling areas how R varies based upon the level of the
standard and the local geographic area emissions.
We have no way to econometrically estimate M. The constant multiplier (M) incorporates many
different influences on the unit costs of control such as technological change in control
technology, change in energy technology, learning by doing, relative price changes, and
distribution of sources with uncontrolled emissions. Using a high value for marginal cost we can
solve for M based on this value and our parameter estimate of $ 15,000 for N, and our highest
7 NOx NonEGU point and Area controls were used for this calculation due to availability of
detailed data across all emission sectors.
8 Although $15,000/ton (2006$) represents the cost of the very first ton of unknown control
needed, marginal costs for the last ton of unknown control are assumed to be no higher than
$46,000/ton (2006$)
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value of R9 (2.19)for areas meeting the current standard in 2020. For the modeled control we
used a maximum marginal cost of control of $23,000 dollars/ton. At this cost 98% of the possible
reductions NOx from nonEGU point and area were applied. To arrive at a high value we doubled
the maximum marginal cost value ($46,000). A number this high is rarely seen in either
implemented controls or other RIAs (e.g. the 1997 Ozone RIA highest cost per ton was $10,000
(1990$) which is $14,000 (2006$)). This leads to our estimate of M of 0.47. To arrive at a low
value we used the maximum marginal cost from the modeled control strategy ($23,000). This
leads to our estimate of M of 0.12. We calculated an M of 0.24 for the middle estimate based
upon the higher and lower M values described above. The results reported in this chapter are for
an M of 0.24, the estimates using the high and low value of M are reported in Appendix 5a.
9 The R for Eastern Lake Michigan was 2.19 for the 0.065 ppm alternative standard. The R for
Houston was higher, yet this value was not used when calculating the highest value of M because
Houston is the only area in our analysis for 2020 that did not meet the current standard, and
therefore not representative of the majority of areas needing to reach a new ozone standard.
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Figure 5.4: Ratio of Unspecified Emission Reductions to Known Emission Reductions
Across Various Standards for Phase 1 Areas"'b'c
2.40
2.00
! "I 1.60
1 ° 1.20
m w
en
ll
| I 0.80
(0
cc
0.40
0.00
0.065 ppm
0.070 ppm
0.075 ppm
0.079 ppm
.Eastern Lake Michigan, IL-IN-WI (NOX)
Northeast Corridor, CT-DE-MD-NJ-NY-PA (NOX)
. Sacramento Metro, CA (NOX)
.Eastern Lake Michigan, IL-IN-WI (VOC)
. Houston, TX (NOX)
a Phase 1 Areas are defined in Chapter 4 Section 4.1.1
b There are values of R for both NOx and VOC for the Eastern Lake Michigan, IL-IN-WI. This is the
only geographic area where unknown control costs were calculated for VOC.
c Houston did not meet the current standard after the modeled control strategy.
The cost of the last ton needed for the unknown control is N(1+2RM). Thus, the per unit control
cost for the unspecified tons in an area starts with N and linearly increases with R. The ratio of
needed unknown control to modeled control (R) can be interpreted as a measure of "the degree
of difficulty" (see Figure 5.4). For example, the per unit control costs would be expected to be
higher if the unknown control needed is twice the modeled control than if it is half the modeled
control. Table 5.2 shows how the cost of the last ton controlled for the highest R value would
vary with different values of M. Figure 5.5 also depicts how the average cost per ton would vary.
Table 5.2: Marginal Cost and Average Cost Values Used in Calculating Ma
Highest Annual Cost/Ton Values (2006$), Given R = 2.19
M = 0.12
M = 0.24
M = 0.47
Marginal Cost (MC)
$23,000
$31,000
$46,000
Average Cost (AC)
$19,000
$23,000
$30,000
a Marginal and average costs could be higher than the values presented above for tighter ozone standards.
Figure 5.5 shows the range of average cost/ton values across geographic areas and standards.
This helps graphically illustrate the interplay of all the variables to create a geographically
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specific average cost/ton that is then multiplied by the amount of unspecified emissions
reductions needed to attain. These average cost per ton values
Figure 5.5: Ranges of Hybrid (Mid) Average Cost/Ton Values across Geographic Areas
and Standards
o $23,000
o
T? $22,000 -
o $21,000
co
o
S $20,000
•§, $19,000
I
£ $18,000
"to
O $17,000
cu
CD
co
a $16,000
$15,000
it
i>
t
I
0.065 ppm
0.070 ppm
0.075 ppm
• Ada Co., ID
• Charlotte-Gastonia-Rock Hill, NC-SC
Detroit-Ann Arbor, Ml
» Buffalo-Niagara Falls, NY
Jefferson Co, NY
• Norfolk-Virginia Beach-Newport News
• Houston, TX(NOX)
Salt Lake City, UT
Atlanta, GA
• Dallas-Fort Worth, TX
Baton Rouge, LA
Dona Ana CO., NM
» Las Vegas, NV
• Pittsburgh-Beaver Valley, PA
• Northeast Corridor, CT-DE-MD-NJ-NY-PA (NOX)
San Juan Co., NM
0.079 ppm
i Boston-Lawrence-Worcester, MA
• Denver-Boulder-Greeley-Ft Collins-Love, CO
• Cleveland-Akron-Lorain, OH
• Huntington-Ashland, WV-KY
A Memphis, TN-AR
• Sacramento Metro, CA (NOX)
• Eastern Lake Michigan, IL-IN-WI (NOX)
• St Louis, MO-IL
5.2.1.4 Fixed Cost Approach
As discussed above the Science Advisory Board advice favored a fixed cost per ton approach as
the simplest and most straightforward. The extrapolated cost equation involves only unspecified
emissions (Ei) and Fixed Cost per ton (F). Thus the total cost (TC) equation is:
TC= EiF
The primary estimate of F is $15,000. The $15,000 per ton amount is commensurate with that
used in the 1997 RIA in using current dollars. It is also consistent with what an advisory
committee to the Section 812 second prospective analysis on the Clean Air Act Amendments
suggested.
Values of $10,000/ton and $20,000/ton are used for the sensitivity analyses found in Appendix
5a.4.3.
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5.2.2 Results
5.2.2.1 Emission Reductions Needed to Attain Various Standards
Application of supplemental control measures (for a complete discussion see Chapter 5
Appendix section 5a.4) mentioned above resulted in some geographic areas no longer needing
extrapolated tons to attain various alternate primary standards. Table 5.3 shows the emission
reductions needed by geographic area, pollutant, and standard. Eastern Lake Michigan is the only
area with both NOx and VOC emission reductions estimates. For the other areas additional
control of NOx only is expected to be a less expensive approach than controlling both NOx and
VOC. As expected, more areas need extrapolated emission reductions when the alternative
standards are more stringent.
Table 5.3: Extrapolated Emission Reductions Needed (Post Application of Supplemental
Controls) to Meet Various Alternate Standards in 2020
2020 Extrapolated
Cost Area
Ada Co., ID
Atlanta, GA
Baton Rouge, LA
Boston-Lawrence-
Worcester, MA
Buffalo -Niagara
Falls, NY
Campbell Co., WY
Charlotte-Gastonia-
Rock Hill, NC-SC
Cincinnati-
Hamilton, OH-KY-
IN
Cleveland- Akron-
Lorain, OH
Dallas-Fort Worth,
TX
Denver-Boulder-
Greeley-Ft Collins-
Love, CO
Detroit- Ann Arbor,
MI
Dona Ana CO., NM
Eastern Lake
Michigan, IL-IN-WI
El Paso Co, TX
Houston, TX
Huntington-
Ashland, WV-KY
Jackson Co, MS
Jefferson Co, NY
Las Vegas, NV
Memphis, TN-AR
Additional Emission Reductions Needed (annual tons/year)
0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
NOX VOC NOX VOC NOX VOC NOX VOC
2,800
5,500
160,000 49,000
8,500
18,000 3,700
50
47,000
(40)a
78,000 11,000
48,000 (30) a
1,600
100,000 8,700
410
320,000 320,000 250,000 250,000 74,000 49,000 (60) a (50) a
a
180,000 160,000 110,000 81,000
800
(200) a
6,200
3,900
1,100
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2020 Extrapolated
Cost Area
Norfolk- Virginia
Beach-Newport
News
Northeast Corridor,
CT-DE-MD-NJ-
NY-PA
Phoenix-Mesa, AZ
Pittsburgh-Beaver
Valley, PA
Richmond-
Petersburg, VA
Sacramento Metro,
CAb
Salt Lake City, UT
San Juan Co., NM
St Louis, MO-IL
Toledo, OH
Additional Emission Reductions Needed (annual tons/year)
0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
NOX VOC NOX VOC NOX VOC NOX VOC
21,000
340,000 220,000 65,000
(60) a
13,000
(600) a
130,000 89,000 44,000 1,800
430
1,300
17,000
(90) a
a negative or zero values indicate the supplemental measures applied yielded equal or greater emission
reductions than were needed for the geographic area to attain the standard being analyzed.
b Sacramento Metro, CA geographic area also contains the South Coast and San Joaquin Valley Areas.
These two areas will still be reducing emissions to meet the 0.08 ozone standard, and therefore the costs
of these emission reductions are not incurred as part of meeting a new ozone standard. The difference
between the emission reductions needed in Table 4.7a and this table are accounted for by the tons that
South Coast and San Joaquin need to reduce to reach the current standard, and to help Sacramento
attain a new ozone standard.
5.2.2.2 Fixed Cost Approach Extrapolated Costs
Figure 5.6 and Table 5.4 presents the extrapolated cost estimates regionally for the various
alternative standards for a fixed cost approach of $15,000/ton. These costs are the values from
Table 5.3 multiplied by $15,000. See the Appendix 5a.4.3 for sensitivity analyses of varying the
fixed dollar per ton to values other than $15,000. When we evaluate the portion of costs for the
extrapolated costs fixed approach by supplemental air quality modeling phase (as described in
Chapter 4), 100% of the costs are allocated to phase 1 geographic areas for the 0.075 ppm and
0.079 ppm standard. For the 0.065 ppm and 0.070 ppm standards 73% to 94% are allocated to
phase 1 areas, 22% to 6% in phase 2 areas, and only 5% to 0% for phase 3 areas. The sensitivity
analysis for the fixed cost approach at $10,000/ton and $20,000/ton resulted in extrapolated costs
of $3.4 to $6.8 billion dollars for the 0.075 ppm standard.
5.2.2.3 Hybrid Approach Extrapolated Cost Results
Table 5.5 presents the extrapolated cost estimates regionally for the various alternative standards
for the hybrid approach (mid). See the Appendix 5a.4.4 for sensitivity analyses of values of M of
0.47 and 0.12. A value of 0.24 is used for M because R goes up with the stringency of the
standard, the differences in costs between cost areas increase with the stringency of the
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Figure 5.6: Extrapolated Cost by Region to Meet Various Alternate Standards
Using Fixed Cost Approach ($15,000/ton)
0.070 ppm
0.075 ppm
0.079 ppm
Table 5.4: Extrapolated Cost by Region to Meet Various Alternate Standards
Using Fixed Cost Approach ($15,000/ton)
a,b
2020 Extrapolated Cost by
Region
East
West
California
Total Extrapolated Cost
Fixed Cost Approach Extrapolated Cost (M
0.065 ppm
$25,000
$160
$2,000
$27,000
0.070 ppm
$14,000
-
$1,300
$16,000
0.075 ppm
$4,500
-
$660
$5,100
2006$)
0.079 ppm
$1,200
-
$28
$1,200
a All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
alternative being considered. When we evaluate the portion of costs for the extrapolated costs
fixed approach by supplemental air quality modeling phase (as described in Chapter 4), 100% of
the costs are allocated to phase 1 geographic areas for the 0.075 ppm and 0.079 ppm standard.
For the 0.065 ppm and 0.070 ppm standards 74% to 95% are allocated to phase 1 areas, 21% to
5% in phase 2 areas, and only 5% to 0% for phase 3 areas.
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Figure 5.7: Extrapolated Cost by Region to Meet Various Alternate Standards Using
Hybrid Approach (Mid)
$40
0.065 ppm
0.070 ppm
0.075 ppm
0.079 ppm
Table 5.5: Extrapolated Cost by Region to Meet Various Alternate Standards Using
Hybrid Approach (Mid)
a,b
2020 Extrapolated Cost by
Region
East
West
California
Total Extrapolated Cost
Hybrid Approach Extrapolated Cost (M 2006$)
0.065 ppm
$36,000
$170
$2,800
$39,000
0.070 ppm
$20,000
$1,700
$22,000
0.075 ppm
$5,500
$770
$6,300
0.079 ppm
$1,700
$28
$1,800
a All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
5-22
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5.3 Summary of Costs
Table 5.6 presents a summary of the total national cost of attaining 0.079, 0.075, 0.070, and
0.065 ppm standards in 2020. This summary includes the engineering costs presented above
from the modeled controls and the extrapolated costs. The range presented in the extrapolated
costs and the total costs represent the upper and lower bound cost estimates. Consistent with
OMB Circular A-4, costs are presented at a 7% discount rate. It is more consistent to present the
extrapolated costs at the same discount rate as the modeled control costs, for which a 7% rate
was determined to be more representative of actual costs (see section 5.1.3). Although the
amount of reduction assumed to occur using unknown controls increases, the uncertainty of the
associated costs and benefits calculations increases.
Table 5.6: Total Costs of Attainment in 2020 for Alternate Levels of the
Ozone Standard a'b'c
Region
Known Control East
Costs ($B) West
California
Known Control Costs6
Extrapolated
Costs ($B) East
West
California
Extrapolated Costs
Total Cost Range
Annual Engineering Costs (M 2006$)
0.065 ppm
$4,100
$230
$160
$4,500
Fixed Hybrid
$25,000 $36,000
$160 $170
$2,000 $2,800
$27,000 $39,000
$32,000 $44,000
0.070 ppm
$3,100
$14
$160
$3,300
Fixed Hybrid
$14,000 $20,000
$0 $0
$1,300 $1,700
$16,000 $22,000
$19,000 $25,000
0.075 ppm d
$2,400
-$4
$160
$2,500
Fixed Hybrid
$4,500 $5,500
$0 $0
$660 $770
$5,100 $6,300
$7,600 $8,800
0.079 ppm d
$960
-$5
$160
$1,100
Fixed Hybrid
$1,200 $1,700
$0 $0
$28 $28
$1,200 $1,800
$2,400 $2,900
a All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for the analysis of these areas.
c These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
d Known control costs for 0.079 ppm and 0.075 ppm include the modeled EGU cap and trade strategy, and
therefore contain greater emission reductions than are needed to attain for some geographic areas.
Therefore these results represent an overestimate of the costs of attainment.
e Known control costs consist of modeled control strategy costs presented in Table 5.1, as well as
supplemental costs and "giveback" costs presented in Appendix 5a.4.1 and 5a.4.2.
Our estimates of costs of attainment in 2020 assume a particular trajectory of aggressive
technological change. This trajectory leads to a particular level of emissions reductions and
costs which we have estimated based on two different approaches, the fixed cost and hybrid
approaches. An alternative storyline might hypothesize a much less optimistic technological
change path, such that emissions reductions technologies for industrial sources would be more
expensive or would be unavailable, so that emissions reductions from many smaller sources
might be required for 2020 attainment, at a potentially greater cost per ton. Under this
alternative storyline, two outcomes are hypothetically possible: Under one scenario, total costs
associated with full attainment might be substantially higher. Under the second scenario, states
may choose to take advantage of flexibility in the Clean Air Act to adopt plan with later
5-23
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attainment dates to allow for additional technologies to be developed and for existing programs
like EPA's Onroad Diesel, CAIR, Nonroad Diesel, and Locomotive and Marine rules to be fully
implemented. If states were to submit plans with attainment dates beyond our 2020 analysis
year, benefits would clearly be lower than we have estimated under our analytical storyline.
However, in this case, state decision makers, seeking to maximize economic efficiency, would
not impose costs, including potential opportunity costs of not meeting their attainment date,
when they exceed the expected health benefits that states would realize from meeting their
modeled 2020 attainment date. In this case, upper bound costs are difficult to estimate because
we do not have an estimate of the point where marginal costs are equal to marginal benefits plus
the costs of nonattainment.
Figure 5.8 shows the total costs for both the fixed and hybrid approaches broken out by region.
Figure 5.8: Annual Total Costs by Region"
Total Cost - Fixed ($15,000/ton)
West - CA • East
Total Cost - Hybrid (Mid)
' These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
Figure 5.9 separates the total cost under both the fixed and extrapolated cost approaches into the
known control costs and the extrapolated costs. This shows graphically the increasing portion of
costs that comes from unknown controls as the standard tightens. Depending upon the standard
and extrapolated cost methodology (fixed or hybrid) the costs from unknown control
technologies ranges from 50% to 89% of the total costs.
5-24
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Figure 5.9: National Known Control Costs and Extrapolated Costs for
Various Standards"'b
o
o
CM
in
+J
O
$45^
$40-
$35-
$30-
$25-
$20-
$10
$5
^ ^
Total Cost ($15,000/ton)
^ ^
Total Cost (Mid)
_ Known Control Costs _ Fixed Extrapolated Cost ($15,000/ton) Hybrid Extrapolated Cost (Mid)
' Known control costs consist of modeled control strategy costs presented in Table 5.1, as well as
supplemental costs and "giveback" costs presented in Appendix 5a.4.1 and 5a.4.2.
: These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
Lastly, Figure 5.10 shows the total cost range by standard. For the final standard of 0.075 ppm
the total cost ranges from $7.6 to $8.8 billion.
5-25
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Figure 5.10: Total Cost Ranges for Various Standards3
$45n
$40-
$35-
1 $30
CM
at
O
o
$25-
§ $20-
$ $15
o
$10-
$5-
0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
-Total Cost - Fixed ($15,000/ton) -Total Cost - Hybrid (Mid)
' These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
5.4 Technology Innovation and Regulatory Cost Estimates
There are 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. Studies10 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
1 Harrington et al. (2000) and previous studies cited by Harrington.
5-26
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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.
Constantly increasing marginal costs are 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 for such
equipment (Figure 5.11)11 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.
Figure 5.11: Technological Innovation Reflected by Marginal Cost Shift
05
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
11 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.
5-27
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• 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
• Chlorofiuorocarbon (CFC) free air conditioners, refrigerators, and solvents
• Water and powder-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
• Idle-reduction technologies for engines, including truck stop electrification efforts
• Market penetration of gas-electric hybrid vehicles, and clean fuels
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.
What is known as "learning by doing" or "learning curve impacts" have also 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. Learning curve impacts can be defined
generally as the extent to which variable costs (of production and/or pollution control) decline as
firms gain experience with a specific technology. Such impacts have been identified to occur in a
number of studies conducted for various production processes. Impacts such as these would
manifest themselves as a lowering of expected costs for operation of technologies in the future
below what they may have been.
The magnitude of learning curve impacts on pollution control costs has been estimated for a
variety of sectors as part of the cost analyses done for the Draft Direct Cost Report for the second
EPA Section 812 Prospective Analysis of the Clean Air Act Amendments of 1990.12 In that
report, learning curve adjustments were included for those sectors and technologies for which
learning curve data was available. A typical learning curve adjustment example is to reduce
either capital or O&M costs by a certain percentage given a doubling of output from that sector
or for that technology. In other words, capital or O&M costs will be reduced by some percentage
for every doubling of output for the given sector or technology.
12 E.H. Pechan and Associates and Industrial Economics, Direct Cost Estimates for the Clean
Air Act Second Section 812 Prospective Analysis: Draft Report, prepared for U.S. EPA, Office
of Air and Radiation, February 2007. Available at
http ://www. epa.gov/oar/sect812/mar07/direct_cost_draft.pdf.
5-28
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T.P. Wright, in 1936, was the first to characterize the relationship between increased productivity
and cumulative production. He analyzed man-hours required to assemble successive airplane
bodies. He suggested the relationship is a log linear function, since he observed a constant linear
reduction in man-hours every time the total number of airplanes assembled was doubled. The
relationship he devised between number assembled and assembly time is called Wright's
Equation (Gumerman and Marnay, 2004).13 This equation, shown below, has been shown to be
widely applicable in manufacturing:
Wright's Equation: CN= C0 * Nb,
where
N = cumulative production
CN = cost to produce Nth unit of capacity
C0 = cost to produce the first unit
B = learning parameter = In (l-LR)/ln(2), where
LR = learning by doing rate, or cost reduction per doubling of capacity or output.
The percentage adjustments can range from 5 to 20 percent, depending on the sector and
technology. Learning curve adjustments were prepared in a memo by lEc (2007) supplied to US
EPA and applied for the mobile source sector (both onroad and nonroad) and for application of
various EGU control technologies within the Draft Direct Cost Report.14 Advice received from
the SAB Advisory Council on Clean Air Compliance Analysis in June 2007 indicated an interest
in expanding the treatment of learning curves to those portions of the cost analysis for which no
learning curve impact data are currently available. Examples of these sectors are non-EGU point
sources and area sources. The memo by lEc outlined various approaches by which learning curve
impacts can be addressed for those sectors. The recommended learning curve impact adjustment
for virtually every sector considered in the Draft Direct Cost Report is a 10% reduction in O&M
costs for two doubling of cumulative output, with proxies such as cumulative fuel sales or
cumulative emission reductions being used when output data was unavailable.
For this RIA, we do not have the necessary data for cumulative output, fuel sales, or emission
reductions for sectors included in our analysis in order to properly generate control costs that
reflect learning curve impacts. Clearly, the effect of including these impacts would be to lower
13 Gumerman, Etan and Marnay, Chris. Learning and Cost Reductions for Generating
Technologies in the National Energy Modeling System (NEMS), Ernest Orlando Lawrence
Berkeley National Laboratory, University of California at Berkeley, Berkeley, CA. January
2004, LBNL-52559.
14 Industrial Economics, Inc. Proposed Approach for Expanding the Treatment of Learning Curve
Impacts for the Second Section 812 Prospective Analysis: Memorandum, prepared for U.S. EPA,
Office of Air and Radiation, August 13, 2007.
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our estimates of costs for our control strategies in 2020, but we are not able to include such an
analysis in this RIA.
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."15
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
lower than originally projected by EPA.16 Note that the original EPA cost analysis also
relied on an optimization model like IPM to approximate the results of emissions trading.
15 Harrington et al., 2000.
16 Carlson et al., 2000; Ellerman, 2003.
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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. The fuel switching from high-sulfur to low-sulfur coal was spurred by a
reduction in rail transportation costs due to deregulation of rail rates during the
1990'sHarrington 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 billion3
$1.0 to $1.4 billion
a 2010 Phase II cost estimate in $1995.
• EPA Fuel Control Rules: A 2002 study by two economists with EPA's Office of
Transportation and Air Quality17 examined EPA vehicle and fuels rules and found a
general pattern that "all ex ante estimates tended to exceed actual price impacts, with the
EPA estimates exceeding actual prices by the smallest amount." The paper notes that cost
is not the same as price, but suggests that a comparison nonetheless can be instructive.18
An example focusing on fuel rules is provided:
Table 5.7: Comparison of Inflation-Adjusted Estimated Costs and Actual Price Changes
for EPA Fuel Control Rules A
Inflation-ad j usted
EPA DOE
Cost Estimates
API
(c/gal)
Other
Actual Price
Changes (c/gal)
Gasoline
Phase 2 RVP Control (7. 8
RVP— Summer) (1995$)
Reformulated Gasoline Phase 1
(1997$)
Reformulated Gasoline Phase 2
(Summer) (2000$)
30 ppm sulfur gasoline (Tier 2)
1.1
3.1-5
4.6-6
1.7-1
1.8
.1 3.4-4.1
.8 7.6-10.2
.9 2.9-3.4
8.2-14.0
10.8-19.4
2.6
0.5
7.4 (CRA)
12
5.7 (NPRA),
3.1 (AIAM)
2.2
7.2 (5.1, when
corrected to Syr
MTBE price)
N/A
Diesel
500 ppm sulfur highway diesel
fuel (1997$)
1 5 ppm sulfur highway diesel
fuel
1.9-2
4.5
.4
4.2-6.0
3.3
(NPRA)
6.2
2.2
4.2-6.1
(NPRA)
N/A
"Anderson et al., 2002.
17 Anderson et al, 2002.
18 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."
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• 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.19
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."
• Additional examples of decreasing costs of emissions controls include: SCR catalyst
costs decreasing from $1 lk-$14k in 1998 to $3.5k-$5k in 2004, and improved low NOx
burners reduced emissions by 50% from 1993-2003 while the associated capital cost
dropped from $25-$38/kw to $15/kw (ICF, 2005).
We can not estimate the interplay between EPA regulation and technology improvement, but it is
clear that a priori cost estimation often results in overestimation of costs because changes in
technology (whatever the cause) make less costly control possible.
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 .
Holmstead, 2002.
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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. "Sulfur
Dioxide 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. Coutts. 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.
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.
ICF Consulting. October 2005. The Clean Air Act Amendment: Spurring Innovation and Growth
While Cleaning the Air. Washington, DC. Available at
http://www.icfi.com/Markets/Environment/doc_files/caaa-success.pdf.
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.
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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.
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Appendix 5a: Additional Cost Information
5a.l Engineering Cost Information for NonEGU Point and Area Sources
(Full details on controls can be found in Appendix Chapter 3)
5a. 1.1 Engineering Costs by Control Measure
Tables 5a. 1 and 5a.2 summarize the total incremental annualized engineering costs in 2020 for
the modeled control strategy by control measure for nonEGU point and Area sources.
Table 5a.l: NOx NonEGU Point and Area Source Control Measure
Annualized Engineering Costs
Control Measure
RACT to 25 tpy (LNB)
Switch to Low Sulfur Fuel
Water Heater + LNB Space
Heaters
Biosolid Injection Technology
LNB
LNB + FOR
LNB + SCR
NSCR
OXY-Firing
Source Type
Industrial Coal Combustion
Industrial NG Combustion
Industrial Oil Combustion
Residential Home Heating
Commercial/Institutional — NG
Residential NG
Cement Kilns
Asphaltic Cone; Rotary Dryer; Conv Plant
Coal Cleaning-Thrml Dryer; Fluidized Bed
Fiberglass Mfg; Textile — Type Fbr; Recup Furn
Fuel Fired Equip; Furnaces; Natural Gas
In-Process Fuel Use; Natural Gas
In-Process Fuel Use; Residual Oil
In-Process; Process Gas; Coke Oven Gas
Lime Kilns
Sec Alum Prod; Smelting Furn
Steel Foundries; Heat Treating
Surf Coat Oper; Coating Oven Htr; Nat Gas
Fluid Cat Cracking Units
Fuel Fired Equip; Process Htrs; Process Gas
In-Process; Process Gas; Coke Oven Gas
Iron & Steel Mills — Galvanizing
Iron & Steel Mills — Reheating
Iron Prod; Blast Furn; Blast Htg Stoves
Sand/Gravel; Dryer
Steel Prod; Soaking Pits
Iron & Steel Mills — Annealing
Process Heaters — Distillate Oil
Process Heaters — Natural Gas
Process Heaters — Other Fuel
Process Heaters — Process Gas
Process Heaters — Residual Oil
Rich Burn 1C Engines — Gas
Rich Burn 1C Engines— Gas, Diesel, LPG
Rich Burn Internal Combustion Engines — Oil
Glass Manufacturing — Containers
Total Cost
(M 2006$)
$11
$o o
j.j
$0.98
$20
$7.7
$12
$0.43
$0.39
$0.79
$1.1
$0.14
$4.3
$0.14
$0.59
$4.7
$0.052
$0.010
$0.095
$14
$3.2
$3.5
$0.030
$0.58
$0.56
$0.049
$0.11
$1.6
$38
$420
$110
$61
$0.29
$13
$2.1
$6.6
$5.1
5a-l
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Control Measure
SCR
SCR + Steam Injection
SCR + Water Injection
SNCR
SNCR— Urea
SNCR— Urea Based
Source Type
Glass Manufacturing — Flat
Glass Manufacturing — Pressed
Ammonia — NG-Fired Reformers
Cement Manufacturing — Dry
Cement Manufacturing — Wet
1C Engines — Gas
ICI Boilers— Coal/Cyclone
ICI Boilers— Coal/Wall
ICI Boilers— Coke
ICI Boilers— Distillate Oil
ICI Boilers — Liquid Waste
ICI Boilers— LPG
ICI Boilers — Natural Gas
ICI Boilers — Process Gas
ICI Boilers— Residual Oil
Natural Gas Prod; Compressors
Space Heaters — Distillate Oil
Space Heaters — Natural Gas
Sulfate Pulping — Recovery Furnaces
Combustion Turbines — Natural Gas
Combustion Turbines — Oil
By-Product Coke Mfg; Oven Underfiring
Comm./Inst. Incinerators
ICI Boilers— Coal/Stoker
Indust. Incinerators
In-Process Fuel Use; Bituminous Coal
Municipal Waste Combustors
Nitric Acid Manufacturing
Solid Waste Disp; Gov; Other Inc
ICI Boilers— MSW/Stoker
ICI Boilers— Coal/FBC
ICI Boilers— Wood/Bark/Stoker— Large
In-Process; Bituminous Coal; Cement Kilns
In-Process; Bituminous Coal; Lime Kilns
Total Cost
(M 2006$)
$48
$22
$10
$120
$93
$220
$2.3
$34
$0.89
$12
$1.6
$1.1
$110
$25
$31
$3.3
$0.088
$2.1
$24
$55
$0.69
$10
$2.3
$10
$0.42
$0.058
$7.2
$2.5
$0.16
$0.29
$0.13
$8.4
$0.33
$0.034
Table 5a.2: VOC NonEGU Point and Area Source Control Measure Annualized
Engineering Costs
Total Cost
Control Measure
CARB Long-Term Limits
Catalytic Oxidizer
Equipment and Maintenance
Gas Collection (SCAQMD/BAAQMD)
Incineration >100,000 Ibs bread
Low Pressure /Vacuum Relief Valve
OTC Mobile Equipment Repair and
Refinishing Rule
OTC Solvent Cleaning Rule
SCAQMD— Low VOC
Source
Consumer Solvents
Conveyorized Charbroilers
Oil and Natural Gas Production
Municipal Solid Waste Landfill
Bakery Products
Stage II Service Stations
Stage II Service Stations — Underground Tanks
Aircraft Surface Coating
Machn, Electric, Railroad Ctng
Cold Cleaning
Rubber and Plastics Mfg
(M 2006$)
$320
$240
$210
$1.1
$5.8
$16
$15
$2
$12
$16
$2.6
5a-2
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Control Measure
SCAQMD Limits
SCAQMDRulell68
Solvent Utilization
Switch to Emulsified Asphalts
Permanent Total Enclosure (PTE)
Petroleum and Solvent Evaporation
Source
Metal Furniture, Appliances, Parts
Adhe sive s — Industrial
Large Appliances
Metal Furniture
Paper SIC 26
Cutback Asphalt
Fabric Printing, Coating and Dyeing
Paper and Other Web Coating
Printing and Publishing
Surface Coating
Total Cost
(M 2006$)
$19
$69
$4.1
$0.90
$3.5
$0
$0.069
$0.85
$4.4
$0.42
5 a. 1.2 Engineering Costs of Supplemental Controls
5a. 1.1.1 Low Emission Combustion (LEG)
The average cost effectiveness for large 1C engines using LEC technology was estimated to be
$760/ton (ozone season, 2006 dollars).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 $600-1,200/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.
5a. 1.1.2 Leak Detection and Repair (LDAR) for Fugitive Leaks
The control efficiency is 80 percent reduction of VOC at an annualized engineering cost of
$6,900 per ton.
5a. 1.1.3 Enhanced LDAR for Fugitive Leaks
The control efficiency of this measure is estimated at 50 percent at a engineering cost of
$4,360/ton of VOC reduced.2
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
5a-3
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5a.l.l.4 Flare Gas Recovery
The control efficiency of this measure is 98 percent reduction of VOC emissions at an
annualized engineering cost of $3,860/ton. Costs may become negligible as the size of the flare
increases due to recovery credit.3
5a. 1.1.5 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 engineering cost for a continuous flow monitor is $90,000- this
is constant over a variety of cooling tower sizes.
5a.l.l.6 Wastewater Drains and Separators
The control efficiency is 65 percent reduction of VOC emissions at an annualized engineering
cost of $4,360/ton. This is based on actual sampling and cost data for 5 refineries in the Bay Area
Air Quality Management District (BAAQMD).5
5a.l.l.7 Work Practices and Use of Low VOC Coatings in Solvent Utilization and Other
Processes
The control efficiency is 90 percent reduction of VOC emissions at an engineering cost of
$l,200/ton (2006 dollars). This is based on analyzes applied to the 2002 National Emissions
Inventory (NEI) and summarized in the proposed CTG for paper, film and foil coatings, metal
furniture, and large appliances published by US EPA in July 2007.6
5a.2 Engineering Cost Information for EGU Sources
(Full details on controls can be found in Appendix Chapter 3)
3 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.
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.
6 U.S. Environmental Protection Agency. Consumer and Commercial Products: Control
Techniques Guidelines in Lieu of Regulations for Paper, Film, and Foil Coatings; Metal
Furniture Coatings; and Large Appliance Coatings. 40 CFR 59. July 10, 2007. Available on the
Internet at http://www.epa.gov/ttncaaa 1 It 1 /fr_notices/ctg_ccp092807.pdf. It should be noted that
this CTG became final in October 2007.
5a-4
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5a.2.1 Cost of Controls as a Result of Lower Nested Caps within the MWRPO, OTC, and East
Texas and other Local Controls Outside of these Regions Nationwide
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 and in the Proposed
Ozone NAAQS RIA (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. Generally, the incremental impacts of these controls on the power
sector are marginal.
Projected Costs. EPA projects that the annualized incremental cost of the new ozone standard
approach is $0.15 billion in 2020 ($2004)7. The additional annualized costs reflect additional
retrofits (SCR and SNCR) and generation shifts. Annualized cost of CAIR is projected to be
$6.17 billion in 2020 ($2004). The approach applied in this RIA would add $0.15 billion
incremental to this cost. Annualized cost of the EGU controls (in $2004) for the entire country
for fossil units > 25MW is about $5,500. Table 5.a3 below summarizes increase in NOx control
(SCR and SNCR) capacity.
Table 5a.3: NOx Control (SCR and SNCR) Capacity (GWs)
Baseline Modeled Control
CAIR/CAMR/CAVR Strategy
Retrofits (GWs)
SCR 57.0 66.4
SNCR 2.1 4.5
Total Controls (GWs) (Existing + Retrofits + New Units)
SCR 219.6 229.9
SNCR 11.8 15.0
Projected Generation Mix. Coal-fired generation and natural gas/oil-fired generation are
projected to remain almost unchanged. Installation of approximately 9.4 GWs of SCR and 2.4
GWs of SNCR incremental to the base case are projected as a result of the lower sub-regional
caps. There are very small changes in the generation mix. Coal-fired generation decreases about
6,000 GWh (a decrease of approximately 0.1% of the total generation) and gas-fired generation
increases a similar amount. Hydro, nuclear, other, and renewable based generation projected to
remain the same. Projected retirements of both coal and oil/gas units remained same compared to
the base case approach.
7IPM calculates costs in 2004$. All costs presented in Chapter 5 are in 2006$. The costs
presented here were converted to 2006$ prior to being compared or added to other control
measure costs.
5a-5
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Projected Nationwide Retail Electricity Prices. Retail electricity prices are projected to decrease
marginally, 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, OTC, and East Texas, and decrease
elsewhere.
5a.3 Engineering Cost Information for Onroad and Nonroad Mobile Sources
(Full details on controls can be found in Appendix Chapter 3)
Table 5a.4 and 5a.5 summarize the total incremental engineering costs for the modeled control
strategy by mobile source control measure.
Table 5a.4: NOx Mobile Modeled Control Strategy Incremental Annualized Engineering
Costs by Control Measure
Sector Control Measure Total Cost (M$)
Onroad Eliminate Long Duration Idling $—
Low RVP $—
Onroad Retrofit $280
Continuous Inspection and Maintenance $—
Commuter Programs $79
Nonroad Nonroad Retrofit $150
Table 5a.5: VOC Mobile Modeled Control Strategy Incremental Annualized Engineering
Costs by Control Measure
Sector Control Measure Total Cost (M$)
Onroad Low RVP $95
Onroad Retrofits $—
Continuous Inspection and Maintenance $—
Commuter Programs $—
Nonroad Low RVP $36
Nonroad Retrofits & Engine Rebuilds $—
International Aircraft NOx Standard $—
5a. 3.1 Diesel Retrofits and Engine Rebuilds
To calculate engineering 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. One study calculated the average estimated cost of this system to be
$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
5a-6
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scale cost reduction of 33%, which is consistent with trends in other mobile source control
technologies that enter large scale production8.
The rebuild/upgrade kit is applied to nonroad equipment. OTAQ estimates the engineering 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.
The cost effectiveness numbers are presented in Tables 5a.6, 5a.7, and 5a.8.
Table 5a.6: Summary of Cost Effectiveness for Rebuild/Upgrade Kit for Various Nonroad
Vehicles
Nonroad Vehicle
Tractors/Loaders/Backho e s
Excavators
Crawler Tractor/Dozers
Skid Steer Loaders
Agricultural Tractors
Retrofit
Technology
Rebuild/
Upgrade kit
Range of $/ton NOx
Emission Reduced
$1,300 $2,200
$1,100 $4,200
$1,100 $4,200
$1,000 $1,600
$1,200 $4,900
Range of $/ton HC
Emission Reduced
$9,600 $18,900
$8,100 $43,400
$8,300 $43,500
$7,400 $14,800
$9,300 $34,300
Table 5a.7: Summary of Cost Effectiveness for SCR for Various Nonroad Vehicles
Nonroad Vehicle
Retrofit Technology
Range of $/ton NOx
Emission Reduced
Range of $/ton HC
Emission Reduced
Tractors/Loaders/Backho e s
Excavators
Crawler Tractor/Dozers
Skid Steer Loaders
Agricultural Tractors
SCR $2,900
$2,700
$2,800
$2,600
$3,000
$5,300
$10,400
$10,400
$4,000
$7,600
$32,200
$27,400
$27,900
$24,900
$31,200
$63,700
$146,200
$146,700
$52,100
$115,500
Table 5a.8: Summary of Cost Effectiveness for SCR for Various Highway Vehicles
Highway Vehicle
Retrofit Technology
Range of $/ton NOx
Emission Reduced
Range of $/ton HC
Emission Reduced
Class 6&7 Truck
Class 8b Truck
SCR
$5,600
$1,100
$14,100
$2,500
$46,900
$14,900
$126,200
$44,600
5a. 3.2 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 the radio-frequency approach as an example, the costs of periodic testing to Remote OBD
can be compared. Note that this is just an example to illustrate the difference in cost of traditional
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.
5a-7
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periodic I/M and Remote OBD. In this scenario, the assumption is that all 1996 and newer
vehicles currently subject to I/M will participate in a mandatory Remote OBD program. The
national fleet of vehicles subject to I/M are considered over a 10 year period a static set of
vehicles. The estimated cost of setting up and maintaining a data processing and reporting
system is shown in Table 5a.9 and ranges from 500 to $3.00 per vehicle in the program per year.9
For the purposes of this example, we will assume $1 to $3 per vehicle per year. These estimates
assume one record per vehicle per month is actually stored (although additional readings will
usually be taken since vehicles will routinely pass receivers many times a month). This cost does
not include installing Remote OBD on the vehicle or the network of receivers to pick up signals
from equipped vehicles, which is covered by the $50 fee discussed above. If we assume an
average vehicle life span of 14 years,10 with the first test at 4 years of age, the typical vehicle 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 Remote OBD
program, an additional cost of $10-$30 will be incurred for each vehicle over its life to cover
data processing and reporting.
Table 5a.9: Remote OBD VID Service Cost Estimate Per Vehicle Per Year
Number of Vehicles Level 1
in Remote OBD Database Design, Installation, Level 2
Program Maintenance, and Communications Add Reporting
250,000
250,001-500,000
500,001-1,500,000
>1,500,000
$1.50
$1.00
$0.75
$0.50
$2.00
$1.50
$1.00
$0.75
Level 3
Add Auditing
$3.00
$2.75
$2.50
$2.00
In addition to test costs, Remote OBD avoids most of the consumer convenience and indirect
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. Hard data are not available on the actual average time motorists spend
driving to a test station, getting a test, and returning to their point of origin or to their next stop in
a trip chain. In some centralized programs, wait times can be very long. In decentralized
programs, motorists often drop off their vehicle (requiring two trips to the test station). For the
sake of illustrating the convenience costs associated with I/M, a reasonable range for the typical
test cycle is one to two hours. If we assign a cost of $20 per hour11 and a half-gallon of gas (10
miles round trip with an average fuel economy of 20 mpg) at $3 per gallon, the total cost of the
typical cycle is $21.50 to $41.50. Over the life of the vehicle, this would amount to $104 to $208
in a biennial program or $208 to $415 in an annual program. Compare this to the one time
installation trip for Remote OBD at a cost of $21.5 to $41.50, it is clear that substantial savings
are realized.
9 Table provided by Systech International, Inc. and Gordon-Darby, Inc. It should be noted that
careful design of the data management system is necessary to achieve these cost levels.
10 Greenspan, A. & D. Cohen, Motor Vehicle Stocks, Scrappage, and Sales; October 1996
11 This is the same dollar amount assumed in EPA's original Technical Support Document
published along with the 1992 Enhanced I/M Rule.
5a-8
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For the purposes of illustrating the nationwide costs and benefits of doing remote OBD, the
following analysis assumes 100% participation. It is likely, however, that in the short run states
will gradually introduce remote OBD initially on a voluntary basis (except possibly for fleets),
and that participation rates will build over time as motorists recognize the cost and convenience
advantages. Another caveat is that those states that require motorists to get safety checks, the
convenience costs may not be fully realized (see Discussion of Issues, below). Table Sa.10
shows the lifetime inspection and convenience costs of a mandatory, nationwide remote OBD
program versus a periodic OBD program (assuming the current nationwide mix of annual and
biennial testing and current test costs; see Appendix 3) for a static fleet of about 80 million
vehicles. Note that in reality, fleet size generally grows over time and vehicles come and go.
Thus, this is a simplifying assumption for the purposes of illustrating the comparative costs. The
"low" and "high" refer to the range of convenience costs (1 to 2 hours) and oversight costs in the
case of Remote OBD ($l-$3). Current periodic OBD testing costs about $12 billion12 over a 10-
year lifecycle with an additional $9 to $17 billion in convenience costs for a total of $21 to $29
billion. By contrast, Remote OBD has a test and install cost of $4 to $5 billion over the same 10
year period, and a convenience cost of $1 to $2 billion for a total of about $5 to $7 billion. Thus,
nationwide installation of Remote OBD would save the nation's motorists about $16 to $22
billion in inspection and convenience costs over a 10 year period.
Table Sa.10: Range of Lifetime Inspection and Convenience Costs of I/M
Test/Install Cost
Convenience Cost
Total
Low
High
Low
High
Low
High
Periodic OBD
($B 2006)
$12
$12
$9
$17
$21
$29
Remote OBD
($B 2006)
$4
$5
$1
$2
$5
$7
Savings
($B 2006)
$8
$7
$8
$15
$16
$22
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.
Cost-Effectiveness of Measure: $0/ton NOx
5a. 3.3 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.
12 Test volumes and costs were derived from Sierra Research's annual I/M summary for 2005
and updated in some cases by members of the workgroup.
5a-9
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Truck Stop Electrification
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
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.
Mobile Idle Reduction Technologies
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/ton NOx
5a. 3.4 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.13 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
13 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-10
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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.
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.l 1.
Table 5a.ll: Cost-Effectiveness of Best Workplaces for Commuters Type Measures from
the 2002 TRB Study
Regional Rideshare
Vanpool Programs
Park-and-ride lots
Regional TDM
Employer trip reduction programs
Average of All Measures
$/ton (2000$) 1:4
Low
$1,200
$5,200
$8,600
$2,300
$5,800
$4,620
VOC:NOx (reported
High
$16,000
$89,000
$70,700
$33,200
$175,500
$76,900
in the RIA as $/ton NOx)
Median
$7,400
$10,500
$43,000
$12,500
$22,700
$19,200
Cost-Effectiveness of Measure: $19,200/tonNOx
5a.3.5 Reduce Gasoline RVPfrom 7.8 to 7.0
Michigan has conducted the most recent study on the cost of reducing RVP to 7.0. The analysis
was undertaken as part of their proposed revision to Michigan's SIP for their 7.0 low vapor
pressure request for Southeast Michigan. According to their analysis, the costs of the program
are:
5a-ll
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• 0.6-3.0 0 per gallon
• $ 1-$ 11 per vehicle per year
• Total annual cost =$6.9-$48.1 million
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 RVP Standards for Conventional
Gasoline Only. EPA420-B-05-012. November 2005
5a.3.6 Aircraft Engine NOx Standard
The Committee on Aviation Environmental Protection (CAEP) is a committee within the
International Civil Aviation Organization (ICAO) that makes recommendations to the ICAO for
environmental standards for aircraft. ICAO is a United Nations body that sets voluntary
international standards for aircraft. Manufacturers in the U.S. and other countries generally
comply with these standards. A few years ago, ICAO set a new standard (CAEP/6) for NOx
emissions from commercial aircraft to reduce emissions 12% compared to the existing standard.
Compliance with this standard is reflected in the analysis. No costs are attributed to EPA
rulemaking.
5a.4 Characterization of Unknown Controls
5a. 4.1 Supplemental Control Information
Supplemental emission controls came from a variety of sources. The 0.065 ppm standard
geographic areas were broader than those for the modeled control strategy; therefore additional
local known controls were available for mobile sources as well as nonEGU point and Area. In
addition, supplemental controls were achieved through controls applied to select natural gas and
oil fired electric generating units. Other supplemental controls applied to nonEGU point and
Area sources are described in the appendix to Chapter 3 (3a.l.6 Supplemental Controls). Lastly,
for the Eastern Lake Michigan area, the cut point for applying VOC controls was raised from
$5,000/ton (2006$) to $15,000/ton (2006$). Table 5a.l2 summarizes the emission reductions
achieved through the application of supplemental control measures. The total annualized cost of
these measures is broken down by extrapolated cost area in Table 5a.l3 and is presented at a
seven percent discount rate.
5a-12
-------
Table 5a.l2: Supplemental Local Control Measure Emission Reductions [annual tons/year]
Applied for Various Standards8
2020 Extrapolated Cost Area
Ada Co., ID
Atlanta, GA
Baton Rouge, LA
Boston-Lawrence-Worcester, MA
Buffalo-Niagara Falls, NY
Campbell Co., WY
Charlotte-Gastonia-Rock Hill, NC-
SC
Cincinnati-Hamilton, OH-KY-IN
Cleveland- Akron-Lorain, OH
Dallas-Fort Worth, TX
Denver-Boulder-Greeley-Ft
Collins-Love, CO
Detroit- Ann Arbor, MI
Dona Ana CO., NM
Eastern Lake Michigan, IL-IN-WI
El Paso Co., TX
Houston, TX
Huntington-Ashland, WV-KY
Jackson Co., MS
Jefferson Co, NY
Las Vegas, NV
Memphis, TN-AR
Norfolk- Virginia Beach -Newport
News
Northeast Corridor, CT-DE-MD-
NJ-NY-PA
Phoenix-Mesa, AZ
Pittsburgh-Beaver Valley, PA
Richmond-Petersburg, VA
Sacramento Metro, CA
Salt Lake City, UT
San Juan Co., NM
St Louis, MO-IL
Toledo, OH
TOTAL by Pollutant
0.065
NOX
2,600
16,000
8,300
5,200
630
2,600
15,000
9,400
5,100
5,100
7,000
2,100
560
33,000
1,700
49
21,000
7,800
1,100
1,000
14,000
9,100
9,500
5,000
4,500
820
5,600
3,600
16,000
18,000
180
230,000
ppm 0.070 ppm 0.075 ppm
VOC NOX VOC NOX VOC
340
3,500
23 7,200
3,600
140 190
69
3,300
3,700
390 2,400
3,100
4,300
2,100
200
82,000 29,000 75,000 29,000 74,000
53
1,200
410
710
1,300
1,100
2,400
750 8,100 7,600
3,300
1,400
530
5,600 5,600
2,200
190
3,400
50
120,000 58,000 75,000 42,000 74,000
0.079 ppm
NOX VOC
8,200 9,800
5,600
14,000 9,800
"These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
5a-13
-------
Table 5a.l3: Supplemental Local Control Measure Total Annualized Costs [M 2006$]
Applied for Various Standards (ppm) a
2020 Extrapolated Cost Area
Ada Co., ID
Atlanta, GA
Baton Rouge, LA
Boston-Lawrence-Worcester, MA
Buffalo-Niagara Falls, NY
Campbell Co., WY
Charlotte-Gastonia-Rock Hill, NC-SC
Cincinnati-Hamilton, OH-KY-IN
Cleveland-Akron-Lorain, OH
Dallas-Fort Worth, TX
Denver-Boulder-Greeley-Ft Collins-Love, CO
Detroit- Ann Arbor, MI
Dona Ana CO., NM
Eastern Lake Michigan, IL-IN-WI
El Paso Co., TX
Houston, TX
Huntington-Ashland, WV-KY
Jackson Co., MS
Jefferson Co, NY
Las Vegas, NV
Memphis, TN-AR
Norfolk- Virginia Beach-Newport News
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Phoenix-Mesa, AZ
Pittsburgh-Beaver Valley, PA
Richmond-Petersburg, VA
Sacramento Metro, CA
Salt Lake City, UT
San Juan Co., NM
St Louis, MO-IL
Toledo, OH
TOTAL by Pollutant
TOTAL COSTS
0.065
NOX
$6.0
$44
$52
$13
$2.6
$10
$50
$30
$27
$16
$20
$10
$1.9
$130
$8.1
$0.7
$81
$37
$3.9
$3.6
$46
$23
$60
$7.9
$19
$2.0
$13
$11
$54
$72
$0.6
$860
ppm
VOC
$0.8
$5.8
$0.1
$1.7
$0.3
$0.2
$7.6
$7.1
$1.0
$4.9
$0.7
$750
$3.40
$1.50
$1.20
$4.50
$2.40
$3.50
$0.99
$6.80
$3.10
$1.20
$1.70
$0.52
$4.80
$0.17
$820
$1,680
0.070 ppm
NOX VOC
$48
$0.9
$13
$15
$10
$120 $690
$0.6
$55
$13
$280 $690
$970
0.075 ppm 0.079 ppm
NOX VOC NOX VOC
$120 $680 $33 $100
$52
$13 $13
$190 $680 $46 $100
$870 $146
These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
5a.4.2 Modeled Control Strategy Costs Not Needed
As presented in Chapter 4, there were areas in our Modeled control strategy that were "over
controlled." Table 4.8 provides the amount of emissions that were not needed to meet the various
ozone standards in 2020. Given these targets, the modeled control strategy emission reductions
were analyzed to asses what measures could be removed. Table 5a.l4 and 5a.l5 respectively,
show the amount of emission reductions and costs that were removed from the analysis. It was
not possible in all extrapolated cost areas to remove all the emissions presented in Table 4.8.
This was due to the nature of the EGU trading program, as well as the application of measures
statewide for mobile sources. The emission reductions that were not able to be removed from the
analysis of attainment for these standards is presented in Table 5a.l6. it is important to note that
since there was "over control" for 0.070ppm, 0.075 ppm, and 0.079ppm, the full costs of
attainment of these levels of the standard will be an overestimate.
5a-14
-------
Table 5a.l4: Modeled Control Strategy Control Measure Emissions Reductions [annual
tons/year] removed from Extrapolated Analysis for Various Standards
2020 Extrapolated Cost Area
Allegan Co., MI
Atlanta, GA
Baton Rouge, LA
Boston-Lawrence-Worcester-Portsmouth, MA-NH
Buffalo-Niagara Falls, NY
Charlotte-Gastonia-Rock Hill, NC-SC
Cincinnati-Hamilton, OH-KY-IN
Cleveland-Akron-Lorain, OH
Dallas-Fort Worth, TX
Denver, CO
Detroit- Ann Arbor, MI
Eastern Lake Michigan, IL-IN-WI-MI
Hancock, Knox, Lincoln & Waldo Cos, ME
0.070
NOX
22,000
3,200
29,000
12,000
7,800
ppm
VOC
3,400
4,000
3,600
0.075
NOX
2,600
22,000
81,000
12,000
6,000
14,000
29,000
24,000
25,000
15,000
30,000
83
9,300
ppm
VOC
240
3,400
3,800
1,300
4,000
4,100
1,800
4,100
3,600
8
460
0.079
NOX
2,600
22,000
110,000
12,000
7,000
14,000
31,000
30,000
25,000
15,000
30,000
83
9,300
ppm
VOC
240
3,400
1,300
3,800
1,400
4,100
4,600
1,800
4,100
3,600
8
460
Houston-Galveston-Brazoria, TX
Huntington-Ashland, WV-KY
Indianapolis, IN
Jefferson Co., NY
Las Vegas, NV
Muskegon Co., MI
Norfolk- Virginia Beach-Newport News, VA
Northeast Corridor, CT-DE-DC-NY-NJ-PA-VA
Phoenix, AZ
Pittsburgh-Beaver Valley, PA
Providence (All RI), RI
Richmond-Petersburg, VA
Salt Lake City, UT
St Louis, MO-IL
Toledo, OH
Rest of VA
Rest of OH
Rest of MI
Rest of NY
Rest of KY
Rest of PA
TOTALS
1,200
760
290
530
7,600
17,000
1,500
310
7,400
29,000
1,500
1,100
140,000
84
190
90
3,200
690
2,100
3,300
42
82
21,000
1,200
760
1,200
1,500
420
640
7,600
23,000
1,500
310
7,400
29,000
1,500
420
1,100
350,000
84
190
630
1,300
100
85
3,200
1,500
690
58
2,100
3,300
42
35
82
40,000
1,200
760
1,700
1,800
420
780
87,000
7,600
25,000
1,500
300
7,400
29,000
1,600
910
46
420
110
1,100
180
470,000
84
190
660
1,300
100
93
19,000
3,200
1,700
690
64
2,100
3,300
49
50
4
35
9
82
14
62,000
5a-15
-------
Table 5a.l5: Modeled Control Strategy Control Measure Annualized Total Costs [M
2006$] Removed from Extrapolated Analysis for Various Standards
2020 Extrapolated Cost Area
Allegan Co., MI
Atlanta, GA
Baton Rouge, LA
Boston-Lawrence-Worcester-Portsmouth, MA-NH
Buffalo-Niagara Falls, NY
Charlotte-Gastonia-Rock Hill, NC-SC
Cincinnati-Hamilton, OH-KY-IN
Cleveland-Akron-Lorain, OH
Dallas-Fort Worth, TX
Denver, CO
Detroit- Ann Arbor, MI
Eastern Lake Michigan, IL-IN-WI-MI
Hancock, Knox, Lincoln & Waldo Cos, ME
0.070
NOX
$66
$3.8
$99
$41
$19
ppm
VOC
$5.7
$9.0
$4.8
0.075
NOX
$10
$66
$180
$32
$17
$33
$99
$110
$80
$49
$130
$0.2
$24
ppm
VOC
$0.9
$5.7
$2.8
$2.3
$9.0
$12
$2.1
$4.8
$12
$0.9
0.079
NOX
$10
$66
$490
$32
$20
$33
$110
$130
$80
$49
$130
$0.2
$24
ppm
VOC
$0.9
$5.7
$4.1
$2.8
$2.3
$9.0
$12
$2.1
$4.8
$12
$0.9
Houston-Galveston-Brazoria, TX
Huntington-Ashland, WV-KY
Indianapolis, IN
Jefferson Co., NY
Las Vegas, NV
Muskegon Co., MI
Norfolk- Virginia Beach-Newport News, VA
Northeast Corridor, CT-DE-DC-NY-NJ-PA-VA
Phoenix, AZ
Pittsburgh-Beaver Valley, PA
Providence (All RI), RI
Richmond-Petersburg, VA
Salt Lake City, UT
St Louis, MO-IL
Toledo, OH
Rest of VA
Rest of OH
Rest of MI
Rest of NY
Rest of KY
Rest of PA
TOTAL by Pollutant
TOTAL
$4.8
$3.4
$0.9
$1.4
$20
$48
$3.0
$0.6
$18
$130
$6.0
$3.1
$460
$0.2
$0.8
$0.4
$6.7
$0.3
$1.7
$4.9
$0.2
$35
$500
$4.8
$3.4
$4.5
$4.7
$1.2
$2.1
$20
$82
$3.0
$0.6
$18
$130
$6.0
$1.2
$3.1
$1,100
$0.2
$0.8
$1.2
$4.4
$0.4
$0.3
$6.7
$3.9
$0.3
$0.3
$1.7
$4.9
$0.2
$78
$1,200
$4.8
$3.4
$5.8
$5.8
$1.2
$2.6
$300
$20
$89
$3.0
$0.8
$18
$130
$6.3
$2.7
$0.2
$1.2
$0.3
$3.1
$0.5
$1,800
$0.2
$0.8
$1.2
$4.4
$0.4
$0.3
$21
$6.7
$3.9
$0.3
$0.3
$1.7
$4.9
$0.2
$100
$1,900
5a-16
-------
Table 5a.l6: Emission Reductions Not Needed [annual tons/year] Remaining After
Removing Control Measures Not Needed to Meet Various Ozone Standards a
2020 Extrapolated Cost Area
Allegan Co., MI
Atlanta, GA
Baton Rouge, LA
Boston-Lawrence-Worcester-Portsmouth, MA-NH
Buffalo-Niagara Falls, NY
Charlotte-Gastonia-Rock Hill, NC-SC
Cincinnati-Hamilton, OH-KY-IN
Cleveland-Akron-Lorain, OH
Dallas-Fort Worth, TX
Denver, CO
Detroit- Ann Arbor, MI
0.070 ppm
NOX
8,700
(10)
12,000
4,300
0.075 ppm
NOX
460
8,700
(1)
1,800
1,000
(40)
12,000
8,900
18,000
11,000
20,000
0.079 ppm
NOX
460
8,700
7,606
1,800
(40)
9,000
14,100
18,000
11,000
20,000
Eastern Lake Michigan, IL-IN-WI-MI
Hancock, Knox, Lincoln & Waldo Cos, ME
2
6
6
Houston-Galveston-Brazoria, TX
Huntington-Ashland, WV-KY
Indianapolis, IN
Jefferson Co., NY
Las Vegas, NV
Muskegon Co., MI
Norfolk- Virginia Beach-Newport News, VA
Northeast Corridor, CT-DE-DC-NY-NJ-PA-VA
Phoenix, AZ
Pittsburgh-Beaver Valley, PA
Providence (All RI), RI
Richmond-Petersburg, VA
10
5,800
130
(8)
(90)
(6)
(4)
(5)
10
5,800
700
6,400
0
140
(90)
6,700
(4)
(5)
10
5,800
250
6,100
0
11,242
(90)
4,400
(4)
8
Salt Lake City, UT
St Louis, MO-IL
Toledo, OH
TOTALS
2
110
30,000
1,200
110
100,000
1,200
120,000
a All estimates rounded to two significant figures. As such, totals will not sum down columns.
5a. 4.3 Fixed Cost Approach Detailed Results and Sensitivities
The range of values from the fixed cost ($10,000/ton) to the fixed cost ($20,000/ton) is presented
in Figure 5a.l. You can see that as the amount of unknown emissions increases for the alternate
primary standards, the range of total extrapolated cost values becomes larger. The detailed costs
by geographic area and alternate primary standard are presented in Tables 5a.l7 through 5a.20.
5a-17
-------
$55
$50
$45
Figure 5a.l: Fixed Cost Approach Sensitivity Analysis Results Ranges
IO
o
° $35
s.
•5 $30
O
o
IS $25
_
i.$20
$15
$10
$5
0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
• Fixed Cost ($15,000/ton) • Fixed Cost ($10,000/ton) • Fixed Cost ($20,000/ton)
Table 5a.l7: Extrapolated Cost by Geographic Area to Meet 0.065 ppm Alternate Standard
Fixed Cost Approach a'b
2020 Extrapolated Cost Area
Ada Co., ID
Atlanta, GA
Baton Rouge, LA
Boston-Lawrence-Worcester, MA
Buffalo-Niagara Falls, NY
Campbell Co., WY
Charlotte-Gastonia-Rock Hill, NC-SC
Cleveland- Akron-Lorain, OH
Dallas-Fort Worth, TX
Denver-Boulder-Greeley-Ft Collins-Love, CO
Detroit- Ann Arbor, MI
Dona Ana CO., NM
Eastern Lake Michigan, IL-IN-WI
Houston, TX
Huntington- Ashland, WV-KY
Jefferson Co, NY
Las Vegas, NV
Memphis, TN-AR
Norfolk- Virginia Beach-Newport News
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Fixed Cost Approach Extrapolated
($10,000/ton)
$28
$55
$1,600
$85
$180
$0.5
$470
$780
$480
$16
$1,000
$4.1
$6,400
$1,800
$8.0
$62
$39
$11
$210
$3,400
($15,000/ton)
$42
$83
$2,500
$130
$270
$0.8
$710
$1,200
$720
$25
$1,500
$6.2
$9,600
$2,700
$12
$93
$59
$16
$310
$5,100
Costs (M 2006$)
($20,000/ton)
$55
$110
$3,300
$170
$360
$1.0
$940
$1,600
$960
$33
$2,000
$8.2
$13,000
$3,600
$16
$120
$78
$21
$410
$6,800
5a-18
-------
2020 Extrapolated Cost Area Fixed Cost Approach Extrapolated Costs (M 2006$)
($10,000/ton) ($15,000/ton) ($20,000/ton)
Pittsburgh-Beaver Valley, PA $130 $190 $250
Sacramento Metro, CA $1,300 $2,000 $2,600
Salt Lake City, UT $4.3 $6.5 $8.6
San Juan Co., NM $13 $19 $25
St Louis, MO-IL $170 $250 $330
Total Extrapolated Cost $18,000 $27,000 $36,000
"All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
Table 5a.l8: Extrapolated Cost by Geographic Area to Meet 0.070 ppm Alternate Standard
Fixed Cost Approach8'b
2020 Extrapolated Cost Area
Extrapolated
Costs (M 2006$)
($10,000/ton) ($15,000/ton) ($20,000/ton)
Baton Rouge, LA
Buffalo-Niagara Falls, NY
Cleveland- Akron-Lorain, OH
Detroit- Ann Arbor, MI
Eastern Lake Michigan, IL-IN-WI
Houston, TX
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Sacramento Metro, CA
Total Extrapolated Cost
$490
$37
$110
$87
$7,000
$1,600
$2,200
$890
$10,000
$740
$56
$170
$130
$7,500
$2,300
$3,300
$1,300
$16,000
$990
$75
$220
$170
$10,000
$3,100
$4,400
$1,800
$21,000
a All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
Table 5a.l9: Extrapolated Cost by Geographic Area to Meet 0.075 ppm Alternate Standard
Fixed Cost Approach8'b
2020 Extrapolated Cost Area
Eastern Lake Michigan, IL-IN-WI
Houston, TX
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Sacramento Metro, CA
Total Extrapolated Cost
Extrapolated Costs (M 2006$)
($10,000/ton)
$740
$1,200
$650
$440
$3,400
($15,000/ton)
$1,800
$1,600
$980
$660
$5,100
($20,000/ton)
$1,500
$2,500
$1,300
$6,800
"All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
5a-19
-------
Table 5a.20: Extrapolated Cost by Geographic Area to Meet 0.079 ppm Alternate Standard
Fixed Cost Approach8'b
2020 Extrapolated Cost Area
Extrapolated Costs (Thousands 2006$)
($10,000/ton)
($15,000/ton)
($20,000/ton)
Houston, TX
$810
$1,200
$1,600
Sacramento Metro, CA
$18
$37
Total Extrapolated Costs (NOX + VOC)
$830
$1,200
$1,700
a All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
5 a. 4.4 Hybrid Approach
5a.4.4.1 Hybrid Approach Equations
We begin with a linear increasing marginal cost (MC) curve represented here as
MC = b + 2cQ
Where (b+2cQ) is a nonnegative function, and b is the intercept and 2c represents the slope, and
Q is the quantity of emissions reduced from unknown controls.
For geographic areas that have reached the baseline in the modeled control strategy the total cost
(TC) is calculated by taking the integral of the marginal cost function from 0 of emission
reductions from unknown controls to all emissions reductions needed from unknown controls
(0-
Figure 5a.2: Example Extrapolated Marginal Cost for Geographic Areas Meeting the
Baseline in the Modeled Control Strategy
Linear MC Curve
o
o
I
o
o
o
o
u
Area=
Emission reductions from unknown controls
5a-20
-------
Evaluate (b + 2cQ]dx = (bQ + cQ2 + a)- (bO + cO2 + a)
Where MC is nonnegative for 0 < (b + 2cQ) < Q the definite integral of MC equals the area of the
shaded region, which is the total cost (TC)
TC = bQ + cQ2
To calculate average cost (AC) divide TC by Q
TC
Q Q
AC=b+cQ
Replace the intercept b with the national cost/ton jumping off point (TV), and the slope (c) of the
NM
average cost curve with - where Mis the multiplier, and E0 represents the known emission
Eo
reductions from the modeled control strategy. This slope represents; control technology changes,
energy technology changes, relative price changes, technological innovation, and geographic
distribution of sources with uncontrolled emissions, and emission reductions from known
controls. Lastly, Q is represented by E, (the total unknown emission reductions)
.t
l£. J
_£•
If we replace — - with R, and pull out TV the equation becomes
Eo
= N(1+RM)
For geographic areas that have not reached the baseline in the modeled control strategy (Houston
and parts of California), the total cost is calculated between Qo and Q, where Qo represents the
quantity of emission reductions from unknown controls to reach the current ozone standard.
Therefore the quantity of emissions that are extrapolated is
Q-Qo.
5a-21
-------
Figure 5a.3: Example Extrapolated Marginal Cost for Geographic Areas Not Meeting the
Baseline in the Modeled Control Strategy
Linear MC Curve
Area=
2cQ)dx
o Qo Q
Emission reductions from unknown controls
Evaluate f(b + 2cQ]dx = (bQ + cQ2 + a)- (bQ0 +cQ02 + a)
Where MC is nonnegative for Q0 <(b + 2cQ) < Q the definite integral of MC equals the area of
the shaded region, which is the total cost (TC)
= bQ-bQ0+cQ2-cQ02
TC=b(Q-Q0)
To calculate average cost (AC) divide TC by (Q - Qo)
TC
~Q
c(Q2-Q02)
(fi-fio)
Replace the intercept b with the national cost/ton jumping off point (TV), and the slope (c) with
NM
where Mis the multiplier, and E0 represents the known emission reductions from the
Eo
modeled control strategy. This slope represents; control technology changes, energy technology
changes, relative price changes, technological innovation, and geographic distribution of sources
with uncontrolled emissions, and emission reductions from known controls. Lastly, Q is
represented by E, (the total unknown emission reductions), and Qo is represented by
EOM (unknown emission reductions to reach the current standard)
5a-22
-------
AC = N-
NM
v o J
E E
If we replace —- with R, replace °84 with RS and pull out TV the equation becomes
-^0 J-'Q
= N(1+RM+RSM)
Figure 5a.4 shows a graphic al example that in the hybrid approach the total cost will be identical
if calculated using the marginal cost framework or average cost framework. The total cost using
the marginal cost framework is the grey area plus the blue area. The total cost using the average
cost framework is the grey area plus the green area. By the nature of geometry, the blue area and
the green area are equal. Therefore the total cost under either framework is equal.
Figure 5a.4: Example Marginal Cost versus Average Cost for the Hybrid Approach
200
400 600 800
Unknown Emission Reductions (Tons)
1000
1200
' Marginal Cost ^™ Average Costl
5a.4.4.3 Hybrid Approach Detailed Results by Geographic Area
Tables 5a.21 through 5a.24 present the detailed results by geographic area and standard for the
hybrid approach (mid).
5a-23
-------
Table 5a.21: Extrapolated Cost by Geographic Area to Meet 0.065 ppm Alternate Primary
Standard Using Hybrid Approach (Mid) a'b'c
2020 Extrapolated Cost Area
Ada Co., ID
Atlanta, GA
Baton Rouge, LA
Boston-Lawrence-Worcester, MA
Buffalo -Niagara Falls, NY
Campbell Co., WY
Charlotte-Gastonia-Rock Hill, NC-SC
Cleveland- Akron-Lorain, OH
Dallas-Fort Worth, TX
Denver-Boulder-Greeley-Ft Collins-Love, CO
Detroit- Ann Arbor, MI
Dona Ana CO., NM
^ ^ T „,„ H ,;„,.; TT T^T ,WT NOX
El Paso Co, TX
Houston, TXd
Huntington- Ashland, WV-KY
Jefferson Co, NY
Las Vegas, NV
Memphis, TN-AR
Norfolk- Virginia Beach-Newport News
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Pittsburgh-Beaver Valley, PA
Sacramento Metro, CA
Salt Lake City, UT
San Juan Co, NM
St Louis, MO-IL
Total Extrapolated Cost
Ratio of Unknown
to Known Emission
Reductions
0.81
0.10
0.95
0.36
1.60
0.01
1.20
1.11
0.85
0.04
1.65
0.27
2.00
2.19
0.00
1.78
0.02
1.18
0.37
0.04
0.64
2.15
0.35
1.90
0.03
0.07
0.23
Average
Cost/Ton
(2006$)
$18,000
$15,000
$18,000
$16,000
$21,000
$15,000
$19,000
$19,000
$18,000
$15,000
$21,000
$16,000
$22,000
$23,000
$15,000
$24,000
$15,000
$19,000
$16,000
$15,000
$17,000
$23,000
$16,000
$22,000
$15,000
$15,000
$16,000
Hybrid Approach
Extrapolated Cost
(M 2006$)
$49
$85
$3,000
$140
$370
$0.75
$910
$1,500
$860
$25
$2,100
$6.6
$14,000
$4,200
$12
$120
$64
$16
$360
$7,700
$210
$2,800
$6.5
$19
$260
$39,000
a All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
c Houston did not reach the baseline, and therefore has an additional R to reach the current standard of
0.62.
d Houston did not reach the baseline, and therefore has an additional R to reach the current standard of
0.62.
5a-24
-------
Table 5a.22: Extrapolated Cost by Geographic Area to Meet 0.070 ppm Alternate Primary
Standard Using Hybrid Approach (Mid) a'b
2020 Extrapolated Cost Area Ratio of Unknown Average
to Known Emission Cost/Ton
Reductions (2006$)
Baton Rouge, LA
Buffalo-Niagara Falls, NY
Cleveland- Akron-Lorain, OH
Detroit- Ann Arbor, MI
Eastern Lake Michigan, IL-IN-WI NOX
VOC
Houston, TXC
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Sacramento Metro, CA
Total Extrapolated Cost
0.31
0.39
0.18
0.14
1.65
1.86
1.63
1.47
1.30
$16,000
$16,000
$16,000
$16,000
$21,000
$22,000
$23,000
$20,000
$20,000
Hybrid Approach
Extrapolated Cost
(M 2006$)
$800
$61
$170
$130
$11,000
$3,600
$4,400
$1,700
$22,000
"All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
c Houston did not reach the baseline, and therefore has an additional R to reach the current standard of
0.62.
Table 5a.23: Extrapolated Cost by Geographic Area to Meet 0.075 ppm Alternate Primary
Standard Using Hybrid Approach (Mid) a'b
2020 Extrapolated Cost Area
r T 1 AT' 1 ' TT TT.T TWT NOX
Houston, TXC
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Sacramento Metro, CA
Total Extrapolated Cost
Ratio of Unknown
to Known Emission
Reductions
0.50
0.36
1.36
0.46
0.67
Average
Cost/Ton
(2006$)
$17,000
$16,000
$22,000
$17,000
$17,000
Hybrid Approach
Extrapolated Cost
(M 2006$)
$2,000
$2,400
$1,100
$770
$6,300
"All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
c Houston did not reach the baseline, and therefore has an additional R to reach the current standard of
0.62.
Table 5a.24: Extrapolated Cost by Geographic Area to Meet 0.075 ppm Alternate Primary
Standard Using Hybrid Approach (Mid) a'b'c
2020 Extrapolated Cost Area
Houston, TXd
Sacramento Metro, CA
Total Extrapolated Cost
Ratio of Unknown
to Known Emission
Reductions
1.17
0.07
Average
Cost/Ton
(2006$)
$21,000
$15,000
Hybrid Approach
Extrapolated Cost
(M 2006$)
$1,700
$28
$1,800
a All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
c These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
5a-25
-------
d Houston did not reach the baseline, and therefore has an additional R to reach the current standard of
0.62.
5a.4.4.3 Hybrid Approach Sensitivity Analysis Results
Sensitivity analysis was performed on the variable M to explore the degree that this variable
effects total costs of attainment across alternate primary standards. The lowest value of M (0.12),
as well as the highest (0.47) was used. The detailed results of these sensitivity analyses are
presented in Tables 5a.25 through 5a.29. Figure 5a.5 shows graphically the range of values for
national extrapolated costs for the four levels of the alternate primary standard analyzed.
Figure 5a.5: Hybrid Approach Sensitivity Analysis Results Ranges
$55
$50
$45
,-. $40
to
o
CM $35
00.
I $30
8
| $25
15
| $20
" $15
$10
$5
0.065 ppm 0.070 ppm 0.075 ppm
• Hybrid Mid Range • Hybrid Lower Bound • Hybrid Upper Bound
i
0.079 ppm
5a-26
-------
Table 5a.25: Extrapolated Cost by Geographic Area to Meet 0.065 ppm Alternate Standard
Hybrid Approach Sensitivities a'b'c
Hybrid Approach (Low)
2020 Extrapolated Cost Area
Ada Co., ID
Atlanta, GA
Baton Rouge, LA
Boston-Lawrence-Worcester, MA
Buffalo -Niagara Falls, NY
Campbell Co., WY
Charlotte -Gastonia-Rock Hill, NC-SC
Cleveland- Akron-Lorain, OH
Dallas-Fort Worth, TX
Denver-Boulder-Greeley-Ft Collins-Love, CO
Detroit- Ann Arbor, MI
Dona Ana CO., NM
^ ^ T „!„ A,r:,,l,: TT nvr nn NOX
Houston, TX
Huntington- Ashland, WV-KY
Jefferson Co, NY
Las Vegas, NV
Memphis, TN-AR
Norfolk- Virginia Beach-Newport News
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Pittsburgh-Beaver Valley, PA
Sacramento Metro, CA
Salt Lake City, UT
San Juan Co., NM
St Louis, MO-IL
Total Extrapolated Cost
Hybrid
Average Approach
Cost/Ton Extrapolated
(2006$) Cost
(M 2006$)
$16,000
$15,000
$17,000
$16,000
$18,000
$15,000
$17,000
$17,000
$17,000
$15,000
$18,000
$15,000
$19,000
$19,000
$19,000
$15,000
$17,000
$16,000
$15,000
$16,000
$19,000
$16,000
$18,000
$15,000
$15,000
$15,000
$46
$84
$2,700
$130
$320
$0.75
$810
$1,300
$790
$25
$1,800
$6.4
ti ° noo
$3,400
$12
$110
$61
$16
$330
$6,400
$200
$2,400
$6.5
$19
$250
$33,000
Hybrid Approach (High)
Hybrid
Average Approach
Cost/Ton Extrapolated
(2006$) Cost
(M 2006$)
$21,000
$16,000
$22,000
$18,000
$26,000
$15,000
$23,000
$23,000
$21,000
$15,000
$27,000
$17,000
$29,000
$31,000
$32,000
$15,000
$23,000
$18,000
$15,000
$20,000
$30,000
$17,000
$28,000
$15,000
$16,000
$17,000
$57
$86
$3,600
$150
$480
$0.75
$1,100
$1,800
$1,000
$25
$2,700
$6.9
$19,000
$5,700
$12
$140
$69
$16
$400
$10,000
$220
$3,700
$6.6
$19
$280
$51,000
"All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
c These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
5a-27
-------
Table 5a.26: Extrapolated Cost by Geographic Area to Meet 0.070 ppm Alternate Standard
Hybrid Approach Sensitivities a'b'c
2020 Extrapolated Cost Area
Baton Rouge, LA
Buffalo-Niagara Falls, NY
Cleveland- Akron-Lorain, OH
Detroit- Ann Arbor, MI
r 1 1 A I' 1 ' TT TTVT TWT NOX
Houston, TX
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Sacramento Metro, CA
Total Extrapolated Cost
Hybrid Approach (Low)
Average
Cost/Ton
(2006$)
$16,000
$16,000
$15,000
$15,000
$18,000
$18,000
$19,000
$18,000
$17,000
Hybrid
Approach
Extrapolated
Cost (M
2006$)
$770
$59
$170
$130
$9,000 •
$3,000
$3,800
$1,500
$19,000
Hybrid Approach (High)
Average
Cost/Ton
(2006$)
$17,000
$18,000
$16,000
$16,000
$27,000
$28,000
$31,000
$25,000
$24,000
Hybrid
Approach
Extrapolated
Cost (M
2006$)
$850
$67
$180
$140
$14,000
$4,800
$5,500
$2,100
$27,000
"All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
c These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
Table 5a.27: Extrapolated Cost by Geographic Area to Meet 0.075 ppm Alternate Standard
Hybrid Approach Sensitivities a'b'c
2020 Extrapolated Cost Area
r T 1 AJ- 1,' TT nvr ™rr NOX
Houston, TX
Northeast Corridor, CT-DE-MD-NJ-NY-PA
Sacramento Metro, CA
Total Extrapolated Cost
Hybrid Approach (Low)
Average
Cost/Ton
(2006$)
$16,000
$16,000
$19,000
$16,000
$16,000
Hybrid
Approach
Extrapolated
Cost (M
2006$)
$2,000 •
$2,000
$1,000
$710
$5,700
Hybrid Approach (High)
Average
Cost/Ton
(2006$)
$19,000
$18,000
$29,000
$18,000
$20,000
Hybrid
Approach
Extrapolated
Cost (M
2006$)
$2,300
$3,100
$1,200
$870
$7,500
a All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
c These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
5a-28
-------
5a-29
-------
Table 5a.28: Extrapolated Cost by Geographic Area to Meet 0.079 ppm Alternate Standard
Hybrid Approach Sensitivities a'b'c
2020 Extrapolated Cost Area Hybrid Approach (Low)
Houston, TX
Sacramento Metro, CA
Total Extrapolated Cost
Average
Cost/Ton
(2006$)
$18,000
$15,000
Hybrid
Approach
Extrapolated
Cost (M
2006$)
$1,500
$28
$1,500
Hybrid Approach (High)
Average
Cost/Ton
(2006$)
$28,000
$15,000
Hybrid
Approach
Extrapolated
Cost (M
2006$)
$2,200
$29
$2,300
"All estimates rounded to two significant figures. As such, totals will not sum down columns.
b These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins.
Please see Appendix 7b for analysis of these areas.
c These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
5a-30
-------
Appendix 5b: Economic Impact of Modeled Controls
5b.l Synopsis
This appendix presents the economic impact results of the illustrative modeled control strategy.
Given the possible impacts of ozone precursor control measures on manufacturing industries, the
transportation sector, electricity generators, consumers, and U.S. Gross Domestic Product (GDP)
as a whole, we believe it is important to gauge the extent to which other parts of the economy
might also be affected by implementing an alternate primary ozone standard. Therefore, an
analysis of the economy-wide effects of implementing the alternate standard is conducted by
inputting estimated direct engineering costs to EPA's computable general equilibrium model
(Economic Model for Policy Analysis, or EMPAX-CGE).
Before the appendix commences with a background and description of EMPAX-CGE followed
by a presentation of the results, three points are highlighted below that will assist the reader in
interpreting the economic impacts and relating these impacts to the modeled control strategy
engineering costs presented in Chapter 5.
(a) The selection criteria for the modeled control strategy, and its related compliance costs, is
designed to select the least cost controls, from an engineering cost standpoint, that
generate the greatest ozone reductions, but not necessarily the lowest economic impact.
Therefore, although the control strategy is selected to reduce ozone at the lowest
engineering cost, it does not necessarily represent the lowest impact strategy from an
economic impact standpoint. Thus, while this economic impact analysis presents results
for the modeled control strategy approach detailed in Chapter 3 of the RIA, it should not
be viewed as reflecting the approach with the smallest economic impact. Instead, the
results should be viewed as guidance or useful information for states preparing their
implementation plans. It is likely that states will design implementation plans that apply
alternative control strategies and in some cases design plans that take into account
secondary impacts to industries and consumers within their boundaries. In such a case,
the end result would be a set of State Implementation Plans (SIPs) that could be more
economically optimal and may have lower impacts than those described below.
(b) The costs analyzed in this economic impact appendix include only the modeled
engineering costs detailed in Chapter 5 for the alternate primary ozone standard. Thus, the
economic impacts presented in this appendix reflect only the modeled engineering costs.
Not included in estimating these economic impacts are the extrapolated cost estimates
detailed in Chapter 5. This is because the extrapolated cost estimates are not available by
industry, a necessary input to the operation of EMPAX. Therefore, the engineering costs
for the illustrative modeled control strategy that are input to EMPAX in this analysis are
those that reflect the $2.8 billion (2006 dollars) in 2020 for the application of known
controls.
(c) In the interest of learning how possible changes in manufactured-goods prices might
affect businesses and households, along with how changes in electricity/energy prices
might affect industry groups that are large energy users, EPA employed the "EMPAX-
CGE" computable general equilibrium (CGE) model, which has been peer reviewed and
5b-l
-------
used in recent analyses of the Clean Air Interstate Rule (CAIR), the Clean Air Visibility
Rule (CAVR), and the PM2.5 NAAQS. As with similar models, EMPAX-CGE focuses on
the cost-side of spillover effects on the economy. This implies its estimated industry-
sector impacts may be overstated because EMPAX-CGE is not configured to capture the
beneficial economic consequences of the increased labor availability and productivity
expected to result from air quality improvements. EPA continues to investigate the
feasibility of incorporating labor productivity gains and other beneficial effects of air
quality improvements in CGE models and will incorporate labor productivity gains and
other effects of air quality model improvements within future versions of EMPAX-CGE
as is feasible.
EMPAX-CGE may also be used to generate the social costs associated with a regulation. The
social costs associated with a regulation are those costs that result from the reaction of
consumers and producers to the direct engineering costs of a regulation. The welfare of
consumers and producers may be affected positively or negatively depending on the nature of the
regulation, and this welfare change is a measure of the social costs. Such a welfare change could
result from higher prices on output, which may lead to less demand by consumers and less output
by producers. These changes due to the higher output prices are estimated as part of social costs.
We apply the equivalent variation (EV) approach to estimate social costs using EMPAX-CGE in
this RIA. This the first application of EV to estimate social costs as part of analysis using a CGE
model in an RIA of this type. We explain how the EV approach can be used to estimate social
costs, and why it is a better approach to estimating social cost than one using GDP in section
5b.4.4 of this appendix . Given a substantial number of caveats on results generated by EMPAX-
CGE, we include social cost and do not compare these costs to the monetized benefits estimates
provided later in this RIA. We also intend to solicit review and the advice of the SAB before we
use this approach to estimate social costs before conducting any future economic impact analyses
using CGE models.
5b.2 Background
To complement the analysis of effects on specific manufacturing sectors from AirControlNET
4.1, implications for mobile sources from MOBILE 6.2, NMIM, and NONROAD, and changes
in electricity generation from IPM, the macroeconomic implications of the modeled control
strategy have been estimated using EPA's EMPAX-CGE model. The focus of this component of
the Ozone RIA is on examining the sectoral and regional distribution of economic effects across
the U.S. economy. This section briefly discusses the BMP AX model and the approach used to
incorporate findings from other models in EMPAX-CGE.
5b.2.1 Background and Summary of EMPAX-CGE Model
BMP AX was first developed in 2000 to support economic analysis of EPA's maximum
achievable control technology (MACT) rules for combustion sources (reciprocating internal
combustion engines, boilers, and turbines). The initial framework consisted of a national
multimarket partial-equilibrium model with linkages only between manufacturing industries and
the energy sector. Modified versions of EMPAX were subsequently used to analyze economic
5b-2
-------
impacts of strategies for improving air quality in the Southern Appalachian mountain region as
part of efforts associated with the Southern Appalachian Mountain Initiative (SAMI).
Recent work on BMP AX has extended its scope to cover all aspects of the U.S. economy at a
regional level in either static or dynamic modes. Although major regulations directly affect a
large number of industries, substantial indirect impacts may also result from changes in
production, input use, income, and household consumption patterns. Consequently, BMP AX
now includes economic linkages among all industrial and energy sectors as well as households
that supply factors of production such as labor and purchase goods (i.e., a CGE framework). This
gives the version of BMP AX called EMPAX-CGE the ability to trace economic impacts as they
are transmitted throughout the economy and allows it to provide critical insights to policy makers
evaluating the magnitude and distribution of costs associated with environmental policies. The
dynamic version of EMPAX-CGE employed n this analysis, and its data sources, are described
later on in Section 5b.3. EMPAX-CGE underwent peer review in 2006, and the results of that
peer review can be found on the EPA Web site.1 We have incorporated a number of
recommendations offered in the peer review, including updating the energy production and
consumption data (from DOE) to allow for more up to date characterization of energy markets
and revising the uncompensated labor supply elasticities used in the model.
5b.2.2 EMPAX Modeling Methodology for the Modeled Control Strategy
EMPAX-CGE can be used to analyze a wide array of policy issues and is capable of estimating
how a change in a single part (or multiple parts) of the economy will influence producers and
consumers across the United States. However, some types of policies, including the Ozone
National Ambient Air Quality Standard, are difficult to capture adequately within a CGE
structure because of the boiler- and firm-specific nature of emission reduction costs.
Consequently, an interface has been developed that allows linkages between EMPAX-CGE and
the detailed technology models discussed in Chapter 5 (AirControlNET 4.1, MOBILE6.2,
NMIM, and IPM 3.0). These linkages give the combined modeling system the advantages of
technology detail and broad macroeconomic coverage, thereby permitting EMPAX-CGE to
investigate economy-wide policy implications.
The technology models mentioned above estimate engineering cost changes by industry and
region of the United States for the sectors of the economy affected by the alternate primary
ozone standard. In order for EMPAX-CGE to effectively incorporate these additional costs, they
have to be expressed in terms of the productive inputs used in CGE models (i.e., capital, labor,
and material inputs produced by other industries). Rather than assume the costs represent a
proportional scaling up of all inputs, Nestor and Pasurka (1995) data on purchases made by
industries for environmental-protection reasons are used to allocate these additional expenditures
across inputs within EMPAX-CGE. Once these expenditures are specified, the incremental
engineering costs from the technology models can be used to adjust the production technologies
in the CGE model. Also, for the modeled control strategy, linkages are made between EMPAX-
1 http://www.epa.gov/ttn/ecas/models/empax_peer review comments responses.pdf.
5b-3
-------
CGE and IPM to handle specific IPM findings related to resource costs and fuel consumption in
electricity generation.2
5b.3 EMPAX-CGE Model Description: General Model Structure
This section provides additional details on the EMPAX-CGE model structure, data sources, and
assumptions. The version of EMPAX-CGE used in this analysis is a dynamic, intertemporally
optimizing model that solves in five year intervals from 2005 to 2050. It uses the classical
Arrow- Debreu general equilibrium framework wherein households maximize utility subject to
budget constraints, and firms maximize profits subject to technology constraints. The model
structure, in which agents are assumed to have perfect foresight and maximize utility across all
time periods, allows agents to modify behavior in anticipation of future policy changes, unlike
dynamic recursive models that assume agents do not react until a policy has been implemented.
Nested CES functions are used to portray substitution possibilities available to producers and
consumers. Figure 5b.l illustrates this general framework and gives a broad characterization of
the model.3 Along with the underlying data, these nesting structures and associated substitution
elasticities determine the effects that will be estimated for policies. These nesting structures and
elasticities used in EMPAX-CGE are generally based on the Emissions Prediction and Policy
Analysis (EPPA) Model developed at the Massachusetts Institute of Technology (Paltsev et al.,
2005). This updated version of the EPPA model incorporates some extensions over the EPPA
version documented in Babiker et al. (2001) such as specification of transportation purchases by
households. These updates to transportation choices have been incorporated in this version of
EMPAX-CGE as shown on the left-hand side of Figure 5b.l. Although the two models continue
to have different focuses (EPPA is a model focused on analysis of national-level climate change
policies while BMP AX is a model focused on regional-level analysis of pollution control
policies), both are intended to simulate how agents will respond to environmental policies and as
such EPPA provides a strong basis to develop the theoretical structure of EMPAX-CGE.
Given this basic similarity, EMPAX-CGE has adopted a comparable structure. EMPAX-CGE is
programmed in the GAMS4 language (Generalized Algebraic Modeling System) and solved as a
mixed complementarity problem (MCP)5 using MPSGE software (Mathematical Programming
2 See Appendix E in the RIA for the Final CAIR rule for additional discussion of these IPM-
EMPAX linkages (http://www.epa.gov/interstateairquality/technical.html).
3 Although it is not illustrated in Figure 6.1, some differences across industries exist in their
handling of energy inputs. In addition, the agriculture and fossil-fuel sectors in EMPAX-CGE
contain equations that account for the presence of fixed inputs to production (land and fossil-fuel
resources, respectively).
4 See Brooke, Kendrick, and Meeraus (1996) for a description of GAMS (http://www.gams.com/).
5 Solving EMPAX-CGE as a MCP problem implies that complementary slackness is a feature of
the equilibrium solution. In other words, any firm in operation will earn zero economic profits
and any unprofitable firms will cease operations. Similarly, for any commodity with a positive
price, supply will equal demand, or conversely any good in excess supply will have a zero price.
5b-4
-------
Subsystem for General Equilibrium).6 The PATH solver from GAMS is used to solve the MCP
equations generated by MPSGE.
See Rutherford (1999) for MPSGE documentation (http://www.mpsge.org/).
5b-5
-------
Figure 5b.l: General Production and Consumption Nesting Structure in EMPAX-CGE
Utility
Consumption is a Cobb-
Douglas composite of
transportation plus goods.
Household utility is a CES function
of consumption and leisure.
Consumption
Leisure
Transportation is a
CES composite of
personal vehicle
transport and
purchased transport.
Personal vehicles
use fuel and
goods/services.
Transportation
Consumption Goods
Petroleum
Personal
Transport
Consumption of
energy is a CES
composite of
energy and other
consumption
goods.
(30 types)
Services
Manufactured
Goods
Domestic goods are a CES
composite of local goods and
goods from other U.S. regions.
Most producer goods use
fixed proportions of
intermediate inputs and KLE.
Local
Output
Regional
Output
Each consumption good
is a CES composite of
foreign and domestically
produced goods.
Imports are a CES
composite across
foreign supply sources.
KLE is a CES
composite of
energy (E) and
value-added (KL).
Energy (E) is a
CES composite of 5
energy types. The
structure of this
function varies across
industries.
KLE
Intermediates
A\
Intermediate inputs are the
30 types of non-energy
goods, in fixed proportion
for each industry.
Energy
Value Added
Energy
(5 Types)
Capital
Value added is a Cobb-
Douglas composite of
capital and labor (KL).
Labor
5b-6
-------
5b.3.1 Data Sources
The economic data come from state level information provided by the Minnesota IMPLAN
Group7 and energy data come from EIA.8 Forecasts for economic growth are taken from EIA's
Annual Energy Outlook 2007 (AEO) and Global Insight.9 Although IMPLAN data contain
information on the value of energy production and consumption in dollars, these data are
replaced with EIA data since the policies being investigated by EMPAX-CGE typically focus on
energy markets, making it essential to include the best possible characterization of these markets
in the model. Although the IMPLAN data are developed from a variety of government data
sources at the U.S. Bureau of Economic Analysis and U.S. Bureau of Labor Statistics, these data
do not always agree with energy information collected by EIA directly from manufacturers and
electric utilities.
EMPAX-CGE combines these economic and energy data to create a balanced social accounting
matrix (SAM) that provides a baseline characterization of the economy. The SAM contains data
on the value of output in each sector, payments for factors of production and intermediate inputs
by each sector, household income and consumption, government purchases, investment, and
trade flows. A balanced SAM for the year 2005 consistent with the desired sectoral and regional
aggregation is produced using procedures developed by Babiker and Rutherford (1997) and
described in Rutherford and Paltsev (2000). This methodology relies on optimization techniques
to maintain the calculated energy statistics (in both quantity and value terms) while minimizing
any changes needed in the other economic data to create a new balanced SAM based on
EIA/IMPLAN data for the baseline model year (in essence, industry production functions are
adjusted, if necessary, to account for discrepancies between EIA energy data and IMPLAN
economic data by matching the energy data and adjusting the use of non-energy inputs so that the
industry is in balance, i.e., the value of inputs to production equals the value of output).
These data are used to define economic conditions in 50 states within the United States (plus the
District of Columbia), each of which contains 80 industries. Prior to solving EMPAX-CGE, the
states and industries are aggregated up to the categories to be included in the analysis.
Aggregated regions have been selected to capture important differences across the country in
electricity generation technologies, while industry aggregations are controlled by available
energy consumption data.
7 See http://www.implan.com/index.html for a description of the Minnesota IMPLAN Group and
its data.
8 These EIA sources include AEO 2007, the Manufacturing Energy Consumption Survey, State
Energy Data Report, State Energy Price and Expenditure Report, and various annual industry
profiles.
9 See http://www.globalinsight.com/ProductsServices/ProductDetaill 100.htm for a description of
the Global Insight U.S. State Forecasting Service.
5b-7
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Table 5b.l presents the 35 industry categories included in EMPAX-CGE for policy analysis.
Their focus is on maintaining as much detail in the energy intensive and manufacturing sectors10
as is
Table 5b.l: Industries in Dynamic EMPAX-CGE
EMPAX Industry NAICS Classifications
Energy
Coal 2121
Crude Oil3 211111,4861
Electricity (fossil and nonfossil) 2211
Natural Gas 211112, 2212, 4862
Petroleum Refining" 324,48691
General
Agriculture 11
Mining (w/o coal, crude, gas) 21
Construction 23
Manufacturing
Food Products 311
Textiles and Apparel 313, 314, 315, 316
Lumber 321
Paper and Allied 322
Printing 323
Chemicals 325
Plastic & Rubber 326
Glass 3272
Cement 3273
Other Minerals 3271, 3274, 3279
Iron and Steel 3311,3312
Aluminum 3313
Other Primary Metals 3314, 3316
Fabricated Metal Products 332
Manufacturing Equipment 333
Computers & Communication Equipment 334
Electronic Equipment 335
Transportation Equipment 336
Miscellaneous remaining 312, 337, 339
Services
Wholesale & Retail Trade 42, 44, 45
Transportation13 481-488
Information 51
Finance & Real Estate 52, 54
Business/Professional 53, 55, 56
Education (w/public) 61
Health Care (w/public) 62
Other Services 71,72,81,92
"Although NAICS 211111 covers crude oil and gas extraction, the gas component of this sector is moved
to the natural gas industry.
b The petroleum refining industry provided oil in delivered terms, which includes pipeline transport.
c Transportation does not include NAICS 4862 (natural gas distribution), which is part of the natural gas
industry.
10 Energy-intensive industry categories are based on EIA definitions of energy-intensive
manufacturers in the Assumptions for the Annual Energy Outlook 2007.
5b-8
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allowed by available energy consumption data and computational limits of dynamic CGE
models. In addition, the electricity industry is separated into fossil fuel generation and nonfossil
generation, which is necessary because many electricity policies affect only fossil fired
electricity.
Figure 5b.2 shows the five regions included in EMPAX-CGE in this analysis, which have been
defined based on the expected regional distribution of policy impacts, availability of economic
and energy data, and computational limits on model size. These regions have been constructed
from the underlying state-level database designed to follow, as closely as possible, the electricity
market regions defined by the North American Electric Reliability Council (NERC).11
Figure 5b.2: Regions Defined in Dynamic EMPAX-CGE
5b.3.2 Production Functions
All productive markets are assumed to be perfectly competitive and have production
technologies that exhibit constant returns to scale, except for the agriculture and natural resource
extracting sectors, which have decreasing returns to scale because they use factors in fixed
supply (land and fossil fuels, respectively). The electricity industry is separated into two distinct
sectors: fossil fuel generation and nonfossil generation. This allows tracking of variables such as
heat rates for fossil fired utilities (Btus of energy input per kilowatt hour of electricity output).
11 Economic data and information on nonelectricity energy markets are generally available only
at the state level, which necessitates an approximation of the NERC regions that follows state
boundaries.
5b-9
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All markets, must clear (i.e., supply must equal demand in every sector) in every period, and the
income of each agent in the model must equal their factor endowments plus any net transfers.
Markets in EMPAX clear in the 5 regions included in the dynamic model. Along with the
underlying data, the nesting structures shown in Figure 5b-l and associated substitution
elasticities define current production technologies and possible alternatives.
5b.3.3 Utility Functions
Each region in the dynamic version of EMPAX-CGE contains four representative households,
classified by income, that maximize intertemporal utility over all time periods in the model
subject to budget constraints, where the income groups are:
• $0 to $14,999,
• $15,000 to $29,999,
• $30,000 to $49,999, and
• $50,000 and above.12
The percentage of U.S. households in each of these household classes is: 13% - $0 to $14,999;
18% - $15,000 to $29,999, 20% - $30,000 to $49,999, and 49% - $50,000 and above.13 These
representative households are endowed with factors of production including labor, capital,
natural resources, and land inputs to agricultural production. Factor prices are equal to the
marginal revenue received by firms from employing an additional unit of labor or capital. The
value of factors owned by each representative household depends on factor use implied by
production within each region. Income from sales of these productive factors is allocated to
purchases of consumption goods to maximize welfare.
Within each time period, intratemporal utility received by a household is formed from
consumption of goods and leisure. All consumption goods are combined using a Cobb Douglas
structure to form an aggregate consumption good. This composite good is then combined with
leisure time to produce household utility. The elasticity of substitution between consumption
goods and leisure depends on empirical estimates of labor supply elasticities and indicates how
willing households are to trade off leisure time for consumption. Over time, households consider
the discounted present value of utility received from all periods' consumption of goods and
leisure.
12 Computational limitations on EMPAX-CGE limit the number of household classes to four, and
this is due to the complex modeling needed for the dynamic version of the model. We intend to
review and potentially increase the number of household classes in future version of EMPAX-
CGE, and will better reflect higher income household classes as part of that effort.
13 U.S. Census Bureau, Current Population Survey, 2007 Annual Social and
Economic Supplement. HINC-01. Selected Characteristics of Households, by
Total Money Income. Found on the Internet at
(http://pubdb3.census.gov/macro/032007/hhinc/newOl 001.htm)
5b-10
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Following standard conventions of CGE models, factors of production are assumed to be
intersectorally mobile within regions, but migration of productive factors is not allowed across
regions. This assumption is necessary to calculate welfare changes for the representative
household located in each region in EMPAX-CGE. EMPAX-CGE also assumes that ownership
of natural resources and capital embodied in nonfossil electricity generation is spread across the
United States through capital markets.
5b.3.4 Trade
In EMPAX-CGE, all goods and services are assumed to be composite, differentiated
"Armington" goods made up of locally manufactured commodities and imported goods. Output
of local industries is initially separated into output destined for local consumption by producers
or households and output destined for export. This local output is then combined with goods
from other regions in the United States using Armington trade elasticities that indicate agents
make relatively little distinction between output from firms located within their region and
output from firms in other regions within the United States. Finally, the domestic composite
goods are aggregated with imports from foreign sources using lower trade elasticities to capture
the fact that foreign imports are more differentiated from domestic output than are imports from
other regional suppliers in the United States.
5b.3.5 Tax Rates and Distortions
Taxes and associated distortions in economic behavior have been included in EMPAX-CGE
because theoretical and empirical literature found that taxes can substantially alter estimated
policy costs (e.g., Bovenberg and Goulder [1996]; Goulder and Williams [2003]). For example,
existing labor taxes distort economic choices because they encourage people to work below the
levels they would choose in an economy without labor taxes and reduces economic efficiency14.
When environmental policies raise production costs for firms and the price of goods and
services, people may choose to work even less; the additional economic costs from this decision
has been described as the "tax interaction" effect.
EMPAX-CGE considers these interaction effects by utilizing tax data from several sources and
by explicitly modeling household labor supply decisions. The IMPLAN economic database
provides information on taxes such as indirect business taxes (all sales and excise taxes) and
social security taxes. However, since IMPLAN reports factor payments for labor and capital at
their gross of tax values, we use additional data sources to determine personal income and capital
tax rates. Information from the TAXSIM model at the National Bureau of Economic Research
(Feenberg and Courts, 1993), along with user cost of capital calculations from Fullerton and
Rogers (1993), are used to establish tax rates. Elasticity parameters describing labor supply
choice ultimately determine how distortionary existing taxes are in the CGE model. EMPAX-
CGE currently uses elasticities based on the relevant literature (i.e., 0.4 for the compensated
labor supply elasticity and 0.15 for the uncompensated labor supply elasticity). These elasticity
14 These efficiency losses are often expressed in terms of overall marginal excess burden; the cost
associated with raising an additional dollar of tax revenue. Estimates range from $0.10 to $0.35
per dollar (Ballard et al, 1985).
5b-ll
-------
values give an overall marginal excess burden associated with the existing tax structure of
approximately 0.3.
5b.3.6 Intertemporal Dynamics and Economic Growth
There are four sources of economic growth in EMPAX-CGE: technological change from
improvements in energy efficiency, growth in the available labor supply (from both population
growth and changes in labor productivity), increases in stocks of natural resources, and capital
accumulation. Energy consumption per unit of output tends to decline over time because of
improvements in production technologies and energy conservation. These changes in energy use
per unit of output are modeled as AEEIs (Autonomous Energy Efficiency Improvements), which
are used to replicate energy consumption forecasts by industry and fuel from EIA.15 The AEEI
values provide the means for matching expected trends in energy consumption that have been
taken from the AEO forecasts. They alter the amount of energy needed to produce a given
quantity of output by incorporating improvements in energy efficiency and conservation. Labor
force and regional economic growth, electricity generation, changes in available natural
resources, and resource prices are also based on the AEO forecasts.
Savings provide the basis for capital formation and are motivated through people's expectations
about future needs for capital. Savings and investment decisions made by households determine
aggregate capital stocks in EMPAX-CGE. The IMPLAN dataset provides details on the types of
goods and services used to produce the investment goods underlying each region's capital stocks.
Adjustment dynamics associated with formation of capital are controlled by using quadratic
adjustment costs experienced when installing new capital, which imply that real costs are
experienced to build and install new capital equipment.
Prior to investigating policy scenarios, it is necessary to establish a baseline path for the
economy that incorporates economic growth and technology changes that are expected to occur
in the absence of the policy actions. Beginning from the initial balanced SAM dataset, the model
is calibrated to replicate forecasts from the AEO 2007. Upon incorporating these forecasts,
EMPAX-CGE is solved to generate a baseline based on them through 2030. Once this baseline is
established, it is possible to run the "counterfactual" policy experiments discussed below.
5b.3.7 Caveats Regarding EMPAX Modeling and the Results of this Analysis
The results generated by EMPAX-CGE that are provided in this PJA appendix, which include
estimates of price and output changes by industry and energy impacts, have a number of caveats
and limitations associated with them that one should be cognizant of. They are as follows:
As mentioned above, the current EMPAX-CGE model only considers the costs of policies and
ignores the beneficial economic consequences of air quality improvements such as increased
labor availability and productivity. If these health-related improvements were included in the
model, any production decreases estimated by the model might be partially offset.
15 See Babiker et al. (2001) for a discussion of how this methodology was used in the EPPA
model (EPPA assumes that AEEI parameters are the same across all industries in a country,
while AEEI values in EMPAX-CGE are industry specific).
5b-12
-------
The extent of these potential benefits, along with current estimates of GDP impacts, depend on
the labor supply elasticities in the model that have been chosen from the CGE literature on labor
markets and tax distortions as discussed above. More flexible labor supply elasticities would
allow additional response in labor markets to policy impacts, potentially with both positive and
negative effects. Other critical assumptions in EMPAX-CGE largely revolve around the
production technologies and input substitution options, which are based on the MIT EPPA
model.
It is also highly uncertain as to which industries will be affected in the future when moving
beyond where known engineering controls can currently apply. This mix of industries affected
may be different than those current controls apply to, and tighter ozone standards may lead to
consideration of controls to industries previously unaffected by measures related to ozone
implementation. Ozone SIPs sometimes provide a "black box" (as per Section 182(e)(5) of the
Clean Air Act) for additional controls to be supplied by unknown measures, and individual
sectors where these controls may apply are never specified.16
BMP AX requires identification of costs by industry (by NAICS or SIC code) in order to operate.
The capability of BMP AX to generate impacts is thus dependent on the extent to which the input
costs by industry are defined. With a lack of knowledge of affected industries, there is also a lack
of knowledge of affected consumers or households (thus, no way to estimate completely
household welfare impacts).
Results from BMP AX are strongly influenced by elements in its baseline data set such as energy
production and consumption data taken from the Energy Information Administration's Annual
Energy Outlook (AEO). The current EMPAX version uses such energy data taken from the latest
AEO version available (2007). This version of the AEO does not incorporate effects on energy
production and consumption data associated with provision of the Energy Independence and
Security Act of 2007 (or EISA) signed by the President on December 19, 2007.17 Such effects
include increased biofuels production, increased vehicle fuel efficiency, and new minimum
energy efficiency standards for many electric appliances and products. The effects of EISA will
be incorporated in a revised version of the AEO that will be released to the public in March,
2008.18
EMPAX keeps the location of labor constant in response to a supply shock. Hence, labor is not
allowed to migrate between regions based on changes in wage rates. By not allowing labor
migration, some inaccuracies in estimated changes in labor and wage rates may take place.
These inaccuracies in estimated labor and wage rate changes may offset the inclusion of other
16 Section 182 (e) (5) of the Clean Air Act allows estimation of reductions (or so-called "black
box" measures) in ozone SIPs that are not allocated by source category or sectors. An example of
this is on pp. 6-12 and 6-13 in the 2003 California Air Quality Management Plan for Ozone and
PM found at http://www.aqmd.gov/aqmp/docs/2003AQMPChap6.pdf.
17 The entire text of this legislation can be read at http://frwebgate.access.gpo.gov/cgi-
bin/getdoc.cgi?dbname= 110 cong bills&docid=f:h6enr.txt.pdf. This is found at the Government
Printing Office's official web site.
18 This is noted on the Energy Information Administration web site at
http://www.eia.doe.gov/oiaf/aeo/index.html.
5b-13
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effects into BMP AX that would lead to reduced economic impact estimates such as how
improvements in air quality lead to increased labor productivity.
Other caveats that can typically be applied to CGE analyses, including this one, cover issues
such as transitional dynamics in the economy. CGE models such as BMP AX, which assume
foresight on the part of businesses and households, will allow agents to adapt to anticipated
policy impacts coming in the future. These adaptations may occur more quickly than if agents
adopted a wait-and-see approach to new regulations. The alternative, recursive-dynamic structure
used in CGE models such as MIT EPPA imply that no anticipation or adjustments will occur
until the policy is in place, which tends to overstate the costs of policies.
Finally, in addition to transition dynamics, while CGE models are ideally suited for analyzing
broad, economy-wide impacts of policies, they are not able to examine firm-specific impacts on
profits/losses or estimate how particular types of disadvantaged households may be affected by
policies. Similarly, environmental justice concerns may not be fully addressed.
5b.4 EMPAX-CGE Results for the Modeled Control Strategy
This section compares the modeled control strategy to a baseline for the economy that includes
the current ozone standard (effectively, 0.084 ppm), along with other rules used to form the basis
of the AEO 2007 forecasts by El A such as the Clean Air Interstate Rule (CAIR) and the Clean
Air Mercury Rule (CAMR). Impacts are measured assuming a 2020 implementation year and are
the result of engineering costs described in Section 5.1. Thus, the following graphs compare the
modeled control strategy to a baseline economic growth path in EMPAX-CGE that includes the
current ozone standard and currently implemented legislation in the AEO 2007 forecasts.
5b.4.1 Projected Energy Impacts and Impacts on U.S. Industries of Incremental Costs From
Modeled Control Strategy
Impacts of the modeled control strategy on manufacturing costs can affect output and prices of
all industries in the EMPAX-CGE model. These effects may increase or decrease output and/or
revenue, depending on their implications for production costs and technologies and shifts in
household demands. In general, the impacts on energy producers and other industries will be
dependent on the control strategy and follow a pattern similar to the stringency of the ozone
standard.
As shown in Figure 5b.3, impacts on energy and industrial output quantities are generally small
across all industries for modeled control strategy. Outside of the energy-intensive sectors,19
estimated changes in output of most manufactured goods are less than five one-hundredths of
one percent (0.05%). Effects on coal output are somewhat higher, but impacts on other types of
energy producers are low and can be positive or negative, which limits any spillover effects to
other businesses and households. These changes in output quantities are different than any
19 Energy-intensive sectors include food processing, pulp and paper, chemicals, glass, cement,
iron and steel, and aluminum manufacturing. The definition of energy-intensive sectors applied
in EMPAX-CGE is identical to that used by the U.S. Energy Information Administration (ElA)
for their AEO modeling.
5b-14
-------
changes in gross output revenues, which include effects of changes in both quantity and output
prices (which reflect changes in production costs) and may be either positive or negative,
regardless of changes in output quantities. Also, across the economy as a whole, although there is
almost no change in the quantity of services produced, these changes in output can potentially be
larger in absolute terms than any changes in energy-related industries, which are much smaller
than service industries in the U.S. economy. For more information on energy impacts at a
nationwide level, please refer to Chapter 8 where we provide energy impact results in response to
Executive Order 13211.
5b-15
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Figure 5b.3: Modeled Control Strategy Impacts on U.S. Domestic Output Quantity, 2020
Percent Change from Baseline
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
0.50%
1.00%
Coal
Crude Oil
Electricity
Natural Gas
Petroleum
Agriculture
Mining (other)
Construction
Food
Textiles & Apparel
Lumber
Pulp & Paper
Printing
Chemicals
Plastics & Rubber
Glass
Cement
Other Minerals
Iron & Steel
Aluminum
Other Primary Metals
Fabricated Metal Products
Machinery & Equipment
Computer Equipment
Electronic Equipment
Transportation Equipment
Miscellaneous Manufacturing
Wholesale & Retail Trade
Transportation Services
Information Services
Finance & Real Estate
Business Services
Education
Health Services
Other Services
Source: EMPAX-CGE.
5b-16
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As described in Chapter 3, selected control options for the modeled control strategy involve
additional actions by electric utilities, which tend to slightly decrease coal consumption
(influencing U.S. coal production) and increase natural gas use. EMPAX-CGE uses these
findings on coal and gas use directly from the IPM model (as described in Appendix E in the
RIA for the Final CAIR rule).20 As part of its economy-wide estimation, EMPAX-CGE then
considers how these changes in electricity markets affect other consumers of energy. Outside of
electricity, other energy-producing industries also engage in additional measures, which can
affect energy users such as energy-intensive manufacturers. Cement, chemicals and glass
production are influenced by direct control costs on their respective industries and any changes
in energy markets. Note, however, that across energy-intensive industries as a group, output
quantities decline on average by less than a two-tenths of a percent (<0.2%).
5b.4.2 Projected Regional Impacts
Regional effects will tend to show variation that does not appear at the national level. To
examine how such variations might occur in response to the modeled control strategy, this
section presents findings for selected industries and groups for the five regions in EMPAX-CGE.
These divergences between average national impacts and regional effects arise from several
sources such as:
• differences in control measures from the AirControlNET, IPM, and MOBILE models;
• differences in regional mixes of generation technologies (coal, gas, oil, and nonfossil
use), which may be averaged out at a national level;
• differences in regional production and consumption patterns for electricity and
nonelectricity energy goods;
• differences in industrial composition of regional economies;
• differences in household consumption patterns; and
• differences in regional growth forecasts.
Figure 5b.4 first presents regional impacts on industrial output from the modeled control
strategy. Except for energy producers (shown in Figure 5b.5), this graph summarizes results for
all the industries shown in Figure 5b.3, where similar industries are grouped together to facilitate
the presentation. Aside from energy-intensive manufacturing (illustrated in more detail in
Figure 5b.6), the adjustments in output are on the order of a few one-hundredths of one percent.
20 See http://www.epa.gov/interstateairquality/technical.html for additional discussion of these
linkages.
5b-17
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Figure 5b.4: Modeled Control Strategy Impacts on Regional Energy-Intensive Output
Quantities, 2020
Percent Change from Baseline
-2.0%
-1.0%
0.0%
1.0%
L
[
[
m
L
1=
•
[
C
c
:
i
Northeast
South
a*
Midwest a
3
u
Plains 'C
M
-------
Figure 5b.5: Modeled Control Strategy Impacts on Regional Industry Output Quantities, 2020
-2.0%
Percent Change from Baseline
-1.0% 0.0%
L
Source: EMPAX-CGE.
1.0%
Northeast
South
Midwest —
o
Plains u
West
US
Northeast
South
Midwest
Plains
T3
a
West
US
Northeast
South
Midwest
Plains
w
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US
Northeast
South
Midwest „
a
Plains
West
a
US
Northeast
South
Midwest
Plains
West
US
|
oj
"3
is
5b-19
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Figure 5b.6: Modeled Control Strategy Impacts on Regional Energy Output Quantities, 2020
Percent Change from Baseline
-2.0%
-1.0%
0.0%
1.0%
1
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5b-20
-------
Unlike the broader industries, energy production that is more directly affected by the standards shows more regional variation than
seen in the U.S. results in Figure 5b.3. However, all impacts are still less than one percent (<1.0%) across the regions with most
adjustments smaller than that. Under the modeled control strategy, coal consumption by electric utilities tends to decrease slightly in
2020, except in the South. Natural gas use in electricity rises, but is offset by declines in other parts of the economy. Such results
reflect the impacts from applying the EGU control strategy discussed in Chapter 3 of the RIA. This control strategy is applying
primarily to EGUs in the Northeast and is applied only to coal-fired units. This leads to the costs of power generation becoming
relatively cheaper in the South relative to the Northeast. Also, there are few controls applied to coal-fired EGUs in the South. The net
impact from these effects is that EMPAX estimates that coal-fired power generation in the South decreases while it increases in the
Northeast. These EMPAX results are shown in percentage and physical terms in Table 5b-2. The crude oil and petroleum refining
industries react to the alternative standard by minor changes in output, although refining in some regions rises in cases where they may
have a small comparative advantage as fewer refiners need to install additional controls.
Table 5b.2 Results from EMPAX in 2020 for Changes in Fuel Use and Generation by EGUs in Northeast and South Regions Under
Modeled Control Strategy
Region
North-
east
South
Baseline
Use of
Coal
(trillion
BTU) by
EGUs
2,622
7,689
Use of
Coal
Under
Modeled
Control
Strategy
(trillion
BTU)
2,598
7,728
Percent
Difference
in Coal
Use (%)
-0.9
0.5
Baseline
Use of
Natural
Gas
(trillion
BTU) by
EGUs
871
1,497
Use of
Natural
Gas
Under
Modeled
Control
Strategy
(trillion
BTU)
870
1,506
Percent
Difference
in Natural
Gas Use
(%)
-0.1
0.1
Electricity
Generation
in Baseline
(millions
kWh)
681,046
1,392,374
Electricity
Generation
Under
Modeled
Control
Strategy
(millions
kWh)
687,175
1,339,336
Percent
Difference
in
Electricity
Generation
(%)
0.9
-0.5
BTU = British Thermal Unit
kWh = kilowatt-hour
5b-21
-------
Figure 5b.6 illustrates how changes in energy markets may affect those industries particularly
reliant on energy inputs to their production processes. As with the U.S. average results from
Figure 5b.3, even though the energy-intensive sectors show more regional variation, based on
differences in production methods and changes in manufacturing costs, the majority of the
impacts are on the order of a few tenths of one percent. However, there are measurable impacts
in the output of specific industries. Under the modeled control strategy, energy-intensive output
tends to be redistributed slightly from eastern to western regions as decreases in industries such
as glass manufacturing in some regions are partially offset by increases in other regions.21
When examining such findings, however, it is important to note that these impacts and
redistributions are directly related to the specific control strategy assumed in this illustrative
analysis. As previously stated, these results represent the impact of the modeled control strategy
presented by EPA. It is expected that States will evaluate the best strategies for achieving
compliance and may choose options that could significantly alter these regional effects.
Therefore, SIPs will likely be different than the strategy developed in this PJA and could be
designed to alleviate any disproportionate impacts on sensitive industries. For example, given the
impact on glass and cement production, assumed with this scenario, affected States may design
SIP strategies that mitigate the impact on these particular industries, perhaps distributing costs
more uniformly among all sectors.
5b.4.3 ProjectedMacroeconomic Impact: GDP
The combination of economic interactions affecting business and household behavior will be
reflected in the changes in GDP estimated by a CGE model. The impacts on GDP are provided
here only for illustration of the macroeconomic impacts of this standard. They are not meant to
illustrate the social costs associated with the modeled control strategy applied to attain the 0.070
alternate Ozone standard
Figure 5b.7 illustrates GDP in the EMPAX-CGE model's baseline forecast and the modeled
control strategy. As shown, the estimated GDP impact is negligible and, in fact, it is not possible
to adjust the scale of the graph to the point where the two lines do not overlap. Projected
decrease in GDP for the modeled control strategy is roughly 0.02 percent (0.02%), respectively,
for the year 2020. This is equivalent to a $3.6 billion decrease in GDP during the implementation
year. In absolute terms, these estimated changes in U.S. GDP are extremely small relative to the
total size of the economy. Even these small costs could be reduced if the CGE analyses were
extended to include benefits associated with any alternate primary ozone standard such as
improvements in labor productivity from environmental improvements.
21 Redistribution of production will also tend to occur among states in each region, with some
states' increasing output to offset any declines in neighboring states.
5b-22
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Figure 5b.7: Change in U.S. GDP Compared to EMPAX-CGE Baseline
$27,50ft
$25,006
an 2006$)
$22,506
GDP
(Billion 2006$)
$20,OOC
$17,506
$15,041
$15,000
*— EMPAX Baseline
n Modeled Control Strategy
$26,629 (Ozone 084)-P $26,625
$23,333 (Ozone OS4)Jf $23,330
$20,218 (Ozone 084)^$20,214
$17,355 (Ozone 084]^M Ji'7 7,354
Ozone 084)
$12,5001
2010 2015 2020 2025
Source: Department of Energy, Energy Information Administration; EMPAX-CGE
2030
5b.4.4 Social Cost Approaches and Estimates
To provide an estimate of the social costs associated with the modeled control strategy, EMPAX-
CGE monetizes welfare changes from the general equilibrium simulation using Hicksian
equivalent variation (EV), which is related in concept to the producer/consumer surplus
measures used in partial-equilibrium models. EV is a long-recognized technique to estimate
welfare gains and losses in economic theory, having been developed by Sir John Hicks in 1939.22
EV provides an estimate of the change in income that would provide an equivalent change in
household welfare as the policy being considered and includes changes in utility households
receive from both consumption and leisure time.23 It is a technique that is widely used by
economists to measure welfare change. For example, Chipman and Moore (1980) showed that
22 Hicks introduced this concept into economic theory in his book "Value and Capital: An inquiry
into some fundamental principles of economic theory," published in 1939.
23 Including leisure time in the model and household decisions allows the labor supply to expand
or contract in response to changes in wage rates, etc. It is also essential when modeling
interactions between tax interactions and the economy.
5b-23
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EV is appropriate for welfare comparisons.24 However, as calculated using EMPAX-CGE
currently, it excludes measures of the standard's environmental benefits (e.g., environment,
public health, and labor productivity). In addition, these social cost estimates from EMPAX-
CGE do not incorporate extrapolated costs since these costs do not have a clear link to specific
industries. The general equilibrium model estimates that the relative change in infinite-horizon25
and average annual welfare losses are extremely small (approximately 0.025%). Over the 2005-
2020 time horizon used in EMPAX-CGE for this analysis, the social costs are 93 percent of the
engineering costs for the illustrative modeled control strategy when estimated in present value
terms (2006 dollars).26'27 We estimate social costs using a 5 percent real interest rate to discount
future production and consumption as per EPA guidance from the SAB provided in 2003.
We use EV to provide an estimate of social costs in this analysis instead of a metric such as GDP
since changes in GDP are a poor measure of impacts on consumer welfare. Although GDP is a
common metric among policymakers for expressing "costs to society," it is a poor measure of
"social costs." GDP as a measure of welfare has been criticized for many years by different
economists. Much of that criticism is well summarized in a response to the 2004 Draft
Thompson Report to Congress prepared by Arik Levinson and quoted as follows: "... GDP
growth is a poor measure of welfare. It measures the flow of economic activity rather than the
flow of assets. If there is over-fishing, regulations that reduce fish catch will reduce GDP in the
short run, but increase long-run economic prosperity... Finally, GDP excludes non-traded
benefits: environmental quality, health, workplace safety..." 28 Changes in household
consumption are much closer to changes in the welfare of households (ignoring leisure) than
changes in GDP. For example, since consumption is around two-thirds of GDP, a ballpark
estimate might be that any changes in consumption will only be around two-thirds as large in
dollar terms as changes in GDP. GDP also does not account for the value of leisure, which is
accounted for directly in estimates of welfare impacts using an EV approach as mentioned
above. Regarding exports and imports, GDP does account directly for the effect of export and
24 Chipman, John S., and James C. Moore. 1980. Compensating Variation, Consumer's Surplus,
and Welfare. American Economic Review 70 (5): 933-49.
25 By infinite horizon, what is meant is an infinite number of time horizons. Since it is not
computationally feasible for EMPAX-CGE to provide estimates to this many time horizons, the
model approximates an infinite horizon. Turn to p. 6-9 of the EMPAX-CGE documentation at
http://www.epa.gov/ttnecas 1 /models/empax model documentation.pdf for details.
26 It should be noted that we will not compare this social cost estimate with the benefits estimates
for alternate primary standards presented later in this RIA. We do not make this comparison for
two key reasons: 1) the lack of linkage between air quality changes and effect categories such as
labor productivity and health care costs among households; and 2) our inability to provide
extrapolated costs by industry to serve as input to BMP AX.
27 As mentioned in Chapter 5, the engineering cost estimate for the modeled control strategy of
$2.8 billion (2006$) is calculated using the Equivalent Uniform Annual Cost (EUAC) method.
The EUAC method does not generate the present value of the annual costs of controls on a year-
by-year basis from 2005 to 2020.
28 Levinson, Arik. Response to 2004 Draft Report to Congress on the Costs and Benefits of
Federal Regulation and Unfunded Mandates on State, Local, and Tribal Entities (or "Thompson
Report"). Submitted to the U.S. Office of Management and Budget. June 2, 2004. Found on the
Internet at http://www.whitehouse.gov/omb/inforeg/2004 cb/c.pdf.
5b-24
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imports upon U.S. expenditure on goods and services. The effect of purchasing imports upon
household welfare as measured by the EV approach is accounted for indirectly through changes
in household consumption and does not account for changes due to exports. We conclude
thatThus, GDP is a poor metric for estimating welfare impacts in comparison to the EV
approach, and therefore social costs.29
As part of being a dynamic, forward-looking model, BMP AX uses an interest rate to place a
value on the future (including both the benefits of consumption and costs of production). We
have been using a 5% real interest rate, based on the MIT EPPA model referred to earlier in this
chapter and SAB guidance as discussed in U.S. EPA (2003). This interest rate will form the basis
for how the model reacts to any engineering costs it sees coming in the future. Following the
guidance provided in OMB's Circular A-4, we also provide social cost estimates in this appendix
over the same 2005-2020 time horizon that reflect a 3% real interest rate, and a 7% real interest
rate. These social cost estimates are 90 and 91 percent, respectively, of the engineering costs
when costs are calculated in present value terms.
This is the first application of EV to estimate social costs as part of analysis using a CGE model
in an PJA of this type. We intend to solicit review and advice from the SAB before its use in
future economic impact analyses using CGE models.
5b.5 References
Babiker, M.H., and T.F. Rutherford. 1997. "Input Output and General Equilibrium Estimates of
Embodied CC^: A Data Set and Static Framework for Assessment." University of Colorado at
Boulder, Working Paper 97 2. Available at http://www.mpsge.org/mainpage/mpsge.htm.
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 .
Ballard, C. J. Shoven, and J. Whalley. 1985. "General Equilibrium Computations of the Marginal
Welfare Costs of Taxation in the United States" American Economic Review 75(1): 128-138.
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.
29 We provide estimates of the changes in GDP in 2020 from implementation of the modeled
control strategy in Chapter 6, but only to provide information on this commonly known
macroeconomic metric.
5b-25
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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.
Global Insight. 2007. "U.S. State, Metropolitan Area, and County-level Forecasting Services."
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
Minnesota IMPLAN Group. 2006. State Level Data for 2004. 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.
Paltsev, S., J.M. Reilly, H.D. Jacoby, R.S. Eckaus, J. McFarland, M. Sarofim, M. Asadoorian,
and M. Babiker. 2005. "The MIT Emissions Prediction and Policy Analysis (EPPA) Model:
Version 4." MIT Joint Program on the Science and Policy of Global Change, Report No. 125.
Cambridge, MA. .
Rutherford, T.F. 1999. "Applied General Equilibrium Modeling with MPSGE as a GAMS
Subsystem: An Overview of the Modeling Framework and Syntax." Computational Economics
14(1): 1 46. Available at http://www.gams.com/solvers/mpsge/syntax.htm.
Rutherford, T.F., and S.V. Paltsev. 2000. "GTAP Energy in GAMS: The Dataset and Static
Model." University of Colorado at Boulder, Working Paper 00 2. Available at
http://www.mpsge.org/mainpage/mpsge.htm.
U.S. Department of Energy, Energy Information Administration. Undated (a). State Energy Data
Report. Washington DC. Available at http://www.eia.doe.gov/emeu/states/ _use_multistate.html.
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. 2003. Manufacturing Energy
Consumption Survey 2002. Washington DC. Available at http://www.eia.doe.gov/emeu/mecs/.
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U.S. Department of Energy, Energy Information Administration. January 2007. Annual Energy
Outlook 2007. DOE/EIA-0383(2007). Washington, DC. Available at
http://www.eia.doe.gov/oiaf/aeo/index.html.
U.S. Environmental Protection Agency (EPA), Office of Policy Analysis and Review. 2003.
"Benefits and Costs of the Clean Air Act 1990-2020: Revised Analytical Plan For EPA's Second
Prospective Analysis."
5b-27
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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, 28 counties in the U.S. with
ozone monitors are anticipated to fail to meet an alternative ozone standard of 0.075 ppm for the
4th highest maximum 8-hour ozone concentration. This number falls to 11 for an ozone standard
of 0.079 ppm, and increases to 89 for a standard of 0.070 ppm, and increases to 231 for an
alternative standard of 0.065 ppm (see Figure 3.4 in Chapter 3). We estimated the health benefits
of attaining these alternative ozone standards across the nation using the EPA Environmental
Benefits Modeling and Analysis Program (BenMAP) using a two-stage analysis.
In the first stage, we estimated the benefits associated with improving modeled air quality using
known control technologies. These control strategies were sufficient to bring some, but not all,
areas into attainment with the alternative standards. Thus, for some areas, the benefits computed
during this first stage only represented partial attainment. In the second stage, we estimated the
benefits of fully attaining the standards in all areas by using a "rollback" method. This method
reduced ozone concentrations at nonattaining monitors to a level that would just meet the
standards. To estimate the benefits for the 0.075 ppm and 0.079 standards, we deviated from this
two-stage approach. Instead, we used an interpolation technique (please see Appendix 6a for
more details on this technique). Benefits for the South Coast and San Joaquin areas of California
(which are not expected to reach attainment of the current standard until after 2020) are
estimated separately and can be found in Appendix 7b.: For all alternative standards, we used
health impact functions based on published epidemiological studies and valuation functions
derived from the economics literature to calculate the monetary value of the adverse health
outcomes potentially avoided due to these reductions in ambient ozone levels.2 Key health
endpoints 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 independent estimates of this association based
upon different functions reported in the scientific literature. We also note that this range of
estimates do not fully capture the uncertainties within each study. Recognizing that additional
research is necessary to clarify the underlying mechanisms causing these effects, 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),
which was used as the primary basis for the risk analysis presented in our Staff Paper and
reviewed by Clean Air Science Advisory Committee (CASAC), we estimated 250 avoided
premature deaths annually in 2020 from reducing ozone levels to meet a standard of 0.070 ppm.
When added to the other projected benefits from reduced ozone, including 3,000 hospital and
1 All subsequent estimates of full attainment ozone benefits and PM2.5 co-benefits found in this
chapter exclude these two areas of California.
2 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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emergency room admissions, 640,000 school absences, and over 1.7 million minor restricted
activity days, we estimated a total ozone-related benefit of $2.2 billion/yr (2006$). Using three
studies that synthesize data across a large number of individual studies, we estimate between 810
and 1,100 avoided premature deaths annually in 2020 from reducing ozone to 0.070 ppm,
leading to total monetized ozone-related benefits of between $6.5 and $9 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 $230 million/yr.
For the selected standard of 0.075 ppm, using the NMMAPS ozone mortality study resulted in 71
premature deaths avoided and total monetized benefits of $620 million/yr, incremental to
attainment of the 0.08 ppm standard. Using the three synthesis studies, estimated premature
deaths avoided for the less stringent standard are between 230 and 320 with total monetized
ozone benefits between $1.9 and $2.6 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 $73 million/yr.
For a less stringent standard of 0.079 ppm, using the NMMAPS ozone mortality study resulted in
24 premature deaths avoided and total monetized benefits of $220 million/yr, incremental to
attainment of the 0.08 ppm standard. Using the three synthesis studies, estimated premature
deaths avoided for the less stringent standard are between 80 and 110, with total monetized
ozone benefits between $640 and $890 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 $28 million/yr.
For a more stringent standard of 0.065 ppm, using the NMMAPS ozone mortality study resulted
in 450 premature deaths avoided and total monetized benefits of $3.9 billion/yr, incremental to
attainment of the 0.08 ppm standard. Using the three synthesis studies, estimated premature
deaths avoided for the more stringent standard are between 1,500 and 2,100, with total
monetized ozone benefits between $12 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
$420 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. We expect to receive this advice later this spring.
The monetary benefits of visibility improvements from PM2.5 reductions associated with from the
0.070 modeled attainment strategy in selected federal Class I Areas in 2020 is $160 million/yr.
In addition to the direct benefits from reducing ozone, attainment of the standards would likely
result in additional health and welfare benefits because reducing the ozone precursors NOx and
VOC will also reduce PM2.5. Using both modeled and extrapolated reductions in these precursor
emissions, we estimated PM-related co-benefits for the four alternative standards. For each
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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 2020 attainment of the 0.075 ppm alternative, incremental to attainment of the 0.08 ppm
standard, we estimate total ozone and PlV^.s-related co-benefits to be between $3.6 and $16
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 PlV^.s-related co-benefits to be between $2 and $11
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 0.070 ppm alternative, incremental to attainment of the 0.08 ppm
standard, we estimate total ozone and PlV^.s-related co-benefits to be between $6.5 and $27
billion/yr (3% and 7% discount rates, 2006$); 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 ozone and PlV^.s-related co-benefits of between $11 and $42
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
The purpose of this analysis is to assess the human health benefits of attaining the selected 8-
hour ozone standard of 0.075 ppm as well as alternative standards, including 0.079 ppm, 0.070
ppm, and 0.065 ppm, incremental to attainment of the current 8-hour ozone standard of 0.08
ppm.3 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. We calculated total benefits simply by summing the values for all non-
overlapping health and welfare endpoints. This analysis largely builds on both the analytical
approach used in the 2006 PM NAAQS RIA 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, please see those documents, as well as
the EPA Guidelines for Economic Analysis (2000).4'5'6
3 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.
4 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.
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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 improving modeled air
quality using known and available control technologies. These control strategies were sufficient
to bring some, but not all, areas into attainment with the various alternative standards. Thus, for
some areas, the benefits computed during this first stage only represented partial attainment (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" method. This method reduced ozone concentrations at residually nonattaining
monitors to a level that would just meet the standards (see Appendix 6a for details on this
methodology). We tested the sensitivity of our results to different assumptions, including the
choice of health effect estimates from epidemiological studies and economic valuation
parameters for those health effects. A quantitative assessment of non-health benefits (e.g.,
benefits from reduced ozone-related crop damage) was beyond the scope of this analysis due to
data and resource limitations.
For this assessment, we estimated the benefits of reducing ozone and PM concentrations by
applying illustrative control strategies on ozone precursor emissions to attain alternative ozone
NAAQS. With the exception of ozone-related premature mortality, we used methods consistent
with previous PM and ozone benefits assessments. Specifically, we used the same approach to
analyze PM co-benefits as the 2006 PM NAAQS RIA (U.S. EPA, 2006). In addition, we used a
nearly identical approach to analyze the ozone benefits as the 2007 Ozone RIA (U.S. EPA,
2007).
All estimates of ozone benefits and PM2.5 co-benefits 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 through an array of federal rules such as the Clean Air Interstate and
Non-Road Diesel Rules, as well as ozone and PM2.5 state implementation plans. Moreover, the
PM2.5 co-benefits are incremental to an assumption of full attainment of the 2006 PM2.5 NAAQS.
See Chapter 3 for a complete discussion of the baseline. 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.
Furthermore, none of the estimates of incidence or monetary benefits provided in this chapter
include South Coast and San Joaquin Valley Air Basins. Attainment dates will be determined in
the future through the SIP process based on criteria in the CAA, future air quality data, and
future rulemakings and are not knowable at this time. For analytical simplicity, and in keeping
5 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
6 U.S. EPA. 2000. Guidelines for Preparing Economic Analyses.
http://vosemitel.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/Guidelines.pdf
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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with the proposal analysis, we have chosen to use an analysis year of 2020 and generally assume
attainment in that year. The exception is the San Joaquin and South Coast California areas where
SIP submittals for the current standard show that they would have current standard attainment
dates later than 2020. For these two areas in California, we are assuming a new standard
attainment date of 2030. Estimates of the costs and benefits of attaining the 0.075 ppm standard
and the alternate air quality standards for these two areas in 2030 not included in the primary
benefit analysis and are provided in Appendix 7b.
For purposes of this analysis, we assume attainment by 2020 for all areas except San Joaquin
Valley and South Coast air basins in California. The state has submitted plans to EPA for
implementing the current ozone standard which propose that these two areas of California meet
that standard by 2024. We have assumed for analytical purposes that the San Joaquin Valley and
South Coast air basin would attain a new standard in 2030. There are many uncertainties
associated with the year 2030 analysis. Between 2020 and 2030 several federal air quality rules
are likely to further reduce emissions of NOx and VOC, such as, but not limited to National rules
for Diesel Locomotives, Diesel Marine Vessels, and Small Nonroad Gasoline Engines. These
emission reductions should lower ambient levels of ozone in California between 2020 and 2030.
Complete emissions inventories as well as air quality modeling were not available for this year
2030 analysis. Due to these limitations, it is not possible to adequately model 2030 air quality
changes that are required to develop robust controls strategies with associated costs and benefits.
In order to provide a rough approximation of the costs and benefits of attaining 0.075 ppm and
the alternate standards in San Joaquin and South Coast air basins, we have relied on the available
data. Available data includes emission inventories, which do not include any changes in
stationary source emissions beyond 2020, and 2020 supplemental air quality modeling. This
data was used to develop extrapolated costs and benefits of 2030 attainment. These results
indicate that benefits would be between $0.13 billion and $2.0 billion for the selected ozone
standard of 0.075 ppm in 2030. To view the complete analysis for the San Joaquin Valley and
South Coast air basins, see Appendix 7b.3
The remainder of this chapter describes the data and methods used in this analysis, along with
the results. Appendix 6a of this RIA provides additional details of the analysis. 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.
6.2 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
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elements on both health incidence and benefits estimates. Components of that strategy include
emissions modeling, air quality modeling, health effects incidence estimation, and valuation.
Two aspects of OAR's strategy have been used in several recent RIAs and are also employed in
this analysis.8'9'10 First, we used 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. The reported
standard errors in the epidemiological studies determined the distributions for individual effect
estimates. Table 6.4 describes the distributions for unit values.
Second, because characterization of random statistical error omits important sources of
uncertainty (e.g., in the functional form of the model—e.g., whether or not a threshold may exist)
we used a recently completed expert elicitation of the concentration response function describing
the relationship between premature mortality and ambient PM2.5 concentration.11 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 fully 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, it is
particularly important to attempt to characterize the uncertainties associated with reductions in
premature mortality. The health impact 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
8 U.S. Environmental Protection Agency, 2004a. Final Regulatory Analysis: Control of
Emissions fromNonroad 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).
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.
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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:
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.
13 Industrial Economics, Inc. 2006. Expanded Expert Judgment Assessment of the Concentration-
Response Relationship Between PM2.s Exposure and Mortality. Prepared for EPA Office of Air
Quality Planning and Standards, September. Available at:
http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm ee report.pdf
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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
outline numerous health effects known or suspected to be linked to exposure to ambient ozone
and PM (US EPA, 2006; US EPA, 2005; 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 RIA, 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 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 alternative standards. Table 6.2 lists the health endpoints included in
this analysis.
In order to select appropriate epidemiological studies to use for our effect estimates, we applied
several criteria to determine the set of studies that is likely to provide the best estimates of effects
in the U.S. To account for the potential effects of different health care systems or underlying
health status of populations, we gave preference to U.S. studies over non-U.S. studies. In
addition, due to the potential for confounding by co-pollutants, we gave preference to effect
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.pdfpp. 5-29.
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estimates from models that included both ozone and PM over effect estimates from single-
pollutant models.15'16
A number of endpoints that are not health-related may also contribute significant 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). Although we estimate the value of increased outdoor worker
productivity, estimation of other welfare effects is beyond the scope of this analysis.
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.
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Table 6.1: Human Health and Welfare Effects of Ozone and PM2.5
Pollutant/Effect
Quantified and Monetized in Base
Estimates"
Unquantified Effects —Changes in:
PM/Health Premature mortality based on both cohort
study estimates and on expert elicitationc'd
Bronchitis: chronic and acute
Hospital admissions: respiratory and
cardiovascular
Emergency room visits for asthma
Nonfatal heart attacks (myocardial 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
Non-asthma respiratory emergency room
visits
UVb exposure (+/-)e
PM/Welfare Visibility in Southeastern, southwestern and
California Class I areas
Visibility in northeastern and Midwestern
Class I areas
Household soiling
Visibility in residential and non-Class I
areas
UVb exposure (+/-)e
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
Non-asthma respiratory emergency room
visits
UVb exposure (+/-)e
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 (+/-)e
a Primary quantified and monetized effects are those included when determining the primary estimate of total monetized benefits
of the alternative standards.
b 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.
c 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).
d 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.
e May result in benefits or disbenefits. Appendix 6d includes a sensitivity analysis that partially quantifies this endpoint. This
analysis was performed for the purposes of this RIA only.
f 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.
6 The categorization of unquantified toxic health and welfare effects is not exhaustive.
h Health endpoints in the unquantified benefits column include both a) those for which there is not consensus on causality and b)
those for which causality has been determined but empirical data are not available to allow calculation of benefits.
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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 (8 -hour max)
O3 (8 -hour max)
O3 (8 -hour max)
O3 (8 -hour max)
PM2.s (annual
avg)
PM2.s (annual
avg)
PM2.s (annual
avg)
Bell et al (2004) (NMMAPS study)
Meta-analyses:
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
Cardiovascular
Asthma-related
ER visits
O3 (24-hour avg)
PM2.5 (24-hour
avg)
PM2.5 (24-hour
avg)
PM2.5 (24-hour
avg)
PM2 5 (24-hour
avg)
PM2.5 (24-hour
avg)
PM2.5 (24-hour
avg)
O3 (8 -hour max)
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)
Pooled estimate:
Moolgavkar (2003)— ICD 390-429 (all
cardiovascular)
Ito (2003)— ICD 410-414, 427-428 (ischemic
heart disease, dysrhythmia, heart failure)
Moolgavkar (2000)— ICD 390-429 (all
cardiovascular)
Pooled estimate:
Jaffe et al (2003)
>64 years
<2 years
>64 years
20-64 years
>64 years
<65 years
>64 years
20-64 years
5-34 years
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Endpoint
Pollutant
Study
Study Population
Asthma-related
ER visits (con't)
PM2.5 (24-hour
avg)
Peel et al (2005)
Wilson et al (2005)
Norrisetal. (1999)
All ages
All ages
0-18 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.s (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)
Dockeryetal. (1996)
Pope etal. (1991)
Schwartz and Neas (2000)
Pooled estimate:
Ostro et al. (2001) (cough, wheeze and
shortness of breath)
Vedal et al. (1998) (cough)
Ostro (1987)
Pooled estimate:
Gilliland etal. (2001)
Chen et al. (2000)
Ostro and Rothschild (1989)
Ostro and Rothschild (1989)
8-12 years
Asthmatics, 9-11
years
7-14 years
6- 18 years3
1 8-65 years
5-17 yearsb
1 8-65 years
1 8-65 years
1 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.
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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 also 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 recent 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
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."
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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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 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. EPA expects to receive 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 later this spring.
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).
Ozone Exposure Metric. Both the NMMAPS analysis and the individual time series studies
upon which the meta analyses were based use the 24-hour average or 1-hour maximum ozone
levels as exposure metrics. The 24-hour average is not the most relevant ozone exposure metric
to characterize population-level exposure. Given that the majority of the people tend to be
outdoors during the daylight hours and concentrations are highest during the daylight hours, the
24-hour average metric is not appropriate. Moreover, the 1-hour maximum metric uses an
exposure window different than that that used for the current ozone NAAQS. Together, this
means that the most biologically relevant metric, and the one used in the ozone NAAQS since
1997 is the 8-hour maximum standard. Thus, although our analysis at proposal calculated impact
functions based on either the 24 hour average or 1-hour maximum ozone levels originally
reported in the epidemiogical studies, for the final rule analysis, we have converted ozone
mortality health impact functions that use a 24-hour average or 1-hour maximum ozone metric to
maximum 8-hour average ozone concentration using standard conversion functions.
This practice is consistent both with the available exposure modeling and with the form of the
current ozone standard. This conversion also does not affect the relative magnitude of the health
impact function. An equivalent change in the 24-hour average, 1-hour maximum and 8-hour
maximum will provide the same overall change in incidence of a health effect. The conversion
ratios are based on observed relationships between the 24-hour average and 8-hour maximum
ozone values. For example, in the Bell et al., 2004 analysis of ozone-related premature mortality,
the authors found that the relationship between the 24-hour average, the 8-hour maximum, and
the 1-hour maximum was 2:1.5:1, so that the derived health impact effect estimate based on the
1-hour maximum should be half that of the effect estimate based on the 24-hour values (and the
8-hour maximum three-quarters of the 24-hour effect estimate).
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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In EPA's risk analysis for the ozone NAAQS rule, mortality risks were estimated for 8 urban
areas based on application of city-specific effect estimates derived from single city studies and
from the Bell et al (2004) and Huang et al (2005) multi-city studies. These effect estimates were
based on 24-hour average daily ozone concentrations. While it may have been preferable to use
shorter averaging times, conversions from daily averages to shorter averaging times was not
appropriate due to the lack of city-specific conversion factors. In our benefits analysis for the
ozone NAAQS, we applied national effect estimates based on the pooled multi-city results
reported in Bell et al (2004) and the three meta-analysis studies. Bell et al (2004), Bell et al
(2005), Levy et al (2005), and Ito et al (2005) all provide national conversion ratios between
daily average and 8-hour and 1-hour maxima, based on national data. However, these
conversions were not specific to the ozone "warm" season which was the period used in the
health risk assessment. As such we were able to convert the national C-R function parameters
from daily average to 8-hour average, albeit with the introduction of additional uncertainty due
to the use of effect estimates based on a mixture of warm season and all year data in the
epidemiological studies. Given the heterogeneity in ratios of daily average to 8-hour and 1-hour
maxima that exists between cities, it would be inappropriate to use national conversion ratios to
adjust C-R functions for individual cities.
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
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
19 Abt Associates, Incorporated. Environmental Benefits Mapping and Analysis Program,
Technical Appendices. May 2005. pp. 1-3
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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
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. 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 most asthma ER visits do not result in a
hospital admission.
6.3.2.4 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
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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.
6.3.2.6 Outdoor 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.
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6.3.2.7 Visibility Benefits
Changes in the level of ambient PM2.5 caused by the reduction in emissions associated with the
alternative 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. This chapter contains an estimate of the monetized benefits of
improved visibility associated with the simulated emission control strategy to attain the 0.070
ppm ozone standard. The methodology we followed to estimate changes in visibility benefits is
consistent with the PIVb.s RIA (EPA, 2006), which is described on page 5-60 of that document.
6.3.2.8 Other Unqualified Effects
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 may affect
crop and forest yields throughout the affected area. Recent scientific studies have also found that
at sufficient concentrations ozone negatively affects the quality or nutritive value of some
sensitive 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. There is considerable
uncertainty, however, in such estimates, including the fact that the extensive management of
agricultural crops may mitigate the potential 03-related effects. For this reason, the estimates of
economic crop loss developed using the updated AGSIM model were not relied on for this
analysis of alternative O3 standards. In addition, these models have not been adapted for use in
analyzing ozone-related forest impacts. Again, because there commercial activities are highly
managed the potential benefits of alternative O3 standards are uncertain. Because of these
uncertainties and resource limitations, we are unable to provide benefits estimates for the
commercial production of agricultural and silvaculture commodities.
An additional welfare benefit of reducing ambient ozone concentrations is the economic value of
reduced aesthetic injury to forests. There is sufficient scientific information available to reliably
establish that ambient ozone causes 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 us from quantifying the benefits of improved forest aesthetics.
Urban ornamentals (floriculture and nursery crops) are an additional vegetation category that
may experience negative effects from exposure to ambient ozone and may affect large economic
sectors. However, the absence of adequate exposure-response functions and economic damage
functions for the potential range of effects relevant to these types of vegetation precludes us from
quantifying these direct economic benefits. 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, but information and valuation methods are not available to allow for
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plausible estimates of the percentage of these expenditures that may be related to impacts
associated with ozone exposure.
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.
Deposition to Agricultural and Forested Land. Implementation strategies for alternative
standards that 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,
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
6-19
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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.
Ultraviolet Radiation. Atmospheric ozone absorbs a harmful band of ultraviolet radiation from
the sun called UV-B, thus 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 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 $6.6 million
(2006$). 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 ozone as a result of any revision to the Ozone 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.
21 Peter M. Vitousek et. al., "Human Alteration of the Global Nitrogen Cycle: Causes and
Consequences" Issues in EcologyNo. 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 Kochy 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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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. EPA has conducted a screening level analysis of the
effects of reduced ozone concentrations on UVB exposures. This analysis is based on the air
quality modeling conducted for the proposed Ozone NAAQS RIA, and is described in Appendix
6d to the this RIA. The screening analysis has been peer-reviewed and a summary of the peer-
review comments and responses are provided with the report.
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 carbon dioxide and methane) 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
24 National Academy of Sciences, "Radiative Forcing of Climate Change: Expanding the
Concept and Addressing Uncertainties," October 2005.
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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 that
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.
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.
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.
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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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 10 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 million people, that number must be multiplied
by the millions of people in the total 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
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).
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).
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Table 6.3: National Average Baseline Incidence Rates
Rate per 100 people per year by Age Group
Endpoint
Source
Notes
18-
24
25-
34
35-
44
45-
54
55-
64
65+
Mortality CDC Compressed non- 0.025 0.022 0.057 0.150 0.383 1.006 4.937
Mortality File, accessed accidental
through CDC Wonder
(1996-1998)
Respiratory
Hospital
Admissions
Asthma ER
visits
Minor
Restricted
Activity Days
(MRADs)
School Loss
Days
1 999 NHDS public use incidence
data filesb
2000 NHAMCS public incidence
use data files0; 1999
NHDS public use data
filesb
Ostro and Rothschild incidence
(1989, p. 243)
National Center for all-cause
Education Statistics
(1996) and 1996 HIS
(Adams et al., 1999,
Table 47); estimate of
180 school days per year
0.043 0.084 0.206 0.678 1.926 4.389 11.629
1.011 1.087 0.751 0.438 0.352 0.425 0.232
— 780 780 780 780 780 —
990.0 _____ _
Endpoint
Asthma
Exacerbations
Source
Ostro et al.
(2001)
Vedal et al.
(1998)
Notes
Incidence (and prevalence)
among asthmatic African-
American children
Incidence (and prevalence)
among asthmatic children
Daily wheeze
Daily cough
Daily dyspnea
Daily wheeze
Daily cough
Daily dyspnea
Rate per 100 People
per Year
0.076(0.173)
0.067(0.145)
0.037 (0.074)
0.038
0.086
0.045
a 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.
b See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/
c See ftp://ftp.cdc.gov/Dub/Health Statistics/NCHS/Datasets/NHAMCS/
d 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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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 2006 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
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 PJA.
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 $6.6 million at 1990 income levels (2006$), and $7.9 million (2006$) at 2020 income
levels. This represents an intermediate value from a variety of estimates in the economics
literature (see the 2006 PM NAAQS PJA 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 $109 multiplied by n.
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Table 6.4: Unit Values for Economic Valuation of Health Endpoints (2006$)
Central Estimate of Value
Per Statistical Incidence
Health Endpoint
1990 Income
Level
2020 Income
Level
Derivation of Distributions of Estimates
Premature Mortality
(Value of a Statistical
Life)
$6,600,000 $7,900,000
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)
$410,000
$500,000
The WTP to avoid a case of pollution-related CB is calculated as * = Wipv, e , where x is
the severity of an average CB case, WTP 13 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).
Nonfatal Myocardial
Infarction (heart
attack)
3% discount rate
Age 0-24 $79,685 $79,685
Age 25-44 $88,975 $88,975
Age 45-54 $93,897 $93,897
Age 55-65 $167,532 $167,532
Age 66 and over $79,685 $79,685
7% discount rate
Age 0-24 $77,769 $77,769
Age 25-44 $87,126 $87,126
Age 45-54 $91,559 $91,559
Age 55-65 $157,477 $157,477
Age 66 and over $77,769 $77,769
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 MI. 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 et al. (1998), 5-year period ($22,33 1 at 3% discount rate; $21,1 13 at 7% discount rate)
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Central Estimate of Value
Per Statistical Incidence
Health Endpoint
1990 Income
Level
2020 Income
Level
Derivation of Distributions of Estimates
Hospital Admissions
Chronic
Obstructive
Pulmonary Disease
(COPD)
$16,606
$16,606
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).
Asthma
Admissions
$8,900
$8,900
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).
All Cardiovascular
Emergency Room
Visits for Asthma
$24,668 $24,668
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).
All respiratory
(ages 65+)
All respiratory
(ages 0-2)
$24,622
$10,385
$24,622
$10,385
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).
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).
No distributional information available. Simple average of two unit COI values:
$384 $384 (l)$311.55,fromSmithetal.(1997)and
(2) $260.67, from Stanford et al. (1999).
Respiratory Ailments Not Requiring Hospitalization
Upper Respiratory
Symptoms (URS)
$30
$30 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.
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Central Estimate of Value
Per Statistical Incidence
1990 Income 2020 Income
Health Endpoint
Lower Respiratory
Symptoms (LRS)
Asthma
Exacerbations
Acute Bronchitis
Work Loss Days
(WLDs)
Minor Restricted
Activity Days
(MRADs)
School Absence
Days
Level
$19
$50
$429
Variable
(U.S. median
= $130)
$61
$89
Level Derivation of Distributions of Estimates
Combinations of the four symptoms for which WTP estimates are available that closely match those
listed by Schwartz et al. result in 1 1 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
$21 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 1 1 different types of LRS. In the absence of information
surrounding the frequency with which each of the 1 1 types of LRS occurs within the LRS symptom
complex, we assumed a uniform distribution between $6.9 and $24.46.
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
a.,- . (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.
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.
dj^co (1994). The low daily estimate of $10 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 10.
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.
$64 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.
$89 No distribution available
6-28
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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)
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.
6-29
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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 workday:
72.85 percent times $103, for a total loss of $75. This valuation approach is similar to that used
by Hall et al. (2003).
6.5 Results and Implications
6.5.1 Ozone Benefit Estimates
Figure 6.1 summarizes the valuation of ozone benefits. Tables 6.6 through 6.21 summarize the
reduction in incidence for ozone- and PM-related health endpoints for each of the alternative
ozone standards evaluated. Tables 6.22 through 6.37 summarize the ozone-related economic
benefits for each of the alternative standards.28 Note that incidence and valuation estimates for
each standard alternative are presented in separate tables In addition to the mean incidence
estimates, we have included 5th and 95th percentile estimates when available, based on the Monte
Carlo simulations described above. In the tables for the 0.065 ppm and 0.070 ppm alternative
standards, the change in ozone-related incidence from attaining the alternative standards is
presented for both the partial attainment scenario and the full attainment scenario (i.e., sum of the
change in incidence associated with achieving the partial attainment increment plus the residual
attainment increment). As described in Appendix 6a, to calculate the additional change in ozone
concentrations to get from partial attainment to 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); estimating benefits that occur at
these downwind monitors as a result of air quality improvements below the standard would be
appropriate because ozone is a non-threshold pollutant. 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 and
0.079 ppm alternatives are derived through an interpolation technique described in Appendix 6a.
As such, these estimates are presented as full attainment only.
We model all ozone-related premature mortality and morbidity to occur in the same year as the
change in exposure rather than assuming a 'lag' in the change in health state, as we do for PM.
Therefore, we do not discount ozone estimates.
28 Note that the valuation estimates for ozone benefits are not discounted due to the fact that there
is no lag between changes in exposure and premature mortality, as is calculated for
benefits.
6-30
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Figure 6.1: Valuation of Ozone Morbidity and Mortality Benefits Results by Standard
Alternative*
National Total Ozone Benefits by Standard Alternative:
Metric Adjusted Ozone Mortality Functions
$18,000
Bell 01 al, 2004
(Bhrmax)
Bell cUl. 2005
(Bhrmax)
Hodal, 2005 (Shr
max)
0.065 ppm Alternative
0.070 ppm Alternative
0.075 ppm Allornalivo
0.079 ppm AlleiTiaLii'C
Levyctal. 2005
(ghrmax)
AssumptionofNo
Qusohlv
* This figure 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 figure do not reflect benefits for the San Joaquin and South Coast Air Basins.
6.5.2 PM.2.5 Co-Benefit Estimation Methodology
Figure 6.2 summarizes the valuation of PM benefits at a 3% and 7% discounted rate,
respectively. A series of tables below present the PIVb.s 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 PIVb.s related co-benefits of full attainment of each
ozone standard alternative, we applied a scaling technique described below. To estimate total
valuation estimates, we applied benefit per-ton metrics; this procedure is detailed further below.
Valuation estimates of the PIVb.s-related full attainment benefits are presented at a 3% discount
rate and at a 7% discount rate. All PM2.5 co-benefit estimates are incremental to the 2006 PM
NAAQS RIA.
6-31
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Figure 6.2: Valuation of PM Co-Benefits by Standard Alternative at 3% and 7%*
Distribution of PM2 5 Benefits by Ozone Standard Alternative
(3% Discount Rate)
as
0.065 ppm Alternative
0.070 ppm Alternative
0.075 ppm Alternative
0.079 ppm Alternative
Epidemiology or Expert Derived PM^ Morta ity Function
Distribution of PM2 5 Benefits by Ozone Standard Alternative
(7% Discount Rate)
0.065 ppm Alternative
0.070 ppm Alternative
0.075 ppm Alternative
0.079 ppm Alternative
Epktomtokw or Expert Darhtd PM^ MorbMy Function
* This figure 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 figure do not reflect benefits for the San Joaquin and South Coast Air
Basins. 6-32
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Estimating PM?^ Co-Benefits Resulting from Full Attainment of the Selected Standard and Each
Standard Alternative
The modeled PIVb.s air quality scenario reflects the PIVb.s changes associated with partially
attaining 0.070 ppm incremental to a partial attainment of 0.08 ppm; due to analytical limitations
it was not possible to model a full-attainment PlV^.s scenario for the selected standard or each
standard alternative. Thus, using this projected air quality change to estimate PM2.5 co-benefits
would under or overstate the benefits of attaining each standard alternative; this is due in part to
the fact that the model run projects the air quality changes from NOx reductions needed to attain
a baseline of 0.08 ppm. Of greater analytical value would be an estimate of the PIVb.s 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 PIVb.s baseline that established the PIVb.s air
quality associated with full attainment of 0.08 ppm. To create such a baseline, EPA utilized
benefit PlV^.s per-ton estimates. These PIVb.s benefit per-ton estimates provide the total
monetized human health benefits (the sum of premature mortality and premature morbidity) of
reducing one ton of PM2.5 from a specified source. EPA has used a similar technique in previous
Regulatory Impact Analyses.29 These estimates are based on the sum of the valuation of the Pope
(2002) estimates of mortality (3% discount rate, 2006$) 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 PIVb.s 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 4
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 incremental to partial attainment ofO. 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. Note that this calculation step allows us to net
out the benefits of attaining the current standard, so that all subsequent benefits are
incremental to the full attainment of 0.080 ppm.
3. Calculate the benefits of partially attaining 0.070 ppm incremental to full attainment of
0.08 ppm. Subtract the benefits of fully attaining 0.080 ppm incremental to the partial
29 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].
6-33
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attainment of 0.08 ppm to create a new estimate of incremental 0.070 ppm partial
attainment.
4. Calculate the PM^j benefits of fully attaining 0.070 ppm. Multiplying the estimate of the
extrapolated NOx tons necessary to attain 0.070 ppm fully (Table 5.3) 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.
5. Repeat step 4 to estimate the benefits of 0.075 ppm, 0.079 ppm and 0.065 ppm. Step 4
may be repeated by substituting the NOx tons necessary to attain the selected alternative
of 0.075 ppm and the remaining alternatives of 0.079 ppm and 0.065 ppm to produce an
estimate of total PM2.5 co-benefits.
The process for estimating the PM2 5 co-benefits of fully attaining 0.065 ppm, 0.075 ppm, and
0.079 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, 0.075 ppm, and 0.079
ppm respectively. Table 7-5 below provides the inputs to the calculation steps described above.
In the example below, we 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 emitting Electrical
Generating Units, Other NOx emitting point sources and NOx emitting Mobile 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.
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 $7.5 billion, $0.6 billion and -$1 billion respectively. Simulated
attainment of the 0.79 ppm alternative required fewer emission reductions than were modeled in
the emissions control strategy to simulate attainment with 0.070 ppm. For this reason, we "netted
out" the benefits of the incremental NOx emission reductions that were present in the 0.070 ppm
control case but not necessary to attain 0.079 ppm.
The benefit per-ton estimates produce estimates of total valuation but not incidence. To estimate
total incidence, 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.
The total PM2 5 benefits of attaining 0.065 ppm, 0.075 ppm and 0.079 ppm are $11 billion, $3.6
billion and $2 billion respectively. The full attainment PM2 5 benefits do not include confidence
intervals. Because this full attainment estimate was derived by summing the modeled PM2 5
6-34
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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 PlV^.s benefits do not
include confidence intervals.
Table 6.5: Estimated PM2.s Co-Benefits Associated with Full Attainment of 0.070 ppm
Incremental to 0.08 ppma
Calculation
Benefits of attaining 0.08 ppm partially and
0.070 ppm partially (i.e. the benefits of the
modeled scenario):
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
Extrapolated NOx
Tons
—
NOx EGU: 37,400
NOx Point: 37,400
NOx Mobile: 37,400
—
NOx EGU: 3 10,000
NOx Point: 3 10,000
NOx Mobile: 3 10,000
VOC: 3 10,000
Benefit per Ton
Estimate
—
$3,200
$3,000
$4,800
—
$3,200
$3,000
$4,800
$430
Valuation of PM2.5
Benefits
(Billions 2006$)"
$3.4
$0.4
$3
$3.5
Benefits of attaining 0.070 ppm incremental
to attainment of 0.08 ppm
$6.5
"Numbers have been rounded to two significant figures and therefore summation may not match table
estimates. PM2.s benefit estimates do not include confidence intervals because they are derived using
benefit per-ton estimates.
b All estimates derived using the Pope et al. (2002) mortality estimate at a 3% and 7% discount rate, in
2006$. 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 do not reflect benefits for the San Joaquin and South Coast Air
Basins.
Estimated reductions in ozone mortality incidence provided in Tables 6.6, 6.10, 6.14, and 6.18
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. 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 alternative standards based on each study.
6-35
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Statistical uncertainty associated with the model estimate for each study is characterized by the
95% credible interval30 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 alternative
standards 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, CASAC31 stated that it seems unlikely that the
observed associations between short-term ozone concentrations and daily mortality are due
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 Sciences32 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. We expect to
receive this advice later this spring.
Using the NMMAPS study that was used as the basis for the risk analysis presented in our Staff
Paper, we estimate 71 avoided premature deaths annually in 2020 from reducing ozone levels to
meet the selected standard of 0.075 ppm, which, when added to the other projected ozone related
benefits, leads to an estimated total benefit of $620 million/yr. Using three studies that synthesize
data across a large number of individual studies, we estimate between 230 and 320, with total
monetized ozone benefits to be between $1.9 and $2.6 billion/yr. Alternatively, if there is no
causal relationship between ozone and mortality, avoided premature deaths would be zero. For a
30 A credible interval is a posterior probability interval used in Bayesian statistics, which is
similar to a confidence interval used in frequentist statistics.
31 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-
07-00 l.pdf
32 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/proiectview.aspx?key=48768
6-36
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standard of 0.079 ppm, using the NMMAPS ozone mortality study, we estimate 24 premature
deaths avoided and total monetized benefits of $220 million/yr. Using the three synthesis studies,
we estimate premature deaths avoided for the less stringent standard to be between 80 and 110,
with total monetized ozone benefits to be between $640 and $890 million/yr. For a standard of
0.070 ppm, using the NMMAPS ozone mortality study, we estimate 250 premature deaths
avoided and total monetized benefits of $2.2 billion/yr. Using the three synthesis studies, we
estimate premature deaths avoided for the less stringent standard to be between 810 and 1,100
avoided premature deaths annually in 2020, leading to total monetized benefits of between $6.5
and $9 billion/yr. For a standard of 0.065 ppm, using the NMMAPS ozone mortality study, we
estimated to result in 450 premature deaths avoided and total monetized benefits of $3.9
billion/yr. Using the three synthesis studies, estimated premature deaths avoided for the more
stringent standard are between 1,500 and 2,100, with total monetized ozone benefits between $12
and $16 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.
As mentioned above, EPA has requested advice from the NAS on this issue.
6.5.3 Estimate of Full Attainment Benefits
Tables 6.38 through 6.41 below summarize the estimates of full attainment and PM2.s co-benefit
estimate for each standard alternative. The presentation of ozone benefits and PM2.5 co-benefits
for each standard alternative is broken into two tables. The first table presents the national ozone
benefits and PM2.5 co-benefits. Tables 6.42 through 6.49 summarize the combined ozone and
PM2.5 co-benefits.
6-37
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Table 7-6: 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, Arithmetic Mean, 95% Confidence Intervals in
Parentheses) B'C'D'E
Model or Assumption4
NMMAPS
Reference
Bell et al. 2004
Bell et al. 2005
Meta- Analysis Ito et al. 2005
Levy et al. 2005
Assumption that association is not causal
National Modeled Partial
Attainment
120
(43-210)
400
(200-610)
550
(340-760)
560
(390-730)
0
National Rolled-Back Full
Attainment
450
(170-730)
1500
(760-2,200)
2000
(1,300-2,700)
2100
(1,500-2,600)
0
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text
in section 6.3.2.1).
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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-38
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Table 7-7: 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)A'B
Morbidity Endpoint
Hospital Admissions (ages 0-1)
Hospital Admissions (ages 65-99)
Emergency Department Visits,
Asthma-Related0
School Absences
Minor Restricted Activity Days
National Modeled Partial
Attainment
700
(310-1,100)
420
(-190-1,100)
550
(-57-1,500)
300,000
(77,000-560,000)
810,000
(350,000-1,300,000)
National Rolled Back Full
Attainment
2,700
(1,300-4,000)
3,200
(74-6,200)
1900
(-130-5,500)
1,100,000
(320,000-1,800,000)
2,900,000
(1,300,000-4,400,000)
A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and
totals will not sum across columns.
B This table reflects fiill 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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
c The 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.
6-39
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Table 7-8: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of PM Premature Mortality associated with PM co-benefit0
Mortality Endpoint
National 2020 Benefits
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study*
Harvard Six-City Study8
Woodruff etal 1997 (infant mortality)
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
1,000
2,300
2.9
4,000
3,100
3,100
2,100
5,000
2,800
1,800
2,300
3,000
2,400
490
2,100
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. All estimates incremental to 2006 PM NAAQS RIA. Estimates
do not include confidence intervals because they were derived through a scaling technique described above. 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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-40
-------
Table 7-9: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of Morbidity Associated with PM Co-benefitA'B
Morbidity Endpoint
National 2020 Benefits
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)
970
940
660,000
17,000
13,000
110,000
2,600
16,000
270
550
2,300
A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA.
Estimates do not include confidence intervals because they were derived through a scaling technique
described above. 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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
B Morbidity Impact Functions Derived from Epidemiology Literature
6-41
-------
Table 7-10: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in
the Incidence of Premature Mortality Associated with Ozone Exposure in 2020
(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals
in Parentheses) B'C'D'E
Model 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
National Modeled Partial
Attainment
120
(43-210)
400
(200-610)
550
(340-760)
560
(390-730)
0
National Rolled Back Full
Attainment
250
(92-410)
810
(410-1,200)
1100
(690-1,500)
1100
(800-1,500)
0
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text
in section 6.3.2.1).
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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-42
-------
Table 7-11: 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)^8
Morbidity Endpoint
Hospital Admissions (ages 0-1)
Hospital Admissions (ages 65-99)
Emergency Department Visits,
Asthma-Related0
School Absences
Minor Restricted Activity Days
National Modeled Partial
Attainment
700
(310-1,100)
420
(-190-1,100)
550
(-57-1,500)
300,000
(77,000-560,000)
810,000
(350,000-1,300,000)
National Rolled Back Full
Attainment
1,500
(720-2,400)
1,400
(-110-3,000)
1000
(-82-3,000)
640,000
(180,000-1,000,000)
1,700,000
(740,000-2,600,000)
A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and
totals will not sum across columns.
B This table reflects fiill 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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
c The 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.
6-43
-------
Table 7-12: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of PM Premature Mortality associated with PM co-benefit0
Mortality Endpoint
National 2020 Benefits
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study*
Harvard Six-City Study8
Woodruff etal 1997 (infant mortality)
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
650
1,500
1.9
2,600
2,000
2,000
1,400
3,200
1,800
1,100
1,500
1,900
1,600
310
1,400
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. All estimates incremental to 2006 PM NAAQS RIA. Estimates
do not include confidence intervals because they were derived through a scaling technique described above. 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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-44
-------
Table 7-13: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of Morbidity Associated with PM Co-benefitA'B
Morbidity Endpoint
National 2020 Benefits
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)
630
610
430,000
11,000
8,100
72,000
1,700
10,000
180
350
1,500
A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA.
Estimates do not include confidence intervals because they were derived through a scaling technique
described above. 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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
B Morbidity Impact Functions Derived from Epidemiology Literature
6-45
-------
Table 7-14: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in
the Incidence of Premature Mortality Associated with Ozone Exposure in 2020
(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals
in Parentheses) B'C'D'E
Model 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
National Full Attainment
71
(27-110)
230
(120-340)
310
(200-430)
320
(230-420)
0
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text
in section 6.3.2.1).
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.
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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-46
-------
Table 7-15: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in
the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current
Ozone Standard, 95% Confidence Intervals in Parentheses)^8
Morbidity Endpoint National Full Attainment
Hospital Admissions (ages 0-1)
470
Hospital Admissions (ages 65-99)
Emergency Department Visits, 280
Asthma-Related0 (-18-830)
c , 200,000
School Absences (58,000-320,000)
500,000
Minor Restricted Activity Days (230,000-760,000)
A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical.
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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
c The 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.
6-47
-------
Table 7-16: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of PM Premature Mortality associated with PM co-benefit0
Mortality Endpoint
National 2020 Benefits
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study*
Harvard Six-City Study8
Woodruff etal 1997 (infant mortality)
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
390
880
1.1
1,600
1,200
1,200
820
2,000
1,100
690
880
1,200
950
190
820
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. All estimates incremental to 2006 PM NAAQS RIA. Estimates
do not include confidence intervals because they were derived through a scaling technique described above. 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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-48
-------
Table 7-17: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of Morbidity Associated with PM Co-benefitA'B
Morbidity Endpoint
National 2020 Benefits
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
370
260,000
6,700
4,900
43,000
1,000
6,100
110
210
890
A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates
do not include confidence intervals because they were derived through a scaling technique described above. 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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
B Morbidity Impact Functions Derived from Epidemiology Literature
6-49
-------
Table 7-18: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in
the Incidence of Premature Mortality Associated with Ozone Exposure in 2020
(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals
in Parentheses) B'C'D'E
Model 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
National Full Attainment
24
(10-39)
80
(42-120)
110
(69-150)
110
(80-140)
0
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text
in section 6.3.2.1).
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.
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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-50
-------
Table 7-19: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in
the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current
Ozone Standard, 95% Confidence Intervals in Parentheses)^8
Morbidity Endpoint National Full Attainment
190
Hospital Admissions (ages 0-1)
190
Hospital Admissions (ages 65-99)
Emergency Department Visits, 87
Asthma-Related0 (-5.2-250)
„ , ... 72,000
School Absences (21,000-110,000)
180,000
Minor Restricted Activity Days (83,000-270,000)
A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical.
B This table reflects fiill 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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
c The 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.
6-51
-------
Table 7-20: Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual
Reductions in the Incidence of PM Premature Mortality associated with PM co-benefit0
Mortality Endpoint
National 2020 Benefits
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study*
Harvard Six-City Study8
Woodruff etal 1997 (infant mortality)
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
250
560
0.71
1,000
760
750
530
1,200
690
440
560
750
600
120
530
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. All estimates incremental to 2006 PM NAAQS RIA. Estimates
do not include confidence intervals because they were derived through a scaling technique described above. 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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-52
-------
Table 7-21: Illustrative 0.079 ppm Full Attainment Scenario:
Estimated Annual Reductions in the Incidence of Morbidity Associated
with PM Co-benefitA'B'c
Morbidity Endpoint
National 2020 Benefits
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)
240
230
160,000
4,200
3,100
28,000
640
3,900
67
140
570
A All estimates rounded to two significant figures. All estimates incremental to 2006 PM
NAAQS RIA. Estimates do not include confidence intervals because they were derived
through a scaling technique described above.
B Morbidity Impact Functions Derived from Epidemiology Literature
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 do not reflect benefits for the
San Joaquin and South Coast Air Basins.
6-53
-------
Table 7-22: 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, Arithmetic Mean, 95% Confidence
Intervals in Parentheses, Millions of 2006$)B'C'D'E
Model 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
National Modeled Partial Attainment
$960
($140-$2,200)
$3,100
($490-6,600)
$4,200
(730-$8,600)
$4,400
($770-$8,500)
0
National Rolled Back
Full Attainment
$3,500
($510-$7,800)
$11,000
($1,800-24,000)
$15,000
(2,700-$3 1,000)
$16,000
($2,800-$31,000)
0
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see
text in section 6.3.2.1).
B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible
interval around the mean estimates of the annual number of lives saved are based on an assumption of a
normal distribution.
c A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a
confidence interval used in frequentist statistics.
D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and
totals will not sum across columns.
E This table reflects full attainment in all locations of the U.S. except two areas of California. These two
areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The
estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-54
-------
Table 7-23: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to
Current Ozone Standard, 95% Confidence Intervals in Parentheses, Millions of 2006$) A'B
Morbidity Endpoint
Hospital Admissions (ages 0-1)
Hospital Admissions (ages 65-
99)c
Emergency Department Visits,
Asthma-Related0
School Absences
Minor Restricted Activity Days
Worker Productivity
National Modeled Partial
Attainment
$6.9
($3.4-10)
$9.9
(-$3.3-$24)
$0.20
($0.0»$0.56)
$27
($8.4-$48)
$48
($18-$89)
$6.8
National Rolled Back Full
Attainment
$26
($14.0-39)
$74
($8.40-$140)
$0.69
($0.0-$2.0)
$99
($34-$150)
$170
($67-$310)
$49
All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and
totals will not sum across columns. This table reflects full attainment in all locations of the U.S. except two areas
of California. These two areas, which have high levels of ozone, are not planning to meet the current standard
until after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas,
which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in
the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
c The negative 5th percentile incidence estimates for this health endpoint are a result of the weak statistical power
of the study and should not be inferred to indicate that decreased ozone exposure may cause an increase in
asthma-related emergency department visits.
6-55
-------
Table 7-24: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual
Valuation of Reductions in the Incidence of PM Premature Mortality associated with PM
co-benefit (Millions of 2006$)c
Mortality Endpoint
National 2020 Benefits
(3% discount rate)
National 2020 Benefits
(7% discount rate)
Mortality Impact Functions Derived from Epidemiology Literature
ACS Stud/
Harvard Six-City Study13
Woodruff etal 1997 (infant mortality)
$9,700
$22,000
$20
$8,800
$20,000
$16
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
$33,000
$25,000
$25,000
$17,000
$41,000
$23,000
$15,000
$19,000
$25,000
$20,000
$4,300
$18,000
$30,000
$23,000
$22,000
$16,000
$37,000
$20,000
$13,000
$17,000
$22,000
$18,000
$3,900
$16,000
The estimate is based on the concentration-response (C-R) function developed from the study of the American
Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate
in recent RIAs.
B Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for
advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
c All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates
do not include confidence intervals because they were derived through a scaling technique described above. This
table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which
have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the
table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-56
-------
Table 7-25: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual
Valuation of Reductions in the Incidence of Morbidity Associated with PM Co-benefit
(Millions of 2006$)A'B'C
Morbidity Endpoint
National 2020 Benefits
Chronic Bronchitis (age >25 and over)
Nonfatal myocardial infarction (age >17)
3% discount rate
7% discount rate
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)
$480
$250
$240
$5.8
$15
$0.35
$1.3
$0.33
$0.39
$0.84
$14
$19
All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA.
Estimates do not include confidence intervals because they were derived through a scaling technique described
above.
B Morbidity Impact Functions Derived from Epidemiology Literature
c This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas,
which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in
the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-57
-------
Table 7-26: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure
(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals
in Parentheses, Millions of 2006$)B'C'D'E
Model 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
National Modeled Partial Attainment
$960
($140-$2,200)
$3,100
($490-6,600)
$4,200
(730-$8,600)
$4,400
($770-$8,500)
0
National Rolled Back
Full Attainment
$1,900
($280-$4,300)
$6,200
($1,000-13,000)
$8,500
(1,500-$17,000)
$8,800
($1,600-$17,000)
0
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text
in section 6.3.2.1).
B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval
around the mean estimates of the annual number of lives saved are based on an assumption of a normal
distribution.
c A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence
interval used in frequentist statistics.
D All estimates rounded to two significant figures.
E This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas,
which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in
the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-58
-------
Table 7-27: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to
Current Ozone Standard, 95% Confidence Intervals in Parentheses, Millions of 2006$) A'B
Morbidity Endpoint
Hospital Admissions (ages 0-1)
Hospital Admissions (ages 65-99)
Emergency Department Visits,
Asthma-Related
School Absences
Minor Restricted Activity Days
Worker Productivity
National Modeled Partial
Attainment
$6.9
($3.4-10)
$9.9
(-$3.3-$24)
$0.20
($0.0»$0.56)
$27
($8.4-$48)
$48
($18-$89)
$6.8
National Rolled Back Full
Attainment
$15
($7.8-23)
$34
($0.59-$67)
$0.37
($0.0-$1.1)
$57
($19-$88)
$98
($38-$180)
$27
All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and
totals will not sum across columns. This table reflects full attainment in all locations of the U.S. except two areas
of California. These two areas, which have high levels of ozone, are not planning to meet the current standard
until after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas,
which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in
the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-59
-------
Table 7-28: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual
Valuation of Reductions in the Incidence of PM Premature Mortality associated with PM
co-benefit (Millions of 2006$)c
Mortality Endpoint
National 2020 Benefits
(3% discount rate)
National 2020 Benefits (7%
discount rate)
Mortality Impact Functions Derived from Epidemiology Literature
ACS Stud/
Harvard Six-City Study13
Woodruff etal 1997 (infant mortality)
$6,000
$13,000
$13
$5,400
$12,000
$11
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
$21,000
$16,000
$16,000
$11,000
$27,000
$15,000
$9,500
$12,000
$16,000
$13,000
$2,700
$12,000
$19,000
$15,000
$15,000
$10,000
$24,000
$13,000
$8,600
$11,000
$14,000
$12,000
$2,500
$10,000
A The estimate is based on the concentration-response (C-R) function developed from the study of the American
Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate
in recent RIAs.
B Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for
advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
c All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates
do not include confidence intervals because they were derived through a scaling technique described above. This
table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which
have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the
table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-60
-------
Table 7-298: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual
Valuation of Reductions in the Incidence of Morbidity Associated with PM Co-benefit
(Millions of 2006$)^ B'c
Morbidity Endpoint
National 2020 Benefits
Chronic Bronchitis (age >25 and over)
Nonfatal myocardial infarction (age >17)
3% discount rate
7% discount rate
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)
$310
$160
$160
$3.7
$9.8
$0.22
$0.85
$0.22
$0.25
$0.54
$8.9
$12
A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates
do not include confidence intervals because they were derived through a scaling technique described above.
B Morbidity Impact Functions Derived from Epidemiology Literature
c This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas,
which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in
the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-61
-------
Table 7-30: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure
(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals
in Parentheses, Millions of 2006$)B'C'D'E
Model 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
National Full Attainment
$550
($81-$1,200)
$1,800
($290-3,800)
$2,400
(420-$4,900)
$2,500
($450-$4,900)
0
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text
in section 6.3.2.1).
B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval
around the mean estimates of the annual number of lives saved are based on an assumption of a normal
distribution.
c A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence
interval used in frequentist statistics.
D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical.
E This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas,
which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in
the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-62
-------
Table 7-31: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to
Current Ozone Standard, 95% Confidence Intervals in Parentheses, Millions of 2006$) A'B
Morbidity Endpoint _ National Full Attainment _
Hospital Admissions (ages 0-1)
^4>2.~ — > .
$11
Hospital Admissions (ages 65-99)
(&V.07--/.L)
Emergency Department Visits, $0.10
Asthma-Related ($0.00-$0.3)
School Absences
($6.1— $27)
$29
Minor Restricted Activity Days
($12--$54)
Worker Productivity $ 1 0
A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical.
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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-63
-------
Table 7-32: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual
Valuation of Reductions in the Incidence of PM Premature Mortality associated with PM
co-benefit (Millions of 2006$)c
Mortality Endpoint
National 2020 Benefits
(3% discount rate)
National 2020 Benefits (7%
discount rate)
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study*
Harvard Six-City Study13
Woodruff etal 1997 (infant mortality)
$3,300
$7,400
$8
$3,000
$6,600
$6
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
$13,000
$9,900
$9,800
$6,900
$16,000
$9,000
$5,800
$7,300
$9,700
$7,900
$1,600
$6,900
$12,000
$8,900
$8,900
$6,200
$15,000
$8,100
$5,200
$6,600
$8,800
$7,100
$1,500
$6,200
A The estimate is based on the concentration-response (C-R) function developed from the study of the American
Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate
in recent RIAs.
B Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for
advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
c All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and
totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA. Estimates do not include
confidence intervals because they were derived through a scaling technique described above. This table reflects
full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels
of ozone, are not planning to meet the current standard until after 2020. The estimates in the table do not reflect
benefits for the San Joaquin and South Coast Air Basins.
6-64
-------
Table 7-33: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual
Valuation of Reductions in the Incidence of Morbidity Associated with PM Co-benefit
(Millions of 2006$)A'B'C
Morbidity Endpoint
National 2020 Benefits
Chronic Bronchitis (age >25 and over)
Nonfatal myocardial infarction (age >17)
3% discount rate
7% discount rate
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)
$180
$97
$94
$2.3
$5.9
$0.13
$0.51
$0.13
$0.15
$0.33
$5.3
$7.2
A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA.
Estimates do not include confidence intervals because they were derived through a scaling technique described
above.
B Morbidity Impact Functions Derived from Epidemiology Literature
c This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas,
which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates
in the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-65
-------
Table 7-34: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure
(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals
in Parentheses, Millions of 2006$)B'C'D'E
Model 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
National Full Attainment
$190
($28-$420)
$620
($100-1,300)
$830
(140-$1,700)
$860
($160-$1,700)
0
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text
in section 6.3.2.1).
B With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval
around the mean estimates of the annual number of lives saved are based on an assumption of a normal
distribution.
c A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence
interval used in frequentist statistics.
D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical.
E This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas,
which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in
the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-66
-------
Table 7-35: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Valuation of
Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to
Current Ozone Standard, 95% Confidence Intervals in Parentheses, Millions of 2006$) A'B
Morbidity Endpoint National Full Attainment
$4 4
Hospital Admissions (ages 0-1) 6Q ' ^
$1 9
Hospital Admissions (ages 65-99) '
^j>u.yo--z. i)
Emergency Department Visits, $0.03
Asthma-Related ($0.00~$0.09)
School Absences ($2.2^9.5)
$11
Minor Restricted Activity Days _
^i^T1.^ iP "/
Worker Productivity $4.7
A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical.
B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas,
which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in
the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-67
-------
Table 7-36: Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual
Valuation of Reductions in the Incidence of PM Premature Mortality associated with PM
co-benefit (Millions of 2006$)c
Mortality Endpoint
National 2020 Benefits
(3% discount rate)
National 2020 Benefits (7%
discount rate)
Mortality Impact Functions Derived from Epidemiology Literature
ACS Stud/
Harvard Six-City Study13
Woodruff etal 1997 (infant mortality)
$1,800
$4,100
$5.0
$1,600
$3,700
$4.0
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
$8,400
$6,400
$6,400
$4,400
$11,000
$5,800
$3,700
$4,700
$6,300
$5,100
$1,000
$4,400
$7,600
$5,700
$5,700
$4,000
$9,500
$5,200
$3,400
$4,300
$5,700
$4,600
$910
$3,900
A The estimate is based on the concentration-response (C-R) function developed from the study of the American
Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate
in recent RIAs.
B Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for
advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
c All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical. All
estimates incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were
derived through a scaling technique described above. This table reflects full attainment in all locations of the U.S.
except two areas of California. These two areas, which have high levels of ozone, are not planning to meet the
current standard until after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South
Coast Air Basins.
6-68
-------
Table 7-37: Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual
Valuation of Reductions in the Incidence of Morbidity Associated with PM Co-benefit
(Millions of 2006$)^ B'c
Morbidity Endpoint
National 2020 Benefits
Chronic Bronchitis (age >25 and over)
Nonfatal myocardial infarction (age >17)
3% discount rate
7% discount rate
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)
$120
$62
$60
$1.4
$3.8
$0.086
$0.33
$0.083
$0.10
$0.21
$3.4
$4.6
A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates
do not include confidence intervals because they were derived through a scaling technique described above.
B Morbidity Impact Functions Derived from Epidemiology Literature
c This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas,
which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in
the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-69
-------
Table 7-38: Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality
(Millions of 2006$) for the 0.065 ppm Full Attainment
Ozone Mortality and Morbidity Benefits of Attaining 0.065 ppm
NMMAPS Bell (2004)
Bell (2005)
Meta-Analysis Ito (2005)
Levy (2005)
No Causality
Total
$3,900
$12,000
$16,000
$16,000
$420
PM2.5 Mortality and Morbidity Benefits of Attaining 0.065 ppm
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study0
Harvard Six-City Study"
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
Total (3%
Discount Rate)
$11,000
$23,000
$34,000
$26,000
$26,000
$18,000
$42,000
$24,000
$15,000
$19,000
$25,000
$21,000
$5,100
$19,000
Total (7%
Discount Rate)
$9,600
$20,000
$31,000
$24,000
$23,000
$17,000
$38,000
$21,000
$14,000
$18,000
$23,000
$19,000
$4,700
$17,000
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text
in section 6.3.2.1).
B A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence
interval used in frequentist statistics. Credible intervals for ozone estimates and confidence intervals for PM2.s
estimates not provided because the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates.
c The estimate is based on the concentration-response (C-R) function developed from the study of the American
Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate
in recent RIAs.
D Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for
advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
E All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates
discounted at 3% and 7%. This table reflects full attainment in all locations of the U.S. except two areas of
California. These two areas, which have high levels of ozone, are not planning to meet the current standard until
after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-70
-------
Table 7-39: Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality
(Millions of 2006$) for the 0.070 ppm Full Attainment
Ozone Mortality and Morbidity Benefits of Attaining 0.070 ppm
NMMAPS Bell (2004)
Bell (2005)
Meta-Analysis Ito (2005)
Levy (2005)
No Causality
Total
$2,200
$6,500
$8,800
$9,000
$230
PM2.5 Mortality and Morbidity Benefits of Attaining 0.070 ppm
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study0
Harvard Six-City Study"
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
Total (3%
Discount Rate)
$6,500
$14,000
$22,000
$17,000
$17,000
$12,000
$27,000
$15,000
$10,000
$13,000
$17,000
$13,000
$3,200
$12,000
Total (7%
Discount Rate)
$5,900
$13,000
$20,000
$15,000
$15,000
$11,000
$24,000
$14,000
$9,100
$11,000
$15,000
$12,000
$3,000
$11,000
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text
in section 6.3.2.1).
B A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence
interval used in frequentist statistics. Credible intervals for ozone estimates and confidence intervals for PM2 5
estimates not provided because the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates.
c The estimate is based on the concentration-response (C-R) function developed from the study of the American
Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate
in recent RIAs.
D Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for
advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
E All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates
discounted at 3% and 7%. This table reflects full attainment in all locations of the U.S. except two areas of
California. These two areas, which have high levels of ozone, are not planning to meet the current standard until
after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-71
-------
Table 7-40: Estimate of Annual Ozone and PM2.s Combined Morbidity and Mortality
(Millions of 2006$) for the 0.075 ppm Full Attainment
Ozone Mortality and
NMMAPS
Meta-Analysis
No Causality
Morbidity Benefits of Attaining 0.075 ppm
Bell (2004)
Bell (2005)
Ito (2005)
Levy (2005)
Total
$620
$1,900
$2,500
$2,600
$73
PM2.s Mortality and Morbidity Benefits of Attaining 0.075 ppm
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study0
Harvard Six-City Study13
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
Total (3%
Discount Rate)
$3,600
$7,700
$13,000
$10,000
$10,000
$7,200
$16,000
$9,300
$6,100
$7,600
$10,000
$8,200
$1,900
$7,200
Total (7%
Discount Rate)
$3,300
$7,000
$12,000
$9,200
$9,200
$6,500
$15,000
$8,400
$5,500
$6,900
$9,100
$7,400
$1,800
$6,500
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text
in section 6.3.2.1).
B A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence
interval used in frequentist statistics. Credible intervals for ozone estimates and confidence intervals for PM2.s
estimates not provided because the valuation estimates were derived through a scaling technique (see above) that
precluded us from generating such estimates.
c The estimate is based on the concentration-response (C-R) function developed from the study of the American
Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate
in recent RIAs.
D Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for
advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
E All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates
discounted at 3% and 7%. This table reflects full attainment in all locations of the U.S. except two areas of
California. These two areas, which have high levels of ozone, are not planning to meet the current standard until
after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-72
-------
Table 7-41: Estimate of Annual Ozone and PM2.s Combined Morbidity and Mortality
(Millions of 2006$) for the 0.079 ppm Full Attainment
Ozone Mortality and
NMMAPS
Meta-Analysis
No Causality
Morbidity Benefits of Attaining 0.079 ppm
Bell (2004)
Bell (2005)
Ito (2005)
Levy (2005)
Total
$220
$640
$860
$890
$28
PM2.s Mortality and Morbidity Benefits of Attaining 0.079 ppm
Mortality Impact Functions Derived from Epidemiology Literature
ACS Study0
Harvard Six-City Study13
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
Total (3%
Discount Rate)
$2,000
$4,300
$8,600
$6,600
$6,600
$4,600
$11,000
$6,000
$3,900
$4,900
$6,500
$5,300
$1,200
$4,600
Total (7%
Discount Rate)
$1,800
$3,900
$7,800
$5,900
$5,900
$4,200
$9,700
$5,400
$3,600
$4,500
$5,900
$4,800
$1,100
$4,100
Does not represent equal weighting among models or between assumption of causality vs. no causality (see text
in section 6.3.2.1).
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 because 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% and 7%. 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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-73
-------
Table 7-42: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
3% Discount Rate) for the 0.065 ppm Alternative Standard
Alternative Standard and Model or AssumptionA
Bell (2004) Bell (2005) Ita (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study8
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 from
$14,000
$26,000
Derived from
$34,000
$26,000
$26,000
$19,000
$42,000
$24,000
$16,000
$20,000
$26,000
$21,000
$5,500
$19,000
Epidemiology Literature
$22,000
$34,000
Expert Elicitation
$38,000
$30,000
$30,000
$22,000
$46,000
$27,000
$19,000
$23,000
$29,000
$25,000
$9,000
$23,000
$26,000
$38,000
$46,000
$38,000
$38,000
$30,000
$54,000
$35,000
$27,000
$31,000
$37,000
$33,000
$17,000
$30,000
$27,000
$39,000
$50,000
$42,000
$42,000
$34,000
$58,000
$39,000
$31,000
$35,000
$41,000
$37,000
$21,000
$35,000
$11,000
$23,000
$50,000
$42,000
$42,000
$35,000
$58,000
$40,000
$32,000
$36,000
$42,000
$37,000
$21,000
$35,000
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in
section 6.3.2.1).
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 a combination of modeling data and benefit per ton estimates.
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
do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-74
-------
Table 7-43: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
7% Discount Rate) for the 0.065 ppm Alternative Standard
Alternative Standard and Model or AssumptionA
Bell (2004) Bell (2005) /to (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study8
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 from
$13,000
$24,000
Derived from
$34,000
$27,000
$27,000
$20,000
$42,000
$25,000
$18,000
$21,000
$27,000
$23,000
$8,600
$21,000
Epidemiology Literature
$21,000
$32,000
Expert Elicitation
$42,000
$35,000
$35,000
$28,000
$50,000
$33,000
$26,000
$29,000
$35,000
$31,000
$16,000
$29,000
$25,000
$36,000
$46,000
$39,000
$39,000
$32,000
$54,000
$37,000
$30,000
$33,000
$39,000
$35,000
$21,000
$33,000
$26,000
$37,000
$47,000
$40,000
$40,000
$33,000
$54,000
$38,000
$30,000
$34,000
$39,000
$35,000
$21,000
$33,000
$10,000
$21,000
$31,000
$24,000
$24,000
$17,000
$38,000
$22,000
$14,000
$18,000
$23,000
$19,000
$5,100
$17,000
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in
section 6.3.2.1).
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 a combination of modeling data and benefit per ton estimates.
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
do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-75
-------
Table 7-44: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
3% Discount Rate) for the 0.070 ppm Alternative Standard
Alternative Standard and Model or AssumptionA
Bell (2004) Bell (2005) Ita (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study8
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 from
$8,700
$16,000
Derived from
$24,000
$19,000
$19,000
$14,000
$29,000
$17,000
$12,000
$15,000
$19,000
$16,000
$5,400
$14,000
Epidemiology Literature
$13,000
$20,000
Expert Elicitation
$28,000
$23,000
$23,000
$18,000
$34,000
$22,000
$16,000
$19,000
$23,000
$20,000
$9,700
$19,000
$15,000
$23,000
$31,000
$26,000
$25,000
$21,000
$36,000
$24,000
$19,000
$21,000
$25,000
$22,000
$12,000
$21,000
$16,000
$23,000
$31,000
$26,000
$26,000
$21,000
$36,000
$24,000
$19,000
$22,000
$26,000
$22,000
$12,000
$21,000
$6,700
$14,000
$22,000
$17,000
$17,000
$12,000
$27,000
$15,000
$10,000
$13,000
$17,000
$14,000
$3,500
$12,000
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in
section 6.3.2.1).
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 a combination of modeling data and benefit per ton estimates.
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
do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-76
-------
Table 7-45: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
7% Discount Rate) for the 0.070 ppm Alternative Standard
Alternative Standard and Model or AssumptionA
Bell (2004) Bell (2005) Ita (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study8
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 from
$8,100
$15,000
Derived from
$22,000
$17,000
$17,000
$13,000
$27,000
$16,000
$11,000
$14,000
$17,000
$14,000
$5,100
$13,000
Epidemiology Literature
$12,000
$19,000
Expert Elicitation
$26,000
$22,000
$22,000
$17,000
$31,000
$20,000
$16,000
$18,000
$21,000
$19,000
$9,500
$17,000
$15,000
$21,000
$29,000
$24,000
$24,000
$19,000
$33,000
$23,000
$18,000
$20,000
$24,000
$21,000
$12,000
$20,000
$15,000
$22,000
$29,000
$24,000
$24,000
$20,000
$33,000
$23,000
$18,000
$20,000
$24,000
$21,000
$12,000
$20,000
$6,100
$13,000
$20,000
$15,000
$15,000
$11,000
$25,000
$14,000
$9,300
$12,000
$15,000
$12,000
$3,200
$11,000
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in
section 6.3.2.1).
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 a combination of modeling data and benefit per ton estimates.
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
do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-77
-------
Table 7-46: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
3% Discount Rate) for the 0.075 ppm Alternative Standard
Alternative Standard and Model or AssumptionA
Bell (2004) Bell (2005) Ita (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study8
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 from
$4,200
$8,300
Derived from
$14,000
$11,000
$11,000
$7,800
$17,000
$9,900
$6,700
$8,300
$11,000
$8,800
$2,600
$7,800
Epidemiology Literature
$5,500
$9,500
Expert Elicitation
$15,000
$12,000
$12,000
$9,000
$18,000
$11,000
$7,900
$9,500
$12,000
$10,000
$3,800
$9,000
$6,100
$10,000
$16,000
$13,000
$13,000
$9,700
$19,000
$12,000
$8,600
$10,000
$13,000
$11,000
$4,400
$9,700
$6,200
$10,000
$16,000
$13,000
$13,000
$9,800
$19,000
$12,000
$8,700
$10,000
$13,000
$11,000
$4,500
$9,800
$3,700
$7,800
$13,000
$10,000
$10,000
$7,300
$17,000
$9,300
$6,100
$7,700
$10,000
$8,300
$2,000
$7,300
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in
section 6.3.2.1).
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 a combination of modeling data and benefit per ton estimates.
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
do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-78
-------
Table 7-47: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
7% Discount Rate) for the 0.075 ppm Alternative Standard
Alternative Standard and Model or AssumptionA
Bell (2004) Bell (2005) Ita (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study8
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 from
$3,900
$7,600
Derived from
$13,000
$9,800
$9,800
$7,100
$16,000
$9,000
$6,100
$7,500
$9,700
$8,000
$2,400
$7,100
Epidemiology Literature
$5,100
$8,800
Expert Elicitation
$14,000
$11,000
$11,000
$8,400
$17,000
$10,000
$7,400
$8,800
$11,000
$9,300
$3,600
$8,400
$5,800
$9,500
$15,000
$12,000
$12,000
$9,000
$17,000
$11,000
$8,000
$9,400
$12,000
$9,900
$4,300
$9,000
$5,900
$9,500
$15,000
$12,000
$12,000
$9,100
$17,000
$11,000
$8,100
$9,500
$12,000
$10,000
$4,300
$9,100
$3,400
$7,000
$12,000
$9,300
$9,200
$6,600
$15,000
$8,500
$5,600
$7,000
$9,100
$7,500
$1,800
$6,600
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in
section 6.3.2.1).
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 a combination of modeling data and benefit per ton estimates.
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
do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-79
-------
Table 7-48: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
3% Discount Rate) for the 0.079 ppm Alternative Standard
Alternative Standard and Model or AssumptionA
Bell (2004) Bell (2005) Ita (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study8
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 from
$2,200
$4,500
Derived from
$8,900
$6,800
$6,800
$4,900
$11,000
$6,200
$4,100
$5,200
$6,700
$5,500
$1,400
$4,800
Epidemiology Literature
$2,700
$4,900
Expert Elicitation
$9,300
$7,200
$7,200
$5,300
$11,000
$6,700
$4,600
$5,600
$7,200
$5,900
$1,900
$5,200
$2,900
$5,200
$9,500
$7,400
$7,400
$5,500
$12,000
$6,900
$4,800
$5,800
$7,400
$6,200
$2,100
$5,400
$2,900
$5,200
$9,500
$7,400
$7,500
$5,500
$12,000
$6,900
$4,800
$5,800
$7,400
$6,200
$2,100
$5,400
$2,100
$4,300
$8,700
$6,600
$6,600
$4,700
$11,000
$6,000
$4,000
$5,000
$6,500
$5,300
$1,200
$4,600
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in
section 6.3.2.1).
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 a combination of modeling data and benefit per ton estimates.
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
do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-80
-------
Table 7-49: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006,
7% Discount Rate) for the 0.079 ppm Alternative Standard
Alternative Standard and Model or AssumptionA
Bell (2004) Bell (2005) Ita (2005) Levy (2005) No Causality
Mortality Impact Functions
ACS Study8
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 from
$2,100
$4,100
Derived from
$8,000
$6,100
$6,200
$4,400
$9,900
$5,600
$3,800
$4,700
$6,100
$5,000
$1,300
$4,300
Epidemiology Literature
$2,500
$4,500
Expert Elicitation
$8,400
$6,600
$6,600
$4,800
$10,000
$6,100
$4,200
$5,100
$6,500
$5,400
$1,800
$4,800
$2,700
$4,800
$8,700
$6,800
$6,800
$5,100
$11,000
$6,300
$4,400
$5,300
$6,700
$5,700
$2,000
$5,000
$2,700
$4,800
$8,700
$6,800
$6,800
$5,100
$11,000
$6,300
$4,400
$5,400
$6,800
$5,700
$2,000
$5,000
$1,900
$3,900
$7,800
$5,900
$6,000
$4,200
$9,700
$5,500
$3,600
$4,500
$5,900
$4,800
$1,100
$4,100
A Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in
section 6.3.2.1).
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 a combination of modeling data and benefit per ton estimates.
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 do not reflect benefits for the San Joaquin and South Coast Air Basins.
6-81
-------
Figure 6.3: Ozone and PM2.s Benefits by Standard Alternative (3% and 7% Discount
Rates)
Ozone and PM2.5 Bcnefiu by Slandard Alternative \¥Z Discount Rale)
II- C. :
"
Orcne and PM2.5 GencFiti by Standard Alternative [7K Discount Rale)
SKI iPP
d UP1 nu.
Figure 7.3 graphically shows the breakdown between ozone and PM morbidity and mortality
monetized benefits for one example combination with PM benefits discounted at 3% and 7%,
respectively. This example combination of Bell 2004 and Pope have been used in previous
RIAs and Risk Assessments.
6-82
-------
Figure 6.4: Example Combined Ozone and PMi.s Monetized Benefits Estimates by
Standard Alternative (3% and 7% Discount Rates)*
Example Combined Qione and PM2.5 Monetized Benefits Estimates by Standard
Alternative [3% Discount Rate)
Exampfe Combined Otane and PM2.5 Monetized Benefits Estitn-ates by Standard
Alternntiwe (7% Discount RateJ
*,
fllflM pp« »h
aKppnMtnTutm ClJlJ ppn ^I
O&ppm Mtaimtan
AUndurd Allmiulnii
Figure 7.4 graphically shows four combinations of ozone and PM benefits estimates. These
intermediate combinations represent reference points:
• Bell 2004 is the epidemiological study that underlies the ozone NAAQS risk assessment and Pope
is the PM mortality function that was in several EPA RIAs, and
• Bell 2005 is one of three ozone meta-analyses and Laden is a more recent PM epidemiological
study that was used as an alternative in the PM NAAQS RIA
6-83
-------
Figure 6.5 and Figure 6.6 show the complete range of combinations of ozone and PM mortality
functions at 3 and 7 percent, respectively. These graphs display all possible combinations of
benefits, utilizing the five different ozone functions and the fourteen different PM functions, for
each standard alternative. Each of the 70 bars represents an independent and equally probably
point estimate of benefits under a certain combination of ozone and PM functions. Thus it is not
possible to infer the likelihood of any single benefit estimate.
Figure 6.5:*
Ozone and PM Total Benefits including all combinations of mortality
estimates
(3% Discount Rate)
60000
* These figures reflect 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 do not reflect benefits for the San Joaquin
and South Coast Air Basins. No causality, Bell, and Levy represent ozone estimates. Expert
K, Pope, Laden, and Expert E represent PM estimates.
6-84
-------
Figure 6.6*:
Ozone and PM Total Benefits including all combinations of mortality
estimates
(7% Discount Rate)
* These figures reflect 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 do not reflect benefits for the San Joaquin
and South Coast Air Basins. No causality, Bell, and Levy represent ozone estimates. Expert K,
Pope, Laden, and Expert E represent PM estimates.
6-85
-------
6.5.4 Estimates of Visibility Benefits
Table 7-50 below summarizes the regional distribution of visibility benefits in Class I areas in
2020. Note that these estimates represent the monetized visibility benefits associated with the
modeled ozone emission control strategy, and do not reflect the visibility benefits of fully
attaining the 0.075 ppm selected alternative. For this reason, they are not added to the human
health-based benefits estimates. The methodology we followed to generate these estimates may
be found in the PM2.5 RIA (EPA, 2006)
Table 7-50: Monetary Benefits Associated with Visibility Improvements from the
0.070 Simulated Ozone Attainment Strategy in Selected Federal Class I Areas in
2020 (in millions of 2006$)A
California Southwest Southeast Total
$5 $95 $56 $160
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.
6.5.5 Discussion of Results and Uncertainties
The results of this analysis 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 benefits and PM2.5 co-benefits would be between $2.0
and $19 billion annually for the 0.075 ppm selected alternative when the emissions reductions
from implementing the new standard are fully realized. The magnitude of these estimated
benefits provide 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 (outside of San Joaquin Valley and South Coast) accounts for a substantial
share of the total benefits for each of the evaluated standards. Benefits are most
uncertain for California due to the unique challenge of modeling attainment with the
standards in this state. These challenges include high levels of ozone, difficulties in
modeling the impacts of emissions controls on air quality, and the very large proportion
of California benefits that were derived through extrapolation. 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
6-86
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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. The extrapolation and interpolation techniques used to estimate the full attainment
benefits of the selected and three alternate standards contributed some uncertainty to the
analysis. The great majority of benefits estimated for the 0.065 ppm standard alternative
were derived through extrapolation. As noted previously in this chapter, these benefits are
likely to be more uncertain than the modeled benefits. The 0.075 ppm and 0.079 ppm
benefits were derived by interpolating the full attainment benefits of the 0.070 ppm
alternative (a process which is described in Appendix 6a). This approach may under- or
over-estimate benefits if the actual geographic distribution of air quality changes is
different than that assumed in the interpolation.
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
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 ofPM2.s co-benefits that were derived through benefit
per-ton estimates. 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 PIVb.s NAAQS RIA (Table 5.5), there are a variety of
uncertainties associated with these PM benefits.
6. PM2.5 co-benefits represent a substantial proportion of total benefits. For the 0.075 ppm
selected standard, we estimate co-benefits from PM to be between 42% and 99% of total
benefits, depending on the PM2.5 and ozone mortality functions used. When calculating
PM2.5 co-benefits we assume that states will pursue an ozone strategy that reduces NOx
6-87
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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. Projecting key variables introduces uncertainty. 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. This analysis omits certain unquantified effects due to lack of data, time and resources.
These unquantified endpoints include the direct effects 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 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.
6.5.6 Summary of Total Benefits
Table 6.51 presents the total number of estimated ozone and PM2.5-related premature mortalities
and morbidities avoided nationwide in 2020. Ranges within the mortality section reflect
variability in the studies upon which the estimates associated with premature mortality were
derived. The lower end of the range reflects the Expert K derived mortality functions, and the
upper end of the range reflects the Expert E derived mortality functions. Figure 6.7 graphically
presents the total number of estimated ozone and PM2.5-related premature mortalities avoided in
2020 by standard. Tables 6.52 through 6.56 show the overall ozone, PM, and combined results
with regional breakdowns.
-------
Table 6.51: Summary of Total Number of Annual Ozone and PM2.s -Related Premature Mortalities and Premature Morbidity Avoided in
Combined Estimate of Mortality
Model or Assumption
NMAPS
Meta-analysis
No Causality
Bell (2004)
Bell (2005)
Ito
Levy
Combined Estimate of Morbidity
Acute Myocardial Infarction8
Upper Respiratory Symptoms8
Lower Respiratory Symptoms8
Chronic Bronchitis8
Acute Bronchitis8
Asthma Exacerbation8
Work Loss Days8
School Loss Days0
Hospital and ER Visits
Minor Restricted Activity Days
202(T
Combined Range of Ozone Benefits and PM2.s Co-benefits by Standard Alternative
0.079 ppm
140 to 1,300
200 to 1,300
230 to 1,400
230 to 1,400
120 to 1,200
0.075 ppm
260 to 2,000
420 to 2,200
500 to 2,300
510 to 2,300
190 to 2,000
0.070 ppm
560 to 3,500
1,100 to 4,100
1,400 to 4,300
1,400 to 4,400
310 to 3,200
0.65 ppm
940 to 5,500
2,000 to 6,500
2,500 to 7,000
2,500 to 7,100
490 to 5,000
Combined Ozone Benefits and PM2 5 Co-benefits by Standard Alternative
570
3,100
4,200
240
640
3,900
28,000
72,000
890
340,000
890
4,900
6,700
380
1,000
6,100
43,000
200,000
1,900
750,000
1,500
8,100
11,000
630
1,700
10,000
72,000
640,000
5,100
2,100,000
2,300
13,000
17,000
970
2,600
16,000
110,000
1,100,000
9,400
3,500,000
A Does not reflect estimates for the San Joaquin and South Coast Air Basins
8 PM-related benefits only
c Ozone-related benefits only
D Includes ozone benefits, and PM2.s 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.s premature mortality functions characterized in the expert elicitation.
6-89
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Figure 6.7: Total Annual Ozone and PM2.s-Related Premature Mortalities Avoided in 2020 by Standard Alternative
7,000 -,
f
>•
•*
03
(Q
<*v fv
vi vj
5 5
03 03
I
o?
I
079 ppm
075 ppm
070 ppm
065 ppm
Ozone Mortality - PM Mortality Expert K - Additional PM Mortality for Expert E
6-90
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Table 6.52: Regional Breakdown of Annual Ozone Benefit Results by Health Endpoint in 2020 (thousands of 2006$)*
*••
I/I
ro
HI
Rest of West
California
National Total
Endpoint Group Author Year
Hospital Admissions, Respiratory (0-1)
Hospital Admissions, Respiratory (65+)
Emergency Room Visits, Respiratory
School Loss Days
Worker Productivity
Acute Respiratory Symptoms
Mortality Bell et al. 2004
Mortality Bell et al. 2005
Mortality Ito et al.
Mortality Levy et al.
Hospital Admissions, Respiratory (0-1)
Emergency Room Visits, Respiratory
School Loss Days
Worker Productivity
Acute Respiratory Symptoms
Hospital Admissions, Respiratory (65+)
Mortality Bell et al. 2004
Mortality Bell et al. 2005
Mortality Ito et al.
Mortality Levy et al.
Hospital Admissions, Respiratory (0-1)
Emergency Room Visits, Respiratory
School Loss Days
Worker Productivity
Acute Respiratory Symptoms
Hospital Admissions, Respiratory (65+)
Mortality Bell et al. 2004
Mortality Bell et al. 2005
Mortality Ito et al.
Mortality Levy et al.
Hospital Admissions, Respiratory (0-1)
Hospital Admissions, Respiratory (65+)
Emergency Room Visits, Respiratory
School Loss Days
Worker Productivity
Acute Respiratory Symptoms
Mortality Bell et al. 2004
Mortality Bell et al. 2005
Mortality Ito et al.
Mortality Levy et al.
079 079
Valuation Incidence
$ 470 47
$ 1,300 54
$ 13 35
$ 1,700 19,000
$ 430 370,000
$ 2,800 47,000
$ 50,000 7
$ 160,000 21
$ 220,000 29
$ 230,000 30
$ 10 1.0
$ 0.14 0.39
$ 33 370
$ 6.3 5,500
$ 61 1,000
$ 30 1.3
$ 1,500 0.20
$ 5,100 0.65
$ 6,800 0.88
$ 7,100 0.92
$ 1,400 140
$ 19 51
$ 4,700 53,000
$ 4,300 3,800,000
$ 7,800 130,000
$ 3,100 130
$ 140,000 18
$ 450,000 58
$ 610,000 78
$ 630,000 81
$ 1,900 190
$ 4,400 190
$ 32 87
$ 6,400 72,000
$ 4,700 4,200,000
$ 11,000 180,000
$ 190,000 24
$ 620,000 80
$ 830,000 110
$ 860,000 110
075 Valuation . °J75
Incidence
$ 2,200 220
$ 5,200 220
$ 68 190
$ 8,500 95,000
$ 2,100 1,800,000
$ 15,000 250,000
$ 290,000 38
$ 940,000 120
$ 1,300,000 170
$ 1,300,000 170
$ 18 1.8
$ 0.27 0.74
$ 60 670
$ 11 9,900
$ 110 1,800
$ 58 2.5
$ 2,700 0.35
$ 8,900 1.2
$ 12,000 1.6
$ 13,000 1.6
$ 2,600 260
$ 36 97
$ 9,000 100,000
$ 8,000 7,100,000
$ 15,000 250,000
$ 5,800 240
$ 260,000 33
$ 840,000 110
$ 1,100,000 150
$ 1,200,000 150
$ 4,800 480
$ 11,000 470
$ 100 280
$ 18,000 200,000
$ 10,000 9,000,000
$ 29,000 500,000
$ 550,000 71
$ 1,800,000 230
$ 2,400,000 310
$ 2,500,000 320
070
070 Valuation . .'
Incidence
$ 8,800 880
$ 20,000 870
$ 290 770
$ 35,000 390,000
$ 8,700 7,500,000
$ 61,000 1,000,000
$ 1,300,000 170
$ 4,100,000 530
$ 5,600,000 730
$ 5,800,000 750
$ 820 83
$ 9.4 26
$ 2,600 29,000
$ 360 310,000
$ 4,500 76,000
$ (39) (1.6)
$ 69,000 9.0
$ 230,000 30
$ 310,000 40
$ 320,000 42
$ 5,800 580
$ 79 220
$ 20,000 220,000
$ 18,000 16,000,000
$ 33,000 550,000
$ 13,000 560
$ 580,000 75
$ 1,900,000 250
$ 2,600,000 330
$ 2,700,000 340
$ 15,000 1,500
$ 34,000 1,400
$ 370 1,000
$ 57,000 640,000
$ 27,000 23,000,000
$ 98,000 1,700,000
$ 1,900,000 250
$ 6,200,000 810
$ 8,500,000 1,100
$ 8,800,000 1,100
065 Valuation . ?f5
Incidence
$ 15,000 1,500
$ 50,000 2,100
$ 530 1,400
$ 61,000 690,000
$ 16,000 14,000,000
$ 110,000 1,800,000
$ 2,400,000 300
$ 7,600,000 980
$ 10,000,000 1,300
$ 11,000,000 1,400
$ 2,000 200
$ 27 74
$ 6,500 72,000
$ 1,900 1,600,000
$ 11,000 180,000
$ 3,200 140
$ 200,000 26
$ 670,000 87
$ 900,000 120
$ 950,000 120
$ 9,100 910
$ 130 340
$ 31,000 350,000
$ 31,000 26,000,000
$ 52,000 880,000
$ 22,000 910
$ 940,000 120
$ 3,100,000 400
$ 4,200,000 540
$ 4,300,000 560
$ 26,000 2,700
$ 74,000 3,200
$ 690 1,900
$ 99,000 1,100,000
$ 49,000 42,000,000
$ 170,000 2,900,000
$ 3,500,000 450
$ 11,000,000 1,500
$ 15,000,000 2,000
$ 16,000,000 2,100
* National Total does not reflect benefits for the South Coast
two significant figures. Valuation results for mortality and
and San Joaquin Air Basins. Confidence intervals not available for PM estimates. All estimates rounded to
nonfatal myocardial infarctions are shown at a 3% discount rate. Does not include visibility benefits.
6-91
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Table 6.53: Regional Breakdown of Annual PM Benefit Results by Health Endpoint in 2020
(thousands of 2006$) at 3%*
"to
&
„
8
g
0
*J
3
K
_TO
'E
,0
(0
o
8
o
H
"re
c
o
«
z
Endpoint Group Author
Chronic Bronchitis
Emergency Room Visits, Respiratory
Acute Respiratory Symptoms
Upper+Lower Respiratory Symptoms
Work Loss Days
Acute Bronchitis
Asthma Exacerbation
Hospital Admissions
Non-fatal myocardial infarction
Infant Mortality Woodruff
Mortality Pope
Mortality Laden
Mortality Expert E
Mortality Expert K
Chronic Bronchitis
Emergency Room Visits, Respiratory
Acute Respiratory Symptoms
Upper+Lower Respiratory Symptoms
Work Loss Days
Acute Bronchitis
Asthma Exacerbation
Hospital Admissions
Non-fatal myocardial infarction
Infant Mortality Woodruff
Mortality Pope
Mortality Laden
Mortality Expert E
Mortality Expert K
Chronic Bronchitis
Emergency Room Visits, Respiratory
Acute Respiratory Symptoms
Upper+Lower Respiratory Symptoms
Work Loss Days
Acute Bronchitis
Asthma Exacerbation
Hospital Admissions
Non-fatal myocardial infarction
Infant Mortality Woodruff
Mortality Pope
Mortality Laden
Mortality Expert E
Mortality Expert K
Chronic Bronchitis
Emergency Room Visits, Respiratory
Acute Respiratory Symptoms
Upper+Lower Respiratory Symptoms
Work Loss Days
Acute Bronchitis
Asthma Exacerbation
Hospital Admissions
Non-fatal myocardial infarction
Infant Mortality Woodruff
Mortality Pope
Mortality Laden
Mortality Expert E
Mortality Expert K
079
079 Valuation . '
Incidence
$ 31,000 64
$ 23 62
$ 1,200 43,000
$ 47 1,900
$ 910 7,300
$ 87 170
$ 55 1,000
$ 1,400 54
$ 16,000 150
$ 1,300 0.19
$ 480,000 66
$ 1,100,000 150
$ 2,800,000 330
$ 270,000 32
$ 740 1 .5
$ 0.54 1.5
$ 29 1,000
$ 1.1 46
$ 21 170
$ 2.0 4.0
$ 1.3 24
$ 32 1.3
$ 390 3.5
$ 31 0.00
$ 11,000 1.5
$ 26,000 3.5
$ 66,000 7.8
$ 6,300 0.8
$ 86,000 180
$ 63 170
$ 3,400 120,000
$ 130 5,300
$ 2,500 20,000
$ 240 460
$ 150 2,800
$ 3,800 150
$ 45,000 410
$ 3,600 0.52
$ 1,300,000 180
$ 3,000,000 410
$ 7,600,000 910
$ 740,000 88
$ 120,000 240
$ 86 230
$ 4,600 160,000
$ 180 7,300
$ 3,400 28,000
$ 330 640
$ 210 3,900
$ 5,200 200
$ 62,000 570
$ 5,000 0.71
$ 1,800,000 250
$ 4,100,000 560
$ 11,000,000 1,200
$ 1,000,000 120
075
075 Valuation . '
Incidence
$ 91,000 190
$ 66 180
$ 3,600 130,000
$ 140 5,700
$ 2,600 21,000
$ 250 490
$ 160 3,000
$ 4,000 160
$ 48,000 440
$ 3,900 0.55
$ 1,600,000 190
$ 3,600,000 430
$ 8,000,000 960
$ 800,000 93
$ 740 1 .5
$ 0.54 1.5
$ 29 1,000
$ 1.1 46
$ 21 170
$ 2.0 4.0
$ 1.3 24
$ 32 1.3
$ 390 3.5
$ 31 0.00
$ 13,000 1.5
$ 29,000 3.5
$ 65,000 7.8
$ 6,500 0.8
$ 93,000 190
$ 68 180
$ 3,600 130,000
$ 140 5,800
$ 2,700 22,000
$ 260 500
$ 160 3,100
$ 4,100 160
$ 49,000 450
$ 3,900 0.56
$ 1,700,000 200
$ 3,700,000 440
$ 8,100,000 980
$ 810,000 95
$ 180,000 380
$ 130 370
$ 7,200 260,000
$ 280 12,000
$ 5,300 43,000
$ 510 1,000
$ 330 6,100
$ 8,100 320
$ 97,000 890
$ 7,800 1.10
$ 3,300,000 390
$ 7,400,000 880
$ 16,000,000 2,000
$ 1,600,000 190
070
070 Valuation . '
Incidence
$ 190,000 390
$ 140 380
$ 7,500 260,000
$ 290 12,000
$ 5,500 45,000
$ 530 1,000
$ 340 6,300
$ 8,400 330
$ 100,000 920
$ 8,100 1.20
$ 3,700,000 400
$ 8,300,000 900
$ 16,000,000 2,000
$ 1,700,000 190
$ 10,000 21
$ 7.4 20
$ 400 14,000
$ 15 630
$ 290 2,400
$ 28 55
$ 18 330
$ 450 17
$ 5,300 49
$ 430 0.06
$ 200,000 21
$ 440,000 48
$ 880,000 110
$ 90,000 10
$ 110,000 220
$ 78 210
$ 4,200 150,000
$ 160 6,700
$ 3,100 25,000
$ 300 580
$ 190 3,500
$ 4,700 180
$ 56,000 510
$ 4,500 0.65
$ 2,100,000 220
$ 4,700,000 510
$ 9,200,000 1,100
$ 950,000 110
$ 310,000 630
$ 220 610
$ 12,000 430,000
$ 460 19,000
$ 8,900 72,000
$ 850 1,700
$ 540 10,000
$ 14,000 530
$ 160,000 1,500
$ 13,000 1.90
$ 6,000,000 650
$ 13,000,000 1,500
$ 27,000,000 3,200
$ 2,700,000 310
065
065 Valuation . °
Incidence
$ 300,000 620
$ 220 600
$ 12,000 420,000
$ 460 19,000
$ 8,800 71,000
$ 840 1,600
$ 530 10,000
$ 13,000 520
$ 160,000 1,500
$ 13,000 1.80
$ 6,200,000 640
$ 14,000,000 1,400
$ 26,000,000 3,200
$ 2,700,000 310
$ 27,000 55
$ 20 53
$ 1,100 37,000
$ 40 1,700
$ 780 6,300
$ 74 140
$ 47 890
$ 1,200 46
$ 14,000 130
$ 1,100 0.16
$ 550,000 56
$ 1,200,000 130
$ 2,300,000 280
$ 240,000 27
$ 150,000 300
$ 110 290
$ 5,800 200,000
$ 220 9,200
$ 4,300 35,000
$ 410 800
$ 260 4,900
$ 6,500 250
$ 77,000 710
$ 6,200 0.89
$ 3,000,000 310
$ 6,700,000 700
$ 13,000,000 1,600
$ 1,300,000 150
$ 480,000 970
$ 350 940
$ 19,000 660,000
$ 720 30,000
$ 14,000 110,000
$ 1,300 2,600
$ 840 16,000
$ 21,000 820
$ 250,000 2,300
$ 20,000 2.90
$ 9,700,000 1,000
$ 22,000,000 2,300
$ 41,000,000 5,000
$ 4,300,000 490
* National Total does not reflect benefits for the South Coast and San Joaquin Air Basins. Confidence intervals not available
for PM estimates. All estimates rounded to two significant figures. Valuation results for mortality and nonfatal myocardial
infarctions are shown at a 3% discount rate. PM incidence and other PM morbidity incidence and valuation estimates are
identical to Table 6.54 because these are not discounted. Does not include visibility benefits.
6-92
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Table 6.54: Regional Breakdown of Annual PM Benefit Results by Health Endpoint in 2020
(thousands of 2006$) at 7%*
tfl
re
HI
„
tn
>
0
"w
ec.
re
'E
,0
re
O
~a
'o
_
c
g
z
Endpoint Group Author
Chronic Bronchitis
Emergency Room Visits, Respiratory
Acute Respiratory Symptoms
Upper+Lower Respiratory Symptoms
Work Loss Days
Acute Bronchitis
Asthma Exacerbation
Hospital Admissions
Non-fatal myocardial infarction
Infant Mortality Woodruff
Mortality Pope
Mortality Laden
Mortality Expert E
Mortality Expert K
Chronic Bronchitis
Emergency Room Visits, Respiratory
Acute Respiratory Symptoms
Upper+Lower Respiratory Symptoms
Work Loss Days
Acute Bronchitis
Asthma Exacerbation
Hospital Admissions
Non-fatal myocardial infarction
Infant Mortality Woodruff
Mortality Pope
Mortality Laden
Mortality Expert E
Mortality Expert K
Chronic Bronchitis
Emergency Room Visits, Respiratory
Acute Respiratory Symptoms
Upper+Lower Respiratory Symptoms
Work Loss Days
Acute Bronchitis
Asthma Exacerbation
Hospital Admissions
Non-fatal myocardial infarction
Infant Mortality Woodruff
Mortality Pope
Mortality Laden
Mortality Expert E
Mortality Expert K
Chronic Bronchitis
Emergency Room Visits, Respiratory
Acute Respiratory Symptoms
Upper+Lower Respiratory Symptoms
Work Loss Days
Acute Bronchitis
Asthma Exacerbation
Hospital Admissions
Non-fatal myocardial infarction
Infant Mortality Woodruff
Mortality Pope
Mortality Laden
Mortality Expert E
Mortality Expert K
079
079 Valuation
Incidence
$ 31,000 64
$ 23 62
$ 1,200 43,000
$ 47 1,900
$ 910 7,300
$ 87 170
$ 55 1,000
$ 1 ,400 54
$ 16,000 150
$ 1,100 0.17
$ 440,000 66
$ 980,000 150
$ 2,500,000 330
$ 240,000 32
$ 740 1 .5
$ 0.54 1.5
$ 29 1,000
$ 1.1 46
$ 21 170
$ 2.0 4.0
$ 1.3 24
$ 32 1.3
$ 370 3.5
$ 25 0.00
$ 10,000 1.5
$ 23,000 3.5
$ 59,000 7.8
$ 5,700 0.8
$ 86,000 180
$ 63 170
$ 3,400 120,000
$ 130 5,300
$ 2,500 20,000
$ 240 460
$ 150 2,800
$ 3,800 150
$ 44,000 410
$ 2,900 0.47
$ 1,200,000 180
$ 2,700,000 410
$ 6,900,000 910
$ 670,000 88
$ 120,000 240
$ 86 230
$ 4,600 160,000
$ 180 7,300
$ 3,400 28,000
$ 330 640
$ 210 3,900
$ 5,200 200
$ 60,000 570
$ 4,000 0.64
$ 1,600,000 250
$ 3,700,000 560
$ 9,500,000 1,200
$ 910,000 120
075
075 Valuation
Incidence
$ 91,000 190
$ 66 180
$ 3,600 130,000
$ 140 5,700
$ 2,600 21,000
$ 250 490
$ 160 3,000
$ 4,000 160
$ 46,000 440
$ 3,100 0.50
$ 1,500,000 190
$ 3,300,000 430
$ 7,200,000 960
$ 720,000 93
$ 740 1 .5
$ 0.54 1.5
$ 29 1,000
$ 1.1 46
$ 21 170
$ 2.0 4.0
$ 1 .3 24
$ 32 1.3
$ 370 3.5
$ 25 0.00
$ 12,000 1.5
$ 27,000 3.5
$ 58,000 7.8
$ 5,800 0.8
$ 93,000 190
$ 68 180
$ 3,600 130,000
$ 140 5,800
$ 2,700 22,000
$ 260 500
$ 160 3,100
$ 4,100 160
$ 47,000 450
$ 3,200 0.51
$ 1,500,000 200
$ 3,300,000 440
$ 7,300,000 980
$ 740,000 95
$ 180,000 380
$ 130 370
$ 7,200 260,000
$ 280 12,000
$ 5,300 43,000
$ 510 1,000
$ 330 6,100
$ 8,100 320
$ 94,000 890
$ 6,300 1.00
$ 3,000,000 390
$ 6,600,000 880
$ 15,000,000 2,000
$ 1,500,000 190
070
070 Valuation
Incidence
$ 190,000 390
$ 140 380
$ 7,500 260,000
$ 290 12,000
$ 5,500 45,000
$ 530 1,000
$ 340 6,300
$ 8,400 330
$ 97,000 920
$ 6,500 1.00
$ 3,300,000 400
$ 7,500,000 900
$ 15,000,000 2,000
$ 1,500,000 190
$ 10,000 21
$ 7.4 20
$ 400 14,000
$ 15 630
$ 290 2,400
$ 28 55
$ 18 330
$ 450 17
$ 5,100 49
$ 350 0.06
$ 180,000 21
$ 400,000 48
$ 790,000 110
$ 81,000 10
$ 110,000 220
$ 78 210
$ 4,200 150,000
$ 160 6,700
$ 3,100 25,000
$ 300 580
$ 190 3,500
$ 4,700 180
$ 54,000 510
$ 3,700 0.58
$ 1,900,000 220
$ 4,200,000 510
$ 8,300,000 1,100
$ 860,000 110
$ 310,000 630
$ 220 610
$ 12,000 430,000
$ 460 19,000
$ 8,900 72,000
$ 850 1,700
$ 540 10,000
$ 14,000 530
$ 160,000 1,500
$ 11,000 1.70
$ 5,400,000 650
$ 12,000,000 1,500
$ 24,000,000 3,200
$ 2,500,000 310
065
065 Valuation
Incidence
$ 300,000 620
$ 220 600
$ 12,000 420,000
$ 460 19,000
$ 8,800 71,000
$ 840 1,600
$ 530 10,000
$ 13,000 520
$ 150,000 1,500
$ 10,000 1.60
$ 5,600,000 640
$ 12,000,000 1,400
$ 23,000,000 3,200
$ 2,500,000 310
$ 27,000 55
$ 20 53
$ 1,100 37,000
$ 40 1,700
$ 780 6,300
$ 74 140
$ 47 890
$ 1,200 46
$ 14,000 130
$ 920 0.15
$ 490,000 56
$ 1,100,000 130
$ 2,100,000 280
$ 220,000 27
$ 150,000 300
$ 110 290
$ 5,800 200,000
$ 220 9,200
$ 4,300 35,000
$ 410 800
$ 260 4,900
$ 6,500 250
$ 75,000 710
$ 5,100 0.80
$ 2,700,000 310
$ 6,000,000 700
$ 11,000,000 1,600
$ 1,200,000 150
$ 480,000 970
$ 350 940
$ 19,000 660,000
$ 720 30,000
$ 14,000 110,000
$ 1,300 2,600
$ 840 16,000
$ 21,000 820
$ 240,000 2,300
$ 16,000 2.60
$ 8,800,000 1,000
$ 20,000,000 2,300
$ 37,000,000 5,000
$ 3,900,000 490
* National Total does not reflect benefits for the South Coast and San Joaquin Air Basins. Confidence intervals not available
for PM estimates. All estimates rounded to two significant figures. Valuation results for mortality and nonfatal myocardial
infarctions are shown at a 7% discount rate. PM incidence and other PM morbidity incidence and valuation estimates are
identical to Table 6.53 because these are not discounted. Does not include visibility benefits.
6-93
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Table 6.55: Regional Breakdown of Annual Ozone and PM Benefit Results by Health
Endpoint in 2020 (3% discount rate, thousands of 2006$)*
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Endpoint Group Author Year
Ozone Morbidity (non-causal)
Ozone Mortality Bell 2004
Ozone Mortality Bell 2005
Ozone Mortality Ito 2005
Ozone Mortality Levy 2005
PM Infant Mortality Woodruff
PM Morbidity
PM Mortality Pope
PM Mortality Laden
PM Mortality Expert E
PM Mortality Expert K
Ozone Morbidity (non-causal)
Ozone Mortality Bell 2004
Ozone Mortality Bell 2005
Ozone Mortality Ito 2005
Ozone Mortality Levy 2005
PM Infant Mortality Woodruff
PM Morbidity
PM Mortality Pope
PM Mortality Laden
PM Mortality Expert E
PM Mortality Expert K
Ozone Morbidity (non-causal)
Ozone Mortality Bell 2004
Ozone Mortality Bell 2005
Ozone Mortality Ito 2005
Ozone Mortality Levy 2005
PM Infant Mortality Woodruff
PM Morbidity
PM Mortality Pope
PM Mortality Laden
PM Mortality Expert E
PM Mortality Expert K
Ozone Morbidity (non-causal)
Ozone Mortality Bell 2004
Ozone Mortality Bell 2005
Ozone Mortality Ito 2005
Ozone Mortality Levy 2005
PM Infant Mortality Woodruff
PM Morbidity
PM Mortality Pope
PM Mortality Laden
PM Mortality Expert E
PM Mortality Expert K
079
Valuation
$6,600
$50,000
$160,000
$220,000
$230,000
$1,300
$51,000
$480,000
$1,100,000
$2,800,000
$270,000
$140
$1,500
$5,100
$6,800
$7,100
$31
$1,200
$11,000
$26,000
$66,000
$6,300
$21,000
$140,000
$450,000
$610,000
$630,000
$3,600
$140,000
$1,300,000
$3,000,000
$7,600,000
$740,000
$28,000
$190,000
$620,000
$830,000
$860,000
$5,000
$190,000
$1,800,000
$4,100,000
$11,000,000
$1,000,000
075
Valuation
$33,000
$290,000
$940,000
$1,300,000
$1,300,000
$3,900
$150,000
$1,600,000
$3,600,000
$8,000,000
$800,000
$260
$2,700
$8,900
$12,000
$13,000
$31
$1,200
$13,000
$29,000
$65,000
$6,500
$40,000
$260,000
$840,000
$1,100,000
$1,200,000
$3,900
$150,000
$1,700,000
$3,700,000
$8,100,000
$810,000
$73,000
$550,000
$1,800,000
$2,400,000
$2,500,000
$7,800
$300,000
$3,300,000
$7,400,000
$16,000,000
$1,600,000
070
Valuation
$130,000
$1,300,000
$4,100,000
$5,600,000
$5,800,000
$8,100
$310,000
$3,700,000
$8,300,000
$16,000,000
$1,700,000
$8,200
$69,000
$230,000
$310,000
$320,000
$430
$17,000
$200,000
$440,000
$880,000
$90,000
$90,000
$580,000
$1,900,000
$2,600,000
$2,700,000
$4,500
$180,000
$2,100,000
$4,700,000
$9,200,000
$950,000
$230,000
$1,900,000
$6,200,000
$8,500,000
$8,800,000
$13,000
$500,000
$6,000,000
$13,000,000
$27,000,000
$2,700,000
065
Valuation
$250,000
$2,400,000
$7,600,000
$10,000,000
$11,000,000
$13,000
$500,000
$6,200,000
$14,000,000
$26,000,000
$2,700,000
$24,000
$200,000
$670,000
$900,000
$950,000
$1,100
$44,000
$550,000
$1,200,000
$2,300,000
$240,000
$140,000
$940,000
$3,100,000
$4,200,000
$4,300,000
$6,200
$240,000
$3,000,000
$6,700,000
$13,000,000
$1,300,000
$420,000
$3,500,000
$11,000,000
$15,000,000
$16,000,000
$20,000
$780,000
$9,700,000
$22,000,000
$41,000,000
$4,300,000
* Totals do not reflect benefits for the South Coast and San Joaquin Air Basins. Confidence intervals not
available for PM estimates. All estimates rounded to two significant figures. Valuation results for mortality and
nonfatal myocardial infarctions are shown at a 3% discount rate. Does not include visibility benefits.
6-94
-------
Table 6.56: Regional Breakdown of Annual Ozone and PM Benefit Results by Health
Endpoint in 2020 (7% discount rate, thousands of 2006$)*
-4_»
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Endpoint Group
Author Year
Ozone Morbidity (non-causal)
Ozone Mortality
Ozone Mortality
Ozone Mortality
Ozone Mortality
PM Infant Mortality
PM Morbidity
PM Mortality
PM Mortality
PM Mortality
PM Mortality
Bell 2004
Bell 2005
Ito 2005
Levy 2005
Woodruff
Pope
Laden
Expert E
Expert K
Ozone Morbidity (non-causal)
Ozone Mortality
Ozone Mortality
Ozone Mortality
Ozone Mortality
PM Infant Mortality
PM Morbidity
PM Mortality
PM Mortality
PM Mortality
PM Mortality
Ozone Morbidity (non-
causal)
Ozone Mortality
Ozone Mortality
Ozone Mortality
Ozone Mortality
PM Infant Mortality
PM Morbidity
PM Mortality
PM Mortality
PM Mortality
PM Mortality
Bell 2004
Bell 2005
Ito 2005
Levy 2005
Woodruff
Pope
Laden
Expert E
Expert K
Bell 2004
Bell 2005
Ito 2005
Levy 2005
Woodruff
Pope
Laden
Expert E
Expert K
Ozone Morbidity (non-causal)
Ozone Mortality
Ozone Mortality
Ozone Mortality
Ozone Mortality
PM Infant Mortality
PM Morbidity
PM Mortality
PM Mortality
PM Mortality
PM Mortality
Bell 2004
Bell 2005
Ito 2005
Levy 2005
Woodruff
Pope
Laden
Expert E
Expert K
079
Valuation
$6,600
$50,000
$160,000
$220,000
$230,000
$1,100
$51,000
$440,000
$980,000
$2,500,000
$240,000
$140
$1,500
$5,100
$6,800
$7,100
$25
$1,200
$10,000
$23,000
$59,000
$5,700
$21,000
$140,000
$450,000
$610,000
$630,000
$2,900
$140,000
$1,200,000
$2,700,000
$6,900,000
$670,000
$28,000
$190,000
$620,000
$830,000
$860,000
$4,000
$190,000
$1,600,000
$3,700,000
$9,500,000
$910,000
075
Valuation
$33,000
$290,000
$940,000
$1,300,000
$1,300,000
$3,100
$150,000
$1,500,000
$3,300,000
$7,200,000
$720,000
$260
$2,700
$8,900
$12,000
$13,000
$25
$1,200
$12,000
$27,000
$58,000
$5,800
$40,000
$260,000
$840,000
$1,100,000
$1,200,000
$3,200
$150,000
$1,500,000
$3,300,000
$7,300,000
$740,000
$73,000
$550,000
$1,800,000
$2,400,000
$2,500,000
$6,300
$300,000
$3,000,000
$6,600,000
$15,000,000
$1,500,000
070
Valuation
$130,000
$1,300,000
$4,100,000
$5,600,000
$5,800,000
$6,500
$310,000
$3,300,000
$7,500,000
$15,000,000
$1,500,000
$8,200
$69,000
$230,000
$310,000
$320,000
$350
$16,000
$180,000
$400,000
$790,000
$81,000
$90,000
$580,000
$1,900,000
$2,600,000
$2,700,000
$3,700
$180,000
$1,900,000
$4,200,000
$8,300,000
$860,000
$230,000
$1,900,000
$6,200,000
$8,500,000
$8,800,000
$11,000
$500,000
$5,400,000
$12,000,000
$24,000,000
$2,500,000
065
Valuation
$250,000
$2,400,000
$7,600,000
$10,000,000
$11,000,000
$10,000
$490,000
$5,600,000
$12,000,000
$23,000,000
$2,500,000
$24,000
$200,000
$670,000
$900,000
$950,000
$920
$44,000
$490,000
$1,100,000
$2,100,000
$220,000
$140,000
$940,000
$3,100,000
$4,200,000
$4,300,000
$5,100
$240,000
$2,700,000
$6,000,000
$11,000,000
$1,200,000
$420,000
$3,500,000
$11,000,000
$15,000,000
$16,000,000
$16,000
$780,000
$8,800,000
$20,000,000
$37,000,000
$3,900,000
* Totals do not reflect benefits for the South Coast and San Joaquin Air Basins. Confidence intervals not
available for PM estimates. All estimates rounded to two significant figures. Valuation results for mortality and
nonfatal myocardial infarctions are shown at a 7% discount rate. Does not include visibility benefits.
6-95
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6-99
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Appendix 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 and 0.079 ppm benefits estimates; (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 calculated 0.070 ppm and 0.065 ppm partial attainment design values at
each monitor to just reach 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 the full attainment air quality grids for 0.075 ppm and 0.079 ppm, we performed an
interpolation of the 0.070 ppm full attainment air quality grid, rather than a monitor rollback. We
used this technique because air quality modeling incorporating control strategies was only
available for 0.070 ppm. This interpolation for 0.075 ppm 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.
1 BenMAP Technical Appendices, Abt Associates: May 2005. Page C-12.
6a-2
-------
3. We then interpolated these monitor-specific ratios to the grid cell-level in BenMAP,
constraining the interpolation to within 200 km of the control buffer.
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.
5. Next, we followed the same process for the 0.079 ppm interpolation.
6a.2 Partial Attainment PM2.s Incidence and Valuation Estimates
Tables 6a.l through 6a.5 below summarize the estimates of PIVb.s incidence and valuation
resulting from the 0.070 ppm partial attainment scenario. These estimates provided the basis for
the full attainment PlV^.s co-benefit estimates found in Chapter 6 of this RIA. Details about the
methodology for this approach can also be found in Chapter 6.
6a-3
-------
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)0
Eastern U.S.
Western U.S.
Excluding
California
California
National PM Co-
Benefits
Mortality Impact Functions
ACS Study3
Harvard Six-City Study
Woodruff etal. 1997
(infant mortality)
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 from Epidemiology
420
(110-730)
950
(420-1,500)
1.1
(0.34-1.8)
Literature
6.3
(2-10)
14
(7-21)
0.15
(0.07-0.23)
5.4
(2-9)
12
(6-18)
0.02
(0.01-0.04)
430
(110-750)
980
(440-1,500)
1.3
(0.42-2.1)
Derived from Expert Elicitation
1,600
(-92-3,200)
1,200
(-100-2,900)
1,200
(-100-2,900)
830
(42-1,500)
2,000
(690-3,300)
1,100
(660-1,700)
690
(0.00-1,400)
880
(-250-2,300)
1,200
(-14-2,400)
950
(44-2,400)
190
(0.00-1,000)
840
(25-1,800)
150
(0.90-310)
110
(-4.3-270)
120
(-0.89-280)
81
(5.7-140)
190
(76-310)
110
(66-160)
68
(0.00-130)
86
(-17-220)
120
(1.5-220)
93
(11-230)
18
(0.00-98)
70
(1.5-170)
32
(3.4-60)
24
(2.3-53)
24
(2.7-54)
17
(1.7-28)
39
(18-62)
22
(15-32)
14
(0.00-27)
18
(-0.93-43)
24
(1.2-44)
19
(5-45)
3.8
(0.00-20)
16
(1.2-33)
1,800
(-87-3,600)
1,300
(-100-3,200)
1,300
(-99-3,200)
920
(49-1,700)
2,200
(790-3,600)
1,200
(740-1,900)
770
(0.00-1,500)
990
(-270-2,600)
1,300
(-11-2,600)
1,100
(60-2,700)
210
(0.00-1,100)
920
(28-2,000)
"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 RIA. Estimates
do not reflect benefits for the San Joaquin Valley or South Coast Air Basins. Negative values indicate
that an increase in incidence could occur.
6a-4
-------
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)8
Western U.S. National PM Co-
Eastern U.S. Excluding California California Benefits
Morbidity Impact Functions Derived from Epidemiolosv
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
(-11-760)
970
(440-1,500)
120
(46-184)
230
(127-340)
400
(200-610)
980
(-310-2,300)
7,100
(2,600-12,000)
5,200
(880-9,500)
6,500
(-78-21,000)
47,000
(39,000-54,000)
280,000
(220,000-330,000)
Literature
38
(4-72)
12
(6-18)
1.3
(1-2)
2.8
(2-4)
3.6
(2-5)
120
(-16-250)
150
(63-230)
110
(27-190)
130
(10-420)
830
(710-950)
4,800
(4,000-5,700)
8.7
(1-17)
11
(5-16)
1.1
(1-2)
2.3
(1-3)
2.4
(1-4)
23
(-3-50)
130
(57-210)
95
(24-170)
120
(9-380)
800
(680-910)
4,700
(3,900-5,500)
420
(-6-850)
1,000
(450-1,500)
120
(46-186)
240
(130-340)
410
(200-620)
1,100
(-320-2,600)
7,400
(2,800-12,000)
5,400
(930-9,900)
6,800
(-60-22,000)
48,000
(41,000-56,000)
290,000
(230,000-340,000)
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 reflect benefits for the San Joaquin Valley or South Coast Air Basins. Negative values indicate
that an increase in incidence could occur.
6a-5
-------
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 2006$) 95th Percentile Confidence
Intervals Provided in Parentheses0
Mortality Impact Functions
ACS Study3
Harvard Six-City Studyb
Woodruff etal., 1997
(infant mortality)
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
Eastern U.S.
Western U.S.
Excluding
California
California
National PM Co-
Benefits
Derived from Epidemiolosv Literature
$3,000
($380»$7,000)
$6,700
($1,000»$14,000)
$7.5
($1.0-$17)
$44
($6.8-$110)
$99
($16-$210)
$1.0
($0.16-$2.3)
$38
($5.8-$95)
$85
($14-$180)
$0.17
($0.03-$0.36)
$3,000
($440-$7,200)
$6,900
($1,000-$15,000)
$8.8
($1.2-$20)
Derived from Expert Elicitation
$11,000
($200»$30,000)
$8,400
(-$600»$28,000)
$8,300
(-$33-$27,000)
$5,800
($480-$15,000)
$14,000
($2,000»$32,000)
$7,600
($1,400-$17,000)
$4,900
($0.00»$13,000)
$6,200
(-$1,700-
$21,000)
$8,200
($430-$22,000)
$6,700
($430-$22,000)
$1,300
($0.00-$8,200)
$5,900
($240-$17,000)
$1,100
($55-$2,800)
$790
(-$23-$2,700)
$810
($32-$2,600)
$570
($53-$l,400)
$1,300
($200-$3,000)
$740
($130-$1,600)
$480
($0.00-$1,300)
$610
(-$100-$2,000)
$810
($53-$2,100)
$650
($61-$2,100)
$130
($0.00-$800)
$490
($7.2-$l,600)
$220
($20-$560)
$170
($9.0-$520)
$170
($15-$500)
$120
($13-$280)
$280
($43-$600)
$150
($27-$330)
$98
($0.00-$260)
$120
($0.26-$390)
$170
($14-$420)
$130
($17-$410)
$27
($0.00-$160)
$110
($5.7-$330)
$12,000
($280-$33,000)
$9,300
(-$620-$3 1,000)
$9,300
($13-$30,000)
$6,500
($540-$16,000)
$15,000
($2,300-$35,000)
$8,500
($1,400-$19,000)
$5,400
($0.00-$14,000)
$6,900
(-$1,700-$23,000)
$9,200
($500-$24,000)
$7,400
($520-$24,000)
$1,500
($0.00-$9,200)
$6,500
($260-$19,000)
"The estimate is based on the concentration-response (C-R) function developed from the study of the
American Cancer Society cohort reported in Pope et al. (2002), which has previously been reported as
the primary estimate in recent RIAs.
b Based on Laden et al. (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB
for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
c All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical
and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA. Estimates
do not reflect benefits for the San Joaquin Valley or South Coast Air Basins. Negative values indicate
that an increase in incidence could occur.
6a-6
-------
Table 6a.4: Illustrative Strategy to Partially Attain 0.070 ppm: Estimated Partial
Attainment Value of Reductions in PM2.s-Related Premature Mortality Associated with
PM Co-Benefit (7 percent discount rate, in millions of 2006$) 95th Percentile Confidence
Intervals Provided in Parentheses0
Mortality Impact Functions
ACS Study3
Harvard Six-City
Studyb
Woodruff etal., 1997
(infant mortality)
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
Eastern U.S.
Derived from Epidemiology
$2,700
($340»$6,300)
$6,000
($920»$13,000)
$6.8
($0.90»$16)
Western U.S.
Excluding
California
Literature
$40
($6.8-$110)
$89
($14-$190)
$0.94
($0.14-$2.0)
California
$34
($5.8-$95)
$77
($12-$160)
$0.15
($0.02-$0.33)
National PM Co-
Benefits
$2,700
($360-$6,400)
$6,200
($940-$13,000)
$7.9
($1.1-$18)
Derived from Expert Elicitation
$9,900
($180»$27,000)
$7,500
(-$550»$25,000)
$7,500
(-$30»$24,000)
$5,200
($430»$13,000)
$12,000
($1,800-$29,000)
$6,800
($1,200-$16,000)
$4,400
($0.00»$12,000)
$5,600
(-$1,500-$19,000)
$7,400
($380»$20,000)
$6,000
($390-$19,000)
$1,200
($0.00-$7,400)
$5,300
($220-$ 16,000)
$970
($50-$2,500)
$720
(-$20-$2,400)
$730
($28-$2,300)
$510
($47-$l,300)
$1,200
($180-$2,700)
$660
($110-$1,500)
$430
($0.00»$1,200)
$550
(-$90-$1,800)
$730
($48-$l,900)
$590
($55-$l,900)
$110
($0.00-$720)
$440
($6.5-$l,500)
$200
($18-$510)
$150
($8.1-$470)
$150
($14-$450)
$110
($11-$250)
$250
($39-$540)
$140
($24-$300)
$88
($0.00-$240)
$110
($0.24-$350)
$150
($12-$380)
$120
($16-$370)
$24
($0.00-$150)
$99
($5.1-$300)
$11,000
($250-$30,000)
$8,400
(-$560-$28,000)
$8,400
($12-$27,000)
$5,800
($490-$15,000)
$14,000
($2,000-$32,000)
$7,600
($1,300-$17,000)
$4,900
($0.00-$13,000)
$6,200
(-$1,600-$21,000)
$8,300
($450-$22,000)
$6,700
($470-$22,000)
$1,300
($0.00-$8,200)
$5,800
($230-$17,000)
"The estimate is based on the concentration-response (C-R) function developed from the study of the
American Cancer Society cohort reported in Pope et al. (2002), which has previously been reported as
the primary estimate in recent RIAs.
b Based on Laden et al. (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB
for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.
c All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical
and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA. Estimates
do not reflect benefits for the San Joaquin Valley or South Coast Air Basins. Negative values indicate
that an increase in incidence could occur.
6a-7
-------
Table 6a.5: Illustrative Strategy to Partially Attain 0.070 ppm: Estimated Partial
Attainment Monetary Value of Reductions in Risk of PM2.s-Related Morbidity Reductions
Associated with PM Co-Benefit (in millions of 2006$) 95th Percentile Confidence Intervals
Provided in Parentheses"
Morbidity Impact Functions
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 1 8-
65)
Minor restricted activity
days (age 1 8-65)
Eastern U.S.
Western U.S.
Excluding
California
California
National PM Co-
Benefits
Derived from Epidemiology Literature
$180
($4.0-$870)
$210
($50-$480)
$2.5
($1.10»$3.80)
$6.5
($3.80»$9.10)
$0.15
($0.07-$0.25)
$0.50
(-$0.14»$1.50)
$0.14
($0.04»$0.29)
$0.16
($0.03»$0.41)
$0.35
($0.01»$1.30)
$5.7
($4.9-$6.6)
$7.8
($0.39~$16)
$19
($1.0-$86)
$2.6
($0.65-$5.8)
$0.03
($0.01»$0.04)
$0.08
($0.05»$0.11)
$0.00
($0.00-$0.00)
$0.06
($0.00»$0.17)
$0.00
($0.00-$0.01)
$0.00
($0.00-$0.01)
$0.01
($0.00-$0.03)
$0.10
($0.09--$0.11)
$0.14
($0.01»$0)
$4.3
($0.24-$20)
$2.3
($0.61-$5.2)
$0.02
($0.01-$0.04)
$0.06
($0.04-$0.09)
$0.00
($0.00»$0.00)
$0.01
($0.00-$0.03)
$0.00
($0.00-$0.01)
$0.00
($0.00-$0.01)
$0.01
($0.00-$0.02)
$0.12
($0.10»$0.13)
$0.13
($0.01-$0)
$210
($5.2-$980)
$210
($50»$490)
$2.5
($l.l-$3.8)
$6.6
($3.9-$9.3)
$0.15
($0.07--$0.25)
$0.57
(-$0.14-$1.7)
$0.14
($0.04»$0.30)
$0.17
($0.03-$0.42)
$0.36
($0.01-$1.4)
$6.0
($5.1-$6.8)
$8.1
($0.40-$17)
1 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. Estimates do
not reflect benefits for the San Joaquin Valley or South Coast Air Basins.
6a-8
-------
Health-Based Cost-Effectiveness of Reductions in Ambient O3 and PM2.s
Associated with Illustrative O3 NAAQS Attainment Strategies
Draft Report
Submitted by:
Ellen Post
Don McCubbin
Nathan Frey
Hardee Mahoney
Abt Associates Inc.
4800 Montgomery Lane
Bethesda, MD 20814
(301)913-0500
Submitted to:
Ronn Dexter, Work Assignment Manager
U.S. Environmental Protection Agency
National Center for Environmental Economics
1200 Pennsylvania Ave., NW
Washington, D.C. 20460
March 11, 2008
-------
Appendix Chapter 7b: Health-Based Cost-Effectiveness of Reductions in Ambient
Os and PM2.s Associated with Illustrative O^ NAAQS Attainment Strategies
7b.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. 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 reduced risk of acute and chronic morbidity, and non-health benefits. 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 quality-adjusted life years
(QALYs). QALYs were developed to evaluate the effectiveness of individual medical
treatments, and EPA is still evaluating the appropriate methods for CEA for environmental
regulations.
In this CEA, we estimated statistical lives saved, statistical life years saved, and QALYs gained.
In addition, where relevant, we used an alternative aggregate effectiveness metric, Morbidity
Inclusive Life Years (MILYs), to address some of the concerns about aggregation of life
extension and quality-of-life impacts. MILYs represent the sum of life years gained due to
reductions in premature mortality and the QALYs 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.
Following the methodology used in the CEA for the PM NAAQS RIA, we did not assign QALY
weights to the life years saved - i.e., we calculated life years saved, rather than QALYs gained
from mortality avoided. Put another way, we assumed weights of 1.0 for all life years saved.
Life years saved in the future, however, were discounted to reflect people's time preference (i.e.,
a benefit received now is worth more than the same benefit received in the future). We used
discount rates of 3 percent and 7 percent.
For each illustrative Os NAAQS attainment strategy, we present several metrics: lives saved, life
years saved, and cost of the regulation per life saved and per life year saved. Where possible,
benefits that could not be quantified in the denominator of our cost-effectiveness ratios were
monetized and subtracted from the cost of the regulation in the numerator.
Although there are indirect PM2.s-related co-benefits associated with all the illustrative Os
NAAQS attainment strategies considered, we were able to model the changes in PM2.s occurring
7b-l
-------
as a result of only one illustrative Os NAAQS attainment strategy1. Therefore PlV^.s-related co-
benefits are included in the cost effectiveness metrics presented only for that one strategy. The
cost effectiveness metrics presented for all of the other illustrative Os NAAQS attainment
strategies omit the PM2.5-related co-benefits and are therefore likely to understate the cost
effectiveness of those strategies.
For the illustrative Os NAAQS attainment strategy for which we were able to include both direct
Os-related health benefits and indirect PlV^.s-related co-benefits, in addition to the cost
effectiveness metrics listed above we also calculated MILYs and the cost of the regulation (net
of the monetized benefits not included in the denominator) per MILY gained.
The results of the analysis are summarized as follows:
• Estimates of Os-related lives saved were substantially affected by the underlying Os-
mortality study used and, to a greater extent, by the attainment scenario considered.
Because all Os-related mortality was assumed to occur in 2020, we did not discount Os-
related lives saved. Non-zero estimates of Os-related lives saved based on Bell et al.
(2004) ranged from 36 (95% CI: 12 - 60), under full attainment of an alternative
standard of 0.079 ppm, to 520 (95% CI: 170 - 880), under full attainment of an
alternative standard of 0.065 ppm. Estimates of Os-related lives saved based on Levy et
al. (2005) ranged from 160 (95% CI: 110 - 210) to 2,400 (95% CI: 1,600 - 3,100),
under full attainment of the 0.079 ppm and 0.065 ppm alternative standards, respectively.
• Non-zero estimates of Os-related life years saved also depended substantially on the
underlying mortality study used and the attainment scenario considered. In addition, we
hypothesized several alternative possible sets of life expectancies associated with age-
specific Os-related deaths avoided, and the choice of life expectancies had a large impact
on the estimates of Os-related life years saved. Using a 3 percent discount rate, the
smallest non-zero estimate of Os-related life years saved was 160 (95% CI: 54 - 270),
under full attainment of the alternative standard of 0.079 ppm, based on Bell et al. (2004),
and assuming that Os-related mortality occurs only in the subpopulation with severe
preexisting conditions (and thus the shortest life expectancies). The largest estimate of
Os-related life years saved was 26,000 (95% CI: 18,000 - 34,000), under full attainment
of the alternative standard of 0.065 ppm, based on Levy et al. (2004), and assuming that
Os-related mortality occurs in the general population.
• Using a 7 percent discount rate, the smallest non-zero estimate of Os-related life years
saved was 140 (95% CI: 46 - 230), under full attainment of the alternative standard of
0.079 ppm, based on Bell et al. (2004), and assuming that Os-related mortality occurs
only in the subpopulation with severe preexisting conditions (and thus the shortest life
expectancies). The largest estimate of Os-related life years saved was 19,000 (95% CI:
13,000 - 25,000), under full attainment of the alternative standard of 0.065 ppm, based
1 This illustrative attainment strategy has a baseline of partial attainment of the current standard
of 0.084 ppm and a control scenario of partial attainment of an alternative standard of 0.070
ppm.
7b-2
-------
on Levy et al. (2004), and assuming that Os-related mortality occurs in the general
population.
• The estimate of PM2.5-related lives saved under the single illustrative attainment strategy
for which we were able to model the indirect changes in PM2 5 concentrations and thus
include PM2.5 co-benefits, was 440 (95% CI: 170 - 700), based on Pope et al. (2002),
and 2,400 (95% CI: 540 - 1,400), based on Laden et al. (2006). Unlike O3-related
mortality, PM2.5-related mortality was not all assumed to occur in the year of exposure.
Estimates of PM2.5-related life years saved were thus discounted twice - first life years
saved were discounted back to the year of avoided death, and then were further
discounted back to 2020. Using a 3 percent discount rate, PM2.5-related life years saved
was estimated to be 4,400 (95% CI: 1,700 - 7000), based on Pope et al. (2002), and
9,900 (95% CI: 5,400 - 14,000), based on Laden et al. (2006). Using a 7 percent
discount rate, the corresponding estimates using Pope et al. (2002) and Laden et al.
(2006) were 3,000 (95% CI: 1,200 - 4,800) and 6,700 (95% CI: 3,700 - 9,800),
respectively.
• Under the single scenario for which we were able to model the indirect changes in PM2 5
concentrations and thus include PM2 5 co-benefits, we estimated PM25-related reductions
in chronic bronchitis (CB) and non-fatal acute myocardial infarction (AMI) and the
corresponding improvements in quality of life as QALYs gained. QALYs gained from
PM2.5-related reductions in CB were estimated to be 1,970 (95% CI: 270 - 4,700), using
a 3 percent discount rate, and 1,300 (95% CI: 180 - 3,000) using a 7 percent discount
rate. QALYs gained from PM2.s-related reductions in AMI were estimated to be 870
(95% CI: 220 - 1,800) and 680 (95% CI: 180 - 1,400), using 3 percent and 7 percent
discount rates, respectively.
• Because both costs (in the numerator) and benefits (in the denominator) increased with
the stringency of the alternative regulations considered, the cost effectiveness ratios
would not necessarily be expected to show a monotonic pattern across the regulations.
Net cost per Os-related life saved (in 2006 $) (in those illustrative attainment strategies
for which we incorporated only Os-related benefits) were greatest in the illustrative
attainment strategy of full attainment of a 0.075 ppm standard. Even under this one
strategy, however, cost effectiveness estimates varied substantially, depending on the
underlying mortality study used and the discount rate (for cost) assumed - from a low
estimate of $18 million per life saved (95% CI: $13 million - $25 million), based on
Levy et al. (2005) and using a lower bound estimate of the 7 percent discounted cost, to a
high estimate of $110 million (95% CI: $55 million - $280 million), based on Bell et al.
(2004) and using an upper bound estimate of the 7 percent discounted cost. Note,
however, that all of the cost effectiveness ratios for illustrative attainment strategies for
which we incorporated only Os-related benefits would tend to overstate the cost per life
saved - i.e., understate cost effectiveness - because PM2 5 co-benefits were not included
in the denominator.
• Net cost per life saved tended to be substantially lower for the single scenario for which
both Os-related and PM2.s-related lives saved were included, ranging from $1.8 million
(95% CI: $1.3 million - $2.6 million), using Levy et al. (2005) and Laden et al. (2006),
7b-3
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to $5.4 million (95% CI: $3.2 million - $9.9 million), using Bell et al. (2004) and Pope
et al. (2002).
• The pattern seen for cost per life year saved was similar to that seen for cost per life
saved. Net costs per Os-related life year saved were greatest in the illustrative attainment
strategy of full attainment of a 0.075 ppm standard. However, there was substantial
variability in cost effectiveness estimates across these illustrative attainment strategies.
The lowest cost per life year saved was estimated to be $ 1.6 million (95% CI: $1.2
million - $2.3 million), under full attainment of a 0.079 ppm standard, using Levy et al.
(2005) and a 3 percent discount rate, and assuming life expectancies of the general
population. The highest cost per life year saved was estimated to be $29 million (95%
CI: $15 million - $75 million), under full attainment of a 0.075 ppm standard, using Bell
et al. (2004) and a 7 percent discount rate, and assuming life expectancies of a
subpopulation with severe preexisting conditions.
• Net costs per life year saved in the single illustrative strategy for which we included both
Os-related and PlV^.s-related benefits were substantially smaller than for the other
scenarios. This is not surprising, since the cost effectiveness of those other scenarios was
understated - and thus the cost per life year saved was overstated - because of the
omission of PlV^.s-related live years saved. The lowest estimate of net cost per life year
saved for this illustrative strategy was $0.14 million (95% CI: $0.1 million - $0.2
million), based on Levy et al. (2005) and Laden et al. (2006), and, for Os-related
mortality avoided, assuming life expectancies of the general population, and using a 3
percent discount rate. The highest estimate was $0.79 million (95% CI: $0.44 million -
$1.6 million), based on Bell et al. (2004) and Pope et al. (2002), and, for O3-related
mortality avoided, assuming life expectancies of a subpopulation with severe preexisting
conditions, and using a 7 percent discount rate.
• Finally, under the single illustrative strategy for which we included both Os-related and
PM2.s-related benefits, the lowest estimate of net costs per MILY gained, using a 3
percent discount rate, was $0.12 million (95% CI: $0.09 million - $0.17 million), based
on Levy et al. (2005) and Laden et al. (2006) and, for Os-related mortality avoided,
assuming life expectancies of the general population; the highest estimate was $0.30
million (95% CI: $0.19 million - $0.53 million), based on Bell et al. (2004) and Pope et
al. (2002) and, for Os-related mortality avoided, assuming life expectancies of a
subpopulation with severe preexisting conditions.
• Using a 7 percent discount rate, the lowest estimate of net costs per MILY gained was
$0.18 million (95% CI: $0.14 million - $0.26 million), based on Levy et al. (2005) and
Laden et al. (2006) and, for Os-related mortality avoided, assuming life expectancies of
the general population; the highest estimate was $0.48 million (95% CI: $0.29 million -
$0.86 million), based on Bell et al. (2004) and Pope et al. (2002) and, for O3-related
mortality avoided, assuming life expectancies of a subpopulation with severe preexisting
conditions.
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7b.2 Introduction
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. 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 reduced risk of acute and chronic morbidity, and non-health benefits. 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 quality-adjusted life years
(QALYs).
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.
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, however,
making application of CEA more difficult and less straightforward.
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.
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 non-health benefits, such as
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visibility improvements associated with reductions in PlV^.s or increases in worker productivity
associated with reductions in Os, 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).
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 than similar interventions
affecting primarily healthy individuals.
In this CEA, we calculated both life years saved and statistical lives saved. Following the
methodology used in the CEA for the PM NAAQS RIA, we did not assign QALY weights to the
life years saved - i.e., we calculated life years saved, rather than QALYs gained from mortality
avoided. Put another way, we assumed weights of 1.0 for all life years saved. Life years saved
in the future, however, were discounted to reflect people's time preference (i.e., a benefit
received now is worth more than the same benefit received in the future). We used discount
rates of 3 percent and 7 percent.
Where possible, benefits that could not be quantified in the denominator of our cost-effectiveness
ratios were monetized and subtracted from the cost of the regulation in the numerator. For
example, developing QALYs for acute health effects is problematic (Bala and Zarkin, 2000).
Therefore, rather than try to derive QALYs for the acute morbidity endpoints, we instead applied
valuation estimates and subtracted the total monetized value of all avoided acute morbidity
effects from the cost of the regulation, in the numerator of the cost-effectiveness ratios. The
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monetized benefits of non-health improvements, where they were estimated, were similarly
subtracted from the cost of the regulation. Finally, although QALY estimates were derived for
the (PM2.s-related) chronic morbidity endpoints, the medical and opportunity costs associated
with these chronic illnesses were also subtracted from the cost of the regulation.
Although there are indirect PlV^.s-related co-benefits associated with all the illustrative Os
NAAQS attainment strategies, we were able to model the changes in PM2.s occurring as a result
of only one illustrative Os NAAQS attainment strategy (see Chapter 7 for a full discussion of this
issue). Therefore PM2.5-related co-benefits are included in the cost effectiveness metrics
presented only for that one strategy. The cost effectiveness metrics presented for all of the other
illustrative Os NAAQS attainment strategies omit the PM2.5-related co-benefits and are therefore
likely to understate the cost effectiveness of those strategies.
The indirect PM2.5-related co-benefits derive not only from avoided cases of premature mortality
and acute morbidity, but from avoided cases of chronic morbidity (chronic bronchitis and non-
fatal myocardial infarction) as well. In the CEA for the PM NAAQS RIA, EPA derived QALYs
for these two chronic morbidity endpoints (see Appendix G of the PM NAAQS RIA,
http://www.epa.gov/ttn/ecas/regdata/RIAs/Appendix%20G--
Health%20Based%20Cost%20Effectiveness%20Analysis.pdf) and used an alternative aggregate
effectiveness metric, Morbidity Inclusive Life Years (MILYs), to address some of the concerns
about aggregation of life extension and quality-of-life impacts. MILYs represent the sum of life
years gained due to reductions in premature mortality and the QALYs 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.
For each illustrative Os NAAQS attainment strategy, we present several metrics: lives saved, life
years saved, and cost of the regulation (net of the monetized benefits not included in the
denominator) per life saved and per life year saved.
For the illustrative Os NAAQS attainment strategy for which we were able to include both direct
Os-related health benefits and indirect PM2.5-related co-benefits, in addition to the cost
effectiveness metrics listed above we also calculated MILYs and the cost of the regulation (net
of the monetized benefits not included in the denominator) per MILY gained.
Note that, like future life years saved, future QALYs gained from avoided cases of chronic
bronchitis and myocardial infarction are discounted. All costs and monetized benefits are in
2006 dollars.
Monte Carlo simulation methods as implemented in the Crystal Ball™ software program were
used to propagate uncertainty in several of the model parameters throughout the analysis. In
particular, we incorporated uncertainty surrounding the coefficients in the concentration-
response (C-R) functions, the unit values for the various morbidity endpoints included in the
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analysis, and the quality of life weights for the two chronic morbidity endpoints for which we
developed QALYs.
We characterized overall uncertainty in the results with 95 percent credible or confidence
intervals based on the Monte Carlo simulations. In addition, we examined the impacts on the cost
effectiveness metrics of changing key parameters and/or assumptions, including
• the discount rate (for the cost of the regulation in the numerator and future lives or life
years saved and QALYs gained in the denominator);
• the C-R functions for Os-related and PlV^.s-related mortality ; and
• the life expectancies (and therefore years of potential life lost) of individuals who die as a
result of exposure to Os (as explained in Section 7b.5 below).
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
direct reductions in ambient Os (and indirect reductions in PIVb.s, where possible) in achieving
improvements in public health. Reductions in ambient Os and PM2.s are estimated to 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 non-health 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.
The remainder of this appendix provides an overview of the methods used to derive the cost
effectiveness metrics developed for this CEA and presents the resulting metrics. Section 7b.3
provides an overview of effectiveness measures. Section 7b.4 discusses general issues in
constructing cost-effectiveness ratios. Section 7b.5 presents the methods and results for those
illustrative Os NAAQS attainment strategies for which we were able to incorporate only the Os-
related benefits; and Section 7b.6 presents the methods and results for the single illustrative Os
NAAQS attainment strategy for which we were able to include both the Os-related benefits and
PM2.s-related co-benefits. Finally, Section 7b.7 presents concluding remarks.
7b.3 Effectiveness Measures
For the purposes of CEA, we focus the effectiveness measures on the quantifiable health impacts
of the reductions in Os and, where possible, PIVb.s, estimated to result from each illustrative Os
NAAQS attainment strategy considered. 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 deaths avoided, or can be characterized in terms of increases in life expectancy or
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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 deaths
avoided are problematic because a gain in life of only a few months would be considered
equivalent to a gain of 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 non-health benefits associated with air pollution regulations, over
90 percent of quantifiable monetized benefits are health-related. Thus, it can be argued that
QALYs are more applicable for these types of regulations than for other environmental policies.
However, the value of non-health benefits should not be ignored. As discussed below, we have
chosen to subtract the value of non-health benefits from the costs in the numerator of the cost-
effectiveness ratio.
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.
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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
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 7b-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 7b-1. Comparison of QALY and WTP Approaches
Parameter
QALY
WTP
Risk aversion
Relation of duration and quality
Proportionality of duration/ quality trade-off
Treatment of time/age in utility function
Preferences
Source of preference data
Treatment of income and prices
Risk neutral
Independent
Constant
Utility linear in time
Community/Individual
Stated
Not explicitly considered
Empirically determined
Empirically determined
Variable
Empirically determined
Individual
Revealed and stated
Constrains choices
7b.4 Construction of Cost-Effectiveness Ratios: General Issues
7b.4.1 Dealing with Morbidity Health Effects and Non-health Effects
Health effects from exposure to Os and PIVb.s air pollution encompass a wide array of chronic
and acute conditions in addition to premature mortality. EPA's Ozone and PM Criteria
Documents outline numerous health effects known or suspected to be linked to exposure to
ambient ozone and PM (US EPA, 2006; US EPA, 2005; Anderson et al, 2004). Although
chronic conditions and premature mortality generally account for the majority of monetized
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.
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benefits, acute symptoms can affect a broad population or sensitive populations (e.g., asthma-
related emergency room visits among asthmatics). In addition, reductions in air pollution may
result in a broad set of non-health environmental benefits, including improved worker
productivity, improved visibility in national parks, increased agricultural and forestry yields,
reduced acid damage to buildings, and a host of other impacts. Lives saved, life years saved, and
QALYs gained 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."
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, andHillman, 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 non-health benefits and subtract
the dollar value (based on WTP or COI) from compliance costs in the CEA.
To address the issues of incorporating acute morbidity and non-health 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
ratio should be described. We followed this recommended "net cost" approach, specifically in
netting out the benefits of health improvements other than reduced mortality and improved
quality of life from avoided chronic illness - in particular, the monetized benefits of acute
morbidity avoided, the medical and opportunity costs ("cost of illness") of avoided chronic
illness, and the benefits of non-health improvements, including increases in worker productivity
associated with reductions in Os and visibility improvements at national parks associated with
reductions in PM2.s (see Chapter 7 for more details on these benefit categories).
7b.4.2 Should Life Years Gained Be Adjusted for Initial Health Status?
The methods outlined below in Sections 7b.5 and 7b.6 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
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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 QALYs 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 "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, 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 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).
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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
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.
7b.4.3 Constructing 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 concentrations of the air
pollutant 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
7b-13
-------
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 costs of illness (COI) associated with
chronic illnesses. In addition, as noted above, to account for the value of reductions in acute
health impacts and non-health benefits, we netted out the monetized value of these benefits from
the numerator to yield a "net cost" estimate.
The denominators of the cost-effectiveness ratios we calculated are either lives saved, life years
saved, or, for the single scenario in which we were able to include both Os-related and PM2.s-
related benefits, MILYs gained. For the MILY aggregate effectiveness measure, the denominator
is simply the sum of life years gained from increased life expectancy and QALYs gained from
the reductions in incidence of chronic illnesses associated with PM2.s - chronic bronchitis (CB)
and nonfatal acute myocardial infarction (AMI).
7b.5 Cost Effectiveness Metrics Incorporating Only Os-Related Benefits
In this section we describe the development of cost effectiveness metrics for those illustrative Os
NAAQS attainment strategies for which we were able to incorporate only Os-related benefits.
This includes the scenarios in which the baseline is full attainment of the current Os standard of
0.084 ppm and the control scenarios are full attainment of the following four alternative
standards: 0.079 ppm, 0.075 ppm, 0.070 ppm, and 0.065 ppm.
To generate health outcomes, we used the same framework as for the benefit-cost analysis
described in Chapter 8. For convenience, we summarize the basic methodologies here. For
more details, see Chapter 8 and the Environmental Benefits Mapping and Analysis Program
(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 C-R functions reported in 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 source of public health statistics, such as CDC), the affected
population, and the estimated change in the relevant pollutant summary measure.
A typical health impact function might look like this:
Ay = y0 • (e^x - 1),
where yo is the baseline incidence, equal to the baseline incidence rate times the potentially
affected population; p is the effect estimate; Ax is the estimated change in the pollutant (e.g., Os
or PM2 5) and Ay is the estimated change in incidence of the health effect (e.g., the number of
deaths avoided) associated with the change in the pollutant, Ax. There are other functional
forms, but the basic elements remain the same.
7b-14
-------
7b.5.1 Reductions in 0^-RelatedPremature Deaths
To calculate Os-related life years saved under a given illustrative Os NAAQS attainment
strategy, we first calculated the numbers of Os-related statistical lives saved within 5-year age
groups, using BenMAP. (For more details on the calculation of statistical lives saved using
BenMAP, see Chapter 8 or the BenMAP user's manual
(http://www.epa.gov/ttn/ecas/benmodels.html). We used two studies used in the benefit analysis
for the O3 NAAQS RIA - Bell et al. (2004) and Levy et al. (2005). Both studies report estimated
C-R functions of the association between premature mortality and short-term exposures to
ambient Os. Bell et al. (2004) is a multi-city study of 95 cities, and as such may avoid the
potential for publication bias that may be inherent in single-city studies or meta-analyses of
single-city studies. This study provides the lowest estimate of Os-related premature deaths
among the mortality studies included in the Os NAAQS RIA benefit analysis. An upper bound
estimate of Os-related premature deaths in the Os NAAQS RIA benefit analysis was provided by
Levy et al. (2005). More extensive discussions of these studies are given in Chapter 8.
We checked to confirm that, for each Os NAAQS attainment strategy, the total number of Os-
related statistical lives saved, summed across all age groups, equals the corresponding number
calculated in the benefit analysis. Age group-specific Os-related premature deaths avoided under
the illustrative Os NAAQS attainment strategies for which we considered only Os-related
benefits are given in Table 7b-2.
7b-15
-------
Table 7b-2. Estimated Reduction in Incidence of O3-Related Premature Mortality Associated with
Illustrative O3 NAAQS Attainment Strategies in 2020
Age
Interval
0-4
5-9
10-14
15- 19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
Total:
Reduction in O3-Related Premature Mortality
(95% Cl)*
Belletal. (2004)
Levyetal. (2005)
Baseline of Full Attainment of Current (0.084 ppm) Standard to Control Scenario of Full Attainment of:
0.079 ppm
0
(0-0)
0
(0-0)
0
(0-0)
0
(0-0)
0
(0-0)
0
(0-0)
0
(0-0)
0
(0-1)
0
(0-1)
1
(0-2)
1
(0-2)
3
(1-4)
2
(1-4)
4
(1-7)
3
(1-6)
5
(2-9)
3
(1-6)
11
(4-19)
36
(12-60)
0.075 ppm
0
(0-1)
0
(0-0)
0
(0-0)
0
(0-0)
0
(0-0)
0
(0-1)
0
(0-1)
1
(0-2)
1
(0-2)
2
(1-4)
2
(1-4)
7
(2-11)
6
(2-10)
11
(4 - 19)
9
(3 - 15)
14
(5 - 23)
9
(3 - 15)
30
(10-50)
94
(31 - 160)
0.070 ppm
1
(0-2)
0
(0-0)
0
(0-0)
0
(0-1)
0
(0-1)
1
(0-2)
1
(0-2)
3
(1-5)
3
(1-5)
7
(2-12)
7
(2-12)
20
(6 - 34)
19
(6 - 32)
36
(11 -61)
28
(9-47)
45
(14 - 75)
29
(9 - 49)
94
(30-160)
300
(93 - 500)
0.065 ppm
2
(1-3)
1
(0-1)
1
(0-1)
1
(0-1)
1
(0-1)
3
(1-4)
2
(1-4)
5
(2-9)
5
(2-8)
12
(4 - 20)
13
(4-21)
35
(12-59)
33
(11 -55)
63
(21 -110)
49
(16-83)
80
(26 - 130)
51
(17-85)
170
(54 - 280)
520
(170-880)
0.079 ppm
1
(1-1)
0
(0-1)
0
(0-1)
1
(1-1)
1
(1-2)
2
(1-2)
2
(1-2)
3
(2-3)
2
(2-3)
5
(3-6)
5
(3-6)
12
(8-15)
11
(7-14)
19
(13-25)
15
(10-19)
23
(16-30)
15
(10-19)
49
(34 - 64)
160
(110-210)
0.075 ppm
2
(2-3)
1
(1-1)
1
(1-1)
2
(1-2)
3
(2-4)
4
(3-6)
4
(3-5)
6
(4-8)
6
(4-8)
12
(8 - 15)
12
(8 - 15)
30
(21 -39)
27
(19-36)
50
(34 - 65)
38
(27 - 50)
61
(42 - 80)
39
(27-51)
130
(90- 170)
430
(300 - 560)
0.070 ppm
7
(5-9)
3
(2-4)
3
(2-4)
5
(4-7)
9
(6-11)
12
(9-16)
12
(8-15)
18
(12-24)
17
(11 -22)
34
(23 - 45)
35
(24 - 47)
91
(63 - 120)
85
(58- 110)
160
(110-210)
120
(84-160)
200
(130 - 260)
130
(86-170)
140
(280 - 540)
1,300
(920 - 1 ,800)
0.065 ppm
12
(8-16)
5
(4-7)
5
(4-7)
9
(6-12)
15
(10-20)
22
(15-29)
21
(14-27)
32
(22 - 42)
29
(20 - 38)
60
(41 - 79)
62
(43 - 82)
160
(110-210)
150
(100-190)
280
(190-360)
220
(150-290)
350
(240 - 460)
220
(150-290)
240
(500 - 960)
2,400
(1,600-3,100)
*95 percent confidence or credible intervals (CIs) are based on the uncertainty about the coefficient in the mortality C-R functions.
All estimates rounded to two significant figures.
7b-16
-------
7b.5.2 Life Years Saved as a Result of Reductions in 0^-RelatedMortalityRisk
The number of life years saved depends not only on the number of statistical lives saved, but also
on the life expectancies associated with those statistical lives. As was pointed out in the CEA for
the PM NAAQS RIA, age-specific life expectancies for the general population are calculated
from mortality rates for the general population, and these reflect the prevalence of chronic
disease, which shortens life expectancies. The only reason one might use lower life expectancies
than those for the general population in the CEA for the Os NAAQS RIA is if the population at
risk from exposure to Os was limited solely or disproportionately to individuals with preexisting
chronic illness, whose life expectancies were, on average, shorter than those of the general
population (unless all of those individuals had preexisting chronic illness because of long-term
exposure to Os).
It is reasonable to assume that someone who dies from exposure to an air pollutant is already in a
compromised state. However, there are both acute and chronic compromised states. If an
individual has an acute illness (e.g., pneumonia) that puts him at risk of mortality when exposed
to a high concentration of an air pollutant, then in the absence of that high concentration he could
be expected to recover from the illness and go on to live the expected number of years for
someone his age - i.e., he would have the age-specific life expectancy of the general population.
If an individual has a chronic illness that makes him vulnerable to a high concentration of an air
pollutant, then an important question is whether or not he would have had that chronic illness if
he had not been exposed over the long term to high levels of the air pollutant.
We can categorize individuals who are at risk of dying because of exposure to an air pollutant
into three groups:
• those who are vulnerable because of a preexisting acute condition;
• those who are vulnerable because of a preexisting chronic condition that they would not
have had, had they not been exposed over the long term to high levels of the air pollutant;
and
• those who are vulnerable because of a preexisting chronic condition that they would have
had even in the absence of long term exposure to high levels of the air pollutant.
The age-specific life expectancies of the general population should apply to the first two groups,
and the age-specific life expectancies of the subpopulation with the relevant chronic condition(s)
should apply to the third group. If we knew the proportions of people who die from exposure to
Os who are in each group, and the life expectancies of people in the third group, we could
calculate the number of life years saved as follows:
Total life years saved = VM; *(pu * LEi + p2i * LEf + p3i * LE*)
i
where
MI denotes the number of Os-related deaths of individuals age /,
i denotes the general population life expectancy for age /,
7b-17
-------
t denotes the life expectancy for age / of the subpopulation with the relevant chronic
condition(s) - i.e., the third group;
pn denotes the proportion of the Mt Os-related deaths that are in the first group;
P2i denotes the proportion of the Mf Os-related deaths that are in the second group; and
pst denotes the proportion of the Mt Os-related deaths that are in the third group.
Unlike for PM2.s (discussed below in Section 7b.6), we currently lack information that would
allow us to estimate the relevant proportions necessary to estimate the set of life expectancies
that would be appropriate to apply to Os-related deaths. Although there is substantial evidence
linking premature mortality to short-term exposures to Os, there is currently not similar evidence
for long-term exposures. We therefore do not know if the second group above is relevant in the
case of Os-related mortality. Nor do we know what proportion of Os-related deaths can be
attributed to preexisting acute conditions (the first group) versus preexisting chronic conditions
that these individuals would have had even in the absence of long term exposure to Os (the third
group).
Because we currently lack the necessary information to determine the appropriate set of life
expectancies to use in calculating life years saved associated with Os-related premature mortality
avoided, we calculated life years saved based on four different underlying assumptions:
• A lower bound assumption of zero life years saved, based on the hypothesis that the
observed statistical association between premature mortality and short-term exposures to
Os is not actually a causal relationship;
• An upper bound assumption that an Os-related premature death of an individual of a
given age will result in a loss of life years equal to the life expectancy in the general
population of that age;
• Two intermediate assumptions: That the proportions of Os-related premature deaths in
the three groups delineated above (pn,p2i, and/^r-) are such that, on average, the age-
specific life expectancies among people who die Os-related premature deaths are those
of
o people with severe preexisting chronic conditions, whose life expectancies are
substantially shorter than those of the general population; and
o people with preexisting chronic conditions of a range of severities, whose life
expectancies are somewhat shorter than those of the general population.
Life years saved based on the upper bound assumption were calculated from age-specific
mortality probabilities for the general population taken from the Centers for Disease Control
(CDC) National Vital Statistics Reports, Vol. 56, No. 9, December 28, 2007, Table 1. Life table
for the total population: United States, 2004.4 We used a simplified method of calculating life
expectancies from these age-specific mortality probabilities that yielded life expectancies that
were close to the life expectancies derived using the more complicated method employed by the
http://www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr56 09.pdf
7b-18
-------
CDC.5 In particular, starting with a cohort of size 1,000,000 at birth, we calculated the life-years
lived between ages x and (jc+1), for x = 0, 1,2, ...,99, using the age-specific mortality
probabilities taken from the CDC Vital Statistics Report (see above) and assuming that all deaths
that occurred between ages x and (x+1) occurred midway through the year (i.e., we assigned 0.5
life-year to each year of death). The life expectancy at age n was then calculated as the sum of
the life-years lived from age n through age 100 divided by the cohort size at age n. The life
expectancy at age n is the number of life years lost due to an Os-related premature mortality of
an individual age n.
To estimate life years saved under the two intermediate assumptions about the life years lost as a
result of Os-related premature mortality, we turned to the epidemiological evidence of a
statistically significant association between short-term exposures to Os and respiratory hospital
admissions. This evidence suggests that these short-term exposures may exacerbate respiratory
conditions that were preexisting. It is reasonable to suppose that some of these hospitalizations
for respiratory illnesses on days of relatively high Os concentrations might result in death. It
may also be the case that some individuals who did not go to the hospital might also die. We
therefore looked for information on life expectancies of people with chronic respiratory
conditions.
While there is information readily available in vital statistics sources on rates of deaihfrom
chronic respiratory diseases, there is not similarly available information on rates of death among
that subpopulation who suffer from those diseases. It is the latter rate - the rate of death among
that subpopulation who suffers from those diseases - that is of interest.
A recent study of people with and without chronic obstructive pulmonary disease (COPD)
provided data from which we were able to construct estimates of the mortality rates of interest.
Mannino et al. (2006) followed a cohort of 15,440 subjects ages 43 to 66 for up to 11 years. The
cohort subjects were selected from the larger cohort of the Atherosclerosis Risk in Communities
(ARIC) study, which selected its subjects from the population of four U.S. communities by
probability sampling.6 The subjects in the Mannino study were limited to the ARIC participants
who provided baseline information on respiratory symptoms and diagnoses, who underwent
pulmonary function testing, and for whom follow-up data were available.
Using a modification of the criteria developed by the Global Initiative on Obstructive Lung
Disease (GOLD), Mannino et al. (2006) classified the study subjects into COPD severity groups
(or stages), with GOLD stage 0 (presence of respiratory symptoms in the absence of any lung
function abnormality) being the least severe COPD group, and GOLD stages 3 and 4 being the
most severe. The unadjusted death rates of the study participants (taken from Table 1 of
Mannino et al., 2006), ratios of (unadjusted) death rates, and hazard ratios, based on Cox
5 We calculated life expectancies from the mortality probabilities rather than using the life
expectancies given in the CDC table because we were going to also calculate life expectancies
for the subpopulations with severe COPD and with "average" COPD by adjusting the age-
specific mortality probabilities and then calculating life expectancies using these adjusted
probabilities.
6 In one of the four communities probability sampling was used to select African-Americans
only.
7b-19
-------
proportional hazard regressions, which took into account several covariates (including, among
others, age, sex, race, smoking status, and education level) are shown in the table below. In
addition, the right-most column of the table below shows the proportion of COPD subjects in the
study in each GOLD category.
Table 7b-3. Death Rates and Hazard Ratios for Subjects with Varying Degrees of Severity of
COPD (from Mannino et al., 2006)
GOLD* Category
GOLD 3 or 4
GOLD 2
GOLD 1
GOLDO
Restricted
Normal
Total
N
271
1,484
1,679
2,244
1,101
8,661
15,440
Deaths
92
232
137
204
150
427
1,242
(%)
33.9%
15.6%
8.2%
9.1%
13.6%
4.9%
8.0%
Person-
Years
2,143
12,852
15,031
20,191
9,644
79,317
139,178
Death Rate
per 1 ,000
Person-Years
42.9
18.1
9.1
10.1
15.6
5.4
8.9
Ratio of Death Rate
to Death Rate for
Normal Population
7.97
3.35
1.69
1.88
2.89
1.00
Hazard
Ratio**
5.7
2.4
1.4
1.5
2.3
1.0
Proportion of COPD
Subjects in GOLD
Category
4.77%
26.14%
29.57%
39.52%
'Global Inititative on Obstructive Lung Disease (GOLD) guidelines for the staging of COPD severity.
"See Mannino et al. (2006), p. 117.
The ratios of unadjusted death rates are somewhat larger than the corresponding hazard ratios
because these ratios were not adjusted for age. COPD is a progressive disease, so it would be
expected that the proportion of older individuals would increase as the stages (and severity)
increased, and this was indeed the case in the Mannino study. The hazard ratios, being based on
regressions that took age into account, avoid this problem. We therefore used the hazard ratios
to derive age-specific mortality rates for individuals with (1) severe COPD and (2) COPD of
"average" severity. In particular, to derive age-specific mortality probabilities for the
subpopulation with severe COPD, we multiplied each age-specific mortality probability for the
general population by 5.7 (the hazard ratio for GOLD 3 or 4); to derive age-specific mortality
probabilities for the subpopulation with "average" COPD, we multiplied each age-specific
mortality probability for the general population by a weighted average of the GOLD category-
specific hazard ratios, where the weight for a GOLD category was the proportion of COPD
subjects in that GOLD category (given in the right-most column of Table 1 above). The
weighted average hazard ratio was 1.906. Age-specific life expectancies were then derived for
the severe COPD and "average" COPD subpopulations using these adjusted mortality
probabilities and the method for calculating life expectancies described above.
Once an appropriate set of life expectancies has been determined (e.g., life expectancies for the
general population or life expectancies for a subpopulation with severe COPD), these then
provide the number of life years lost for an individual who dies at a given age. This information
can then be combined with the estimated number of Os-related premature deaths at each age
calculated with BenMAP (see previous subsection). Because BenMAP calculates numbers of
premature deaths avoided within age intervals, we can either allocate the premature deaths
avoided within an age interval uniformly to the ages within the interval or, alternatively, we can
calculate average life expectancies for the age intervals. We illustrate the first approach in
7b-20
-------
calculating Os-related life years saved and the second approach in calculating PlV^.s-related life
years saved (see Section 7b.6).
Total Os-related life years gained was calculated as the sum of life years gained at each age:
N
Total life years gained = = LEf x Mi
i=0
where LEt is the remaining life expectancy for age i, Mi is the number of premature deaths
avoided among individuals age z, and TV is the oldest age considered.
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. 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).
Discounted total life years gained is calculated as follows:
rLE
Discounted LY = I e rtdt,
where r is the discount rate, t indicates time, and LE is the life expectancy at the time when the
premature death would have occurred. Because Os-related premature mortality is associated
only with short-term exposures, all Os-related premature deaths are assumed to occur in the year
of exposure. We therefore did not discount Os-related premature deaths avoided.
Undiscounted age-specific life expectancies, and age-specific life expectancies using discount
rates of 3 percent and 7 percent are given for the general population, the subpopulation of
individuals with severe COPD, and the subpopulation of individuals with COPD of average
severity in Tables 7b-4, 7b-5, and 7b-6, respectively. The Os-related (discounted) life years
saved, based on each of the two Os-mortality studies and each of the assumptions about relevant
life expectancies, are given, using 3 percent and 7 percent discount rates, in Tables 7b-7 and 7b-
8, respectively. The Os-related (discounted) life years saved, under the first assumption - that
the observed statistical association between premature mortality and short-term exposures to Os
is not actually a causal relationship - is zero in all cases (i.e., regardless of the mortality study
used and the scenario considered), and is therefore not shown in these Tables.
7b-21
-------
Table 7b-4. Undiscounted and Discounted Age-Specific Life Expectancies for the General
Population
Age at
Beginning
of Year
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
Mortality
Probability*
0.006799
0.000483
0.000297
0.000224
0.000188
0.000171
0.000161
0.000151
0.000136
0.000119
0.000106
0.000112
0.000149
0.000227
0.000337
0.000460
0.000579
0.000684
0.000763
0.000819
0.000873
0.000926
0.000960
0.000972
0.000969
0.000960
0.000954
0.000952
0.000958
0.000973
0.000994
0.001023
0.001063
0.001119
0.001192
0.001275
0.001373
0.001493
0.001634
0.001788
0.001945
0.002107
0.002287
0.002494
0.002727
0.002982
0.003246
Cohort Size
1,000,000
993,201
992,721
992,427
992,204
992,017
991,847
991,688
991,538
991,403
991,286
991,180
991,070
990,922
990,697
990,363
989,907
989,334
988,657
987,902
987,093
986,231
985,318
984,372
983,415
982,462
981,519
980,583
979,650
978,712
977,759
976,787
975,788
974,750
973,659
972,499
971,259
969,925
968,477
966,895
965,166
963,290
961,260
959,062
956,670
954,061
951,216
Deaths in
Year
6,799
480
295
222
187
170
159
149
135
118
105
111
148
225
333
456
573
677
755
809
862
913
946
957
953
943
936
933
939
952
972
999
1,038
1,091
1,160
1,240
1,334
1,448
1,582
1,729
1,877
2,029
2,198
2,392
2,609
2,845
3,088
Life- Years
in Year
996,600
992,961
992,574
992,315
992,111
991,932
991,768
991,613
991,471
991,345
991,233
991,125
990,996
990,809
990,530
990,135
989,621
988,996
988,280
987,498
986,662
985,775
984,845
983,893
982,939
981,991
981,051
980,117
979,181
978,235
977,273
976,287
975,269
974,205
973,079
971,879
970,592
969,201
967,686
966,031
964,228
962,275
960,161
957,866
955,366
952,639
949,672
Age-Specific
Life
Expectancy
77.8
77.3
76.4
75.4
74.4
73.4
72.4
71.4
70.4
69.5
68.5
67.5
66.5
65.5
64.5
63.5
62.5
61.6
60.6
59.7
58.7
57.8
56.8
55.9
54.9
54.0
53.0
52.1
51.1
50.2
49.2
48.3
47.3
46.4
45.4
44.5
43.5
42.6
41.7
40.7
39.8
38.9
38.0
37.0
36.1
35.2
34.3
3% Discounted
Remaining Life
Expectancy
30.9
30.8
30.7
30.6
30.5
30.4
30.3
30.2
30.1
29.9
29.8
29.7
29.5
29.4
29.2
29.1
28.9
28.8
28.6
28.4
28.3
28.1
27.9
27.8
27.6
27.4
27.2
27.0
26.8
26.5
26.3
26.1
25.9
25.6
25.4
25.1
24.9
24.6
24.3
24.0
23.7
23.5
23.2
22.8
22.5
22.2
21.9
7% Discounted
Remaining Life
Expectancy
15.2
15.2
15.2
15.2
15.2
15.2
15.2
15.2
15.2
15.1
15.1
15.1
15.1
15.1
15.1
15.1
15.1
15.0
15.0
15.0
15.0
15.0
15.0
14.9
14.9
14.9
14.9
14.8
14.8
14.8
14.7
14.7
14.7
14.6
14.6
14.5
14.5
14.4
14.4
14.3
14.3
14.2
14.1
14.0
14.0
13.9
13.8
7b-22
-------
Table 7b-4. Undiscounted and Discounted Age-Specific Life Expectancies for the General
Population (cont'd)
Age at
Beginning
of Year
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
Mortality
Probability*
0.003520
0.003799
0.004088
0.004404
0.004750
0.005113
0.005488
0.005879
0.006295
0.006754
0.007280
0.007903
0.008633
0.009493
0.010449
0.011447
0.012428
0.013408
0.014473
0.015703
0.017081
0.018623
0.020322
0.022104
0.024023
0.026216
0.028745
0.031561
0.034427
0.037379
0.040756
0.044764
0.049395
0.054471
0.059772
0.065438
0.071598
0.078516
0.085898
0.093895
0.102542
0.111875
0.121928
0.132733
0.144318
0.156707
0.169922
0.183975
0.198875
0.214620
0.231201
0.248600
0.266786
1 .000000
Cohort Size
948,129
944,792
941,203
937,355
933,227
928,794
924,045
918,974
913,571
907,820
901,689
895,125
888,051
880,384
872,027
862,915
853,037
842,435
831,140
819,111
806,249
792,477
777,719
761,915
745,073
727,174
708,110
687,756
666,050
643,120
619,080
593,849
567,266
539,246
509,873
479,397
448,026
415,949
383,290
350,366
317,468
284,915
253,040
222,187
192,695
164,886
139,047
115,420
94,186
75,454
59,260
45,559
34,233
25,100
Deaths in
Year
3,337
3,589
3,848
4,128
4,433
4,749
5,071
5,403
5,751
6,131
6,564
7,074
7,667
8,357
9,112
9,878
10,601
1 1 ,295
12,029
12,863
13,771
14,758
15,805
16,841
1 7,899
19,064
20,355
21,706
22,930
24,039
25,231
26,583
28,020
29,373
30,476
31,371
32,078
32,659
32,924
32,897
32,554
31,875
30,853
29,492
27,809
25,839
23,627
21,234
18,731
16,194
13,701
1 1 ,326
9,133
25,100
Life-Years
in Year
946,460
942,997
939,279
935,291
931,010
926,419
921,510
916,273
910,696
904,755
898,407
891,588
884,217
876,205
867,471
857,976
847,736
836,788
825,126
812,680
799,363
785,098
769,817
753,494
736,124
717,642
697,933
676,903
654,585
631,100
606,465
580,558
553,256
524,560
494,635
463,712
431,987
399,619
366,828
333,917
301,192
268,977
237,613
207,441
178,791
151,967
127,234
104,803
84,820
67,357
52,410
39,896
29,667
12,550
Age-Specific
Life
Expectancy
33.5
32.6
31.7
30.8
30.0
29.1
28.2
27.4
26.6
25.7
24.9
24.1
23.3
22.5
21.7
20.9
20.1
19.4
18.6
17.9
17.2
16.5
15.8
15.1
14.4
13.7
13.1
12.5
11.9
11.3
10.7
10.1
9.6
9.0
8.5
8.1
7.6
7.1
6.7
6.3
5.9
5.5
5.1
4.8
4.4
4.1
3.7
3.4
3.0
2.7
2.3
1.8
1.2
0.5
3% Discounted
Remaining Life
Expectancy
21.6
21.2
20.9
20.5
20.2
19.8
19.4
19.1
18.7
18.3
17.9
17.5
17.1
16.7
16.2
15.8
15.4
15.0
14.5
14.1
13.7
13.2
12.8
12.3
11.9
11.5
11.0
10.6
10.2
9.7
9.3
8.9
8.5
8.1
7.7
7.3
6.9
6.5
6.2
5.8
5.5
5.1
4.8
4.5
4.2
3.9
3.6
3.3
3.0
2.6
2.2
1.8
1.2
0.5
7% Discounted
Remaining Life
Expectancy
13.7
13.6
13.5
13.4
13.3
13.2
13.0
12.9
12.7
12.6
12.4
12.3
12.1
11.9
11.8
11.6
11.4
11.2
11.0
10.7
10.5
10.3
10.0
9.8
9.5
9.3
9.0
8.7
8.4
8.2
7.9
7.6
7.3
7.0
6.7
6.4
6.1
5.8
5.6
5.3
5.0
4.7
4.5
4.2
3.9
3.7
3.4
3.1
2.8
2.5
2.2
1.8
1.2
0.5
'Mortality probabilities for the general population taken from Table 1
United States, 2004. CDC National Vital Statistics Reports, Vol. 56,
http://www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr56 09.pdf
Life table for the total population:
No. 9, December 28, 2007
7b-23
-------
Table 7b-5. Undiscounted and Discounted Age-Specific Life Expectancies for the Subpopulation
with Severe COPD
Age at
Beginning
of Year
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
Mortality
Probability*
0.038755
0.002752
0.001692
0.001277
0.001074
0.000978
0.000916
0.000859
0.000777
0.000677
0.000606
0.000636
0.000850
0.001295
0.001918
0.002625
0.003301
0.003901
0.004351
0.004671
0.004976
0.005278
0.005472
0.005542
0.005522
0.005470
0.005436
0.005425
0.005461
0.005547
0.005668
0.005830
0.006061
0.006380
0.006792
0.007269
0.007827
0.008510
0.009312
0.010191
0.011084
0.012008
0.013035
0.014215
0.015546
0.016996
0.018503
Cohort Size
1 ,000,000
961,245
958,599
956,977
955,755
954,729
953,796
952,923
952,104
951,365
950,721
950,145
949,540
948,733
947,505
945,687
943,205
940,092
936,424
932,350
927,995
923,377
918,504
913,478
908,415
903,399
898,458
893,573
888,726
883,873
878,970
873,988
868,893
863,626
858,117
852,289
846,094
839,472
832,328
824,577
816,174
807,128
797,436
787,041
775,854
763,792
750,811
Deaths in
Year
38,755
2,646
1,622
1,222
1,026
933
873
819
739
644
576
605
807
1,229
1,818
2,482
3,113
3,667
4,075
4,355
4,618
4,873
5,026
5,063
5,016
4,942
4,884
4,847
4,853
4,903
4,982
5,095
5,266
5,510
5,828
6,195
6,622
7,144
7,750
8,403
9,047
9,692
10,395
11,187
12,061
12,981
13,892
Life-Years
in Year
980,622
959,922
957,788
956,366
955,242
954,263
953,359
952,513
951,734
951,043
950,433
949,842
949,137
948,119
946,596
944,446
941,648
938,258
934,387
930,172
925,686
920,941
915,991
910,947
905,907
900,928
896,016
891,150
886,300
881,422
876,479
871,440
866,260
860,872
855,203
849,191
842,783
835,900
828,452
820,376
811,651
802,282
792,238
781,447
769,823
757,301
743,865
Age-Specific
Life
Expectancy
54.5
55.7
54.9
53.9
53.0
52.1
51.1
50.2
49.2
48.2
47.3
46.3
45.3
44.4
43.4
42.5
41.6
40.8
39.9
39.1
38.3
37.5
36.7
35.9
35.1
34.2
33.4
32.6
31.8
31.0
30.1
29.3
28.5
27.6
26.8
26.0
25.2
24.4
23.6
22.8
22.0
21.3
20.5
19.8
19.1
18.4
17.7
3% Discounted
Remaining Life
Expectancy
27.5
27.7
27.5
27.4
27.2
27.0
26.8
26.5
26.3
26.1
25.8
25.6
25.3
25.1
24.8
24.6
24.3
24.0
23.8
23.5
23.3
23.0
22.7
22.4
22.2
21.9
21.6
21.2
20.9
20.6
20.2
19.9
19.5
19.2
18.8
18.4
18.0
17.6
17.2
16.8
16.4
16.0
15.6
15.2
14.8
14.4
14.0
7% Discounted
Remaining Life
Expectancy
14.9
14.9
14.9
14.9
14.9
14.8
14.8
14.8
14.7
14.7
14.7
14.6
14.6
14.5
14.5
14.4
14.4
14.3
14.3
14.2
14.1
14.1
14.0
13.9
13.9
13.8
13.7
13.6
13.5
13.4
13.3
13.2
13.1
12.9
12.8
12.7
12.5
12.3
12.2
12.0
11.8
11.7
11.5
11.3
11.1
10.9
10.7
7b-24
-------
Table 7b-5. Undiscounted and Discounted Age-Specific Life Expectancies for the Subpopulation
with Severe COPD (cont'd)
Age at
Beginning
of Year
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
Mortality
Probability*
0.020061
0.021652
0.023303
0.025103
0.027075
0.029144
0.031280
0.033512
0.035880
0.038497
0.041497
0.045046
0.049211
0.054108
0.059560
0.065249
0.070839
0.076425
0.082495
0.089507
0.097361
0.106149
0.115833
0.125993
0.136933
0.149433
0.163847
0.179896
0.196231
0.213062
0.232309
0.255152
0.281552
0.310486
0.340699
0.372994
0.408108
0.447543
0.489619
0.535199
0.584489
0.637689
0.694992
0.756579
0.822612
0.893232
Cohort Size
736,919
722,135
706,500
690,036
672,714
654,500
635,425
615,549
594,921
573,575
551,494
528,609
504,797
479,956
453,986
426,947
399,089
370,818
342,478
314,225
286,100
258,245
230,833
204,094
178,380
153,954
130,948
109,493
89,795
72,175
56,797
43,603
32,477
23,333
16,089
10,607
6,651
3,937
2,175
1,110
516
214
78
24
6
1
Deaths in
Year
14,784
15,636
16,464
17,322
18,214
19,075
19,876
20,628
21 ,346
22,081
22,885
23,812
24,842
25,969
27,040
27,858
28,271
28,340
28,253
28,125
27,855
27,413
26,738
25,714
24,426
23,006
21,455
19,697
17,621
15,378
13,194
11,125
9,144
7,245
5,481
3,956
2,714
1,762
1,065
594
302
137
54
18
5
0
Life-Years
in Year
729,527
714,317
698,268
681,375
663,607
644,963
625,487
605,235
584,248
562,535
540,052
516,703
492,376
466,971
440,467
413,018
384,953
356,648
328,352
300,163
272,173
244,539
217,463
191,237
166,167
142,451
120,220
99,644
80,985
64,486
50,200
38,040
27,905
19,711
13,348
8,629
5,294
3,056
1,642
813
365
146
51
15
3
0
Age-Specific
Life
Expectancy
17.0
16.3
15.7
15.0
14.4
13.8
13.2
12.6
12.0
11.5
10.9
10.3
9.8
9.3
8.8
8.3
7.9
7.4
7.0
6.6
6.2
5.8
5.4
5.1
4.7
4.4
4.1
3.8
3.5
3.2
3.0
2.7
2.5
2.3
2.1
1.9
1.7
1.5
1.4
1.3
1.1
1.0
0.9
0.8
0.6
0.0
3% Discounted
Remaining Life
Expectancy
13.6
13.1
12.7
12.3
11.9
11.5
11.1
10.7
10.3
9.9
9.5
9.0
8.6
8.2
7.9
7.5
7.1
6.8
6.4
6.1
5.7
5.4
5.1
4.8
4.5
4.2
3.9
3.6
3.4
3.1
2.9
2.7
2.4
2.2
2.0
1.9
1.7
1.5
1.4
1.3
1.1
1.0
0.9
0.8
0.6
0.0
7% Discounted
Remaining Life
Expectancy
10.4
10.2
10.0
9.8
9.5
9.3
9.0
8.8
8.5
8.2
8.0
7.7
7.4
7.1
6.9
6.6
6.3
6.0
5.8
5.5
5.2
5.0
4.7
4.4
4.2
3.9
3.7
3.5
3.2
3.0
2.8
2.6
2.4
2.2
2.0
1.8
1.7
1.5
1.4
1.2
1.1
1.0
0.9
0.8
0.6
0.0
*Mortality probabilities derived from mortality probabilities for the general population by multiplying
by the hazard ratio (5.7) for GOLD 3 or 4, from Mannino et al. (2006).
7b-25
-------
Table 7b-6. Undiscounted and Discounted Age-Specific Life Expectancies for the Subpopulation
with COPD of Average Severity
Age at
Beginning
of Year
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
Mortality
Probability*
0.012960
0.000920
0.000566
0.000427
0.000359
0.000327
0.000306
0.000287
0.000260
0.000226
0.000203
0.000213
0.000284
0.000433
0.000642
0.000878
0.001104
0.001304
0.001455
0.001562
0.001664
0.001765
0.001830
0.001853
0.001846
0.001829
0.001818
0.001814
0.001826
0.001855
0.001896
0.001949
0.002027
0.002133
0.002271
0.002431
0.002617
0.002846
0.003114
0.003408
0.003707
0.004016
0.004359
0.004753
0.005199
0.005683
0.006187
Cohort Size
1 ,000,000
987,040
986,132
985,574
985,153
984,799
984,477
984,176
983,893
983,638
983,415
983,216
983,006
982,727
982,302
981,671
980,810
979,727
978,449
977,025
975,499
973,876
972,157
970,378
968,580
966,792
965,023
963,269
961,521
959,766
957,985
956,169
954,305
952,371
950,339
948,181
945,876
943,400
940,716
937,786
934,591
931,127
927,388
923,345
918,956
914,179
908,983
Deaths in
Year
12,960
908
558
421
354
322
301
283
256
223
199
209
279
426
630
862
1,083
1,278
1,424
1,526
1,623
1,719
1,779
1,798
1,788
1,769
1,754
1,747
1,756
1,780
1,816
1,864
1,934
2,032
2,158
2,305
2,476
2,685
2,929
3,196
3,464
3,739
4,042
4,389
4,777
5,196
5,624
Life-Years
in Year
993,520
986,586
985,853
985,363
984,976
984,638
984,326
984,034
983,765
983,526
983,315
983,111
982,867
982,514
981,986
981,241
980,268
979,088
977,737
976,262
974,688
973,017
971,268
969,479
967,686
965,907
964,146
962,395
960,643
958,875
957,077
955,237
953,338
951,355
949,260
947,028
944,638
942,058
939,251
936,189
932,859
929,257
925,366
921,151
916,567
911,581
906,171
Age-Specific
Life
Expectancy
69.6
69.5
68.6
67.6
66.7
65.7
64.7
63.7
62.7
61.8
60.8
59.8
58.8
57.8
56.8
55.9
54.9
54.0
53.1
52.1
51.2
50.3
49.4
48.5
47.6
46.7
45.7
44.8
43.9
43.0
42.1
41.1
40.2
39.3
38.4
37.5
36.6
35.7
34.8
33.9
33.0
32.1
31.2
30.4
29.5
28.7
27.8
3% Discounted
Remaining Life
Expectancy
29.9
29.9
29.8
29.7
29.5
29.4
29.3
29.1
29.0
28.8
28.6
28.5
28.3
28.1
27.9
27.8
27.6
27.4
27.2
27.0
26.8
26.6
26.4
26.1
25.9
25.7
25.5
25.2
25.0
24.7
24.4
24.2
23.9
23.6
23.3
23.0
22.7
22.4
22.0
21.7
21.4
21.0
20.7
20.3
20.0
19.6
19.2
7% Discounted
Remaining Life
Expectancy
15.1
15.1
15.1
15.1
15.1
15.1
15.1
15.1
15.1
15.1
15.0
15.0
15.0
15.0
15.0
14.9
14.9
14.9
14.9
14.8
14.8
14.8
14.7
14.7
14.7
14.6
14.6
14.5
14.5
14.5
14.4
14.3
14.3
14.2
14.1
14.1
14.0
13.9
13.8
13.7
13.6
13.5
13.4
13.3
13.2
13.1
13.0
7b-26
-------
Table 7b-6. Undiscounted and Discounted Age-Specific Life Expectancies for the Subpopulation
with COPD of Average Severity (cont'd)
Age at
Beginning
of Year
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
Mortality
Probability*
0.006709
0.007241
0.007793
0.008395
0.009054
0.009746
0.010460
0.011207
0.011999
0.012874
0.013877
0.015064
0.016456
0.018094
0.019917
0.021820
0.023689
0.025557
0.027587
0.029932
0.032558
0.035497
0.038735
0.042133
0.045791
0.049971
0.054791
0.060158
0.065621
0.071249
0.077685
0.085324
0.094152
0.103828
0.113932
0.124731
0.136473
0.149661
0.163731
0.178974
0.195456
0.213247
0.232409
0.253004
0.275086
0.298702
0.323890
0.350677
0.379078
0.409089
0.440695
0.473858
0.508523
1 .000000
Cohort Size
903,359
897,298
890,801
883,860
876,440
868,505
860,040
851,044
841,507
831,410
820,707
809,318
797,127
784,009
769,823
754,490
738,028
720,545
702,130
682,760
662,324
640,760
618,015
594,076
569,046
542,989
515,855
487,591
458,258
428,187
397,679
366,785
335,489
303,902
272,349
241,319
211,219
182,394
155,096
129,702
106,489
85,675
67,405
51,740
38,649
28,017
19,649
13,285
8,626
5,356
3,165
1,770
931
458
Deaths in
Year
6,060
6,497
6,942
7,420
7,935
8,464
8,996
9,537
10,097
10,703
1 1 ,389
12,191
13,118
14,186
15,333
16,463
17,483
18,415
19,370
20,436
21,564
22,745
23,939
25,030
26,057
27,134
28,264
29,333
30,071
30,508
30,894
31,296
31,587
31,554
31,029
30,100
28,826
27,297
25,394
23,213
20,814
18,270
15,666
13,090
10,632
8,369
6,364
4,659
3,270
2,191
1,395
839
474
458
Life-Years
in Year
900,329
894,050
887,331
880,150
872,472
864,273
855,542
846,276
836,458
826,058
815,012
803,222
790,568
776,916
762,157
746,259
729,286
711,337
692,445
672,542
651,542
629,388
606,046
581,561
556,017
529,422
501,723
472,924
443,223
412,933
382,232
351,137
319,696
288,125
256,834
226,269
196,806
168,745
142,399
118,096
96,082
76,540
59,572
45,194
33,333
23,833
16,467
10,955
6,991
4,261
2,468
1,351
695
229
Age-Specific
Life
Expectancy
27.0
26.2
25.3
24.5
23.7
23.0
22.2
21.4
20.6
19.9
19.1
18.4
17.7
17.0
16.3
15.6
14.9
14.3
13.6
13.0
12.4
11.8
11.2
10.6
10.1
9.6
9.0
8.5
8.0
7.6
7.1
6.7
6.2
5.8
5.5
5.1
4.8
4.4
4.1
3.8
3.5
3.3
3.0
2.8
2.6
2.4
2.2
2.0
1.9
1.7
1.5
1.3
1.0
0.5
3% Discounted
Remaining Life
Expectancy
18.9
18.5
18.1
17.7
17.3
16.9
16.5
16.1
15.7
15.3
14.8
14.4
14.0
13.5
13.1
12.7
12.3
11.8
11.4
11.0
10.5
10.1
9.7
9.3
8.9
8.4
8.0
7.6
7.3
6.9
6.5
6.1
5.8
5.4
5.1
4.8
4.5
4.2
3.9
3.7
3.4
3.2
3.0
2.7
2.5
2.4
2.2
2.0
1.8
1.7
1.5
1.3
1.0
0.5
7% Discounted
Remaining Life
Expectancy
12.8
12.7
12.5
12.4
12.2
12.1
11.9
11.7
11.5
11.3
11.1
10.9
10.7
10.4
10.2
10.0
9.7
9.5
9.2
8.9
8.7
8.4
8.1
7.8
7.6
7.3
7.0
6.7
6.4
6.1
5.8
5.6
5.3
5.0
4.7
4.5
4.2
4.0
3.7
3.5
3.3
3.1
2.8
2.7
2.5
2.3
2.1
2.0
1.8
1.6
1.5
1.3
1.0
0.5
'Mortality probabilities derived from mortality probabilities for the general population (see Table 2) by multiplying
by the weighted average of hazard ratios for the GOLD severity categories (1.906) from Mannino et al. (2006).
7b-27
-------
Table 7b-7. Estimated Discounted O3-Related Life Years Saved Under Alternative Illustrative O3 NAAQS Attainment Strategies in 2020,
Using a 3 Percent Discount Rate
Estimated O3-Related Life Years Saved*
(95% Cl)**
Bell et al. (2004)
Levy etal. (2005)
Baseline: Full Attainment of Current (0.084 ppm) Standard; Control Scenario: Full Attainment of
Alternative Standard of:
0.079 ppm
0.075 ppm
0.070 ppm
0.065 ppm
0.079 ppm
0.075 ppm
0.070 ppm
0.065 ppm
Assuming Life Expectancies of the General Population
380
(130-630)
980
(320-1,600)
3,000
(960-5,100)
5,400
(1,700-9,000)
1,800
(1,300-2,400)
4,700
(3,300 - 6,200)
15,000
(10,000-19,000)
26,000
(18,000-34,000)
Assuming Life Expectancies of the Sub-Population with COPD of Average Severity
290
(97 - 480)
750
(250-1,300)
2,300
(740 - 3,900)
4,100
(1,300-6,900)
1,400
(1,000-1,900)
3,700
(2,500-4,800)
11,000
(7,800-15,000)
20,000
(14,000-26,000)
Assuming Life Expectancies of the Sub-Population with Severe COPD
160
(54 - 270)
420
(140-700)
1,300
(400 - 2,200)
2,300
(730 - 3,800)
840
(580-1,100)
2,100
(1,500-2,800)
6,500
(4,500 - 8,600)
11,000
(7,900-15,000)
*The O3-related (discounted) life years saved, under the first assumption - that the observed statistical association between premature mortality and short-
term exposures to O3 is not actually a causal relationship - is zero in all cases (i.e., regardless of the mortality study used and the scenario considered, and
is therefore not shown.
**95 percent confidence or credible intervals (CIs) are based on the uncertainty about the coefficient in the mortality C-R functions. All estimates rounded to
two significant figures.
7b-28
-------
Table 7b-8. Estimated Discounted O3-Related Life Years Saved Under Alternative Illustrative O3 NAAQS Attainment Strategies in 2020,
Using a 7 Percent Discount Rate
Estimated O3-Related Life Years Saved*
(95% Cl)**
Bell et al. (2004)
Levyetal. (2005)
Baseline: Full Attainment of Current (0.084 ppm) Standard; Control Scenario: Full Attainment of
Alternative Standard of:
0.079 ppm
0.075 ppm
0.070 ppm
0.065 ppm
0.079 ppm
0.075 ppm
0.070 ppm
0.065 ppm
Assuming Life Expectancies of the General Population
290
(96 - 480)
750
(250-1,200)
2,300
(740 - 3,900)
4,100
(1,300-6,900)
1,400
(940-1,800)
3,500
(2,400 - 4,600)
11,000
(7,500-14,000)
19,000
(13,000-25,000)
Assuming Life Expectancies of the Sub-Population with COPD of Average Severity
230
(77 - 390)
600
(200-1,000)
1,900
(590 - 3,200)
3,300
(1,100-5,500)
1,100
(770-1,500)
2,900
(2,000 - 3,800)
8,900
(6,100-12,000)
16,000
(11,000-21,000)
Assuming Life Expectancies of the Sub-Population with Severe COPD
140
(46 - 230)
350
(120-590)
1,100
(340-1,800)
1,900
(620 - 3,200)
690
(480 - 900)
1,800
(1,200-2,300)
5,400
(3,700-7,100)
9,500
(6,500-12,000)
*The O3-related (discounted) life years saved, under the first assumption - that the observed statistical association between premature mortality and short-
term exposures to O3 is not actually a causal relationship - is zero in all cases (i.e., regardless of the mortality study used and the scenario considered, and
is therefore not shown.
**95 percent confidence or credible intervals (CIs) are based on the uncertainty about the coefficient in the mortality C-R functions. All estimates rounded to
two significant figures.
7b-29
-------
7b.5.3 Cost-Effectiveness Ratios
For each illustrative Os NAAQS attainment strategy for which we considered only Os-related
benefits, we calculated one set of cost-effectiveness ratios using total lives saved, based on the
Bell study and the Levy study, as the denominator, and another set using total life years saved as
the denominator. As discussed above in Section 7b.4, we netted out the monetized benefits of
avoided cases of Os-related acute morbidity (respiratory hospital admissions, asthma-related ER
visits, school absence days, and minor restricted activity days) as well as avoided Os-related
worker productivity losses from the direct costs of the controls necessary to achieve the
reductions in ambient concentrations of Os in the numerator. Incidences of avoided acute
morbidity are given in Chapter 8.
We used Monte Carlo procedures to incorporate the uncertainty surrounding the Os coefficient in
each of the C-R functions (including C-R functions for each of the acute morbidity endpoints as
well as the C-R function for mortality) as well as the uncertainty surrounding the unit value
(monetized benefit of an avoided case) of each acute morbidity endpoint. This procedure was
repeated separately for each of the two mortality C-R functions used, and, for cost-effectiveness
ratios using life years saved, for each combination of mortality C-R function and assumption
about relevant life expectancies. The results are shown in Table 7b-9 for cost-effectiveness
ratios using lives saved. As noted above, Os-related premature mortality avoided (lives saved)
are assumed to be related only to short-term exposures and are not discounted. The cost of the
regulation, however, which occurs over a period of time, is discounted (using discount rates of 3
percent and 7 percent). Tables 7b-10 and 7b-l 1 show cost-effectiveness ratios using life years
saved, using discount rates of 3 and 7 percent, respectively. Both the costs of the regulation and
the lives saved are discounted.
As noted in Section 1, these cost-effectiveness ratios omit the PlV^.s-related co-benefits of these
illustrative Os NAAQS strategies and are therefore likely to understate the cost effectiveness of
these strategies. As can be seen in Tables 7b-9 through 7b-l 1, the direct costs of the controls
necessary to achieve the reductions in ambient concentrations of Os, in the numerators of the
cost-effectiveness ratios, increase with the stringency of the alternative standards. The lives and
life years saved, in the denominators of the cost-effectiveness ratios, similarly increase with the
stringency of the alternative standards. It is therefore not surprising that we do not see a
monotonic trend in these ratios across the increasingly more stringent alternative standards.
7b-30
-------
Table 7b-9. Estimated Net Cost (2006$) per O3-Related Life Saved Under Alternative Illustrative O3 NAAQS Attainment Strategies in 2020
Mortality Study
Cost Effectiveness Ratio: Net Cost (in Million $) per Life Saved*
(95% Cl)**
Change From Full Attainment of the Current (0.084 ppm) Std. To Full Attainment of Alternative Std. of:
0.079 ppm
Estimated 3% discounted cost of the regulation (in
$2.9
Bell etal. (2004)
Levy et al. (2005)
$93
($48 - $240)
$18
($13 -$25)
Using lower bound estimate of 7% discounted cost
$2.4
Bell etal. (2004)
Levy et al. (2005)
$76
($40 - $200)
$15
($11 -$21)
Using upper bound estimate of 7% discounted cos
$2.9
Bell etal. (2004)
Levy et al. (2005)
$93
($48 - $240)
$18
($13 -$25)
0.075 ppm
Billion $):***
$8.8
$110
($55 - $280)
$21
($15 -$29)
of the regulation (in Billion
$7.6
$92
($48 - $240)
$18
($13 -$25)
f of the regulation (in Billion
$8.8
$110
($55 - $280)
$21
($15 -$29)
0.070 ppm
$25
$98
($50 - $260)
$19
($14 -$27)
$):
$19
$74
($38 - $200)
$14
($1 1 - $20)
$):
$25
$98
($50 - $260)
$19
($14 -$27)
0.065 ppm
$44
$96
($50 - $260)
$19
($14 -$27)
$32
$70
($36 -$190)
$14
($10 -$19)
$44
$96
($50 - $260)
$19
($14 -$27)
*Because PM2.5-related benefits are not incorporated in these cost effectiveness ratios, the cost effectiveness of full attainment of
each alternative O3 standard shown in this table will tend to be understated.
**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 coefficients in the mortality and
morbidity endpoints as well as the uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two
significant figures.
***Uses the upper bound estimates of the 7% discounted costs of the regulations as proxies for the 3% discounted costs.
7b-31
-------
Table 7b-10. Estimated Net Cost (2006$) per O3-Related Life Year Saved Under Alternative Illustrative O3 NAAQS Attainment
Strategies in 2020, Using a 3 Percent Discount Rate
Mortality Study
Life Expectancy Assumption
Estimated 3% discounted cost
of the regulation (in Billion $):***
Bell et al. (2004)
Bell et al. (2004)
Bell et al. (2004)
Levy etal. (2005)
Levyetal. (2005)
Levy etal. (2005)
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
Cost Effectiveness Ratio: Net Cost (in Million $) per Life Year Saved*
(95% Cl)**
Change From Full Attainment of the Current (0.084 ppm) Std. To Full Attainment of Alternative Std. of:
0.079 ppm
$2.9
$8.7
($4.6 - $23)
$11
($5.9 - $29)
$20
($11 -$53)
$1.6
($1.2 -$2.3)
$2.0
($1.5 -$2.9)
$3.5
($2.6 - $4.9)
0.075 ppm
$8.8
$10
($5.3 - $27)
$13
($6.9 - $35)
$24
($13 -$63)
$1.9
($1.4 -$2.7)
$2.4
($1.8 -$3.4)
$4.2
($3.1 -$5.9)
0.070 ppm
$25
$9.5
($4.8 - $26)
$12
($6.3 - $34)
$22
($11 -$61)
$1.7
($1.3 -$2.5)
$2.2
($1.7 -$3.2)
$3.9
($2.9 - $5.5)
0.065 ppm
$44
$9.6
($4.8 - $25)
$12
($6.3 - $33)
$22
($12 -$59)
$1.7
($1.3 -$2.5)
$2.2
($1.7 -$3.2)
$3.9
($2.9 - $5.5)
'Because PM2.s-related benefits are not incorporated in these cost effectiveness ratios, the cost effectiveness of full attainment of each alternative O3 standard shown in
this table will tend to be understated.
**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 coefficients in the mortality and morbidity C-R functions as well as the
uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures.
***Uses the upper bound estimates of the 7% discounted costs of the regulations as proxies for the 3% discounted costs.
7b-32
-------
Table 7b-11. Estimated Net Cost (2006$) per O3-Related Life Year Saved Under Alternative Illustrative O3 NAAQS Attainment Strategies
in 2020, Using a 7 Percent Discount Rate
Mortality Study
Life Expectancy Assumption
Using lower bound estimate of 7% discounted cost
of the regulation (in Billion $):
Bell etal. (2004)
Bell etal. (2004)
Bell etal. (2004)
Levy etal. (2005)
Levy etal. (2005)
Levy etal. (2005)
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
Using upper bound estimate of 7% discounted cost
of the regulation (in Billion $):
Bell etal. (2004)
Bell etal. (2004)
Bell etal. (2004)
Levy etal. (2005)
Levy etal. (2005)
Levy etal. (2005)
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
Cost Effectiveness Ratio: Net Cost (in Million $) per Life Year Saved*
(95% Cl)**
Change From Full Attainment of the Current (0.084 ppm) Std. To Full Attainment of Alternative Std. of:
0.079 ppm
$2.4
$9.4
($4.9 - $25)
$12
($6.1 -$31)
$20
($10 -$52)
$1.8
($1.3 -$2.5)
$2.2
($1.6 -$3.1)
$3.5
($2.6 - $5)
$2.9
$11
($6 - $30)
$14
($7.4 - $37)
$24
($13 -$63)
$2.2
($1.6- $3)
$2.6
($2 - $3.7)
$4.3
($3.2 - $6)
0.075 ppm
$7.6
$12
($6 - $30)
$14
($7.5 - $38)
$25
($13 -$64)
$2.2
($1.6 -$3.1)
$2.7
($2 - $3.8)
$4.4
($3.3 - $6.2)
$8.8
$14
($7 - $35)
$17
($8.7 - $44)
$29
($15 -$75)
$2.5
($1.9 -$3.6)
$3.1
($2.3 - $4.4)
$5.1
($3.8 - $7.2)
0.070 ppm
$19
$9.5
($4.8 - $25)
$12
($5.9 - $32)
$20
($10 -$55)
$1.7
($1.3 -$2.5)
$2.2
($1.6 -$3.1)
$3.6
($2.6 -$5.1)
$25
$13
($6.3 - $34)
$16
($7.8 - $42)
$27
($13 -$72)
$2.3
($1.7 -$3.3)
$2.9
($2.1 -$4.1)
$4.7
($3. 5 -$6.7)
0.065 ppm
$32
$8.8
($4.6 - $24)
$11
($5.7 - $29)
$19
($9.8 - $50)
$1.7
($1.2 -$2.4)
$2.1
($1.5 -$2.9)
$3.4
($2.5 - $4.8)
$44
$12
($6.3 - $32)
$15
($7.9 -$41)
$26
($14 -$70)
$2.3
($1.7 -$3.3)
$2.8
($2.1- $4)
$4.7
($3. 5 -$6.7)
'Because PM2.s-related benefits are not incorporated in these cost effectiveness ratios, the cost effectiveness of full attainment of each alternative O3 standard shown in
this table will tend to be understated.
**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 coefficients in the mortality and morbidity C-R functions as well as the
uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures.
7b-33
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7b.6 Cost-Effectiveness Metrics Incorporating Both Os-Related and PM2.s-Related
Benefits
In this section we describe the development of cost-effectiveness metrics for the single
illustrative Os NAAQS attainment strategy for which we were able to incorporate both Os-
related benefits and PlV^.s-related co-benefits, in which the baseline is partial attainment of the
current Os standard of 0.084 ppm and the control scenario is partial attainment of an alternative
standard of 0.070 ppm.
7b.6.1 Os-related Lives Saved and Life Years Saved
The methods used to calculate Os-related lives saved and Os-related life years saved under this
scenario are the same as those described above in Section 7b.5. Estimated numbers of Os-related
premature deaths avoided are shown in Table 7b-12. The corresponding Os-related life years
saved, discounted using 3 percent and 7 percent discount rates, are shown in Tables 7b-13 and
7b-14, respectively.
7b-34
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Table 7b-12. Estimated Reduction in Incidence of O,-Related Premature Mortality Associated with
Illustrative O3 NAAQS Attainment Strategy in 2020: Changing from Partial Attainment
of the Current O3 NAAQS to Partial Attainment of an Alternative O3 NAAQS of 0.07
ppm
Age
Interval
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
Total:
Reduction in O3-Related Premature Mortality
(95% Cl)*
Baseline of Partial Attainment of Current (0.084 ppm) Standard to Control
Scenario of Partial Attainment of 0.07 ppm
Belletal. (2004)
0
(0-1)
0
(0-0)
0
(0-0)
0
(0-0)
0
(0-0)
1
(0-1)
0
(0-1)
1
(0-2)
1
(0-2)
3
(1-5)
3
(1-5)
8
(2-14)
8
(2-14)
16
(5 - 27)
12
(4-21)
20
(6 - 33)
12
(4-21)
40
(12-68)
130
(36 - 220)
Levyetal. (2005)
3
(2-3)
1
(1-2)
1
(1-1)
2
(1-3)
3
(2-4)
4
(3-6)
4
(3-6)
7
(4-9)
6
(4-8)
13
(9-17)
14
(9-18)
37
(25 - 50)
36
(24 - 48)
70
(47 - 94)
55
(37 - 73)
86
(58-110)
55
(37 - 73)
170
(120-230)
570
(380 - 760)
*95 percent confidence or credible intervals (CIs) are based on the uncertainty about
the coefficient in the mortality C-R functions. All estimates rounded to two significant
figures.
7b-35
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Table 7b-13. Estimated O,-Related Life Years Saved Associated with Illustrative O3 NAAQS
Attainment Strategy in 2020: Changing from Partial Attainment of the Current O3
NAAQS to Partial Attainment of an Alternative O3 NAAQS of 0.07 ppm, Using a 3
Percent Discount Rate
Estimated O3-Related Life Years Saved
(95% CI)*
Baseline: Partial Attainment of Current (0.084 ppm) Standard; Control Scenario: Partial
Attainment of Alternative Standard of 0.070 ppm
Mortality Study:
Assuming Life Expectancies of the General Population
Assuming Life Expectancies of the Sub-Population with
COPD of Average Severity
Assuming Life Expectancies of the Sub-Population with
Severe COPD
Bell et al (2004)
1,300
(370 - 2,200)
980
(280-1,700)
530
(150-910)
Levyetal. (2005)
6,100
(4,100-8,100)
4,700
(3,200 - 6,300)
2,700
(1,800-3,500)
*95 percent confidence or credible intervals are based on the uncertainty about the coefficient in
the mortality C-R functions. All estimates rounded to two significant figures.
Table 7b-14. Estimated O,-Related Life Years Saved Associated with Illustrative O3 NAAQS
Attainment Strategy in 2020: Changing from Partial Attainment of the Current O3
NAAQS to Partial Attainment of an Alternative O3 NAAQS of 0.07 ppm, Using a 7
Percent Discount Rate
Estimated O3-Related Life Years Saved
(95% CI)*
Baseline: Partial Attainment of Current (0.084 ppm) Standard; Control Scenario: Partial
Attainment of Alternative Standard of 0.070 ppm
Mortality Study:
Assuming Life Expectancies of the General Population
Assuming Life Expectancies of the Sub-Population with
COPD of Average Severity
Assuming Life Expectancies of the Sub-Population with
Severe COPD
Bell et al (2004)
990
(280-1,700)
790
(230-1,400)
450
(130-780)
Levyetal. (2005)
4,600
(3,100-6,100)
3,700
(2,500-4,900)
2,200
(1,500-2,900)
*95 percent confidence or credible intervals are based on the uncertainty about the coefficient in
the mortality C-R functions. All estimates rounded to two significant figures.
7b.6.2 Reductions in PM2.s-Related Premature Deaths
To generate PlV^.s-related health outcomes, we used the same framework as for the benefit-cost
analysis described in Chapter 8 and briefly summarized above in the introductory portion of
Section 8.4.
7b-36
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As in several recent air pollution health impact assessments (e.g., Kunzli et al., 2000; EPA,
2004), we focused 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., 2001;
NRC, 2002). We selected an effect 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.s mortality expert elicitation, we 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 PM2.s following implementation of
PM2.5 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 PM2.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 PIVb.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
A recent follow up to the Harvard 6-city study (Laden et al., 2006) both confirmed the effect size
from the first study and provided additional confirmation that reductions in PIVb.s directly result
in reductions in the risk of premature death. This additional evidence stems from the observed
reductions in PIVb.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
PM2.5. The effect estimate obtained from Laden et al. (2006) is 0.0148, which is equivalent to a
relative risk of 1.16 for a 10 (j,g/m3 change in
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 PlV^.s-related premature mortality within each age group
associated with the illustrative 0.07 ppm partial attainment strategy in 2020 are summarized in
Table 7b-15.
7b-37
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Table 7b-15: Estimated Reduction in Incidence of PM2.s-Related All-Cause Premature Mortality
Under an Illustrative Strategy of Changing from Partial Attainment of the Current
(0.084 ppm) O, NAAQS to Partial Attainment of an Alternative 0.070 ppm O, NAAQS
in 2020
Reduction in All-Cause Premature Mortality
(95% C/T
Age Interval
30-34
35-44
45-54
55-64
65-74
75-84
85+
Total
Pope (2002)
4
(1-6)
11
(4-18)
23
(9 - 36)
56
(22 - 90)
93
(37-150)
110
(43-180)
140
(56 - 230)
440
(170-700)
Laden (2006)
8
(5-12)
25
(13-36)
51
(28 - 75)
130
(69-180)
210
(120-310)
250
(130-360)
320
(180-470)
990
(540-1,400)
*95% confidence intervals are based on the uncertainty surrounding the effect estimate (coefficient) in the
mortality C-R function. All estimates rounded to two significant figures.
7b.6.3 Life Years Saved as a Result of Reductions in PM2.s-Related Mortality Risk
To calculate life years saved 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 7b-16 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.
From the abridged life table (Table 7b-16), 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
7b-38
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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:
TotalLife Years-
x M
where LEt is the remaining life expectancy for age interval i, Mt is the change in incidence of
mortality in age interval i, and TV is the number of age intervals.
As noted above, 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. 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).
Table 7b-16. Abridged Life Table for the Total Population, United States, 2000
Age Interval
Probability
of Dying
Between
Ages x to
x+1
Number
Surviving to
Age x
Number
Dying
Between
Ages x to
x+1
Person
Years
Lived
Between
Ages x to
x+1
Total
Number of
Person
Years
Lived
Above Age
x
Expectation
of Life at
Age x
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
Ix
97,696
97,132
95,210
91,113
82,131
64,244
34,959
7,252
1,781
dx
564
1,922
4,097
8,982
17,887
29,285
27,707
5,471
1,781
Lx
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
ex
48.3
43.6
34.4
25.7
17.9
11.3
6.3
3.5
2.6
Unlike Os-related premature deaths, PM2.s-related premature deaths are associated with long-
term exposures. We therefore did not assume that these deaths all occur in 2020. The PM2.5-
related premature deaths avoided and associated life years saved are thus further discounted to
account for the lag between the reduction in ambient PM2.5 and the corresponding reduction in
mortality risk. We used the same 20-year segmented lag structure that is used in the benefit-cost
analysis (see Chapter 8).
7b-39
-------
The most complete estimate of the impacts of PIVb.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 PIVb.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 PIVb.s 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
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. PM2.s-Related life years saved associated with the illustrative 0.07 ppm partial
attainment strategy in 2020 are given in Table 7b-17.
Table 7b-17. Estimated PM2.5-Related Life Years Saved Associated with Illustrative O3 NAAQS
Attainment Strategy in 2020: Changing from Partial Attainment of the Current O3
NAAQS to Partial Attainment of an Alternative O3 NAAQS of 0.07 ppm
Estimated PM2.5-Related Life Years Saved
(95% Cl)*
Discounted back to 2020,
Discounted back to 2020,
using a 3 percent discount rate:
using a 7 percent discount rate:
Pope et al (2002)
4,400
(1,700-7,000)
3,000
(1,200-4,800)
Laden etal. (2006)
9,900
(5,400-14,000)
6,700
(3,700-9,800)
*95 percent confidence or credible intervals (CIs) are based on the uncertainty about the coefficient in
the mortality C-R functions. All estimates rounded to two significant figures.
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
7b-40
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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.
7b.6.4 Calculating Changes in the Quality of Life Years (PM2.s-Related Chronic 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. The indirect reductions in levels of PM2.s
also lead to reductions in serious illnesses that affect quality of life. These include chronic
bronchitis (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 the cost effectiveness analysis for the PM NAAQS RIA, we developed estimates of the
QALYs gained from reductions in the incidence of CB and nonfatal heart attacks associated with
reductions in ambient PIVb.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.
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 7b-18 summarizes the key inputs for calculating QALYs associated with chronic health
endpoints.
7b-41
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Table 7b-18. 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).
7b. 6. 4.1 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 QALYGAINED =
x D* x w - w
B
where ACB; is the number of incidences of CB avoided in age interval i, wr is the average QALY
weight for the z'th age interval, wfB is the QALY weight associated with CB in the z'th age
7b-42
-------
interval, and/)* is the discounted duration of life with CB for individuals with onset of disease in
the z'th age interval, equal to \e~"dt , where A is the duration of life with CB for individuals
with onset of disease the fth age interval.
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. Only the Abbey et al. (1995) study was used,
because it is the only study focusing on the relationship between PIVb.s and new incidences of
CB. The number of cases of CB in each age interval was derived by applying the impact
function from Abbey et al. (1995) to the population in each age interval with the appropriate
baseline incidence rate.7 The effect estimate from the Abbey et al. (1995) study is 0.0137,
which, based on the logistic specification of the model, is equivalent to a relative risk of 1.15 for
a 10 |ig change in PIVb.s. Table 7b-19 presents the estimated reduction in new incidences of CB
associated with the 0.070 ppm partial attainment strategy.
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 subtracted 4.26 from
the remaining life expectancy for each age group, up to age 75. For age groups over 75, we
applied the ratio of 4.26 to the life expectancy for the 65 to 74 year group (0.237) to the life
expectancy for the 75 to 84 and 85 and up age groups to estimate potential life years lost and
then subtracted that value from the base life expectancy.
7 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).
7b-43
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Table 7b-19. Estimated Reduction in Incidence of Chronic Bronchitis Under an Illustrative
Strategy of Changing from Partial Attainment of the Current (0.084 ppm) O3 NAAQS
to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 2020
Age Interval Reduction in Incidence
(95% Confidence Interval)*
25 ~34 (14-140)
35 - 44 85
M 44 (16-150)
45-54 80
^ ^ (15-150)
_ KA 85
00 ° (16-160)
65 ~74 (11-°110)
75-84 (6-°54)
(2^24)
T°tal (78-3770)
*95% confidence intervals are based on the uncertainty surrounding the effect estimate (coefficient) in
the CB C-R function. All estimates rounded to two significant figures.
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 selected 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.
7b-44
-------
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.8 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 7b-20. Table 7b-20 presents both the undiscounted and discounted QALYs
gained per incidence, using a 3 percent discount rate.
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).
7b-45
-------
Table 7b-20. 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+
QAL Ys 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)
7b.6.4.2 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 coronary heart disease (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 estimated only 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 7b-l shows the heart attack QALY model in
diagrammatic form.
The total gain in QALYs is calculated as:
7b-46
-------
DISCOUNTED AM QALY GAINED =
X
y y hAM. x P.D:POS'AM x (w. - wpostAM
Z_l Z_l i rj y \'Yi 'Yy
i >1
where A AMI; is the number of nonfatal acute myocardial infarctions avoided in age interval z,
wfM is the QALY weight associated with the acute phase of the AMI, PJ is the probability of
being in they'th post-AMI status, wj™' is the QALY weight associated with post-AMI health
fDfM
statusy", w; is the average QALY weight for age interval i, A ~ J , e r dt ^ the discounted
Dpo,lAMI
value of DAM, the duration of the acute phase of the AMI, and A = Jf=1 e ^, is the
discounted value of Dtj ost , the duration of post-AMI health statusy".
7b-47
-------
Acute Treatment Stage
Chronic Post-AMI Follow up Stage
Post AMI QALY with Angina and CHF
Nonfatal AMI
Post AMI QALY with CHF without Angina
Post AMI QALY with Angina without CHF
Post AMI QALY without Angina or CHF
Figure 7b-1. Decision Tree Used in Modeling Gains in QALYs from Reduced Incidence of
Nonfatal Acute Myocardial Infarctions
Nonfatal heart attacks have been linked with short-term exposures to PIVb.s 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.s
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
7b-48
-------
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 was derived by applying the impact
function from Peters et al. (2001) to the population in each age interval with the appropriate
baseline incidence rate.9 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 PM2.5. Table 7b-21 presents the estimated reduction in nonfatal AMI associated with
the illustrative Ozone NAAQS attainment strategies.
Table 7b-21. Estimated Reduction in Nonfatal Acute Myocardial Infarctions Under an Illustrative
Strategy of Changing from Partial Attainment of the Current (0.084 ppm) O3 NAAQS
to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 2020
Age Interval
Reduction in Incidence
(95% Confidence Interval)1
18-24
25-34
35-44
45-54
55-64
65-74
75-84
85+
Total
1
(0-1)
5
(2-7)
32
(17-46)
97
(52-140)
240
(130-350)
290
(150-420)
210
(120-310)
130
(71 -190)
1,000
(540-1,500)
*95% confidence intervals are based on the uncertainty surrounding the effect estimate (coefficient) in
the AMI C-R function.
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.
7b-49
-------
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).10 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.
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
10 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.
7b-50
-------
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 7b-22 provides the duration (both discounted and undiscounted) of CHF assumed for post-
AMI cases by age interval.
Table 7b-22. 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 7b-23. These
life expectancies were then used to represent the duration of non-CHF post-AMI health states (III
and IV).
7b-51
-------
Table 7b-23. 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.
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.
7b-52
-------
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 7b-24, along with the 95 percent
confidence interval resulting from the Monte Carlo simulation. Table 7b-24 presents both the
undiscounted and discounted QALYs gained per incidence.
Table 7b-24. 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
(0.88-4.74)
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)
a Mean of Monte Carlo generated distribution; 95% confidence interval presented in parentheses.
7b.6.5 Aggregating Life Expectancy and Quality-of-Life Gains
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 for the denominator of the cost-effectiveness ratio.
7b-53
-------
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 chronic morbidity endpoints (CB and acute myocardial infarctions). The
resulting measure of effectiveness then forms the denominator in the cost-effectiveness ratio.
This combined measure of effectiveness 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 a scaled morbidity equivalent to the standard life
years calculation. 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 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
under the illustrative attainment strategy with a baseline of partial attainment of the current
(0.084 ppm) Os NAAQS and a control scenario of partial attainment of an alternative 0.070 ppm
Os NAAQS. These distributions reflect both the quantified uncertainty in estimates of avoided
incidence and the quantified uncertainty in QALYs gained per incidence avoided.
Tables 7b-25 and 7b-26 present the discounted life years, QALYs, and MILYs gained, based on
each combination of Os-mortality study, PM2.5-mortality study, and life expectancy assumption
for Os-related life years saved used for the analysis of this attainment strategy, using a 3 percent
and 7 percent discount rate, respectively.
7b-54
-------
Table 7b-25. Estimated Gains in Discounted MILYs, Using a 3 Percent Discount Rate, Under an Illustrative Strategy of Changing from
Partial Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in
2020
O3 Mortality Study
Bell et al. (2004)
Bell et al. (2004)
Bell et al. (2004)
Levy et al. (2005)
Levy et al. (2005)
Levy et al. (2005)
Bell et al. (2004)
Bell et al. (2004)
Bell et al. (2004)
Levy et al. (2005)
Levy et al. (2005)
Levy et al. (2005)
PM2.5 Mortality
Study
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Laden et al. (2006)
Laden et al. (2006)
Laden et al. (2006)
Laden et al. (2006)
Laden et al. (2006)
Laden et al. (2006)
Life Expectancy Assumption for O3-
Related Mortality
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
OS-Related Life Years
Gained from Mortality
Risk
Reductions
(95% Cl)
1,300
(400 - 2,200)
1,000
(300-1,700)
500
(200 - 900)
6,100
(4,100-8,100)
4,700
(3,200 - 6,300)
2,700
(1,800-3,500)
1,300
(400 - 2,200)
1,000
(300-1,700)
500
(200 - 900)
6,100
(4,100-8,100)
4,700
(3,200 - 6,300)
2,700
(1,800-3,500)
PM2.5-Related Life
Years Gained from
Mortality Risk
Reductions
(95% Cl)
4,400
(1,700-7,000)
9,900
(5,400- 14,000)
QALYs Gained from
Reductions in PM2.5-
Related Chronic
Bronchitis
(95% Cl)
1,970
(270 - 4,700)
QALYs Gained from
Reductions in PM2.5-
Related Non-Fatal
Myocardial Infarction
(95% Cl)
870
(220 - 1 ,800)
Total MILYs
Gained
(95% Cl)
8,500
(4,700-12,000)
8,200
(4,500-12,000)
7,700
(4,100-12,000)
13,000
(9,100-18,000)
12,000
(7,900- 16,000)
9,900
(6,200 - 14,000)
14,000
(8,500 - 20,000)
14,000
(8,200-19,000)
13,000
(7,800-19,000)
19,000
(13,000-25,000)
17,000
(12,000-23,000)
15,000
(9,900 - 21 ,000)
"Life years, QALYs, and MILYs are discounted back to 2020. 95% confidence or credible intervals (CIs) around the point estimates are based on the uncertainty surrounding the effect
estimates (coefficients) in the C-R functions and, for QALYs and MILYs, the uncertainty surrounding the quality of life weights. All estimates rounded to two significant figures.
7b-55
-------
Table 7b-26. Estimated Gains in Discounted MILYs, Using a 7 Percent Discount Rate, Under an Illustrative Strategy of Changing from
Partial Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in
2020
O3 Mortality Study
Bell et al. (2004)
Bell et al. (2004)
Bell et al. (2004)
Levyetal. (2005)
Levyetal. (2005)
Levyetal. (2005)
Bell et al. (2004)
Bell et al. (2004)
Bell et al. (2004)
Levyetal. (2005)
Levyetal. (2005)
Levyetal. (2005)
PM2.5 Mortality
Study
Popeetal. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Laden et al. (2006)
Laden et al. (2006)
Laden et al. (2006)
Laden et al. (2006)
Laden et al. (2006)
Laden et al. (2006)
Life Expectancy Assumption for O3-
Related Mortality
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
OS-Related Life Years
Gained from Mortality
Risk
Reductions
(95% Cl)
990
(280 - 1 ,700)
790
(230 - 1 ,400)
450
(130-780)
4,600
(3,100-6,100)
3,700
(2,500 - 4,900)
2,200
(1,500-2,900)
990
(280 - 1 ,700)
790
(230 - 1 ,400)
450
(130-780)
4,600
(3,100-6,100)
3,700
(2,500 - 4,900)
2,200
(1,500-2,900)
PM2.5-Related Life
Years Gained from
Mortality Risk
Reductions
(95% Cl)
3,000
(1,200-4,800)
6,700
(3,700 - 9,800)
QALYs Gained from
Reductions in PM2.5-
Related Chronic
Bronchitis
(95% Cl)
1,300
(180-3,000)
QALYs Gained from
Reductions in PM2.5-
Related Non-Fatal
Myocardial Infarction
(95% Cl)
680
(180- 1,400)
Total MILYs
Gained
(95% Cl)
5,900
(3,300 - 8,700)
5,700
(3,100-8,500)
5,400
(2,800-8,100)
9,500
(6,600- 13,000)
8,600
(5,800-12,000)
7,100
(4,400-10,000)
9,700
(5,900-13,000)
9,500
(5,700- 13,000)
9,200
(5,400- 13,000)
13,000
(9,400- 17,000)
12,000
(8,600- 16,000)
1 1 ,000
(7,200-15,000)
'Life years, QALYs, and MILYs are discounted back to 2020. 95% confidence or credible intervals (CIs) around the point estimates are based on the uncertainty surrounding the effect
estimates (coefficients) in the C-R functions and, for QALYs and MILYs, the uncertainty surrounding the quality of life weights. All estimates rounded to two significant figures.
7b-56
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7 b. 6.6 Estimating the Avoided Costs of Chronic Illness
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. As noted above (see Section 7b.4.1), our estimate of costs in the
numerator is net of the avoided costs (cost savings) associated with the reductions in morbidity
(Gold et al., 1996). Among the morbidity costs subtracted from the direct costs of controls in the
numerator are the avoided costs of illness (COI) associated with PM2.5-related CB and nonfatal
AMI.
Avoided costs for CB and nonfatal AMI are based on estimates of lost earnings and medical
costs.11 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 2006$) due to CB of $185,774 for someone between the ages of 27 and 44; $121,177 for
someone between the ages of 45 and 64; and $14,293 for someone over 65. The corresponding
age-specific estimates of lifetime present discounted value (in 2006$) using a 7 percent discount
rate are $105,974, $89,506, and $11,641, respectively. These estimates assumed that 1) lost
earnings continue only until age 65, 2) medical expenditures are incurred until death, and 3) life
expectancy is unchanged by CB.
Because the costs associated with a myocardial infarction extend beyond the initial event itself,
we consider costs incurred over several years. Using age-specific annual lost earnings estimated
by Cropper and Krupnick (1990) and a 3 percent discount rate, we estimated a present
discounted value in lost earnings (in 2006$) over 5 years due to a myocardial infarction of
$10,758 for someone between the ages of 25 and 44, $15,856 for someone between the ages of
45 and 54, and $91,647 for someone between the ages of 55 and 65. The corresponding age-
specific estimates of lost earnings (in 2006$) using a 7 percent discount rate are $9,631, $14,195,
and $82,051, 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 $141,124 in year 2006$. 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
11 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.
7b-57
-------
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 $1,051
annually thereafter. Converting to year 2006$, that would be $28,787 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 $84,956, and add it to the 5-year opportunity cost estimate. The resulting estimates
are given in Table 7b-27.
Table 7b-27. Estimated Costs Over a 5-Year Period (in 2006$) of a Nonfatal Myocardial Infarction
Age Group
Opportunity Cost
Medical Cost3
Total Cost
0-24
25-44
45-54
55-65
>65
$0
$10,757b
$15,856b
$91,647b
$0
$84,956
$84,956
$84,956
$84,956
$84,956
$84,956
$95,714
$100,812
$176,603
$84,956
a 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 (using a 3 percent discount rate) is provided in Table 7b-28. The total
avoided COI associated with this illustrative attainment strategy (using a 3 percent discount rate)
is about $172 million. Note that these estimates do not include any direct avoided medical costs
associated with premature mortality. Nor do they include any medical costs that occur more than
5 years from the onset of a nonfatal AMI. Therefore, they are likely underestimates of the true
avoided COI associated with this illustrative attainment strategy.
7b-58
-------
Table 7b-28. Avoided Costs of Illness Associated with Reductions in Chronic Bronchitis and
Nonfatal Acute Myocardial Infarctions Under an Illustrative Strategy of Changing
from Partial Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of
an Alternative 0.070 ppm O3 NAAQS in 2020
Avoided Cost of Illness
(in millions of 2006$)
Age
Range
18-24
25-34
35-44
45-54
55-64
65-74
75-84
85+
Total
Chronic Bronchitis
—
$17
$19
$12
$13
$1.1
$0.5
$0.2
$63
Nonfatal Acute Myocardial Infarction
$0.07
$0.4
$3
$9.8
$42
$24
$18
$11
$110
7b. 6.7 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. As noted above (see Section 7b.4.1), the estimate of costs in the
numerator should include both the direct costs of the controls necessary to achieve the reduction
in ambient Os (and, indirectly, PIVb.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
Os- and PlV^.s-related acute health impacts and non-health benefits, we netted 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 (Os- and PlV^.s-related)
life years gained from increased life expectancy and QALYs gained from the (PlV^.s-related)
reductions in CB and nonfatal AMI. The separate Os- and PM2.s-related inputs to the
denominators of the cost-effectiveness ratios are summarized above in Tables 7b-25 through 7b-
26. The cost-effectiveness ratios and 95 percent confidence (credible) intervals resulting from all
of the sources of uncertainty considered, using Monte Carlo procedures as implemented in the
Crystal Ball™ software program and incorporating both the Os- and PM2.5-related benefits are
shown in the tables below. Tables 7b-29 and 7b-30 show cost per life saved, using a 3 percent
7b-59
-------
and 7 percent discount rate, respectively. Tables 7b-31 and 7b-32 show cost per life year saved
at the two discount rates; and Tables 7b-33 and 7b-34 show cost per MILY gained.
Table 7b-29. Estimated Net Cost (2006$) per O3- and PM2.5-Related Life Saved Under an Illustrative
Strategy of Changing from Partial Attainment of the Current (0.084 ppm) O3 NAAQS
to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 2020, Using a 3
Percent Discount Rate
O3 Mortality Study
Bell etal. (2004)
Bell etal. (2004)
Levy etal. (2005)
Levy etal. (2005)
PM2.5 Mortality Study
Pope etal. (2002)
Laden etal. (2006)
Pope etal. (2002)
Laden etal. (2006)
Cost Effectiveness Ratio:
Net Cost (in Million $) per Life Saved*
(95% Cl)**
$4.5
($2.7 - $8.7)
$2.3
($1.5 -$3.8)
$2.3
($1.7 -$3.4)
$1.5
($1.1 -$2.2)
The 3 percent discounted cost of the regulation is estimated to be $2.6 billion. PM2.5-related avoided deaths
are discounted back to 2020. O3-related deaths are assumed to occur in 2020.
**95 percent confidence or credible intervals incorporate uncertainty surrounding the O3 and PM2 5 coefficients
in the mortality and morbidity C-R functions as well as the uncertainty surrounding unit values of morbidity
endpoints. All estimates rounded to two significant figures.
Table 7b-30. Estimated Net Cost (2006$) per O3- and PM2.5-Related Life Saved Under an Illustrative
Strategy of Changing from Partial Attainment of the Current (0.084 ppm) O3 NAAQS
to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 2020, Using a 7
Percent Discount Rate
O3 Mortality Study
Bell etal. (2004)
Bell etal. (2004)
Levy etal. (2005)
Levy etal. (2005)
PM2.5 Mortality Study
Pope etal. (2002)
Laden etal. (2006)
Pope etal. (2002)
Laden etal. (2006)
Cost Effectiveness Ratio:
Net Cost (in Million $) per Life Saved*
(95% Cl)**
$5.4
($3.2 - $9.9)
$2.7
($1.8 -$4.5)
$2.6
($1.9 -$3.8)
$1.8
($1.3 -$2.6)
*The 7 percent discounted cost of the regulation is estimated to be $2.8 billion. PM25-related avoided deaths
are discounted back to 2020. O3-related deaths are assumed to occur in 2020.
**95 percent confidence or credible intervals incorporate uncertainty surrounding the O3 and PM2.5 coefficients
in the mortality and morbidity C-R functions as well as the uncertainty surrounding unit values of morbidity
endpoints. All estimates rounded to two significant figures.
7b-60
-------
Table 7b-31. Estimated Net Cost (2006$) per O3- and PM2.5-Related Life Year Saved Under an Illustrative Strategy of Changing from
Partial Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in
2020, Using a 3 Percent Discount Rate
O3 Mortality Study
Bell etal. (2004)
Bell etal. (2004)
Bell etal. (2004)
Levy etal. (2005)
Levy etal. (2005)
Levy etal. (2005)
Bell etal. (2004)
Bell etal. (2004)
Bell etal. (2004)
Levy etal. (2005)
Levy etal. (2005)
Levy etal. (2005)
PM2.5 Mortality Study
Pope etal. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope etal. (2002)
Pope et al. (2002)
Pope et al. (2002)
Laden etal. (2006)
Laden etal. (2006)
Laden etal. (2006)
Laden etal. (2006)
Laden etal. (2006)
Laden etal. (2006)
Life Expectancy Assumption for O3-Related
Mortality
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
Cost Effectiveness Ratio:
Net Cost (in Million $) per Life Year Saved*
(95% Cl)**
$0.42
($0.25 -$0.81)
$0.45
($0.26 - $0.89)
$0.50
($0.28 -$1)
$0.22
($0.1 6 -$0.32)
$0.25
($0.1 8 -$0.38)
$0.33
($0.22 - $0.54)
$0.21
($0.1 3 -$0.35)
$0.21
($0.14 -$0.36)
$0.22
($0.14 -$0.38)
$0.14
($0.1 - $0.2)
$0.16
($0.11 -$0.23)
$0.18
($0.1 2 -$0.29)
*The 3 percent discounted cost of the regulation is estimated to be $2.6 billion. All life years are discounted back to the year of death. PM2.5-related avoided
deaths are discounted back to 2020. O3-related deaths are assumed to occur in 2020.
**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 and PM2.5 coefficients in the mortality and morbidity C-R functions
as well as the uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures.
7b-61
-------
Table 7b-32. Estimated Net Cost (2006$) per O3- and PM2.5-Related Life Year Saved Under an Illustrative Strategy of Changing from
Partial Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in
2020, Using a 7 Percent Discount Rate
O3 Mortality Study
Bell etal. (2004)
Bell etal. (2004)
Bell etal. (2004)
Levy etal. (2005)
Levy etal. (2005)
Levy etal. (2005)
Bell etal. (2004)
Bell etal. (2004)
Bell etal. (2004)
Levy etal. (2005)
Levy etal. (2005)
Levy etal. (2005)
PM2.5 Mortality Study
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Laden etal. (2006)
Laden et al. (2006)
Laden et al. (2006)
Laden etal. (2006)
Laden et al. (2006)
Laden et al. (2006)
Life Expectancy Assumption for O3-Related
Mortality
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
Cost Effectiveness Ratio:
Net Cost (in Million $) per Life Year Saved*
(95% Cl)**
$0.67
($0.39 -$1.2)
$0.71
($0.41 - $1 .4)
$0.79
($0.44 - $1 .6)
$0.33
($0.24 - $0.47)
$0.37
($0.26 - $0.55)
$0.49
($0.33 - $0.78)
$0.33
($0.21 - $0.55)
$0.34
($0.22 - $0.56)
$0.35
($0.23 - $0.6)
$0.22
($0.1 6 -$0.31)
$0.24
($0.1 7 -$0.34)
$0.28
($0.1 9 -$0.42)
*The 7 percent discounted cost of the regulation is estimated to be $2.8 billion. All life years are discounted back to the year of death. PM2 5-related avoided
deaths are discounted back to 2020. O3-related deaths are assumed to occur in 2020.
**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 and PM2.5 coefficients in the mortality and morbidity C-R functions
as well as the uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures.
7b-62
-------
Table 7b-33. Estimated Net Cost (2006$) per O3- and PM2.5-Related MILY Gained Under an Illustrative Strategy of Changing from Partial
Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 2020,
Using a 3 Percent Discount Rate
O3 Mortality Study
Bell etal. (2004)
Bell etal. (2004)
Bell etal. (2004)
Levy etal. (2005)
Levy etal. (2005)
Levy etal. (2005)
Bell etal. (2004)
Bell etal. (2004)
Bell etal. (2004)
Levy etal. (2005)
Levy etal. (2005)
Levy etal. (2005)
PM2.5 Mortality Study
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Laden etal. (2006)
Laden etal. (2006)
Laden etal. (2006)
Laden etal. (2006)
Laden etal. (2006)
Laden etal. (2006)
Life Expectancy Assumption for O3-Related
Mortality
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
Cost Effectiveness Ratio:
Net Cost (in Million $) per MILY Gained*
(95% Cl)**
$0.27
($0.1 7 -$0.46)
$0.28
($0.1 8 -$0.49)
$0.30
($0.1 9 -$0.53)
$0.17
($0.1 2 -$0.24)
$0.19
($0.14 -$0.28)
$0.23
($0.1 6 -$0.35)
$0.16
($0.11 -$0.26)
$0.17
($0.11 -$0.27)
$0.17
($0.1 2 -$0.28)
$0.12
($0.09 -$0.17)
$0.13
($0.09 -$0.18)
$0.15
($0.1 -$0.22)
*The 3 percent discounted cost of the regulation is estimated to be $2.6 billion. All life years are discounted back to the year of death. PM2.5-
related avoided deaths are discounted back to 2020. All QALYs are discounted back to 2020. O3-related deaths are assumed to occur in 2020.
**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 and PM2.5 coefficients in the mortality and morbidity
C-R functions as well as the uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures.
7b-63
-------
Table 7b-34. Estimated Net Cost (2006$) per O3- and PM2.5-Related MILY Gained Under an Illustrative Strategy of Changing from Partial
Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 2020,
Using a 7 Percent Discount Rate
O3 Mortality Study
Bell etal. (2004)
Bell etal. (2004)
Bell etal. (2004)
Levy etal. (2005)
Levy etal. (2005)
Levy etal. (2005)
Bell etal. (2004)
Bell etal. (2004)
Bell etal. (2004)
Levy etal. (2005)
Levy etal. (2005)
Levy etal. (2005)
PM2.5 Mortality Study
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Pope et al. (2002)
Laden etal. (2006)
Laden etal. (2006)
Laden etal. (2006)
Laden etal. (2006)
Laden etal. (2006)
Laden etal. (2006)
Life Expectancy Assumption for O3-Related
Mortality
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
General Population
Subpopulation with Average COPD
Subpopulation with Severe COPD
Cost Effectiveness Ratio:
Net Cost (in Million $) per MILY Gained*
(95% Cl)**
$0.43
($0.27 - $0.73)
$0.45
($0.28 - $0.77)
$0.48
($0.29 - $0.86)
$0.26
($0.1 9 -$0.37)
$0.29
($0.2 -$0.41)
$0.35
($0.24 - $0.54)
$0.26
($0.1 7 -$0.41)
$0.26
($0.1 8 -$0.42)
$0.27
($0.1 8 -$0.44)
$0.18
($0.14 -$0.26)
$0.20
($0.14 -$0.28)
$0.23
($0.1 6 -$0.33)
*The 7 percent discounted cost of the regulation is estimated to be $2.8 billion. All life years are discounted back to the year of death. PM2.5-
related avoided deaths are discounted back to 2020. All QALYs are discounted back to 2020. O3-related death are assumed to occur in 2020.
**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 and PM2.5 coefficients in the mortality and morbidity
C-R functions as well as the uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures.
7b-64
-------
7b.7 Conclusions
We estimated the effectiveness of several illustrative Os NAAQS attainment strategies based on
reductions in premature deaths and, in the case of the one strategy for which we were able to
estimate both direct Os-related benefits and indirect PlV^.s-related co-benefits, incidence of
chronic disease. We measured effectiveness using several different metrics, including lives
saved, life years saved, and QALYs gained (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. As noted above, however, the cost effectiveness metrics presented for
all but one of the illustrative Os NAAQS attainment strategies omit the PlV^.s-related co-benefits
and are therefore likely to understate the cost effectiveness of those strategies
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 non-health effects. The
benefit-cost analysis for the Os 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 Os NAAQS will likely result in improvements in overall public
welfare.
7b-65
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Appendix 6c: 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.s, 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 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
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
-------
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 PM2.s, and 30 percent occurring evenly over the years 6 to 20 after the reduction in
PM2.5. 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) = -^ - - - (C
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., $6.6 million per incidence in 2006$) and are presented for both a 3 and 7
percent discount rate over the lag period.
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 17 percent when a 7 percent discount rate is
used. If no lag is assumed, benefits are increased by approximately 10 percent relative to the
segmented lag with a 3 percent discount rate and 22 percent with a 7 percent discount rate.
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
Value
(billions of 2006$)"
Percent
Difference from
Base Estimate
None
8 -year
15-year
Alternative
Segmented
5-Year
Distributed
Incidences all occur in the first year
3% discount rate
7% discount rate
Incidences all occur in the 8th year
3% discount rate
7% discount rate
Incidences all occur in the 1 5th 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
$3.4
$3.4
$2.8
$2.1
$2.2
$1.3
$3.0
$2.6
$3.2
$3.0
10.4%
22.5%
-10.3%
-23.7%
-27.0%
-52.5%
-3.2%
-6.6%
4.9%
9.4%
Exponential
Incidences occur at an exponentially declining
rate following year of change in exposure
3% discount rate
7% discount rate
$3.2
$3.1
5.6%
11.3%
1 All valuations rounded to two significant figures. 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 do not reflect benefits for the
San Joaquin and South Coast Air Basins.
6c.2 Threshold Sensitivity Analysis
Chapter 6 presents the results of the PIVb.s 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 PIVb.s 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).
6c-4
-------
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 PlV^.s-related premature mortality resulting from changes in the
presumed threshold. We also provide a corresponding estimate of the valuation of these changes
in incidence.
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) 95th
Percentile Confidence Intervals Provided in Parentheses a
Less Certain That Benefits
Are at Least as Large
1
More Certain That Benefits
are at Least as Large
No Threshold
Threshold at 7.5 jo,g
Threshold at 1 0 jo,g
Threshold at 12 jo,g
Threshold at 14 jo,g
East
580
(120-1,000)
570
(130-1,000)
420
(110-730)
46
(14-79)
1.0
(0.35-1.7)
Western U.S.
Excluding CA
56
(15-98)
49
(16-81)
6.3
(2.1-10)
0.00
(0.00-0.00)
0.00
(0.00-0.00)
California
12
(3.9-19)
11
(3.6-18)
5.4
(2-9)
3.7
(1.2-6.2)
2.9
(1.0-4.9)
Total
640
(140-1,100)
630
(150-1,100)
430
(110-750)
50
(15-85)
4.0
(1.3-6.6)
11 estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded
estimates. As such, totals will not sum across columns. Estimates do not include South Coast and San
Joaquin Air Basins.
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 PM2.5
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-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, 95th Percentile Confidence
Intervals Provided in Parentheses, in billions of 2006$)a
Less Certain
that Benefits
Are at Least
as Large
1
More Certain
that Benefits
Are at Least
as Large
3%
7%
3%
7%
3%
7%
3%
7%
3%
Threshold at 14 jo,g
7%
Eastern U.S.
4.0
($0.49»$10)
3.6
($0.44»$8.8)
4.0
($0.49~$10)
3.6
($0.44»$8.6)
3.0
($0.38»$7.0)
2.7
($0.35»$6.3)
0.33
($0.04»$0.76)
0.29
($0.04»$0.68)
0.01
($0.00»$0.02)
0.01
($0.00-$0.01)
Western U.S.
Excluding CA
0.40
($0.05»$0.94)
0.36
($0.05-$0.84)
0.34
($0.05-$0.78)
0.31
($0.04-$0.70)
0.04
($0.01»$0.10)
0.04
($0.01-$0.09)
0.00
($0.00-$0.0)
0.00
($0.00-$0.00)
0.00
($0.00-$0.0)
0.00
($0.00»$0.00)
California
0.08
($0.01-$0.19)
0.02
($0.01-$0.17)
0.08
($0.01-$0.17)
0.07
($0.01-$0.16)
0.04
($0.01»$0.09)
0.03
($0.00-$0.08)
0.03
($0.00-$0.06)
0.02
($0.00-$0.05)
0.02
($0.00-$0.05)
0.02
($0.00»$0.04)
Total
Nationwide
Attainment
4.5
($0.55-$11)
4.1
($0.49-$10)
4.4
($0.55»$11)
4.0
($0.49-$9.5)
3.0
($0.39-$7.2)
2.7
($0.36-$6.5)
0.35
($0.05-$0.82)
0.32
($0.04»$0.73)
0.03
($0.00-$0.06)
0.03
($0.00-$0.06)
1 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. Estimates do not include South Coast and San
Joaquin Air Basins.
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.
6c-6
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Table 6c-4. Ranges of Elasticity Values Used to Account for Projected Real Income
Growth8
Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound
Minor Health Effect 0.04 0.30
Severe and Chronic Health Effects 0.25 0.60
Premature Mortality 0.08 1.00
Visibility13 — —
"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.
b 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
Growth8
Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound
Minor Health Effect 1.018 1.147
Severe and Chronic Health Effects 1.121 1.317
Premature Mortality 1.037 1.591
Visibilityb — —
"Based on elasticity values reported in Table C-4, U.S. Census population projections, and projections of
real GDP per capita.
bNo range was applied for visibility because no ranges were available in the current published literature.
Table 6c-6. Sensitivity of Monetized Benefits to Alternative Income Elasticities8
Ozone Analysis PM Analysis
Benefit Category Lower Sensitivity Upper Sensitivity Lower Sensitivity Upper Sensitivity
Bound Bound Bound Bound
Minor Health Effect $48 $48 $8.3 $8.5
Severe and Chronic Health c c
Tnoo ^ — — 4>1 /U LpZUU
Effects
Premature Mortality13 $340 $520 $2,600 $4,000
Total Benefits'3 $380 $560 $2,800 $4,200
"All estimates rounded to two significant digits. All Benefits Incremental to 080 ppm Partial Attainment
Strategy (Millions of 2006$). 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 do not reflect benefits for the San Joaquin
and South Coast Air Basins.
b Using mortality effect estimate from Bell (2004) and mortality effect estimate from Pope et al (2002) to
estimate PM2.s mortality at a 3% discount rate.
°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-7
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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.
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-AD V-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-AD V-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." International Journal 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-8
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Appendix Chapter 6d: Exploring the Effects of Changes in Tropospheric Ozone on UVB
Atmospheric ozone filters harmful solar ultraviolet radiation (UV-B), thereby reducing the
amount of UV-B reaching the Earth's surface. The majority of ozone—about 90%—is located in
the stratosphere, and the stratospheric ozone layer provides most of this protective filtration.
Tropospheric ozone, located at ground level, accounts for the remaining 10% of atmospheric
ozone. Although only a portion of ground level ozone can be attributed to anthropogenic sources,
it is reasonable to assume that reducing ground level ozone would reduce the UV-B filtration
provided, and thus would lead to increases in health effects normally associated with reductions
in stratospheric ozone. UV radiation-induced health effects are primarily related to the skin (e.g.,
melanoma and nonmelanoma skin cancer), eyes (e.g., cataracts), and immune system.
The attached preliminary report entitled "Analysis of the Impact of Emissions Changes on
Tropospheric Ozone" represents the EPA's first attempt to develop a methodology for capturing
the changes in skin cancers and their economic value that might be associated with changes in
tropospheric ozone. This initial effort was designed as a scoping analysis to determine the
potential magnitude of impacts, and is not intended to serve as a standard methodology for
assessing UVB impacts in future RIAs. This scoping analysis focuses on a scenario reflecting
the likely distribution of ground level ozone in the Eastern United States domain under an
illustrative set of controls intended to reduce ozone concentrations towards attainment of an
ozone standard of 70 parts per billion (ppb), as compared to the current ozone National Ambient
Air Quality Standard (NAAQS) for 2020.l The report only examines the effects of this reduced
UV filtration on incidence of and mortality associated with skin cancers - specifically, basal cell
carcinoma (BCC), squamous cell carcinoma (SCC) and cutaneous malignant melanoma (CMM).
The general methodology developed for this draft scoping analysis was applied in four steps.
First, changes in ground level UV radiation (for geographical extent) were estimated using the
Community Multiscale Air Quality model results as an input to the Tropospheric Ultraviolet -
Visible radiation model (TUV). The CMAQ model runs provided data for each of 14 altitude
layers for each location on a 12x12 km grid at hourly intervals for 24 hours of each day from
June 1, 2020 to August 31, 2020. Using these data, the TUV model produced estimates of the
daily integrated dose of UV exposure. Second, population-weighted exposure estimates were
derived using county based population projections developed using a cohort-component
methodology. Third, the resulting estimates were used in the Atmospheric Health Effects
Framework model to quantify expected changes in incidence in and mortality from basal cell
carcinoma (BCC), squamous cell carcinoma (SCC) and cutaneous malignant melanoma (CMM)
associated with the given change in ground level ozone. Fourth, the resulting health effects were
monetized using a combination of estimates of the value of statistical life and willingness to pay
to avoid a case of skin cancer.
This research makes use of results from the CMAQ, TUV and AHEF models. These models
have all been applied extensively in other contexts but this is their first application to estimate
1 This scenario was developed for the Ozone NAAQS Proposal and does not match runs produced for the Ozone
NAAQS Final.
6d-l
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skin cancer effects associated with changes in tropospheric ozone. While all of these models
have been extensively peer reviewed and validated in different contexts, the reviews were
focused on different model applications and did not extend necessarily to the current problem.
We subjected this scoping analysis to peer review by five experts external to the Agency,
including Dr. Edward DeFabo, George Washington University; Dr. Hugh Ellis, Johns Hopkins
University; Dr. Scott Farrow, University of Maryland - Baltimore County; Dr. Randy Kawa,
National Atmospheric Sciences Administration; and Dr. Helen Suh, Harvard School of Public
Health.3 Unfortunately, due to time constraints, we were unable to incorporate the
recommendations from the reviewers in time for this rule. However, the Agency plans to
respond to peer reviewer remarks in the near future as we continue our efforts on exploring this
topic.
Although the draft report addresses a number of sources of uncertainty, we recognize that others
may remain including, but not limited to, the applicability of epidemiologic studies of long-term
UV-B exposures over broad geographic regions to scenarios involving impacts of smaller, more
variable, localized changes in ground level ozone; the variation in activity patterns and other
factors that determine population exposures and sensitivities to UV-B radiation; as well as the
effects of aerosols. These uncertainties have been recognized by the Agency and discussed in
Chapter 10 of the most recent Ozone Criteria Document (U.S. EPA, 2006). The Agency will
consider whether to conduct additional exploratory analyses related to UVB screening as we
continue our efforts to quantify health effects associated with reduced tropospheric ozone in a
rigorous and defensible manner.
Because the CMAQ modeling runs used for this scoping analysis do not match those used for the
Ozone NAAQS Final Regulatory Impact Assessment (RIA), direct comparisons of the monetized
skin cancer effects associated with reduced UV-B filtration presented in this report cannot be
made with health benefit results presented in the RIA for the final rule. Still, comparing the
results of this scoping analysis with the estimates of benefits presented in the proposal RIA,
provides a general sense of the order of magnitude of the resulting effects. The estimates of
monetized disbenefits resulting from increased UVB levels due to reduced tropospheric ozone as
captured by this scoping analysis amount to approximately 0.3 to 0.6 percent of the monetized
health benefits associated with the modeled set of ozone precursor control strategies reported in
the proposal RIA.
2 TUV and AHEF were developed to estimate health effects associated with changes in stratospheric ozone.
3 The individual reports from each of the peer reviewers are contained in the docket for this rule.
6d-2
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IGF
INTERNATIONAL
Analysis of the Impact of Emissions Changes on
Tropospheric Ozone
DRAFT Report
February 18, 2008
Prepared by:
ICF International
6d-3
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ICF
Health Impel' '•• << n Ozone Changes
Table of Contents
1. Introduction 5
2. Methodology 5
2.1. CMAQ Modeling 5
2.2. TUV Modeling 5
2.2.1. Tropospheric Ozone Scenarios 5
2.2.2. TUV Model Calculations 9
2.2.3. TUV Results 12
2.3. Population Adjustments 13
2.3.1. Cohort-Component Methodology Overview 13
2.3.2. Component Data and Methods 15
2.3.3. Use of these Projections 16
2.4. AHEF Modeling 17
2.4.1. Overview of Methodology to Estimate Changes in Health Effects 17
2.4.2. Selected Action Spectrum and Derived Dose-Response Relationships 18
2.5. Valuation of Human Health Effects 19
3. Results 20
3.1. Changes in Ground Level SCUP-h UV 20
3.2. Changes in Human Health Effects 24
4. Uncertainty 25
4.1. Uncertainty in estimated impacts 25
4.2. Uncertainty in CMAQ Modeling 26
4.3. Uncertainty in TUV Modeling 26
4.3.1. Uncertainty Analysis of TUV Calculations 26
4.3.2. Comparison with UV Changes Due to Other Factors 29
4.4. Uncertainty in Population Adjustments 30
4.5. Uncertainty in AHEF Modeling 31
4.5.1. Uncertainties in Selected Derived Dose-Response Relationships 31
4.5.2. Behavioral Uncertainties 32
4.5.3. Latency 32
4.6. Uncertainty in Valuation of Human Health Effects 33
4.7. Unquantified Sources of Uncertainty 33
4.8. Summary of Quantified and Unquantified Sources of Uncertainty 36
References 37
Appendix A: Ground Level SCUP-h UV with 70 and 84 ppb by Day 41
Appendix B: Overview of Evaluation Methodology 49
Glossary 50
Draft report 6d-4
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Atmospheric ozone filters harmful solar ultraviolet radiation (UV-B), thereby reducing the
amount of the UV-B reaching the Earth's surface. The majority of ozone—about 90%—is
located in the stratosphere, and the stratospheric ozone layer provides most of this protective
filtration. Tropospheric ozone, located at ground level, accounts for the remaining 10% of
atmospheric ozone. Although only a portion of ground level ozone can be attributed to
anthropogenic sources, it is reasonable to assume that reducing ground level ozone would reduce
the UV-B filtration provided, and thus would lead to increases in health effects normally
associated with reductions in stratospheric ozone. UV radiation-induced health effects are
primarily related to the skin (e.g., melanoma and nonmelanoma skin cancer), eyes (e.g.,
cataracts), and immune system.
The purpose of this report is to assess these human health effects of reduced UV filtration
associated with the reduction of ground level ozone under an ozone standard of 70 parts per
billion (ppb) compared to the current ozone National Ambient Air Quality Standard (NAAQS)
for 2020.
The remainder of this paper is organized as follows:
• Section 2 describes the methodology used to carry out this assessment, including
modeling using the Tropospheric Ultraviolet-Visible radiation model (TUV) and the
U.S. EPA's Atmospheric and Health Effects Framework (AHEF);
• Section 3 presents the results of the analysis, including changes in ground level UV
and health effects; and
• Section 4 addresses the uncertainties associated with modeling undertaken for this
analysis.
1.1. CMAQ Modeling
The inputs for this analysis were generated through Community Multiscale Air Quality (CMAQ)
ozone modeling runs. The CMAQ model produced spatial fields of gridded ozone
concentrations on an hourly basis for the Eastern United States domain with 12 km horizontal
resolution and 14 vertical layers topping out at 16,200 meters.
1.2. TUV Modeling
1.2.1. Tropospheric Ozone Scenarios
The CMAQ model provided ozone concentrations in parts per billion (ppb) for each of the 14
altitude layers given in Table 1. These values are specified for each location (latitude, longitude)
on a 12 x 12 km grid (66920 locations) at hourly intervals for 24 hours (UT) of each day from 1
Draft report 6d-5
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Health Impel'
n Ozone Changes
Jun to 31 Aug 2020. Two scenarios are considered with identifiers:
2020bk_v4.5_084_12km.o3_hr_shift_LST, and
2020bk_v4.5_070b_12km.o3_hr_shift_LST.
For brevity, these scenarios will be called 084 and 070, respectively.
In order to model a hypothetical control strategy incremental to attainment of the current
standard (84 ppb), EPA approached the analysis in stages. First, EPA identified controls to be
included in the baseline. These included current state and federal programs plus controls to
attain the current ozone standard and PM2.5 PM standards (see
http://www.epa.gov/ttnecasl/ria.html for a complete list of controls). Then, EPA applied
additional known controls within geographic areas designed to bring areas predicted to exceed
70 ppb in 2020 into attainment (U.S. EPA, 2008).
Table 1 gives the vertical structure of the model. The 14 layers are bounded by 15 levels defined
on unequally spaced modified normalized pressure coordinates (sigma = 1 at the surface, 0 at the
top of the model). The actual atmospheric pressures, and corresponding geometric altitudes, are
determined by the meteorological input to CMAQ and vary in time and space. Approximate
values are given in the table. For the purposes of the radiative transfer calculations, the
approximate heights given in Table 1 were used, and sensitivity calculations were made to
bracket the effect of this approximation. The last column of Table 1 gives the number of air
molecules, per square centimeter, in a vertical column within each layer, and their calculation is
described in the following section.
Table 1: Vertical Structure for 14 Layer CMAQ (heights are the top of layer).
Layer
Number
0
1
2
3
4
5
6
7
8
9
10
11
Sigma
1.000
0.995
0.990
0.980
0.960
0.940
0.910
0.860
0.800
0.740
0.650
0.550
Approximate
Height (m)
0
38
77
154
310
469
712
1,130
1,657
2,212
3,108
4,212
Approximate
Pressure (mb)
1000
995
991
982
964
946
919
874
820
766
685
595
Air column between
levels (molecules cm"2)
—
9.67 x 1022
9.89 x 1022
1.94x IQ2
3.89 x 102
3.90 x 102
5.85 x 102
9.73 x 102
1.17x 1024
1.17x 1024
1.75 x 1024
1.95 x 1024
Draft report
6d-6
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Health Impacts from Ozone Changes
Layer
Number
12
13
14
Sigma
0.400
0.200
0.000
Approximate
Height (m)
6,153
9,625
15,674
Approximate
Pressure (mb)
460
280
100
Air column between
levels (molecules cm" )
2.91 x IQ24
3.85 x 1024
3.58 x 1024
The detailed CMAQ ozone values were used in the calculations of the UV radiation. However,
to illustrate the magnitude of the changes in ozone, Figure 1 shows the concentration changes
from the 084 to the 070 CMAQ scenarios, averaged over the entire geographic domain and over
hours of all days of each month. Also shown are the values for 15 July (the mid-time of the
simulation), since this date will be used in some sensitivity studies in Section 4.3. The largest
changes are seen to occur between ca. 500 and 1000 m above the surface (layers 5-7, see
Table 1) and are non-negligible even in the highest layers.
Figure 1: Domain-averaged ozone concentration changes (ppb) in each CMAQ layer.
Vertically averaged changes (ppb) are given in the legend.
0.8
0.7
0.6
0.5
a. 0.4
Q.
0.3
0.2
0.1
0.0
0
15JUL/0.479
JUN/0.336
JUL/0.405
AUG/0.401
5 10
Layer number
15
The contribution of each layer to the ozone column change is given in Figure 2. This was
obtained by multiplying the concentration changes (ppb) by 10"9 times the air column in each
Draft report
6d-7
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IBT[ •«*'!'» 41
Health Impacts from Ozone Change;
corresponding layer (from Table 1), and converting to Dobson Units (DU) by dividing by
2.687 x 1016 molecules cm"3 DU"1. The total ozone column change is the sum of the
contributions in each layer, and is shown in the figure legend. The small contribution from the
lowest levels is due mainly to their small vertical thickness, while the decreasing contribution of
the uppermost layers is due to the exponential decrease in air density with altitude. Notably, the
highest contributions are from layers 7-10 (ca. 1-3 km altitudes), with non-negligible
contributions from the upper troposphere as already noted above.
Figure 2: Domain-averaged ozone column changes (Dobson Units) in each CMAQ
layer. The sum of the ozone changes (Dobson Units) is given in the legend,
and is the total ozone column change.
0.030
0.025
co 0.020
*j
'c
^
c 0.015
o
(A
Q 0.010
0.005
0.000
15JUL/0.196
JUN/0.150
JUL/0.193
AUG/0.191
0
10
15
Layer number
The ozone changes shown in Figures 1 and 2 cannot be translated directly into changes of
surface UV radiation, because they are averaged over different locations and times. For
example, they include night-time values when the UV radiation is non-existent. This can be seen
in Table 2, where the domain-averaged ozone changes for 15 July were divided according to
whether they occur for solar zenith angels (sza) smaller than 45 degrees (high sun) and lower
than 45 degrees (low sun). The table shows that the changes in surface and column values are
largest for high sun, consistent with photochemical formation near sources, and coincident with
times of highest surface biologically active irradiances. Mid-tropospheric values have a weaker
dependence on sza, consistent with long-range transport and a relatively long ozone lifetime.
Draft report
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Health
v'i • •••? Ozone Changes
Table 2: Ozone change statistics for 15 July 2020, CMAQ scenarios 084-070
All sza
sza < 45°
45° < sza < 90°
sza > 90°
high sun
low sun
night
Number of
points
1,605,600
426,139
538,529
640,932
Average
ozone
change
(ppb) at
surface
0.58
0.69
0.60
0.50
Average
ozone change
(ppb) at level
12 (~ 500 mb)
0.10
0.11
0.09
0.11
Average
ozone column
change,
Dobson Units
0.20
0.22
0.19
0.19
An independent albeit rough estimate of the ozone column (DU) change can be obtained from
the concentrations given in Table 2. Considering only the values for sza < 45° (c.f, the simple
rule that UV exposure should be avoided when a person's shadow is shorter than a person's
height), even a simple linear average of the ozone changes at the surface and 500 mb yields
~ 0.4 ppb, and can be taken as applicable to the lower half of the atmosphere (below 500 mb).
The total atmospheric column of air is about 2.0 x 1025 molecules cm"2, so taking half of this and
0.4 ppb ozone yields 4 x 1015 molecules cm"2 of ozone (1.0 x 1025 x 0.4 x 1Q"9), or
~ 0.15 Dobson Units. This is reasonably close to the column value in the table, 0.22 DU, which
was calculated within TUV from the full vertical variation of ozone and air concentrations, and
included changes above 500 mb as well as an exponential profile of air density which of course
gives more weight to the lower altitudes.
1.2.2. TUV Model Calculations
The surface ultraviolet radiation was calculated with the Tropospheric Ultraviolet-Visible (TUV)
model developed by Madronich and co-workers at the National Center for Atmospheric Research
(NCAR). The TUV model is widely used for the calculation of atmospheric and surface UV
radiation including international assessments of the environmental effects of stratospheric ozone
depletion (e.g., Madronich et al., 1998), and has been evaluated in numerous model-
measurement intercomparison studies (e.g., Koepke et al., 1998; Bais et al., 2003). An early
version of TUV, of similar accuracy but lesser flexibility, is used within the CMAQ atmospheric
chemistry module to compute photolysis frequencies. The model has been described in the
literature (e.g., Madronich and Flocke, 1999) and the latest version (version 4.5, used here) is
freely available to the scientific community through NCAR Community Data Portal
(http ://cdp .ucar.edu).
Several modifications to the TUV model were made for the present purposes, specifically to (i)
interface the model with the CMAQ ozone concentrations, and (ii) to speed up the computational
time in view of the large number of locations reported by the CMAQ model.
The altitude grid was modified to match the values given in Table 1, then continuing to 16 km
and increasing by 2 km to 40 km, and by 5 km to 80 km. These represent altitude levels, while
layers (to which the ozone concentrations are applied) are the volume between these levels. The
TUV model used the U.S. Standard Atmosphere (USSA, 1976) vertical profiles of temperature
Draft report
6d-9
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Health Impel' '•• > < n Ozone Changes
(K), air density and ozone (both molecules cm"3), specified from sea level to 80 km in 1 km
increments, and then interpolated to the altitude grid described above. Because the CMAQ
model has layers that are both smaller and larger than the standard USSA 1 km grid, some
attention was given to proper vertical interpolation of air density. Specifically, the logarithm of
the USSA air number density (molecules cm"3) was interpolated linearly to obtain the logarithm
of the air density at the CMAQ levels. Then, the vertical air column (molecules cm" ) of each
layer was obtained by logarithmic integration:
Air column in layer k = dz [X^+l) ~X^) ] / MX^+l) /X^) ]
where dz = z(k+l) - z(k) = vertical thickness of the layer. The air column of each layer was then
multiplied by the CMAQ ozone concentrations (ppb x 10"9) to yield the ozone column in each
layer (molecules cm"2), so overwriting the USSA ozone values for these altitudes. For altitudes
above the highest level of Table 1, the interpolated USSA ozone values were used.
For each wavelength interval (see below), the radiative transfer solution was expressed
analytically using the delta-Eddington approximation (Joseph et al., 1976) formulated in
generalized 2-stream equations (Toon et al., 1989) corrected for atmospheric curvature using a
pseudo-spherical approximation (Petropavlovskih, 1995). The resulting set of coupled 2N
equations (N = number of layers) was solved by tridiagonal matrix inversion to obtain the
spectral irradiance, I(A,) in W m"2 nm"1 for a given wavelength, time, and location. This
calculation was repeated for the center of each wavelength interval, for each location, for each
hour (on the half-hour) of each day of June, July, and August for each of the two given CMAQ
scenarios. The spectral irradiance was multiplied by a biological sensitivity function (action
spectrum) B(A,), then integrated numerically all wavelengths with non-zero contributions, to
obtain the surface biological exposure (biologically effective irradiance) Ibio (W m" ). Two
different action spectra were considered: (1) the CIE standard erythemal (skin-reddening)
spectrum (McKinlay and Diffey, 1987) which forms the basis of the WMO/WHO-recognized
UV Index computed operationally in the United States by NOAA and highlighted by the EPA,
and (2) the spectrum for the induction of non-melanoma skin cancer in mice, corrected for
human skin transmission (deGruijl and van der Leun, 1994). The latter spectrum has been used
extensively in the assessments of ozone depletion, and is named SCUP-h (Skin Cancer Utrecht-
Philadelphia, reflecting the location of the research groups that originated it), and its sensitivity
to ozone changes is quite similar to that of the erythemal spectrum (as shown by Madronich et
al., 1998). For brevity, biologically effective irradiances computed from these two spectra are
hereafter called IERY and ISCUP- Values of ISCUP are used in ICF's AHEF model as measures of
human exposure to UV radiation.
The TUV wavelength (nm) grid extended from 294 to 330 by 2 nm, to 350 by 5 nm, and to 400
by 10 nm. The higher resolution at the shorter wavelengths is required to represent accurately
the absorption by ozone which is strongly dependent on wavelength, while the coarser resolution
provides computational efficiency. A resolution of 2 nm in the ozone-dependent region has been
show to be sufficiently accurate for photolysis calculations, including Os + h v -> C>2 + O(1D)
which has a spectral dependence similar or steeper (and therefore more sensitive to spectral
resolution) than the action spectra used here (Madronich and Weller, 1990).
Draft report 6d-10
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Health Impacts from Ozone Changes
For each location on the 12 x 12 km grid, the values of IERY and ISCUP were integrated over 24
hours to provide daily integrated doses, and over each month (June, July, and August) to provide
monthly integrated values. Otherwise identical calculations were performed for the 070 and 084
scenarios, and the difference between scenarios was computed for each location.
Tropospheric ozone causes a larger change, on a per molecule basis, than stratospheric ozone
(Bruhl and Crutzen, 1989), at least for high sun. This is because of coupling between molecular
(Rayleigh) scattering and ozone absorption: Scattering increases the tropospheric photon path
lengths and therefore increases the probability of absorption by tropospheric absorbers including
ozone. Figure 3 shows the normalized sensitivity of SCUP-h weighted UV as a function of the
altitude where the ozone perturbation occurs. The normalized sensitivity (also called the
Radiation Amplification Factor, RAF) is the % increase in radiation for each % decrease in
ozone column. For this plot, a 1 DU of ozone was inserted in a 1 km layer at various altitudes
(the altitudes of ozone perturbation in the figure), and the resultant surface UV-SCUP values
compared to the reference calculation (without the 1 DU). The RAF is then:
RAF = - \n(UV2/UVi)/\n(DU2/DUi)
where the subscripts 2 and 1 refer to the perturbed and reference calculations (Micheletti et al.,
2003).
Figure 3: Normalized sensitivity (% for %) of UV-SCUP changes to the altitude at
which ozone perturbations are made.
RAF for SCUP-h Action Spectrum
0
5 10 15 20 25
Altitude of O3 perturbation, km
30
Draft report
6d-ll
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Health Impacts from Ozone Changes
For example, a 0.22 DU increment (from Table 1,15 July, high sun) represents about 0.06%
change in the ozone column (349 DU for the USSA, but actually somewhat different and
variable when using the CMAQ values up to 16 km). From Figure 3, a RAF of 1.6-1.7 is
reasonable for ozone perturbations in the low-mid troposphere and relatively high sun when UV
matters. Multiplying (0.06 % x 1.65), the surface UV-SCUP radiation is expected to change by
about 0.1 %. This is the approximated magnitude of the UV-SCUP changes expected between
the two CMAQ scenarios.
Of course, the full TUV calculations were done with high spectral resolution (not simple scaling
with RAFs), time integration over actual sza values, full vertical distributions of tropospheric
ozone given by the CMAQ, and fully coupled scattering-absorption multi-layer radiative
transfer. Therefore they are expected to be more accurate, and more firmly anchored in the state-
of-the-science.
1.2.3. TUV Results
Detailed maps of UV-SCUP distributions and percent changes are given in Appendix A. Here,
the results are summarized in Figure 4 as domain-averaged UV-SCUP percent changes for each
day. They range from 0.05 % to 0.16 %, with most values near 0.1 % or slightly higher (note that
% changes of the monthly UV increments are not strictly equal to the monthly averages of daily
% changes, though they happen to be quite similar). Figure 5 shows the frequency distribution
of the monthly increments expressed as percent. The most common value is near zero, and few
values above 0.3%. A few negative values were noted. Finally, it should be clear that the data in
these figures are not yet weighted by the affected populations, and therefore should be viewed as
changes in the physical state of the atmosphere, not as measures of population exposure.
Figure 4: Domain-averaged percent changes in SCUP-weighted daily doses changes
between 070 and 084 scenarios
070-084 scenarios
Domain-averaged changes (%) in daily UV-SCUP
0.05
15
Day of the month
22
29
Draft report
6d-12
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Health Impacts from Ozone Changes
Figure 5: Frequency distribution of percent changes in erythemal surface UV radiation
Distrbution of percent changes in erythemal (Q^ surface UV
CM AQ 12x12 km grid, scenarios 084-070b
14000
12000
10000
£ 8000
£
Z 6000
4000
2000
-JUN
•XL
AUG
o rAWAVAvro
-0.1 0
1.3. Population Adjustments
This analysis required county-based population projections for two purposes: to calculate the
population-weighted changes in ground level UV for each latitude band modeled in the AHEF
model for the year 2020 and to provide future population projections for the years 2005-2050.
Although the U.S. Census Bureau provides population projections, they could not be used for
this purpose because the publicly available datasets lack the level of detail needed by the AHEF
model: population by county, race, gender, and five-year age cohorts. Existing population
projections traditionally used by the AHEF model also could not be used because they cover the
entire United States, while the area analyzed by CMAQ model covers all or part of 42 states. To
meet the data needs of this analysis, county-based population projections were developed using a
simple cohort-component methodology.
1.3.1. Cohort-Component Methodology Overview
The cohort-component methodology is a common technique for projecting population changes
over time. In this case, three independent components of population change were used: fertility,
mortality, and net international migration (i.e., migrations between U.S. counties and foreign
countries). Domestic migration (i.e., migrations between U.S. counties) was not included in this
projection exercise for reasons discussed below. To project population changes over time, the
Draft report
6d-13
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Health Impacts from Ozone Changes
population was divided into cohorts that were age-, gender-, and race-specific. Changes due to
these three components of change were estimated over time as each cohort was tracked
separately, hence the term "cohort-component."
The population of a county in any year t as estimated by the model is determined using the
following equation:
= Pt-i + Et - Dt + NIM,
Equation 1
where:
Pt = Population in year t
Pt-i = Population in the previous year
B^ = Births in year t
Dt = Deaths in year t
NIM; = Net International Migration in year t
Beginning with an initial set of populations, annual components of change were applied in the
following process, which were repeated annually until the desired end year was reached:
1. Add births by cohort
2. Deduct deaths by cohort
3. Add net international migration
4. Age population one year and repeat for the next year
This methodology is illustrated in Figure 6 below. The cycle begins with an initial Year 2000
population and is repeated until reaching Year 2100.
Figure 6: Demographic model flow
Initial Population in Year?
Apply Components of Change
I
2. Subtract
Deaths
1
3. Add Net
International Migration
Population in Year t + 1
Draft report
6d-14
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Health Impa- '•
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Health //n/wv'i - •••? Ozone Changes
provided in five year increments only. We assumed that 2010 mortality rates held steady from
2010-2014, 2015 mortality rates held steady from 2015-2019, and so on.
Net International Migration
The projections for net international migration utilized a simple method based on the Census
Bureau's international migration projections for the entire country. These files contain the
projected net international migration for each gender, age, and race cohort for the years 2000-
2100. Like the fertility and morality rates, these data are part of the Census Bureau's
"Component Assumptions of the Resident Population by Age, Sex, Race, and Hispanic Origin"
(U.S. Census Bureau 2000). Since the tables "Foreign-born Net Migration to the United States"
contain only national level data, it was necessary to allocate the national migrants to the counties.
Using 2000 Census data (Summary File 3, Table P22), we determined each county's share of the
total population of recent immigrants (i.e., those who entered within the last five years). These
county shares were then used to allocate each cohort of immigrants among the nation's counties.
The estimated number of immigrants in each cohort was then added to the existing county
population of each cohort. This method assumes a constant distribution of recent immigrants
based on Year 2000 immigration patterns. While it is likely that new settlement patterns for
immigrants will develop in the future, this is the same method the Census Bureau uses for
assigning immigrants to states in its state projections (U.S. Census Bureau 2005). The Census
Bureau provides a low, medium, and high series for net international immigration. In the base
case, the middle series was used.
Domestic Migration
Although domestic migration is also a major component of local population change, it could not
be accurately modeled here. The Census Bureau's methodology for state estimates does contain
data about state-to-state migration rates based on the observed trend from 1975-2000, but that
method does not consider county-to-county migration patterns. The commonly used Woods and
Poole projections do consider domestic migration, but are only available to 2030. Developing a
method for estimating future migrations was beyond the requirements of this analysis, and likely
to introduce more error. The potential impacts of excluding domestic migration from this
analysis are discussed in the Section 4.4 which addresses uncertainty in the population
adjustments.
1.3.3. Use of these Projections
The population projections developed using the above methodology were used for two purposes
in this analysis. First, they were used to calculate the population-weighted change in UV
exposure based on the CMAQ and TUV modeling discussed above. These models provided the
percent change in ground-level UV exposure for each 12x12 km cell in a grid that roughly
covers the eastern two-thirds of the United States. To link the change in UV exposure to the
population in each county, the average percent change in UV exposure was calculated for each
county. In calculating the average for any given county, each cell was given a weighting equal
to the percentage of its area of that is located in that county. These county averages were then
used to calculate the population-weighted average change in UV exposure for each sex, age
group, and latitude band. The modeled population for 2020 was aggregated into male and
female, 18 age cohorts (0-4 years, 5-9 years, 10-14 years, and...85-plus years), and three
Draft report 6d-16
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Health Irnpa- • . • 7 Ozone Changes
latitude bands (20-30°, 30-40°, and 40-50°), or 108 population groups (2 sexes x 18 age groups
x 3 latitude bands =108 population groups). For each population group, the population-
weighted average exposure was calculated by summing the product of the population in each
county multiplied by the change in UV exposure in each county divided by the total population
of that population group across all counties.
These projections were also used in the AHEF model runs. Model outputs for each five-year
increment from 2005 to 2050 were aggregated for the 108 population groups. Because the
CMAQ model did not cover the entire United States, those counties that were not included in the
CMAQ modeling area were not included in the aggregated populations.
1.4. AHEF Modeling
The projections of population-weighted percentage change in UV exposure and future
populations, as described in Section 2.3.3 above, were inputted into the AHEF model to estimate
associated changes in health effects—specifically basal cell carcinoma (BCC), squamous cell
carcinoma (SCC), and cutaneous malignant melanoma (CMM) incidences and mortalities.
1.4.1. Overview of Methodology to Estimate Changes in Health Effects
To yield health effects estimates, the AHEF first projected future baseline skin cancer incidence
and mortality; this calculation was based on the future population estimates derived in Section
2.3 and baseline incidence and mortality rates for each health effect (based on a scenario of
compliance with the Montreal Adjustments to the Montreal Protocol). Then the AHEF
multiplied the population-weighted percentage changes in UV exposure in a future year by the
appropriate dose-response relationship (described in Section 4.2.2 below) to yield the percentage
change in future skin cancer incidence/mortality attributable to the proposed change in the
NAAQS ozone standard (from 84 ppb to 70 ppb). These percentages were then multiplied by the
baseline incidence and/or mortality for that health effect to compute the absolute number of
additional future cases or deaths attributable to the tropospheric ozone standard change.4 These
calculations are shown in Equation 2 below using BCC as an example health effect.
(Cumulative Percentage Increase in UV Exposure) x (Biological Amplification Factor for
BCC) x (Baseline Incidence of BCC for the Population Group) = Absolute Increase in BCC
Incidence
Equation 2
These calculations were performed for each health effect and for each future population group5
to produce predictions of the incremental health effects in each future year through 2100
associated with a one-pulse change in the NAAQS ozone standard from 84 ppb to 70 ppb in
4 This method of multiplying the changes in UV exposure by the biological amplification factor (BAF) and the
underlying baseline incidence or mortality is the same as that used by other researchers to estimate changes in health
effects based on changes in ozone concentrations (e.g., Madronich and de Gruijl 1994, Pitcher and Longstreth 1991).
5 The future population group is a subset of the total U.S. population, calculated specifically for this analysis, as
described in Section Error! Reference source not found, above.
Draft report 6d-17
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Health Impci- • ••• n Ozone
2020. It is important to note that because the percentage increase in UV exposure associated
with the tightening of the NAAQS standard from 84 ppb to 70 ppb is being used as the
environmental input in the AHEF, only the incremental number of health effects associated with
the standard change were modeled. The absolute number of health effects associated with the
current NAAQS standard was not expressly calculated.
1.4.2. Selected Action Spectrum and Derived Dose-Response
Relationships
The calculation of incremental health effects in Equation 2 above involves the use of a derived
dose-response relationship, or biological amplification factor (BAF). Determining the health
effects caused by UV exposure first requires information on the relative weights to be placed on
each discrete UV wavelength to reflect the degree to which each wavelength causes biologic
damage. Such a weighting function is called an action spectrum—an experimentally derived
function that describes the relative effectiveness of each UV wavelength in the induction of skin
cancers. The AHEF relies on action spectra for each health effect because action spectra provide
information regarding which wavelengths of the total UV spectrum are most effective at causing
the particular health effect. Based on the available action spectra, the Skin Cancer Utrecht-
Philadelphia-human (SCUP-h) action spectrum (derived based on the induction of SCC in
hairless mice and corrected for human skin transmission) was selected for modeling SCC, BCC,
and CMM in the AHEF.6
Based on the action spectrum selected for each health effect, the relationship between those
health effects and the intensity of UV exposure can then be explored. These dose-response
relationships are derived by correlating measurements or estimates of UV exposure received for
a specific action spectrum and given health effect at various locations, and the level of incidence
or mortality for that health effect at those same locations. In the AHEF, statistical regression
analyses were used to estimate the dose-response relationship, known in technical terms as the
BAF, for each health effect. The BAF measures the degree to which changes in UV exposure
weighted by the appropriate action spectrum (as measured in Watts/m2) cause incremental
changes in health effects (incidence or mortality), and is estimated after accounting for the
influence of birth year and age, as necessary.
BAFs are defined as the percent change in a health effect resulting from a one-percent change in
the intensity of UV radiation (weighted by the chosen action spectrum). For example, for BCC
incidence in white males, a one-percent change in the intensity of UV radiation results in a 1.5
percent change in BCC incidence. For each health effect, the AHEF applies the BAF to predict
future incidence and mortality as shown in Equation 2 above.
Table 3 presents a summary of calculated BAFs and selected action spectra for each health
effect.
6 Since a mammalian action spectrum for CMM still remains to be determined, the SCUP-h is also used to model
CMM.
Draft report 6d-18
-------
Table 3: Summary of Calculated BAFs, Selected Action Spectra, and Key Inputs
Health Effect
CMM
Incidence/
Mortality
BCC Incidence
SCC Incidence
Nonmelanoma
Mortality
Data Sources
Incidence: Ratios from SEER data set
Mortality: EPA/NCI data set
BAF: Developed using econometric analysis
Incidence: Based on methods used in U.S. EPA (1987)
and Fears and Scotto (1983)
BAF: de Gruijl and Forbes (1995)
Incidence: Based on methods used in U.S. EPA (1987)
and Fears and Scotto (1983)
BAF: de Gruijl and Forbes (1995)
Mortality: EPA/NCI data set
BAF: Developed using econometric analysis
Selected Action
Spectrum
SCUP-h(1993)
SCUP-h(1993)
SCUP-h(1993)
SCUP-h(1993)
BAF: Used in AHEF
(Annual Exposures)
Males
0.5846
1.5
2.6
0.7094
Females
0.5047
1.3
2.6
0.4574
1.5. Valuation of Human Health Effects
The monetary value of incremental cases of basal cell carcinoma (BCC), squamous cell
carcinoma (SCC), and cutaneous malignant melanoma (CMM) was calculated as the number of
additional cases multiplied by the medical and productivity loss cost per case. Cost per case is
for cancer care only and excludes the costs of unrelated care, such as increased costs for treating
other medical conditions later in life that might have occurred after the projected skin cancer
mortality. For a change in the NAAQS ozone standard in one year (2020) only, the AHEF
output gave the associated increase in skin cancer incidence and mortality, by health effect type,
in each year through 2150. Total incremental costs were calculated over 2020-2150 and
discounted to 2020 using discount rates of 3 percent and 7 percent, consistent with the guidance
provided in the Office of Management and Budget's (OMB) (2003) Circular A-4.
The medical costs and productivity loss per case are shown in Table 4. These monetary values
(in 2005$) were employed in a peer-reviewed publication (Kyle et al. forthcoming).
Table 4: Total Cost per Case of Non-fatal Skin Cancer and Mortality (2005 $)
Medical Cost
Productivity Loss Cost
Total Cost per
Case/Mortality
Non-fatal Skin Cancer Case
Basal Cell Carcinoma
Squamous Cell Carcinoma
Cutaneous Malignant Melanoma
Skin Cancer Mortality
$1,066*
$1,066*
--
$l,161f
$4,477f
--
$2,228
$5,543
$37,220J
$6.6 million§
* Chen et al. (2001), adjusted to 2005 $ using the medical care component of the Consumer Price Index (CPI-U).
Draft report
6d-19
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Health tnipa- ' - >i Ozone Changes
f Calculated by ICF, based on U.S. EPA (1988) and U.S. BLS (2007).
| U.S. EPA (1988), adjusted to 2005 $ using the CPI-U for medical care.
§ Adjusted from $5.5 million at 1990 income levels (2000 $) to $6,600,000 at 2020 income levels. $5.5 million is
the mean of a normal distribution with a 95% confidence interval between $ 1 million (Mrozek and Taylor 2002) and
$10 million (Viscusi and Aldy 2003).
Medical costs per case of BCC and SCC were based on Chen et al. (2001); this study used data
from the Medicare Current Beneficiary Survey (1999-2000) to estimate medical treatment costs
associated with BCC and SCC in different practice settings. To determine an average medical
treatment cost per case, weighted averages were calculated based on the percentage of episodes
managed in each setting.
Productivity loss costs were based on a U.S. EPA analysis supporting the Regulatory Impact
Analysis: Protection of Stratospheric Ozone (U.S. EPA 1988). The cost per case was calculated
by multiplying EPA's estimates of the loss of work due to illness and care giving performed by
others for the patient for BCC and SCC by the national mean annual wage for 2005 (U.S. BLS
2007). For CMM, EPA's estimate of the total medical cost and productivity loss per case was
used and adjusted to 2005 $ using the CPI-U for medical care (U.S. EPA 1988).
The value of a statistical life (VSL) is estimated to be $5.5 million at 1990 income levels and
$6.6 million at 2020 income levels. The estimate of $5.5 million is the mean of a normal
distribution with a 95 % confidence interval between $1 and $10 million. The 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 VSL represents the value of a small change in mortality risk aggregated over
the affected population.
This section provides an overview of the results of this analysis, including changes in ground
level SCUP-h UV, changes in health effects (i.e., incremental skin cancer incidence and
mortalities), and the resulting monetized disbenefits.
1.6. Changes in Ground Level SCUP-h UV
Using the methodology described in Section 2 above, the percent change in ground-level
SCUP-h UV was calculated for each day and averaged across each month. The figures below
represent average changes in SCUP-h UV associated with achieving an ozone standard of 70 ppb
(down from 84 ppb) in the summer months of June, July, and August.
Draft report 6d-20
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ICF
INTEHUTHMUL
Health Impacts from Ozone Changes
Figure 7: Ground Level UV Percent Change between 70 and 84 ppb, June
U.-.ml
% Chsngt
. 0 •'.!-_.
JD3-01
~:j: 0 v'"
013-0:
•02-035
Between 70 and 04 ppb
June
Draft report
6d-21
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ICF
INTEHUTHMUL
Health Impacts from Ozone Changes
Figure 8: Ground Level SCUP-h UV Percent Change between 70 and 84 ppb, July
U.-.ml
% Chsngt
. 0 •'.!-_.
JD3-01
~:j: 0 v'"
013-0:
•02-035
B «rw ft en 70 and 04 ppb
June
Draft report
6d-22
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ICF
INTEHUTHMUL
Health Impacts from Ozone Changes
Figure 9: Ground Level SCUP-h UV Percent Change between 70 and 84 ppb, August
Ww-
I lann-ai
0.1-0.16
din-Da
0.3-025
035-03
03-03}
SCUP UV % Change
Mid 84 ppb
August
Draft report
6d-23
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1.7. Changes in Human Health Effects
This section presents results in terms of the changes in skin cancer incidence and mortality
associated with a one-year change in the ozone standard in 2020. Table 5 below gives the
cumulative change in incidence and premature mortality associated with a one-time pulse (i.e., a
change in the ozone standard from 84 ppb to 70 ppb in one year, 2020). As shown, 3,538
additional cases of skin cancer and about 16 additional mortalities are expected. For the age
cohorts relevant to this analysis (those populations potentially alive in 2020 and thereafter; i.e.,
those born from 1930 to 2100) and for the population subset analyzed, baseline skin cancer
modeled in the AHEF through 2150 totals more than 188 million cases and about 2.6 million
mortalities. Thus, the additional cases and mortalities associated with changing the ozone
standard represent less than 0.002% and less than 0.001% of baseline, respectively.
This section also provides the monetary value of those future health effects, discounted back to
2020, in Table 6 and Table 7.
Table 5: Additional Skin Cancer Incidence and Mortality Associated with a Change
in the Ozone Standard in 2020
Skin Cancer Type
Nonmelanoma Skin Cancer
Cutaneous Malignant Melanoma
Total
Incidence
Mortality
Central Estimate*
(Uncertainty Range f)
3,454
(2,348-4,560)
84
(57-110)
3,538
(2,405-4,671)
5.7
(3.9-7.5)
10.5
(7.1-13.8)
16.2
(11.0-21.3)
* From the AHEF model.
f The uncertainty range is derived by applying the quantified uncertainty (approximately 32%), as calculated in
Section 1.15, to the central estimate.
Table 6: Monetized Summary Table (3% discount rate, discounted to 2020 with prices
in 2005 $)
Skin Cancer Type
Nonmelanoma Skin Cancer
Cutaneous Malignant Melanoma
Total
Incidence
Mortality
Central Estimate*
(Uncertainty Range f)
$4,717,452
($3,207,130-$6,227,773)
$1,399,631
($951,531-$1,847,732)
$6,117,083
($4,158, 661-$8,075,505)
$13,334,430
($9, 065, 329-$l 7, 603, 530)
$28,630,454
($19, 464,2 36-$3 7, 796, 672)
$41,964,884
($28, 529, 566-$55, 400,202)
Draft report
6d-24
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Health ttnpa-
•i Ozone Changes
* Based on incidence and mortality projected by the AHEF model.
f The uncertainty range is derived by applying the quantified uncertainty (approximately 32%), as calculated in
Section 1.15, to the central estimate.
Table 7: Monetized Summary Table (7% discount rate, discounted to 2020 with prices
in 2005 $)
Skin Cancer Type
Nonmelanoma Skin Cancer
Cutaneous Malignant Melanoma
Total
Incidence
Mortality
Central Estimate*
(Uncertainty Range f)
$2,234,469
($l,519,090-$2,949,848)
$720,816
($490,042-$951,590)
$2,955,285
($2, 009, 1 32-$3, 901, 438)
$5,819,781
($3, 956, 542-$ 7, 683, 021)
$14,068,218
($9, 564, 191-$18, 572,246)
$19,888,000
($13,520, 733-$26,255,267)
* Based on incidence and mortality projected by the AHEF model..
f The uncertainty range is derived by applying the quantified uncertainty (approximately 32%), as calculated in
Section 1.15, to the central estimate.
1.8. Uncertainty in estimated impacts
Uncertainty in the estimation of human health impacts arising from a tightening of the NAAQS
standards from 84 ppb to 70 ppb arise from various sources. These uncertainties are addressed in
the following sections:
• CMAQ Modeling—uncertainty in the prediction of precise tropospheric ozone column
changes under the NAAQS scenarios
• TUV Modeling—uncertainty in the calculation of consequent changes in surface UV-
SCUP
• Population Adjustments—uncertainty in the determination of county based population
projections
• AHEF Modeling—uncertainty in the estimate of associated changes in health effects
including latency
• Valuation Of Human Health Effects—uncertainty in the monetary value of incremental
skin cancer incidence/mortality
• Unquantified Sources of Uncertainty—other qualitative sources of uncertainty
• Summary of Quantified and Unquantified Sources of Uncertainty
The sources and magnitudes of the uncertainties associated with each step of the analysis were
identified and are discussed in the relevant sections below.
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6d-25
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Health Impacts from Ozone Changes
1.9. Uncertainty in CMAQ Modeling
Output from CMAQ modeling runs from U.S. EPA (CMAQ version 4.5) were provided for the
two NAAQS scenarios with identifiers:
• 2020bk_v4.5_084_12km.o3_hr_shift_LST, and
• 2020bk_v4.5_070b_12km.o3_hr_shift_LST
The CMAQ model did not cover the entire United States, the area analyzed covers all or part of
42 states in the eastern two-thirds of the country
Eder and Yu (2006) have conducted performance evaluations comparing annual simulations
(2001) of CMAQ (version 4.4) covering the contiguous United States against monitoring data for
four nationwide networks. This effort represents one of the most spatially and temporally
comprehensive performance evaluations of the model. Simulations of the peak 1- and 8-h ozone
concentrations during the summer (April-September) were "relatively good" (correlation
(r)=0.68, 0.69; normalized mean bias = 4.0 %, 8.1 % and normalized mean error = 18.3 % and
19.6 % respectively). No performance evaluation could be assessed for the provided scenarios;
however, analysis for the CMAQ review process (see http://www.cmascenter.org/index.cfm)
typically returns normalized mean errors for ozone ~ 20%.
As described in Section 2.2, the CMAQ ozone concentrations are accommodated into the TUV
model to determine overall column ozone values.
1.10. Uncertainty in TUV Modeling
1,10.1. Uncertainty Analysis of TUV Calculations
The uncertainties in the TUV calculations can be divided into two types:
1) Uncertainties inherent in the TUV numerical model, primarily from the approximate 2-
stream (delta-Eddington) solution of the radiative transfer equation, and the discretization
of altitudes and wavelength and related interpolations. These uncertainties have been
shown in many earlier studies to be negligible, on the order of 5% or less, when
compared to higher stream models and higher vertical and spectral resolution.
2) Uncertainties in the input parameters that describe atmospheric composition (vertical
profiles of air, ozone, other absorbing gases, aerosols, and clouds) and the earth's surface
reflectivity.
If the input parameters are well known (e.g., cloud-free and pollution-free conditions with
measured total ozone column as inputs), the TUV results are accurate to a few percent, which is
also the accuracy of the best instruments for measuring atmospheric UV radiation. For the
present purposes, the inherent TUV uncertainty (item 1) is taken, conservatively, as 5%.
The atmospheric input parameters (item 2 above) are generally not well known in any specific
situation, and are highly variable spatially and temporally, with long-term trends also a
possibility. For the purposes of these calculations, we adopt the principle that UV changes
Draft report 6d-26
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Health Impel'
n Ozone Changes
stemming from CMAQ scenario changes in tropospheric ozone can be calculated under the
premise that all other atmospheric conditions remain exactly the same between the two
scenarios, including clouds, aerosols, and surface reflectivity. This is consistent with the
approach used in calculations relating stratospheric ozone changes to surface UV increases.
Table 8 shows the predicted changes in UV-SCUP calculated by the TUV model, between the
two CMAQ scenarios (084 to 070), for 15 July. The changes are expressed as percent changes in
daily UV-SCUP doses at each location, then domain-averaged to give the values in the third
column of the table. The reference model (test number 0) would be used in the AHEF estimates
of skin cancer changes. The other entries in the table (tests number 1-6), show the UV-SCUP %
change between scenarios, if other atmospheric conditions are changed individually and equally
for both scenarios, as described in the second column. The last column gives the % effect of
changing the atmospheric conditions. For example, the reference calculation (test 0) gives a UV-
SCUP increase of 0.118% in going from scenario 084 to 070. If aerosols are removed from the
model (test 1), the UV-SCUP increase between scenarios is only 0.112, which is a 5.1 %
reduction relative to the reference case. A brief explanation of the effects from each factor is
given below.
Table 8: UV-SCUP changes between CMAQ scenarios 084 and 070 on 15 July, for
different values of other factors (aerosol, surface albedo, clouds, and
stratospheric ozone).
Test
number
0
1
2
3
4
5
6
Description
Reference (Elterman* aerosols, 10% surface
albedo, no clouds, sea level, USSA
stratospheric Os)
No aerosols
0% surface albedo
High thin cloud, at 9-10 km, optical depth
=2
Low moderately heavy cloud, at 1-2 km,
optical depth =16
850 mb surface pressure
20 DU reduction in stratospheric Os (above
16km)
Domain-
averaged
change in
UV-SCUP,
%
0.118
0.112
0.112
0.132
0.169
0.098
0.123
Effect of
other factors,
%
= 0
-5.1
-5.1
11.9
43
-17
4.2
(*) Elterman continental aerosol vertical profile, with total optical depth (at 550 nm) = 0.235,
Angstrom alpha = 1.0, single scattering albedo = 0.99, asymmetry factor = 0.61.
1. Aerosols increase the photons' pathlengths, and therefore increase the probability of
absorption by tropospheric ozone. By removing aerosols from the reference run, the UV
increase from changing ozone scenarios is somewhat smaller.
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Health Impel' '•• > < n Ozone Changes
2. Surface albedo reflects light back to the atmosphere, and a fraction of this can be scattered
back toward the surface, effectively increasing the photons' path-lengths for absorption by
tropospheric ozone. If the surface is not reflecting (albedo = 0%), these photon reflections do not
occur and the interaction with tropospheric ozone is smaller.
3. High clouds (e.g., cirrus) make the incident (down-welling) light more diffuse and therefore
more slanted as it passes the troposphere. They also reflect a fraction of the up-welling radiation
(up-scattered by tropospheric molecules), back to the lower troposphere (much like surface
albedo, but in the opposite direction). Both effects increase tropospheric photon pathlengths and
therefore the probability of absorption from any additional tropospheric ozone.
4. Low thick clouds (e.g., stratocumulus, marine stratus) have a larger effect because they are at
altitudes closer to where the ozone changes are largest. In-cloud increases of ozone are
particularly significant because of the long in-cloud photon pathlengths, as has been observed
and modeled (e.g., Mayer et al., 1997). Broken clouds (e.g., fair-weather cumulus) are expected
to be intermediate between fully overcast and fully clear (Nack and Green, 1974).
5. Decreases in atmospheric pressure reduce, in direct proportion according to the ideal gas law,
the conversion factor between ozone molar mixing ratios (ppb, specified by CMAQ) and the
ozone number density (molecules cm"3, which is integrated to obtain the ozone column in
Dobson Units) used for atmospheric transmission. Also, lower pressures decrease the Rayleigh
optical depth and therefore the photon path coupling between scattering and absorption. These
factors combine to yield a smaller SCUP-UV change. The pressure reduction chosen here, 850
mb, is roughly representative of cities at high elevation. Thus, this case can also be considered a
surrogate test for the effect of surface elevation (varying the surface elevation directly is possible
within the TUV code, but would have created some ambiguity between the nominal CMAQ
altitudes and the TUV geometric grid).
6. Reductions in stratospheric ozone imply that any tropospheric ozone changes are a larger
fraction of the total column ozone. Therefore the sensitivity to CMAQ scenario changes is
greater if the stratospheric ozone is smaller. This is consistent with the power law first proposed
byMadronich(1993):
UVbw o
for which the theoretical basis is described by Micheletti et al. (2003).
The sensitivity studies (cases 1-6) show that how the baseline environmental conditions, under
which the difference between the two tropospheric ozone scenarios was assessed, could
contribute to the uncertainties of the TUV-calculated changes in surface SCUP-UV radiation.
The worst case is that of low clouds: If the entire domain were actually covered by low clouds
for the entire period of interest (June- August), the TUV calculations made under cloud- free
assumption would underestimate the UV increases stemming from the changes in tropospheric
ozone, by about 43%. This extreme case is patently unrealistic. Conservatively, if it is assumed
Draft report 6d-28
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that low clouds are present no more than 1/4 of the time, their error is reduced to about 11%.
Thus, the uncertainty budget can be summarized as follows:
Inherent TUV uncertainties 5 %
Aerosols 5.1 %
0
Surface albedo 5.1 %
High clouds 11.9 %
Low clouds (1 /4 of the time) 11 %
Surface pressure 17
Stratospheric ozone 4.2 %
TOTAL (quadrature) 25 %
For example, for the 15 July case, the reference UV-SCUP change of 0.118 % is estimated to be,
with high certainty, in the range 0.088-0.148 %.
Finally, it should be noted that these estimates are generally overly conservative. For example,
high clouds are likely to be present only a fraction of the time, and the 850 mb pressure may
apply to only a few locations. Therefore the 25% uncertainty estimated here should be viewed as
a very conservative upper limit.
The TUV model also has the option of calculating radiation incidence on a sphere or on a
horizontal plane. Incidence on a sphere is presently considered a better metric for UV exposure
and was therefore used in this analysis. A small uncertainty is introduced over incidence on a
horizontal plane, the previous standard. The percent change in UV is reduced by about 8 % by
taking the spherical output in preference to the planar output (i.e., for the 15 July domain-
average, from 0.126 % to 0.118 %). This is a small effect and it should be noted that the average
SCUP-UV changes are still near 0.1 % using either output.
1.10.2. Comparison with UV Changes Due to Other Factors
In Section 2.2.3, the UV-SCUP change resulting from tropospheric ozone change between the
two CMAQ scenarios was calculated and shown to be of order ~ 0.1 %, if all other
environmental factors are kept constant between the two scenarios. Below, we consider, for
comparison only, the UV changes that would result if these other factors are allowed to vary
between two scenarios. To illustrate this, Table 9 shows the UV changes, calculated for the
CMAQ 084 tropospheric ozone scenario, when other environmental conditions, rather than
tropospheric ozone, are changed relative to the reference conditions. The magnitude of changes
in the conditions is the same as used for Table 8. It should be emphasized that the % UV
changes shown in Table 9 are NOT those associated with changes in tropospheric ozone,
but rather with direct changes in the other environmental conditions.
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Table 9:
Effect on surface SCUP-UV radiation of varying environmental conditions
other than tropospheric Os.
Test
number
0
la
2a
3a
4a
5a
6a
Description
Reference (Elterman aerosols, 10% surface
albedo, no clouds, sea level, USSA
stratospheric Os), tropospheric Os scenario
084
No aerosols
0% surface albedo
High thin cloud, at 9-10 km, optical depth
=2
Low moderately heavy cloud, at 1-2 km,
optical depth =16
850 mb surface pressure
20 DU reduction in stratospheric Os (above
16km)
Domain-
averaged
% change in
UV-SCUP
= 0
7.3
-3.8
-12.1
-50.
10.4
7.6
Should the baseline environmental conditions actually change between the two CMAQ
tropospheric ozone scenarios (084 and 070), the SCUP-UV changes could be far larger. Of
course, there is no solid scientific basis for expecting such environmental changes in response to
relatively small changes in tropospheric ozone. Some interactions are known, (e.g. oxidant
photochemistry leading to the formation of sulfate and secondary organic aerosols, which can
affect radiation directly as well as change cloud nucleation and lifetimes) but these effects are
still poorly quantified, and although subjects of active current research, are not expected to be as
large as the variations used in this sensitivity analysis.
1.11. Uncertainty in Population Adjustments
The Cohort-Component Methodology (see Section 2.3.1) for population adjustment used in the
analysis gave a 2020 total population of 336.1 million in very close agreement with the U.S.
Census Bureau projection for 2020 of 335.8 million—a difference of less than 0.1 %. However,
as discussed above, the model did not consider domestic migration between counties due to the
lack of suitable alternative estimates. It is assumed that migration between neighboring counties
within the same metropolitan area is not likely to have an impact on the results because the
change in ozone concentration is similar in adjacent areas. When aggregated across broad
latitude bands with hundreds of counties, small differences from one county to the next due to
migration are likely to cancel each other out.
Interregional migration—such as the observed historic migrations from the Northeast and upper
Midwest to the Sun Belt states—is a potential source of uncertainty in this analysis. Since the
model estimated that all local populations change only through births, deaths, and the arrival of
international immigrants, it is possible that populations of regions that are losing migrants to
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Health iin)u-^ -- -7 Ozone Chanes
other parts of the country are overrepresented in this analysis, while the populations of fast-
growing regions attracting these migrants are underrepresented. Because the population-
weighted change in UV exposure is higher in the southern latitude band than in the northern
latitude band, this analysis may be underestimating the change in exposure if the historic north-
to-south migration pattern holds. However, this effect is not uniform—Florida, for example,
exhibits much lower changes in UV than other areas of the South, but has traditionally received a
large portion of migrants from the North.
Ultimately, it was decided that the uncertainty associated with predicting migration patterns
outweighed the uncertainty introduced by excluding domestic migration from this model.
Because migration between regions is a matter of percentage points rather than degrees of
magnitude, it is assumed that the overall uncertainty associated with the population projections is
relatively small.
The CMAQ model area also has population implications. The area analyzed covers all or part of
42 states in the eastern two-thirds of the country. As a result, those counties that were not
included in the CMAQ modeling area were not included in the aggregated populations (26.2 %
of the total population). It would be reasonable to assume, given this truncation of population
(e.g., 13.5 % of the population reside in California) and the historically high proportion of cases
of skin cancer and/or mortality on the West Coast (e.g., California counties, especially Los
Angeles), that this input alone would introduce a disproportional large, unquantifiable
uncertainty if the estimated health effects from the analysis were extrapolated to the rest of the
population. Therefore, the results of this analysis must be viewed in this context when drawing
comparisons with other studies which consider the continuous United States (e.g., Lutter and
Wolz, 1997).
1.12. Uncertainty in AHEF Modeling
AHEF modeling contributes uncertainties to the estimates of human health effects—resulting
from a change in NAAQS standards—in two major areas:
1) the dose-response relationships (expressed as a BAF) for the three endpoints of concern
(i.e., BCC, SCC, and CMM), and
2) the future size, behavior, and distribution of the populations that will be affected (see
Section 4.4. Uncertainty in Population Adjustments).
It should be noted that for this analysis, only estimated uncertainty in the BAF parameter is
quantifiable.
1.12.1. Uncertainties in Selected Derived Dose-Response Relationships
The AHEF model (described in Section 2.4) incorporates information on the dose-response
relationships for BCC, SCC, and CMM through the use of a BAF (i.e., the slope of the dose-
response relationship). . The estimate of BAF and associated standard error generated for CMM
incidence/mortality using the SCUP-h action spectrum is 0.5846 ± 0.02 for males, 0.5047 ± 0.02
for females which yields an uncertainty range of approximately 3 % for changes in these health
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Health Imptr '•- -i Ozone Changes
effects estimates; the BAF and associated standard errors generated for NMSC mortality
0.7094 ± 0.03 for males, 0.4574 ± 0.03 for females which yields an uncertainty range of
approximately 4 and 7 % respectively; and BAFs and associated standard errors generated for
BCC and SCC are 1.5 ± 0.5 for males, 1.3 ± 0.4 for females and 2.6 ± 0.7 for males, 2.6 ± 0.8,
respectively (deGruijl and Forbes, 1995) which yields an uncertainty range of approximately
30% for changes in these health effects estimates.
1.12.2. Behavioral Uncertainties
While the AHEF assumes that human exposure behavior remains constant over time, changes in
human behavior affect the amount of UV radiation exposure received. For example, changes in
(1) the amount of time spent outdoors, (2) in socioeconomic profiles that impact travel to areas
where high UV exposure can be expected (i.e., the beach), or (3) in the use and/or efficacy of sun
protection technologies such as sunglasses and sunscreens can impact the extent of UV exposure
received.
A number of recent studies have examined UV exposure behaviors in the U.S. Godar et al.
(2003) found that Americans get about 23 % of their lifetime UV dose by the age of 18, 46 % by
the age of 40, and about 74 % by the age of 59, assuming that individuals live up to the age of
78. Among U.S. youth ages 11-18, Cokinnides et al. (2001) found that about 10 % reported
practicing three or more sun protection behaviors regularly and nearly 60 % practiced one or two
routinely; however, about one-third of the youth overall did not practice any recommended sun
protection behaviors.
1.12.3. Latency
Another source of uncertainty in the AHEF health effects estimate is associated with the
exposure period over a person's lifetime that is most likely to be the cause of UV-related health
effects. This is especially relevant for CMM, since it has been hypothesized that CMM is largely
the product of intense exposures early in life (e.g., through age 20) rather than cumulative
lifetime exposure. The AHEF uses whole life exposure for all skin cancer types as the default
assumption. Using early life exposure for CMM is not the same as evaluating a latency effect,
but can be used as a proxy for latency in this health end point. Figure 10 shows the effect of this
proxy measure for latency on CMM mortality changes by ~ 10 percent when the exposure
assumptions (early life versus whole life) are changed (U.S. EPA, 2003), with uncertainty
concerning the appropriate exposure dose manifesting itself less in the total incremental risks
predicted, than in when those incremental effects are predicted to occur, and who will bear them
(i.e., shifting the risk to future generations). Modeling this lag time further is difficult given the
current state of knowledge about latency and its mechanisms (Madronich, 1999).
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Health impacts from Ozone Changes
Figure 10: Excess CMM mortality for the Montreal Adjustment scenario for equal-age
exposure weighting and weighting for exposures only for ages 1-20:
cumulative annual exposure (U.S. EPA/NASA, 2001).
400 n
1.13. Uncertainty in Valuation of Human Health Effects
An extensive literature review was conducted to determine the best medical cost estimates for
NMSC for the Economic Evaluation of the U.S. Environmental Protection Agency's Sun Wise
Program: Sun Protection Education for Young Children (Kyle et al, forthcoming). Values were
taken from Chen et al. (2001), considered to be the best available source of data for these health
endpoints; however, the authors did not include uncertainty bands around their central estimates.
The national mean annual wage for 2005 (U.S. BLS, 2007) is $37, 870 (mean annual wage for
all occupations) which has a mean relative standard error of 0.1%.
1.14. Unquantified Sources of Uncertainty
There are a number of other sources of uncertainty in the analysis' health effects predictions.
Some of these sources of uncertainty are possible to quantify, but are not central to the structure
of the analysis. Others cannot be quantified because any assumptions or estimates would be
simply speculative. These other sources of uncertainty include:
• Composition of the future atmosphere;
• Future conditions of the ozone column;
• Effect of climate change;
• Compliance with modeled policy scenarios;
• Laboratory techniques and instrumentation for deriving action spectra;
• Improvements in medical care/increased longevity; and
• Baseline information.
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Health Impel' '•• << n Ozone Changes
These uncertainties are described qualitatively in more detail below.
Composition of the Future Atmosphere
The exact composition of the future atmosphere as a result of compliance with different policies
(i.e., ODS phaseout under the Montreal Adjustments to the Montreal Protocol) is unknown. As
levels of atmospheric chlorine are reduced, the impact of ozone depletion from chlorine and
bromine radical species generated from ODS would change. In addition, long-term systematic
changes in atmospheric opacity (e.g., clouds, aerosols, other pollutants) will also impact the
ability to model changes in ozone. Likewise, future changes in climate could result in changes in
the atmospheric circulation patterns and therefore could change cloud cover. The impacts of such
changes on the predicted recovery of the ozone layer and subsequently tropospheric ozone are
unknown. All of these uncertainties could influence the ability to model atmospheric processes
accurately.
Future Conditions of the Ozone Column
Uncertainties also can be contributed by assumptions regarding the future conditions of the
ozone column in response to the phaseout of ODS. Some computer models predict that the
phaseout of ODS will slow and eventually stop the rate of ozone depletion, and suggest that
natural ozone-making processes will enable stratospheric ozone to return to 1979-1980 ozone
conditions. These models also predict that the recovery will eventually result in increased
concentrations beyond 1979-1980 levels7 (see Chapter 12 in WMO
1999 for more detail). Because there is incomplete knowledge about the behavior of ozone prior
to the satellite measurements taken in 1979-1980, the AHEF imposes a limit on future ozone
recovery to the conditions observed in 1979-1980.
Effect of Climate Change
The effects of global climate variations on stratospheric temperature and, in turn, on ozone
depletion, are not well understood, and have therefore not been assessed in the analysis. While
this effect is not typically incorporated into models used to assess future ozone depletion, it does
represent a modeling constraint that should be noted.
Compliance with Modeled Policy Scenarios
This analysis assumes compliance with each of the modeled NAAQS policy scenarios. To the
extent that these limitations are not adhered to, future ozone column conditions could be
different.
Laboratory Techniques and Instrumentation
Additional uncertainty can be contributed by the laboratory techniques and instrumentation used
for deriving the action spectra used to weight UV exposure. Discrepancies between the
wavelengths of UV radiation intended to be administered and the wavelengths actually received
by the test organism can result in orders of magnitude differences in the measured response. In
7 Whether this recovery scenario, called "ozone superabundance," is likely to occur is open to debate, particularly
because of the potential for complex interactions between global climate change and stratospheric ozone dynamics.
Model computations have predicted both higher and lower amounts of ozone in the future.
Draft report 6d-34
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Health Impel' '•• > < n Ozone Changes
addition, many action spectra are derived using monochromatic light sources that do not fully
simulate the polychromatic light received directly from the sun.
Improvements in medical care/increased longevity
Improvements in medical care and predictions of increased longevity for many population
subgroups could affect estimates of future skin cancer incidence and mortality significantly.
Changes in socioeconomic factors
Changes in socioeconomic factors (e.g., demographics and human behavioral changes) that could
affect the accuracy of the analysis include:
• Changes in human UV exposure behavior: This evaluation assumes that human exposure
behavior remains constant through time, and does not take into account innovations in
sun protection technology (e.g., improved sunglasses and sunscreens), increased public
awareness of the effects of overexposure to UV, and increased sensitization to the need
for early treatment of suspicious lesions.
• Changes in socioeconomic profiles: Socioeconomic profiles can impact a variety of
factors, ranging from demand for air travel to areas where high UV exposure is expected
(i.e., the beach), to the types of skin cancer most commonly observed.
• Changes in population composition and size: Population composition changes such as the
expected increase in Hispanic populations, whose more pigmented skin is thought to
decrease skin cancer risk, could have significant effects on future U.S. skin cancer rates.
The above factors are either not easily quantified (e.g., human behavior; see Section 4.5.2.
Behavioral Uncertainties), or they are not central to the analysis (e.g., improvements in medical
care), and are therefore not addressed further in this evaluation.
Baseline Information
It is possible that error is introduced to the AHEF's results through misreporting of skin cancer
incidence and mortality data (i.e., the AHEF's baseline estimates). With disease data, under-,
over-, and misreporting are not uncommon. For example, a studies have revealed that the
incidence of CMM has been systematically under-reported in the SEER data (Clegg et al. 2002).8
The original SEER data indicated that CMM rates in white males were relatively flat or even
falling (ranging from -11.1 percent to 3.3 percent annually after 1996). However, after adjusting
for underreporting, CMM rates were actually found to have increased between 3.8 to 4.4 percent
annually since 1981 (Clegg et al. 2002). Underreporting of CMM incidence is largely
attributable to diagnosis in doctors' offices, as opposed to hospitals and other treatment centers
with better reporting accuracy. However, the AHEF results are not significantly affected by this
underreporting because CMM incidence estimates in the AHEF are not based directly on SEER
incidence data. Rather, because the AHEF estimates CMM incidence based on the ratio of SEER
8 There is little reason to believe that the SEER CMM incidence under-reporting extends to the NCI-based CMM
mortality input information.
Draft report 6d-35
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incidence data to projected annual mortality estimates, and because underreporting would affect
both baseline and scenario estimates, the effects on incremental changes in CMM incidence
would be second order.
1.15. Summary of Quantified and Unquantified Sources of
Uncertainty
Of the major sources of uncertainty associated with the analysis, the total quantified uncertainty
is roughly 32 percent, as summarized in Table 10.
Table 10: Major Sources of Quantified Uncertainty
Source of Uncertainty
Translating column ozone to ground-level UV
TUV Model
Translating UV exposure to human health effects
Uncertainty in BAFs
• CMM mortality (3 %)
• NMSC mortality (4-7 %)
• NMSC incidence (30 %)
Early life exposure versus whole life exposure
Total V(52 + 302 + 102)
Quantified Uncertainty
-5%
< 30 %
~ 10 %
» 32 %
There are a variety of other unquantified sources of uncertainty that may contribute to overall
analytical uncertainty associated with modeled ozone changes, changes in UV radiation, and
changes in health effects. Table 11 summarizes the parameters that relate to these unquantified
uncertainties.
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Health Impacts from. Ozone Changes
Table 11: Factors with unknown contributions to uncertainty
Factor
Changes in ozone estimates
Change in UV radiation
estimates
Change in health effect
estimates
Parameter
Composition of future atmosphere
Ability to model atmospheric processes accurately
Response of tropospheric ozone to ozone layer recovery
Effect of climate change
Compliance with modeled NAAQS policy scenarios
Long-term systematic changes in atmospheric opacity
(e.g., clouds, aerosols, other pollutants)
Changes in human UV exposure behavior
Laboratory techniques and instrumentation for deriving
an action spectrum
Uncertainty with choice of action spectra
Improvements in medical care/increased longevity
Changes in socioeconomic factors (e.g., demographics
and human behavioral changes)
Baseline information (e.g., misreporting of skin cancer
incidence and mortality data)
Changes in population composition and size (including
truncation of CMAQ model analysis area)
Accurate prediction of future changes in human health effects would require consideration of the
net effect of all the factors described above. This challenge is beyond the ability of the current
state of atmospheric and epidemiological science. In addition, direct measurements (e.g., of
future UV levels or skin cancer incidence) cannot attribute explicitly observed changes to any
specific factor, unless that factor is far more important than all the others combined. However,
the principle of superposition can be used to examine the NAAQS impact (i.e., one effect in
isolation) under the assumption that the other factors remain constant at current conditions. The
validity of this principle is based on the assumption that the NAAQS impacts are independent of
the other factors (e.g., behavioral changes will occur regardless of whether a new NAAQS
standard is in place).
!(Jii u'jvos
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National Vital Statistics Reports 53:20. Available online at:
http://www.cdc.gov/nchs/data/nvsr/nvsr53/nvsr53_20.pdf
McKinlay, A. F. and Diffey, B. L., A reference action spectrum for ultraviolet induced erythema in
human skin. In Human Exposure to Ultraviolet Radiation: Risks and Regulations (Edited by W. R.
Passchier and B. F. M. Bosnjakovic), pp. 83-87. Elsevier, Amsterdam, 1987.
Mrozek, J.R., and L.O. Taylor. 2002. "What Determines the Value of Life? A Meta-Analysis."
Journal of Policy Analysis and Management 21(2):253-270.
Nack, M. L. and A. E. S. Green, Influence of clouds, haze, and smog on the middle ultraviolet
reaching the ground., 12,2405-2415, 1974.
National Center for Health Statistics (2007). Bridged-race Vintage 2006 Postcensal Population
Estimates for July 1, 2000-July 1, 2006. Available online at:
http://www.cdc.gov/nchs/about/major/dvs/popbridge/datadoc.htm
Petropavlovskikh, I. Evaluation of Photodissociation Coefficient Calculations for Use in Atmospheric
Chemical Models, Ph.D. thesis, U. of Brussels/NCAR Cooperative Thesis No. 159, National Center
for Atmospheric Research, Boulder, 1995.
Pitcher, H.M., and J.D. Longstreth (1991), "Melanoma mortality and exposure to ultraviolet radiation:
An empirical relationship," Environment International 17:7-21.
Toon, O. B., C. P. McKay, T. P. Ackerman, and K. Santhanam, J. Geophys. Res., 94, 16287, 1989.
U.S. Bureau of Labor Statistics, Department of Labor. Occupational Employment Statistics (OES)
Survey: May 2005 National OES Estimates, [cited 2007 Dec 26]. Available from:
http://www.bls.gov/oes/.
U.S. Census Bureau (2000). Component Assumptions of the Resident Population by Age, Sex, Race, and
Hispanic Origin. Available online at: http://www.census.gov/population/www/projections/natdet-
D5.html
U.S. Census Bureau (2005). State Interim Population Projections by Age and Sex: 2004-2030.
Available online at: http://www.census.gov/population/www/projections/projectionsagesex.html
U.S. Environmental Protection Agency. EPA Guidelines for Preparing Economic Analyses.
Washington, DC: Office of the Administrator, U.S. Environmental Protection Agency; 2001. Report
No. 240-R-00-003.
U.S. EPA (2003). Human Health Benefits of Stratospheric Ozone Protection. Global Programs Division,
Office of Air and Radiation, U.S. EPA, Washington, DC.
U.S. EPA and NASA (2001). Human Health Effects of Ozone Depletion From Stratospheric Aircraft.
U.S. EPA, Washington, DC
U.S. EPA (2008). Personal communication.
Draft report 6d-39
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Health Impel' '•• > < n Ozone Changes
U.S. Environmental Protection Agency. Regulatory Impact Analysis: Protection of Stratospheric Ozone,
E. Final Report. 1988.
USSA, US Standard Atmosphere National Oceanic and Atmospheric Administration, National
Aeronautics and Space Administration, United States Air Force, Washington B.C., 1976.
Viscusi, V.K., and J.E. Aldy. 2003. "The Value of a Statistical Life: A Critical Review of Market
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Draft report 6d-40
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ICF
r. 11 i.i.» i -.-. ,-.i
Health Impacts from Ozone Changes
Appendix A: Ground Level SCUP-h UV with 70 and 84 ppb by Day
This Appendix will provide a series of maps showing ground level SCUP-h UV levels under 70 and 84 ppb NAAQS for ozone for several
specific days in the summer months - June 1, June 20, July 1, and August 1.
Figure A-l: Ground Level SCUP UV, June 1; 70 ppb Scenario
Jtn *. iD pft SnnMiil
Draft report
6d-41
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ICF
r. 11 i.i.» i -.-. ,-.i
Health Impacts from Ozone Changes
Figure A-2: Ground Level SCUP UV, June 1; 84 ppb Scenario
WTJ-
70 v<
Ground level SCUP UV
June 1; S4 ppb Scenario
Draft report
6d-42
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IHT[RN.iTU!MM
Health Impacts from Ozone Changes
Figure A-3: Ground Level SCUP UV, June 20; 70 ppb Scenario
Wtt-
Ground lewlSCUPUV
June 20; 70 ppb Scenario
Draft report
6d-43
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ICF
r. 11 i.i.» i -.-. ,-.i
Health Impacts from Ozone Changes
Figure A-4: Ground Level SCUP UV, June 20; 84 ppb Scenario
aott-
Grr,unri level SCUP UV
June 20; 84 ppb Scenario
Draft report
6d-44
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ICF
r. 11 i.i.» i -.-. ,-.i
Health Impacts from Ozone Changes
Figure A-5: Ground Level SCUP UV, July 1; 70 ppb Scenario
Ground lew! SCUPUV
July 1; 70 ppb Scenario
Draft report
6d-45
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ICF
r. 11 i.i.» i -.-. ,-.i
Health Impacts from Ozone Changes
Figure A-6: Ground Level SCUP UV, July 1; 85 ppb Scenario
Ground level SCUPUV
July 1; 34 ppb Scenario
Draft report
6d-46
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ICF
r. 11 i.i.» i -.-. ,-.i
Health Impacts from Ozone Changes
Figure A-7: Ground Level SCUP UV, August 1; 70 ppb Scenario
Ground lewl SCUPUV
; 70 ppb Scenario
Draft report
6d-47
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ICF
r. 11 i.i.» i -.-. ,-.i
Health Impacts from Ozone Changes
Figure A-8: Ground Level SCUP UV, August 1; 84 ppb Scenario
Ground lew) SCUPUV
Aurjusr -I; 34 ppb Scenario
Draft report
6d-48
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Health Impacts from Ozone Changes
Appendix B: Overview of Evaluation Methodology
The schematic presented below provides a graphical summary of the method used in this
evaluation. Atmospheric inputs to the process are listed along the left-hand side and the various
process stages are described along the bottom.
1979-80 column Oa
I "baseline")
column ozone
column ozone
"baseline'
health effec
projections
RP.EI
P = projected
2020-2150
US population
E - empirical
epidemiologies!
data
AlUV) 7W
i = each health
effect endpoint
(BCC,SCC,CMM)
AS = action spectrum
BAF= biological
amplification
factor
"baseline"
health effects
2020-2150
A
Column
Ozone inputs
B
UV Transfer
Calculation
C
Health Effect
Computation
D
Estimated
Health Effects
6d-47
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:.-
AHEF Atmospheric and Health Effects Framework
BAF Biological Amplification Factor
BAU Business as Usual
BCC Basal Cell Carcinoma
BLS Bureau of Labor Statistics
CDC Centers for Disease Control and Prevention
CMAQ Community Multiscale Air Quality
CMM Cutaneous Malignant Melanoma
DU Dobson Units
EPA United States Environmental Protection Agency
NAAQS National Ambient Air Quality Standard
NCEE National Center for Environmental Economics
NCI National Cancer Institute
OAR Office of Air and Radiation
SCC Squamous Cell Carcinoma
SCUP-h Skin Cancer Utrecht-Philadelphia-human
sza solar zenith angle
TOCOR Task Order Contracting Representative
USSA United States Standard Atmosphere
6d-47
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Chapter 7: Conclusions and Implications of the Illustrative Benefit-Cost Analysis
7.1 Synopsis
EPA has performed an illustrative analysis to estimate the costs and human health benefits of
nationally attaining alternative 0.075 ppm ozone standard. We have also considered 3 alternative
standards incremental to attaining the current ozone standard: 0.079 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 PJA for full attainment of the PM NAAQS 15/35 promulgated
in September, 2006). This PJA presents the costs and benefits of full attainment in all locations
except two areas of California, which would not be required to meet an alternate primary
standard until 2024. Estimates for these two areas are presented in Appendix 7b. This chapter
provides additional context for the PJA analysis and a discussion of limitations and uncertainties.
7.2 Results
7.2.1 Presentation of Results
For analytical purposes explained previously, we assume that almost all areas of the country will
meet each alternate primary 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 2024; specifically, two California areas have adopted plans
for post-2020 attainment as noted above. To perform an analysis beyond 2020 involves the use
of highly speculative assumptions that introduce a much higher level of uncertainty to the results.
Thus, in these locations, we provide estimates of the costs and benefits of fully attaining the
alternate primary standards at a later date (2030) in Appendix 7b. It is important to note that, as a
result, the 2020 results presented here do not represent a complete "full attainment" scenario for
the entire nation. Due to the differences in attainment year and other assumptions underlying
2020 analysis presented here and the 2030 analysis in the appendix, it is not appropriate to add
the results together to get a national "full attainment" scenario. Finally, Appendix 6b contains a
health-based cost effectiveness analysis that complements the results found below.
The following two tables summarize the costs and benefits of attaining the alternate primary
standards in 2020 for all places except South Coast and San Joaquin. For purposes of this
analysis, we assume attainment by 2020 for all areas except San Joaquin Valley and South Coast
air basins in California. The state has submitted plans to EPA for implementing the current ozone
standard which propose that these two areas of California meet that standard by 2024. We have
assumed for analytical purposes that the San Joaquin Valley and South Coast air basin would
7-1
-------
attain a new standard in 2030. There are many uncertainties associated with the year 2030
analysis. Between 2020 and 2030 several federal air quality rules are likely to further reduce
emissions of NOx and VOC, such as, but not limited to National rules for Diesel Locomotives,
Diesel Marine Vessels, and Small Nonroad Gasoline Engines. These emission reductions should
lower ambient levels of ozone in California between 2020 and 2030. Complete emissions
inventories as well as air quality modeling were not available for this year 2030 analysis. Due to
these limitations, it is not possible to adequately model 2030 air quality changes that are required
to develop robust controls strategies with associated costs and benefits. In order to provide a
rough approximation of the costs and benefits of attaining 0.075 ppm and the alternate standards
in San Joaquin and South Coast air basins, we have relied on the available data. Available data
includes emission inventories, which do not include any changes in stationary source emissions
beyond 2020, and 2020 supplemental air quality modeling. This data was used to develop
extrapolated costs and benefits of 2030 attainment. To view the complete analysis for the San
Joaquin Valley and South Coast air basins see Appendix 7b.
The costs presented here are based on reducing emissions primarily within 200 km of counties
projected to fail to attain a particular standard. Changes in emissions translate into changes in
ozone within and beyond the 200 km control areas. Air quality modeling is used to estimate
where the changes in ozone resulting from emission changes takes place. Benefits are then
estimated based on the modeled changes in ozone.
Tables 7.1a-d present benefits and costs. Table 7.2 provides the estimated reductions in
premature mortality and morbidity.
7-2
-------
Table 7.1a: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.s
Co-Benefits: 0.075 ppm Standard in 2020 in Billions of 2006$*
Ozone
Mortality
Function or
Assumption
NMMAPS
Meta-analysis
Assumption that
not causal****
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is
Total Benefits**
3% 7%
2.6-17
3.8-18
4.4-19
4.5-19
2.0-17
2.4-16
3.6-17
4.3-17
4.4-17
1.8-15
Total
Costs***
7%
7.6-8.8
7.6-8.8
7.6-8.8
7.6-8.8
7.6-8.8
Net Benefits
3% 7%
-6.3 - 9.5
-5.0-11
-4.4 - 1 1
-4.3-11
-6.8 - 9
-6.4-
-5.2-
-4.5-
-4.5-
-7.0-
7.9
9.1
9.8
9.9
7.4
Table 7.1b: Estimated Range of Annual Monetized Costs and Ozone Benefits and
Co-Benefits: 0.079 ppm Standard in 2020 in Billions of 2006$*
Ozone
Mortality
Function or
Assumption
NMMAPS
Meta-analysis
Assumption that
not causal****
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is
Total Benefits**
3% 7%
1.4-11
1.9-11
2.1-12
2.1-12
1.2-11
1.3-9.9
1.8-10
2.0-11
2.0-11
1.1-9.7
Total
Costs***
7%
2.4-2.9
2.4-2.9
2.4-2.9
2.4-2.9
2.4-2.9
Net Benefits
3% 7%
-1.5-8.5
-1.1-8.9
-0.83-9.2
-0.80-9.2
-1.7-8.3
-1.6
-1.2
-0.9
-0.9
-1.8
-7.5
-7.9
-8.1
-8.2
-7.3
Table 7.1c: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.s
Co-Benefits: 0.070 ppm Standard in 2020 in Billions of 2006$*
Ozone
Mortality
Function or
Assumption
NMMAPS
Meta-analysis
Assumption that
not causal****
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is
Total Benefits**
3% 7%
5.4-29
9.7-34
12-36
12-36
3.5-27
5.1-27
9.5-31
12-33
12-33
3.2-25
Total
Costs***
7%
19-25
19-25
19-25
19-25
19-25
Net Benefits
3% 7%
-20-10
-15-15
-13-17
-13-17
-22-8
-20-
-16-
-13-
-13-
-22-
7.6
12
14
14
5.7
7-3
-------
Table 7.1d: Estimated Range of Annual Monetized Costs and Ozone Benefits and
Co-Benefits: 0.065 ppm Standard in 2020 in Billions of 2006$*
Ozone
Mortality
Function or
Assumption
NMMAPS
Meta-analysis
Assumption that
not causal****
Reference
Bell et al. 2004
Bell et al. 2005
Ito et al. 2005
Levy et al. 2005
association is
Total Benefits**
3% 7%
9.0-46
17-54
21-58
21-58
5.5-42
8.6-42
16-50
21-54
21-54
5.1-38
Total
Costs***
7%
32-44
32-44
32-44
32-44
32-44
Net Benefits
3% 7%
-35 - 14
-27 - 22
-23 - 26
-23 - 26
-39-10
-35-
-28-
-23-
-23-
-39-
9.7
18
22
22
6.2
*A11 estimates rounded to two significant figures. As such, they may not sum across columns. These estimates do
not include visibility benefits. Only includes areas required to meet the current standard by 2020, does not include
San Joaquin and South Coast areas in California. Appendix 7b shows the costs and benefits of attaining alternate
standards in San Joaquin and South Coast California.
**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. Tables exclude unquantified and nonmonetized benefits.
***Range reflects lower and upper bound cost estimates. Data for calculating costs at a 3% discount rate was not
available for all sectors, and therefore total annualized costs at 3% are not presented here. Additionally, these
estimates assume a particular trajectory of aggressive technological change. An alternative storyline
might hypothesize a much less optimistic technological trajectory, with increased costs, or with
decreased benefits in 2020 due to a later attainment date.
****Total includes ozone morbidity benefits and total PM co-benefits only.
The individual row estimates for benefits reflect the variability in the functions available for
estimating a major 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
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. These tables do not include
visibility benefits, which are estimated at $160 million/yr.
Below are three graphs illustrating the net benefits of the selected and alternative standards.
Figures 7.1 and 7.2 provide visual depictions of all available net benefit estimates. Figure 7.3
contains a subset of estimates from the graphic above, displaying four combinations of ozone
and PM benefits estimates with the two primary cost estimates for each alternative. These figures
depict the richness and variability in the estimates of costs and benefits that may not be captured
by the truncated summary tables above.
Figure 7.1 displays all possible combinations of net benefits, utilizing the five different ozone
functions, the fourteen different PM functions, and the two cost methods. Each of the 140 bars in
7-4
-------
each graph represents an independent and equally probability point estimate of net benefits under
a certain combination of cost and benefit estimation methods. Thus it is not possible to infer the
likelihood of any single net benefit estimate. The blue bars indicate combinations where the net
benefits are negative, whereas the green bars indicate combinations where net benefits are
positive.
Figure 7.2 displays a close-up view of the range of net benefits for the selected standard. For the
selected standard of 0.075 ppm, the median value of all of the independent point estimates is
$0.8 billion, and the majority (64%) of the combinations indicate positive net benefits for this
standard.
Figure 7.3 illustrates a subset of the net benefit estimates shown in Figure 7.1. While we treat
each combination of costs and benefit estimates as being equally probable in our model, here we
select a series of combinations of an ozone benefits estimate, a PM2.s co-benefit estimate, and a
cost estimate. Consistent with the distribution shown in Figure 7.1 above, the net benefits
estimate is very sensitive to the choice of ozone mortality function, PlV^.s mortality function, and
cost estimation approach. These intermediate combinations (which are discussed more
completely in the benefits chapter) represent reference points:
• Bell 2004 is the epidemiological study that underlies the ozone NAAQS risk assessment and Pope
is the PM mortality function that was in several EPA RIAs, and
• Bell 2005 is one of three ozone meta-analyses and Laden is a more recent PM epidemiological
study that was used as an alternative in the PM NAAQS RIA
These figures show that for the intermediate points on the distribution the costs and benefits of
the selected standard are slightly positive or slightly negative. The tails of the distribution,
depending on the specific combination of assumptions, show that benefits are either significantly
higher than costs (over $10 billion in net benefits) or that the benefits are significantly lower than
costs (roughly negative $6 billion in net benefits).
7-5
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Figure 7.1: Range of Net Benefits (2006$) for All Standard Alternatives (7% discount)
Range of Net Benefits Across Standard Alternatives'
11D
»
41D
-J3D
-HO
Range- nl Nel Bmefts io 0.07SJ ppm <^UJ Cnmbrubon: of Cosh
and fitncfb Hi 7't CTSCTUNI Rate^
^ muni
Costs exceed
Benefits
Benefits exceed Costs
Rinflfl dl Ntfl Benrfis far 0 075 ppoi (41 CDnibrafohi of Cvitte
Hi-.C B«n*lt5 at TO DtaMrtl Rflt*|
:•'-
•*»
Benefits exceed Costs
osts exceed
Benefits
sir.
•no
far 0 07D
Costs exceed Benefits
Benefits exceed
Costs
u.llllll
ii:
Haroe cf hct Fjrnrrtts tar D [Hii ppm |AH Ocmbmatnns tfucats
aid &err!Ms =t I"* iBccunt RateJ
Benefits exceed
Costs
..ill
Costs exceed Benefits
"This graph shows all 140 combinations of the 5 different ozone mortality functions and assumptions, the 14 different PM mortality functions, and the 2 cost methods. All
combinations are treated as independent and equally probable. These estimates do not include visibility benefits, which are estimated at $160 million/yr. Only includes areas
required to meet the current standard by 2020, does not include San Joaquin and South Coast areas in California.
7-6
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$10 -i
CO
O
O
.2
LLJ
Figure 7.2: Range of Net Benefits (2006$) for Selected Standard
Range of Net Benefits for 0.075 ppm (All Combinations of
Costs and Benefits at 7% Discount Rate) *
pun11"
Benefits exceed Costs
$6 -
$4-
$2
$0
-$2
-$4
-$6
-$8 -
-$10 -
* This graph shows all 140 combinations of the 5 different ozone mortality functions and assumptions, the 14 different PM mortality functions, and the 2 cost methods. All
combinations are treated as independent and equally probable.
For the selected standard of 0.075 ppm, the median value of all of the independent point estimates is $0.8 billion, and the majority (64%) of the combinations indicate positive net
benefits for this standard.
These estimates do not include visibility benefits, which are estimated at $160 million/yr. Only includes areas required to meet the current standard by 2020, does not include
San Joaquin and South Coast areas in California.
Costs exceed Benefits
7-7
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Figure 7.3: Range of Net Benefits for Select Combinations at 3% and 7%*
Net Benefits by Standard Alternative and Combination of Cost
and Benefit Estimate (3% Discount Rate)
1
= -S20
-$30
-$40
-$SO
No CiwsaHlv (Ozone), Expert K (PMJ, Hybrid (Cost)
- No Causal iry (Ozone), Expert K (PM|, Fixed (Cost)
• Bell W (OrorwL Pope (PM(, Hybrid (Cost)
• Bell W (Ojorwf, Pope (PM(, Fixed (Cost)
Bell 'OS (Qzon*f, La Jen (PM|. Hybrid |tt>it)
• Bell TO (OioneK laden (PM|, Fhced (Cost!
• Levy '05 (Ozone). Expert E [PMK Hybrid [Cost)
• levy '05 (Owne), Expert E (PMf, fined (Cost)
0.079 ppm Alternative
Q.07S ppm Aliernatlve 0.070 ppm Alternative 0.065 ppm Alternative
Ozone Si* ndircf Alternative
Net Benefits by Standard Alternative and Combination of Cost
and Benefit Estimate [7% Discount Rate)
S30
$20
$10
-$20
-$30
-$40
-$50
.11
No Causality (Ozone), Expert K (PM^ Hybrid (Cost)
No Causality (Ozone), Enpert K (PMf, Fixed (Cossf
• Bell '04 (Qzon*}, Pop* (PMJ, rtybfid (Cost)
• Bell XM (Oronst, Pope (PMh, Fined (Co5*J
Bell ttS (Ojon*l, Laden (PM(, Hybrid (Cost)
• Bell X» (Ozone^ Laden (PM|, Fined (Cosll
• Levy '05 (Ozone), Expert E (PM|, Hybrid (Cost)
• levy '05 (Ozone), Expert E (PM|, Fixed (Cost)
O.OJ9 ppm Alternative 0.075 ppm Alternative 0.070 ppm Alternative
Own* Stvndird AltemstFv*
0-065 ppm Alternative
*See Section 7.3 for discussion of the ozone and PM premature mortality estimates. See
Section 5.2 for discussion of the hybrid and fixed cost estimates.
7-8
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Table 7.2: Summary of Total Number of Annual Ozone and PMi.s-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 5 Co-Benefits**
NMMAPS Bell (2004)
Bell (2005)
Meta-Analysis Ito (2005)
Levy (2005)
Assumption that association is not
causal
Combined Estimate of Morbidity
Acute Myocardial Infarction
Upper Respiratory Symptoms
Lower Respiratory Symptoms
Chronic Bronchitis
Acute Bronchitis
Asthma Exacerbation
Work Loss Days
School Loss Days
Hospital and ER Visits
Minor Restricted Activity Days
0.079 ppm
140-1,300
200-1,300
230-1,300
230-1,400
120-1,200
570
3,100
4,200
240
640
3,900
28,000
72,000
890
340,000
0.075 ppm
260-2,000
420 - 2,200
500-2,300
510-2,300
190-2,000
890
4,900
6,700
380
1,000
6,100
43,000
200,000
1,900
750,000
0.070 ppm
560-3,500
560-4,100
1,100-4,300
1,400-4,400
310-3,200
1,500
8,100
11,000
630
1,700
10,000
72,000
640,000
5,100
2,100,000
0.065 ppm
940-5,500
2,000-6,500
2,500-7,000
2,500-7,100
490-5,000
2,300
13,000
17,000
970
2,600
16,000
110,000
1,100,000
9,400
3,500,000
*Only includes areas required to meet the current standard by 2020, does not include San Joaquin Valley
and South Coast air basins in California. Appendix 7b shows the costs and benefits of attaining
alternate standards in San Joaquin and South Coast California.
"""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 described in Chapter 6.
7.3 Discussion of Results
7.3.1 Sensitivity of Changes to Costs and Benefits Under an Alternate Baseline Scenario
Circular A-4 of the Office of Management and Budget's (OMB) guidance under Executive Order
12866 defines a no-action baseline as "what the world will be like if the proposed rule is not
adopted". The illustrative analysis in this RIA assesses the costs and benefits of moving from this
"no-action" baseline to a suite of possible new standards. Circular A-4 states that the choice of
an appropriate baseline may require consideration of a wide range of potential factors, including:
• evolution of the market,
• changes in external factors affecting expected benefits and costs,
• changes in regulations promulgated by the agency or other government entities, and
• the degree of compliance by regulated entities with other regulations. (OMB 2003)
7-9
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Circular A-4 also recommends that...
When more than one baseline is reasonable and the choice of baseline will significantly
affect estimated benefits and costs, you should consider measuring benefits and costs
against alternative baselines. In doing so you can analyze the effects on benefits and costs
of making different assumptions about other agencies' regulations, or the degree of
compliance with your own existing rules. (OMB 2003)
This sensitivity analysis is intended to provide information about how the no-action baseline
would differ under different assumptions about mobile technologies. It also assesses nationally
what the change would be to costs and benefits of all standards. Cost for all standards would
increase by $1.81 billion and benefits for all standards would increase by $360 million to $3.1
billion using 2006$ and a 3% discount rate, and $330 million to $2.8 billion when using a 7%
discount rate.
The primary analysis baseline included some mobile controls characterized as additional
technology changes in the onroad transportation sector. The application of these controls to the
baseline assumes an optimistic future where reductions in emissions are achieved through the
implementation nationally of cutting-edge mobile technologies. This sensitivity analysis
estimates nationally how the costs and benefits of attaining 0.075 and the alternate primary
standards would change if these technology changes were not implemented to meet the current
standard, but were instead implemented as part of the strategy for attaining a new tighter
standard.
In this sensitivity analysis scenario, 169,000 tons of NOx would not be reduced prior to the
benefit/cost analysis. The alternate baseline or starting point for assessing the costs and benefits
of the standard of 0.075 and the alternate primary standards would be higher across the board.
Benefits from improved ozone and co-controlled PM2.5 air quality would increase. The costs of
control would increase, as well The air quality improvements would be accomplished by
including additional onroad transportation control measures in the control scenario, equivalent to
the reductions 'removed' from the alternate baseline. The value in benefits of those
improvements is estimated on a $/ton emissions reduced basis derived from the Locomotive
Marine Diesel Rule.
It should be noted that these benefits are only a partial accounting of the total benefits associated
with the mobile controls included in this sensitivity analysis. The sensitivity analysis does not
estimate the benefits of other co-controlled emission reductions achieved by the mobile controls,
such as VOCs (a precursor to ozone formation) and direct PM. The benefits presented here are
therefore an underestimate of total benefits. Furthermore, these estimates are highly uncertain
and are purely illustrative estimates of the potential costs and benefits of these mobile source
1 This cost could be offset in states that choose to replace existing periodic physical inspection of
vehicles with remote onboard diagnostic device inspection in I/M programs. As explained in
Appendix 9a, Remote OBD eliminates the need for periodic inspections of OBD-equipped
vehicles by car owners. EPA estimates that the nationwide installation of Remote OBD would
save the nation's motorists about $16 to $22 billion in inspection and convenience costs over a
10 year period.
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control strategies. We present them only as screening-level estimates to provide a bounding
estimate of the costs and benefits of including these emissions controls in the ozone NAAQS
baseline. As such, it would be inappropriate to apply these benefit per-ton estimates to other
policy contexts, including other regulatory impact analyses. For more details on the baseline
sensitivity analysis, please reference Appendix 7a.
7.3.2 Relative Contribution of PMBenefits to Total Benefits
Because of the relatively strong relationship between PM2.s 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.
For the 0.075ppm alternative, PM2.5 co-benefits account for between 42 and 99 percent of total
benefits. The lower end of the range assumes a combination of Levy et al. (2005) & Expert K.
The upper end of the range assumes a combination of the assumption of no causality & Expert E.
7.3.3 Challenges to Modeling Full A ttainment in AII Areas
Because of relatively higher ozone levels in several large urban areas (Southern California,
Chicago, Houston, and the Northeastern urban corridor) 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 some 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, because 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
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estimates of the benefits associated with the modeled control scenario which provides the
foundation upon which benefits for full attainment are extrapolated for all four alternate primary
standards (0.079, 0.075, 0.070, and 0.065 ppm).
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 of 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 ambient 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.
The 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 extrapolated tons generated from
air quality sensitivity modeling 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 details).
Our estimates of costs of attainment in 2020 assume a particular trajectory of aggressive
technological change. This trajectory leads to a particular level of emissions reductions and
costs which we have estimated based on two different approaches, the fixed cost and hybrid
approaches. An alternative storyline might hypothesize a much less optimistic technological
change path, such that emissions reductions technologies for industrial sources would be more
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expensive or would be unavailable, so that emissions reductions from many smaller sources
might be required for 2020 attainment, at a potentially greater cost per ton. Under this
alternative storyline, two outcomes are hypothetically possible: Under one scenario, total costs
associated with full attainment might be substantially higher. Under the second scenario, states
may choose to take advantage of flexibility in the Clean Air Act to adopt plan with later
attainment dates to allow for additional technologies to be developed and for existing programs
like EPA's Onroad Diesel, CAIR, Nonroad Diesel, and Locomotive and Marine rules to be fully
implemented. If states were to submit plans with attainment dates beyond our 2020 analysis
year, benefits would clearly be lower than we have estimated under our analytical storyline.
However, in this case, state decision makers, seeking to maximize economic efficiency, would
not impose costs, including potential opportunity costs of not meeting their attainment date,
when they exceed the expected health benefits that states would realize from meeting their
modeled 2020 attainment date. In this case, upper bound costs are difficult to estimate because
we do not have an estimate of the point where marginal costs are equal to marginal benefits plus
the costs of nonattainment.
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
emissions reductions and ozone changes in specific locations, and because of the difficulties in
extrapolating costs well beyond the observed data. Of course, these benefits and costs will only
be realized if the emission reductions projected in this extrapolated approach actually occur in
the future.
7.4 What Did We Learn through this Analysis?
1. As in our analysis for the PM NAAQS 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
appear cost-effective relative to NOX reductions (see Figures 5.1 and 5.2). However, the
air quality sensitivity analysis showed that NOx reductions were more effective than
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VOC reductions in reducing Ozone concentrations except 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 we were able to identify controls that reduced overall NOX emissions nationwide
by 6 percent and VOC by 2 percent. After these reductions, remaining emissions were
still substantial, with over 9 million tons of NOX and 12 million tons of VOCs remaining
nationwide. 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 nonEGUpoint 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 nonEGU point sector than in the mobile or EGU sectors.
However, the remaining uncontrolled NOX emissions from EGU and mobile sectors are
still greater than nonEGU point sources2, and additional reductions from these sectors
may need to be considered in developing strategies to achieve full attainment.
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.
4. 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
2 NonEGU point source emission projections currently do not include estimated activity or
economic growth.
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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 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.
5. The 0.079ppm and 0.075ppm benefits estimates reflect special uncertainties. EPA
interpolated the benefits of the 0.070 ppm alternative to estimate the full attainment
benefits of the less stringent 0.075 ppm and 0.079 ppm alternatives. These two
interpolated benefits estimates are subject to two sources of uncertainty: (1) the
uncertainties inherent in the original 0.070 ppm benefits analysis that was the basis for
the interpolation; (2) the incremental uncertainty added through the interpolation
approach. A chief source of uncertainty in the 0.070 ppm analysis was the use of the
monitor rollback technique to estimate full attainment benefits. This approach likely
understates the benefits that would result from state implementation of emissions controls
because controls implemented to reduce ozone concentrations at the highest monitor
would likely result in some reductions in ozone concentrations at nearby attaining
monitors. Therefore, air quality improvements and resulting health benefits from full
attainment would be more widespread than we estimated in our rollback analysis for the
0.070 ppm alternative. The interpolation approach adds its own uncertainties. We made a
reasonable judgment regarding the geographic area within which to interpolate benefits.
However, this area may not match the ultimate geographic distribution of air quality
improvements under a state-implemented control strategy to attain either the 0.075 ppm
or 0.079 ppm alternative; this could result in an under- or over-estimate of benefits. The
complexity of the various uncertainties makes it challenging to draw conclusions about
their combined directional influence on the benefits estimates.
6. 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
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known control costs ($3.3 billion per year3 (2006$)) is only slightly lower than the Clean
Air Interstate Rule (approximately $4 billion per year (2006$)) and roughly one and half
to just over four times higher than the PM NAAQS 15/35 control strategy with annual
engineering costs of $1.0 billion (2006$). It should be noted that for the Ozone NAAQS
$3.3 billion represent the engineering 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 $19 to $25 billion (2006$). 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 several techniques for extrapolation of the costs based upon the
degree of difficulty to reach attainment Based on air quality supplemental modeling,
there is clearly significant spatial variability in the relationship between local and
regional NOX emission reductions and ozone levels across urban areas. 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
uncertainty associated with costs increases.
7. NonEGUpoint source controls dominate the estimated costs. These costs account for
about 54 percent of modeled control costs. The average cost per ton for these reductions
is approximately $3,800 (2006$) and the highest marginal cost for the last known control
applied is $22,000 (2006$). Mobile source controls were also significant contributors to
overall costs, accounting for over 23 percent of total modeled control costs.
8. 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.
7.5 References
U.S. Office of Management and Budget. September 2003. Circular A-4, Regulatory Analysis
Guidance sent to the Heads of Executive Agencies and Establishments. Washington, DC.
http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf.
3 Known controls include the modeled control strategy ($2.8 billion dollars per year (2006$)) as
well as any supplemental and giveback controls applied (Appendix 5a.4).
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Appendix 7a: National Baseline Sensitivity Analysis
7a.l Synopsis
Circular A-4 of the Office of Management and Budget's (OMB) guidance under
Executive Order 12866 defines a no-action baseline as "what the world will be like if the
proposed rule is not adopted." The illustrative analysis in this RIA assesses the costs and
benefits of moving from this "no-action" baseline to a suite of possible new standards.
Circular A-4 states that the choice of an appropriate baseline may require consideration
of a wide range of potential factors, including:
• evolution of the market,
• changes in external factors affecting expected benefits and costs,
• changes in regulations promulgated by the agency or other government entities,
and
• the degree of compliance by regulated entities with other regulations. (OMB
2003)
Circular A-4 also recommends that...
When more than one baseline is reasonable and the choice of baseline will
significantly affect estimated benefits and costs, you should consider measuring
benefits and costs against alternative baselines. In doing so you can analyze the
effects on benefits and costs of making different assumptions about other
agencies' regulations, or the degree of compliance with your own existing rules.
(OMB, 2003)
This sensitivity analysis is intended to provide information about how the no-action
baseline would differ under different assumptions about mobile technologies. It also
assesses nationally what the change would be to costs and benefits of a new standard of
0.075 ppm and alternate primary standards of 0.079, 0.070, and 0.065 ppm. Cost for all
standards would increase by $1.8 billion1 and benefits for all standards would increase by
1 This cost could be offset in states that choose to replace existing periodic physical
inspection of vehicles with remote onboard diagnostic device inspection in Inspection
and Maintenance programs. As explained in the Appendix to Chapter 3, Remote On
Board Diagnostics (OBD) eliminates the need for periodic inspections of OBD-equipped
vehicles by car owners. EPA estimates that the nationwide installation of Remote OBD
would save the nation's motorists about $16 to $22 billion in inspection and convenience
costs over a 10 year period. Refer to the Appendix 5a for more details on the cost
savings of remote OBD.
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$360 million to $3.1 billion using 2006$ and a 3% discount rate, and $330 million to $2.8
billion when using a 7% discount rate.2
The process of analysis of costs and benefits of attaining 0.075 and the alternate primary
standard is, in some ways, an incremental building exercise. EPA begins with a Base
Case (that includes promulgated rules, consent decrees, existing promulgated programs)
and layers onto that illustrative control strategies from previous NAAQS RIA analyses,
and finally, a simulated control strategy for attaining the current NAAQS in question (O3
at 0.084 ppm). This is the point at which the "no-action baseline" is established.
Once the no-action baseline is established, EPA begins assessing the costs and benefits of
moving to a tighter standard. EPA does not assess the costs and benefits of reaching the
no-action baseline. Decisions about what is in the baseline affect the starting point of the
assessment of costs and benefits, and thus affect the total incremental cost and benefit
estimates.
The primary analysis baseline included some mobile controls characterized as additional
technology changes in the onroad transportation sector. The application of these controls
to the baseline assumes an optimistic future where reductions in emissions are achieved
through the implementation nationally of cutting-edge mobile technologies. This
sensitivity analysis estimates nationally how the costs and benefits of attaining 0.075 and
the alternate primary standards would change if these technology changes were not
implemented to meet the current standard, but were instead implemented as part of the
strategy for attaining a new tighter standard.
In this sensitivity analysis scenario, 169,000 tons of NOx would not be reduced prior to
the benefit/cost analysis. The alternate baseline or starting point for assessing the costs
and benefits of the standard of 0.075 and the alternate primary standards would be higher
across the board. Benefits from improved ozone and co-controlled PM2.5 air quality
would increase. The costs of control would increase, as well. The air quality
improvements would be accomplished by including additional onroad transportation
control measures in the control scenario, equivalent to the reductions 'removed' from the
alternate baseline. The value in benefits of those improvements is estimated on a $/ton
emissions reduced basis derived from the Locomotive Marine Diesel Rule.
A description of the control measures added to the alternate control scenario for this
sensitivity analysis follows.
2 These estimates are highly uncertain and are purely illustrative estimates of the potential
costs and benefits of these mobile control strategies. We present them only as screening-
level estimates to provide a bounding estimate of the costs and benefits of including these
emissions controls in the ozone NAAQS control case for all standards. As such, it would
be inappropriate to apply these benefit per-ton estimates to other policy contexts,
including other regulatory impact analyses. Furthermore, the benefits only reflect a
partial accounting of the total benefits associated with emission reductions related to the
mobile controls included in this sensitivity analysis.
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7a.2 Control Details
7a.2.1 Improved Catalyst Design
Improved Catalyst Design is a nationwide strategy that results in tailpipe emission
reductions for new vehicles. The principle technologies used to achieve the Improved
Catalyst Design standards are improved catalysts and increased use of electronically
controlled air injection, reducing NOx and VOC emissions for new light duty gasoline
vehicles.
We modeled a program that would achieve Bin 23 emission levels (see Table 7a. 1) for a
program starting in 2013 and fully phased in by 2015.
Phase-in Scenario Cars: 50% in 2013 Trucks: 100% in 2015
100% in 2014
Table 7a.l: Emission Standards
NOx NMOG
Bin 2 0.02 0.01
Bin 5 (reference) 0.07 0.09
Table 7a.2: Nationwide 2020 Tailpipe Emission Reductions (tons(%))
2020 NOx 2020 HC
Bin 2 87,705 (7%) 93,676 (6%)
In comparison, Tier 2 reduced NOx by about 2.2 million tons in 2020 and nearly 3
million tons in 2030, a 74% reduction.
The above results are modeled relative to a Bin 5 baseline
• Modeled difference in level of the standards: Bin 5 vs. Bin 2.
• In reality, new standards would likely provide fewer benefits because many Bin 5
vehicles are certified well below the standard, and many are in fact 50-state
vehicles certified in California as ULEVs.
• The costs are also modeled relative to a Bin 5 baseline, so the fact that many
vehicles are actually cleaner today will also result in lower total costs.
3 For information on Bin emission levels, see:
http://www.epa.gov/greenvehicles/summarychart.pdf
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The technology is described as follows:
• The technology relies on catalyst improvements—adding Rhodium, improved
substrate/washcoat, and 900 cpsi density (all vehicles are assumed to need these
changes)
• All vehicles are assumed to have close-coupled catalysts (1 or 2)
• Increased use of electronically controlled air injection—100% implementation on
everything except 4-cylinder engines
Engineering costs for this program are estimated to be approximately $90-250 per vehicle
for LDVs to LDT4s.
• Based on an analysis similar to that done for Tier 2 and LEV-II, estimating
penetration rates of emission control technologies, coupled with estimated costs
for each technology.
• A significant driver of costs is the market price of Rhodium, which has varied in
the last 5 years from below $1000 to above $6000 per Troy ounce. We used the 5-
year average of $2200.
• These costs are the result of a preliminary analysis intended to achieve rough
estimates. An in-depth bottom-up detailed cost analysis would need to be done to
support an actual Improved Catalyst Design regulatory program.
• Most of the costs are for catalyst improvements—adding Rhodium, improved
substrate/washcoat, and 900 cpsi density (all vehicles are assumed to need these
changes)
Cost-effectiveness is $8,400 per ton for HC+NOx, and $17,500 per ton for NOx alone.
Based on assumptions and variables in the analysis, these numbers can vary +/- 30%.
7a.2.2 Plug-In Hybrid Electric Vehicles
Plug-In Hybrid Electric Vehicles (PHEVs) are very similar to Hybrid Electric Vehicles,
but with three significant functional differences. The first is the addition of a means to
charge the battery pack from an outside source of electricity (usually the electric grid).
Second, a PHEV would have a larger battery pack with more energy storage, and a
greater capability to be discharged. Finally, a PHEV would have a control system that
allows the battery pack to be significantly depleted during normal operation.
PHEVs offer a significant opportunity to replace petroleum used for transportation
energy with domestically-produced electricity. The reduction in petroleum usage does, of
course, depend on the amount of electric drive the vehicle is capable of under its duty
cycle. PHEVs can lower localized emissions of criteria pollutants and air toxics
especially in urban areas by operating on electric power. The emissions with this
technology occur more from power generation outside the urban area at the power
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generation plant rather than from the vehicle tailpipe, which may provide health benefits
for residents of the more densely populated urban areas. Unlike most other oil-saving
technologies, PHEVs also use existing infrastructure for fueling with gasoline and
electricity so large investments in fueling infrastructure are not required. Since emissions
from utilities are capped by existing programs, increases in power generation are
generally not expected to impact attainment of air quality standards.
For this analysis, we assumed that PHEVs would be available as passenger cars and as
light trucks in all light truck weight classes by 2012. We assumed the following phase-in
schedule for PHEVs (Table 7a.3) as a fraction of new vehicle sales for the period from
2012 to 2020. This is an illustrative example of what could be feasible for the market
penetration of PHEVs based on reductions that are needed for attainment of the revised
ozone NAAQS and EPA's internal expertise and judgment. Recent announcements by
Toyota and General Motors that they plan to introduce PHEVs by 2010 provide
additional support for these assumptions.
Table 7a.3: Plug-In Hybrid Percentage of Total Sales of New Vehicles by Year
Year Percentage of New Vehicles
2012 1%
2013 3%
2014 7%
2015 12%
2016 18%
2017 25%
2018 30%
2019 30%
2020 30%
We believe that the first consumers of PHEVs are likely to be the ones who can take best
advantage of the PHEV while still operating on an overnight charge, i.e., urban and
suburban residents with shorter commutes. We also assume continuing improvements in
the range of PEHVs while operating on the overnight charge. For this analysis, we
assumed that 70% of the VMT of PHEVs would be powered by the overnight charge
rather than the vehicle engine and would have no direct exhaust emissions.4 We used that
estimate, and the assumptions of vehicle sales given above, to adjust the travel fractions
in EPA's MOBILE6.2 emission model to account for the impact of reduced emissions for
each model year of PHEVs.
All light-duty gasoline vehicles and trucks: Affected SCC:
• 2201001000 Light Duty Gasoline Vehicles (LDGV), Total: All Road Types
4 Note that this assumption is different than the assumption used in the payback analysis
used to determine costs of PHEVs in: Interim Report: New Powertrain Technologies and
Their Projected Costs. U.S. E.P.A, October 2005.
http://epa.gov/otaq/technology/420r05012.pdf. That study assumes that only 30% of
PHEV VMT is powered by overnight charge, but still shows a positive payback potential.
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• 2201020000 Light Duty Gasoline Trucks 1 (LDGT1), Total: All Road Types
• 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Total: All Road Types
Using the assumptions and methods described above, we estimated that HC emissions
would be reduced by a range of 2.4% to 3.9% for passenger cars and light trucks
(reductions vary by vehicle class). For NOx, we estimate reductions in the range of 1.6%
to 2.5% for passenger cars and light trucks.
For purposes of this RIA, we identified this measure as a no cost strategy i.e., $0/ton
NOx. Plug-in hybrids have upfront capital costs, but these costs can be fully recovered by
the fuel savings during the life of the vehicle. According to research conducted by the
EPA, the potential consumer payback for the hypothetical PHEV midsize car and large
SUV can be calculated from the modeled fuel economy and projected cost of the vehicle
package5. Using a retail price markup factor of 1.26 from the projected cost, the
additional cost of a PHEV midsize car over the base vehicle is $6,072. The large SUV is
projected to cost $7,884 more than the comparable base vehicle.
Appling these costs, the modeled fuel economy, and the standard economic assumptions
used in this analysis of $2.50 per gallon gasoline price, 7% discount rate, and a 14 year
life with annual VMT taken from the MOBILE6 model, results in consumer payback
shown below. The payback period for the midsize car is 10.7 years, and 7.5 years for the
large SUV.
Table 7a.4: Cost Effectiveness of PHEV Midsize Car and SUV
Incremental Vehicle Price
Fuel Economy Gain
Tailpipe CO2 decrease
Discounted Fuel Savings
Discounted Electricity Cost
Discounted Brake Savings
Reduced Fueling Time Savings
Lifetime Savings
Payback Period
Midsize Car
$5,646
126%
56%
$6,493
$929
$376
$395
$688
10.7 years
Large SUV
$8,577
92%
48%
$11,751
$1,346
$533
$428
$2,789
7.5 years
Improved After-Market Catalysts
Both EPA and CARS have standards in place for aftermarket catalysts. CARS now
requires higher quality replacement catalysts for OBDII vehicles and is considering
expanding that requirement to pre-OBDII vehicles as well. (Even though higher quality,
these replacement catalysts do not constitute a new standard for the vehicle—they just
bring it closer to its original as-new performance level.) CARS has done testing and has
5 Draft Revision to: Interim Report: New Powertrain Technologies and Their Projected
Costs. U.S. E.P.A., October 2005. http://epa.gov/otaq/technology/420r05012.pdf
7a-6
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found that substantial emission reductions can be had by upgrading the quality of
aftermarket catalysts.
Applying the proposed aftermarket catalyst requirements to the national fleet would bring
about nationwide reductions. According to the Manufacturers of Emission Controls
Association (MECA), approximately 3 million aftermarket catalysts are sold each year.
Estimated benefits are derived by comparing performance of existing replacement
catalysts to that of the proposed catalysts. The difference is applied to the 3 million
vehicles in the fleet that get aftermarket replacement catalysts.
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
The table below (Table 7a.5) shows the emissions of the current aftermarket catalysts at
25,000 miles and the performance of the OBDII-type aftermarket catalysts at the same
mileage. The emission reductions from improved aftermarket catalysts are substantial,
even for Tier 0 vehicles.
Table 7a.5: Emissions of Aftermarket Catalysts
Category
TierO
Tier 1
TLEV
LEV
ULEV
LEV II LEV
LEV II ULEV
LEV II SULEV
Current Aftermarket
Catalysts
HC NOx
0.600 2.4
0.600 2.4
0.600 1.6
0.600 1.6
0.450 1.2
0.450 1.2
0.450 0.8
0.375 0.8
Proposed Aftermarket
Catalysts
HC NOx
0.1750 0.20
0.1350 0.15
0.0580 0.20
0.0250 0.05
0.0125 0.07
0.0300 0.07
0.0125 0.07
0.0100 0.02
Percent
HC
71%
78%
90%
96%
97%
93%
97%
97%
Reduction
NOx
92%
94%
88%
97%
94%
94%
91%
98%
Based on this information, if starting in 2010 we required the 3 million replacement
catalysts installed each year to meet these standards, by 2020 there would be 15 million
vehicles with such catalysts left in the fleet (the other 15 million are assumed to be
scrapped during this time period). In 2020, the emission reductions we calculate are as
follows:
7a-7
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Table 7a.6: Emission Reductions from Replacement Catalysts
HC NOx
LDGV 3.5% 7.1%
LDGT1 3.4% 7.0%
LDGT2 3.6% 7.1%
LDGT3 3.7% 7.2%
LDGT4 3.9% 7.3%
Both EPA and CARS have standards in place for aftermarket catalysts. CARS now
requires higher quality replacement catalysts for OBDII vehicles and is considering
expanding that requirement to pre-OBDII vehicles as well. (Even though higher quality,
these replacement catalysts do not constitute a new standard for the vehicle—they just
bring it closer to its original as-new performance level.) CARS has done testing and has
found that substantial emission reductions can be had by upgrading the quality of
aftermarket catalysts.
Estimated engineering cost of the proposed replacement catalyst is $275, compared to
approximately $100 for current replacement catalysts. These cost numbers are based on a
review of prices published on the internet for OBDII and pre-OBDII replacement
catalysts.6
Table 7a.7: CARB Cost Effectiveness for Improved After Market Catalysts
Category
TierO
Tierl
TLEV
LEV
ULEV
LEV II LEV
LEV II ULEV
LEV II SULEV
NOx + HC
$1,423
$1,353
$1,889
$1,659
$2,275
$2,090
$2,736
$2,782
NOx only
$1,722
$1,665
$2,774
$2,378
$3,329
$2,887
$4,419
$4,232
HC only
$8,187
$7,238
$5,917
$5,488
$7,186
$7,567
$7,186
$8,120
For the O3 RIA, we used an average cost of $3,700/ton NOx reduced.
7a.2.3 Summary of Emission Reductions and Costs
Total emission reductions and costs for the 3 control measures included in the alternative
baseline analysis are presented in Table 7a.8:
Table 7a.8: NOx Emission Reductions and Costs for Alternative Baseline Analysis
Sector
Onroad
Control Measure
Improved Catalyst Design
Plug-In Hybrid
Annual Emission Reductions (Tons)
77,000
22,000
Total Cost (M$)
$1,600
c
j> —
See: www.discountconverters.com and autopartswharehouse.com
7a-8
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Improved After-market Catalyst 70,000 $260
TOTAL 169,000 $1,900
7a.3 Methods for Estimation of Benefits ($/ton NOx reduced)
We estimated the monetary value of the 169,000 tons of mobile source NOx emission
reductions in our baseline through a benefit per ton approach. Because NOx is both an
ozone and PM2.5 precursor, these reductions will yield both reductions in the ambient
levels of these pollutants as well as monetized benefits. Because these reductions occur in
the mobile source sector, we decided to estimate total ozone benefits by imputing an
ozone benefit per-ton estimate from the soon-to-be-promulgated Locomotive and Marine
Diesel Rule. While this rule does not affect an identical set of sources, it is a reasonable
representation of the benefits of emission reductions in mobile source emissions, which is
the sector of interest. We have included these benefit per-ton calculations in a separate
Technical Support Document (TSD). To estimate the PM2.5 co-benefits we used a set of
benefit per-ton estimates consistent with the main analysis. The process for deriving these
estimates can be found in the same TSD.
The range of total combined ozone and PM2.5-related 2020 benefits associated with the
emission reductions are between $360 million to $3.1 billion in 2006$ using a 3%
discount rate. The lower-end of this range represents the combination of the assumption
of no causality for ozone benefits and the Expert K PM mortality function for PM2.5 co-
benefits (US EPA, 2006; US EPA, 2005). Using these same two combinations of
studies, the range changes to between $330 million to $2.8 billion when using a 7%
discount rate. It should be noted that these benefits are only a partial accounting of the
total benefits associated with the mobile controls included in this sensitivity analysis. The
sensitivity analysis does not estimate the benefits of other co-controlled emission
reductions achieved by the mobile controls, such as VOCs (a precursor to ozone
formation) and direct PM. The benefits presented here are therefore an underestimate of
total benefits. Furthermore, these estimates are highly uncertain and are purely illustrative
estimates of the potential costs and benefits of these mobile control strategies. We present
them only as screening-level estimates to provide a bounding estimate of the costs and
benefits of including these emissions controls in the ozone NAAQS control case for all
standards. As such, it would be inappropriate to apply these benefit per-ton estimates to
other policy contexts, including other regulatory impact analyses.
7a.4 References
U.S. Office of Management and Budget. September 2003. Circular A-4, Regulatory
Analysis Guidance sent to the Heads of Executive Agencies and Establishments.
Washington, DC. http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf.
Draft Revision to: Interim Report: New Powertrain Technologies and Their Projected
Costs. U.S. E.P.A. October 2005. http://epa.gov/otaq/technology/420r05012.pdf
7a-9
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U.S. Environmental Protection Agency Science Advisory Board. 2005. EPA's Review of
the National Ambient Air Quality Standards for Particulate Matter (Second Draft PM
Staff Paper, January 2005). EPA-SAB-CASAC-05-007. June.
U.S. Environmental Protection Agency, 2006. Air Quality Criteria for Ozone and
Related Photochemical Oxidants Volume I of III. National Center for Environmental
Assessment, Office of Research and Development, U.S. Environmental Protection
Agency, Research Triangle Park, NC EPA 600/R-05/004aF
Industrial Economics, Incorporated (lEc). April 2004. "Expert Judgment Assessment of
the Relationship Between PlV^.s Exposure and Mortality." Available at
.
7a-10
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Appendix 7b: Post 2020 Attainment Analysis
7b.l Uncertainties of Post 2020 Attainment Analysis
Attainment dates will be determined in the future through the SIP process based on criteria in the
CAA, future air quality data, and future rulemakings and are not knowable at this time. For
analytical simplicity, and in keeping with the proposal analysis, we have chosen to use an
analysis year of 2020 and generally assume attainment in that year. The exception is the San
Joaquin and South Coast California areas where SIP submittals for the current standard show that
they would have current standard attainment dates later than 2020. For these two areas in
California, we are assuming a new standard attainment date of 2030. Estimates of the benefits
and costs of attaining .075 and the alternate air quality standards for these two areas in 2030 are
included below.
There are many uncertainties associated with the year 2030 analysis. Between 2020 and 2030
several onroad mobile and nonroad mobile source federal air quality rules are expected to further
reduce emissions of NOx and VOC. Because mobile source rules affect new vehicles and
equipment, they reduce inventories over a long period of time, as older vehicles and equipment
are gradually scrapped and are replaced by new, regulated, lower-emitting vehicles and
equipment. Among the onroad rules that contribute to the expected decline in mobile-source
emissions between 2020 and 2030 are the Tier 2 Rule (light-duty cars and trucks) that went into
effect in 2004, the 2007 Onroad Heavy-Duty Rule, and the Mobile Source Air Toxics Rule
("MSAT Final", EPA, 2007a and EPA, 2007b) that goes into effect in 2011. Major nonroad rules
also contribute to this decline, including the Locomotive Emissions Final Rulemaking (EPA,
1998), the Locomotive-Marine Final Rule (EPA, 2007c), the Clean Air Nonroad Diesel Final
Rule—Tier 4 (EPA, 2004), and Control of Air Pollution from Aircraft (EPA, 2005), among
others. California has also regulated most of these same categories, often more stringently than
the Federal government, resulting in substantial expected inventory decreases between 2020 and
2030. The emission reductions from these programs should lower ambient levels of ozone
between 2020 and 2030 across the state of California; this would facilitate the process of
reaching attainment with a revised ozone standard in San Joaquin and South Coast by 2030. In
addition, activity data beyond 2025 does not exist for aircraft data; therefore, 2030 aircraft
emissions are held at year 2025 levels.
However, the onroad mobile and nonroad mobile sectors are the only sectors projected to 2030 in
our emission inventories; we do not have 2030 inventories for any stationary sources and
therefore do not have a comprehensive estimate of Ozone precursor emissions around which to
craft control strategies to determine costs. All stationary source emissions are held at year 2020
levels because of uncertainties in how to project stationary emissions beyond 2020, and the lack
of consistent projection methodologies beyond 2020 (e.g., the model used to create future year
EGU emissions does not project to year 2030). Without a complete set of future 2030 emission
inventories and control strategies, it is not possible to adequately model either baseline air
quality or changes from control strategies. Without modeled changes in Ozone ambient
concentrations, it is not possible to perform a sophisticated benefits analysis. In order to provide
some idea of costs and benefits of attaining 0.075 and the alternate standards in San Joaquin and
7b-l
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South Coast air basins, we've relied on the available data. Due to the previously mentioned
limitations, these analysis results do not capture potential economic growth, or changes in
emissions beyond 2020.
7b.2 Post 2020 Attainment Analysis
7b.2.1 Air Quality and Emissions Targets
We have used the 2020-based supplemental air quality modeling as a rough indicator of the
percent control needed to meet the four alternate standards by 2030. Table 7b. 1 shows the NOx
targets estimated to get the Los Angeles and San Joaquin Valley areas into attainment by 2030.
The supplemental air quality modeling showed (see Fig4-2d) that there was a sharp dropoff in
ozone between the 60% and 90% additional NOx control cases. This may be due to the South
Coast region transitioning from VOC-limited to NOx-limited conditions at this level of NOx
emissions reductions.
Table 7b.l: Estimated Percentage Reductions of NOx beyond the RIA Control Scenario
Necessary to Meet the Various Ozone Standards in Los Angeles and the San Joaquin
Valley in 2030
All 2030 Extrapolated Cost Areas
(NOx only)
Los Angeles South Coast Air Basin, CA
San Joaquin Valley, CA
2020 Design Value after RIA
Control Scenario (ppm)
0.122
0.096
Additional local control needed to meet various
standards
0.065
> 90%
76%
0.070
88%
67%
0.075
83%
59%
0.079
79%
49%
0.084
75%
37%
Table 7b.2 shows the NOx reductions needed to get the Los Angeles and San Joaquin Valley
areas, into attainment by 2030. These reductions are based on the NOx targets for Los Angeles
South Coast Air Basin in Table 7b.l. The higher reductions for Los Angeles compared to the San
Joaquin Valley should enable all of California to attain, even after transport effects. Inventory
reductions in 2030 from the onroad mobile, nonroad mobile, and aircraft/locomotive/commercial
marine sources were credited to the estimates prior to creating the estimated extrapolated
reductions needed in Table 7b.2. This table reveals that the majority of emission reductions are
needed for these areas to reach the current ozone standard The reductions also include the Final
Loco-Marine controls for 2030 (EPA, 2008). Overall, the loco-marine 2030 inventory contains
about 120,000 fewer tons of NOx than the 2020 loco-marines inventory for the geographic area
in California being analyzed.
Table 7b.2: Estimated Extrapolated Emissions Reductions of NOx Beyond the RIA Control
Scenario Necessary to Meet the Various Ozone Standards in Los Angeles and the San
Joaquin Valley in 2030
All 2030 Extrapolated Cost Areas
(NOx only)
Los Angeles-San Joaquin Valley, CA
Additional local emissions reductions [annual tons/year] needed to meet
various standards (ppm)
0.065
390,000
0.070
380,000
0.075
350,000
0.079
330,000
0.084
300,000
' The Los Angeles South Coast Air Basin and San Joaquin Valley are included in the Sacramento Metro
buffer.
7b-2
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To calculate the incremental costs of attainment for the Los Angeles and San Joaquin Valley
areas the reductions to meet the current standard are removed from the reductions needed for the
various standards.1 Table 7b.3 contains the remaining 2030 emissions reductions needed for Los
Angeles and the San Joaquin Valley.
Table 7b.3: Additional Local Emissions Reductions [annual tons/year] Needed to Meet
Various Standards (ppm) Incremental to the Current Standard
All 2030 Extrapolated Cost Areas
(NOx only)
Los Angeles-San Joaquin Valley, CA"
Additional local emissions reductions [annual tons/year]
needed to meet various standards (ppm) incremental to
the current standard
0.065"
78,000
0.070
73,000
0.075
45,000
0.079
23,000
a The 0.065 ppm emission reductions required are incremental to the reductions achieved by Sacramento
in 2020 (see Table 4.6a).
b The Los Angeles South Coast Air Basin and San Joaquin Valley are included in the Sacramento Metro
buffer.
The additional tons of reductions needed to attain the various standards may appear relatively
low at first glance. It is important to note that these are incremental to progress made in San
Joaquin and South Coast air basins toward attainment of the various standards in Sacramento.
Additionally, between 2020 and 2030 other rules are expected to reduce emissions. Among
these are the Tier 2 Rule (light-duty cars and trucks) that went into effect in 2004, the 2007
Onroad Heavy-Duty Rule, and the Mobile Source Air Toxics Rule ("MSAT Final", EPA, 2007a
and EPA, 2007b) that goes into effect in 2011. Major nonroad rules also contribute to this
decline, including the Locomotive Emissions Final Rulemaking (EPA, 1998), the Locomotive-
Marine Final Rule (EPA, 2007c), the Clean Air Nonroad Diesel Final Rule—Tier 4 (EPA, 2004),
and Control of Air Pollution from Aircraft (EPA, 2005), among others. California has also
regulated most of these same categories, often more stringently than the Federal government,
resulting in substantial expected inventory decreases between 2020 and 2030. A final factor that
influences the total number of tons needed to attain in 2030 is the relatively greater effectiveness
in California of NOx reductions that happen in the higher range of percentage reduced from the
total NOx inventory. For example, a ton reduced when 80% of the total NOx inventory has
already been controlled and reduced has a greater effect on ozone concentrations than a ton
reduced when only 30% of the total NOx inventory has been thus far reduced.
7b.2.2 Extrapolated Costs
The same two methodologies (fixed and hybrid) were used to estimate the costs of the additional
local emission reductions for this 2030 analysis as were used in the national 2020 analysis. There
is even more uncertainty associated with this analysis because there is more time for all types of
change. Technological change, change in energy policy, changes in the sources of emissions are
all expected to be more important for 2030 than for 2020. Because the South Coast and San
1 In one case, the 0.065 ppm alternate standard, the reductions for the Sacramento Metro area in
2020 (again, includes Los Angeles and the San Joaquin Valley areas that do not require
attainment in 2020) are greater than the reductions required to meet the current standard, and
these reductions are the subtracted from the increment needed for California to meet the 0.065
ppm standard..
7b-3
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Joaquin Valley cost area has historically had a difficult time attaining air quality standards, it
might be expected that the 2020 cost methodologies might underestimate the costs of the
additional local emission reductions. However, the additional time for technological change
between 2020 and 2030 might be expected to lower costs and result in an overestimate of costs
from using the 2020 methodologies. The net bias of using the methodology employed for 2020 in
the 2030 analysis is unknown. Additionally it is important to note, most of the air quality
improvement needed for these areas is to reach the 0.08 ozone standard. The cost analysis below
represents the incremental costs of attaining alternate ozone standards.
7b.2.2.1 Fixed Cost Approach Results
Table 7b.4 shows the estimated costs using the fixed cost methodology with a $15,000 a ton cost
applied to the local emission reductions from Table 7b.3
Table 7b.4: Extrapolated Cost to Meet Various Alternate Standards
Using Fixed Cost Approach ($15,000/ton)a
All 2030 Extrapolated Cost Areas Fixed Cost Approach Extrapolated Cost (M 2006$).
(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
Los Angeles-San Joaquin Valley, CA $1,200 $1,100 $680 $340
a All estimates rounded to two significant figures.
7b.2.2.2 Hybrid Approach Results
Table 7b.6 shows the estimated costs using the fixed cost methodology with the hybrid approach
using the average costs shown in Table 7b.5 applied to the local emission reductions from Table
7b.3. The calculations for average cost used for Los Angeles and San Joaquin Valley use the
same formulas presented in the Appendix 5a. There are large uncertainties when extrapolating to
2030, therefore keeping the approach consistent yielded the average cost numbers seen in Table
7b.5.
Table 7b.5: Hybrid Approach (Mid) Parameter Values for Various Standards a'b
All 2030 Extrapolated Cost Areas
(NOx only)
Los Angeles-San Joaquin Valley, CA
0.065 ppm
Rc
1.42
Average
Cost/Ton
(2006$)
$24,000
0.070 ppm
Rd
1.37
Average
Cost/Ton
(2006$)
$24,000
0.075 ppm
Rd
1.27
Average
Cost/Ton
(2006$)
$23,000
0.079 ppm
Rd
1.19
Average
Cost/Ton
(2006$)
$23,000
a All estimates rounded to two significant figures.
b These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
°Los Angeles-San Joaquin Valley, CA did not meet the baseline and therefore has an addition R to reach
the current standard of 1.11.
dLos Angeles-San Joaquin Valley, CA have an R of 1.13 for the 0.65 ppm standard only, due to the
emission reductions from Sacramento being limiting.
7b-4
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Table 7b.6: Extrapolated Cost to Meet Various Standards Using
Hybrid Approach (Mid) a'b
All 2030 Extrapolated Cost Areas Hybrid Approach Extrapolated Cost (M 2006$).
(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
Los Angeles-San Joaquin Valley, CA $1,900 $1,700 $1,000 $520
a All estimates rounded to two significant figures.
b These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
7b.2.2.3 Sensitivity Analysis Results
Extrapolated cost ensitivity results for the fixed cost approach using a lower ($10,000/ton) and a
higher ($20,000/ton) are presented in Table 7b.7 and Table 7b.8. Tables 7b.9 and 7b.l 1 present
the average cost/ton for a higher and lower value of M (0.47 for the high and 0.12 for the low in
place of the 0.24 used in the mid estimate). The total extrapolated costs for the Hybrid (Low)
and Hybrid (High) are presented in Tables 7b.lO and 7b.l2.
Table 7b.7: Extrapolated Cost to Meet Various Alternate Standards
Using Fixed Cost Approach ($10,000/ton)a
All 2030 Extrapolated Cost Areas Fixed Cost Approach Extrapolated Cost (M 2006$).
(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
Los Angeles-San Joaquin Valley, CA $780 $730 $450 $230
a All estimates rounded to two significant figures.
Table 7b.8: Extrapolated Cost to Meet Various Alternate Standards
Using Fixed Cost Approach ($20,000/ton) a
All 2030 Extrapolated Cost Areas Fixed Cost Approach Extrapolated Cost (M 2006$).
(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
Los Angeles-San Joaquin Valley, CA $1,600 $1,500 $900 $450
a All estimates rounded to two significant figures.
Table 7b.9: Hybrid Approach (Low) Average Cost/Ton for Various Standards a'b
All 2030 Extrapolated Cost Areas Hybrid Approach Average Cost/Ton (2006$)
(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
Los Angeles-San Joaquin Valley, CA $19,000 $19,000 $19,000 $19,000
a All estimates rounded to two significant figures.
b These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
7b-5
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Table 7b.lO: Extrapolated Cost to Meet Various Standards Using
Hybrid Approach (Low)a'b
All 2030 Extrapolated Cost Areas Hybrid Approach Extrapolated Cost (M 2006$)
(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
Los Angeles-San Joaquin Valley, CA $1,500 $1,400 $860 $430
a All estimates rounded to two significant figures.
b These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
Table 7b.ll: Hybrid Approach (High) Average Cost/Ton for Various Standards a'b
All 2030 Extrapolated Cost Areas Hybrid Approach Average Cost/Ton (2006$)
(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
Los Angeles-San Joaquin Valley, CAA $33,000 $33,000 $32,000 $31,000
a All estimates rounded to two significant figures.
b These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
Table 7b.l2: Extrapolated Cost to Meet Various Standards Using
Hybrid Approach (High)a'b
All 2030 Extrapolated Cost Areas Hybrid Approach Extrapolated Cost (M 2006$)
(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm
Los Angeles-San Joaquin Valley, CAA $2,600 $2,400 $1,400 $700
a All estimates rounded to two significant figures.
b These estimates assume a particular trajectory of aggressive technological change. An alternative
storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or
with decreased benefits in 2020 due to a later attainment date.
7b.2.3 Benefits
The Estimated Benefits of 2030 Attainment with Alternate Ozone Standards
The ozone analysis for San Joaquin and South Coast applies the same methods described
elsewhere in the benefits chapter with the exception of: (1) the population year and (2) the year
for the income growth adjustment. We updated both to 2030 to be consistent with the attainment
year. Table 7b.l3 below summarizes the updated benefits estimates.
7b-6
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Table 7b.l3: Total Estimated Ozone Benefits of Attaining Alternate Ozone Standards in
2030 in San Joaquin and South Coast (2006$)
Mortality Function or Assumption Valuation Estimate
0.079 ppm
No Causality $13,000,000
Bell et al. (2004) $130,000,000
Bell et al. (2005) $380,000,000
Ito et al. (2005) $520,000,000
Levy et al. (2005) $530,000,000
0.075 ppm
No Causality $25,000,000
Bell et al. (2004) $250,000,000
Bell et al. (2005) $770,000,000
Ito et al. (2005) $1,000,000,000
Levy et al. (2005) $1,100,000,000
0.070 ppm
No Causality $64,000,000
Bell et al. (2004) $530,000,000
Bell et al. (2005) $1,600,000,000
Ito et al. (2005) $2,100,000,000
Levy et al. (2005) $2,200,000,000
0.065 ppm
No Causality $97,000,000
Bell et al. (2004) $800,000,000
Bell et al. (2005) $2,400,000,000
Ito et al. (2005) $3,100,000,000
Levy et al. (2005) $3,300,000,000
Estimating the Monetized Benefit per ton of PM^ Precursor Reduced
The NOx emission reductions necessary to reach attainment with an alternate revised standard
would also reduce levels of PM2.s. The process for estimating the PM2.5 co-benefit for these two
airsheds is very similar to the national co-benefit analysis described in the body of the RIA, with
a single exception noted further below. The steps are as follows:
1. Estimate the number of tons of NOx necessary to attain a baseline of 0.08 ppm. As noted
above, Table 7b.2 includes the estimate of extrapolated NOx tons necessary to attain each
standard alternative.
2. Calculate the benefits of attaining 0.08 ppm incremental to partial attainment of 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. Note that this calculation step allows us to net
7b-7
-------
out the benefits of attaining the current standard, so that all subsequent benefits are
incremental to the full attainment of 0.080 ppm.
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 (Table 7b.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.
5. Repeat step 4 to estimate the benefits of 0.075ppm, 0.079ppm and 0.065ppm. Step 4
may be repeated by substituting the NOx tons necessary to attain the selected alternative
of 0.075 ppm and the remaining alternatives of 0.079 ppm and 0.065 ppm to produce an
estimate of total PIVb.s co-benefits.
Because this analysis estimates the PM2.s co-benefits of full attainment for these two airsheds in
2030, it was necessary to apply a PlV^.s benefit per ton estimate that incorporates this population
year. The Technical Support Document for this RIA describes the technique for calculating a
benefit per ton estimate that reflected population growth to 2030 (EPA, 2008). Table 7b. 14
below summarizes the total monetized PIVb.s co-benefits associated with attainment of each
standard alternative.
Total Estimate of Combined Ozone Benefits and PM^ Co-Benefits
Table 7b.l5 summarizes the total combined benefits for each standard alternative.
The following tables summarize the costs, benefits, and net benefits of attaining the alternate
primary standards for South Coast and San Joaquin.
7b-8
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Table 7b.l4: Total Estimated PM2.5 Co-Benefits of Attaining Alternate Ozone Standards
in 2030 in San Joaquin and South Coast (2006$)
Valuation Estimate
Mortality Function 3% Discount Rate 7% Discount Rate
0.079 ppm
ACS Study $120,000,000 $110,000,000
Harvard Six-City Study $260,000,000 $240,000,000
Expert K $54,000,000 $50,000,000
Expert E $450,000,000 $410,000,000
0.075 ppm
ACS Study $240,000,000 $220,000,000
Harvard Six-City Study $530,000,000 $480,000,000
Expert K $110,000,000 $100,000,000
Expert E $900,000,000 $820,000,000
0.070 ppm
ACS Study $400,000,000 $360,000,000
Harvard Six-City Study $860,000,000 $780,000,000
Expert K $180,000,000 $160,000,000
Expert E $1,500,000,000 $1,300,000,000
0.065 ppm
ACS Study $420,000,000 $380,000,000
Harvard Six-City Study $910,000,000 $820,000,000
Expert K $190,000,000 $170,000,000
Expert E $1,600,000,000 $1,400,000,000
7b-9
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Table 7b.l5: Total Combined Ozone Benefits and PM2.5 Co-Benefits of Attaining Alternate Ozone Standards in 2030 in San
Joaquin and South Coast (2006$, 3% Discount Rate)
Alternative Standard and Model or Assumption
0.079 ppm Alternative
ACS Study
Harvard Six-City Study
Expert K
Expert E
0.075 ppm Selected Alternative
ACS Study
Harvard Six-City Study
Expert K
Expert E
0.070 ppm Alternative
ACS Study
Harvard Six-City Study
Expert K
Expert E
0.065 ppm Alternative
ACS Study
Harvard Six-City Study
Expert K
Expert E
Bell et al. (2004)
$250,000,000
$390,000,000
$180,000,000
$580,000,000
$500,000,000
$780,000,000
$360,000,000
$1,200,000,000
$930,000,000
$1,400,000,000
$710,000,000
$2,000,000,000
$1,200,000,000
$1,700,000,000
$990,000,000
$2,400,000,000
Bell et al. (2005)
$510,000,000
$650,000,000
$440,000,000
$840,000,000
$1,000,000,000
$1,300,000,000
$870,000,000
$1,700,000,000
$2,000,000,000
$2,400,000,000
$1,800,000,000
$3,100,000,000
$2,800,000,000
$3,300,000,000
$2,600,000,000
$3,900,000,000
Ito et al. (2005)
$640,000,000
$780,000,000
$570,000,000
$970,000,000
$1,300,000,000
$1,600,000,000
$1,100,000,000
$1,900,000,000
$2,500,000,000
$3,000,000,000
$2,300,000,000
$3,600,000,000
$3,500,000,000
$4,000,000,000
$3,300,000,000
$4,700,000,000
Levy et al. (2005)
$650,000,000
$800,000,000
$590,000,000
$990,000,000
$1,300,000,000
$1,600,000,000
$1,200,000,000
$2,000,000,000
$2,600,000,000
$3,100,000,000
$2,400,000,000
$3,700,000,000
$3,700,000,000
$4,200,000,000
$3,500,000,000
$4,900,000,000
Assumption of No
Causality
$130,000,000
$280,000,000
$67,000,000
$460,000,000
$270,000,000
$550,000,000
$130,000,000
$930,000,000
$460,000,000
$920,000,000
$240,000,000
$1,500,000,000
$520,000,000
$1,000,000,000
$280,000,000
$1,700,000,000
7b-10
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Table 7b.l6: Total Combined Ozone Benefits and PM2.5 Co-Benefits of Attaining Alternate Ozone Standards in 2030 in San
Joaquin and South Coast (2006$, 7% Discount Rate)
Alternative Standard and Model or Assumption
0.079 ppm Alternative
ACS Study
Harvard Six-City Study
Expert K
Expert E
0.075 ppm Selected Alternative
ACS Study
Harvard Six-City Study
Expert K
Expert E
0.070 ppm Alternative
ACS Study
Harvard Six-City Study
Expert K
Expert E
0.065 ppm Alternative
ACS Study
Harvard Six-City Study
Expert K
Expert E
Bell et al. (2004)
$240,000,000
$370,000,000
$180,000,000
$540,000,000
$480,000,000
$730,000,000
$350,000,000
$1,100,000,000
$1,300,000,000
$700,000,000
$1,900,000,000
$1,200,000,000
$1,600,000,000
$970,000,000
$2,200,000,000
Bell et al. (2005)
$490,000,000
$620,000,000
$430,000,000
$790,000,000
$990,000,000
$1,200,000,000
$860,000,000
$1,600,000,000
$2,400,000,000
$1,700,000,000
$2,900,000,000
$2,800,000,000
$3,200,000,000
$2,600,000,000
$3,800,000,000
Ito et al. (2005)
$630,000,000
$760,000,000
$570,000,000
$930,000,000
$1,300,000,000
$1,500,000,000
$1,100,000,000
$1,900,000,000
$2,900,000,000
$2,300,000,000
$3,500,000,000
$3,500,000,000
$3,900,000,000
$3,300,000,000
$4,500,000,000
Levy et al. (2005)
$640,000,000
$770,000,000
$580,000,000
$940,000,000
$1,300,000,000
$1,500,000,000
$1,200,000,000
$1,900,000,000
$3,000,000,000
$2,400,000,000
$3,500,000,000
$3,700,000,000
$4,100,000,000
$3,500,000,000
$4,700,000,000
Assumption of No
Causality
$120,000,000
$250,000,000
$63,000,000
$420,000,000
$250,000,000
$500,000,000
$130,000,000
$840,000,000
$840,000,000
$230,000,000
$1,400,000,000
$480,000,000
$920,000,000
$270,000,000
$1,500,000,000
7b-ll
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Table 7b.l7: Annual Monetized Costs and Benefits in 2030 in San Joaquin and South
Coast: 0.075 ppm Standard in Billions of 2006$*
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***
Total Be
3%
0.36 - 1.2
0.87- 1.7
1.1 -1.9
1.2-2.0
0.13-0.93
nefits**
7%
0.35- 1.1
0.86-1.6
1.1 - 1.9
1.2-1.9
0.13-0.84
Total
Costs**
7%
0.68-1.0
0.68- 1.0
0.68- 1.0
0.68-1.0
0.68- 1.0
NetB
3%
-0.64 - 0.48
-0.13-0.99
0.14- 1.26
0.17- 1.29
-0.87 - 0.25
enefits
7%
-0.65 - 0.39
-0.14-0.90
0.13- 1.2
0.16- 1.20
-0.87-0.16
Table7b.l8: Annual Monetized Costs and Benefits in 2030 in San Joaquin and South
Coast: 0.079 ppm Standard in Billions of 2006$*
Mortality
Function or
Assumption Reference
NMMAPS Bell et al. 2004
Bell et al. 2005
Meta-analysis Ito et al. 2005
Levy et al. 2005
Assumption that association is not
causal***
Total Be
3%
0.18 -0.58
0.44 - 0.84
0.57 - 0.97
0.59 - 0.99
0.07 - 0.46
nefits**
7%
0.18-0.54
0.43 - 0.79
0.57-0.93
0.58-0.94
0.06-0.42
Total
Costs**
7%
0.34 - 0.52
0.34 - 0.52
0.34 - 0.52
0.34 - 0.52
0.34 - 0.52
NetB
3%
-0.34 - 0.24
-0.08 -0.50
0.05 - 0.63
0.07 - 0.65
-0.45 -0.12
enefits
7%
-0.34 -0.20
-0.09 -0.45
0.05 -0.59
0.06 -0.60
-0.46 -0.08
Table 7b.l9: Annual Monetized Costs and Benefits in 2030 in San Joaquin and South
Coast: 0.070 ppm Standard in Billions of 2006$*
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***
Total Be
3%
0.71 -2.0
1.8-3.1
2.3-3.6
2.4-3.7
0.24-1.5
nefits**
7%
0.70-1.9
1.7-2.9
2.3-3.5
2.4-3.5
0.23- 1.4
Total
Costs**
7%
1.1 -1.7
1.1 -1.7
1.1 - 1.7
1.1 -1.7
1.1 - 1.7
NetB
3%
-0.99 - 0.90
0.06-2.0
0.62 - 2.5
0.67-2.6
-1.5-0.43
enefits
7%
-1.0-0.76
0.05- 1.8
0.60 - 2.4
0.66 - 2.4
-1.5-0.29
7b-12
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Table 7b.20: Annual Monetized Costs and Benefits in 2030 in San Joaquin and South
Coast: 0.065 ppm Standard in Billions of 2006$*
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***
Total Be
3%
0.99 - 2.4
2.6-3.9
3.3-4.7
3.5-4.9
0.28-1.7
nefits**
7%
0.97-2.2
2.6-3.8
3.3-4.5
3.5-4.7
0.27- 1.5
Total
Costs**
7%
1.2- 1.9
1.2-1.9
1.2- 1.9
1.2-1.9
1.2- 1.9
NetB
3%
-0.91 - 1.2
0.67-2.7
1.4-3.5
1.6 -3.7
-1.6-0.46
enefits
7%
-0.93- 1.0
0.65 - 2.6
1.4-3.3
1.6-3.5
-1.63-0.31
*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. Tables exclude unquantified and
nonmonetized benefits. All estimates rounded to two significant figures, so totals may not sum across
columns.
**Range reflects lower and upper bound cost estimates. Data for calculating costs at a 3% discount rate
was not available for all sectors, and therefore total annualized costs at 3% are not presented here.
***Total includes ozone morbidity benefits only.
7b.3 References
U.S. Environmental Protection Agency (EPA). 1998. Locomotive Emission Standards:
Regulatory Support Document, U.S. Environmental Protection Agency, Office of Transportation
and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105. Available at
http://www.epa.gov/otaq/regs/nonroad/locomotv/frm/locorsd.pdf
U.S. Environmental Protection Agency (EPA). 2004. Final Regulatory Analysis: Control of
Emissions from Nonroad Diesel Engines, U.S. Environmental Protection Agency, Office of
Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105,
EPA420-R-04-007, May 2004. Available at http://www.epa.gov/nonroad-
diesel/2004fr/420r04007.pdf
U.S. Environmental Protection Agency (EPA). 2005. Emission Standards and Test Procedures
for Aircraft and Aircraft Engines: Summary and Analysis of Comments, U.S. Environmental
Protection Agency, Office of Transportation and Air Quality, Assessment and Standards Division,
Ann Arbor, MI 48105, EPA420-R-05-004, November, 2005. Available at
http://www.epa.gov/otaq/regs/nonroad/aviation/420r05004.pdf.
U.S. Environmental Protection Agency (EPA). 2007a. Regulatory Impact Analysis for Final
Rule: Control of Hazardous Air Pollutants from Mobile Sources, U.S. Environmental Protection
Agency, Office of Transportation and Air Quality, Assessment and Standards Division, Ann
Arbor, MI 48105, EPA420-R-07-002, February 2007. Available at
http://www.epa.gov/otaq/regs/toxics/420r07002.pdf.
7b-13
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U.S. Environmental Protection Agency (EPA). 2007b. National Scale Modeling for the Final
Mobile Source Air Toxics Rule, Office of Air Quality Planning and Standards, Emissions
Analysis and Monitoring Division, Research Triangle Park, NC 27711, EPA 454/R-07-002,
February 2007. Available at http://www.epa.gov/otaq/regs/toxics/454r07002.pdf.
U.S. Environmental Protection Agency (EPA). 2007c. Draft Regulatory Impact Analysis:
Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression-
Ignition Engines Less than 30 Liters per Cylinder, Chapter 3: Emission Inventory, U.S.
Environmental Protection Agency, Office of Transportation and Air Quality, Assessment and
Standards Division, Ann Arbor, MI 48105. EPA420-D-07-001, March 2007. Available at
http://www.epa.gov/otaq/regs/nonroad/420d07001 chp3 .pdf.
U.S. Environmental Projections Agency (EPA). 2008. Technical Support Document: Preparation
of Emissions Inventories For the 2002-based Platform, Version 3, Criteria Air Pollutants,
USEPA, January, 2008.
7b-14
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Chapter 8: 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.
8.1 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 0.079 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.
8.2 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.
8-1
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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.
8.3 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).
8.4 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
8-2
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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.
8.5 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 the proposal, EPA concluded 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.
8.6 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
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.
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This 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..
8.7 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.
8.8 Executive Order 13211: Actions that Significantly Affect Energy Supply,
Distribution or Use
Executive Order 13211, "Actions Concerning Regulations That Significantly Affect Energy
Supply, Distribution, or Use" (66 FR 28355 (May 22, 2001), requires EPA to prepare and submit
a Statement of Energy Effects to the Administrator of the Office of Information and Regulatory
Affairs, Office of Management and Budget, for certain actions identified as "significant energy
actions." Section 4(b) of Executive Order 13211 defines "significant energy actions" as "any
action by an agency (normally published in the Federal Register) that promulgates or is expected
to lead to the promulgation of a final rule or regulation, including notices of inquiry, advance
notices of proposed rulemaking, and notices of proposed rulemaking: (l)(i) that is a significant
regulatory action under Executive Order 12866 or any successor order, and (ii) is likely to have a
significant adverse effect on the supply, distribution, or use of energy; or (2) that is designated by
the Administrator of the Office of Information and Regulatory Affairs as a significant energy
8-4
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action." OMB has designated this rulemaking as a significant energy action. We have prepared a
Statement of Energy Effects for this action as follows.
Application of the modeled illustrative control strategy containing only known controls shown
Chapter 5 of this RIA leads to an estimated decrease nationwide in 2020 in coal production of
less than 0.2 percent, an estimated decrease in crude oil production of about 0.1 percent, an
estimated decrease in natural gas production of less than 0.1 percent, and an estimated increase
in electricity production of less than 0.1 percent. Estimates of price changes for these energy
products are of the same magnitude nationwide in 2020 as the estimates of output changes. For
more details on how energy impacts are modeled in this analysis and the caveats and limitations
that should be understood in interpreting these impacts, please refer to Appendix 5B of this RIA.
For the electricity generating sector, installation of approximately 9.4 gigawatts (GWs) of SCR
and 2.4 GWs of SNCR are projected in 2020 as a result of applying the illustrative EGU control
strategy mentioned earlier in this RIA. There are very small changes expected in the mix of
electricity generation (i.e., the number of coal-fired EGUs compared to the number of natural
gas-fired and oil-fired EGUs) as a response to the illustrative EGU control strategy. Hydro,
nuclear, other, and renewable based generation are projected to remain the same. Projected
retirements of both coal and oil/gas units remained the same after applying the illustrative EGU
control strategy. For more details on the energy impacts estimated for EGUs, please refer to
Chapter 5 of this RIA and its appendix.
We provide the energy impact results reflecting only the modeled illustrative control strategy
because these results have a greater degree of certainly associated with them when compared to
results associated with the other alternate primary ozone standards analyzed. This greater degree
of certainty is due to the application of photochemical air quality modeling (i.e., CMAQ) to
assess where precursor emission reductions are most needed to attain a particular alternate
primary ozone standard. Since such CMAQ modeling was not applied for these other alternate
primary ozone standards, we thus have a differing degree of certainty with regards to impacts
associated with the modeled illustrative control strategy as opposed to other strategies applied for
the other alternate primary ozone standards. Other caveats associated with our illustrative control
strategies and results from applying them are explained in Chapter 3 of this RIA. Finally, the
energy impacts reported in this RIA do not incorporate the extrapolated costs estimated for
Chapter 5 of this RIA. The proportion of the engineering costs that are extrapolated can also be
found in that RIA chapter.
8.9 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.
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8.10 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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