Regulatory Impact Analysis of the 

Proposed Revisions to the

National Ambient Air Quality Standards for 

Ground-Level Ozone 
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EPA-452/R-07-008

 July 2007 

Regulatory Impact Analysis of the 

 Proposed Revisions to the

National Ambient Air Quality Standards 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impact Division 

Air Benefit-Cost Group 

Research Triangle Park, North Carolina 
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Table of Contents 

Chapter Page

1 Introduction and Background 

 Synopsis.............................................................................................1-1 

1.1 Background.................................................................................1-1 

1.2 Role of the Regulatory Impact Analysis in the NAAQS Setting Process.........1-2 

1.2.1 Legislative Roles.................................................................1-2 

1.2.2 Role of Statutory and Executive Orders.......................................1-2 

1.2.3 Market Failure or Other Social Purpose.......................................1-3 

1.2.4 Illustrative Nature of the Analysis.............................................1-4 

 1.3 Overview and Design of the RIA.......................................................1-5 

 1.3.1 Baseline and Years of Analysis................................................1-5 

 1.3.2 Control Scenarios Considered in this RIA....................................1-6 

 1.3.3 Evaluating Costs and Benefits..................................................1-6 

 1.4 Ozone Standard Alternatives Considered..............................................1-7 

 1.5 References..................................................................................1-9 

2 Characterizing Ozone and Modeling Tools Used in This Analysis 

 Synopsis...............................................................................................2-1 

 2.1 Ozone Chemistry...........................................................................2-1

 2.1.1 Temporal Scale....................................................................2-2

 2.1.2 Geographic Scale and Transport................................................2-2 

 2.1.3 Effects of Ozone..................................................................2-3 

 2.2 Sources of Ozone...........................................................................2-3

 2.3 Modeling Ozone Levels in the Future...................................................2-4 

 2.3.1 Emissions Inventory..............................................................2-4 

 2.3.2 CMAQ Model......................................................................2-5 

 2.4 References..................................................................................2-7 

3 Modeled Control Strategy: Design and Analytical Results 

 Synopsis............................................................................................3-1 
 3.1 Establishing the Baseline..................................................................3-2 

 3.1.1 National Rules.....................................................................3-4

 3.1.2 Additional Controls...............................................................3-6 

 3.1.3 Ozone Levels for Baseline......................................................3-12 

 3.2 Developing the Control Strategy Analysis............................................3-14 

3.2.1 Controls Applied for a 0.070 ppm Standard: Non-EGU and Area 

 Sectors......................................................................................3-17 

 3.2.2 Controls Applied for a 0.070 ppm Standard: EGU Sector................3-19 

 3.2.3 Controls Applied for a 0.070 ppm Standard: Onroad and Nonroad 

 Mobile Sectors............................................................................3.21 

 3.2.4 Data Quality for this Analysis.................................................3-22 

3.3 Geographic distribution of Emissions reductions....................................3-23 

 3.4 Ozone Design Values for partial attainment..........................................3-27 

3.5 References.................................................................................3-31

Appendix Chapter 3

3a.1 Non-EGU and Area Source Controls Applied in the Baseline and Control 

Scenarios.................................................................................3a-1

3a.1.1 Non-EGU and Area Source Control Strategies for Ozone NAAQS 

Proposal...................................................................................3a-1

 3a.1.2 NOx Control Measures for Non-EGU Point Sources.......................3a-1 

3a.1.3 VOC Control Measures for Non-EGU Point Sources...........................3a-2 

3a.1.4 NOx Control Measures for Area Sources............................................3a-2 

3a.1.5 VOC Control Measures for Area Sources........................................3a-2 

3a.1.6 Supplemental Controls..............................................................3a-3 

3a.2 Mobile Controls/Rules Used in Baseline and Control Scenarios...............3a-11 

3a.2.1 Diesel Retrofits and Vehicle Replacement.................................3a-11 

3a.2.2 Implement Continuous Inspection and Maintenance Using Remote 

Onboard Diagnostics (OBD).........................................................3a-14 

3a.2.3 Eliminating Long Duration Truck Idling....................................3a-15 

3a.2.4 Commuter Programs...........................................................3a-16 

3a.2.5 Reduce Gasoline RVP from 7.8 to 7.0 in Remaining Nonattainment 

Areas.....................................................................................3a-18

3a.2.6 Application order for Onroad and Nonroad Mobile Controls............3a-18

3a.3 EGU Controls Used in the Control Strategy.......................................3a-19 

3a.4 Emissions Reductions by Sector......................................................3a-22 
3a.5 Change in Ozone Concentrations between Baseline and Post-0.070 ppm Control 

Strategy Modeling.......................................................................3a.27 

4 Approach for Estimating Reductions for Full Attainment Scenario 

Synopsis..............................................................................................4-1

 4.1 Development of Air Quality Impact Ratios for Determination of Extrapolated 

 Costs.........................................................................................4-1 

 4.1.1 Approach A: Use of Sensitivity Modeling of Local Emissions 

 Reductions...................................................................................4-1 

 4.1.2 Approach B: Use of 2020 Baseline and RIA Control Scenario............4-3 

 4.2 Results from Impact Ratio Analyses..................................................................4-5 

 4.3 Determination of Extrapolated Tons Control Areas..................................4-7 

 4.4 Selection of Air Quality Goal for this analysis.........................................4-9 

 4.5 National 2020 Estimates of Additional Emissions Reductions Needed to 

Meet Four Potential Air Quality Targets...........................................4-12 

4.6 Estimates of Additional Tons Needed for Four Potential Air Quality Targets 

(California Only, Post-2020 Attainment).............................................4-16 

5 Cost Estimates 

 Synopsis...............................................................................................5-1 

 5.1 Modeled Controls..........................................................................5.2 

 5.1.1 Sector Methodology. ...............................................................5-2 

 5.1.1.1 Non-EGU Point and Area Sources: AirControlNet.................5-2 

 5.1.1.2 EGU Sources: the Integrated Planning Model......................5-3 

 5.1.1.3 Onroad and Nonroad Mobil Sources: MOBILE Model............5-4 

 5.1.2 Known Controls- Cost by Sector...............................................5-4 

 5.1.3 Limitations and Uncertainties Associated with Engineering Cost 

 Estimates.....................................................................................5-6 

 5.2 Extrapolated Costs.........................................................................5-7 

 5.2.1 Increasing Marginal Cost Methodology.......................................5-9 

 5.2.1.1 Marginal Cost Regions..................................................5-9 

 5.2.1.2 Derivation of the Marginal Cost Slopes............................5-11 

 5.2.1.3 Calculating Extrapolated Costs using Marginal Cost 

 Approach.........................................................................5-12 

 5.2.2 Fixed Cost per Ton Values.....................................................5-12 
 5.2.3 Results............................................................................5-13 

 5.3 Summary of Costs........................................................................5-19

 5.4 Technology Innovation and Regulatory Cost Estimates............................5-20 

 5.4.1 Examples of Technological Advances in Pollution Control..............5-22 

 5.4.2 Influence on Regulatory Cost Estimates.....................................5-23 

5.5 References.................................................................................5-26

Appendix Chapter 5 

5a.1 Cost Information for Non-EGU and area sources....................................5a.1 

5a.2 Cost Information for EGU sources....................................................5a-2 

5a.3 Cost information for Onroad and Nonroad Mobile Sources........................5a-3 

6 Incremental Benefits of Attaining Alternative Ozone Standards Relative to the 

Current 8-hour Standard (0.08 ppm) 

 Synopsis...............................................................................................6-1 

 6.1 Background..................................................................................6-3 

 6.2 Characterizing Uncertainty: Moving Toward a Probabilistic Framework for 

 Benefits Assessment................................................................................6-5 

 6.3 Health Impact Functions..................................................................6-6 

 6.3.1 Potentially Affected Populations...............................................6-7 

 6.3.2 Effect Estimate Sources..........................................................6-7 

 6.3.2.1 Premature Mortality Effects Estimates..............................6-13 

 6.3.2.2 Respiratory Hospital Admissions Effect Estimates...............6-14 

 6.3.2.3 Asthma-Related Emergency Room Visits Effect Estimates......6-15 

 6.3.2.4 Minor Restricted Activity Days Effect Estimates.................6-16 

 6.3.2.5 School Absences Effect Estimate....................................6-16 

 6.3.2.6 Worker Productivity...................................................6-17

 6.3.2.7 Visibility Benefits.......................................................6-17 

 6.3.2.8 Other Unquantified Effects...........................................6-17 

 6.3.2.8.1 Direct Ozone Effects on Vegetation.....................6-17 

 6.3.2.8.2 Nitrogen Deposition.......................................6-18 

 6.3.2.8.3 Ultraviolet Radiation.......................................6-19 

 6.3.2.8.4 Climate Implications of Tropospheric Ozone..........6-21 

 6.3.3 Baseline Incidence Rates......................................................6-22 
 6.4 Economic Values for Health Outcomes...............................................6-24 

 6.4.1 Mortality Valuation.............................................................6-25 

 6.4.2 Hospital Admissions Valuation................................................6-25 

 6.4.3 Asthma-Related Emergency Room Visits Valuation......................6-25 

 6.4.4 Minor Restricted Activity Days Valuation...................................6-25 

 6.4.5 School Absences.................................................................6-26 

 6.5 Results and Implications.................................................................6-31 

 6.5.1 Glidepath Incidence and Valuation Estimates for 0.065 ppm and 

 0.075 ppm Alternatives...................................................................6-31 

 6.5.2 PM2.5 Co-Benefits Estimates...................................................6-32 

 6.5.3 PM2.5 Co-Benefits Resulting from Attainment of 0.070 ppm 

 incremental to 0.08 ppm.................................................................6-74 

 6.5.4 Estimate of Full Attainment Benefits.........................................6-77 

 6.5.5 Discussion of Results and Uncertainties.....................................6-98 

 6.5.6 Summary of Total Benefits...................................................6-101 

6.6 References................................................................................6-104

Appendix Chapter 6 

 6a Additional Benefits Information 

 Summary..................................................................................6a-1 

 6a.1 Developing an air quality estimate of full attainment with the alternative 

 ozone standards..................................................................6a-1 

6a.2 Partial Attainment PM2.5 Incidence and Valuation Estimates.............6a-2 

6b Health-Based Cost-Effectiveness of Reductions in Ambient PM2.5 Associated 

 with Illustrative Ozone NAAQS 0.070ppm Attainment Strategy 

6b.1 Summary..........................................................................6b-1 

6b.2 Introduction.......................................................................6b-4 

6b.3 Effectiveness Measures.........................................................6b-7 

6b.4 Changes in Premature Death, Life Years, and Quality of Life............6b-9 

6b.4.1 Calculating Reductions in Premature Deaths.....................6b-10 

6b.4.2 Calculating Changes in Life Years from Direct Reductions in 

PM2.5-Related Mortality Risk................................................6b-11 
6b.4.2.1 Should Life Years Gained Be Adjusted for Initial Health 

Status?.......................................................................................6b-14 

6b.5 Calculating Changes in the Quality of Life Years (Morbidity) .........6b-16 

6b.5.1 Calculating QALYs Associated with Reductions in the Incidence 

of Chronic Bronchitis.........................................................6b-17 

6b.5.2 Calculating QALYs Associated with Reductions in the Incidence 

of Nonfatal Myocardial Infarctions.........................................6b-21 

 6b.6 Cost-Effectiveness Analysis..................................................6b-28 

6b.6.1 Aggregating Life Expectancy and Quality-of-Life Gains......6b-28 

6b.6.2 Dealing with Acute Health Effects and Non-health Effects....6b-31 

6b.6.3 Cost-Effectiveness Ratios...........................................6b-33 

 6b.7 Discount Rate Sensitivity Analysis..........................................6b-35 

 6b.8 Conclusions.....................................................................6b-37 

 6b.9 References.......................................................................6b-38 

6c Additional Sensitivity Analyses Related To the Benefits Analysis 

 6c.1 Premature Mortality Cessation Lag Structure...............................6c-1 

 6c.2 Threshold Sensitivity Analysis.................................................6c-5 

 6c.3 Income Elasticity of Willingness to Pay......................................6c-8 

 6c.4 References.........................................................................6c-9 

7 Discussion of Ozone Secondary Standard 

8 Conclusions and Implications of the Illustrative Benefit-Cost Anlaysis 

 Synopsis.............................................................................................8-1 

 8.1 Results.......................................................................................8-1 

 8.2 Discussion of Results.....................................................................8-10 

 8.3 What did we Learn Through this Analysis? ..........................................8-13 

9 Statutory and Executive Order Impact Analysis 
 Synopsis..............................................................................................9-1 

9.1 Executive Order 12866: Regulatory Planning and Review..........................9-1 

 9.2 Paperwork Reduction Act.................................................................9-1 

 9.3 Regulatory Flexibility Act................................................................9-2 

 9.4 Unfunded Mandates Reform Act.........................................................9-2 

 9.5 Executive Order 13132: Federalism.....................................................9-3 

 9.6 Executive Order 13175: Consultation and Coordination with Indian Tribal 

 Governments................................................................................9-3 

 9.7 Executive Order 13045: Protection of Children from Environmental Health 

 & Safety Risks.............................................................................9-4 

 9.8 Executive Order 13211: Actions that Significantly Affect Energy Supply, 

 Distribution, or Use.......................................................................9-4 

 9.9 National Technology Transfer Advancement Act....................................9-5 

 9.10 Executive Order 12898: Federal Actions to Address Environmental Justice 

 in Minority Populations and Low-Income Populations..............................9-5 
List of Tables 

Number Page

2.1 Geographic Specifications of Modeling Domains....................................2-7 

3.1 National Rules and Control Measures, by Sector, Contributing to the Baseline..3-5 

3.2 Controls by Sector Included in the Baseline Determination for 2020............3-10 

3.3 Controls for Emissions Reductions, by Sector, for the 0.070 ppm Control Strategy 

(Incremental to Baseline)...............................................................3-15 

3.4 Annual Tons of Emissions Remaining after Application of the 0.070 ppm Control 

Strategy (35 States + DC Analysis Area).............................................3-29 

4.1 Summary of site-specific impact ratios over the five analysis zones of

Approach A...........................................................................................................4-3 

4.2 Summary of site-specific impact ratios over the four analysis zones of

Approach B.................................................................................4-4 

4.3 The NOx impact ratios at the controlling counties for each methodology over the 

analysis zones..............................................................................4-5 

4.4 List of counties that did not reach 0.070 in the RIA control scenario and how they 

were aggregated into extrapolated tons control areas................................4-8 

4.5 2020 Air Quality Glidepath Targets for LA and Kern County.....................4-11 

4.6 Estimated Annual Incremental Tons Needed for an 0.065 ppm Air Quality Target 

in 2020 (29 areas)........................................................................4-13 

4.7. Estimated Annual Incremental Tons Needed for an 0.070 ppm air quality target in 

2020 (20 areas)...........................................................................4-14 

4.8 Estimated Annual Incremental Tons Needed for an 0.075 ppm air quality target in 

2020 (11 areas)...........................................................................4-15 

4.9 Estimated Annual Incremental Tons Needed for an 0.079 ppm air quality target in 

2020 (6 areas)............................................................................4-15 

4.10 California: Estimated Tons Needed for Attainment of 0.065 ppm Air Quality 

Target (beyond 2020)...................................................................4-16 

4.11 California: Estimated Tons Needed for Attainment of 0.070 ppm Air Quality 

Target (Beyond 2020)...................................................................4-16 

4.12 California: Estimated Tons Needed for Attainment of 0.075 ppm air quality target 

(beyond 2020)............................................................................4-16 

4.13 California: Estimated Tons Needed for Attainment of 0.079 ppm air quality 

target (beyond 2020).....................................................................4-16 

5.1 Comparison of Modeled Annual Control Costs Nationwide, by sector, for a 0.070 

ppm control scenario ($1999)............................................................5-6 

5.2 Regions and Slopes for Extrapolated Costs..........................................5-10 

5.3 Data Selection Criteria for Extrapolated Costs.......................................5-11 

5.4 Extrapolated Costs of Meeting the 0.079 ppm Standard............................5-15 

5.5 Extrapolated Costs of Meeting the 0.075 ppm Standard...........................5-16 

5.6 Extrapolated Costs of Meeting the 0.070 ppm Standard...........................5-17 
5.7 Extrapolated Costs of Meeting the 0.065 ppm Standard...........................5-18 

5.8 Total Costs of Attainment in 2020 for Different Levels of the Ozone Standard 

(National Attainment in 2020).........................................................5-19 

5.9 California Extrapolated Costs ($M)...................................................5-20 

5.10 Comparison of Inflation-Adjusted Estimated Costs and Actual Price Changes 

for EPA Fuel Control Rules............................................................5-25 

6.1 Human Health and Welfare Effects of Pollutants Affected by the Alternate 

Standards...................................................................................6-9

6.2 Ozone and PM Related Health Endpoints basis for the concentration-response 

function associated with that endpoint, and sub-populations for which they were 

computed.................................................................................6-11 

6.3 National Average Baseline Incidence Rates..........................................6-23 

6.4 Unit Values for Economic Valuation of Health Endpoints (2000$)..............6-27 

6.5 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the 

Incidence of Premature Mortality Associated with Ozone Exposure in 2020 

(Incremental to Current Ozone Standard)............................................6-33 

6.6 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the 

Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current 

Ozone Standard, 95% Confidence Intervals in Parentheses)......................6-34 

6.7 Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual 

Reductions in the Incidence of Premature Mortality Associated with Ozone 

Exposure (Incremental to Current Ozone Standard)................................6-35 

6.8 Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure...6-36 

6.9 Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Reductions in 

the Incidence of PM Premature Mortality associate with PM co-benefit.........6-37 

6.10 Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Reductions in 

the Incidence of Morbidity Associated with PM Co-benefit.......................6-38 

6.11 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in the 

Incidence of Premature Mortality Associated with Ozone Exposure (Incremental 

to Current Ozone Standard)............................................................6-39 

6.12 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in the 

Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current 

Ozone Standard, 95% Confidence Intervals in Parentheses)......................6-40 

6.13 Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual 

Reductions in the Incidence of Premature Mortality Associated with Ozone 

Exposure (Incremental to Current Ozone Standard)................................6-41 

6.14 Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure 

(Incremental to Current Ozone Standard, 95% Confidence Intervals in 

Parentheses)..............................................................................6-42
6.15 Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Reductions in 

the Incidence of PM Premature Mortality associate with PM co-benefit.........6-43 

6.16 Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Reductions in 

the Incidence of Morbidity Associated with PM Co-benefit.......................6-44 

6.17 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in the 

Incidence of Premature Mortality Ozone Exposures (Incremental to Current 

Ozone Standard).........................................................................6-45 

6.18 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in the 

Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current 

Ozone Standard).........................................................................6-46 

6.19 Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual 

Reductions in the Incidence of Premature Mortality Associated with Ozone 

Exposure (Incremental to Current Ozone Standard)................................6-47 

6.20 Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure 

(Incremental to Current Ozone Standard, 95% Confidence Intervals in 

Parentheses)..............................................................................6-48

6.21 Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Reductions in 

the Incidence of PM Premature Mortality associate with PM co-benefit.........6-49 

6.22 Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Reductions in 

the Incidence of Morbidity Associated with PM Co-benefit......................6-50 

6.23 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in the 

Incidence of Premature Mortality Ozone Exposures (Incremental to Current 

Ozone Standard).........................................................................6-51 

6.24 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in the 

Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current 

Ozone Standard).........................................................................6-52 

6.25 Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual 

Reductions in the Incidence of Premature Mortality Associated with Ozone 

Exposure (Incremental to Current Ozone Standard)................................6-53 

6.26 Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure 

(Incremental to Current Ozone Standard, 95% Confidence Intervals in 

Parentheses)..............................................................................6-54

6.27 Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Reductions in 

the Incidence of PM Premature Mortality associate with PM co-benefit.........6-55 

6.28 Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Reductions in 

the Incidence of Morbidity Associated with PM Co-benefit (95

th percentile 

confidence intervals provided in parentheses).......................................6-56 

6.29 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone 

Exposure (Incremental to Current Ozone Standard, Millions of 1999$).........6-57 

6.30 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the 

Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current 

Ozone Standard, 95% Confidence Intervals in Parentheses, 

Millions of 1999$).......................................................................6-58 
6.31 Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual 

Valuation of Reductions in the Incidence of Premature Mortality Associated with 

Ozone Exposure (Incremental to Current Ozone Standard)........................6-59 

6.32 Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual 

Valuation of Reductions in the Incidence of Morbidity Associated with Ozone 

Exposure (Incremental to Current Ozone Standard)................................6-60 

6.33 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone 

Exposure (Incremental to Current Ozone Standard, Millions of 1999$).........6-61 

6.34 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure 

(Incremental to Current Ozone Standard, 95% Confidence Intervals in 

Parentheses, Millions of 1999$).......................................................6-62 

6.35 Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual 

Valuation of Reductions in the Incidence of Premature Mortality Associated with 

Ozone Exposure (Incremental to Current Ozone Standard)........................6-63 

6.36 Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual 

Valuation of Reductions in the Incidence of Morbidity Associated with Ozone 

Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in 

Parentheses)..............................................................................6-64

6.37 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Monetary Value of 

Reductions in the Incidence of Mortality Associated with Exposure to Ozone 

(Millions of 1999$, Incremental to Current Standard)..............................6-65 

6.38 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Monetary Value of 

Reductions in the Incidence of Morbidity Associated with Exposure to Ozone 

(Millions of 1999$, Incremental to Current Standard)..............................6-66 

6.39 Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual 

Valuation of Reductions in the Incidence of Premature Mortality Associated with 

Ozone Exposure (Incremental to Current Ozone Standard)........................6-67 

6.40 Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual 

Valuation of Reductions in the Incidence of Morbidity Associated with Ozone 

Exposure (Incremental to Current Ozone Standard)................................6-68 

6.41 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Monetary Value of 

Reductions in the Incidence of Premature Mortality Associated with Exposure to 

Ozone (Millions of 1999$, Incremental to Current Standard).....................6-69 

6.42 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Monetary Value of 

Reductions in the Incidence of Morbidity Associated with Exposure to Ozone 

(Millions of 1999$, Incremental to Current Standard)..............................6-70 

6.43 Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual 

Valuation of Reductions in the Incidence of Premature Mortality Associated with 

Ozone Exposure (Incremental to Current Ozone Standard)........................6-71 

6.44 Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual 

Valuation of Reductions in the Incidence of Morbidity Associated with Ozone 

Exposure (Incremental to Current Ozone Standard)................................6-72 

6.45 Estimated PM2.5 Co-Benefits Associated with Full Attainment of 0.070 ppm 

incremental to 0.08 ppm................................................................6-76 
6.46 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals, 

Millions of $1999) for the 0.065 ppm Standard Alternative: National Glidepath 

Attainment................................................................................6-78 

6.47 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals, 

Millions of $1999) for the 0.065 ppm Standard Alternative: California

 Attainment................................................................................6-79 

6.48 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals, 

Millions of $1999) for the 0.070 ppm Standard Alternative: National Glidepath 

Attainment................................................................................6-80 

6.49 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals, 

Millions of $1999) for the 0.070 ppm Standard Alternative: California

 Attainment................................................................................6-81 

6.50 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals, 

Millions of $1999) for the 0.075 ppm Standard Alternative: National Glidepath 

Attainment................................................................................6-82 

6.51 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals, 

Millions of $1999) for the 0.075 ppm Standard Alternative: California

 Attainment................................................................................6-83 

6.52 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals, 

Millions of $1999) for the 0.079 ppm Standard Alternative: National Glidepath 

Attainment................................................................................6-84 

6.53 Estimate of Total Annual Ozone and PM2.5 Benefits (95% Confidence Intervals, 

Millions of $1999) for the 0.079 ppm Standard Alternative: California

 Attainment................................................................................6-85 

6.54 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: National 

Glidepath Attainment...................................................................6-86 

6.55 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: California 

Glidepath Attainment...................................................................6-87 

6.56 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: Incremental 

Benefits of California Post 2020 Attainment........................................6-88 

6.57 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: Total 

California Benefits of Post 2020 Attainment........................................6-89 

6.58 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: National 

Glidepath Attainment...................................................................6-90 

6.59 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: California 

Glidepath Attainment...................................................................6-91 

6.60 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: Incremental 

Benefits of California Post 2020 Attainment........................................6-92 
6.61 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: California 

Post 2020 Attainment...................................................................6-93 

6.62 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.075 ppm Alternative Standard: National 

Glidepath Attainment...................................................................6-94 

6.63 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.075 ppm Alternative Standard: California 

Post 2020 Attainment...................................................................6-94 

6.64 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.079 ppm Alternative Standard: National 

Glidepath Attainment...................................................................6-96 

6.65 Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.079 ppm Alternative Standard: California 

Post 2020 Attainment...................................................................6-97 

6.66 Summary of Total Number of Annual Ozone and PM2.5-Related Premature 

Mortalities and Premature Morbidity Avoided: 2020 National Benefits.......6-102 

6.67 Summary of Total Number of Annual Ozone and PM2.5-Related Premature 

Mortalities and Premature Morbidity Avoided: California Post 2020

 Attainment..............................................................................6-103 

7.1 Comparison of number of counties exceeding various W126 levels when meeting 

various levels of the 8-hr standard for the 3-year period 2003-2005...............7.3 

8.1a National Annual Costs and Benefits: 0.079 ppm Standard in 2020 (including 

California glidepath)......................................................................8-4 

8.1b National Annual Costs and Benefits: 0.075 ppm Standard in 2020 

 (including California glidepath).........................................................8-4 

8.1c National Annual Costs and Benefits: 0.070 ppm Standard in 2020 (including 

California glidepath)......................................................................8-4

8.1d National Annual Costs and Benefits : 0.065 ppm Standard in 2020 (including

California glidepath)...........................................................................8-5 

8.2: Summary of Total Number of Annual Ozone and PM2.5-Related Premature 

Mortalities and Premature Morbidity Avoided: 2020 National Benefits............8-6 

8.3a California: Annual Costs and Benefits of Attaining 0.079 ppm Standard (beyond 

2020)........................................................................................8-7

8.3b California: Annual Costs and Benefits of Attaining 0.070 ppm Standard (beyond 

2020)........................................................................................8-7

8.3c California: Annual Costs and Benefits of Attaining 0.075 ppm Standard (beyond 

2020).........................................................................................8-7

8.3d California: Annual Costs and Benefits of Attaining 0.065 ppm Standard (beyond 

2020)........................................................................................8-8

8.4: Summary of Total Number of Annual Ozone and PM2.5-Related Premature 

Mortalities and Premature Morbidity Avoided: California Post 2020

 Attainment.................................................................................8-9 
List of Figures 

Number Page

1.1 Process used to create this RIA .........................................................1-5 

2.1 Map of the CMAQ Modeling Domains Used for Ozone NAAQS RIA............2-6 

3.1 Counties Where Controls for Nitrogen Oxides (NOx) Were Included for NonEGU Point and Area Sources, for the Baseline (Current Standard, 0.08 ppm)....3-7 

3.2 Counties Where Controls for Volatile Organic Chemicals (VOCs) Were Applied 

to Non-EGU Point and Area Sources in Baseline (Current Standard, 0.08 

ppm).........................................................................................3-8 

3.3 Areas Where NOx and VOC Controls Were Included for Mobile Onroad and 

Nonroad Sources in Addition to National Mobile Controls in Baseline (Current 

Standard, 0.08 ppm).......................................................................3-9 

3.4 Baseline Annual Ozone Air Quality in 2020..........................................3-13 

3.5 Counties Where Controls for Nitrogen Oxides (NOx) Were Applied to Non-EGU 

Point and Areas Sources for RIA Control Strategy Designed to meet 0.070 ppm 

(Incremental to Baseline)...............................................................3-18 

3.6 Counties Where VOC Controls Were Applied to Non-EGU Point and Areas 

Sources for the Control Strategy Designed to Meet 0.070 ppm (Incremental to 

Baseline)..................................................................................3-19

3.7 States Where Nitrogen Oxide (NOx) Controls Were Applied to Electrical 

Generating Units (EGUs) for the Control Strategy Designed to Meet 0.070 ppm 

(Incremental to Baseline)...............................................................3-21 

3.8 Areas Where NOx and VOC Controls Were Applied to Mobile Onroad and 

Nonroad Sources in Addition to National Mobile Controls for the 0.070 ppm 

Control Strategy (incremental to Baseline)..........................................3-22 

3.9 Annual Tons of Nitrogen Oxide (NOx) Emission Reductions from Controls 

Designed to Meet 0.070 ppm Standard...............................................3-24 

3.10 Percentage of Total Annual NOx Emissions Reduced from Various Sources...3-25 

3.11 Annual Tons of Volatile Organic Compound (VOC) Emission Reductions from 

Controls Designed to Meet 0.070 ppm Standard.................................... 3-26 

3.12 Percentage of Total Annual VOC Emissions Reduced from Various Sources..3-27 

3.13 Projected Ozone Air Quality in 2020 After Application of Known Controls...3-28 

3.14 Annual NOx Emissions Remaining after PM NAAQS 15/35, Ozone Current 

Standard, and 0.070 ppm Control Strategies (35 States + DC Analysis Area)..3-30 

3.15 Annual VOC Emissions Remaining after PM NAAQS 15/35, Ozone Current 

Standard, and 0.070 ppm Control Strategies (35 States + DC Analysis Area)..3-31 

4.1 Nine Local Control Areas in Existing 2010 Sensitivity Runs........................4-2 

4.2 The NOx impact ratios at each county (sorted from lowest to highest) for the 

Approach B over the four analysis areas...............................................4-6 

5.1 Extrapolated Cost Example (MC Approach).........................................5-12 

5.2 Technological Innovation Reflected by Marginal Cost Shift......................5-22
Acronyms

AHRQ Agency for Healthcare Research and Quality 

AQS Air Quality System 

BenMAP Benefits Mapping and Analysis Program 

BWC Best workplaces for commuters 

CAAA Clean Air Act Amendments 

CAIR Clean Air Interstate Rule 

CAMR Clean Air Mercury Rule 

CAPMS Criteria Air Pollutant Modeling System 

CAVR Clean Air Visibility Rule 

CDC Centers for Disease Control 

CDC WONDER Centers for Disease Control Wide-Ranging Online Data for 

Epidemiological Research 

CFR Code of Federal Regulations 

CI Confidence interval 

CMAQ Community Multi-Scale Air Quality 

COI Cost of illness 

COPD Chronic obstructive pulmonary disease 

CPI-U Consumer price index – urban 

C-R concentration-response 

DOE Department of Energy 

DOI Department of the Interior 

DPF Diesel Particulate Filters 

EGU electric generating unit 

EIA Economic Impact Analysis 

EO Executive Order 

EPA Environmental Protection Agency 

ER Emergency room 

HIS National Health Interview Survey 

ICD International Classification of Disease 

IPM Integrated Planning Model 

km kilometer 

kWh kilowatt hour 

MACT Maximum Achievable Control Technology 

MRAD Minor restricted activity days 

MWRPO Mid-West Regional Planning Organization 

NAAQS National Ambient Air Quality Standards 

NAS National Academy of Sciences 

NCHS National Center for Health Statistics 

NEI National Emissions Inventory 
NHAMCS National Hospital Ambulatory Medical Care Survey 

NHANES National Health and Nutrition Examination Survey 

NHDS National Hospital Discharge Survey 

NMMAPS National Morbidity, Mortality and Air Pollution Study 

Non-EGU Non-Electricity Generating Unit 

NOx nitrogen oxides 

O&M operation and maintenance 

OAQPS Office of Air Quality Planning and Standards (EPA) 

OH Hydroxide 

OMB Office of Management and Budget 

ORD Office of Research and Development (EPA) 

OTAQ Office of Transportation and Air Quality (EPA) 

OTC Ozone Transport Commission 

PM particulate matter 

PM10 Particulate matter less than or equal to 10 microns 

PM2.5 Particulate matter less than or equal to 2.5 microns 

POC Parameter occurrence code 

ppb parts per billion 

ppm parts per million 

RIA Regulatory Impact Analysis 

RVP Reid vapor pressure 

SCR selective catalytic reduction 

SIP state implementation plan 

SMR standard mortality rate 

SNCR selective non-catalytic reduction 

SO2 Sulfur dioxide 

US United States 

USC US code 

USDA United States Department of Agriculture 

USEPA United States Environmental Protection Agency 

USFWS United States Fish and Wildlife Service 

USGS United States Geological Survey 

UV-B ultraviolet light, type B 

VNA Voronoi neighbor averaging 

VOC Volatile Organic Compounds 

VSL Value of statistical life 

WHO World Health Organization 

WTP Willingness to pay 
ES-1

Executive Summary 

Overview 

EPA has performed an illustrative analysis of the potential costs and human health benefits of 

nationally attaining alternative ozone standards. Per Executive Order 12866 and the guidelines of 

OMB Circular A-4, this Regulatory Impact Analysis (RIA) presents analyses of the range of 

standards proposed by the Administrator in the Notice of Proposed Rulemaking (0.070 – 0.075 

ppm), as well as one more stringent option (0.065 ppm). The less stringent option is the 

baseline, or the current primary standard for ozone (0.08 ppm, effectively 0.084 ppm due to 

current rounding conventions). The benefit and cost estimates below are calculated incremental 

to a 2020 baseline that incorporates air quality improvements achieved through the projected 

implementation of existing regulations and full attainment of the existing ozone and particulate 

matter (PM) National Ambient Air Quality Standards (NAAQS). The baseline includes the 

Clean Air Interstate Rule and mobile source programs, which will help many areas move toward 

attainment of the current standard. 

We present two sets of results. The first reflects full attainment of the alternative ozone 

standards in all locations of the U.S. except two areas of California in 2020. These two areas of 

California are not planning to meet the current standard by 2020, so the estimated costs and 

benefits for these areas are based on reaching an estimated attainment point in 2020 (their 

“glidepath” targets). The second set of results, for California only, estimate the costs and 

benefits from California fully attaining the alternative standards in a year beyond 2020 (glidepath 

estimates, plus the increment needed to reach full attainment beyond 2020, added together for a 

California total). Further explanation about these unique circumstances is provided in Chapter 4. 

In addition, EPA designed a two-stage approach to estimating costs and benefits because we 

recognized from the outset that known and reasonably anticipated emissions controls would 

likely be insufficient to bring many areas into attainment with either the current, or alternative, 

more stringent ozone standards. The individual chapters of the RIA present more detail 

regarding estimated costs and benefits based on both partial attainment (manageable with current 

technologies) and full attainment (manageable in some locations only with hypothetical 

technologies). The post-2020 estimates for California are entirely based on hypothetical 

technologies. 

In setting primary ambient air quality standards, EPA’s responsibility under the law is to 

establish standards that protect public health. The Clean Air Act (“Act”) requires EPA, for each 

criteria pollutant, to set a standard that protects public health with “an adequate margin of 

safety.” As interpreted by the Agency and the courts, the Act requires EPA to base this decision 

on health considerations only; economic factors cannot be considered. 

The prohibition against the consideration of cost in the setting of the primary air quality 

standards, however, does not mean that costs, benefits or other economic considerations are 

unimportant or should be ignored. The Agency believes that consideration of costs and benefits 

is an essential decision making tool for the efficient implementation of these standards. The 
ES-2

impacts of cost, benefits, and efficiency are considered by the States when they make decisions 

regarding what timelines, strategies, and policies make the most sense. 

This RIA is focused on development and analyses of illustrative control strategies to meet these 

alternative standards in 2020. This analysis does not prejudge the attainment dates that will 

ultimately be assigned to individual areas under the Clean Air Act, which contains a variety of 

potential dates and flexibility. For purposes of this analysis, though, we assume attainment by 

2020 for all areas except for two areas in California 

Because States are ultimately responsible for implementing strategies to meet revised standards, 

this RIA provides insights and analysis of a limited number of illustrative control strategies that 

states might adopt to meet any revised standard. These illustrative strategies are subject to a 

number of important assumptions, uncertainties and limitations, which we document in the 

relevant portions of the analysis. 

ES.1 Approach to the Analysis 

This RIA consists of multiple analyses including an assessment of the nature and sources of 

ambient ozone; estimates of current and future emissions of relevant precursors that contribute to 

the problem; air quality analyses of baseline and alternative strategies; development of 

illustrative control strategies to attain the standard alternatives in future years; estimates of the 

incremental costs and benefits of attaining the alternative standards, together with an 

examination of key uncertainties and limitations; and a series of conclusions and insights gained 

from the analysis. 

The air quality modeling results for the regulatory baseline (explained in Chapter 3) provide the 

starting point for developing illustrative control strategies to attain the alternative standards that 

are the focus of this RIA. The baseline shows that by 2020, while ozone air quality would be 

significantly better than today under current requirements, several eastern and western states 

would need to develop and adopt additional controls to attain the alternative standards. 

In selecting controls, we focused more on ozone cost-effectiveness (measured as $/ ppb) than on 

the NOx or VOC cost-effectiveness (measured as $/ton). Most of the overall reductions in NOx 

achieved our illustrative control strategy were from non-EGU point sources. The NOx based 

illustrative control strategies we analyzed are also expected to reduce ambient PM 2.5 levels in 

many locations. The total benefits estimates described here include the co-benefits of reductions 

in fine particulate levels (PM) associated with year-round application of NOx control strategies 

beyond those in the regulatory baseline. 

Estimated reductions in premature mortality from reductions in ambient ozone and PM dominate 

the benefits estimates. For this reason, our assessment provides a range of estimates for both PM 

and ozone premature mortality. Although we note that there are uncertainties that are not fully 

captured by this range of estimates, and that additional research is needed to more fully establish 

underlying mechanisms by which such effects occur, such ranges are illustrative of the extent of 

uncertainly associated with some different modeling assumptions. 
ES-3

 Fig ES.1 Projected Ozone Air Quality in 2020 After Application of Known Controls

1 Modeled emissions reflect the expected reductions from federal programs including the Clean Air 

Interstate Rule, the Clean Air Mercury Rule, the Clean Air Visibility Rule, the Clean Air Nonroad Diesel 

Rule, the Light-Duty Vehicle Tier 2 Rule, the Heavy Duty Diesel Rule, proposed rules for Locomotive 

and Marine Vessels and for Small Spark-Ignition Engines, and state and local level mobile and stationary 

source controls identified for additional reductions in emissions for the purpose of attaining the current 

PM 2.5 and Ozone standards. 

2 Controls applied are illustrative. States may choose to apply different control strategies for 

implementation. 

3 The current standard of 0.08 ppm is effectively expressed as 0.084 ppm when rounding conventions are

applied.

4 Modeled design values in ppm are only interpreted up to 3 decimal places. 

5 Map shows results from a total of 491 counties with projected design values. Consistent with current

modeling guidance, EPA did not project 2020 concentrations for counties where 2001 base year 

concentrations were less than recommended criterion. Such projections may not represent expected 

future levels.
ES-4

ES-2. Results of Benefit-Cost Analysis 

There are two sets of results presented below. The first set of national results assumes 

attainment of revised standards by 2020 in all areas, except for two areas in Southern California. 

It is expected that benefits and costs will begin occurring earlier, as states begin implementing 

control measures to show progress towards attainment. Some areas with high ozone levels, such 

as the two areas in Southern California, are not planning to attain even the current standard until 

after 2020. In these locations, our national 2020 estimate includes the cost and benefits of 

reaching an estimated progress point in 2020 (known as a “glidepath” target). The 2020 results 

will thus not represent a true “full attainment” scenario for the entire nation. In order to gain an 

understanding of the possible additional costs and benefits of fully attaining in California in a 

year beyond 2020, we provide an additional set of results for California only. Tables ES-1 to 

ES-3 present national benefits and costs in 2020, including the “glidepath” targets for California; 

companion Table ES-4 provides the national estimated reductions in premature mortality and 

morbidity in 2020, including the “glidepath” targets for California. 

Tables ES-5 to ES-7 present the costs and benefits of full attainment for California (“glidepath” 

in 2020 plus the additional increment achieved between 2020 and a future year added together 

into one California total); Table ES-8 is the companion table showing estimated reductions in 

premature mortality and morbidity for California. Because various mobile source rules, such as 

the onroad and nonroad diesel rule, among others, would be expected to be implemented 

between 2020 and a future year, the tons of emission reduction expected to occur as a result of 

those rules has been taken out of the calculated costs and benefits for the estimates of additional 

tons of emission reduction needed in California between 2020 and a future year. EPA did the 

analysis this way because to force full attainment in an earlier year than would be required under 

the Clean Air Act would likely lead to an overstatement of costs because those areas might 

benefit from these existing federal or state programs that would be implemented between 2020 

and the attainment year; because additional new technologies may become available between 

2020 and the attainment year; and because the cost of existing technologies might fall over time. 

As such, we use the best available data to estimate costs and benefits of full attainment for 

California in a future year, while recognizing that the estimates of costs and benefits for 

California in a year between 2020 and a future year are likely to be relatively more uncertain 

than the national attainment estimates for 2020. It is not appropriate to add together the 2020 

national attainment, California glidepath estimate and the estimate of California full attainment 

as an estimate of national full attainment in 2020. The extra increment of attainment that is 

estimated for California will not occur in 2020, so it is not accurate to add it to our nationwide 

estimate of the “glidepath” benefits and costs to arrive at a “full attainment” estimate for 20201

.

It is also not accurate to add the two estimates together to arrive at an estimate of future, post2020 full attainment benefits and costs, because our nationwide full attainment estimates do not 

1

 The California full attainment costs calculated using the offset in NOx emissions from mobile 

programs would understate the costs of fully attaining in 2020, however, California will not be 

required to attain in 2020. 
ES-5

allow other areas of the nation to take credit for the reductions in NOx from the mobile source 

rules that will occur after 2020.2

In these tables, the individual row estimates reflect the different studies available to describe the 

ozone premature mortality relationship. Ranges within the total benefits column reflect 

variability in the studies upon which the estimates associated with premature mortality were 

derived. PM co-benefits account for between 13 and 99 percent of co-benefits, depending on the 

standard analyzed and on the choice of ozone and PM mortality functions used. 

Ranges in the total costs column reflect different assumptions about the extrapolation of costs. 

The low end of the range of net benefits is constructed by subtracting the highest cost from the 

lowest benefit, while the high end of the range is constructed by subtracting the lowest cost from 

the highest benefit. The presentation of the net benefit estimates represents the widest possible 

range from this analysis. 

Table ES-1 National Annual Costs and Benefits: 0.079 ppm Standard in 2020 

(including California glidepath ) 

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function or 

Assumption Reference 

Total Benefits* Total Costs** Net Benefits 

NMMAPS Bell et al. 2004 $1.2 to $11 $3 to $3.3 -$2.1 to $8.5 

Bell et al. 2005 $1.6 to $12 $3 to $3.3 -$1.7 to $8.9 

Meta-analysis Ito et al. 2005 $1.7 to $12 $3 to $3.3 -$1.7 to $8.9 

Levy et al. 2005 $1.6 to $12 $3 to $3.3 -$1.7 to $8.9 

Assumption that association is not 

causal*** 

$1.1 to $11 $3 to $3.3 -$2.2 to $8.4 

2

 This approach would be an overestimate of national full attainment costs in a future year after 

2020 because it would not take into account that other states (not just California) could replace 

more expensive NOx reductions from other sources with the post-2020 reductions obtained from 

implementation of mobile source rules that are included in the regulatory baseline. 
ES-6

Table ES-2 National Annual Costs and Benefits: 0.075 ppm Standard in 2020 

(including California glidepath ) 

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function or 

Assumption Reference 

Total Benefits* Total Costs** Net Benefits 

NMMAPS Bell et al. 2004 $3 to $16 $5.5 to $8.8 -$5.8 to $10.5 

Bell et al. 2005 $7.3 to $20 $5.5 to $8.8 -$1.5 to $15 

Meta-analysis Ito et al. 2005 $7.8 to $21 $5.5 to $8.8 -$1. to $15

Levy et al. 2005 $8.7 to $22 $5.5 to $8.8 -$0.1 to $16 

Assumption that association is not 

causal*** 

$1.5 to $15 $5.5 to $8.8 -$7.3 to $9 

Table ES-3 National Annual Costs and Benefits: 0.070 ppm Standard in 2020 

(including California glidepath)

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function or 

Assumption Reference 

Total Benefits* Total Costs** Net Benefits 

NMMAPS Bell et al. 2004 $4.3 to $26 $10 to $22 -$17 to $16 

Bell et al. 2005 $9.7 to $31 $10 to $22 -$12 to $21 

Meta-analysis Ito et al. 2005 $10 to $32 $10 to $22 -$11 to $22 

Levy et al. 2005 $11 to $33 $10 to $22 -$10 to $23 

Assumption that association is not 

causal*** 

$2.5 to $24 $10 to $22 -$20 to $14 

Table ES-4 National Annual Costs and Benefits : 0.065 ppm Standard in 2020 

(including California glidepath)

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function or 

Assumption Reference 

Total Benefits* Total Costs** Net Benefits 

NMMAPS Bell et al. 2004 $7.7 to $45 $17 to $46 -$38 to $28 

Bell et al. 2005 $18 to $55 $17 to $46 -$28 to $38 

Meta-analysis Ito et al. 2005 $19 to $56 $17 to $46 -$27 to $39 

Levy et al. 2005 $20 to $57 $17 to $46 -$27 to $40 

Assumption that association is not 

causal*** 

$4.3 to $42 $17 to $46 -$42 to $25 

*Includes ozone benefits, and PM 2.5 co-benefits. Range was developed by adding the estimate 

from the ozone premature mortality function to both the lower and upper ends of the range of the 

PM2.5 premature mortality functions characterized in the expert elicitation 

**Range reflects lower and upper bound cost estimates

***Total includes ozone morbidity benefits only 
ES-7

Table ES-5: Summary of Total Number of Annual Ozone and PM2.5-Related Premature 

Mortalities and Premature Morbidity Avoided: 2020 National Benefits 

Combined Estimate of Mortality

Standard Alternative and 

Model or Assumption

Combined Range of Ozone Benefits and 

 PM2.5 Co-Benefits

 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 

NMMAPS Bell (2004) 200 to 1,900 430 to 2,600 670 to 4,300 1,200 to 7,400 

Bell (2005) 260 to 2,000 1,100 to 3,300 1,500 to 5,100 2,800 to 9,000 

Meta-Analysis Ito (2005) 270 to 2,000 1,200 to 3,300 1,600 to 5,200 3,000 to 9,200 

Levy (2005) 260 to 2,000 1,300 to 3,500 1,800 to 5,400 3,000 to 9,200 

No Causality 180 to 1,900 230 to 2,400 390 to 4,000 660 to 6,900

Combined Estimate of Morbidity

Acute Myocardial Infarction 1,100 1,400 2,300 4,000 

Hospital and ER Visits 1,300 5,600 7,600 13,000 

Chronic Bronchitis 370 470 780 1,300 

Acute Bronchitis 950 1,200 2,000 3,500 

Asthma Exacerbation 7,300 9,400 16,000 27,000 

Lower Respiratory Symptoms 8,100 10,000 17,000 29,000 

Upper Respiratory Symptoms 5,900 7,500 13,000 22,000

School Loss Days 50,000 610,000 780,000 1,300,000 

Work Loss Days 51,000 65,000 110,000 190,000 

Minor Restricted Activity Days 430,000 2,000,000 2,700,000 4,700,000 
ES-8

Table ES-6 California: Annual Costs and Benefits of Attaining 0.079 ppm Standard 

(beyond 2020)* 

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function 

or Assumption Reference 

Total Benefits** Total Costs*** Net Benefits 

NMMAPS Bell et al. 2004 $0.1 to $0.6 $0.3 to $1.7 -$1.6 to $0.2 

Bell et al. 2005 $0.2 to $0.7 $0.3 to $1.7 -$1.5 to $0.4 

Meta-analysis Ito et al. 2005 $0.3 to $0.7 $0.3 to $1.7 -$1.4 to $0.4 

Levy et al. 2005 $0.2 to $0.7 $0.3 to $1.7 -$1.5 to $0.4 

Assumption that association is not 

causal**** 

$0.05 to $0.5 $0.3 to $1.7 -$1.6 to $0.2 

Table ES-7 California: Annual Costs and Benefits of Attaining 0.070 ppm Standard 

(beyond 2020)* 

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function 

or Assumption Reference 

Total Benefits** Total Costs*** Net Benefits 

NMMAPS Bell et al. 2004 $0.7 to $3.5 $2 to $13 -$12 to $1.5 

Bell et al. 2005 $1.9 to $4.7 $2 to $13 -$11 to $2.7 

Meta-analysis Ito et al. 2005 $2.1 to $4.8 $2 to $13 -$11 to $2.9 

Levy et al. 2005 $2.1 to $4.8 $2 to $13 -$11 to $2.9 

Assumption that association is not 

causal**** 

$0.4 to $3.1 $2 to $13 -$13 to $1.2 

Table ES-8 California: Annual Costs and Benefits of Attaining 0.075 ppm Standard 

(beyond 2020)* 

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function 

or Assumption Reference 

Total Benefits** Total Costs*** Net Benefits 

NMMAPS Bell et al. 2004 $0.4 to $1.9 $1.1 to $6.2 -$5.8 to $0.8 

Bell et al. 2005 $1.1 to $2.6 $1.1 to $6.2 -$5.1 to $1.5 

Meta-analysis Ito et al. 2005 $1.2 to $2.7 $1.1 to $6.2 -$5.1 to $1.6 

Levy et al. 2005 $1.2 to $2.7 $1.1 to $6.2 -$5 to $1.6 

Assumption that association is not 

causal**** $0.2 to $1.7 $1.1 to $6.2 -$6 to $0.6 
ES-9

Table ES-9 California: Annual Costs and Benefits of Attaining 0.065 ppm Standard 

(beyond 2020)*

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function 

or Assumption Reference 

Total Benefits** Total Costs*** Net Benefits 

NMMAPS Bell et al. 2004 $1.1 to $5.2 $2.9 to $21 -$19 to $2.3 

Bell et al. 2005 $3.1 to $7.2 $2.9 to $21 -$17 to $4.3 

Meta-analysis Ito et al. 2005 $3.4 to $7.4 $2.9 to $21 -$17 to $4.5 

Levy et al. 2005 $3.3 to $7.4 $2.9 to $21 -$17 to $4.5 

Assumption that association is not 

causal**** $0.5 to $4.6 $2.9 to $21 -$20 to $1.7 

* Tables present the total of CA glidepath in 2020, plus the additional increment needed to reach 

full attainment in a year beyond 2020 

** Includes ozone benefits and PM 2.5 co-benefits. Range was developed by adding the 

estimate from the ozone premature mortality function to both the lower and upper ends of the 

range of the PM2.5 premature mortality functions characterized in the expert elicitation 

***Range reflects lower and upper bound cost estimates 

 Total includes ozone morbidity benefits only 
ES-10

Table ES-10: Summary of Total Number of Annual Ozone and PM2.5-Related Premature 

Mortalities and Premature Morbidity Avoided: California Post 2020 Attainment 

Combined Estimate of Mortality

Standard Alternative and 

Model or Assumption

Combined Range of Ozone Benefits and 

 PM2.5 Co-Benefits

 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 

NMMAPS Bell (2004) 17 to 93 61 to 310 110 to 570 180 to 840 

Bell (2005) 42 to 120 170 to 410 300 to 760 490 to 1,200 

Meta-Analysis Ito (2005) 45 to 120 180 to 430 320 to 780 530 to 1,200 

Levy (2005) 46 to 120 180 to 430 320 to 780 520 to 1,200 

No Causality 8.2 to 84 26 to 270 49 to 500 72 to 740 

Combined Estimate of Morbidity

Acute Myocardial Infarction 49 160 290 430 

Hospital and ER Visits 200 790 1,400 2,200 

Chronic Bronchitis 17 53 99 150 

Acute Bronchitis 43 140 260 380 

Asthma Exacerbation 330 1,100 2,000 2,900 

Lower Respiratory Symptoms 360 1,200 2,200 3,200 

Upper Respiratory Symptoms 270 850 1,600 2,300 

School Loss Days 30,000 120,000 210,000 340,000 

Work Loss Days 2,300 7,400 14,000 20,000 

Minor Restricted Activity Days 87,000 340,000 600,000 960,000 

** *Range was developed by adding the estimate from the ozone premature mortality function to both the lower and upper ends of the

range of the PM2.5 premature mortality functions characterized in the expert elicitation 
ES-11

ES-3. Caveats and Conclusions 

Of critical importance to understanding these estimates of future costs and benefits is that they 

not intended to be forecasts of the actual costs and benefits of implementing revised standards. 

There are many challenges in estimating the costs and benefits of attaining a tighter ozone 

standard, which are fully discussed in Chapter 8. Analytically, the characterization of ozone 

mortality benefits and the estimation of the costs and benefits of the nation fully attaining a 

tighter standard are being subject to further review by science advisory boards. 

There are significant uncertainties in both cost and benefit estimates. Below we summarize some 

of the more significant sources of uncertainty. 

x Benefits estimates are influenced by our ability to correctly model relationships between 

ozone and PM and their associated health effects (e.g., premature mortality). 

x Benefits estimates are also heavily dependent upon the choice of statistical estimates for 

values associated with each of the health benefits. 

x 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. 

x 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. 

x EPA employed a monitor rollback approach to estimate the benefits of attaining an 

alternative standard of 0.079 ppm nationwide. This approach likely understates the 

benefits that would occur due to implementation of actual controls because controls 

implemented to reduce ozone concentrations at the highest monitor would likely result in 

some reductions in ozone concentrations at attaining monitors down-wind (i.e. the 

controls would lead to concentrations below the standard in down-wind locations). The 

estimated benefits of attaining a standard of 0.075 ppm, however, are likely overstated. 

EPA will develop and present consistent approaches for the alternative standards for the 

final RIA. 

x There are several nonquantified benefits (e.g. effects of reduced ozone on forest health 

and agricultural crop production) and disbenefits (e.g. decreases in tropospheric ozone 

lead to reduced screening of UV-B rays and reduced nitrogen fertilization of forests and 

cropland) discussed in this analysis in chapter 6. 

x 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 

x As explained in chapter 5, there are several uncertainties in our cost estimates. For 

example, the states are likely to use different approaches for reducing NOx and VOCs in 

their state implementation plans to reach a tighter standard. In addition, since we are 

unable to use known controls to get all areas into attainment, we needed to use simple 

$/ton costs to estimate the overall national cost of meeting the tighter alternatives. 

x As discussed in chapter 5, recent advice from EPA’s Science Advisory Board has 

questioned the appropriateness of an approach similar to that used here for estimating 
ES-12

extrapolated costs. EPA will consider this advice and other guidance as it develops the 

methodology for analyzing the final rule. 

x 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. 

x Technological advances over time will tend to increase the economic feasibility of 

reducing emissions, and will tend to reduce the costs of reducing emissions. 

x These sources of uncertainty are discussed in more detail in subsequent chapters of the 

RIA. In addition to considering any advice which comes from advisory bodies prior to 

the publication of the final ozone NAAQS, EPA will undertake an updated approach with 

improvements to emissions inventories, models and control strategies for the RIA which 

will accompany that rulemaking. 
1-1

Chapter 1: Introduction and Background 

Synopsis

This document estimates the incremental costs and monetized human health and welfare 

benefits of attaining possible revised primary ozone National Ambient Air Quality 

Standards (NAAQS) nationwide. This document contains illustrative analyses that 

consider limited emission control scenarios that states, tribes and regional planning 

organizations might implement to achieve a revised ozone NAAQS. In some cases, EPA 

weighed the available empirical data to make judgments regarding the proposed 

attainment status of certain urban areas in the future. According to the Clean Air Act, 

EPA must use health-based criteria in setting the NAAQS and cannot consider estimates 

of compliance cost. This Regulatory Impact Analysis (RIA) is intended to provide the 

public a sense of the benefits and costs of meeting new alternative ozone NAAQS, and to 

meet the requirements of Executive Order 12866 and OMB Circular A-4 (described 

below in Section 1.2.2).

1.1 Background 

Two sections of the Clean Air Act (“Act”) govern the establishment and revision of 

NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify pollutants 

which “may reasonably be anticipated to endanger public health or welfare,” and to issue 

air quality criteria for them. These air quality criteria are intended to “accurately reflect 

the latest scientific knowledge useful in indicating the kind and extent of all identifiable 

effects on public health or welfare which may be expected from the presence of [a] 

pollutant in the ambient air.” Ozone is one of six pollutants for which EPA has 

developed air quality criteria.

Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate 

“primary” and “secondary” NAAQS for pollutants identified under section 108. Section 

109(b)(1) 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 
1-2

retain or revise the NAAQS. After promulgation or revision of the NAAQS, the 

standards are implemented by the States. 

1.2 Role of the Regulatory Impact Analysis in the NAAQS Setting Process 

1.2.1 Legislative Roles 

In setting primary ambient air quality standards, EPA’s responsibility under the law is to 

establish standards that protect public health. The Clean Air Act requires EPA, for each 

criteria pollutant, to set a standard that protects public health with “an adequate margin of 

safety.” As interpreted by the Agency and the courts, the Act requires EPA to create 

standards based on health considerations only. Economic factors cannot be considered. 

The prohibition against the consideration of cost in the setting of the primary air quality 

standard, however, does not mean that costs or other economic considerations are 

unimportant or should be ignored. The Agency believes that consideration of costs and 

benefits are essential to making efficient, cost effective decisions for implementation of 

these standards. The impact of cost and efficiency are considered by states during this 

process, as they decide what timelines, strategies, and policies make the most sense. This 

RIA is intended to inform the public about the potential costs and benefits that may result 

when a new ozone standard is implemented, but is not relevant to establishing the 

standards themselves. 

1.2.2 Role of Statutory and Executive Orders 

There are several statutory and executive orders that dictate the manner in which EPA 

considers rulemaking and public documents. This document is separate from the 

NAAQS decision making process, but there are several statutes and executive orders that 

still apply to any public documentation. A summary of the pertinent orders is included in 

Appendix 1. The analysis required by these statutes and executive orders is presented in 

Chapter 9. 

EPA presents this RIA pursuant to Executive Order 12866 and the guidelines of OMB 

Circular A-4. 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 costeffectiveness analysis for rules where health is the primary effect. Within this RIA we 

provide a cost benefit analysis. We also provide a cost-effectiveness analysis for that 

portion of the benefits which occur from concurrent reductions in particulate matter as a 

result of controls on NOx emissions to reduce ozone levels (see Appendix 6b). We are 

investigating options for conducting a cost-effectiveness analysis for the ozone portion of 

the benefits and expect to provide estimates based on that analysis in the final RIA. 

1

 U.S. Office of Management and Budget. Circular A-4, September 17, 2003. Found on 

the Internet at <http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf>.
1-3

1.2.3 Market Failure or Other Social Purpose 

OMB Circular A-4 indicates that one of the reasons a regulation such as the NAAQS may 

one may be issued is to address market failure. The major types of market failure include: 

externality, market power, and inadequate or asymmetric information. Correcting market 

failures is one reason for regulation, but it is not the only reason. Other possible 

justifications include improving the function of government, removing distributional 

unfairness, or promoting privacy and personal freedom.

An externality occurs when one party’s actions impose uncompensated benefits or costs 

on another party. Environmental problems are a classic case of externality. For example, 

the smoke from a factory may adversely affect the health of local residents while soiling 

the property in nearby neighborhoods. If bargaining was costless and all property rights 

were well defined, people would eliminate externalities through bargaining without the 

need for government regulation. From this perspective, externalities arise from high 

transaction costs and/or poorly defined property rights that prevent people from reaching 

efficient outcomes through market transactions. 

Firms exercise market power when they reduce output below what would be offered in a 

competitive industry in order to obtain higher prices. They may exercise market power 

collectively or unilaterally. Government action can be a source of market power, such as 

when regulatory actions exclude low-cost imports. Generally, regulations that increase 

market power for selected entities should be avoided. However, there are some 

circumstances in which government may choose to validate a monopoly. If a market can 

be served at lowest cost only when production is limited to a single producer of local gas 

and electricity distribution services, a natural monopoly is said to exist. In such cases, the 

government may choose to approve the monopoly and to regulate its prices and/or 

production decisions. Nevertheless, it should be noted that technological advances often 

affect economies of scale. This can, in turn, transform what was once considered a 

natural monopoly into a market where competition can flourish. 

Market failures may also result from inadequate or asymmetric information. Because 

information, like other goods, is costly to produce and disseminate, an evaluation will 

need to do more than demonstrate the possible existence of incomplete or asymmetric 

information. Even though the market may supply less than the full amount of 

information, the amount it does supply may be reasonably adequate and therefore not 

require government regulation. Sellers have an incentive to provide information through 

advertising that can increase sales by highlighting distinctive characteristics of their 

products. Buyers may also obtain reasonably adequate information about product 

characteristics through other channels, such as a seller offering a warranty or a third party 

providing information. 

There are justifications for regulations in addition to correcting market failures. A 

regulation may be appropriate when there are clearly identified measures that can make 

government operate more efficiently. In addition, Congress establishes some regulatory 

programs to redistribute resources to select groups. Such regulations should be examined 

to ensure that they are both effective and cost-effective. Congress also authorizes some 

regulations to prohibit discrimination that conflicts with generally accepted norms within 
1-4

our society. Rulemaking may also be appropriate to protect privacy, permit more 

personal freedom or promote other democratic aspirations. 

From an economics perspective, setting an air quality standard is a straightforward case 

of addressing an externality, in this case where firms are emitting pollutants, which cause 

health and environmental problems without compensation for those suffering the 

problems. Although this economics perspective is reflected in Clean Air Act legislative 

history, there is also legislative history in which members of Congress stated that the 

purpose of setting national air quality standards solely based on health considerations 

(without considering costs) is to protect a fundamental right of Americans to be protected 

from air pollution levels that adversely affect their health. Setting a standard with a 

reasonable margin of safety attempts to place the cost of control on those who emit the 

pollutants and lessens the impact on those who suffer the health and environmental 

problems from higher levels of pollution. 

1.2.4 Illustrative Nature of the Analysis

This ozone NAAQS RIA is an illustrative analysis that provides useful insights into a 

limited number of emissions control scenarios that states might implement to achieve a 

revised ozone NAAQS. Because states are ultimately responsible for implementing 

strategies to meet any revised standard, the control scenarios in this RIA are necessarily 

hypothetical in nature. They are not forecasts of expected future outcomes. Important 

uncertainties and limitations, are documented in the relevant portions of the analysis. 

The illustrative goals of this RIA are somewhat different from other EPA analyses of 

national rules, or the implementation plans states develop, and the distinctions are worth 

brief mention. This RIA does not assess the regulatory impact of an EPA-prescribed 

national or regional rule such as the Clean Air Interstate Rule, nor does it attempt to 

model the specific actions that any state would take to implement a revised ozone 

standard. This analysis attempts to estimate the costs and human and welfare benefits of 

cost-effective implementation strategies which might be undertaken to achieve national 

attainment of new standards. These hypothetical strategies represent a scenario where 

states use one set of cost-effective controls to attain a revised ozone NAAQS. Because 

states—not EPA—will implement any revised NAAQS, they will ultimately determine 

appropriate emissions control scenarios. State implementation plans would likely vary 

from EPA’s estimates due to differences in the data and assumptions that states use to 

develop these plans. 

The illustrative attainment scenarios presented in this RIA were constructed with the 

understanding that there are inherent uncertainties in projecting emissions and controls. 

Furthermore, certain emissions inventory, control, modeling and monitoring limitations 

and uncertainties inhibit EPA’s ability to model full attainment in all areas. An 

additional limitation is that this analysis is carried out for the year 2020, before some 

areas are required to reach the current ozone standard. Section 1.3.1 below explains why 

EPA selected the analysis year of 2020. Despite these limitations, EPA has used the best 

available data and methods to produce this RIA. 
1-5

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. 

Figure 1.1: the process used to create this RIA 

1.3.1 Baseline and Years of Analysis 

The analysis year for this regulatory impact analysis is 2020, which allows EPA to build 

the ozone RIA analysis on the previously completed PM NAAQS RIA analysis. Many 

areas will reach attainment of the current ozone standard or any alternative standard by 

2020. For purposes of this analysis, we assume attainment by 2020 for all areas except 

for two areas in California with unique circumstances described in chapter 4. Some 

areas for which we assume 2020 attainment may in fact need more time to meet one or 

more of the analyzed standards, while others will need less time. This analysis does not 

Estimate 2020 Emissions 

Model 2020 Baseline Air Quality 

Identify Areas Projected to Exceed 

Alternate Standard and Calculate 

Needed Reduction Targets 

Select Control Strategies 

Estimate Modeled 

Control Costs 

Estimate Modeled Human 

Health Effects and Dollar 

Benefits

Determine Post-Control Air 

Quality and Compare pre- and 

post-control strategy air quality 

(partial attainment in most areas) 

Extrapolate Tons to Reach Full 

Attainment and Compare pre-and 

post- extrapolation air quality 

Estimate Extrapolated 

Control Costs 

Estimate Extrapolated Human 

Health Effects and Dollar Benefits 
1-6

prejudge the attainment dates that will ultimately be assigned to individual areas under 

the Clean Air Act, which contains a variety of potential dates and flexibility to move to 

later dates (up to 20 years), provided that the date is as expeditious as practicable.

The methodology first estimates what baseline ozone levels might look like in 2020 with 

existing Clean Air Act programs, including application of controls to meet the current 

ozone standard and the newly revised PM NAAQS standard. and then models how ozone 

levels would be predicted to change following the application of additional controls to 

reach a tighter standard. This allows for an analysis of the incremental change between 

the current standard and an alternative standard. This timeline is also consistent with 

expected attainment in 2020 of the revised Particulate Matter (PM) NAAQS covered in 

the PM NAAQS RIA issued in September 2006. As explained in Chapter 2, since one of 

the principal precursors for ozone, NOx, is also a precursor for PM, it is important that 

we account for the impact on ozone concentrations of NOx controls used in the 

hypothetical control scenario used in the PM NAAQS RIA, so as to avoid double 

counting the benefits and costs of these controls. 

1.3.2 Control Scenarios Considered in this RIA

A hypothetical control strategy was developed for an alternative 8-hr ozone standard of 

0.070 ppm, in order to illustrate one national scenario for how such a tighter standard 

might be met. First, EPA modeled the predicted air quality changes that would result 

from the application of emissions control options that are known to be available to 

different types of sources in portions of the country that were predicted to be in nonattainment with 0.070 ppm in 2020. However, given the limitations of current 

technology and the amount of improvement in air quality needed to reach a standard of 

0.070 ppm in some areas, it was also expected that modeling these known controls would 

not reduce ozone concentrations sufficiently to allow all areas to reach the more stringent 

standard. This required a second step to calculate the number of tons of emission 

reductions that would be needed to reach full attainment. This required calculating a 

conversion factor to quantify the estimated tons of emissions that needed to be reduced to 

generate a particular change in air quality concentrations of ozone (in ppm). This factor, 

coupled with the estimated remaining increment (in ppm) of ozone necessary to reach the 

alternative standard in each area, allowed for an extrapolation of how many tons of 

additional emissions reductions were estimated to be needed to reach the alternate 

standard.

1.3.3 Evaluating Costs and Benefits

Applying a two step methodology for estimating emission reductions needed to reach full 

attainment enabled EPA to evaluate nationwide costs and benefits of attaining a tighter 

ozone standard, albeit with substantial additional uncertainty regarding the second step 

estimates. Costs and benefits are presented in this RIA in the same two steps that 

emissions reductions were estimated. First, the costs associated with applying known 

controls were quantified, and presented along with an estimate of their economic impact. 

Second, EPA estimated costs of the additional tons of extrapolated emission reductions 

estimated which were needed to reach full attainment. The analysis of the benefits of 

setting an alternative standard included both mortality and morbidity calculations 
1-7

matching the costs of applying known controls and then the benefits of reaching full 

attainment. The costs and monetized benefits were then compared to provide an estimate 

of net benefits nationwide. 

The RIA presents two sets of results for estimated costs and benefits. The first reflects 

full attainment in 2020 in all locations of the U.S. except two areas of California. These 

two areas are not planning to meet the current standard by 2020, so the estimated costs 

and benefits for these areas are based on reaching an estimated progress point in 2020 

(their “glidepath” targets). The second set of results for California only, estimate the 

costs and benefits from California fully attaining the alternative standards in a year 

beyond 2020 (glidepath estimates for 2020, plus further increments needed to reach full 

attainment beyond 2020, added together for California total).

To streamline this RIA, it refers to several previously published documents, including 

two technical documents EPA produced to prepare for the ozone NAAQS proposal. The 

first was a Criteria Document created by EPA’s Office of Research and Development 

(published in 2006), which presented the latest available pertinent information on 

atmospheric science, air quality, exposure, dosimetry, health effects, and environmental 

effects of ozone. The second was a “Staff Paper” (published in 2007) that evaluated the 

policy implications of the key studies and scientific information contained in the Criteria 

Document, as well as presented a risk assessment for various standard levels. The Staff 

Paper also includes staff conclusions and recommendations to the Administrator 

regarding potential revisions to the standards. In addition to the Criteria Document and 

Staff Paper, this ozone RIA relies heavily on the 2006 RIA for particulate matter (PM). 

Many of the models and methodology used here are the same as in the PM NAAQS RIA. 

This RIA identifies methodologies used to generate data, but refers readers to the PM 

NAAQS RIA for many technical details. The focus of this RIA is to explain in detail 

how the approach or methodologies have changed from the PM NAAQS RIA analysis, 

and to present the results of the methodologies employed in this analysis, which 

compares attainment of tighter levels of the ozone standard to the baseline of the current 

standard.

1.4 Ozone Standard Alternatives Considered 

Per Executive Order 12866 and the guidelines of OMB Circular A-4, this RIA presents 

analyses of the range of standards proposed by the Administrator in the Notice of 

Proposed Rulemaking (0.070 – 0.075 ppm), as well as one more stringent option (0.065 

ppm), and one less stringent option (0.079 ppm). EPA will also model a baseline as the 

current primary standard for ozone (0.08 parts per million (ppm), calculated as the 3-year 

average of the annual fourth-highest daily maximum 8-hour average ozone concentration 

measured at each monitor within an area -- effectively 0.084 ppm using current data 

rounding conventions).

The EPA Administrator received recommendations to revise the current primary ozone 

standard from both the Clean Air Act Scientific Advisory Council (CASAC) and EPA 
1-8

staff. Both CASAC and staff expressed the view that the current standard was not 

adequately protective of human health and should be tightened to provide additional 

public health protection. Specifically, CASAC recommended that “...the current 

primary ozone NAAQS [should] be revised and that the level that should be considered 

for the revised standard be from 0.060 to 0.070 ppm.” 2 In the Staff Paper, EPA staff 

suggested a slightly broader range, recommending “that consideration be given to a 

standard level within the range of somewhat below 0.080 ppm to 0.060 ppm.”

 3 Both of 

these recommendations encourage maintaining the same averaging time and form as the 

current standard. Additionally, both recommendations suggested it was appropriate to 

specify the level of the standard out to three significant digits.

In the concurrent ozone NAAQS proposal, EPA proposes to revise the 8-hour standard to 

a level within the range of 0.070 to 0.075 ppm, and to request comment on a wider range 

of an 8-hour standard from 0.060 ppm to 0.084 ppm, to provide increased protection for 

children and other sensitive populations against an array of ozone-related adverse health 

effects that range from decreased lung function and increased respiratory symptoms to 

serious indicators of respiratory morbidity including emergency department visits and 

hospital admissions for respiratory causes, and possibly cardiovascular-related morbidity 

as well as total nonaccidental and cardiopulmonary mortality. The EPA also proposes to 

specify the level of the primary standard to the nearest thousandth ppm. 

This RIA presents benefit and cost estimates for both ends of the proposal range of 0.070 

to 0.075 ppm. It also assesses the costs and benefits of attaining one more stringent 

standard option (0.065 ppm), and will include a less stringent option of 0.079 ppm.. 

Since EPA is not considering loosening the standard, the less stringent option to the 

proposed range is the current standard itself. Since this RIA presents an analysis of the 

costs and benefits incremental to the current standard, retaining the current standard is 

assumed to have no additional incremental costs or benefits. 

For the secondary standard, EPA proposes to revise the current 8-hour standard with one 

of two options to provide increased protection against ozone-related adverse impacts on 

vegetation and forested ecosystems. One option is to replace the current standard with a 

cumulative, seasonal standard expressed as an index (called the W126) of the annual sum 

of weighted hourly concentrations, cumulated over 12 hours per day (8:00 am to 8:00 

pm) during the consecutive 3-month period within the ozone season with the maximum 

index value, set at a level within the range of 7 to 21 ppm-hours. The other option is to 

make the secondary standard identical to the proposed primary 8-hour standard in all 

respects. This RIA provides a limited discussion of an alternative secondary standard 

showing the number of additional counties with monitors that may violate the standards, 

2

 (Henderson, 2006c, p. 5). Henderson, R. (2006c) Letter from CASAC Chairman 

Rogene Henderson to EPA Administrator Stephen Johnson, October 24, 2006, EPACASAC-07-001.

3

 U.S EPA. 2007. Review of the National Ambient Air Quality Standards for Ozone: 

Policy Assessment of Scientific and Technical Information. OAQPS Staff Paper. North 

Carolina. EPA-452/R-07-003 
1-9

depending on the level of the secondary standard. Costs and benefits of attaining the 

different levels of the secondary were not estimated in this RIA; this analysis focused on 

the health benefits of the primary standard. An analysis of a separate secondary will be 

included in the final RIA as appropriate. Hereafter, any reference to the ozone standard 

will be assumed to be the primary standard, unless otherwise noted. 

1.5 References: 

Henderson, R. 2006. October 24, 2006. Letter from CASAC Chairman Rogene 

Henderson to EPA Administrator Stephen Johnson, , EPA-CASAC-07-001. 

U.S. EPA. 1970. Clean Air Act. 40CFR50. 

U.S. EPA. 2006 .Air Quality Criteria for Ozone and Related Photochemical Oxidants 

(Final). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aFcF,

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 
1-10

Pertinent Statutory and Executive Orders 

1. Executive Order 12866: Regulatory Planning and Review 

http://www.archives.gov/federal-register/executive-orders/pdf/12866.pdf

2. Paperwork Reduction Act, 44 U.S.C. 3501 et seq

http://www.archives.gov/federal-register/laws/paperwork-reduction/

3. Regulatory Flexibility Act, 5 U.S.C. 601 et seq.

http://www.archives.gov/federal-register/laws/regulatory-flexibility/

4. Unfunded Mandates Reform Act, Public Law 104-4 

http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=104_cong_public_laws&docid=f:publ4.104.pdf

5. Executive Order 13132: Federalism, Executive Order 13132

http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=1999_register&docid=fr10au99-133.pdf

6. Executive Order 13175: Consultation and Coordination with Indian Tribal 

Governments 

http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=2000_register&docid=fr09no00-167.pdf

7. Executive Order 13045: Protection of Children from Environmental Health & 

Safety Risks 

http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=1997_register&docid=fr23ap97-130.pdf

8. Executive Order 13211: Actions that Significantly Affect Energy Supply, 

Distribution or Use

http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=2001_register&docid=fr22my01-133.pdf

9. National Technology Transfer Advancement Act, Public Law No. 104-113, §12(d)

http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=104_cong_public_laws&docid=f:publ113.104.pdf

10. Executive Order 12898: Federal Actions to Address Environmental Justice in 

Minority Populations and Low-Income Populations 

http://www.archives.gov/federal-register/executive-orders/pdf/12898.pdf
2-1

Chapter 2: Characterizing Ozone and Modeling Tools Used in This Analysis 

Synopsis

This chapter describes the chemical and physical properties of ozone, general ozone air 

quality patterns, key health and environmental impacts associated with exposure to 

ozone, and key sources of ozone precursor emissions. In order to evaluate the health and 

environmental impacts of trying to reach a tighter ozone standard in the year 2020, it was 

necessary to use models to predict concentrations in the future. The tools and 

methodology used for the air quality modeling are described in this chapter. Subsequent 

chapters of this RIA rely heavily on the results of this modeling. 

2.1 Ozone Chemistry 

Ozone occurs both naturally in the stratosphere to provide a protective layer high above 

the earth, and at ground-level (troposphere) as the prime ingredient of smog.

Tropospheric ozone, which is regulated by the NAAQS, is formed by both naturally 

occurring and anthropogenic sources. Ozone is not emitted directly into the air, but is 

created when its two primary components, volatile organic compounds (VOC) and oxides 

of nitrogen (NOx), combine in the presence of sunlight. VOC and NOx are often referred 

to as ozone precursors, which are, for the most part, emitted directly into the atmosphere. 

Ambient ozone concentrations are directly affected by temperature, solar radiation, wind 

speed and other meteorological factors. Ultraviolet radiation from the sun plays a key 

role in initiating the processes leading to ozone formation. However, there is little 

empirical evidence directly linking day-to-day variations in observed surface ultraviolet 

radiation levels with variations in tropospheric ozone levels. 

The rate of ozone production can be limited by either VOCs or NOx. In general, ozone 

formation using these two precursors is reliant upon the relative sources of hydroxide 

(OH) and NOx. When the rate of OH production is greater than the rate of production of 

NOx, indicating that NOx is in short supply, the rate of ozone production is NOx-limited. 

In this situation, ozone concentrations are most effectively reduced by lowering current 

and future NOx emissions, rather than lowering emissions of VOCs. When the rate of OH 

production is less than the rate of production of NOx, ozone production is VOC-limited. 

Here, ozone is most effectively reduced by lowering VOCs. Between the NOx- and 

VOC- limited extremes there is a transitional region where ozone is nearly equally 

sensitive to each species. However ozone is relatively insensitive to marginal changes in 

both NOx and VOC in this situation. In urban areas with a high population 

concentration, ozone is often VOC-limited. Ozone is generally NOx-limited in rural areas 

and downwind suburban areas. 

Due to the complex photochemistry of ozone production, NOx emissions lead to both the 

formation and destruction of ozone, depending on the local quantities of NOx, VOC, and 

ozone catalysts such as the OH and HO2 radicals. In areas dominated by fresh emissions 
2-2

of NOx, ozone catalysts are removed via the production of nitric acid, which slows the 

ozone formation rate. Because NOx is generally depleted more rapidly than VOC, this 

effect is usually short-lived and the emitted NOx can lead to ozone formation later and 

further downwind. The terms “NOx disbenefits” or “ozone disbenefits” refer to the 

ozone increases that can result from NOx emission reductions in these localized areas.1

2.1.1 Temporal Scale 

Ground-level ozone forms readily in the atmosphere, usually during hot weather. The 

effects of sunlight on ozone formation depend on its intensity and its spectral distribution. 

Ozone levels tend to be highest during the daytime, during the summer or warm season. 

Changing weather patterns contribute to day to day and interannual differences in ozone 

concentrations. Differences in climatic regime, amount and mixture of emissions, and 

the extent of transport contribute to variations in ozone from city to city. 

2.1.2 Geographic Scale and Transport 

In many urban areas, ozone nonattainment is not caused by emissions from the local area 

alone. Due to atmospheric transport, contributions of precursors from the surrounding 

region can also be important. Thus, in designing control strategies to reduce ozone 

concentrations in a local area, it is often necessary to account for regional transport 

within the U.S. 

In some areas, such as California, global transport of ozone from beyond North America 

can contribute to nonattainment areas. In a very limited number of areas, including 

areas such as Buffalo, Detroit and El Paso, which are located near borders, emissions 

from Canada or Mexico may contribute to nonattainment. In these areas, our illustrative 

implementation strategies may have included more controls on domestic sources than 

would be required if cross-border transport did not occur. However, we have not 

conducted formal analysis, and as such cannot determine the contribution of non-U.S. 

sources to ozone design values. The transport of ozone is determined by meteorological 

and chemical processes which typically extend over spatial scales of several hundred 

kilometers. Additionally, convection is capable of transporting ozone and its precursors 

vertically through the troposphere, with resulting mixing of stratospheric ozone for 

periods of a month or more with tropospheric ozone.

The Technical Support Document (TSD) for the Clean Air Interstate Rule (CAIR) 

suggests that ozone transport constitutes a sizable portion of projected nonattainment in 

most eastern areas based on a 2010 analysis. A listing of Eastern states and the extent of 

transported ozone they receive in the CAIR analysis is located in the CAIR TSD.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 
2-3

2.1.3 Effects of Ozone 

Exposure to ground-level ozone is associated with a wide array of human health effects. 

Short-term exposure to ozone can cause acute respiratory problems; aggravate asthma; 

cause significant temporary decreases in lung capacity; cause inflammation of lung 

tissue; lead to hospital admissions and emergency room visits; and impair the body's 

immune system defenses, making people more susceptible to respiratory illnesses, 

including bronchitis and pneumonia. In addition, recent studies also provide evidence of 

additional health impacts associated with exposure to ozone, including premature 

mortality and possibly cardiac-related effects. (For a complete discussion of these 

effects, see Chapter 3 of the Staff Paper.) 

Ground-level ozone is also associated with numerous environmental impacts. For 

example, ozone interferes with the ability of plants to produce and store food, so that 

growth, reproduction and overall plant health are compromised. By weakening sensitive 

vegetation, ozone makes plants more susceptible to disease, pests, and environmental 

stresses. Ground-level ozone has been shown to reduce agricultural yields for many 

economically important crops (e.g., soybeans, kidney beans, wheat, cotton). The effects 

of ground-level ozone on long-lived species such as trees are believed to add up over 

many years so that whole forests or ecosystems can be affected. For example, ozone can 

adversely impact ecological functions such as water movement, mineral nutrient cycling, 

and habitats for various animal and plant species. Furthermore, one of the key 

components of ozone, nitrogen oxides, contributes to fish kills and algae blooms in 

sensitive waterways, such as the Chesapeake Bay. (For a complete discussion of these 

effects, see Chapter 7 of the Staff Paper.) 

2.2 Sources of Ozone 

The anthropogenic precursors of ozone originate from a wide variety of stationary and 

mobile sources. In urban areas, both biogenic (natural) and anthropogenic VOCs are 

important for ozone formation. Hundreds of VOCs are emitted by evaporation and 

combustion processes from a large number of anthropogenic sources. Current data show 

that solvent use and highway vehicles are the two main sources of VOCs, with roughly 

equal contributions to total emissions. Emissions of VOCs from highway vehicles 

account for roughly two-thirds of the transportation-related 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.

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 
2-4

EGU NOx is approximately 40% less than onroad mobile NOx in 2001. Both decrease 

between 2001 and 2020, with onroad mobile NOx decreasing more, so that their 

emissions are similar in 2020. It is not possible to make an overall statement about their 

relative impacts on ozone in all local areas because EGUs are more sparse than mobile 

sources, particularly in the west and south (See chapter 3 for a discussion of emission 

reductions projected in 2020 for the 8-hr ozone current standard baseline and the more 

stringent alternative control scenario). Natural NOx sources include stratospheric 

intrusions, lightning, soils, and wildfires. Lightning, fertilized soils, and wildfires are the 

major natural sources of NOx in the United States. Uncertainties in natural NOx 

inventories are much larger than for anthropogenic NOx emissions. 

A complete list of emissions source categories, for both NOx and VOCs, is compiled in 

the final ozone Staff Paper (EPA, 2007, pp. 2-3 to 2-6). 

2.3 Modeling Ozone Levels in the Future 

In order to evaluate the predicted air quality in 2020, it is necessary to use modeling to 

derive estimated air quality concentrations. The modeling analysis uses an emissions 

inventory and historical meteorological conditions to simulate pollutant concentrations. 

The predictions from the modeling are used to (a) project future ozone design values (a 

representation of the resultant air quality concentration in 2020 equal to the 4th highest 

maximum 8-hr concentration) and (b) create spatial fields of ozone and PM2.5 for

characterizing human health impacts from reducing ozone precursors, which in the case 

of NOx will also affect the formation of PM2.5. The air quality model used in this RIA is 

the Community Multi-Scale Air Quality (CMAQ) model4

. The modeling to capture the 

PM2.5 was performed for a one year time period. Modeling to calculate ozone-related 

benefits and the projection of ozone design values was performed for the period June 

through August. All controls in the illustrative 0.070 scenario were applied similarly to 

all months. There were no controls applied specifically for PM2.5 co-benefits because the 

controls developed to reduce summer ozone were applied to all months (see Chapter 3). 

2.3.1 Emissions Inventory 

The 2020 inventory from the Final PM NAAQS emissions platform was used as the 

starting point for the baseline and all subsequent analyses5

. This included emissions from 

Canada as of 20006, and Mexico as of 1999.7 As first discussed in the PM NAAQS RIA, 

an examination of the historical data suggests our previous methods have over-predicted 

future-year emissions for stationary non-EGU point and non-point sources, especially in 

the longer-forecast periods required for the NAAQS and other programs. To address this 

issue, we developed an ‘interim’ emission projection approach that assumes no growth to 

4 See CMAQ references listed at end of this chapter 

5 Final PM NAAQS Inventory is in the public docket EPA-HQ-OAR-2006-0834-0048.3

6

 http://www.epa.gov/ttn/chief/net/canada.html#data 

7

 http://www.epa.gov/ttn/chief/net/mexico.html 
2-5

emissions for many stationary non-EGU sources in estimating future-year emissions. In 

the future, we intend to pursue improved methods and models that provide more 

consistency with the historical record and reasonable assumptions regarding future 

conditions. More information is provided in Appendix D of the PM NAAQS RIA on the 

interim approach and a sensitivity analysis of the implications of this method relative to 

our previous forecasting methods. An updated 2020 inventory based on the EPA's 2002 

modeling platform is currently being developed and will be used for the final ozone 

NAAQS RIA In addition, all national and local controls used in the illustrative control 

scenario for the revised PM NAAQS RIA were included in this 2020 baseline. These 

controls were included to prevent double counting of costs and benefits, especially in the 

case of NOx controls which are precursors to both PM2.5 and ozone.

2.3.2 CMAQ Model 

A national scale air quality modeling analysis was performed to estimate future year 

attainment/nonattainment of the current and alternative ozone standards. In addition, the 

model-based projections of ozone and PM2.5 were used as inputs to the calculation of 

expected incremental benefits from the alternative ozone standards considered in this 

assessment. The 2001-based CMAQ modeling platform (version 4.5) was used as the 

basis for air quality modeling of future baseline emissions and control scenarios designed 

to bring areas into attainment with specific standards. This modeling platform was used 

in the 2006 PM NAAQS RIA. In addition to the CMAQ model, the modeling platform 

includes the emissions, meteorology, and initial and boundary condition data which are 

inputs to this model. The model produces spatial fields of gridded air quality 

concentrations on an hourly basis for the entire modeling domain. The concentrations 

that are produced can be averaged to produce a number of air quality metrics, including 

the 8-hr ozone design values, and can be used as inputs for the analysis of costs and 

benefits.

The key inputs to the CMAQ model include emissions from anthropogenic and biogenic

sources, meteorological data, and initial and boundary conditions. The CMAQ 

meteorological input files were derived from simulations of the Pennsylvania State 

University / National Center for Atmospheric Research Mesoscale Model (Grell, Dudhia, 

and Stauffer, 1994). This model, commonly referred to as MM5, is a limited-area, 

nonhydrostatic, terrain-following system that solves for the full set of physical and 

thermodynamic equations which govern atmospheric motions. The lateral boundary and 

initial species concentrations were obtained from a three-dimensional global atmospheric 

chemistry model, the GEOS-CHEM model (Yantosca, 2004). The global GEOS-CHEM 

model simulates atmospheric chemical and physical processes driven by assimilated 

meteorological observations from the NASA’s Goddard Earth Observing System 

(GEOS).

EPA performed an extensive evaluation of the 2001-based CMAQ modeling platform as 

part of the analyses for CAIR and the PM NAAQS RIA. These evaluations have been 

updated as part of the Locomotive/Marine Proposed Rule to focus on model performance 

for ozone from the 12 km CMAQ base year simulations in the East. Details of the model 

performance methodology are described in the Locomotive/Marine Rule Air Quality 
2-6

36km Domain Boundary

12km East Domain Boundary

12km West Domain Boundary

Modeling Technical Support Document. For the months of June, July, and August 2001, 

which were used as the basis for the Ozone NAAQS modeling, the 8-hour daily 

maximum modeled concentrations underestimate the corresponding observed values by 

10 to 15 percent in the East and West. As in the evaluation for previous model 

applications, the “acceptability” of model performance for the ozone RIA modeling was 

judged by comparing the results to those found in recent regional ozone model

applications for other EPA and non-EPA studies. Overall, the performance for the 

CMAQ application is generally within the range of these other applications. 

Figure 2-1 shows the modeling domains that were used as a part of this analysis. The 

geographic specifications for these domains are provided in Table 2-1. All three 

modeling domains contain 14 vertical layers with a top at about 16,200 meters, or 100 

mb. Two domains with 12 km horizontal resolution were used for episodic ozone 

modeling. These domains are labeled as the Eastern and Western 12 km domains in 

Figure 2-1. Also shown in this figure is the 36 km domain which was used for modeling 

PM2.5 concentrations. For this analysis, predictions from the Eastern domain were used 

to provide data for all areas east of 100 degrees longitude. Model predictions from the 

Western domain we used for all areas west of this longitude.

Figure 2.1. Map of the CMAQ Modeling Domains Used for Ozone NAAQS RIA 

The selection of 12 km grid resolution for ozone modeling and 36 km resolution for 

PM2.5 modeling is consistent with recommendations on grid resolution for regional 

analyses in EPA’s air quality modeling guidance for ozone and PM. Specifically, the 

guidance recommends modeling at 12 km or finer resolution, but not greater than 36 km 

resolution for regional scale modeling. The recommendations in the guidance are based 
2-7

largely on analyses of model performance and model response at various grid resolutions 

which indicate that results are generally similar at these grid resolutions for secondarily 

formed pollutants like ozone, nitrate, and sulfate which are components of PM2.5.

Another factor weighed in the selection of grid resolution is the computation requirement 

for modeling at 12 km versus 36 km. Specifically, national modeling at 12 km resolution 

requires roughly 10 times more computer time compared to modeling at 36 km. We were 

able to minimize the computer burden of modeling at 12 km for ozone because we 

limited the duration of the ozone model simulations to just the summer months when 

ozone concentrations are typically at their peak. For PM2.5, however, it is useful to model 

a full year in order to determine annual average PM2.5 concentrations in a manner 

consistent with the annual PM NAAQS. In view of the computer requirements for 

modeling at 12 km, it would not have been possible to model a full year at 12 km 

resolution for the Eastern and Western domains as part of the analysis for this proposal 

RIA.

Table 2.1. Geographic Specifications of Modeling Domains.

36 km Domain 

(148 x 112 Grid Cells) 

12 km Eastern Domain 

(279 x 240 Grid Cells) 

12 km Western Domain 

(213 x 192 Grid Cells) 

 Lon lat lon lat lon lat 

SW -121.77 18.17 SW -106.79 24.99 SW -121.65 28.29 

NE -58.54 52.41 NE -65.32 47.63 NE -94.94 51.91 

2.4 References 

Amar, P., R. Bornstein, H. Feldman, H. Jeffries, D. Steyn, R. Yamartino, and Y. Zhang. 

2004. Final Report Summary: December 2003 Peer Review of the CMAQ Model. pp. 7. 

Byun, D.W., and K.L. Schere. 2006. “Review of the Governing Equations, 

Computational Algorithms, and Other Components of the Models-3 Community 

Multiscale Air Quality (CMAQ) Modeling System.” J. Applied Mechanics Reviews

59(2):51-77.

Dennis, R.L., D.W. Byun, J.H. Novak, K.J. Galluppi, 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. 

U.S. Environmental Protection Agency (EPA). 1999. “Science Algorithms of EPA 

Models-3 Community Multiscale Air Quality.” (CMAQ Modeling System D.W. Byun 

and J.K.S. Ching, Eds. EPA/600/R-99/030, Office of Research and Development). 

U.S. Environmental Protection Agency (EPA). March 2005. CMAQ Model Evaluation 

Report, Office of Air Quality Planning and Standard, Research Triangle Park, NC. 

(Docket No. OAR-2005-0053-2149).
2-8

U.S. Environmental Protection Agency (EPA). 2006 .Air Quality Criteria for Ozone and 

Related Photochemical Oxidants (Final). U.S. Environmental Protection Agency, 

Washington, DC, EPA/600/R-05/004aF-cF,

U.S Environmental Protection Agency (EPA). 2007. Review of the National Ambient Air 

Quality Standards for Ozone: Policy Assessment of Scientific and Technical Information. 

OAQPS Staff Paper. North Carolina. EPA-452/R-07-003 

U.S. Environmental Protection Agency (EPA). 2007. “Guidance on the Use of Models 

and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, 

PM2.5, and Regional Haze.” EPA-454/B-07-002,

http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf

U.S. Environmental Protection agency (EPA). “Technical Support Document for the 

Proposed Locomotive/Marine Rule: Ozone Modeling.” EPA-454/R-07-004. 

Yantosca, B. 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry 

Modeling Group, Harvard University, Cambridge, MA, October 15, 2004. 
3-1

Chapter 3: Modeled Control Strategy: Design and Analytical Results 

Synopsis

In order to estimate the costs and benefits of alternate ozone standards, EPA has analyzed 

one possible hypothetical scenario to illustrate the control strategies that areas across the 

country might employ to attain an alternative more stringent primary standard of 0.070 

ppm. Specifically, EPA has modeled the impact that additional emissions controls across 

numerous sectors would have on predicted ambient ozone concentrations, incremental to 

meeting the current standard (baseline). Thus, the modeled analysis for a revised 

standard focuses specifically on incremental improvements beyond the current standard, 

and uses control options that might be available to states for application by 2020. The 

hypothetical scenario presented in this RIA is one illustrative option for achieving 

emissions reductions to move towards a national attainment of a tighter standard. It is not 

a recommendation for how a tighter ozone standard should be implemented, and states 

will make all final decisions regarding implementation strategies once a final NAAQS 

has been set.

In order to model a hypothetical control strategy to achieve national attainment of 0.070 

ppm incremental to attainment of the current standard, EPA approached the analysis in 

stages. First, EPA identified controls to be included in the baseline (current state and 

federal programs plus controls to attain the current ozone and PM standards). Then, EPA 

applied additional known controls within geographic areas designed to bring areas 

predicted to exceed 0.070 ppm in 2020 into attainment. This chapter presents the 

hypothetical control strategy, the geographic areas where controls were applied, and the 

results of the modeling which predicted ozone concentrations in 2020 after application of 

the strategy. The strategy to attain a 0.070 ppm level was the only strategy modeled by 

EPA. EPA did not expect the modeled control strategy to result in attainment at 0.070 

ppm everywhere, so the control will result in only partial attainment. Chapter 4 will 

explain how EPA used the results of the modeled control strategy for 0.070 ppm to 

estimate total tons of emissions reductions needed to achieve ozone concentrations for the 

bounds of the range of the proposed more stringent standard (0.075 and 0.070 ppm, and 

the more stringent option analyzed of 0.065 ppm). Chapters 5 and 6 present the estimated 

costs and benefits of the modeled costs and benefits for partial attainment. 

Because EPA’s baseline indicated that some areas were not likely to be in attainment 

with the current standard by 2020 (0.08 ppm, effectively 0.084 ppm based on current 

rounding conventions) – (Fig 3.4) EPA expected that known controls would not be 

enough to bring those areas, and likely others, into attainment with 0.070 ppm in 2020. 

Modeling results showed that to be the case (see Fig 3.13).

Because it was impossible to meet either the current or any tighter ozone standard 

nationwide using only known controls, EPA conducted a second step in the analysis, and 

estimated the number of further tons of emission reductions needed to attain 0.070 ppm 

(presented in Chapter 4). It is uncertain what controls States would put in place to attain 
3-2

a tighter standard, since additional control measures are not currently recognized as being 

commercially available. However, existing emissions inventories for the areas that were 

predicted to be in non-attainment after application of all known controls, do indicate that 

substantial amounts of ozone precursor emissions (i.e. tons of NOx or VOC) are available 

for control, pending future technology. Chapter 4 describes the methodology EPA used 

to estimate the amount of tons available for control to reach attainment, and Chapters 5 

and 6 present the extrapolation-based costs and benefits of achieving the reductions in 

ozone necessary to fully attain the standards, except for a few areas in California, which 

will be more fully explained in Chapter 4. 

3.1 Establishing the Baseline

The regulatory impact analysis (RIA) is intended to evaluate the costs and benefits of 

reaching attainment with potential alternative ozone standards. In order to develop and 

evaluate a control strategy for attaining a more stringent (0.070 ppm) primary standard, it 

is important to first estimate ozone levels in 2020 given the current ozone standard and 

trends (more information is provided in chapter 1). This scenario is known as the 

baseline. Establishing this baseline allows us to estimate the incremental costs and 

benefits of attaining any alternative standard.

This focus on the assessment of the incremental costs and benefits of attaining any 

alternative standard is an important difference from the focus of the risk assessment used 

in developing the standard. For purposes of the Staff Paper-risk assessment, risks are 

estimated associated with just meeting recent air quality and upon just meeting the 

current and alternative standards as well as incremental reductions in risks in going from 

the current standard to more stringent alternative standards. When considering risk 

estimates remaining upon attaining a given standard, EPA is only interested in the risks in 

excess of policy relevant background (PRB). PRB is defined in the ozone Criteria 

Document and Staff Paper as including (1) O3 in the U.S. from natural sources of 

emissions in the U.S., Canada, and Mexico, and (2) O3 in the U.S. from the transport of 

O3 or the transport of emissions from both natural and man-made sources, from outside 

of the U.S. and its neighboring countries (Staff Paper, p.2-54). Emissions of ozone 

precursors from natural sources (e.g. isoprenes emitted from trees) and from sources 

outside of the U.S. are uncertain, as are the specific impacts those emissions will have on 

ozone concentrations in areas exceeding alternative standards. Our models use available 

information on these emissions in generating future projections of baseline ozone 

concentrations, and our modeled reductions in U.S. emissions of NOx and VOC are 

based on these baseline levels that include the contribution of natural and non-U.S. 

emissions. To the extent that these emissions contribute a greater (lesser) proportion of 

ozone on high ozone days, more (less) reductions in emissions from U.S. sources might 

be required to reduce ozone levels below the analyzed alternative standards. 

In contrast, the RIA only examines the incremental reduction, not the remaining risk, 

which results from changes in U.S. anthropogenic emissions. The air quality modeling 

used to establish the baseline for the RIA explicitly includes contributions from natural 
3-3

and anthropogenic emissions in Canada, Mexico, and other countries abroad, as well as 

the contributions to ozone levels from natural sources in the U.S. Since the RIA does not 

attempt to estimate the risk remaining upon meeting a given standard, and the alternative 

standards are clearly above the Staff Paper estimates of PRB, we do not consider PRB a 

component of the RIA costs and benefits estimates. 

In developing the baseline it was important to recognize that there are several areas that 

are not required to meet the current standard by 2020. The Clean Air Act allows areas 

with more significant air quality problems to take additional time to reach the current 

standard. Two areas in Southern California, are not planning to meet the current standard 

by 2020, so the estimated emission reductions for these areas are based on reaching an 

estimated progress point in 2020 (their “glidepath” targets). We provide an estimate of 

the additional amount of tons these areas would need to reduce to meet the standard and 

the additional costs and benefits of reducing those tons in those few areas.

The baseline includes controls which EPA estimates need to be included to attain the 

current standard (0.08 ppm, effectively 0.084 ppm based on current rounding 

conventions) for 2020. Two steps were used to develop the baseline. First, the 

reductions expected in national ozone concentrations from national rules in effect or 

proposed today were considered. Because these alone were not predicted to bring all 

areas into attainment with the tighter standard, EPA used a hypothetical control strategy 

to apply additional known controls. Additional control measures were used in four 

sectors to establish the baseline1: Non-Electricity Generating Unit Point Sources (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. 

x 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. 

x Non-Point Area Sources (Area) are stationary sources that are too numerous or 

whose emissions are too small to be individually included in a stationary source 

emissions inventory. Area sources are the activities where aggregated source 

emissions information is maintained for the entire source category instead of each 

point source, and are reported at the county level. 

x Onroad Mobile Sources are mobile sources that travel on roadways. These 

sources include automobiles, buses, trucks, and motorcycles traveling on roads 

and highways. 

1

 In establishing the baseline, EPA selected a set of cost-effective controls to simulate 

attainment of the current ozone and PM2.5 standards. These control sets are hypothetical 

as states will ultimately determine controls as part of the SIP process. 
3-4

x Nonroad Mobile Sources are any portable engine that travels by other means than 

roadways. These sources include railroad locomotives; marine vessels; aircraft; 

off-road motorcycles; snowmobiles; pleasure craft; and farm, construction, 

industrial and lawn/garden equipment. 

x Electricity Generating Unit Point Sources (EGUs) are stationary sources 

producing electricity, such as fossil-fuel-fired boilers and combustion turbines. 

3.1.1 National Rules

To reduce ambient ozone concentrations, it was necessary to control emissions of ozone 

precursors, NOx and VOC. Establishing the baseline required identifying the national 

rules which were expected to contribute to reductions in NOx and VOCs between now 

and 2020. Some of these include the Clean Air Interstate Rule (CAIR), Clean Air 

Mercury Rule (CAMR), and the Clean Air Visibility Rule (CAVR); and the 2007 

proposed Locomotive/Marine rule. A complete listing of these rules is provided in table 

3.1. In addition, EPA included the control set developed for the hypothetical national 

attainment strategy presented in the PM NAAQS RIA in the baseline for this ozone 

analysis. 

At the time that EPA established the regulatory baseline -- to capture how existing rules 

affect the emissions inventory over time even in the absence of this new NAAQS 

standard -- EPA focused on information that was readily available in the emission 

inventories and other data sources. Typically, a RIA analysis baseline includes only 

reductions from final rules and not reductions from regulatory proposals or other actions 

being contemplated. However, for this analysis, EPA did not include the recently 

promulgated Renewable Fuel Standard (RFS), due to a lack of readily available 

quantitative information. In addition, EPA did include reductions from some upcoming 

rules in an attempt to better characterize reductions that we anticipate to occur in the 

future (e.g. Ocean Going Vessel Rule). For the analysis to support the Final Rule, EPA 

will be using an updated emission inventory and improved models and sets of control 

information. The starting point for the analysis will include only and all promulgated 

rules, including the Renewable Fuel Standard rule. Any potential reductions resulting 

from proposed or upcoming rules will be discussed separately. 

The RFS RIA provides an analysis of the energy, emissions, air quality, and economic 

impacts of expanding the use of renewable fuels in comparison to a reference case of 4 

billion gallons of renewable fuel use that represents 2004 conditions projected out to 

2012. Depending on the anticipated volume of renewable fuel usage in 2012, EPA 

estimates that this transition to renewable fuels will reduce petroleum consumption 

between 2.0 and 3.9 billion gallons or roughly 0.8 to 1.6 percent of the petroleum that 

would otherwise be used by the transportation sector2

.

2

 http;//www.epa.gov/otaq/renewablefuels
3-5

With regard to emissions impacts, carbon monoxide emissions from gasoline-powered 

vehicles and equipment will be reduced between 0.9 and 2.5 percent. Emissions of 

benzene (a mobile source air toxic) will be reduced between 1.8 and 4.0 percent. Further, 

the use of renewable fuel will reduce carbon dioxide equivalent greenhouse gas emissions 

between 8.0 and 13.1 million metric tons, about 0.4 to 0.6 percent of the anticipated 

greenhouse gas emissions from the transportation sector in the United States in 2012. 

At the same time, other vehicle emissions may increase as a result of greater renewable 

fuel use. Nationwide, EPA estimates an increase in total emissions of volatile organic 

compounds and nitrogen oxides (VOC + NOx) between 41,000 and 83,000 tons. 

However, the effects will vary significantly by region. Areas that already are using 

ethanol will experience little or no change in emissions or air quality. In some contexts 

and situations, however, the use of renewable fuels may impact compliance with a 

reduced ozone NAAQS standard. 

In addition to changes in NOx and VOC emissions resulting from increased use of 

ethanol in gasoline, fugitive ethanol emissions may also increase peroxacetyl nitrate 

(PAN) concentrations. Fugitive emissions of ethanol in a photochemical smog polluted 

environment will generate acetaldehyde, a precursor to PAN. PAN, in turn, can lead to 

increase ozone levels. As part of the analysis to support the final rule, EPA will examine 

whether this increase in PAN will affect baseline ozone concentrations in some areas and 

how this effect can be quantified and incorporated into the baseline. 

For the final analysis, EPA will be using an updated emission inventory and improved 

models and sets of control information. The starting point for the analysis will include all 

promulgated rules, including the increases in regional VOC and NOx emissions from 

increased combustion of ethanol. 

Table 3.1 National Rules and Control Measures, by Sector, Contributing to the 

Baseline3,4

Sources of Controls-National 

Sector NOx VOC 

Non-EGUs PM 15/35* (west only) (none used) 

Area PM 15/35* (west only) (none used) 

Onroad

Mobile

-Onroad Diesel Particulate 

Filters and Retirement 

- Commuter Reduction 

Strategies

-Idling Elimination 

-Intermodal Transfer from 

Trucks to Rail 

-Onroad Diesel Particulate Filters 

and Retirement 

- Commuter Reduction Strategies 

3

 References for these rules are provided at the end of this chapter. Controls are 

explained in Appendix 3. 

4

 0.08 ppm, effectively 0.084 ppm based on current rounding conventions
3-6

Nonroad

Mobile

-Diesel Marine & Locomotives 

Rule

-Ocean-Going Vessels Rule 

-Small Spark-Ignition Engine 

Rule

-Nonroad Diesel Particulate 

Filters & Engine Rebuilds 

-Small Spark-Ignition Engine Rule 

-Nonroad Diesel Particulate Filters 

& Engine Rebuilds 

EGU -CAIR/CAMR/ CAVR 

-PM 15/35* (West only) 

(none used) 

*NOx controls are included as part of the hypothetical control scenario modeled 

in the 2006 PM NAAQS RIA. These controls included low NOx burners and SNCR for 

industrial boilers. Further examples can be found in Table 3-5 (page 3-18) of the 2006 

PM NAAQS RIA. http://www.epa.gov/ttn/ecas/ria.html 

3.1.2 Additional Controls

Additional known controls were also included as needed in the baseline, to simulate 

attainment with current ozone NAAQS. The applicable controls and their respective 

sectors are listed in table 3.2 and described below. Details regarding the individual 

controls are provided in appendix 3. Due to the extensive reductions from EGUs already 

implemented in CAIR/CAMR/CAVR, no additional EGU controls were included in the 

baseline. The East was evaluated separately from the West, due to the nature of the 

controls available in each area and the specific features of the areas needing reductions in 

ozone, as explained in more detail below.

In the East, controls included in the baseline for Non-EGU and area sources came from a 

variety of geographic areas and scales. Almost all available controls in Chicago, 

Houston, and the Northeast Corridor were included in the baseline because these areas 

contain counties that were projected to be nonattainment of the current ozone NAAQS in 

2020 (based on air quality modeling performed as part of the PM NAAQS RIA). 

NOx controls from Non-EGU/Area sources were included in two ways in the East. First, 

controls were included in 22 counties with monitors that were projected to violate the 

current standard in 2020. Second, controls were included in all surrounding counties 

within the same state that were completely contained within 200 km of the county 

containing the projected violating monitor. These counties were chosen based upon an 

examination of previous ozone air quality modeling and emissions inventories as well as 

existing EPA guidance. VOC controls were applied (for area sources only) in 26 

counties where VOC emissions were high (>5,000 tpy), and screening analysis indicated 

that mean ozone concentrations were predicted to be markedly reduced by local VOC 

controls ( 0.5 ppb) by local VOC controls of 25%. Two additional counties that did not 

meet these criteria were also included.5

 In the West, Non-EGU and Area Controls were 

5

 Porter County, IN, was included, despite being below the emissions threshold, due to its 

close proximity to Chicago. Harris County, TX, was included because of local 
3-7

included in the baseline only for California, where they were included state-wide. In 

California, all controllable tons of NOx and VOC emissions were reduced using known 

Non-EGU and Area Controls in the baseline. (See Fig 3.1 and Fig 3.2)

Fig. 3.1 Counties Where Controls for Nitrogen Oxides (NOx) Were Included for 

Non-EGU Point and Area Sources, for the Baseline 

(Current Standard, 0.08 ppm) 

Fig. 3.2 Counties Where Controls for Volatile Organic Chemicals (VOCs) Were 

Applied to Non-EGU Point and Area Sources in Baseline 

(Current Standard, 0.08 ppm) 

information about the benefits of VOC control, and concerns about the screening tool 

performance in the 36km region of which Houston is a part. 
3-8

In the Onroad Mobile sector, local controls were included as necessary in the baseline for 

both East and West. Counties projected to have a monitor that exceeded the current 

standard were surrounded by a 200km buffer zone, and controls were included in the 

counties within this buffer that were within the same state as the exceeding monitor. 

Where some control measures overlapped for a given county, controls with the lowest 

costs were included first. This is the only instance in which controls were included in a 

certain order. For a complete list of the controls and the order in which they were 

included, see Appendix 3. Both onroad and nonroad diesel retrofits and idling 

elimination were included statewide in California with an assumed 75% market 

penetration, and elsewhere in the nation with an assumed 25% market penetration for all 

states with a county projected to be in nonattainment with the current standard in 2020. 

EPA determined that 25% would have a significant impact, but was reasonably easy to 

achieve and was applied for reduction areas outside of California. EPA further 

determined that for southern California a higher level of reduction was required. 75% 

was the highest penetration rate that EPA felt could be reasonably accomplished. The 

remainder of mobile controls were included statewide in Ozone Transport Commission 

(OTC) states (see section 3.2.2 for more information on OTC states), with the exceptions 

of Vermont, Maine, New Hampshire, and Massachusetts, which were not projected to 

have counties in nonattainment with the current standard in 2020. These additional 

mobile controls were included statewide in California (See Fig. 3.3) 
3-9

Fig. 3.3 Areas Where NOx and VOC Controls Were Included for Mobile Onroad 

and Nonroad Sources in Addition to National Mobile Controls in Baseline 

(Current Standard, 0.08 ppm) 

*Onroad retrofits and elimination of long duration idling 

**Onroad retrofits, elimination of long duration idling, nonroad retrofits,

Commuter Reduction Strategies and Reid Vapor Pressure (RVP) 
3-10

Table 3.2 Controls by Sector Included in the Baseline Determination for 2020 

Controls- East Controls- West 

Sector NOx VOC NOx VOC 

Non-EGUs -LEC (Low Emission 

Combustion) 

-LNB (Low NOx Burner) 

-LNB + FGR (Flu-Gas 

Sulfurization) 

-LNB + SCR (Selective 

Catalytic Reduction) 

-Mid-Kiln Firing 

-NSCR (Non-selective 

Catalytic Reduction) 

-OXY-Firing

-SCR

-SCR + Steam Injection 

-SCR + Water Injection 

-SNCR (Selective Noncatalytic Reduction)

-SNCR - Urea 

-SNCR - Urea Based 

(none used) -LNB 

-Mid-Kiln Firing 

-NSCR

-OXY-Firing

-SCR

-SCR + Steam Injection 

-SNCR

-SNCR - Urea Based 

(none used) 

Area -RACT to 25 tpy (LNB) 

-Water Heater + LNB Space 

Heaters

-CARB Long-Term Limits 

-Catalytic Oxidizer 

-Equipment and Maintenance 

-Gas Collection (SCAQMD/ 

BAAQMD) 

-Incineration 

-Incineration >100,000 lbs bread 

-Low Pressure/Vacuum Relief 

Valve

-OTC Mobile Equipment Repair 

-RACT to 25 tpy (LNB) 

-Switch to Low Sulfur Fuel 

-Water Heater + LNB Space 

Heaters

-Add-On Controls 

-Airtight Degreasing System 

-Catalytic Oxidizer 

-Equipment and Maintenance 

-FIP Rule (VOC content & TE) 

-Gas Collection 

(SCAQMD/BAAQMD) 

-Incineration 

-Incineration >100,000 lbs bread 

-Low Pressure/ Vacuum Relief 
3-11

and Refinishing Rule 

-OTC Solvent Cleaning Rule 

-SCAQMD - Low VOC 

-SCAQMD Limits 

-SCAQMD Rule 1168 

-Switch to Emulsified Asphalts 

-Use of Low or No VOC Materials 

Valve

-OTC Solvent Cleaning Rule 

-Reformulation - FIP Rule 

-SCAQMD Limits 

-SCAQMD Rule 1168 

-South Coast Phase III 

-Switch to Emulsified Asphalts 

-Use of Low or No VOC Materials 

Onroad

Mobile

-Onroad Selective Catalytic Reduction (SCR) and Diesel 

Particulate Filters (DPF)6

-Reduce Gasoline Reid Vapor Pressure (RVP) 

-Onroad Selective Catalytic Reduction (SCR) and Diesel 

Particulate Filters (DPF) 6

-Reduce Gasoline Reid Vapor Pressure (RVP) 

-Nonroad Selective Catalytic Reduction (SCR) and Diesel 

Particulate Filters (DPF) 6

-Reduce Gasoline Reid Vapor Pressure (RVP) 

-Nonroad Selective Catalytic Reduction (SCR) and Diesel 

Particulate Filters (DPF) 6

-Reduce Gasoline Reid Vapor Pressure (RVP) 

Nonroad

Mobile

-Aircraft NOx Engine 

Standard

-Ocean-Going Vessels – 

reductions for vessels burning 

residual fuels7

(none used for VOC only) -Aircraft NOx Engine 

Standard

(none used for VOC only) 

EGU (none used) (none used) (none used) (none used) 

6

 Onroad and Nonroad DPF were applied in the baseline, and SCR retrofit technologies were chosen because of the need to reduce 

NOx emissions.. 

7

 Reductions from Ocean-Going Vessels burning residual fuels were applied in the Baseline analysis for the east, but inadvertently

omitted for the west. The omission was not identified in time to include it in the initial Baseline analysis for the west. 

Table 3.2 Controls by Sector Included in the Baseline Determination for 2020 (continued) 
3-12

3.1.3 Ozone Levels for Baseline 

Establishing the baseline required design values (predicted concentrations) of ozone 

across the country. Because the intention of this evaluation was to achieve attainment of 

the current ozone standard, controls were included to reduce ambient ozone 

concentrations to 0.08 ppm (effectively 0.084 ppm based on current rounding 

conventions). A map of the country is presented in figure 3.4, which shows predicted 

concentrations for the 491 counties with ozone monitors that were included in the 

baseline. Modeling projections were developed for all appropriate counties according to 

procedures outline in EPA modeling guidance8

.

The baseline shows that 10 counties would not meet the current ozone standard in 2020, 

even after inclusion of all known controls. After including known controls as described 

above, the analysis predicted that the remaining 481 counties would attain the current 

standard by 2020. The baseline forms the foundation for the cost-benefit analysis 

conducted in this RIA, where EPA compares more stringent primary ozone standard 

alternatives incrementally to national attainment of the current standard. 

8

 Available online at: http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf
3-13

Fig. 3.4 Baseline Annual Ozone Air Quality in 2020 

a

 Modeled emissions reflect the expected reductions from federal programs including the 

Clean Air Interstate Rule, the Clean Air Mercury Rule, the Clean Air Visibility Rule, the 

Clean Air Nonroad Diesel Rule, the Light-Duty Vehicle Tier 2 Rule, the Heavy Duty 

Diesel Rule, proposed rules for Locomotive and Marine Vessels and for Small SparkIgnition Engines, and state and local level mobile and stationary source controls 

identified for additional reductions in emissions for the purpose of attaining the current 

PM 2.5 and Ozone standards.

b

 Controls applied are illustrative. States may choose to apply different control strategies 

for implementation. 

c

 The current standard of 0.08 ppm is effectively expressed as 0.084 ppm when rounding 

conventions are applied.

d

 Modeled design values in ppm are only interpreted up to 3 decimal places. 

e

 Map shows results from a total of 491 counties with projected design values. Consistent 

with current modeling guidance, EPA did not project 2020 concentrations for counties 

where 2001 base year concentrations were less than recommended criterion. Such 

projections may not represent expected future levels.
3-14

3.2 Developing the Control Strategy Analysis

After developing the baseline, EPA developed a hypothetical control strategy to illustrate 

one possible national control strategy that could be adopted to reach an alternative 

primary standard of 0.070 ppm by 2020. The stricter standard alternative of 0.070 ppm 

was chosen as being representative of the set of alternatives being considered by EPA in 

its notice of proposed rulemaking on the ozone NAAQS. Controls for five sectors were 

used in developing the control analysis, as discussed previously: non-EGU stationary, 

Area, onroad mobile and nonroad mobile, along with EGU controls only in the East 

(EGU controls for the West were included in the hypothetical PM NAAQS 15/35 

national control strategy, and were therefore already in the ozone baseline). Reductions 

in both NOx and VOC ozone precursors were needed in all four remaining sectors to 

meet a tighter standard. 

As depicted in the flow diagram in figure 1.1, the control strategy modeled in this RIA 

first applied and exhausted nearly all known controls (see section 3.2.1 an explanation of 

which controls were excluded from this analysis). After controls were identified, the 

expected emissions reductions were input to an air quality model that projected design 

values for ozone in 2020. Following the control strategy, there were some areas 

projected not to attain 0.070 ppm in 2020 using all known control measures. EPA was 

then required to extrapolate the additional emission reductions required to reach 

attainment. The methodology used to develop those estimates and those calculations are 

presented in Chapter 4. 

As in the analysis for the baseline, parts of the hypothetical national control strategy for 

0.070 ppm focused on the Eastern (East) United States (U.S.) separately from the 

Western U.S. (West). However, this RIA presents estimates of the costs and benefits of 

attaining alternative ozone standards on a national basis. Table 3.3 presents the specific 

control technologies that were applied within each sector for the 0.070 ppm control 

strategy.
3-15

Controls- East Controls- West 

Sector NOx VOC NOx VOC 

NonEGUs-Biosolid Injection Technology 

-LEC (Low Emission 

Combustion) 

-LNB

-LNB + FGR 

-LNB + SCR 

-LNB+SCR

-Mid-Kiln Firing 

-NGR

-NSCR

-OXY-Firing

-SCR

-SCR + Steam Injection 

-SCR + Water Injection 

-SNCR

-SNCR - Urea 

-SNCR – Urea Based 

-LDAR (Leak Detection and Repair) 

-Enhanced LDAR 

-Flares Gas Recovery 

Monitoring Program 

-Permanent Total Enclosure (PTE) 

-Wastewater Drain Control 

-Biosolid Injection Technology 

-LNB

-LNB + FGR 

-LNB + SCR 

-Mid-Kiln Firing 

-NSCR

-OXY-Firing

-SCR

-SCR + Steam Injection 

-SCR + Water Injection 

-SNCR

-SNCR - Urea Based 

(none used) 

Area -RACT to 25 tpy (LNB) 

-Water Heater + LNB Space 

Heaters

-CARB Long-Term Limits 

-Catalytic Oxidizer 

-Equipment and Maintenance 

-Gas Collection (SCAQMD/BAAQMD) 

-Incineration 

-Incineration >100,000 lbs bread 

-Low Pressure/Vacuum Relief Valve 

-OTC Mobile Equipment Repair and 

Refinishing Rule 

-OTC Portable Gas Container Rule 

-RACT to 25 tpy (LNB) 

-Switch to Low Sulfur Fuel 

-Water Heater + LNB Space 

Heaters

(none used) 

Table 3.3: Controls for Emissions Reductions, by Sector, for the 0.070 ppm Control Strategy (Incremental to Baseline)
3-16

9

 For Onroad and Nonroad Mobile Source control measures, all measures applied for the Baseline analysis were applied to additional

geographic areas in the .070 analysis. 

10 Reductions from Ocean-Going Vessels burning diesel fuel were applied in the Base Case analysis. However, we inadvertently 

omitted the associated reductions that would occur in vessels burning residual fuels. These additional reductions were applied in the 

Baseline analysis for the east and in the .070 analyses for the east and west. The omission was not identified in time to include it in 

the initial Baseline analysis for the west, but was included in the Baseline national PM co-benefits analysis for the east and west.

-OTC Solvent Cleaning Rule 

-SCAQMD - Low VOC 

-SCAQMD Limits 

-SCAQMD Rule 1168 

-Switch to Emulsified Asphalts 

-Use of Low or No VOC Materials 

Onroad

Mobile9

-Increased Penetration of Onroad SCR and DPF from 25% to 75% 

-Continuous Inspection and Maintenance (OBD) 

-Continuous Inspection and Maintenance (OBD) 

Nonroad

Mobile9

-Increased Penetration of Nonroad SCR and DPF from 25% to 75% -Ocean-Going Vessels – 

reductions for vessels burning 

residual fuels10

EGU -Lower nested caps in OTC and 

MWRPO states 

-Application of SCR and SNCR in 

coal fired units in NA counties 

outside of OTC and MWRPO 

(none used) (none used) (none used) 

Table 3.3 (Continued): Controls for Emissions Reductions, by Sector, for the 0.070 ppm Control Strategy

(Incremental to Baseline) 
3-17

3.2.1 Controls Applied for a 0.070 ppm Standard: Non-EGU and Area Sectors 

Non-EGU and Area control measures were identified using AirControlNET 4.1.11,12 To 

reduce NOx and VOC levels, all known control measures, within a given cost-cap, were 

applied, allowing for the largest emission reduction per source over the widest 

geographic area. The cost-caps were pollutant specific and applicable only in the East 

portion of the analysis. For reductions of NOx emissions the cap was $16,000/ton, based 

upon the approximate benefit per ton of reductions. In some instances, controls were too 

costly due to the large capital component of installing these controls. A similar process 

was followed for reductions from VOCs. The marginal cost curve was analyzed, and 

there was a clear break in the curve at approximately $6,000/ton. Because all available 

controls up to the cost cap were used in counties needing emission reductions, there was 

no ordering of which controls were applied first. VOCs were cut at this level because 

approximately 75% of reductions were coming from controls below that number. 

Additionally, the relative effectiveness of VOC controls is not high. See Chapter 5 for 

more information on cost caps 

Additionally, controls were added that appeared in preliminary State Implementation 

Plans (SIPs) from States and Regional Planning Bodies. Supplemental controls that 

estimated near-term source controls based on similar technology were included in the 

Non-EGU and Area Source sectors as well. Supplemental controls are described in 

further detail in Appendix 3. 

NOx controls were applied in the East for the 233 counties that were projected to have 

concentrations of greater than 0.070 ppm in the 2020 baseline. Additional controls were 

applied in surrounding counties within 200 km of the county projected to be out of 

attainment (at 0.070 ppm), but not crossing state boundaries. In the West, NOx controls 

were applied statewide, rather than only to counties with violating monitors and their 

immediate neighbors (See Fig. 3.5). This was due to modeling methodology, in which 

the 200 km buffer was only validated for the East. 

11 See http://www.epa.gov/ttnecas1/AirControlNET.htm for a description of how 

AirControlNET operates and what data is included in this tool. 

12 While AirControlNET has not undergone a formal peer review, this software tool has 

undergone substantial review within EPA's OAR and OAQPS, and by technical staff in 

EPA's Regional offices. Much of the control measure data has been included in a control 

measure database that will be distributed to EPA Regional offices for use by States as 

they prepare their ozone, regional haze, and PM2.5 SIPs over the next 10 months. In 

addition, the control measure data within AirControlNET has been used by Regional 

Planning Organizations (RPOs) such as the Lake Michigan Air District Commission 

(LADCO), the Ozone Transport Commission (OTC), and the Visibility Improvement 

State and Tribal Assocation of the Southeast (VISTAS) as part of their technical analyses 

associated with SIP development over the last 3 years. All of their technical reports are 

available on their web sites.
3-18

Fig 3.5 Counties Where Controls for Nitrogen Oxides (NOx) Were Applied to NonEGU Point and Areas Sources for RIA Control Strategy Designed to Meet 0.070 

ppm (Incremental to Baseline) 

In the East, VOC controls were applied (for area sources only) in 47 counties where the 

following criteria were met (including the 26 counties which included VOC controls in 

their baselines): VOC emissions within the county or an adjacent county were high (e.g. 

>5000 tons per year of area source emissions), and screening analyses indicated that 

ozone design values would be markedly reduced (> 0.5 ppb) by local VOC controls of 

25%, and the county design value was projected to be  0.070 ppm in the 2020 baseline 

(See Fig 3.6). No VOC controls were used in the West. 
3-19

Fig. 3.6 Counties Where VOC Controls Were Applied to Non-EGU Point and Areas 

Sources for the Control Strategy Designed to Meet 0.070 ppm (Incremental to 

Baseline)

3.2.2 Controls Applied for a 0.070 ppm Standard: EGU Sector

For the East only, a control strategy was applied for the EGU sector (Fig. 3.7) (EGU 

controls for the West were already included in the ozone baseline since they were applied 

for the hypothetical national control strategy in the PM NAAQS RIA.) Annual and 

ozone season CAIR caps remained unchanged, but coal-fired units were targeted for this 

shifted strategy within those caps. This strategy was appropriate to consider because 

transport of NOx pollution is more of a concern in the East, and NOx from EGUs still 

accounts for a significant portion of emissions in this region. California, while in need of 

reductions as well, was not included in this strategy because all known controls 

(including EGU controls) had already been applied in the baseline. The development of 

an EGU-component to this control strategy was based exclusively on NOx emissions 

during the ozone season, although the hypothetical controls applied would operate yearround. 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 
3-20

eastern U.S. by 202013. A map of the CAIR region is presented in appendix 3.4. 

Emissions and air quality impacts of CAIR are documented in detail in the Regulatory 

Impact Analysis of the Final Clean Air Interstate Rule14

To address nonattainment in the CAIR region (especially the Midwest, Mid-Atlantic, and 

Northeast), lower nested caps (a limit lower than the current CAIR cap) were applied in 

these areas for NOx, while holding the CAIR cap unchanged for the entire region. This 

provides an opportunity to reduce emissions in a cost effective manner in targeted 

regions. Two geographic regions were targeted for emissions reductions: the Midwest 

Regional Planning Organization (MWRPO) consisting WI, IL, IN, MI, and OH; and the 

Ozone Transport Commission (OTC), consisting of DC, MD, PA, DE, NJ, CT, NY, RI, 

MA, VT, NH, and ME. These areas were chosen because the MWRPO and OTC states 

are currently investigating ways of reducing EGU emissions further in their states and 

because most of the potential ozone nonattainment areas are found within these two 

regions. Considering transport, as well as the local effects, reducing emissions in these 

areas expected to help bringing the Lake Michigan and Northeast corridor nonattainment 

areas into attainment. 

Lower nested caps were applied in the MWRPO and OTC states, for the ozone season 

only. The caps that were applied lead to reductions that could be obtained by installing 

post-combustion controls to all of the coal-fired units that were not projected to have 

previously installed post-combustion controls in the base-case. Following this, 75% of 

the reduction that could be obtained from these units was subtracted from the sum of 

State level ozone control season NOx caps in CAIR15. The CAIR cap for the entire 

region was kept unchanged.

In order to address non-attainment in the CAIR region outside of the MWRPO and OTC, 

a “command and control” type strategy for coal-fired units has been designed. Annual 

and ozone season CAIR caps remained unchanged, and coal-fired units were targeted for 

this reduction. Preliminary analysis showed that most of the needed NOx reductions in 

the EGU sector can be achieved through application of post-combustion controls (e.g. 

Selective Catalytic Reductions (SCR) and Selective Non-Catalytic Reductions (SNCR)) 

on coal units that are projected to remain without controls under the 

CAIR/CAMR/CAVR cap-and-trade scheme. 

13 See http://www.epa.gov/airmarkets/progress/progress-reports.html for more 

information

14 See http://www.epa.gov/CAIR/technical.html

15 Detailed analysis showed that 75% reduction provides the most cost-effective way of 

reducing emissions at the targeted non-attainment areas, considering transport, with the 

most air quality impacts. 
3-21

Fig 3.7 States Where Nitrogen Oxide (NOx) Controls Were Applied to Electrical 

Generating Units (EGUs) for the Control Strategy Designed to Meet 0.070 ppm 

(Incremental to Baseline) 

3.2.3 Controls Applied for a 0.070 ppm Standard: Onroad and Nonroad Mobile Sectors 

As in other sectors, there are several mobile source control strategies that have been, or 

are expected to be, implemented through previous national or regional rules. Although 

many expected reductions from these rules are included in the baseline, additional mobile 

source controls were required to illustrate attainment of a 0.070 ppm standard (See Fig 

3.8). Modeling of the onroad and nonroad mobile sectors was done using MOBILE6.

See Appendix 3 for more information. 

All of the local mobile source controls included in the ozone baseline were expanded for 

the hypothetical national control strategy to attain 0.070 ppm standard. In the case of 

onroad and nonroad Selective Catalytic Reduction (SCR) and Diesel Particulate Filters 

(DPF), the measure was applied at a greater penetration rate – to 75% of the equipment 

population. 75% was the highest penetration rate that EPA felt could be reasonably 

accomplished. All local measures were applied to sources in additional geographic areas. 

Continuous inspection and maintenance, which allows for much more rapid identification 

of vehicles failing their emissions standard, was added. Descriptions of the mobile 

source rules and measures can be found in appendix 3.3. 
3-22

As in the baseline, onroad SCR and DPF and elimination of idling were applied statewide 

for all states with a county projected to exceed the 0.070 ppm standard. All other 

controls were applied to counties within a 200 km buffer from counties projected to 

exceed the 0.070 ppm alternative standard with the following exceptions: 

x counties in neighboring states were omitted from the buffer zone 

x controls were applied statewide to Ozone Transport Commission (OTC) states, 

with the exception of Vermont 

x controls were applied statewide in California, Colorado, Utah, New Mexico, 

Arizona, and Nevada . 

Fig. 3.8 Areas Where NOx and VOC Controls Were Applied to Mobile Onroad 

and Nonroad Sources in Addition to National Mobile Controls for the 0.070 ppm 

Control Strategy (incremental to Baseline)

*Onroad retrofits and elimination of long duration idling 

**Onroad retrofits, elimination of long duration idling, nonroad retrofits, Best Workplace 

for Commuters programs (BWC), low Reid Vapor Pressure (RVP)

3.2.4 Data Quality for this Analysis 

The estimates of emission reductions associated with our control strategies above are 

subject to important limitations and uncertainties. EPA’s analysis is based on its best 

judgment for various input assumptions that are uncertain. As a general matter, the 

Agency selects the best available information from available engineering studies of air 

pollution controls and has set up what it believes is the most reasonable framework for 
3-23

analyzing the cost, emission changes, and other impacts of regulatory controls. EPA is 

working on approaches to quantify the uncertainties in these areas and will incorporate 

them in future RIAs as appropriate. 

3.3 Geographic Distribution of Emissions Reductions

The following maps break out NOx and VOC reductions into the controlling sectors. The 

maps for NOx and VOC reductions are presented in Figures 3.9 and 3.11, respectively.

Figures 3.10 and 3.12 indicate the emission reductions attributed to each sector. 

Appendix 3 contains maps of emissions reductions by sector, nationwide. 

Prior to reading the maps, there is an important caveat to consider. The control strategy 

above focuses on reducing emissions of VOCs and NOx, the two precursors to ozone 

formation. However, in some cases, the application of the control strategy actually 

increased the level of NOx or VOC emissions. This is due to controls that affect multiple 

pollutants and complex interactions between air pollutants, as well as trading aspects 

under the CAIR rule. 

Emissions of NOx do not decrease everywhere within the CAIR region. As explained 

earlier, the NOx EGU control strategy was designed to achieve emission reductions 

specifically in the non-attainment areas, while retaining the overall CAIR cap.

Application of nested and lower (ozone season) caps for the states in the MWRPO and 

OTC regions and local controls (SCR and SNCR) on the uncontrolled coal units in the 

non-attainment counties outside of the OTC and MWRPO within CAIR region result in 

increase of emissions elsewhere within CAIR region. While there are substantial NOx 

emission reductions within the OTC and MWRPO expected for the 2020 ozone season 

(roughly 55,500 tons) as a result of cap-and-trade program with lower caps and local 

command-and-control reductions in other non-attainment counties where uncontrolled 

coal units exist, there is the possibility of increased emissions from the remainder of 

sources within CAIR region. This approach provides a cost effective opportunity for 

reducing emissions where the reductions are most needed to help reach attainment. It is 

important to recall that this is a hypothetical control strategy, the states or other 

authorities may take additional steps to minimize these increases if warranted. 
3-24

Fig 3.9 Annual Tons of Nitrogen Oxide (NOx) Emission Reductions From Controls 

Designed to Meet 0.070 ppm Standard,* incremental to the current standard

*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. 
3-25

Fig. 3.10 Percentage of Total Annual NOx Emissions Reduced from Various Sources 

* Note that on a national basis, NOx emissions are reduced by <1%. However, the EGU 

strategy used in this analysis gains reductions in nonattainment areas, balanced by 

increases in attainment areas (described in Section 3.3) 

NonElectrical

Generating

Unit Point 

(79%)

Electrical

Generating Unit 

Point(<1%)

On-Road (16%)

Non-Road (<1%)

Area (3%)
3-26

Fig. 3.11 Annual Tons of Volatile Organic Compound (VOC) Emission Reductions 

From Controls Designed to Meet 0.070 ppm Standard*, incremental to the current 

standard

*Reductions are negative and increases are positive 

**The -99 - +57 range is shown without color because these are small county-level VOC 

reductions or increases that likely had little to no impact on ozone estimates. Most 

counties in this range had VOC differences less than 1 ton. 
3-27

Fig. 3.12 Percentage of Total Annual VOC Emissions Reduced from Various 

Sources*

 

3.4 Ozone Design Values for partial attainment 

After determining the emissions reductions from NOx and VOC, we used modeling tools 

(see section 2.3.2) to determine ozone design values for 2020. Figure 3.13 shows a map 

of the design values after modeling the control strategy to reach 0.070 ppm. The map 

legend is broken out to demonstrate under this control strategy, with no adjustments, 

which counties would reach the targeted standard of 0.070 ppm, the more stringent 

alternative standard analyzed (0.065 ppm), and the other end of the proposal range (0.075 

ppm). It is understood that this illustrative strategy would not be the exact hypothetical 

strategy used to try to attain either of these alternative standards, due to over- and underattainment in many counties. (Chapter 4 describes EPA’s methodology for estimating 

tons of reductions needed to hypothetically attain these other two possible alternative 

standards.) In addition, because ozone formation is dependent on a variety of factors, it is 

not possible to directly attribute changes in predicted ozone concentrations to emission 

reductions of a specific precursor from a specific sector. 

A full listing of the counties and their design values is provided in Appendix 3. 

Figures 3.14 and 3.15 show the tons of emissions reduced by the hypothetical RIA 0.070 

ppm control strategy, and the tons of emissions remaining after application of those 

controls, by sector.

Using this strategy, it is possible to reach attainment in 365 counties. However, there are 

still an additional 126 counties that will remain out of attainment with an alternative 

standard of 0.070 ppm using this control strategy. All known controls were applied to 

this scenario, but attainment was not achieved everywhere. Because of this partial 

attainment outcome, it will be necessary to identify additional reductions in NOx and 

VOC in order to assess the costs and benefits of full attainment nationwide. Chapter 4 

Area

(45%)

Non-Road

(7%)

On-Road

(46%)

Non-Electrical Generating 

Unit Point (2%) 
3-28

will address the methodology for determining the additional tons that were needed to 

reach full attainment. 

Fig. 3.13 Projected Ozone Air Quality in 2020 After Application of Known Controls 

1 Modeled emissions reflect the expected reductions from federal programs including the Clean 

Air Interstate Rule, the Clean Air Mercury Rule, the Clean Air Visibility Rule, the Clean Air 

Nonroad Diesel Rule, the Light-Duty Vehicle Tier 2 Rule, the Heavy Duty Diesel Rule, proposed 

rules for Locomotive and Marine Vessels and for Small Spark-Ignition Engines, and state and 

local level mobile and stationary source controls identified for additional reductions in emissions 

for the purpose of attaining the current PM 2.5 and Ozone standards. 

2 Controls applied are illustrative. States may choose to apply different control strategies for 

implementation. 

3 The current standard of 0.08 ppm is effectively expressed as 0.084 ppm when rounding 

conventions are applied. 

4 Modeled design values in ppm are only interpreted up to 3 decimal places. 

5 Map shows results from a total of 491 counties with projected design values. Consistent with 

current modeling guidance, EPA did not project 2020 concentrations for counties where 2001 

base year concentrations were less than recommended criterion. Such projections may not 

represent expected future levels.
3-29

Table 3.4 Annual Tons of Emissions Remaining after Application of the 0.070 ppm 

Control Strategy (35 States + DC Analysis Area)16

Pollutant Sector 2020 Emissions 

After Controls 

Applied for 

PM2.5 15/35 

(tons) 

2020 Emissions 

After Controls 

Applied for 

PM2.5 15/35 and 

Ozone 0.084 

Control Strategy 

Baseline (tons) 

0.070 ppm 

Reductions (tons) 

2020 Emissions 

After Controls 

Applied for 

PM2.5 15/35 and 

Ozone 0.070 ppm 

Control Strategy 

(tons) 

Area 1,200,000 1,200,000 30,000 1,200,000 

Onroad 1,800,000 1,700,000 170,000 1,600,000 

Nonroad 1,900,000 1,800,000 8,000 1,800,000 

EGU 1,500,000 1,500,000 7,800 1,500,000 

NOX 

Non-EGU 2,200,000 1,900,000 800,000 1,100,000 

Area 5,800,000 5,600,000 84,000 5,500,000 

Onroad 1,500,000 1,500,000 86,000 1,400,000 

Nonroad 1,000,000 1,000,000 12,000 1,000,000 

EGU 39,000 39,000 26 38,000 

VOC 

Non-EGU 1,100,000 1,100,000 3,400 1,100,000 

16 Numbers may not add up due to rounding 
3-30

Fig 3.14 Annual NOx Emissions Remaining after PM NAAQS 15/35, Ozone 

Current Standard, and 0.070 ppm Control Strategies 

(35 States + DC Analysis Area) 

1,700,000

1,600,000

1,900,000

1,800,000

1,800,000

1,500,000

1,500,000

1,500,000

2,200,000

1,

1,900,000

200,000

1,200,000

1,200,000

1,800,000

1,100,000

2020 Emissions After Controls

Applied for PM 2.5 15/35 and

Accounting for National Rules

on the Books

2020 Emissions After Controls

Applied for PM2.5 15/35 and

Ozone 0.084 Control Strategy

Baseline

2020 Emissions After Controls

Applied for PM2.5 15/35 and

Ozone 0.070 Control Strategy

Tons

NOX Area NOX Onroad NOX Nonroad NOX EGU NOX NonEGU
3-31

Fig. 3.15 Annual VOC Emissions Remaining after PM NAAQS 15/35, Ozone 

Current Standard, and 0.070 ppm Control Strategies

(35 States + DC Analysis Area) 

 5,800,000

 5,600,000 

5,500,000

1,500,000

1,500,000

1,400,000

 1,000,000

1,000,000

 1,100,000 

 1,000,000 

 39,000 

38,000

39,000 

1,100,000

 1,100,000

2020 Emissions After Controls

Applied for PM 2.5 15/35 and

Accounting for National Rules

on the Books

2020 Emissions After Controls

Applied for PM2.5 15/35 and

Ozone 0.084 Control Strategy

Baseline

2020 Emissions After Controls

Applied for PM2.5 15/35, O3

Current Standard and 0.070

Control Strategies

Tons

VOC Area VOC Onroad VOC Nonroad VOC EGU VOC NonEGU

3.5 References

Michigan Department of Environmental Quality and Southeast Michigan Council of 

Governments. Proposed Revision to State of Michigan State Implementation Plan for 7.0 

Low Vapor Pressure Gasoline Vapor Request for Southeast Michigan. May 24, 2006. 

National Ambient Air Quality Standards for Particulate Matter, 40 CFR Part 50 (2006) 

Rule To Reduce Interstate Transport of Fine Particulate Matter and Ozone (Clean Air 

Interstate Rule); Revisions to Acid Rain Program; Revisions to the NOX SIP Call; Final 

Rule, 40 CFR Parts 51, 72, 73, 74, 77, 78 and 96 (2005). 

Standards of Performance for New and Existing Stationary Sources: Electric Utility 

Steam Generating Units, 40 CFR Parts 60, 63, 72, and 75 (2005)
3-32

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 from Nonroad Spark-Ignition Engines and Equipment; proposed 

rule, 40 CFR Parts 60, 63, 85, 89, 90, 91, 1027, 1045, 1048, 1051, 1054, 1060, 1065, 

1068, and 1074 (2007) 

USEPA. Guide on Federal and State Summer RVP Standards for Conventional Gasoline 

Only. EPA420-B-05-012. November 2005 

USEPA. 2007, Regulatory Announcement: EPA Proposal for More Stringent Emissions 

Standards for Locomotives and Marine Compression-Ignition Engines. EPA420-F-07-

015

USEPA. 2007, Proposed Emission Standards for New Nonroad Spark-Ignition Engines, 

Equipment, and Vessels. EPA420-F-07-032 
3a-1

Appendix- Chapter 3 

3a.1 Non-EGU and Area Source Controls Applied in the Baseline and Control 

Scenarios 

3a.1.1 Non-EGU and Area Source Control Strategies for Ozone NAAQS Proposal 

In the Non-EGU and Area Sources portion of the control strategy, maximum control 

scenarios were used from the existing control measure dataset from AirControlNET 4.1 

for 2020 (for Geographic Areas defined for each level of the standard being analyzed). 

This existing control measure dataset reflects changes and updates made as a result of the 

reviews performed for the final PM2.5 RIA. Following this, an internal review was 

performed by the OAQPS engineers in the Sector Policies and Programs Division (SPPD) 

to examine the controls applied by AirControlNET and decide if these controls were 

sufficient or could be more aggressive in their application, given the 2020 analysis year.

This review was performed for non-EGU NOx control measures. The result of this 

review was an increase in control efficiencies applied for many control measures, and 

more aggressive control measures for over 80 SCC’s. For example, SPPD recommended 

that we apply SCR to cement kilns to reduce NOx emissions in 2020. Currently, there 

are no SCRs in operation at cement kilns in the U.S, but there are several SCRs in 

operation at cement kilns in France now. Based on the SCR experience at cement kilns 

in France, SPPD believes SCR could be applied at U.S. cement kilns by 2020. Following 

this, it was recommended that supplemental controls could be applied to 8 additional 

SCC’s from non-EGU NOx sources. We also looked into sources of controls for highly 

reactive VOC non-EGU sources. Four additional controls were applied for highly 

reactive VOC non-EGU sources not in AirControlNET.

3a.1.2 NOx Control Measures for Non-EGU Point Sources. 

Several types of NOx control technologies exist for non-EGU sources: SCR, selective 

noncatalytic reduction (SNCR), natural gas reburn (NGR), coal reburn, and low-NOx 

burners. In some cases, LNB accompanied by flue gas recirculation (FGR) is applicable, 

such as when fuel-borne NOx emissions are expected to be of greater importance than 

thermal NOx emissions. When circumstances suggest that combustion controls do not 

make sense as a control technology (e.g., sintering processes, coke oven batteries, sulfur 

recovery plants), SNCR or SCR may be an appropriate choice. Finally, SCR can be 

applied along with a combustion control such as LNB with overfire air (OFA) to further 

reduce NOx emissions. All of these control measures are available for application on 

industrial boilers.

Besides industrial boilers, other non-EGU source categories covered in this RIA include 

petroleum refineries, kraft pulp mills, cement kilns, stationary internal combustion 

engines, glass manufacturing, combustion turbines, and incinerators. NOx control 

measures available for petroleum refineries, particularly process heaters at these plants, 

include LNB, SNCR, FGR, and SCR along with combinations of these technologies. 
3a-2

NOx control measures available for kraft pulp mills include those available to industrial 

boilers, namely LNB, SCR, SNCR, along with water injection (WI). NOx control 

measures available for cement kilns include those available to industrial boilers, namely 

LNB, SCR, and SNCR. Non-selective catalytic reduction (NSCR) can be used on 

stationary internal combustion engines. OXY-firing, a technique to modify combustion at 

glass manufacturing plants, can be used to reduce NOx at such plants. LNB, SCR, and 

SCR + steam injection (SI) are available measures for combustion turbines. Finally, 

SNCR is an available control technology at incinerators. For more information on these 

measures, please refer to the AirControlNET 4.1 control measures documentation report. 

3a.1.3 VOC Control Measures for Non-EGU Point Sources. 

VOC controls were applied to a variety of non-EGU point sources as defined in the emissions 

inventory in this RIA. These controls are: permanent total enclosure (PTE) applied to paper 

and web coating operations and fabric operations, and incinerators or thermal oxidizers 

applied to wood products and marine surface coating operations. A PTE confines VOC 

emissions to a particular area where can be destroyed or used in a way that limits emissions 

to the outside atmosphere, and an incinerator or thermal oxidizer destroys VOC emissions 

through exposure to high temperatures (2,000 degrees Fahrenheit or higher). For more 

information on these measures, refer to the AirControlNET 4.1 control measures 

documentation report. 

3a.1.4 NOx Control Measures for Area Sources 

There were two controls applied for NOx emissions from area sources. The first is 

RACT (reasonably available control technology) to 25 tpy (LNB). This control is the 

addition of a low NOx burner to reduce NOx emissions. This control is applied to 

industrial oil, natural gas, and coal combustion sources. The second control is water 

heaters plus LNB space heaters. This control is based on the installation of low-NOx 

space heaters and water heaters in commercial and institutional sources for the reduction 

of NOx emissions. For additional information regarding these controls please refer to the 

AirControlNET 4.1 control measures documentation report. 

3a.1.5 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 

controls was the use of low or no VOC materials for graphic art source categories. The 

South Coast Air District’s SCAQMD Rule 1168 control applies to wood furniture and 

solvent source categories sets limits for adhesive and sealant VOC content. The OTC 

solvent cleaning rule control establishes hardware and operating requirements for 

specified vapor cleaning machines, as well as solvent volatility limits and operating 

practices for cold cleaners. The Low Pressure/Vacuum Relief Valve control measure is 

the addition of low pressure/vacuum (LP/V) relief valves to gasoline storage tanks at 
3a-3

service stations with Stage II control systems. LP/V relief valves prevent breathing 

emissions from gasoline storage tank vent pipes. SCAQMD Limits control establishes 

VOC content limits for metal coatings along with application procedures and equipment 

requirements. Switch to Emulsified Asphalts control is a generic control measure 

replacing VOC-containing cutback asphalt with VOC-free emulsified asphalt. The 

equipment and maintenance control measure applies to oil and natural gas production. 

The Reformulation - FIP Rule control measure intends to reach the VOC limits by 

switching to and/or encouraging the use of low-VOC pesticides and better Integrated Pest 

Management (IPM) practices. For additional information regarding these controls please 

refer to the AirControlNET 4.1 control measures documentation report. 

3a.1.6 Supplemental Controls 

The table below summarizes the supplemental control measures added to our control 

measures database by providing the pollutant it controls and its control efficiency. These 

controls were applied in the baseline scenario to Houston and Chicago, and the Northeast 

as well as in the incremental control strategy applied to the Eastern U.S. However, these 

controls are not located in AirControlNET.

Table 3a.1 Supplemental Emission Control Measures Applied in Modeled 

Attainment Strategies for the Ozone NAAQS RIA – New Control Technologies 

Added to the Control Measures Database 

Pollutant SCC SCC Description Control 

Technology 

Percent 

Reduction 

(%)

NOx 20200252 Internal Comb. 

Engines/Industrial/Natur

al Gas/2-cycle Lean Burn 

LEC (Low 

Emission

Combustion)

87

 20200254 Internal Comb. 

Engines/Industrial/Natur

al Gas/4-cycle Lean Burn 

LEC (Low 

Emission

Combustion)

87

VOC* 3018001- 

30600701

and 

30600999

-

Fugitive Leaks 

Flares 

Enhanced 

LDAR 

50

98

 3018001 - Fugitive Leaks LDAR 80 

 30600702

-

Cooling towers Monitoring 

Program 

No one 

general 

estimate 
3a-4

 30600503

-

Wastewater Drains and 

Separators 

Inspection 

and 

Maintenance 

Program 

(Separators) 

Water Seals 

(Drains) 

65

*Note: the cost of these measures are not included in our incremental annualized cost 

estimates since these controls are found in the Harris-Galveston-Brazoria Cos. SIP 

(Texas), and they will be incurred by 2020 in any event. We do quantify the 

emission reductions since these controls are not accounted for in our baseline

inventory for 2020, however. 

Low Emission Combustion (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 control efficiency, EPA estimates that it ranges from 82 to 91 percent for LEC 

technology applications. The EPA believes application of LEC would achieve 

average NOx emission levels in the range of 1.5-3.0 g/bhp-hr. This is an 82-91 

percent reduction from the average uncontrolled emission levels reported in the 

ACT document. An EPA memorandum summarizing 269 tests shows that 96 

percent of IC engines with installed LEC technology achieved emission rates of 

less than 2.0 g/bhp-hr.1

 The 2000 EC/R report on IC engines summarizes 476 

tests and shows that 97% of the IC engines with installed LEC technology achieve 

emission rates of 2.0 g/bhp-hr or less.2

1

 “Stationary Reciprocating Internal Combustion Engines Technical Support Document 

for NOx SIP Call Proposal,” U.S. Environmental Protection Agency. September 5, 2000. 

Available on the Internet at http://www.epa.gov/ttn/naaqs/ozone/rto/sip/data/tsd9-00.pdf.

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.
3a-5

Major Uncertainties: The EPA acknowledges that specific values will vary from 

engine to engine. The amount of control desired and number of operating hours 

will make a difference in terms of the impact had from a LEC retrofit. Also, the 

use of LEC may yield improved fuel economy and power output, both of which 

may affect the emissions generated by the device. 

Leak Detection and Repair (LDAR) for Fugitive Leaks

Overview: This control measure is a program to reduce leaks of fugitive VOC 

emissions from chemical plants and refineries. The program includes special 

“sniffer” equipment to detect leaks, and maintenance schedules that affected 

facilities are to adhere to. This program is one that is contained within the 

Houston-Galveston-Brazoria 8-hour Ozone SIP. 

Major Uncertainties: The degree of leakage from pipes and processes at 

chemical plants is always difficult to quantify given the large number of such 

leaks at a typical chemical manufacturing plant. There are also growing 

indications based on tests conducted by TCEQ and others in Harris County, Texas 

that fugitive leaks have been underestimated from chemical plants by a factor of 6 

to 20 or greater. 3

Enhanced LDAR for Fugitive Leaks

Overview: This control measure is a more stringent program to reduce leaks of 

fugitive VOC emissions from chemical plants and refineries that presumes that an 

existing LDAR program already is in operation. 

Major Uncertainties: The calculations of control efficiency and cost presume use 

of LDAR at a chemical plant. This should not be an unreasonable assumption, 

however, given that most chemical plants are under some type of requirement to 

have an LDAR program. However, as mentioned earlier, there is growing 

evidence that fugitive leak emissions are underestimated from chemical plants by 

a factor of 6 to 20 or greater.4

3

 VOC Fugitive Losses: New Monitors, Emissions Losses, and Potential Policy Gaps. 

2006 International Workshop. U.S. Environmental Protection Agency, Office of Air 

Quality Planning and Standards and Office of Solid Waste and Emergency Response. 

October 25-27, 2006. 

4

 VOC Fugitive Losses: New Monitors, Emissions Losses, and Potential Policy Gaps. 

2006 International Workshop. U.S. Environmental Protection Agency, Office of Air 

Quality Planning and Standards and Office of Solid Waste and Emergency Response. 

October 25-27, 2006.
3a-6

Flare Gas Recovery

Overview: This control measure is a condenser that can recover 98 percent of the 

VOC emitted by flares that emit 20 tons per year or more of the pollutant. 

Major Uncertainties: Flare gas recovery is just gaining commercial acceptance in 

the US and is only in use at a small number of refineries. 

Cooling Towers

Overview: The control measure is continuous monitoring of VOC from the 

cooling water return to a level of 10 ppb. This monitoring is accomplished by 

using a continuous flow monitor at the inlet to each cooling tower. 

There is not a general estimate of control efficiency for this measure; one is to 

apply a continuous flow monitor until VOC emissions have reached a level of 1.7

tons/year for a given cooling tower.5

Major Uncertainties: The amount of VOC leakage from each cooling tower can 

greatly affect the overall cost-effectiveness of this control measure. 

Wastewater Drains and Separators

Overview: This control measure includes an inspection and maintenance 

program to reduce VOC emissions from wastewater drains and water seals on 

drains. This measure is a more stringent version of measures that underlie 

existing NESHAP requirements for such sources. 

Major Uncertainties: The reference for this control measures notes that the VOC 

emissions inventories for the five San Francisco Bay Area refineries whose data 

was a centerpiece of this report are incomplete. In addition, not all VOC species 

from these sources were included in the VOC data that is a basis for these 

calculations.6

In addition to the new supplemental controls presented above, there were a number of 

changes made to existing AirControlNET controls. These changes were made based 

upon an internal review performed by EPA engineers to examine the controls applied by 

AirControlNET and determine if these controls were sufficient or could be more 

aggressive in their application, given the 2020 analysis year. This review was performed 

for non-EGU NOx control measures. The result of this review was an increase in control 

5

 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of 

Regulation 8, Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004. 

6

 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of 

Regulation 8, Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004.
3a-7

efficiencies applied for many control measures, and more aggressive control measures for 

over 70 SCCs. The changes apply to the control strategies performed for the Eastern US 

only. These changes are listed in the table below. 

Table 3a.2 Supplemental Emission Control Measures Applied in Modeled 

Attainment Strategies for the Ozone NAAQS RIA – Changes to Control 

technologies currently in our Control Measures Database 

Pollutant SCC AirControlNET 

Source 

Description 

AirControlNET 

Control 

Technology 

New 

Control 

Technology 

New 

Control 

Efficiency 

(%) 

Old

Control 

Efficiency 

(%) 

NOX 10200104 

10200204

10200205

10300207

10300209

10200217

10300216

ICI Boilers - 

Coal-Stoker 

SNCR SCR 90.0 40.0 

NOX 10200901 

10200902

10200903

10200907

10300902

10300903

ICI Boilers - 

Wood/Bark/ 

Waste 

SNCR SCR 90.0 55.0 

NOX 10200401 

10200402

10200404

10200405

10300401

ICI Boilers - 

Residual Oil 

SCR SCR 90.0 80 

NOX 10200501 

10200502

10200504

ICI Boilers - 

Distillate Oil 

SCR SCR 90.0 80 

NOX 10200601 

10200602

10200603

10200604

10300601

10300602

10300603

10500106

10500206

ICI Boilers - 

Natural Gas 

SCR SCR 90.0 80 

NOX 30500606 Cement 

Manufacturing - 

Dry

SCR SCR 90.0 80 
3a-8

Pollutant SCC AirControlNET 

Source 

Description 

AirControlNET 

Control 

Technology 

New 

Control 

Technology 

New 

Control 

Efficiency 

(%) 

Old

Control 

Efficiency 

(%) 

NOX 30500706 Cement 

Manufacturing - 

Wet 

SCR SCR 90.0 80 

NOX 30300934 Iron & Steel 

Mills - Annealing 

SCR SCR 90.0 85 

NOX 10200701 

10200704

10200707

10200710

10200799

10201402

10300701

10300799

ICI Boilers - 

Process Gas 

SCR SCR 90.0 80 

NOX 10200802 

10200804

ICI Boilers - 

Coke 

SCR SCR 90.0 70 

NOX 10201002 ICI Boilers - 

LPG 

SCR SCR 90.0 80 

NOX 10201301 

10201302

ICI Boilers - 

Liquid Waste 

SCR SCR 90.0 80 

NOX 30700110 Sulfate Pulping - 

Recovery 

Furnaces

SCR SCR 90.0 80 

NOX 30100306 Ammonia 

Production –

Pri. Reformer, 

Nat. Gas 

SCR SCR 90.0 80 

 30500622 

30500623

Cement Kilns Biosolid 

Injection 

Biosolid 

Injection 

40.0 23 

NOX 30590013 

30190013

30190014

39990013

Industrial and 

Manufacturing 

Incinerators 

SNCR SCR 90.0 45 

NOX 30101301 

30101302

Nitric Acid 

Manufacturing 

SNCR SCR 90.0 908 

NOX 30600201 Fluid Cat. 

Cracking Units 

LNB + FGR SCR 90.0 901 

NOX 30590003 

Process Heaters - 

Process Gas LNB + SCR LNB + SCR 90.0 

88

NOX

30600101

30600103

30600111

Process Heaters - 

Distillate Oil LNB + SCR LNB + SCR 90.0 

90
3a-9

Pollutant SCC AirControlNET 

Source 

Description 

AirControlNET 

Control 

Technology 

New 

Control 

Technology 

New 

Control 

Efficiency 

(%) 

Old

Control 

Efficiency 

(%) 

NOX

30600106

30600199

Process Heaters - 

Residual Oil LNB + SCR LNB + SCR 90.0 

80

NOX

30600102

30600105

Process Heaters - 

Natural Gas LNB + SCR LNB + SCR 90.0 

80

NOX 30700104 Sulfate Pulping - 

Recovery 

Furnaces

SCR SCR 90.0 80 

NOX 30790013 Pulp and Paper - 

Natural Gas - 

Incinerators 

SNCR SCR 90.0 45 

NOX 39000201 In-Process; 

Bituminous Coal; 

Cement Kiln 

SNCR - urea 

based 

SCR 90.0 50 

NOX 39000203 In-Process; 

Bituminous Coal; 

Lime Kiln 

SNCR - urea 

based 

SCR 90.0 50 

NOX 39000289 In-Process Fuel 

Use;Bituminous 

Coal; Gen 

SNCR SCR 90.0 40 

NOX 39000489 In-Process Fuel 

Use; Residual 

Oil; Gen 

LNB SCR 90.0 37 

NOX 39000689 In-Process Fuel 

Use; Natural Gas; 

Gen 

LNB SCR 90.0 50 

NOX 39000701 In-Proc;Process 

Gas;Coke 

Oven/Blast Furn 

LNB + FGR SCR 90.0 55 

NOX 39000789 In-Process; 

Process Gas; 

Coke Oven Gas 

LNB SCR 90.0 50 

NOX 50100101 

50100506

50200506

50300101

50300102

50300104

50300506

50100102

Solid Waste 

Disp;Gov;Other 

Incin;Sludge 

SNCR SCR 90.0 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. 
3a-10

Table 3a.3 Supplemental Emission Control Measures Applied in Modeled 

Attainment Strategies for the Ozone NAAQS RIA –Control technologies currently 

in our Control Measures Database Applied to New Source types 

Pollutant SCC SCC Description Control 

Technology 

Control 

Efficiency 

NOX 39000602 Cement Manufacturing - Dry SCR 90.0 

NOX 30501401 Glass Manufacturing - General OXY-Firing 85.0 

NOX 30302351 

30302352

30302359

Taconite Iron Ore Processing - 

Induration - Coal or Gas 

SCR 90.0 

NOX 10100101 External Combustion Boilers;Electric 

Generation;Anthracite Coal;Pulverized 

Coal 

SNCR 40.0 

NOX 10100202 External Combustion Boilers;Electric 

Generation;Bituminous/Subbituminous 

Coal;Pulverized Coal: Dry Bottom 

(Bituminous Coal) 

SNCR 40.0 

NOX 10100204 External Combustion Boilers;Electric 

Generation;Bituminous/Subbituminous 

Coal;Spreader Stoker (Bituminous Coal)

SNCR 40.0 

NOX 10100212 External Combustion Boilers;Electric 

Generation;Bituminous/Subbituminous 

Coal;Pulverized Coal: Dry Bottom 

(Tangential) (Bituminous Coal) 

SNCR 40.0 

NOX 10100401 External Combustion Boilers;Electric 

Generation;Residual Oil;Grade 6 Oil: 

Normal Firing 

SNCR 50.0 

NOX 10100404 External Combustion Boilers;Electric 

Generation;Residual Oil;Grade 6 Oil: 

Tangential Firing 

SNCR 50.0 

NOX 10100501 External Combustion Boilers;Electric 

Generation;Distillate Oil;Grades 1 and 2 

Oil

SNCR 50.0 

NOX 10100601 External Combustion Boilers;Electric 

Generation;Natural Gas;Boilers > 100 

Million Btu/hr except Tangential 

NGR 50.0 

NOX 10100602 External Combustion Boilers;Electric 

Generation;Natural Gas;Boilers < 100 

Million Btu/hr except Tangential 

NGR 50.0 

NOX 10100604 External Combustion Boilers;Electric 

Generation;Natural Gas;Tangentially 

Fired Units 

NGR 50.0 
3a-11

NOX 10101202 External Combustion Boilers;Electric 

Generation;Solid Waste;Refuse Derived 

Fuel 

SNCR 50.0 

NOX 20200253 Internal Comb. 

Engines/Industrial/Natural Gas/4-cycle 

Rich Burn 

NSCR 90 

3a.2Mobile Controls/Rules Used in Baseline and Control Scenarios 

3a.2.1 Diesel Retrofits and Vehicle Replacement 

Retrofitting heavy-duty diesel vehicles and equipment manufactured before stricter 

standards are in place – in 2007-2010 for highway engines and in 2011-2014 for most 

nonroad equipment – can provide NOX and HC benefits. The retrofit strategies included 

in the RIA retrofit measure are: 

 Installation of emissions after-treatment devices called selective catalytic 

reduction (“SCRs”) 

 Rebuilding nonroad engines (“rebuild/upgrade kit”) 

We chose to focus on these strategies due to their high NOx emissions reduction potential 

and widespread application. Additional retrofit strategies include, but are not limited to, 

lean NOx catalyst systems – which are another type of after-treatment device – and 

alternative fuels. Additionally, SCRs are currently the most likely type of control 

technology to be used to meet EPA’s NOx 2007-2010 requirements for HD diesel trucks 

and 2008-2011 requirements for nonroad equipment. Actual emissions reductions may 

vary significantly by strategy and by the type and age of the engine and its application.

To estimate the potential emissions reductions from this measure, we applied a mix of 

two retrofit strategies (SCRs and rebuild/upgrade kits) for the 2020 inventory of: 

 Heavy-duty highway trucks class 6 & above, Model Year 1995-2009 

 All diesel nonroad engines, Model Year 1991-2007, except for locomotive, 

marine, pleasure craft, & aircraft engines 

Class 6 and above trucks comprise the bulk of the NOx emissions inventory from heavyduty 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 
3a-12

risk of failure for earlier model years. We expect the engines manufactured before the 

lower bound year that are still in existence in 2020 to be retired quickly due to natural 

turnover, therefore, we have not included strategies for pre-1995/1991 engines because of 

the strategies’ relatively small impact on emissions. The upper bound in the model year 

range reflects the last year before more stringent emissions standards will be fully 

phased-in.

We chose the type of strategy to apply to each model year of highway vehicles and 

nonroad equipment based on our technical assessment of which strategies would achieve 

reliable results at the lowest cost. After-treatment devices can be more cost-effective 

than rebuild and vice versa depending on the emissions rate, application, usage rates, and 

expected life of the engine. The performance of after-treatment devices, for example, 

depends heavily upon the model year of the engine; some older engines may not be 

suitable for after-treatment devices and would be better candidates for rebuild/upgrade 

kit. In certain cases, nonroad engines may not be suitable for either after-treatment 

devices or rebuild, which is why we estimate that retrofits are not suitable for 5% of the 

nonroad fleet. The mix of strategies employed in this RIA for highway vehicles and 

nonroad engines are presented in Table 3a.4 and Table 3a.5, respectively. The groupings 

of model years for highway vehicles reflect changes in EPA’s published emissions 

standards for new engines. 

Table 3a.4 Application of Retrofit Strategy for Highway Vehicles by Percentage of 

Fleet

Model Year SCR

<1995 0%

1995-2006 100%

2007-2009 50%

>2009 0%

Table 3a.5 Application of Retrofit Strategy for Nonroad Equipment by Percentage 

of Fleet 

Model Year Rebuild/Upgrade kit SCR

1991-2007 50% 50%

The expected emissions reductions from SCR’s are based on data derived from EPA 

regulations (Control of Emissions of Air Pollution from 2004 and Later Model Year 

Heavy-duty Highway Engines and Vehicles published October 2000), interviews with 

component manufacturers, and EPA’s Summary of Potential Retrofit Technologies. This 

information is available at www.epa.gov/otaq/retrofit/retropotentialtech.htm. The 

estimates for highway vehicles and nonroad engines are presented in Table 3a.6 and 

Table 3a.7, respectively. 
3a-13

Table 3a.6: Percentage Emissions Reduction by Highway Vehicle Retrofit Strategy 

PM CO HC NOx

SCR (+DPF) 90% 90% 90% 70%

Table 3a.7: 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 best available estimates of emissions 

reductions that can be expected from retrofitting the heavy-duty diesel fleet. 

Using the retrofit module in EPA’s National Mobile Inventory Model (NMIM) available 

at http://www.epa.gov/otaq/nmim.htm, we calculated the total percentage reduction in 

emissions (PM, NOx, HC, and CO) from the retrofit measure for each relevant engine 

category (source category code, or SCC) for each county in 2020. To evaluate this 

change in the emissions inventory, we conducted both a baseline and control analysis. 

Both analyses were based on NMIM 2005 (version NMIM20060310), NONROAD2005 

(February 2006), and MOBILE6.2.03 which included the updated diesel PM file 

PMDZML.csv dated March 17, 2006. 

For the control analysis, we applied the retrofit measure corresponding to the percent 

reductions of the specified pollutants in Tables 3a.6 and 3a.7 to the specified model years 

in Tables 1 and 2 of the relevant SCCs. Fleet turnover rates are modeled in the NMIM, 

so we applied the retrofit measure to the 2007 fleet inventory, and then evaluated the 

resulting emissions inventory in 2020. The timing of the application of the retrofit 

measure is not a factor; retrofits only need to take place prior to the attainment date target 

(2020 for this RIA). For example, if retrofit devices are installed on 1995 model year 

bulldozers in 2007, the only impact on emissions in 2020 will be from the expected 

inventory of 1995 model year bulldozer emissions in 2020. 
3a-14

We then compared the baseline and control analyses to determine the percent reduction in 

emissions we estimate from this measure for the relevant SCC codes in the targeted 

nonattainment areas. 

Pollutants and Source Categories Affected by Measure (SCC) 

NOx, and HC

3a.2.2 Implement Continuous Inspection and Maintenance Using Remote Onboard 

Diagnostics (OBD) 

Continuous Inspection and Maintenance (I/M) is a new way to check the status of OBD 

systems on light-duty OBD-equipped vehicles. It involves equipping subject vehicles 

with some type of transmitter that attaches to the OBD port. The device transmits the 

status of the OBD system to receivers distributed around the I/M area. Transmission may 

be through radio-frequency, cellular or wi-fi means. Radio frequency and cellular 

technologies are currently being used in the states of Oregon, California and Maryland. 

Current I/M programs test light-duty vehicles on a periodic basis – either annually or 

biennially. Emission reduction credit is assigned based on test frequency. Using 

Continuous I/M, vehicles are continuously monitored as they are operated throughout the 

non-attainment area. When a vehicle experiences an OBD failure, the motorist is notified 

and is required to get repairs within the normal grace period – typically about a month. 

Thus, Continuous I/M will result in repairs happening essentially whenever a malfunction 

occurs that would cause the check engine light to illuminate. The continuous I/M 

program is applied to the same fleet of vehicles as the current periodic I/M programs. 

Currently, MOBILE6 provides an increment of benefit when going from a biennial 

program to an annual program. The same increment of credit applies going from an 

annual program to a continuous program. 

Pollutants and Source Categories Affected by Measure (SCC): 

x All 1996 and newer light-duty gasoline vehicles and trucks: 

x All 1996 and newer 2201001000 Light Duty Gasoline Vehicles (LDGV), Total: 

All Road Types 

x All 1996 and newer 2201020000 Light Duty Gasoline Trucks 1 (LDGT1), Total: 

All Road Types 

x All 1996 and newer 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Total: 

All Road Types 

OBD systems on light duty vehicles are required to illuminate the malfunction 

indicator lamp whenever emissions of HC, CO or NOx would exceed 1.5 times the 

vehicle’s certification standard. Thus, the benefits of this measure will affect all three 

criteria pollutants. MOBILE6 was used to estimate the emission reduction benefits of 

Continuous I/M, using the methodology discussed above. 
3a-15

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 

targets. For more information on determining baseline idling activity see EPA’s 

"Guidance for Quantifying and Using Long-Duration Truck Idling Emission Reductions 

in State Implementation Plans and Transportation Conformity” available at

http://www.epa.gov/smartway/idle-guid.htm.

Pollutants and Source Categories Affected by Measure (SCC): NOx

Table 3a.8 Class 8a and 8b heavy duty diesel trucks (decrease NOx for all SCCs) 

SCC Note: All SCC Descriptions below begin with "Mobile Sources; Highway Vehicles - Diesel;" 

2230074110 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rural Interstate: Total 

2230074130 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rural Other Principal Arterial: Total 

2230074150 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rural Minor Arterial: Total 

2230074170 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rural Major Collector: Total 

2230074190 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B;Rural Minor Collector: Total 
3a-16

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.7

 The BWC survey found that, on average, employees at 

workplaces with comprehensive commuter programs emit 15% fewer emissions than 

employees at workplaces that do not offer a comprehensive commuter program. 

We believe that getting 10-25% of the workforce involved in commuter programs is 

realistic. For modeling purposes, we divided the commuter programs measure into two 

program penetration rates: 10% and 25%. This was meant to provide flexibility to model 

a lower penetration rate for areas that need only low levels of emissions reductions to 

achieve attainment. 

According to the 2001 National Household Transportation Survey (NHTS) published by 

DOT, commute VMT represents 27% of total VMT. Based on this information, we 

calculated that BWC would reduce light-duty gasoline emissions by 0.4% and 1% with a 

10% and 25% program penetration rate, respectively.

Pollutants and Source Categories Affected by Measure (SCC): NOx, and VOC 

7

 Herzog, E., Bricka, S., Audette, L., and Rockwell, J., 2005. Do Employee Commuter 

Benefits Reduce Vehicle Emissions and Fuel Consumption? Results of the Fall 2004 Best 

Workplaces for Commuters Survey, Transportation Research Record, Journal of the 

Transportation Research Board: Forthcoming. 
3a-17

Table 3a.9 All light-duty gasoline vehicles and trucks 

SCC Note: All SCC Descriptions below begin with "Mobile Sources; Highway Vehicles - 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 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Rural Interstate: Total 

2201020130 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Rural Other Principal Arterial: Total 

2201020150 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Rural Minor Arterial: Total 

2201020170 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Rural Major Collector: Total 

2201020190 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Rural Minor Collector: Total 

2201020210 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Rural Local: Total 

2201020230 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Urban Interstate: Total 

2201020250 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Urban Other Freeways and Expressways: Total 

2201020270 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Urban Other Principal Arterial: Total 

2201020290 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Urban Minor Arterial: Total 

2201020310 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Urban Collector: Total 

2201020330 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5);Urban Local: Total 

2201040110 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Rural Interstate: Total 

2201040130 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Rural Other Principal Arterial: Total 

2201040150 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Rural Minor Arterial: Total 

2201040170 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Rural Major Collector: Total 

2201040190 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Rural Minor Collector: Total 

2201040210 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Rural Local: Total 

2201040230 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Urban Interstate: Total 

2201040250 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Urban Other Freeways and Expressways: Total 

2201040270 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Urban Other Principal Arterial: Total 

2201040290 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Urban Minor Arterial: Total 

2201040310 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Urban Collector: Total 

2201040330 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5);Urban Local: Total 

Estimated Emissions Reduction from Measure (%): 

With a 10% program penetration rate: 0.4% 

With a 25% program penetration rate: 1% 
3a-18

3a.2.5 Reduce Gasoline RVP from 7.8 to 7.0 in Remaining Nonattainment Areas 

Volatility is the property of a liquid fuel that defines its evaporation characteristics. RVP 

is an abbreviation for "Reid vapor pressure," a common measure of gasoline volatility, as 

well as a generic term for gasoline volatility. EPA regulates the vapor pressure of all 

gasoline during the summer months (June 1 to September 15 at retail stations). Lower 

RVP helps to reduce VOCs, which are a precursor to ozone formation. This control 

measure represents the use of gasoline with a RVP limit of 7.0 psi from May through 

September in counties with an ozone season RVP value greater than 7.0 psi. 

Under section 211(c)(4)(C) of the CAA, EPA may approve a non-identical state fuel 

control as a SIP provision, if the state demonstrates that the measure is necessary to 

achieve the national primary or secondary ambient air quality standard (NAAQS) that the 

plan implements. EPA can approve a state fuel requirement as necessary only if no other 

measures would bring about timely attainment, or if other measures exist but are 

unreasonable or impracticable. 

Pollutants and Source Categories Affected by Measure (SCC): 

x All light-duty gasoline vehicles and trucks: Affected SCC: 

x 2201001000 Light Duty Gasoline Vehicles (LDGV), Total: All Road Types 

x 2201020000 Light Duty Gasoline Trucks 1 (LDGT1), Total: All Road Types 

x 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Total: All Road Types 

x 2201070000 Heavy Duty Gasoline Vehicles (HDGV), Total: All Road Types 

x 2201080000 Motorcycles (MC), Total: All Road Types 

3a.2.6 Application order for Onroad and Nonroad Mobile Controls 

Application order- 0.084 Mobile

x Eliminate Long Duration Idling 

x ONRetrofit 

x LOWRVP 

x Best Workplaces for Commuters 

Application order- 0.084 Nonroad

x Diesel C1&C2 Marine/Diesel C3 Marine - 90% Rule (adding controls for 

SCCs for residual fuel) 

x ICAO Engine NOx Standards for Commercial Aircraft 

x NRRetrofit 

x LOWRVP 

Application order- 0.070 Mobile

x Eliminate Long Duration Idling 

x Inspection and Maintenance 

x ONRetrofit 
3a-19

x LOWRVP 

x Best Workplaces for Commuters 

Application order – 0.070 Nonroad

x Diesel C1&C2 Marine/Diesel C3 Marine - 90% Rule (adding controls for 

SCCs for residual fuel) 

x ICAO Engine NOx Standards for Commercial Aircraft 

x NRRetrofit 

x LOWRVP 

3a.3 EGU Controls Used in the Control Strategy 

CAIR

The data and projections presented in Section 3.2.2 cover the electric power sector, an 

industry that will achieve significant emission reductions under the Clean Air Interstate 

Rule (CAIR) over the next 10 to 15 years. Based on an assessment of the emissions 

contributing to interstate transport of air pollution and available control measures, EPA 

determined that achieving required reductions in the identified States by controlling 

emissions from power plants is highly cost effective. CAIR will permanently cap 

emissions of sulfur dioxide (SO2) 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. 

Figure 3a.1 CAIR Affected Region 

States controlled for fine particles (annual SO2and NOx)

States not covered by CAIR

States controlled for ozone (ozone season NOx)

States controlled for both fine particles (annual SO2and NOx) and ozone (ozone season NOx)
3a-20

When fully implemented, CAIR will reduce SO2 emissions in these states by over 70% 

and NOx emissions by over 60% from 2003 levels (some of which are due to NOx SIP 

Call). This will result in significant environmental and health benefits and will 

substantially reduce premature mortality in the eastern United States. The benefits will 

continue to grow each year with further implementation. CAIR was designed with current 

air quality standard in mind, and requires significant emission reductions in the East, 

where they are needed most and where transport of pollution is a major concern. CAIR 

will bring most areas in the Eastern US into attainment with the current ozone and current 

PM2.5 standards. Some areas will need to adopt additional local control measures beyond 

CAIR. CAIR is a regional solution to address transport, not a solution to all local 

nonattainment issues. The large reductions anticipated with CAIR, in conjunction with 

reasonable additional local control measures for SO2, NOx, and direct PM, will move 

States towards attainment in a deliberate and logical manner. 

Based on the final State rules that have been submitted and the proposed State rules that 

EPA has reviewed, EPA believes that all States intend to use the CAIR trading programs 

as their mechanism for meeting the emission reduction requirements of CAIR. 

The analysis in this section reflects these realities and attempts to show, in an illustrative 

fashion, the costs and impacts of meeting a proposed 8-hr ozone standard of 0.070 for the 

power sector. 

Integrated Planning Model and Background

CAIR was designed to achieve significant emissions reductions in a highly cost-effective 

manner to reduce the transport of fine particles that have been found to contribute to 

nonattainment. EPA analysis has found that the most efficient method to achieve the 

emissions reduction targets is through a cap-and-trade system on the power sector that 

States have the option of adopting. The modeling done with IPM assumes a region-wide 

cap and trade system on the power sector for the States covered. 

It is important to note that the analysis herein uses the Integrated Planning Model (IPM) 

v2.1.9 to ensure consistency with the analysis presented in 2006 PM NAAQS RIA and 

report incremental results. EPA’s IPM v2.1.9 incorporates Federal and State rules and 

regulations adopted before March 2004 and various NSR settlements. A detailed 

discussion of uncertainties associated with the EGU sector can be found in 2006 PM 

NAAQS RIA (pg. 3-50). A newer version of the model (IPM v3.0) is available which 

includes input and model assumption updates in modeling power sector. IPM v3.0 will 

be used in the Final Ozone NAAQS RIA as part of the updated modeling platform. 

Additionally, other control strategies are being considered that may be applicable to the 

EGU sector, which would be presented in the final Ozone RIA. 

The economic modeling using IPM presented in this and other chapters has been 

developed for specific analyses of the power sector. EPA’s modeling is based on its best 

judgment for various input assumptions that are uncertain, particularly assumptions for 

future fuel prices and electricity demand growth. To some degree, EPA addresses the 

uncertainty surrounding these two assumptions through sensitivity analyses. More detail 
3a-21

on IPM can be found in the model documentation, which provides additional information 

on the assumptions discussed here as well as all other assumptions and inputs to the 

model (http://www.epa.gov/airmarkets/progsregs/epa-ipm/past-modeling.html).

EGU NOx Emission Control Technologies

The Integrated Planning Model v2.1.9 (IPM) includes SO2, NOx, and mercury (Hg) 

emission control technology options for meeting existing and future federal, regional, and 

state, SO2, NOx and Hg emission limits. The NOx control technology options include 

Selective Catalytic Reduction (SCR) system and Selective Non-Catalytic Reduction 

(SNCR) systems. It is important to note that beyond these emission control options, IPM 

offers other compliance options for meeting emission limits. These include fuel 

switching, re-powering, and adjustments in the dispatching of electric generating units. 

Table 3a.10 summarizes retrofit NOx emission control performance assumptions. 

Table 3a.10. Summary of Retrofit NOx Emission Control Performance 

Assumptions

Selective Catalytic Reduction 

(SCR) 

Selective Non-Catalytic Reduction 

 (SNCR) 

Unit Type Coal Oil/Gas* Coal Oil/Gas* 

Percent Removal 

90%

down to 0.06 

lb/mmBtu 80% 35% 50% 

Size Applicability Units Қ 100 MW Units Қ 25 MW 

Units Қ 25 MW 

and

Units < 200 MW Units Қ 25 MW 

* Controls to oil- or gas-fired EGUs are not applied as part of the EGU control strategy 

included in this RIA. 

Existing coal-fired units that are retrofit with SCR have a NOx removal efficiency of 

90%, with a minimum controlled NOx emission rate of 0.06 lb/mmBtu in IPM v2.1.9.. 

Potential (new) coal-fired, combined cycle, and IGCC units are modeled to be 

constructed with SCR systems and designed to have emission rates ranging between 0.02 

and 0.06 lb 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/pastmodeling.html). 
3a-22

3a.4 Emissions Reductions by Sector 

Figures 3a.2- 3a.6 show the NOx reductions for each sector under the 0.070 ppm control 

strategy. 

Figure 3a.2 Tons of Nitrogen Oxide (NOx) Emissions Reduced from Electrical 

Generating Unit (EGU) Sources* 

*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 Tons of Nitrogen Oxide (NOx) Emissions Reduced from Non-EGU 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 Tons 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 Tons 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 Tons 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

3a.5 Change in Ozone Concentrations Between Baseline and Post-0.070 ppm 

Control Strategy Modeling 

Table 3a.11 Changes in Ozone Concentrations between Baseline and Post-0.070 

ppm Control Strategy Modeling 

State County 

Baseline 

8-hour ozone 

DV (ppm) 

Control Scenario 

8-hour ozone 

DV (ppm) 

Change 

(ppm)

Alabama Baldwin 0.067 0.066 0.001 

Alabama Clay 0.060 0.056 0.004 

Alabama Elmore 0.062 0.060 0.002 

Alabama Jefferson 0.064 0.063 0.001 

Alabama Madison 0.063 0.061 0.002 

Alabama Mobile 0.068 0.068 0.000 

Alabama Montgomery 0.061 0.060 0.001 

Alabama Morgan 0.066 0.065 0.001 

Alabama Shelby 0.066 0.065 0.001 

Alabama Tuscaloosa 0.057 0.056 0.001 

Arizona Maricopa 0.078 0.077 0.001 

Arizona Pinal 0.072 0.071 0.001 

Arkansas Crittenden 0.075 0.072 0.003 

Arkansas Pulaski 0.069 0.068 0.001 

California Alameda 0.067 0.067 0.000 

California Amador 0.068 0.068 0.000 

California Butte 0.069 0.069 0.000 

California Calaveras 0.073 0.073 0.000 

California Colusa 0.059 0.059 0.000 

California Contra Costa 0.070 0.070 0.000 

California El Dorado 0.080 0.080 0.000 

California Fresno 0.092 0.092 0.000 

California Glenn 0.060 0.060 0.000 

California Imperial 0.072 0.072 0.000 

California Kern 0.096 0.096 0.000 

California Kings 0.079 0.079 0.000 

California Lake 0.053 0.053 0.000 

California Los Angeles 0.105 0.105 0.000 

California Madera 0.075 0.075 0.000 

California Mariposa 0.073 0.073 0.000 

California Merced 0.080 0.080 0.000 

California Monterey 0.054 0.054 0.000 
3a-28

California Napa 0.051 0.051 0.000 

California Nevada 0.076 0.076 0.000 

California Orange 0.066 0.065 0.001 

California Placer 0.076 0.076 0.000 

California Riverside 0.102 0.102 0.000 

California Sacramento 0.076 0.076 0.000 

California San Benito 0.067 0.067 0.000 

California San Bernardino 0.129 0.129 0.000 

California San Diego 0.077 0.077 0.000 

California San Joaquin 0.067 0.067 0.000 

California San Luis Obispo 0.053 0.053 0.000 

California Santa Barbara 0.065 0.065 0.000 

California Santa Clara 0.065 0.065 0.000 

California Santa Cruz 0.054 0.055 -0.001 

California Shasta 0.058 0.058 0.000 

California Solano 0.057 0.057 0.000 

California Sonoma 0.049 0.049 0.000 

California Stanislaus 0.076 0.076 0.000 

California Sutter 0.065 0.065 0.000 

California Tehama 0.066 0.066 0.000 

California Tulare 0.088 0.088 0.000 

California Tuolumne 0.073 0.073 0.000 

California Ventura 0.079 0.080 -0.001 

California Yolo 0.064 0.064 0.000 

Colorado Adams 0.061 0.060 0.001 

Colorado Arapahoe 0.073 0.072 0.001 

Colorado Boulder 0.066 0.064 0.002 

Colorado Denver 0.068 0.067 0.001 

Colorado Douglas 0.076 0.076 0.000 

Colorado El Paso 0.064 0.063 0.001 

Colorado Jefferson 0.078 0.076 0.002 

Colorado Larimer 0.069 0.067 0.002 

Colorado Weld 0.067 0.065 0.002 

Connecticut Fairfield 0.088 0.087 0.001 

Connecticut Hartford 0.069 0.067 0.002 

Connecticut Litchfield 0.063 0.061 0.002 

Connecticut Middlesex 0.081 0.080 0.001 

Connecticut New Haven 0.084 0.083 0.001 

Connecticut New London 0.072 0.070 0.002 

Connecticut Tolland 0.071 0.069 0.002 

D.C. Washington 0.076 0.073 0.003 
3a-29

Delaware Kent 0.072 0.070 0.002 

Delaware New Castle 0.075 0.073 0.002 

Delaware Sussex 0.070 0.068 0.002 

Florida Bay 0.067 0.066 0.001 

Florida Brevard 0.055 0.053 0.002 

Florida Duval 0.058 0.057 0.001 

Florida Escambia 0.069 0.069 0.000 

Florida Hillsborough 0.072 0.071 0.001 

Florida Manatee 0.067 0.065 0.002 

Florida Pasco 0.061 0.060 0.001 

Florida Pinellas 0.064 0.063 0.001 

Florida Santa Rosa 0.065 0.064 0.001 

Florida Sarasota 0.063 0.061 0.002 

Georgia Bibb 0.073 0.069 0.004 

Georgia Chatham 0.057 0.056 0.001 

Georgia Cherokee 0.055 0.052 0.003 

Georgia Cobb 0.072 0.068 0.004 

Georgia Coweta 0.072 0.064 0.008 

Georgia Dawson 0.058 0.055 0.003 

Georgia De Kalb 0.076 0.072 0.004 

Georgia Douglas 0.071 0.067 0.004 

Georgia Fayette 0.069 0.066 0.003 

Georgia Fulton 0.080 0.076 0.004 

Georgia Glynn 0.058 0.057 0.001 

Georgia Gwinnett 0.067 0.064 0.003 

Georgia Henry 0.072 0.068 0.004 

Georgia Murray 0.062 0.059 0.003 

Georgia Muscogee 0.065 0.061 0.004 

Georgia Paulding 0.068 0.065 0.003 

Georgia Richmond 0.067 0.063 0.004 

Georgia Rockdale 0.071 0.067 0.004 

Illinois Adams 0.062 0.057 0.005 

Illinois Champaign 0.064 0.062 0.002 

Illinois Clark 0.057 0.056 0.001 

Illinois Cook 0.083 0.083 0.000 

Illinois Du Page 0.065 0.064 0.001 

Illinois Effingham 0.062 0.061 0.001 

Illinois Hamilton 0.066 0.064 0.002 

Illinois Jersey 0.074 0.069 0.005 

Illinois Kane 0.067 0.066 0.001 

Illinois Lake 0.074 0.073 0.001 
3a-30

Illinois Macon 0.060 0.059 0.001 

Illinois Macoupin 0.064 0.060 0.004 

Illinois Madison 0.071 0.066 0.005 

Illinois McHenry 0.070 0.068 0.002 

Illinois McLean 0.063 0.061 0.002 

Illinois Peoria 0.066 0.064 0.002 

Illinois Randolph 0.065 0.062 0.003 

Illinois Rock Island 0.058 0.057 0.001 

Illinois Sangamon 0.060 0.058 0.002 

Illinois St Clair 0.072 0.069 0.003 

Illinois Will 0.068 0.067 0.001 

Illinois Winnebago 0.061 0.060 0.001 

Indiana Allen 0.070 0.068 0.002 

Indiana Boone 0.072 0.069 0.003 

Indiana Carroll 0.066 0.064 0.002 

Indiana Clark 0.076 0.074 0.002 

Indiana Delaware 0.069 0.067 0.002 

Indiana Floyd 0.071 0.070 0.001 

Indiana Gibson 0.056 0.054 0.002 

Indiana Greene 0.069 0.067 0.002 

Indiana Hamilton 0.076 0.073 0.003 

Indiana Hancock 0.074 0.071 0.003 

Indiana Hendricks 0.071 0.069 0.002 

Indiana Huntington 0.067 0.065 0.002 

Indiana Jackson 0.068 0.065 0.003 

Indiana Johnson 0.070 0.068 0.002 

Indiana La Porte 0.075 0.073 0.002 

Indiana Lake 0.084 0.083 0.001 

Indiana Madison 0.071 0.068 0.003 

Indiana Marion 0.075 0.072 0.003 

Indiana Morgan 0.070 0.066 0.004 

Indiana Perry 0.071 0.071 0.000 

Indiana Porter 0.078 0.077 0.001 

Indiana Posey 0.071 0.070 0.001 

Indiana Shelby 0.077 0.074 0.003 

Indiana St Joseph 0.069 0.067 0.002 

Indiana Vanderburgh 0.068 0.066 0.002 

Indiana Vigo 0.070 0.065 0.005 

Indiana Warrick 0.068 0.067 0.001 

Iowa Clinton 0.063 0.062 0.001 

Iowa Scott 0.066 0.065 0.001 
3a-31

Kansas Wyandotte 0.070 0.069 0.001 

Kentucky Bell 0.063 0.062 0.001 

Kentucky Boone 0.067 0.066 0.001 

Kentucky Boyd 0.072 0.067 0.005 

Kentucky Bullitt 0.067 0.064 0.003 

Kentucky Campbell 0.077 0.073 0.004 

Kentucky Carter 0.064 0.061 0.003 

Kentucky Christian 0.066 0.065 0.001 

Kentucky Daviess 0.062 0.062 0.000 

Kentucky Edmonson 0.067 0.066 0.001 

Kentucky Fayette 0.063 0.061 0.002 

Kentucky Graves 0.068 0.066 0.002 

Kentucky Greenup 0.068 0.064 0.004 

Kentucky Hancock 0.067 0.067 0.000 

Kentucky Hardin 0.068 0.065 0.003 

Kentucky Henderson 0.066 0.065 0.001 

Kentucky Jefferson 0.072 0.070 0.002 

Kentucky Jessamine 0.062 0.063 -0.001 

Kentucky Kenton 0.073 0.069 0.004 

Kentucky Livingston 0.071 0.069 0.002 

Kentucky McCracken 0.069 0.067 0.002 

Kentucky McLean 0.065 0.064 0.001 

Kentucky Oldham 0.072 0.070 0.002 

Kentucky Pulaski 0.065 0.064 0.001 

Kentucky Scott 0.056 0.055 0.001 

Kentucky Simpson 0.066 0.065 0.001 

Kentucky Trigg 0.060 0.058 0.002 

Kentucky Warren 0.066 0.065 0.001 

Louisiana Ascension 0.071 0.066 0.005 

Louisiana Bossier 0.073 0.070 0.003 

Louisiana Caddo 0.068 0.065 0.003 

Louisiana Calcasieu 0.072 0.067 0.005 

Louisiana East Baton Rouge 0.077 0.074 0.003 

Louisiana Grant 0.063 0.059 0.004 

Louisiana Iberville 0.076 0.072 0.004 

Louisiana Jefferson 0.072 0.069 0.003 

Louisiana Lafayette 0.070 0.064 0.006 

Louisiana Lafourche 0.072 0.068 0.004 

Louisiana Livingston 0.072 0.068 0.004 

Louisiana Orleans 0.060 0.058 0.002 

Louisiana Ouachita 0.068 0.064 0.004 
3a-32

Louisiana Pointe Coupee 0.064 0.060 0.004 

Louisiana St Bernard 0.067 0.065 0.002 

Louisiana St Charles 0.068 0.066 0.002 

Louisiana St James 0.069 0.065 0.004 

Louisiana 

St John The 

Baptist 0.072 0.069 0.003 

Louisiana St Mary 0.068 0.062 0.006 

Louisiana 

West Baton 

Rouge 0.074 0.071 0.003 

Maine Cumberland 0.065 0.063 0.002 

Maine Hancock 0.070 0.067 0.003 

Maine Kennebec 0.060 0.057 0.003 

Maine Knox 0.063 0.060 0.003 

Maine Penobscot 0.062 0.059 0.003 

Maine York 0.069 0.066 0.003 

Maryland Anne Arundel 0.076 0.074 0.002 

Maryland Baltimore 0.077 0.075 0.002 

Maryland Calvert 0.065 0.063 0.002 

Maryland Carroll 0.068 0.066 0.002 

Maryland Cecil 0.078 0.075 0.003 

Maryland Charles 0.070 0.068 0.002 

Maryland Frederick 0.067 0.065 0.002 

Maryland Harford 0.084 0.082 0.002 

Maryland Kent 0.075 0.072 0.003 

Maryland Montgomery 0.072 0.070 0.002 

Maryland Prince Georges 0.075 0.072 0.003 

Maryland Washington 0.067 0.063 0.004 

Massachusetts Barnstable 0.072 0.070 0.002 

Massachusetts Berkshire 0.067 0.066 0.001 

Massachusetts Bristol 0.072 0.069 0.003 

Massachusetts Essex 0.071 0.070 0.001 

Massachusetts Hampden 0.070 0.068 0.002 

Massachusetts Hampshire 0.068 0.066 0.002 

Massachusetts Middlesex 0.067 0.064 0.003 

Massachusetts Suffolk 0.067 0.065 0.002 

Massachusetts Worcester 0.064 0.062 0.002 

Michigan Allegan 0.075 0.072 0.003 

Michigan Benzie 0.070 0.068 0.002 

Michigan Berrien 0.072 0.070 0.002 

Michigan Cass 0.069 0.067 0.002 

Michigan Clinton 0.066 0.062 0.004 
3a-33

Michigan Genesee 0.067 0.064 0.003 

Michigan Huron 0.070 0.067 0.003 

Michigan Ingham 0.066 0.062 0.004 

Michigan Kalamazoo 0.065 0.062 0.003 

Michigan Kent 0.067 0.064 0.003 

Michigan Lenawee 0.069 0.063 0.006 

Michigan Macomb 0.080 0.078 0.002 

Michigan Mason 0.072 0.070 0.002 

Michigan Missaukee 0.064 0.061 0.003 

Michigan Muskegon 0.074 0.071 0.003 

Michigan Oakland 0.077 0.075 0.002 

Michigan Ottawa 0.070 0.067 0.003 

Michigan St Clair 0.073 0.071 0.002 

Michigan Washtenaw 0.076 0.072 0.004 

Michigan Wayne 0.076 0.073 0.003 

Minnesota Anoka 0.058 0.057 0.001 

Minnesota Washington 0.059 0.059 0.000 

Mississippi De Soto 0.069 0.067 0.002 

Mississippi Hancock 0.070 0.068 0.002 

Mississippi Harrison 0.064 0.067 -0.003 

Mississippi Hinds 0.055 0.053 0.002 

Mississippi Jackson 0.069 0.070 -0.001 

Mississippi Warren 0.054 0.051 0.003 

Missouri Clay 0.070 0.068 0.002 

Missouri Jefferson 0.076 0.072 0.004 

Missouri Platte 0.069 0.068 0.001 

Missouri St Charles 0.076 0.072 0.004 

Missouri St Louis 0.079 0.075 0.004 

Missouri St Louis City 0.078 0.075 0.003 

Missouri Ste Genevieve 0.068 0.064 0.004 

Nevada Clark 0.072 0.072 0.000 

Nevada Washoe 0.063 0.063 0.000 

New 

Hampshire Hillsborough 0.063 0.060 0.003 

New 

Hampshire Rockingham 0.063 0.061 0.002 

New Jersey Atlantic 0.071 0.069 0.002 

New Jersey Bergen 0.077 0.075 0.002 

New Jersey Camden 0.082 0.080 0.002 

New Jersey Cumberland 0.073 0.071 0.002 

New Jersey Essex 0.056 0.055 0.001 
3a-34

New Jersey Gloucester 0.080 0.078 0.002 

New Jersey Hudson 0.074 0.073 0.001 

New Jersey Hunterdon 0.078 0.077 0.001 

New Jersey Mercer 0.083 0.081 0.002 

New Jersey Middlesex 0.081 0.079 0.002 

New Jersey Monmouth 0.078 0.077 0.001 

New Jersey Morris 0.077 0.075 0.002 

New Jersey Ocean 0.084 0.081 0.003 

New Jersey Passaic 0.071 0.069 0.002 

New Mexico Dona Ana 0.071 0.070 0.001 

New Mexico San Juan 0.071 0.068 0.003 

New York Albany 0.064 0.063 0.001 

New York Bronx 0.069 0.068 0.001 

New York Chautauqua 0.074 0.070 0.004 

New York Dutchess 0.067 0.066 0.001 

New York Erie 0.079 0.075 0.004 

New York Jefferson 0.075 0.072 0.003 

New York Monroe 0.073 0.072 0.001 

New York Niagara 0.076 0.075 0.001 

New York Orange 0.063 0.061 0.002 

New York Putnam 0.070 0.068 0.002 

New York Queens 0.068 0.067 0.001 

New York Richmond 0.074 0.072 0.002 

New York Saratoga 0.066 0.065 0.001 

New York Suffolk 0.086 0.084 0.002 

New York Ulster 0.065 0.063 0.002 

New York Wayne 0.070 0.068 0.002 

New York Westchester 0.075 0.074 0.001 

North

Carolina Alexander 0.066 0.064 0.002 

North

Carolina Buncombe 0.065 0.065 0.000 

North

Carolina Camden 0.063 0.062 0.001 

North

Carolina Caswell 0.063 0.059 0.004 

North

Carolina Chatham 0.063 0.061 0.002 

North

Carolina Cumberland 0.065 0.063 0.002 

North

Carolina Davie 0.067 0.065 0.002 
3a-35

North

Carolina Durham 0.063 0.060 0.003 

North

Carolina Edgecombe 0.066 0.064 0.002 

North

Carolina Forsyth 0.068 0.065 0.003 

North

Carolina Franklin 0.063 0.060 0.003 

North

Carolina Granville 0.067 0.065 0.002 

North

Carolina Guilford 0.064 0.061 0.003 

North

Carolina Johnston 0.062 0.059 0.003 

North

Carolina Lincoln 0.069 0.067 0.002 

North

Carolina Mecklenburg 0.074 0.072 0.002 

North

Carolina New Hanover 0.062 0.061 0.001 

North

Carolina Northampton 0.067 0.064 0.003 

North

Carolina Person 0.071 0.068 0.003 

North

Carolina Randolph 0.063 0.060 0.003 

North

Carolina Rockingham 0.064 0.061 0.003 

North

Carolina Rowan 0.073 0.071 0.002 

North

Carolina Union 0.065 0.063 0.002 

North

Carolina Wake 0.066 0.064 0.002 

Ohio Allen 0.071 0.067 0.004 

Ohio Ashtabula 0.077 0.073 0.004 

Ohio Butler 0.073 0.070 0.003 

Ohio Clark 0.068 0.063 0.005 

Ohio Clermont 0.071 0.068 0.003 

Ohio Clinton 0.074 0.070 0.004 

Ohio Cuyahoga 0.071 0.068 0.003 

Ohio Delaware 0.071 0.068 0.003 

Ohio Franklin 0.076 0.073 0.003 

Ohio Geauga 0.080 0.076 0.004 

Ohio Greene 0.068 0.062 0.006 
3a-36

Ohio Hamilton 0.074 0.070 0.004 

Ohio Jefferson 0.067 0.064 0.003 

Ohio Knox 0.069 0.065 0.004 

Ohio Lake 0.076 0.073 0.003 

Ohio Lawrence 0.069 0.065 0.004 

Ohio Licking 0.069 0.066 0.003 

Ohio Lorain 0.071 0.068 0.003 

Ohio Lucas 0.072 0.069 0.003 

Ohio Madison 0.068 0.063 0.005 

Ohio Mahoning 0.071 0.068 0.003 

Ohio Medina 0.069 0.066 0.003 

Ohio Miami 0.065 0.061 0.004 

Ohio Montgomery 0.068 0.062 0.006 

Ohio Portage 0.074 0.070 0.004 

Ohio Preble 0.061 0.058 0.003 

Ohio Stark 0.071 0.068 0.003 

Ohio Summit 0.075 0.071 0.004 

Ohio Trumbull 0.073 0.070 0.003 

Ohio Warren 0.071 0.068 0.003 

Ohio Washington 0.064 0.061 0.003 

Ohio Wood 0.070 0.067 0.003 

Oklahoma Cleveland 0.065 0.064 0.001 

Oklahoma Marshall 0.069 0.067 0.002 

Oklahoma Mc Clain 0.067 0.065 0.002 

Oklahoma Oklahoma 0.067 0.065 0.002 

Oklahoma Tulsa 0.073 0.070 0.003 

Pennsylvania Allegheny 0.079 0.076 0.003 

Pennsylvania Armstrong 0.072 0.069 0.003 

Pennsylvania Beaver 0.076 0.073 0.003 

Pennsylvania Berks 0.071 0.068 0.003 

Pennsylvania Blair 0.065 0.063 0.002 

Pennsylvania Bucks 0.084 0.082 0.002 

Pennsylvania Cambria 0.071 0.068 0.003 

Pennsylvania Centre 0.066 0.064 0.002 

Pennsylvania Chester 0.075 0.073 0.002 

Pennsylvania Clearfield 0.068 0.065 0.003 

Pennsylvania Dauphin 0.070 0.068 0.002 

Pennsylvania Delaware 0.074 0.073 0.001 

Pennsylvania Erie 0.069 0.067 0.002 

Pennsylvania Franklin 0.070 0.068 0.002 

Pennsylvania Greene 0.069 0.066 0.003 
3a-37

Pennsylvania Lackawanna 0.064 0.062 0.002 

Pennsylvania Lancaster 0.071 0.068 0.003 

Pennsylvania Lawrence 0.063 0.059 0.004 

Pennsylvania Lehigh 0.071 0.069 0.002 

Pennsylvania Luzerne 0.064 0.063 0.001 

Pennsylvania Lycoming 0.059 0.057 0.002 

Pennsylvania Mercer 0.073 0.069 0.004 

Pennsylvania Montgomery 0.078 0.076 0.002 

Pennsylvania Northampton 0.072 0.070 0.002 

Pennsylvania Perry 0.063 0.061 0.002 

Pennsylvania Philadelphia 0.080 0.078 0.002 

Pennsylvania Washington 0.070 0.067 0.003 

Pennsylvania Westmoreland 0.070 0.067 0.003 

Pennsylvania York 0.071 0.067 0.004 

Rhode Island Kent 0.074 0.072 0.002 

Rhode Island Providence 0.071 0.068 0.003 

Rhode Island Washington 0.075 0.072 0.003 

South

Carolina Anderson 0.067 0.065 0.002 

South

Carolina Berkeley 0.058 0.057 0.001 

South

Carolina Charleston 0.057 0.055 0.002 

South

Carolina Cherokee 0.063 0.061 0.002 

South

Carolina Chester 0.064 0.061 0.003 

South

Carolina Edgefield 0.063 0.058 0.005 

South

Carolina Pickens 0.065 0.063 0.002 

South

Carolina Richland 0.069 0.066 0.003 

South

Carolina Spartanburg 0.066 0.063 0.003 

South

Carolina Union 0.062 0.059 0.003 

South

Carolina York 0.063 0.061 0.002 

Tennessee Anderson 0.064 0.061 0.003 

Tennessee Blount 0.071 0.067 0.004 

Tennessee Davidson 0.064 0.063 0.001 

Tennessee Hamilton 0.066 0.063 0.003 
3a-38

Tennessee Haywood 0.067 0.063 0.004 

Tennessee Jefferson 0.068 0.065 0.003 

Tennessee Knox 0.071 0.066 0.005 

Tennessee Meigs 0.066 0.063 0.003 

Tennessee Rutherford 0.065 0.063 0.002 

Tennessee Shelby 0.072 0.069 0.003 

Tennessee Sullivan 0.072 0.062 0.010 

Tennessee Sumner 0.068 0.067 0.001 

Tennessee Williamson 0.068 0.066 0.002 

Tennessee Wilson 0.066 0.065 0.001 

Texas Brazoria 0.078 0.076 0.002 

Texas Collin 0.075 0.072 0.003 

Texas Dallas 0.079 0.077 0.002 

Texas Denton 0.078 0.075 0.003 

Texas El Paso 0.070 0.069 0.001 

Texas Ellis 0.074 0.069 0.005 

Texas Galveston 0.078 0.075 0.003 

Texas Gregg 0.079 0.073 0.006 

Texas Harris 0.092 0.090 0.002 

Texas Harrison 0.065 0.062 0.003 

Texas Hood 0.068 0.066 0.002 

Texas Jefferson 0.079 0.072 0.007 

Texas Johnson 0.073 0.069 0.004 

Texas Marion 0.069 0.065 0.004 

Texas Montgomery 0.072 0.070 0.002 

Texas Orange 0.068 0.064 0.004 

Texas Parker 0.068 0.066 0.002 

Texas Rockwall 0.067 0.063 0.004 

Texas Smith 0.071 0.068 0.003 

Texas Tarrant 0.079 0.076 0.003 

Utah Box Elder 0.066 0.065 0.001 

Utah Cache 0.055 0.054 0.001 

Utah Davis 0.071 0.070 0.001 

Utah Salt Lake 0.073 0.072 0.001 

Utah Utah 0.070 0.069 0.001 

Utah Weber 0.067 0.066 0.001 

Vermont Bennington 0.060 0.059 0.001 

Virginia Alexandria City 0.072 0.069 0.003 

Virginia Arlington 0.078 0.075 0.003 

Virginia Caroline 0.063 0.062 0.001 

Virginia Charles City 0.074 0.073 0.001 
3a-39

Virginia Chesterfield 0.071 0.070 0.001 

Virginia Fairfax 0.077 0.074 0.003 

Virginia Fauquier 0.062 0.061 0.001 

Virginia Frederick 0.067 0.064 0.003 

Virginia Hampton City 0.077 0.076 0.001 

Virginia Hanover 0.074 0.072 0.002 

Virginia Henrico 0.074 0.073 0.001 

Virginia Loudoun 0.070 0.068 0.002 

Virginia Madison 0.067 0.065 0.002 

Virginia Prince William 0.066 0.064 0.002 

Virginia Roanoke 0.069 0.067 0.002 

Virginia Stafford 0.064 0.062 0.002 

Virginia Suffolk City 0.080 0.080 0.000 

West Virginia Berkeley 0.068 0.063 0.005 

West Virginia Cabell 0.073 0.069 0.004 

West Virginia Hancock 0.068 0.065 0.003 

West Virginia Kanawha 0.069 0.064 0.005 

West Virginia Monongalia 0.064 0.063 0.001 

West Virginia Ohio 0.067 0.064 0.003 

West Virginia Wood 0.065 0.062 0.003 

Wisconsin Brown 0.065 0.063 0.002 

Wisconsin Columbia 0.062 0.061 0.001 

Wisconsin Dane 0.062 0.060 0.002 

Wisconsin Dodge 0.063 0.062 0.001 

Wisconsin Door 0.074 0.072 0.002 

Wisconsin Fond Du Lac 0.061 0.060 0.001 

Wisconsin Jefferson 0.066 0.065 0.001 

Wisconsin Kenosha 0.086 0.085 0.001 

Wisconsin Kewaunee 0.074 0.072 0.002 

Wisconsin Manitowoc 0.074 0.072 0.002 

Wisconsin Milwaukee 0.075 0.073 0.002 

Wisconsin Outagamie 0.059 0.057 0.002 

Wisconsin Ozaukee 0.079 0.077 0.002 

Wisconsin Racine 0.079 0.077 0.002 

Wisconsin Rock 0.069 0.068 0.001 

Wisconsin Sheboygan 0.082 0.080 0.002 

Wisconsin Walworth 0.066 0.065 0.001 

Wisconsin Washington 0.065 0.063 0.002 

Wisconsin Waukesha 0.067 0.065 0.002 

Wisconsin Winnebago 0.063 0.062 0.001 
4-1

Chapter 4: Approach for Estimating Reductions for Full Attainment Scenario 

Synopsis

This chapter presents the methodology used to estimate emission reductions that may be 

needed to reach national attainment of the proposed tighter alternate primary 8-hour 

ozone standard of 0.070-0.075 ppm. After applying the hypothetical control strategy 

described in Chapter 3, there were many areas that were still not projected to attain the 

more stringent standard modeled of 0.070 ppm. This chapter presents the methodology 

EPA developed to determine emissions reductions needed for national attainment of the 

alternate standards on each end of the proposed range (e.g. 0.070 and 0.075 ppm). It also 

presents estimated emission reductions needed to attain a more stringent option analyzed 

of 0.065 ppm, and a less stringent option of 0.079 ppm. 

4.1 Development of Air Quality Impact Ratios for Determination of 

Extrapolated Costs 

Table 3a.11 lists the highest projected design value in each monitored county for the 

2020 baseline (current standard – effectively 0.084 ppm) and after application of the 

illustrative national control strategy designed to attain an alternate primary standard of 

0.070 ppm. From this table one can determine the counties that did not meet the target 

air quality levels after implementation of the national hypothetical 0.070 control scenario.

Because the goal of the RIA is to estimate the estimated incremental costs of full 

attainment, some estimate of the remaining emissions needed to reach these targets is 

required for each of these areas. 

It was beyond the scope of this illustrative analysis to perform detailed area-specific 

analyses of the predicted additional emissions reductions needed to meet various air 

quality goals. Instead, based on existing air quality sensitivity modeling, EPA developed 

several simple, generic relationships of the expected air quality improvement to be 

achieved as a result of ozone precursor reductions. These relationships are referred to 

here as "impact ratios" and have units of ppb of ozone improvement per thousand tons of 

ozone precursor emissions reduction (ppb/kton). Two separate approaches were used to 

develop the impact ratios. The following paragraphs describe the development of the 

impact ratios used in the extrapolated cost analysis of this RIA. Based on data presented 

later in this chapter and considering the uncertainties and limitations of both approaches, 

we decided to use a single impact ratio for NOx and a single impact ratio for VOC for the 

purposes of this illustrative analysis for all areas in the U.S. that are included in the 

extrapolated cost analysis. 

4.1.1 Approach A: Use of Sensitivity Modeling of Local Emissions Reductions 

In this approach, the impact ratios were calculated based on modeling results from four 

existing, 36 km CMAQ 2010 emissions sensitivity simulations and a 2010 base case 

simulation also derived from previously completed modeling: 
4-2

1. 90% NOx reduction in all anthropogenic sectors in nine specific local areas, 

2. 90% NOx reduction in all anthropogenic sectors over the rest of the U.S., 

3. 90% VOC reduction in all anthropogenic sectors in nine specific local areas, 

4. 90% VOC reduction in all anthropogenic sectors over the rest of the U.S.

We calculated the ppb/kton ratios for five of the nine zones shown in Figure 4.1 that are 

included in the extrapolated costs analysis: Dallas, Atlanta, the Lake Michigan area, the 

Northeast Corridor, and central California. It is expected that these five zones would 

provide a representative range of ratios, so the analysis was not done for Denver, 

Phoenix, and Salt Lake City. Because we were not calculating extrapolated tons for 

Seattle, the ratio determination was not done for that region. For monitoring sites in each 

of these five geographic areas we compared the ozone improvement in the 90% control 

cases (simulations 1 and 3) against the tons of NOx and VOC reduced within the 

corresponding control area. The impact ratio for each site was calculated by dividing the 

ozone improvement by the corresponding tons reduced. Impact ratios were calculated for 

88 sites over the five analysis zones. The results from Approach A are summarized in 

Table 4.2 

Figure 4.1: Nine Local Control Areas in Existing 2010 Sensitivity Runs
4-3

A sample calculation for one of the sites in the five analysis zones (a monitoring site 

located in Denton TX) is shown below: 

x A 90% NOx reduction equals 130.4 ktons in the local Dallas area. 

x The ozone improvement from this reduction was 17.6 ppb (87.9 to 70.3). 

x This yields an impact ratio of 0.135 ppb/kton for this county. 

The advantage to this approach is that it allows for all-sector, local-only controls without 

consideration of transport effects. This approach is best-suited for areas in which ozone 

transport is not a large contributor to the local ozone problem, relative to local emissions 

(e.g., Atlanta, Dallas). 

Table 4.1: Summary of site-specific impact ratios over the five analysis zones of 

Approach A. 

 Minimum 

Impact Ratio 

Maximum 

Impact Ratio 

Average

Impact Ratio 

Controlling County 

Impact Ratio 

Atlanta 0.051 0.187 0.123 0.187 

Central CA 0.077 0.106 0.095 0.106 

Dallas 0.118 0.138 0.130 0.135 

Lake Michigan Area -0.0221 0.052 0.010 0.032 

Northeast Corridor 0.002 0.035 0.022 0.035 

It is important to note that we are not able to factor in impacts of controls outside of the 

local regions using this methodology and thus, the impact ratios are likely to be 

conservative. Additionally, depending upon the source-receptor relationship at a 

particular location, some impact ratios would be expected to be lower than others due to 

prevailing transport direction. For instance, one would not expect a location in the 

southern portion of the Northeast Corridor to show much local air quality improvement 

when the majority of the controls were implemented upwind. Other limitations to this 

approach include: the assumption that response to NOx and VOC reductions is linear 

between 0% and 90% control, the assumption that ratios developed from 2010 base case 

modeling are applicable to 2020 post-strategy ozone, and the fact that impact ratios 

calculated from a single month of 36 km modeling may not be appropriate for an analysis 

of urban scale ozone. 

4.1.2 Approach B: Use of 2020 Baseline and RIA Control Scenario 

In the second approach, we used the results from the 2020 baseline and the 2020 

hypothetical control scenario to calculate impact ratios for Atlanta, Houston, the Lake 

1 The negative value of minimum impact ratio in the Lake Michigan area indicates that 

ozone levels at one monitoring site are projected to increase slightly with 90% local NOx 

control. This 'ozone disbenefit' has been projected by the model to occur in a very few, 

highly localized, areas with large amounts of NOx emissions. This lone negative value is 

not representative of regional impact ratios." 
4-4

Michigan Area and the Northeast Corridor. We focused on these four analysis zones 

because they were expected to require extensive extrapolated tons to attain the air quality 

targets. We did not use this approach in the western U.S. because there was very little 

difference in the controls between those two cases in California. We again calculated 

impact ratios for all monitoring sites in each zone by dividing the ozone change at each 

site by the NOx emissions reductions that led to that ozone reduction. For the specific 

purpose of estimating impact ratios, we have made the unrealistic but simplifying 

assumption that the air quality change can be fully ascribed to the total NOx emissions 

changes within 200 km of the area. Different assumptions about which emissions are 

responsible for the air quality change would yield different impact ratios.

A sample calculation for one of the counties (Kenosha WI) is shown below: 

x The RIA control scenario resulted in a NOx reduction of 16.8 ktons in the 

Chicago zone (including 200 km buffer). 

x The ozone improvement from this reduction was 1.6 ppb (86.6 to 85.0). 

x This yields an impact ratio of 0.095 ppb/kton for this county. 

The advantage to Approach B is that it allows for an estimate of the impact of actual 

controls applied regionally because controls in the hypothetical scenario cover nearly the 

entire eastern US. Thus, this approach is best suited for areas in which ozone transport is 

a large contributor to the local ozone problem (e.g., the Lake Michigan area and the 

Northeast Corridor). The primary disadvantage to this approach is that the 2020 control 

scenario is weighted toward non-EGU point source controls which may result in nonhomogeneous reductions and thereby affect individual county impact ratios. Impact 

ratios were calculated for 47 counties over the four analysis zones. The results from 

Approach B are summarized in Table 4.2. 

Table 4.2: Summary of site-specific impact ratios over the four analysis zones of 

Approach B. 

 Minimum 

Impact Ratio 

Maximum 

Impact Ratio 

Average

Impact Ratio 

Controlling County 

Impact Ratio 

Atlanta 0.041 0.129 0.068 0.070 

Houston 0.050 0.057 0.054 0.057 

Lake Michigan Area -0.006 0.095 0.064 0.095 

Northeast Corridor 0.068 0.155 0.110 0.105 
4-5

4.2 Results from Impact Ratio Analyses 

In general, both approaches indicate that impact ratios could range between 0.03 and 0.20 

ppb/kton. However, the approaches did not yield consistent impact ratios for individual 

analysis zones. Individual local impact ratios are likely influenced by: the importance of 

transport, the local NOx/VOC ratio, the meteorology within the region, and the location 

of monitors relative to specific source areas. 

Table 4.3 shows the impact ratios for each of the controlling counties2

 within the areas 

considered. Figure 4.2 shows the range of county-specific impact ratios calculated over 

the four areas included in the calculations for Approach B. Based on these data and 

considering the uncertainties and limitations of both approaches, we decided to use a 

single impact ratio for NOx and a single impact ratio for VOC for the purposes of this 

illustrative analysis for all areas in the U.S. that are included in the extrapolated cost 

analysis. These general impact ratios are: 

x NOx impact ratio = 0.100 ppb/kton 

x VOC impact ratio = 0.025 ppb/kton 

Table 4.3. The NOx impact ratios at the controlling counties for each methodology 

over the analysis zones. 

Impact Ratio at controlling county Analysis Area Approach A Approach B 

Atlanta 0.187 0.070 

Central California 0.106 

Dallas 0.135 

Houston 0.057 

Lake Michigan area 0.032 0.095 

Northeast Corridor 0.035 0.105 

2

 The controlling county is the county within an area whose design value is farthest away 

from attaining the air quality target. 
4-6

Figure 4.2. The NOx impact ratios at each county (sorted from lowest to highest) 

for the Approach B over the four analysis areas3

Range of Impact Ratios: 084-070 methodology

-0.05

0.00

0.05

0.10

0.15

0.20

2%6%

11%15%19%23%28%32%36%40%45%49%53%57%62%66%70%74%79%83%87%91%96%

100%

Percentile (Counties in the Analysis)

Impact Ratio (ppb/kton)

The selection of a 0.100 ppb/kton ratio was based on a consideration of all estimated 

impact ratios from the bounding exercise of both approaches and the technical limitations 

of approaches. There were three specific reasons why we thought 0.100 ppb/kton 

represented the best choice for extrapolating the tons needed to attain an air quality target 

beyond the reductions from the RIA control scenario: 

x 0.100 is within, and near the midpoint of, the 0.03 to 0.20 range 

x 0.100 is close to the median value from Approach B (0.093 ppb/kton) 

x 0.100 is close to the average value at the key sites (0.091 ppb/kton). 

As noted above, the various methods did not generate consistent area-specific NOx 

impact ratios. As the impact ratios are used to estimate extrapolated costs, one should 

keep in mind that higher impact ratios would yield lower estimates of needed 

extrapolated tons and lower impact ratios would yield higher estimates of extrapolated 

tons/costs.

As an example, if in a given area X, our impact ratio of 0.1 ppb/kton for NOx is defined 

as equivalent to 10 extrapolated ktons of emission reductions needed to achieve a 

particular air quality target, then a doubling of the impact ratio (thus, this ratio becomes 

3

 The lone negative value of impact ratio occurs in the Lake Michigan area and indicates 

that ozone levels at that site are projected to increase slightly in response to the RIA 

control scenario. This 'ozone disbenefit' has previously been projected by the model to 

occur in a very few, highly localized, areas with large amounts of NOx emissions. This 

lone negative value is not representative of regional impact ratios. 
4-7

0.2 ppb/kton) means that 5 extrapolated ktons of NOx, or one-half the original tonnage 

reduction needed, to achieve the same air quality target. As a further example, a 

reduction in the NOx impact ratio by half (thus, the ratio becomes 0.05 ppb/kton) means 

that 20 extrapolated ktons of NOx emission reductions, or twice the original tonnage 

reduction needed, to achieve the same air quality target.

We intend to conduct additional sensitivity analysesfor the final RIA to improve the 

estimates of extrapolated tons needed to meet various targets. While it is premature to 

specify the exact nature of these analyses, we expect this will include modeling to 

provide more information about the non-linear responsiveness of ozone, the geographic 

variation in ozone responsiveness, the impacts of local versus upwind emissions 

reductions, and the relationship between NOx and VOC controls in various areas. It will 

also include an analysis of the geographic application of impact ratios. 

4.3 Determination of Extrapolated Tons Control Areas 

The extrapolated tons analysis varied slightly from the geographic areas in which controls 

were applied for the illustrative 0.070 control strategy described in Chapter 3. In the 

extrapolated tons analysis, we aggregated all counties that were above the air quality goal 

into discrete control areas, that is, areas from which the tons would need to be extracted 

in order to meet the target. These control areas were either regional, statewide, or local 

depending upon the nature of the ozone problem within the area. Two regional areas 

were identified: the Ozone Transport Region and the Lake Michigan region. Both of 

these areas have traditionally employed multi-State control plans to lower ozone in those 

regions. For states with multiple areas above the air quality target, we assumed that 

statewide control programs would be developed to bring these areas into attainment. For 

example, for the 0.065 ppm target, Ohio exceeds the air quality target in Cincinnati, 

Cleveland, and Columbus. We assumed that extrapolated tons could be achieved 

anywhere in Ohio to meet the targets in all three areas. All remaining counties were 

treated as places where local controls would be effective. The only exceptions to the 

statewide assumption were in Texas and California. We separated the El Paso area into 

its own area due to its distance (i.e., far greater than 200 km, which was the distance used 

for the 0.070 control strategy) from the Eastern Texas areas (Dallas, Houston). In 

California, we combined the Sacramento and San Joaquin Valley counties into a single 

control area, but created a separate control area for Southern California. Table 4.4 shows 

how the monitoring counties were aggregated into the extrapolated tons control areas for 

the 0.070 ppm target. 
4-8

Table 4.4 List of counties that did not reach 0.070 in the RIA control scenario and 

how they were aggregated into extrapolated tons control areas. 

Control Region State County Control Region State County

Atlanta, GA Georgia Fulton Las Vegas, NV Nevada Clark

Georgia De Kalb Los Angeles South Coast Air Basin, CA California San Bernardino

Baton Rouge, LA Louisiana East Baton Rouge California Los Angeles

Louisiana Iberville California Riverside

Louisiana West Baton Rouge California Ventura

Central Califronia California Kern California San Diego

California Fresno California Imperial

California Tulare Louisville, KY-IN Indiana Clark

California Merced Indiana Perry

California El Dorado Memphis, TN-AR Arkansas Crittenden

California Kings Ozone Transport Region Connecticut Fairfield

California Stanislaus New York Suffolk

California Nevada Connecticut New Haven

California Placer Pennsylvania Bucks

California Sacramento Maryland Harford

California Madera New Jersey Ocean

California Mariposa New Jersey Mercer

California Tuolumne New Jersey Camden

California Calaveras Connecticut Middlesex

Charlotte-Gastonia-Rock Hill, NC-SC North Carolina Mecklenburg New Jersey Middlesex

North Carolina Rowan New Jersey Gloucester

Cleveland-Columbus-Cincinnati Ohio Geauga Pennsylvania Philadelphia

Ohio Ashtabula New Jersey Hunterdon

Kentucky Campbell New Jersey Monmouth

Ohio Franklin Pennsylvania Allegheny

Ohio Lake Pennsylvania Montgomery

Ohio Summit Maryland Cecil

Denver-Boulder Colorado Jefferson New Jersey Bergen

Colorado Douglas New York Erie

Colorado Arapahoe Virginia Arlington

Detroit-Ann Arbor, MI Michigan Macomb Maryland Baltimore

Michigan Oakland New Jersey Morris

Michigan Wayne New York Niagara

Michigan Washtenaw New York Westchester

Michigan St Clair Virginia Fairfax

Houston-Dallas Texas Harris Maryland Anne Arundel

Texas Dallas D.C. Washington

Texas Brazoria Delaware New Castle

Texas Tarrant Pennsylvania Beaver

Texas Denton Pennsylvania Chester

Texas Galveston Pennsylvania Delaware

Texas Gregg New Jersey Hudson

Texas Jefferson Maryland Prince Georges

Texas Collin Maryland Kent

Indianapolis, IN Indiana Shelby New York Richmond

Indiana Hamilton Rhode Island Washington

Indiana Marion New York Jefferson

Indiana Hancock Rhode Island Kent

Lake Michigan region Wisconsin Kenosha New York Monroe

Indiana Lake New Jersey Cumberland

Illinois Cook Phoenix-Mesa, AZ Arizona Maricopa

Wisconsin Sheboygan Arizona Pinal

Indiana Porter Richmond-Norfolk Virginia Suffolk City

Wisconsin Ozaukee Virginia Hampton City

Wisconsin Racine Virginia Henrico

Wisconsin Milwaukee Virginia Charles City

Indiana La Porte Virginia Hanover

Illinois Lake Salt Lake City, UT Utah Salt Lake

Wisconsin Kewaunee St Louis, MO-IL Missouri St Louis City

Michigan Allegan Missouri St Louis

Wisconsin Manitowoc Missouri Jefferson

Wisconsin Door Missouri St Charles

Michigan Muskegon Tampa Bay, FL Florida Hillsborough
4-9

4.4 Selection of Air Quality Goal for this analysis 

Under the Clean Air Act, areas are required to reach the air quality standards as 

expeditiously as practicable and within certain statutorily defined time periods. In 

advance of formal designations and ozone pollution level classifications, which will 

depend upon future air quality data, it is uncertain when areas would be required to attain 

a new ozone standard. In addition, states may request, and EPA must grant, a higher 

classification which under the law provides flexibility for a state to justify a later 

attainment date. (The state implementation plan must show that the attainment date 

selected for an area is as expeditious as practicable, and no later than the maximum 

statutory date for the area's classification.) In view of these and other factors, it is beyond 

our capability to simulate in advance the state implementation process to determine the 

appropriate attainment date and required controls for each potential nonattainment area 

for a new standard. Instead, we have constructed an illustrative analysis that provides a 

level playing field for comparison of the impacts of potential new, alternative standards. 

An important consideration in the determination of the amount of air quality 

improvement needed to reach a tighter ozone standard is the dates by which each area 

must come into attainment. As discussed earlier, for several analytical reasons we 

selected the year 2020 (i.e., approximately 10 years from designations), as the analytical 

target year for this analysis. Therefore, this analysis presents two sets of results. The 

first reflects attainment of the alternative ozone standards in all locations of the U.S. 

except two areas of California in 2020. These two areas of California are not planning to 

meet the current standard by 2020 (see discussion below), so the estimated costs and 

benefits for these areas are based on reaching an estimated progress point (their 

“glidepath” targets) in 2020. The second set of results, for California only, estimate the 

costs and benefits from California fully attaining the alternative standards in a year 

beyond 2020 (glidepath estimates, plus the increment needed to reach full attainment 

beyond 2020, added together for a California total). However, as noted above, we are not 

attempting to prejudge the attainment dates and controls that ultimately will be 

determined through the SIP process, and it may turn out that attainment occurs later than 

2020 for additional areas, particularly in areas where the future SIP process shows that 

very high-cost controls would be needed to attain by 2020. For reasons explained below, 

assuming longer attainment dates would reduce costs and benefits of meeting the current 

and alternative standards, and would reduce costs more sharply in areas assumed to 

employ high-cost controls to meet an artificial deadline.

The South Coast (Los Angeles area) and San Joaquin Air Quality Management Districts 

recently have proposed for comment state implementation plans with the statutory 

maximum 20-year attainment dates (June 2024, with attainment-level reductions by 

2023) for meeting the current 8-hour standard, which would involve a request to 

reclassify those two areas to the “extreme” classification4

. This presented an analytical 

4

 Proposed State Strategy for California's State Implementation Plan (SIP) for the New 

PM2.5 and 8-Hour Ozone Standard, California Air Resources Board web page, 

http://www.arb.ca.gov/planning/sip/2007sip/2007sip.html, update May 30, 2007. 
4-10

dilemma for this analysis because assuming that these areas would be classified severe or 

extreme for purposes of a new standard, these areas would not be required to attain any 

new standard until after the analytical year of 2020. If an area is initially classified 

severe, the law still would allow the state to request reclassification to extreme and to 

demonstrate that a 20-year attainment date (e.g., in 2030, if designations occurred in 

2010) is as expeditious as practicable. Thus, an assumption that these areas would attain 

a tighter standard by 2020, significantly earlier than would be required under the Clean 

Air Act, would artificially inflate both the costs and benefits of the nation attaining the 

new standard in 2020 on a national level.

A further reason that we believe it would be inappropriate for the analysis to assume 

attainment by 2020 with new, more stringent alternative standards in the San Joaquin and 

South Coast areas is that existing rules, especially for on-road and non-road mobile 

sources, will achieve substantial additional reductions in NOx and VOC after 2020 before 

reaching their full impact in 2030. If San Joaquin and South Coast received the 

maximum statutory 20-year attainment dates for new alternative standards, for example, 

they would have an attainment date in 2030, and would benefit from reductions in NOx 

and VOC from existing rules between 2020 and 2030. By 2029, existing rules for onroad 

and nonroad engines would achieve 62,000 tons5

 of residual emissions reductions needed 

for attainment at no cost beyond the baseline for this analysis. By contrast, assuming 

2020 attainment for these areas would result in assuming additional high-cost reductions 

from unknown control measures. Therefore, assuming 2020 attainment for these areas 

could result in a significant overestimate of costs. Likewise, the benefits would be 

overestimated because the tons are attributable to these existing rules that have reductions 

occurring after 2020 and are not part of our hypothetical control strategy.

5

 62,000 tons was estimated by subtracting the California county level Onroad and 

Nonroad 2030 NOx and VOC emissions from their totals in 2020. These differences 

were estimated for counties listed under the CA control regions (Los Angeles and Kern 

County) detailed in Table 4.4. (Los Angeles accounted for 38,500 tons, while Kern 

accounted for 23,300 tons, for a total of 61,800 rounded to 62,000). In order to estimate 

total emissions for both VOC and NOx, VOC emission reductions for these counties were 

adjusted using the adjustment factor detailed earlier in the chapter (4 VOC tons = 1 NOx 

ton). Given that the San Joaquin and South Coast air quality management districts have 

adopted plans allowing until June 2024 (20 years from designation) to meet the current 

standard, EPA believes that it would be consistent for purposes of this analysis to assume 

a 20-year period for attainment of a new more stringent standard. If designations 

occurred in 2010, the 20-year attainment period would end in 2030. Significant 

emissions reductions from implementation of mobile source rules are anticipated between 

2020 and 2030. Consistent with the non-EGU growth assumptions for the rest of the 

RIA, non-EGU emissions are assumed to stay constant. EGU emissions are a small 

fraction of the California inventory and are assumed not to significantly affect the change 

in the state’s emissions between 2020 and 2030. Accordingly, we have used the 

difference between 2020 and 2030 mobile emissions to estimate the post-2020 emissions 

reductions that will assist the two California areas in reaching attainment. 
4-11

Thus, for this analysis we have chosen to present the 2020 costs and benefits in a way 

that reflects partial attainment in certain California areas, an outcome consistent with the 

Clean Air Act. This national estimate includes full attainment in all locations except two 

areas of California, which do not plan to meet the current standard by 2020, and so have 

estimates for a progress point in 2020 (their “glidepath” targets). The second set of results 

presents a total for California only, which adds the 2020 progress point to the additional 

tons of emissions that may be needed in California to fully attain the standards in a year 

beyond 2020. 

The following table shows the results of the calculation of the glidepath targets for these 

two areas used in this analysis6

. The glidepath targets reflect the more stringent of two 

air quality targets: (1) the improvement assumed by 2020 to meet the current standard by 

years specified below, or the improvement needed by 2020 to make linear air quality 

progress between 2010 and a post-2020 attainment date for the more stringent, alternative 

standards. For Los Angeles County, the glidepath air quality targets below for all four 

alternative standards are set based on reductions needed to meet the current standard, 

with the level of the current standard being achieved in 2021. For Kern County in the 

San Joaquin area, the glidepath for the 0.075 ppm and 0.079 ppm alternative standards is 

based on achieving the level of the current standard by 2020. For the other two 

alternative standards, the 2020 glide path targets for San Joaquin are based on meeting 

the level of the alternative, more stringent standards in 2025. 

Table 4-5: 2020 Air Quality Glidepath Targets for LA and Kern County

Alternative Standard Level LA County Kern County 

0.079 ppm 86.9 ppb* 84.9 ppb 

0.075 ppm 86.9 ppb 84.9 ppb 

0.070 ppm 86.9 ppb 82.9 ppb 

0.065 ppm 86.9 ppb 79.9 ppb 

* targets are expressed in ppb for clarity of presentation 

As noted above, since our glidepath calculations and cost-benefit estimates were made, 

the two California districts have proposed state implementation plans for the current 

standard that allow the statutory maximum 20-year period for attainment. This in turn 

suggests that that it would be reasonable solely for purposes of this analysis to assume a 

20-year period for implementation of new standards. In part because decisions regarding 

6

 Assumptions made for the purposes of this analysis were made prior to two California 

areas adopting SIPs which assumed attainment by June 2024. For purposes of this 

analysis, San Joaquin (including Kern County) was assumed to meet the current standard 

by 2020 (consistent with the analysis assumption for most areas), and South Coast 

(including Los Angeles County) was assumed to meet the current standard in 2021 (the 

maximum attainment date of a severe-17 area is in June 2021). Because the San Joaquin 

and South Coast air districts have adopted SIPs with later attainment dates for the current 

standard, we recognize that the assumptions used in this analysis are not likely to be the 

actual years of attainment for these two areas.
4-12

our analysis were made prior to the California proposals, the assumed time periods in this 

analysis for attainment by the San Joaquin and Los Angeles areas are shorter -- both for 

the current standards, and for the potential alternative standards. This suggests that the 

glidepath figures above are all more stringent than likely implementation of the Clean Air 

Act, and that as a result our analysis applies more unknown controls than would be 

needed in these areas for the current and alternative standards assuming 20-year 

deadlines. Thus, our estimated 2020 costs and benefits for the two California areas, 

which influence the total cost and benefit figures in this draft RIA, are higher than would 

likely occur under the Clean Air Act for the current and the potential alternative 

standards. We intend to consider this issue further in the final RIA. 

4.5 National 2020 Estimates of Additional Emissions Reductions Needed to Meet 

Four Potential Air Quality Targets 

This analysis presents two sets of estimates: national 2020 estimates, and California-only 

estimates. This section presents the national 2020 estimates. 

The national 2020 estimates assume full attainment in all locations except two areas of 

California, which are assumed to meet 2020 air quality glidepath targets on their way 

toward full attainment after 2020. These 2020 national estimates present incremental 

tons that may be needed to meet the four separate air quality targets were considered as 

part of this analysis: a less stringent alternative standard of 0.079 ppm, 0.075 ppm and 

0.070 ppm, which bound the range that is being proposed, and a more stringent 

alternative of 0.065 ppm. After the RIA control scenario, there were 24, 50, 126, and 280 

counties above these four thresholds, respectively. The aggregation technique discussed 

above grouped these counties into 6, 11, 20, and 29 extrapolated ton control areas for the 

four targets. The calculation of additional tons needed does not account for the ancillary 

effects of ozone transport reductions (e.g. the impact of Lake Michigan region reductions 

on the OTR). The national 2020 total estimated incremental tons that may be needed to 

attain the four targets are summarized below and presented in Tables 4.6 - 4.9. 

x 0.079 = 102,000 tons of additional NOx control 

x 0.075 = 321,000 tons of additional NOx control 

x 0.070 = 1,004,000 tons of additional NOx control 

x 0.065 = 2,239,000 tons of additional NOx control7

7 While the proposed rule takes comment on a range of alternate standards from 0.060 

ppm to 0.084 ppm, the RIA analysis focused on a more limited range 
4-13

Table 4.6 Estimated Annual Incremental Tons Needed for an 0.065 ppm Air 

Quality Target in 2020 (29 areas) 

Control Region Controlling 

County

Post-scenario 

design value 

(ppb) 

Incremental

Extrapolated 

NOx Tons 

Lake Michigan region Kenosha WI 85.0 174,000 

Ozone Transport Region Fairfield CT 87.1 173,000 

Eastern TX areas (Houston/Dallas/Beaumont) Harris TX 90.5 166,000 

VA areas (Norfolk/Richmond/Roanoke) Suffolk City VA 80.8 149,000 

Detroit, MI Macomb MI 78.4 125,000 

Phoenix, AZ Maricopa AZ 77.6 117,000 

Denver, CO Jefferson CO 76.9 110,000 

OH areas (Cleveland/Columbus/Cincinnati) Geauga OH 76.5 106,000 

Atlanta, GA Fulton GA 76.0 101,000 

St Louis, MO-IL St Louis City MO 75.7 98,000 

Indiana areas (Indianapolis / Evansville) Shelby IN 74.5 86,000 

LA areas (Baton Rouge/New 

Orleans/Shreveport) E Baton Rouge LA 74.4 85,000 

KY areas (Louisville/Paducah/Bowling Green) Clark IN 74.0 81,000 

TN areas (Knoxville/Memphis/Nashville) Crittenden AR 72.9 70,000 

NC areas (Charlotte / Raleigh) Mecklenburg NC 72.3 64,000 

Salt Lake City, UT Salt Lake UT 72.2 63,000 

Las Vegas, NV Clark NV 72.0 61,000 

FL areas (Tampa / Panama City / Pensacola) Hillsborough FL 71.4 55,000 

Sacramento / San Joaquin Valley / S Fran Kern CA** 96.3 50,000 

Jackson, MS Jackson MS 70.6 47,000 

New Mexico areas (Farmington / Las Cruces) Dona Ana NM 70.3 44,000 

OK areas (Tulsa, Marshall) Tulsa OK 70.3 44,000 

Huntington, WV-KY Cabell WV 69.9 40,000 

El Paso, TX El Paso TX 69.3 34,000 

Kansas City, MO/KS Wyandotte KS 69.0 31,000 

Little Rock, AR Pulaski AR 68.7 28,000 

Mobile AL Mobile AL 68.6 27,000 

Columbia, SC Richland SC 66.9 10,000 

Los Angeles South Coast Air Basin, CA Los Angeles CA** 105.0 0* 

*In EPA’s illustrative analysis for the PM NAAQS RIA, there were reductions of NOx in 

California. The amount of reductions assumed there are sufficient for these counties to 

achieve their glidepath targets in 2020.

** Los Angeles and Kern Counties have expected attainment dates after 2020. This 

analysis counts the portion of reductions assumed by this analysis by 2020 or earlier.
4-14

Table 4.7. Estimated Annual Incremental Tons Needed for an 0.070 ppm air 

quality target in 2020 (20 areas) 

Control Region Controlling 

County

Post-scenario 

design value (ppb) 

Incremental

Extrapolated NOx 

Tons 

Lake Michigan region Kenosha WI 85.0 124,000 

Ozone Transport Region Fairfield CT 87.1 123,000 

Eastern TX areas (Houston/Dallas) Harris TX 90.5 116,000 

VA areas (Norfolk/Richmond) Suffolk City VA 80.8 99,000 

Detroit, MI Macomb MI 78.4 75,000 

Phoenix, AZ Maricopa AZ 77.6 67,000 

Denver, CO Jefferson CO 76.9 60,000 

OH areas 

(Cleveland/Columbus/Cincinnati) Geauga OH 76.5 56,000 

Atlanta, GA Fulton GA 76.0 51,000 

St Louis, MO-IL St Louis City MO 75.7 48,000 

Indiana areas (Indianapolis) Shelby IN 74.5 36,000 

LA areas (Baton Rouge) E Baton Rouge LA 74.4 35,000

KY areas (Louisville) Clark IN 74.0 31,000 

Sacramento / San Joaquin Valley Kern CA** 96.3 20,000 

TN areas (Memphis) Crittenden AR 72.9 20,000 

NC areas (Charlotte) Mecklenburg NC 72.3 14,000 

Salt Lake City, UT Salt Lake UT 72.2 13,000 

Las Vegas, NV Clark NV 72.0 11,000 

FL areas (Tampa) Hillsborough FL 71.4 5,000 

Los Angeles South Coast Air Basin, CA Los Angeles CA** 105.0 0* 

* In EPA’s illustrative analysis for the PM NAAQS RIA, there were reductions of NOx

in California. The amount of reductions assumed there are sufficient for these counties to 

achieve their glidepath targets in 2020.

** Los Angeles and Kern Counties have expected attainment dates after 2020. This 

analysis counts the portion of reductions assumed by this analysis by 2020 or earlier.
4-15

Table 4.8. Estimated Annual Incremental Tons Needed for an 0.075 ppm air quality 

target in 2020 (11 areas) 

*In EPA’s illustrative analysis for the PM NAAQS RIA, there were reductions of NOx in 

California. The amount of reductions assumed there are sufficient for these counties to 

achieve their glidepath targets in 2020. 

** Los Angeles and Kern Counties have expected attainment dates after 2020. This 

analysis counts the portion of reductions assumed by this analysis by 2020 or earlier.

Table 4.9. Estimated Annual Incremental Tons Needed for an 0.079 ppm air quality 

target in 2020 (6 areas) 

*In EPA’s illustrative analysis for the PM NAAQS RIA, there were reductions of NOx in 

California. The amount of reductions assumed there are sufficient for these counties to 

achieve their glidepath targets in 2020. 

** Los Angeles and Kern Counties have expected attainment dates after 2020. This 

analysis counts the portion of reductions assumed by this analysis by 2020 or earlier.

Control Region Controlling 

County

Post-scenario 

design value (ppb) 

Incremental

Extrapolated NOx 

Tons 

Lake Michigan region Kenosha WI 85.0 74,000 

Ozone Transport Region Fairfield CT 87.1 73,000 

Eastern TX areas (Houston/Dallas) Harris TX 90.5 66,000 

VA areas (Norfolk) Suffolk City VA 80.8 49,000 

Detroit, MI Macomb MI 78.4 25,000 

Phoenix, AZ Maricopa AZ 77.6 17,000 

Denver, CO Jefferson CO 76.9 10,000 

OH areas (Cleveland) Geauga OH 76.5 6,000 

Atlanta, GA Fulton GA 76.0 1,000 

Sacramento / San Joaquin Valley Kern CA** 96.3 0* 

Los Angeles South Coast Air Basin, CA Los Angeles CA** 105.0 0* 

Control Region Controlling 

County

Post-scenario 

design value (ppb) 

Incremental

Extrapolated NOx 

Tons 

Lake Michigan region Kenosha, WI 85.0 34,000

Ozone Transport Region Fairfield. CT 87.1 33,000

Eastern TX areas (Houston/Dallas) Harris, TX 90.5 26,000

VA areas (Norfolk) Suffolk City, VA 80.8 9,000

Sacramento / San Joaquin Valley Kern CA** 96.3 0* 

Los Angeles South Coast Air Basin, CA Los Angeles CA** 105.0 0* 
4-16

4.6 Estimates of Additional Tons Needed for Four Potential Air Quality Targets 

(California only, post-2020 Attainment) 

The second estimates presented are for California only. Tables 4.10 – 4.13 below, 

present the estimated tons needed to attain California’s glidepath targets in 2020 and the 

additional increment of tons that may be needed for full attainment in a year beyond 2020 

Table 4.10 California: Estimated Tons Needed For Attainment 0.065 ppm air 

quality target (beyond 2020) 

Table 4.11 California: Estimated Tons Needed for Attainment of 0.070 ppm air 

quality target (beyond 2020) 

Table 4.12 California: Estimated Tons Needed for Attainment of 0.075 ppm air 

quality target (beyond 2020) 

Control Region Controlling 

County

Postscenario 

design

value (ppb) 

Total 

Extrapolated 

NOx Tons 

Glidepath

Extrapolated 

NOx Tons 

Remaining 

tons needed 

(2020- 

attainment)

“Credit” tons 

from mobile 

rules

2020-2030 

Tons needed 

after mobile 

reductions 

Sacramento / San 

Joaquin Valley Kern CA 96.3 90,000 0 90,000 23,300 66,700 

Los Angeles South 

Coast Air Basin, CA Los Angeles CA 105.0 88,000 0 88,000 38,500 49,500 

Table 4.13 California: Estimated Tons Needed for Attainment of 0.079 ppm air 

quality target (beyond 2020) 

Control Region Controlling 

County

Postscenario 

design

value (ppb) 

Total 

Extrapolated 

NOx Tons 

Glidepath

Extrapolated 

NOx Tons 

Remaining 

tons needed 

(2020- 

attainment)

“Credit” tons 

from mobile 

rules

2020-2030 

Tons needed 

after mobile 

reductions 

Sacramento / San 

Joaquin Valley Kern CA 96.3 50,000 0 50,000 23,300 26,700 

Los Angeles South 

Coast Air Basin, CA Los Angeles CA 105.0 48,000 0 48,000 38,500 9,500 

Control Region Controlling 

County

Postscenario 

design

value (ppb) 

Total 

Extrapolated 

NOx Tons 

Glidepath

Extrapolated 

NOx Tons 

Remaining 

tons needed 

(2020- 

attainment)

“Credit” tons 

from mobile 

rules

2020-2030 

Tons needed 

after mobile 

reductions 

Sacramento / San 

Joaquin Valley / S 

Fran

Kern CA 96.3 190,000 50,000 140,000 23,300 116,700 

Los Angeles South 

Coast Air Basin, CA Los Angeles CA 105.0 188,000 0 188,000 38,500 149,500 

Control Region Controlling 

County

Postscenario 

design

value (ppb) 

Total 

Extrapolated 

NOx Tons 

Glidepath

Extrapolated 

NOx Tons 

Remaining 

tons needed 

(2020- 

attainment)

“Credit” tons 

from mobile 

rules

2020-2030 

Tons needed 

after mobile 

reductions 

Sacramento / San 

Joaquin Valley Kern CA 96.3 140,000 20,000 120,000 23,300 96,700 

Los Angeles South 

Coast Air Basin, CA Los Angeles CA 105.0 138,000 0 138,000 38,500 99,500 
5-1

Chapter 5: Cost Estimates 

Synopsis

This chapter summarizes the data sources and methodology used to estimate the costs of 

attaining the alternative more stringent levels for the ozone primary standard analyzed in this 

RIA. This chapter estimates the costs of the bounds of the proposed range, 0.075- 0.070 ppm, a 

more stringent alternative of 0.065 ppm, as well as a less stringent option of 0.079 ppm. The 

chapter presents cost estimates for the illustrative control strategy outlined in Chapter 3 (which 

uses currently available known controls). The control strategy discussion is followed by a 

presentation of estimates for the costs of the additional tons of emissions that are needed to move 

to full attainment of the alternate standards analyzed (methodology and numbers discussed in 

Chapter 4).

As noted in Chapter 3, EPA first modeled an illustrative control strategy aimed at attaining a 

tighter standard of 0.070 ppm in 2020. These known controls were insufficient to bring all areas 

into attainment with 0.070 ppm, and EPA then developed methodology to estimate additional 

tons of emissions needed to attain the bounds of the proposed range, 0.075 and 0.070 ppm, the 

tighter alternative of 0.065 ppm and the less stringent alternative option of 0.079 ppm. This 

chapter presents the costs associated with each portion of the control analysis, clearly identifying 

the relative costs of modeled versus extrapolated emissions reductions as well as providing an 

estimate of the total cost of attainment nationwide in 2020. Section 5.1 summarizes the 

methodology and the engineering costs associated with applying known and supplemental 

controls to partially attain a 0.070 ppm alternative standard, incremental to reaching the current 

baseline (effectively 0.084 ppm) in 2020. 

Section 5.2 describes the methodology used to estimate the cost of extrapolated tons needed to 

reach attainment of the bounds of the proposed alternative standard (0.070 and 0.075 ppm, the 

less stringent alternative of 0.079ppm, as well as the more stringent alternative of 0.065 ppm) 

and provides estimates of how much additional cost will be associated with moving from the 

modeled partial attainment scenario to the nationwide attainment scenario (see Chapter 4 for 

discussion of extrapolated tons needed to attain 0.079, 0.075, 0.070, and 0.065 ppm). In general, 

EPA increased the tons required for each area using the same impact/ton estimate ( 5 ppb = 

50,000 tons) and extrapolated cost approaches in order to estimate additional costs for reaching a 

standard level of 0.065 ppm as well as estimate cost savings for the 0.075 and 0.079 ppm 

standard levels (compared to the 0.070 ppm case). 

Section 5.3 then combines the results from Sections 5.1 and 5.2 to describe the total estimated 

cost of full attainment in 2020, including both the costs of modeled controls for reaching partial 

attainment (engineering costs) and the additional costs of tons of extrapolated emissions 

reductions needed to reach attainment. This section includes two sets of costs. The first reflects 

full attainment in 2020 in all locations of the U.S. except two areas of California. These two 

areas are not planning to meet the current standard by 2020, so the estimated costs for these areas 

are based on reaching an estimated progress point in 2020 (their “glidepath” targets). The 

second set of results for California only, estimate the costs from California fully attaining the 
5-2

alternative standards in a year beyond 2020 (glidepath estimates for 2020, plus further 

increments needed to reach full attainment beyond 2020, added together for California total).

The costs described in this chapter generally include the costs of purchasing, installing, and 

operating the referenced technologies. For a variety of reasons, actual control costs may vary 

from the estimates EPA presents here. As discussed throughout this report, the technologies and 

control strategies selected for analysis are illustrative of one way in which nonattainment areas 

could meet a revised standard. There are numerous ways to construct and evaluate potential 

control programs that would bring areas into attainment with alternative standards, and EPA 

anticipates that state and local governments will consider programs that are best suited for local 

conditions. Furthermore, based on past experience, EPA believes that it is reasonable to 

anticipate that the marginal cost of control will decline over time due to technological 

improvements and more widespread adoption of previously niche control technologies. Also, 

EPA recognizes the extrapolated portion of the cost estimates reflects substantial uncertainty 

about which sectors, and which technologies, might become available for cost-effective 

application in the future. This is explained in further detail in Section 5.4. 

It is also important to recognize that the cost estimates are limited in their scope. Because we are 

not certain of the specific actions that states will take to design State Implementation Plans to 

meet the revised standards, we do not present estimated costs that government agencies may 

incur for managing the requirement and implementation of these control strategies or for offering 

incentives that may be necessary to encourage or motivate the implementation of the 

technologies, especially for technologies that are not necessarily market driven. This analysis 

does not assume specific control measures that would be required in order to implement these 

technologies on a regional or local level.

5.1 Modeled Controls 

5.1.1 Sector methodology 

5.1.1.1 Non-EGU Point and Area Sources: AirControlNET 

After designing a national hypothetical control strategy to meet an alternative standard of 0.070 

ppm using the methodology discussed in Chapter 3 (see sub-section 3.2.1), EPA used 

AirControlNET to estimate engineering control costs. AirControlNET calculates costs using 

three different methods: (1) by multiplying an average annualized cost-per-ton estimate against 

the total tons of a pollutant reduced to derive a total cost estimate; (2) by calculating cost using 

an equation that incorporates information regarding key plant information; or (3) by using both 

cost per ton and cost equations. Most control cost information within AirControlNET has been 

developed based on the cost-per-ton approach. This is because estimating cost using an equation 

requires more data, and parameters used in other non-cost per ton methods may not be readily 

available or broadly representative across sources within the emissions inventory. The costing 

equations used in AirControlNET require either plant capacity or stack flow to determine annual, 

capital and/or operating and maintenance (O&M) costs. Capital costs are converted to annual 
5-3

costs, in dollars per ton, using the capital recovery factor.1 Applied controls and their respective 

costs are provided in Ozone NAAQS RIA docket. 

The control strategy for Non-EGU Point and Area Sources incorporated cost-per-ton caps.

These caps were pollutant specific and applicable only in the eastern U.S. portion of the analysis.

For reductions of NOx emissions the cap was $16,000/ton. This was based upon the approximate 

benefit per ton of reductions in NOx, as well as an examination of the marginal cost curve for 

NOx reductions from these sectors. There were only two controls whose cost per ton were 

greater than this cap, and subsequently not included in this analysis, due to the large capital 

component of installing these controls. A similar process was followed for reductions from 

VOCs. The marginal cost curve was analyzed and there was a clear break in the curve at 

approximately $6,000/ton. At this cap, over sixty percent of the possible reductions are being 

controlled at less than thirty percent of the total cost of the VOC reductions. 

Supplemental controls were applied in this illustrative analysis in order to achieve the highest 

possible emission reduction from Non-EGU point and area sources. Supplemental control 

measures are those controls that are 1) applied in these analyses but are not found in 

AirControlNET, and 2) are in AirControlNET but whose data have been modified to better 

approximate their applicability to source categories in 2020. The controls and associated data 

such as control cost estimates not found in AirControlNET are taken from technical reports 

prepared to support preliminary 8-hour ozone State Implementation Plans (SIPs) prepared by 

States and from various reports prepared by the staffs of various local air quality regulatory 

agencies (e.g. Bay Area Air Quality Management District). The reports that are the sources of 

additional controls data are included within footnotes in the Chapter 3 Appendix. Modification 

of control data, including percent reduction levels and control cost data, in AirControlNET 

occurred as a result of a review of the nonEGU point and area NOx control measures by 

technical staff. The changes EPA supplied are provided later in the Chapter 3 Appendix.

5.1.1.2 EGU Sources: the Integrated Planning Model

Costs for the electric power sector are estimated using the Integrated Planning Model (IPM).

The model determines the least-cost means of meeting energy and peak demand requirements 

over a specified period, while complying with specified constraints, including air pollution 

regulations, transmission bottlenecks, fuel market restrictions, and plant-specific operational 

constraints. IPM is unique in its ability to provide an assessment that integrates power, 

environmental, and fuel markets. The model accounts for key operating or regulatory constraints 

(e.g. emission limits, transmission capabilities, renewable generation requirements, fuel market 

constraints) that are placed on the power, emissions, and fuel markets. IPM is particularly wellsuited to consider complex treatment of emission regulations involving trading and banking of 

1

 For more information on this cost methodology and the role of AirControlNext, see Section 6 

of the 2006 PM RIA, AirControlNET 4.1 Control Measures Documentation (Pechan, 2006b), or 

http://www.epa.gov/ttn/catc/products.html#cccinfo
5-4

emission allowances, as well as traditional command-and-control emission policies.2 Applied 

controls and their respective costs are provided in the docket. IPM is described in further detail 

in Appendix 3. 

5.1.1.3 Onroad and Nonroad Mobile Sources: MOBILE model 

Cost information for mobile source controls was taken from studies conducted by EPA for 

previous rulemakings and studies conducted for development of voluntary and local measures 

that could be used by state or local programs to assist in improving air quality. Applied controls 

and their respective costs are provided in the docket.3

Cost analysis of the onroad and nonroad mobile sector was performed using the MOBILE6 

model. MOBILE is an EPA model for estimating pollution from highway vehicles. MOBILE 

calculates emissions of hydrocarbons (HC), oxides of nitrogen (NOx), and carbon monoxide 

(CO) from passenger cars, motorcycles, light- and heavy-duty trucks. The model accounts for the 

emission impacts of factors such as changes in vehicle emission standards, changes in vehicle 

populations and activity, and variation in local conditions such as temperature, humidity and fuel 

quality4.

5.1.2 Known Controls— Cost by Sector 

In this section, we provide engineering cost estimates of the control strategies identified in 

Chapter 3 that include control technologies on non-EGU stationary sources, area sources, EGUs, 

and onroad and nonroad mobile sources. Engineering costs generally refer to the capital 

equipment expense, the site preparation costs for the application, and annual operating and 

maintenance costs. 

The total annualized cost of control in each sector in the control scenario is provided in Table 

5.1. These numbers reflect the engineering costs across sectors annualized at a discount rate of 

7% and 3%, consistent with the guidance provided in the Office of Management and Budget’s 

(OMB) (2003) Circular A-4. However, it is important to note that it is not possible to estimate 

2

 The application of the 0.070 EGU control strategy results in NOx allowance price decreasing 

from $1340/ton in the baseline to $715/ton. See Technical Support Document on EGU Control 

Strategies for more details. Further detailed information on IPM is available in Section 6 of the 

2006 PM RIA or at http://www.epa.gov/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 MOBILE6 model can be found at 

http://www.epa.gov/otaq/mobile.htm 
5-5

both 7% and 3% discount rates for each source (see section 5.1.3). In Table 5.1, an annualized 

control cost is provided to allow for comparison across sectors, and between costs and benefits.

A 7% discount rate was used for control measures applied to non-EGU point, area, and mobile 

sources. Costs from EGU sources, which are calculated using the IPM model and variable 

interest rates, are captured in this table at an annualized 7% discount rate5

.

Total annualized costs were calculated using a 3% discount rate for controls which had a capital 

component and where equipment life values were available. In this RIA, the non-EGU point 

source sector was the only sector with available data to perform a sensitivity analysis of our 

annualized control costs to the choice of interest rate. Sufficient information on annualized 

capital calculations was not available for area source and mobile controls to provide a reliable 3 

percent discount rate estimate. As such, the 3% value in figure 5.1 is representative of the sum of 

the non-EGU Point Source sector at a 3% discount rate, and the EGU, mobile, and Area Source 

sector at a 7% discount rate. It is expected that the 3% discount rate value is overestimated due 

to the addition of cost sectors at a higher discount rate. With the exception of the 3 % Total 

Annualized Cost estimate on Table 5.1, cost estimates presented throughout this and subsequent 

chapters are based on 7% discount rate. 

The total annualized engineering costs associated with the application of known and 

supplemental controls to reach a revised 0.070 ppm standard, incremental to the current standard, 

are approximately $3.9 billion. 

5

 A different plant-specific interest rate is applied in estimating control costs within IPM. See 

PM RIA for details.
5-6

Table 5.1 Comparison of Modeled Annual Control Costs Nationwide, by sector, for a 0.070 

ppm control scenario ($1999)6

0.070 ppm Control Strategy 

Source Category Total Cost 

($B 1999) 

East West 

Average

Cost per Ton 

($1999)

A. Electric Generating Units (EGU) Sector 

Controls for NOx Cap-and-Trade Program and Local 

Measures in Projected Nonattainment Areas 

$0.20 $0 $2,000

 Total $0.20 $0 

 B. Onroad $0.51 $0.11 $2,300

 C. Nonroad $0.09 $0.02 $4,400

 Total $0.60 $0.13 

D. Non-EGU Sector 

 Point Sources (Ex: Pulp & Paper, Iron & Steel,

 Cement, Chemical Manu.) 

$2.30 $0.34 $3,600

E. Area Sector 

 Area Sources (Ex: Res. Woodstoves, Agriculture) $0.31 $0.01 $2,000

 Total $2.6 $0.35 

 Total Annualized Costs 

(using a 7% interest rate) $3.90

 Total Annualized Costs 

(using a 3% interest rate) $3.60

5.1.3 Limitations and Uncertainties Associated with Engineering Cost Estimates 

EPA bases its estimates of emissions control costs on the best available information from 

engineering studies of air pollution controls and has developed a reliable modeling framework 

for analyzing the cost, emissions changes, and other impacts of regulatory controls. The 

annualized cost estimates of the private compliance costs are meant to show the increase in 

production (engineering) costs to the various affected sectors in our control strategy analyses.

To estimate these annualized costs, EPA uses conventional and widely-accepted approaches that 

are commonplace for estimating engineering costs in annual terms. However, our cost analysis 

is subject to uncertainties and limitations. 

There are some 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 

6 All estimates provided reflect the cost of a control strategy for 0.070 pm, incremental to a 

2020 baseline of compliance with the current standard of 0.084 ppm. Note, for the final RIA 

we will be updating our estimates to $2006.
5-7

"no cost" may require limited government agency resources for administration and oversight of 

the program not included in this analysis; those costs are generally outweighed by the saving to 

the industrial, commercial, or private sector. The Agency also did not consider transactional 

costs and/or effects on labor supply in the illustrative analysis.

The economic impacts (i.e. social costs) of the cost of these modeled controls were not included 

in this analysis. Incorporating the economic impact of the extrapolated portion of the costs was 

too uncertain to be included as part of these estimates, and it was determined best to keep the 

modeled and extrapolated costs on the same basis. However, incorporating any economic 

impacts would increase the total cost of attainment in 2020 for a revised ozone standard. 

The illustrative analysis does quantify the potential for advancements in the capabilities of 

pollution control technologies as well as reductions in their costs over time. This is discussed in 

Section 5.4. 

5.2 Extrapolated Costs 

This section presents the results and methodology behind the extrapolated cost calculations of 

attainment of the alternate standards (the ends of the proposed range – 0.075 and 0.070 ppm, the 

less stringent alternative of 0.079 ppm, and the more stringent alternative of 0.065 ppm). 

Consistent with the rest of this RIA, this section presents two sets of results. The first reflects 

full attainment in 2020 in all locations of the U.S. except two areas of California. These two 

areas are not planning to meet the current standard by 2020, so the estimated costs for these areas 

are based on reaching an estimated progress point in 2020 (their “glidepath” targets). The 

second set of results for California only, estimate the costs from California fully attaining the 

alternative standards in a year beyond 2020 (glidepath estimates for 2020, plus further 

increments needed to reach full attainment beyond 2020, added together for California total).

As discussed in Chapter 3, the application of the 0.070 ppm control strategy was not successful 

in reaching nationwide attainment of the alternate ozone standards. Many areas remained in 

non-attainment for all three alternate standard scenarios; therefore, the engineering costs detailed 

in Section 5.1 represent only the costs of partial attainment. 

The estimation of the costs of unidentified controls needed to reach attainment is inherently a 

difficult issue. The degree to which unspecified controls are needed to achieve attainment 

depends upon other variables in the analysis, such as attainment date assumptions. We will 

better understand the true scope of the issue in the future as states conduct detailed area-by-area 

analyses to determine available controls and attainment dates that are appropriate under the 

Clean Air Act. We do not attempt to determine specific attainment dates in this analysis. 

This draft RIA used two different approaches to estimating the costs of unspecified control 

measures. This reflects the difficulty in defining a “best” approach to this issue as well as the 

uncertainty related to the extrapolated costs. One approach assumes that the marginal cost of 

abatement increases at a constant rate. The other approach assumes a fixed cost for abatement 
5-8

tons and provides estimates for two fixed cost/ton values. Use of the fixed cost per ton approach 

reduces the possibility of inflated extrapolated costs that would result from an infinitely 

increasing marginal cost curve. However it does not take into account the probability that 

abatement costs would increase as an industry or state reduces a higher portion of available NOx 

or VOC tons. In turn, the marginal cost approach captures this increase in abatement cost, but its 

lack of “cost caps” can result in overestimates of costs. 

Our approaches have yet to be peer reviewed and reflect a range of views about the likely cost of 

future techniques and strategies that reduce air pollutant emissions. (The higher-cost estimation 

approaches are implicitly more pessimistic about prospects for technological advances that avoid 

large increases in the cost per ton of emission reduction relative to controls employed in the 

past.) Section 5.4 discusses historical experience which has shown numerous technological 

advances in emission reduction technologies, and provides a few examples of today’s emerging 

technologies. EPA will continue to consider these issues between now and the publication of the 

final RIA for the final ozone NAAQS rule. 

This section provides the additional costs of reaching nationwide full attainment of the alternate 

ozone standards utilizing three values: a lower fixed cost per ton estimate based on the majority 

of the known cost/ton control values, an upper fixed cost per ton estimate based on the cost of 

the last few known control measures used and an increasing marginal cost estimate similar to 

that used in the PM NAAQS Final RIA. In addition to presenting the full attainment cost, this 

section will provide the methodology behind each approach. 

Prior to presenting the aforementioned full attainment costs, it is important to provide 

information from EPA’s Science Advisory Board Council Advisory7

, dated June 8, 2007, on the 

issue of estimating costs of unidentified control measures. In that letter, the Council advises 

against any approach that deviates from using a fixed cost/ton estimate such as the increasing 

marginal cost approach provided below. This increasing marginal cost approach ‘grows’ 

extrapolated costs that have an unquantifiable level of uncertainty at a rate with an equivalent 

level of uncertainty. This approach is presented in this Proposal RIA in order to maintain a 

consistency with the PM NAAQS Final RIA cost extrapolation. EPA is going to reconsider its

approach to estimating the full attainment costs in the final RIA, in light of this advice. 

Consideration of this advice will be balanced with the requirements of E.O. 12866 and OMB 

circular A-4, which provides guidance on the estimation of benefits and costs of regulations. 

812 Council Advisory, Direct Cost Report, Unidentified Measures (charge question 2.a)

“The Project Team has been unable to identify measures that yield sufficient emission 

reductions to comply with the National Ambient Air Quality Standards (NAAQS) and 

relies on unidentified pollution control measures to make up the difference. Emission 

reductions attributed to unidentified measures appear to account for a large share of 

7

 U.S. Environmental Protection Agency. June 2007. Advisory Council on Clean Air 

Compliance Analysis (COUNCIL), Council Advisory on OAR’s Direct Cost Report and 

Uncertainty Analysis Plan. Washington, DC 
5-9

emission reductions required for a few large metropolitan areas but a relatively small 

share of emission reductions in other locations and nationwide. 

“The Council agrees with the Project Team that there is little credibility and hence 

limited value to assigning costs to these unidentified measures. It suggests taking great 

care in reporting cost estimates in cases where unidentified measures account for a 

significant share of emission reductions. At a minimum, the components of the total cost 

associated with identified and unidentified measures should be clearly distinguished. In 

some cases, it may be preferable to not quantify the costs of unidentified measures and to 

simply report the quantity and share of emissions reductions attributed to these 

measures.

“When assigning costs to unidentified measures, the Council suggests that a simple, 

transparent method that is sensitive to the degree of uncertainty about these costs is best. 

Of the three approaches outlined, assuming a fixed cost/ton appears to be the simplest 

and most straightforward. Uncertainty might be represented using alternative fixed costs 

per ton of emissions avoided.”

5.2.1 Increasing Marginal Cost Methodology 

This approach stems from the assumption that each unit of incremental reduction in nonattainment areas will result in an increase in cost per ton or marginal cost of abatement. 

Therefore, similar to the approach used in the PM NAAQS RIA, EPA estimated constantly 

increasing marginal cost curves for emission reductions using cost per ton values from control 

strategy data in representative non-attainment areas. These curves were then used to estimate a 

cost of full attainment using the emission reduction targets detailed in Chapter 4 of this report. 

5.2.1.1 Marginal Cost Regions 

EPA grouped the non-attainment areas described in Chapter 4 along with their emission 

reduction targets into six regions of the country (Table 5.2) in order to acquire sufficient and 

representative data for deriving the slopes of the marginal cost curves. 

x Nonattainment areas in Virginia were grouped with the Northeast due to the fact that 

Northern Virginia is part of the Ozone Transport Region (OTR) which makes up the 

Northeast. Resources were not available to disaggregate states by counties.

x Nonattainment areas in Louisiana were grouped with Texas and Oklahoma (Plains 

region) due to the similarity in industry mix among those states. 

x California was separated from the rest of the west due to the severity of the ozone 

problem in the state, the glide path targets unique to the state, and because EPA 

determined the rest of the west was not an ideal representation of California.
5-10

Table 5.2 Regions and Slopes for Extrapolated Costs

Region Marginal Cost Slope

Northeast (OTR) 0.035

Midwest 0.045

Southeast 0.036

Plains (TX/LA) 0.033

West (Not CA) 0.152

CA 0.211
5-11

5.2.1.2 Derivation of the Marginal Cost Slopes 

Due to the efficaciousness and efficiency of NOx controls compared to VOC controls, control 

strategy cost per ton data was acquired for each region using a selection criteria defined in Table 

5.3 and applied in Ordinary Least Squares regression equations. Results of these equations 

provided the slope for the marginal cost curves. For each equation, the dependant variable (Y = 

cost/ton)8

 and was regressed conditional to (X = cumulative emissions reductions).9

Y = c + ȕX + ɟ

The regression equations are not intended to be used for statistical inference in regards to the 

relation between cost/ton and cumulative emission reductions. They represent a rough 

approximation of an increasing rate or slope of cost/ton which could be used to project 

extrapolated costs. This slope would then provide an increasing cost/ton rate in the extrapolated 

portion of the cost that was equivalent to the rate observed under the modeled costs. As can be 

seen by the range of the goodness of fit estimates (R2

 = 0.5 to 0.8), there is a high level of 

uncertainty with these slope estimates which is propagated through the extrapolated cost 

estimates. 

Table 5.3 Data Selection Criteria for Extrapolated Costs 

1) Determine if area has sufficient NOx emissions remaining to reach 

attainment 

2) If area has sufficient NOx remaining to reach attainment, then use NOx 

cost/ton data due to their cost effectiveness compared VOC controls 

3) If area does not have sufficient NOx emissions to reach attainment, then 

include VOC controls in the data set if: 

x VOC controls were part of the control strategy for the area in question 

x VOC control cost/ton inclusions to the regression data set would 

significantly alter the value of the slope for the marginal cost curve 

derived using only NOx cost/ton data 

Note: Data analysis demonstrated that VOC control data would only be needed for California.

Due to lack of available ozone data, NOx controls from California also include control cost from 

the PM NAAQS RIA control strategies. 

8

 For the east regions, the full cost/ton data set was applied and had a maximum value of 

$15,267/ton. For the west, the cost/ton data was truncated at $15,267/ton in order to maintain a 

consistent comparison with the east regions and because the few remaining controls had costs 

greater $35,000/ton and were therefore judged to be not economically feasible. 

9

 EPA recognizes that these regression equations may be misspecified. As stated above, the 

objective was not to accurately capture the relation between control cost/ton and emission 

reduction for statistical or economic inference purposes. These equations represent the most 

statistically adequate models that could be specified given the data, time, and resource 

constraints.

c = constant 

ȕ = slope 

ɟ = residual
5-12

5.2.1.3 Calculating Extrapolated Costs Using Marginal Cost Approach 

Once the slope of the marginal cost curve was derived, the extrapolation was calculated by 

multiplying that slope with the emission reduction target and adding that value to the highest of 

the observed cost/ton value (Figure 5.1). For this illustrative analysis, the highest of the 

observed cost/ton values was roughly $15,267/ton which represented the intercept of the 

marginal cost equation. Total costs could then be estimated by adding the area under the 

marginal cost curve in Figure 5.1 or by taking the integral of the marginal cost function and 

inputting the emission reduction target into the equation for total cost.10

Figure 5.1 Extrapolated Cost Example (MC Approach)11

Extrapolated Costs

Portion

0 50 100 

Reductions Needed (thousands)

Cost/Ton ($)

$15,267

Slope = ȕ

Lower Fixed Cost/Ton 

Estimate

5.2.2 Fixed Cost per Ton Values

Similar to the 1997 Ozone NAAQS RIA, a fixed cost/ton value was also applied to estimate the 

extrapolated costs of nationwide full attainment. Total costs for each non-attainment area was 

calculated by multiplying the fixed cost/ton value with the emission reduction targets for each 

region. For this particular illustrative analysis, a pair of fixed cost/ton values was used to 

calculate costs. 

NOx control strategy data for the East and West were examined for ‘clustering’ within their 

individual distributions. Cost/ton data for the east and west were stratified into thousands (Ex. 

$0-$1000, $1000-$2000) with individual source counts aggregated within each interval. 

California was separated from the west so source cost/ton counts were conducted separately for 

the state. This was the result of limited ozone NOx data availability for the state, the low number 

10 Total Cost = $15,267x + (ȕ/2)x2

, where x = emission reduction target 

11 In the case of 0.075 ppm, negative reductions are needed in order to estimate extrapolated cost 

savings.
5-13

of NOx emissions remaining for CA, and the inclusion of ozone VOC controls as well as NOx 

controls from PM NAAQS RIA control strategies which were required to resolve these data and 

emissions issues. 

For the East, 90% of the controls were below $6,000/ton. As a result, the control cost closest to 

$6,000/ton ($6,012) was selected to represent the cost of the majority of the modeled controls as 

well as the lower estimate of the Eastern fixed cost/ton approach. For the West, 94% of the 

controls had a cost/ton value below $4,000/ton. Therefore, $4,213 was selected as the lower 

estimate for the western fixed cost/ton approach. For California, the lower estimate was $9,035 

using the same method but including VOC and PM NAAQS NOx control data from the PM 

NAAQS RIA hypothetical control scenario. This lower estimate for CA captured 81% of the 

cost/ton data below $15,267 and 65% of all cost/ton data for controls applied in the ozone and 

PM control strategies after multiplying VOC controls by a 4 to 1 substitution factor. 

In addition to a lower fixed cost/ton estimate, an upper fixed cost/ton value was used for 

calculating extrapolated costs. This upper value as estimated at $15,267 for all regions for the 

following reasons. 

x This value represented the highest, in terms of cost/ton, of the controls applied in the 

East. The East control strategy made up the majority of the modeled controls for the 

ozone standard. 

x In the case of the West, the next highest controls were roughly $35,000 and $39,000 per 

ton. Controls with these costs were determined to be significantly less feasible to 

implement compared other controls. 

x This value provides a consistent platform from which to incorporate and compare 

marginal cost values derived using the increasing marginal cost approach. 

5.2.3 Results 

Tables 5.4 to 5.7 provide the extrapolated cost values for 2020 attainment of the 0.079, 0.075, 

0.070, 0.065 ppm standards in each area (including the California 2020 glidepath targets) 

applying the increasing marginal cost value as well as the two fixed cost/ton values. The reader 

should be aware of the following stipulations prior to making inferences from the extrapolated 

costs presented in the following tables. 

x The two extrapolated cost approaches provide three rough estimates of potential costs 

with the marginal cost approach providing the highest value. Neither result includes a 

probability or a link to sectors where reductions will be attained. Therefore, there are no 

expected values within this range of outcomes and no assumptions made about the types 

of controls that would be applied in 2020. Although the amount of reduction assumed to 

occur using unknown controls increases, the uncertainty of the associated costs and 

benefits calculations increases. 

x 0.070 ppm extrapolated costs were estimated using data from the 0.070 ppm control 

strategy. Therefore, although the degree of uncertainty is still significant, these results 

can be expected to have a higher level of confidence than results for the 0.079, 0.075, and 

0.065 ppm alternate standards. 
5-14

x The use of the 0.070 ppm control strategy as a starting point for extrapolating the 0.079 

and 0.075 ppm standard resulted in over attainment of these targets in some areas. For 

over attaining areas, cost savings and emission increases were extrapolated using the 

impact/ton estimates derived in Chapter 4 and their appropriate emission targets until 

reaching the respective alternate standard. 

x Several new non-attainment counties were added to the analysis as a result of moving to 

the 0.065 ppm alternate standard. Most of these counties were in states within the 0.070 

ppm control strategy region described in Chapter 3. For the east, this region was made up 

of counties with 0.070 ppm violating monitors and their 200 km buffers which made up 

most of the eastern part of the US. In the west, this region was made up of six states (AZ, 

CA, CO, NM, NV, UT). Due to the geographic scope of the 0.070 control strategy, no 

additional controls were available and costs had to be extrapolated using the same 

impact/ton estimates applied in the 0.070 ppm estimates. Two new states were added to 

the non-attainment region (KS and AL). Since controls were available for these states, 

AirControlNET was used to identify controls that would achieve the required emission 

reduction targets. 

x 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) 
5-15

aDue to the limited amount of controls in the modeled control strategy which had this value, 

deducting this amount would likely result in an over estimate of the savings. For example, 

estimating cost savings by multiplying changes in emission reduction by $15,267 would 

result in an overestimate of cost savings since not all of the applied controls had a cost/ton as 

high as $15,267.

b

Los Angeles and Kern Counties have expected attainment dates after 2020. This analysis 

counts the portion of reductions expected by 2020 or earlier.

c

 We did not calculate the cost savings which would result from using the upper bound 

values. This would result in an unrealistic savings because none of these cities with less 

significant air quality problems would be expected to be getting all their expected emission 

reductions at such a high cost/ton. We therefore used the lower cost estimate which

included roughly 90% of the controls applied. 

Table 5.4 Extrapolated Costs of Meeting the 0.079 ppm Standard

MC Curve 

Estimate 

Lower Fixed 

Cost/Ton 

Estimate 

Upper Fixed 

Cost/Ton 

Estimatea

Cost/Ton Estimate of 

Last Control Applied on 

MC Curve Estimate Extrapolated Costs for 0.079 

Standard ($M 1999) ($M 1999) ($M 1999) ($1,999)

Extrapolated Costs

CA – Los Angelesb

$0 $0 $0

CA – Kern Countyb

$0 $0 $0

Houston / Dallas $477 $158 $400 $18,765

Ozone Transport Region $568 $200 $504 $17,787

Lake Michigan region $571 $206 $519 $17,562

Richmond / Norfolk $139 $55 $137 $15,582

Total Cost $1,755 $619

Extrapolated Cost Savings

Atlanta, GA ($568) ($236) 

c

Cleveland, OH ($493) ($206) 

c

Detroit, MI ($224) ($91) 

c

Control Deletions

Charlotte, Memphis, Las Vegas, Salt 

Lake City, Tampa ($119) ($119) 

c

Colorado, Arizona ($676) ($676) 

c

Baton Rouge, Indianapolis, Louisville, 

St. Louis ($241) ($241) 

c

 

Total Cost Savings ($2,321) ($1,569)

Total Extrapolated Cost ($566) ($950) 

Average cost per ton $2,113 $3,549
5-16

Table 5.5 Extrapolated Costs of Meeting the 0.075 ppm Standard

Extrapolated Costs for 075 

Standard

MC Curve 

Estimate 

($M 1999) 

Lower Fixed 

Cost/Ton 

Estimate 

($M 1999) 

Upper Fixed 

Cost/Ton 

Estimatea

($M 1999) 

Cost/Ton Estimate of 

Last Control Applied 

on MC Curve Estimate 

($ 1999) 

Extrapolated Costs

CA – Los Angelesb

$0 $0 $0

CA – Kern Countyb

$0 $0 $0

Houston / Dallas $1,254 $400 $1,008 $20,085

Ozone Transport Region $1,307 $443 $1,114 $19,187

Lake Michigan region $1,310 $449 $1,130 $19,362

Richmond / Norfolk $790 $297 $748 $16,982

Detroit $396 $152 $382 $16,392

Phoenix $282 $72 $260 $17,851

Denver $160 $42 $153 $16,787

Cleveland/Columbus/Cincinnati $92 $36 $92 $15,537

Atlanta $15 $6 $15 $15,303 

Total Cost $5,606 $1,896

Extrapolated Cost Savings

Baton Rouge, LA ($225) ($91)

c

Indianapolis, IN ($209) ($85)

c

Louisville, KY-IN ($284) ($115)

c

St. Louis, MO-IL ($30) ($12)

c

Total Cost Savings ($748) ($303)

Total Extrapolated Cost $4,858 $1,593 

Average cost per ton $23,000 $7,600

aDue to the limited amount of controls in the modeled control strategy which had this value, 

deducting this amount would likely result in an over estimate of the savings. For example, 

estimating cost savings by multiplying changes in emission reduction by $15,267 would 

result in an overestimate of cost savings since not all of the applied controls had a cost/ton as 

high as $15,267.

b

Los Angeles and Kern Counties have expected attainment dates after 2020. This analysis 

counts the portion of reductions expected by 2020 or earlier.

c

 We did not calculate the cost savings which would result from using the upper bound 

values. This would result in an unrealistic savings because none of these cities with less 

significant air quality problems would be expected to be getting all their expected emission 

reductions at such a high cost/ton. We therefore used the lower cost estimate which

included roughly 90% of the controls applied.
5-17

Table 5.6 Extrapolated Costs of Meeting the 0.070 ppm Standard a

Extrapolated Costs for 0.070

Standard

MC Curve Estimate 

($M 1999) 

Lower Fixed 

Cost/Ton 

Estimate 

($M 1999) 

Upper Fixed 

Cost/Ton 

Estimate 

($M 1999) 

Cost/Ton Estimate 

of Last Control 

Applied on MC 

Curve Estimate 

($ 1999) 

CA – Los Angelesb $0 $0 $0

CA – Kern County b $829 $181 $305 $43,541

Houston / Dallas $2,299 $703 $1,771 $21,735

Ozone Transport Region $2,310 $746 $1,878 $20,937

Lake Michigan region $2,334 $752 $1,893 $21,612

Richmond / Norfolk $1,683 $600 $1,511 $18,732

Detroit $1,272 $455 $1,145 $18,642

Phoenix $1,364 $282 $1,023 $25,451

Denver $1,190 $253 $916 $24,387

Cleveland/Columbus/Cincinnati $926 $339 $855 $17,787

Atlanta $825 $309 $779 $17,103

St. Louis $785 $291 $733 $17,427

Indianapolis $579 $218 $550 $16,887

Baton Rouge $555 $212 $534 $16,422

Louisville $491 $188 $473 $16,383

Memphis $313 $121 $305 $15,987

Charlotte $217 $85 $214 $15,771

Salt Lake City $211 $55 $198 $17,243

Las Vegas $177 $46 $168 $16,939

Tampa $77 $30 $76 $15,447

Total Extrapolated Cost $18,441 $5,867 $15,328

Average cost per ton $18,400 $5,900 $15,300

 a EPA was not able to estimate benefit changes when moving the standard from 0.070 to 

0.075 for Memphis, Charlotte, Salt Lake City, Las Vegas, and Tampa. Therefore, in order to 

maintain a consistent comparison, cost savings were not estimated for these locations. 

b

 Los Angeles and Kern Counties have expected attainment dates after 2020. This analysis 

counts the portion of reductions expected by 2020 or earlier.
5-18

Table 5.7 Extrapolated Costs of Meeting the 0.065 ppm Standard

Extrapolated Costs for 065 

Standard

MC Curve 

Estimate

($M 1999)

Lower Fixed 

Cost/Ton 

Estimate

($M 1999)

Upper 

Fixed

Cost/Ton 

Estimate

($M 1999)

Cost/Ton 

Estimate of Last 

Control Applied 

on MC Curve 

Estimate 

($ 1999)

CA – Los Angelesa$0 $0 $0 $15,267

CA – Kern Countya$2,230 $452 $763 $49,871

Houston / Dallas $3,427 $1,006 $2,534 $23,385

Ozone Transport Region $3,401 $1,049 $2,641 $22,687

Lake Michigan region $3,471 $1,055 $2,656 $23,862

Richmond / Norfolk $2,663 $903 $2,275 $20,482

Detroit $2,260 $758 $1,908 $20,892

Phoenix $2,827 $493 $1,786 $33,051

Denver $2,599 $463 $1,679 $31,987

Cleveland/Columbus/Cincinnati $1,871 $643 $1,618 $20,037

Atlanta $1,726 $612 $1,542 $18,903

St. Louis $1,712 $594 $1,496 $19,677

Indianapolis $1,479 $521 $1,313 $19,137

Baton Rouge $1,417 $515 $1,298 $18,072

Louisville $1,355 $491 $1,237 $18,183

Memphis $1,157 $424 $1,069 $17,787

Charlotte $1,051 $388 $977 $17,571

Salt Lake City $1,263 $265 $962 $24,843

Las Vegas $1,214 $257 $931 $24,539

Tampa $894 $333 $840 $17,247

Jackson, MS $757 $285 $718 $16,959

New Mexico areas (Farmington / Las 

Cruces) $819 $185 $672 $21,955

OK areas (Tulsa, Marshall) $704 $267 $672 $16,719

Huntington, WV-KY $639 $242 $611 $16,707

El Paso, TX $538 $206 $519 $16,389

Kansas City, MO/KS $325 $142 $317 $16,122

Little Rock, AR $442 $170 $427 $16,275

Mobile AL $70 $70 $70 $16,239

Columbia, SC $154 $61 $153 $15,627

Extrapolated Total $42,465 $12,851 $33,684

Average cost per ton $19,300 $5,800 $15,300

a

 Los Angeles and Kern Counties have expected attainment dates after 2020. This analysis 

counts the portion of reductions expected by 2020 or earlier.
5-19

5.3 Summary of Costs 

Table 5.8 presents a summary of the total national cost of attaining 0.079, 0.075, 0.070, and 

0.065 ppm standards in 2020 (including the California glidepath). This summary includes the 

costs presented above from the modeled controls and the extrapolated costs. The range 

presented in the extrapolated costs and the total costs represent the upper and lower bound cost 

estimates. Consistent with OMB Circular A-4, costs are presented at a 7% discount rate. It is 

more consistent to present the extrapolated costs at the same discount rate as the modeled control 

costs, for which a 7% rate was determined to be more representative of actual costs (see section 

5.1.3). Although the amount of reduction assumed to occur using unknown controls increases, 

the uncertainty of the associated costs and benefits calculations increases. 

Table 5.8 Total Costs of Attainment in 2020 for Different Levels of the Ozone Standard 

(National Attainment in 2020) 

Level of Standard in 2020 

 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

Modeled Costs ($B) $3.9 $3.9 $3.9 $3.9 

Extrapolated Costs ($B) $13 to $42 $5.9 to $18 $1.6 to $4.9 ($0.95) to ($0.57)* 

Total Costs ($B) $17 to $46 $10 to $22 $5.5 to $8.8 $3 to $3.3 

* The use of the 0.070 ppm control strategy as a starting point for extrapolating the 0.079 

standard resulted in over attainment in some areas. For over attaining areas, cost savings were 

applied. For the 0.079 ppm standard the cost savings from over attaining areas was greater than 

the costs for areas still needing extrapolated tons (see Table 5.4). 

Table 5.9 presents an estimate of total costs of California only, for fully attaining the alternative 

standards in a year beyond 2020 (glidepath estimates for 2020, plus further increments needed to 

reach full attainment beyond 2020, added together for California total). 
5-20

* Due to the limited amount of controls in the modeled control strategy which had this value, 

deducting this amount would likely result in an over estimate of the savings. 

It is not appropriate to add together the 2020 national attainment, California glidepath estimate 

and the estimate of California full attainment as an estimate of national full attainment in 2020. 

The extra increment of attainment that is estimated for California will not occur in 2020, so it is 

not accurate to add it to our nationwide estimate of the “glidepath” benefits and costs to arrive at 

a “full attainment” estimate for 202012. It is also not accurate to add the two estimates together to 

arrive at an estimate of future, post-2020 full attainment benefits and costs, because EPA’s 

nationwide full attainment estimates do not allow other areas of the nation to take credit for the 

reductions in NOx from the mobile source rules that will occur after 2020.13

5.4 Technology Innovation and Regulatory Cost Estimates 

The history of the Clean Air Act provides many examples in which technological innovation and 

“learning by doing” have made it possible to achieve greater emissions reductions than had been 

feasible earlier, or have reduced the costs of emission control in relation to original estimates. 

Innovative companies have successfully responded to the regulatory challenges and market 

opportunities provided by the Act, producing breakthrough technologies for multiple sectors. 

12 The California full attainment costs calculated using the offset in NOx emissions from mobile 

programs would understate the costs of fully attaining in 2020, however, California will not be 

required to attain in 2020. 

13 This approach would be an overestimate of national full attainment costs in a future year after 

2020 because it would not take into account that other states (not just California) could replace 

more expensive NOx reductions from other sources with the post-2020 reductions obtained from 

implementation of mobile source rules that are included in the regulatory baseline.

Table 5.9 California Extrapolated Costs ($M) 

0.079 0.075 0.070 0.065

CA (Glidepath) 

Marginal Cost Approach $0 $0 $829 $2,230

Lower Estimate (fixed cost) $0 $0 $181 $452

Upper Estimate (fixed cost) * * $305 $763

CA Increment Needed for 

Full Attainment Marginal Cost Approach $4,566 $6,227 $12,022 $18,301

Lower Estimate (fixed cost) $1,187 $1,050 $1,773 $2,405

Upper Estimate (fixed cost) * * $2,995 $4,064

CA (Full Attainment-Later 

Year)

Marginal Cost Approach $4,566 $6,227 $12,851 $20,530

Lower Estimate (fixed cost) $1,187 $1,050 $1,953 $2,857

Upper Estimate (fixed cost) * * $3,301 $4,827
5-21

Studies14 have suggested that costs of some EPA programs have been less than originally 

estimated due in part to inadequate inability to predict and account for future technological 

innovation in regulatory impact analyses. 

Technological change will affect baseline conditions for our analysis. This change may lead to 

potential improvements in the efficiency with which firms produce goods and services, for 

example, firms may use less energy to produce the same quantities of output. In addition, 

technological change may result in improvements in the quality of health care, which can have 

impacts on the baseline health of the population, potentially reducing the susceptibility of the 

population to the effects of air pollution. While our baseline mortality incidence rates account 

for increasing life expectancy, and thus reflect projected improvements in health care, our 

baseline incidence rates for other health endpoints such as hospital admissions do not reflect any 

future advances in health care, and thus, our estimates of avoided health impacts for these 

endpoints will potentially be overstated. For other endpoints, such as asthma, there has been an 

observed upward trend in prevalence, which we have not captured in our incidence rates. For 

these endpoints, our estimates will potentially be understated. In general, for non-mortality 

endpoints, there is increased uncertainty in our estimates due to our use of current baseline 

incidence and prevalence rates. 

A constantly increasing marginal cost curve similar to the one utilized for estimating 

extrapolated costs in this RIA is likely to induce the type of innovation that would result in lower 

costs than estimated early in this chapter. Breakthrough technologies in control equipment could 

by 2020 result in a rightward shift in the marginal cost curve (Figure 5.2)15 as well as perhaps a 

decrease in its slope, reducing marginal costs per unit of abatement, and thus deviate from the 

assumption of one constantly increasing marginal cost curve. In addition, elevated abatement 

costs may result in significant increases in the cost of production and would likely induce 

production efficiencies, in particular those related to energy inputs, which would lower emissions 

from the production side. 

14 Harrinton et al ,2000, and previous studies cited by Harrington 

15 Figure 5.2 shows a linear marginal abatement cost curve. It is possible that the shape of the 

marginal abatement cost curve is non-linear. 
5-22

MC0 MC1

Cumulative NOx Reductions

Cost/Ton

Figure 5.4 Technological 

Innovation Reflected by MC Shift

Induced Technology Shift

Slope = ȕ0

Slope = ȕ1

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: 

x Selective catalytic reduction (SCR) and ultra-low NOx burners for NOx emissions 

x Scrubbers which achieve 95% and even greater SO2 control on boilers 

x Sophisticated new valve seals and leak detection equipment for refineries and chemical 

plans

x Low or zero VOC paints, consumer products and cleaning processes 

x Chlorofluorocarbon (CFC) free air conditioners, refrigerators, and solvents 

x Water and power-based coatings to replace petroleum-based formulations 

x 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 

x Continued development of activated carbon injection (ACI) technology for control of 

mercury from electric generating units 

x Development of integrated gasification combined cycle (IGCC) and ultra-super critical 

pulverized coal technologies for electricity generation 

x Idle-reduction technologies for engines, including truck stop electrification efforts 

x Market penetration of gas-electric hybrid vehicles, biodiesel and other clean fuels 

Figure 5.2 Technological Innovation 

Reflected by Marginal Cost Shift 
5-23

These technologies were not commercially available two decades ago, and some were not even 

in existence. Yet today, all of these technologies are on the market, and many are widely 

employed. Several are key components of major pollution control programs, 

5.4.2 Influence on Regulatory Cost Estimates 

Studies indicate that it is not uncommon for pre-regulatory cost estimates to be higher than later 

estimates, in part because of inability to predict technological advances. Over longer time 

horizons, such as the time allowed for areas with high levels of ozone pollution to meet the 

ozone NAAQS, the opportunity for technical advances is greater. 

x Multi-rule study: Harrington et al. of Resources for the Future (2000) conducted an 

analysis of the predicted and actual costs of 28 federal and state rules, including 21 issued 

by EPA and the Occupational Safety and Health Administration (OSHA), and found a 

tendency for predicted costs to overstate actual implementation costs. Costs were 

considered accurate if they fell within the analysis error bounds or if they fall within 25 

percent (greater or less than) the predicted amount. They found that predicted total costs 

were overestimated for 14 of the 28 rules, while total costs were underestimated for only 

three rules. Differences can result because of quantity differences (e.g., overestimate of 

pollution reductions) or differences in per-unit costs (e.g., cost per unit of pollution 

reduction). Per-unit costs of regulations were overestimated in 14 cases, while they were 

underestimated in six cases. In the case of EPA rules, the agency overestimated per-unit 

costs for five regulations, underestimated them for four regulations (three of these were 

relatively small pesticide rules), and accurately estimated them for four. Based on 

examination of eight economic incentive rules, “for those rules that employed economic 

incentive mechanisms, overestimation of per-unit costs seems to be the norm,” the study 

said.

Based on the case study results and existing literature, the authors identified 

technological innovation as one of five explanations of why predicted and actual 

regulatory cost estimates differ: “Most regulatory cost estimates ignore the possibility of 

technological innovation ... Technical change is, after all, notoriously difficult to forecast 

... In numerous case studies actual compliance costs are lower than predicted because of 

unanticipated use of new technology.”16

It should be noted that many (though not all) of the EPA rules examined by Harrington 

had compliance dates of several years, which allowed a limited period for technical 

innovation. Much longer time periods (ranging up to 20 years) are allowed by the statute 

for meeting the ozone NAAQS in areas with high ozone levels, where a substantial 

fraction of the estimated cost in this analysis is incurred. 

x Acid Rain SO2 Trading Program: Recent cost estimates of the Acid Rain SO2 trading 

program by Resources for the Future (RFF) and MIT have been as much as 83 percent 

16 Harrington et al., 2000. 
5-24

lower than originally projected by EPA.17 Note that the original EPA cost analysis also 

relied on an optimization model like IPM to approximate the results of emissions trading. 

As noted in the RIA for the Clean Air Interstate Rule, the ex ante numbers in 1989 were 

an overestimate in part because of the limitation of economic modeling to predict 

technological improvement of pollution controls and other compliance options such as 

fuel switching. Harrington et al report that scrubbing turned out to be more efficient 

(95% removal vs. 80-85% removal) and more reliable (95% vs. 85% reliability) than 

expected, and that unanticipated opportunities arose to blend low and high sulfur coal in 

older boilers up to a 40/60 mixture, compared with the 5/95 mixture originally estimated. 

x EPA Fuel Control Rules: A 2002 study by two economists with EPA’s Office of 

Transportation and Air Quality19 examined EPA vehicle and fuels rules and found a 

general pattern that “all ex ante estimates tended to exceed actual price impacts, with the 

EPA estimates exceeding actual prices by the smallest amount.” The paper notes that cost 

is not the same as price, but suggests that a comparison nonetheless can be instructive.20

An example focusing on fuel rules is provided:

17 Carlson et al., 2000; Ellerman, 2003.

18 2010 Phase II cost estimate in $1995. 

19 Anderson et al, 2002. 

20 The paper notes: “Cost is not the same as price. This simple statement reflects the fact that a 

lot happens between a producer’s determination of manufacturing cost and its decisions about 

what the market will bear in terms of price change.”

Phase 2 Cost Estimates

Ex ante estimates $2.7 to $6.2 billion18

Ex post estimates $1.0 to $1.4 billion 
5-25

Table 5.10 Comparison of Inflation-Adjusted Estimated Costs and Actual Price Changes 

for EPA Fuel Control Rules21

Inflation-adjusted Cost Estimates 

(c/gal) 

Actual Price 

Changes

(c/gal) 

EPA DOE API Other 

Gasoline

 Phase 2 RVP Control (7.8 RVP - 

Summer) (1995$) 1.1 1.8 0.5 

 Reformulated Gasoline Phase 1 

(1997$)

3.1-

5.1 3.4-4.1

8.2-

14.0 7.4 (CRA) 2.2 

Reformulated Gasoline Phase 2 

(Summer) (2000$) 

4.6-

6.8

7.6-

10.2

10.8-

19.4 12 

7.2 (5.1, when 

corrected to 

5yr MTBE 

price)

30 ppm sulfur gasoline (Tier 2) 

1.7-

1.9 2.9-3.4 2.6 

5.7

(NPRA),

3.1

(AIAM) N/A 

Diesel

 500 ppm sulfur highway diesel 

fuel (1997$) 

1.9-

2.4 

3.3

(NPRA) 2.2 

 15 ppm sulfur highway diesel 

fuel 4.5 4.2-6.0 6.2 

4.2-6.1

(NPRA) N/A 

x Chlorofluorocarbon (CFC) Phase-Out: EPA used a combination of regulatory, market 

based (i.e., a cap-and-trade system among manufacturers), and voluntary approaches to

phase out the most harmful ozone depleting substances. This was done more efficiently 

than either EPA or industry originally anticipated. The phaseout for Class I substances 

was implemented 4-6 years faster, included 13 more chemicals, and cost 30 percent less 

than was predicted at the time the 1990 Clean Air Act Amendments were enacted.22

The Harrington study states, “When the original cost analysis was performed for the CFC 

phase-out it was not anticipated that the hydrofluorocarbon HFC-134a could be 

substituted for CFC-12 in refrigeration. However, as Hammit (1997) notes, ‘since 1991 

most new U.S. automobile air conditioners have contained HFC-134a (a compound for 

which no commercial production technology was available in 1986) instead of CFC-12” 

(p.13). He cites a similar story for HCFRC-141b and 142b, which are currently 

substituting for CFC-11 in important foam-blowing applications.” 

21 Anderson et al., 2002. 

22 Holmstead, 2002.
5-26

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 <http://www.aeaweb.org/aer/>. 

Brooke, A., D. Kendrick, A. Meeraus, and R. Raman. 1998. GAMS: A User’s Guide. GAMS 

Development Corporation. Available at http://www.gams.com.

Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000. 

“SulfurDioxide Control by Electric Utilities: What Are the Gains from Trade?” Journal of 

Political Economy 108(#6):1292-1326.

Ellerman, Denny. January 2003. Ex Post Evaluation of Tradable Permits: The U.S. SO2 

Cap-and-Trade Program. Massachusetts Institute of Technology Center for Energy and 

Environmental Policy Research. 

Feenberg, D., and E. 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. 
5-27

Holmstead, Jeffrey, 2002. “Testimony of Jeffrey Holmstead, Assistant Administrator, Office of 

Air and Radiation, U.S. Environmental Protection Agency, Before the Subcommittee on Energy 

and air Quality of the committee on Energy and Commerce, U.S. House of Representatives, May 

1, 2002, p. 10. 

Minnesota IMPLAN Group. 2003. State Level Data for 2000. Available from 

http://www.implan.com/index.html.

Nestor, D.V., and C.A. Pasurka. 1995. The U.S. Environmental Protection Industry: A 

Proposed Framework for Assessment. U.S. Environmental Protection Agency, Office of Policy, 

Planning, and Evaluation. EPA 230-R-95-001. Available at 

http://yosemite.epa.gov/ee/epa/eermfile.nsf/11f680ff78df42f585256b45007e6235/41b8b642ab93

71df852564500004b543/$FILE/EE 0217A 1.pdf.

U.S. Department of Energy, Energy Information Administration. Undated (b). State Energy 

Price and Expenditure Report. Washington DC. Available at 

http://www.eia.doe.gov/emeu/states/price_multistate.html. 

U.S. Department of Energy, Energy Information Administration. 2001. Manufacturing Energy 

Consumption Survey 1998. Washington DC. Available at http://www.eia.doe.gov/emeu/mecs/. 

U.S. Department of Energy, Energy Information Administration. January 2003. Annual Energy 

Outlook 2003. DOE/EIA 0383(2003). Washington DC. Available at 

http://www.eia.doe.gov/oiaf/archive/aeo03/pdf/0383(2003).pdf.

U.S. Department of Energy, Energy Information Administration. January 2004. Annual Energy 

Outlook 2004. DOE/EIA-0383(2003). Washington, DC. Available at 

http://www.eia.doe.gov/oiaf/aeo/pdf/0383 (2001). 

U.S. Environmental Protection Agency. June 2007. Advisory Council on Clean Air Compliance 

Analysis (COUNCIL), Council Advisory on OAR’s Direct Cost Report and Uncertainty 

Analysis Plan. Washington, DC. http://www.epa.gov/sab/pdf/council-07-002.pdf
5a- 1

Appendix- Chapter 5 

5a.1 Cost Information for Non-EGU and area sources 

(Full details on controls can be found in Appendix Chapter 3) 

Low Emission Combustion (LEC) 

The average cost effectiveness for large IC engines using LEC technology was estimated 

to be $532/ton (ozone season).1

 The EC/R report on IC engines (Ec/R, September 1, 

2000) estimates the average cost effectiveness for IC engines using LEC technology to 

range from $420-840/ton (ozone season) for engines in the 2,000-8,000 bhp range. The 

key variables in determining average cost effectiveness for LEC technology are the 

average uncontrolled emissions at the existing source, the projected level of controlled 

emissions, annualized costs of the controls, and number of hours of operation in the 

ozone season. The ACT document uses an average uncontrolled level of 16.8 g/bhp-hr, a 

controlled level of 2.0 g/bhp-hr (87% decrease), and nearly continuous operation in the 

ozone season. The EPA believes the ACT document provides a reasonable approach to 

calculating cost effectiveness for LEC technology. 

Leak Detection and Repair (LDAR) for Fugitive Leaks 

The control efficiency is 80 percent reduction of VOC at an annualized cost of $4,800 per 

ton. We do not include the costs of this control measure in our analyses in the Houston 

nonattainment area since these controls are already included in the 8-hour Ozone SIP for 

this area. 

Enhanced LDAR for Fugitive Leaks 

The control efficiency of this measure is estimated at 50 percent at a cost of $3,050/ton of 

VOC reduced2

.

Flare Gas Recovery 

The control efficiency of this measure is 98 percent reduction of VOC emissions at a cost 

of $2,700/ton. Costs may become negligible as the size of the flare increases due to 

recovery credit.3

1

 “NOx Emissions Control Costs for Stationary Reciprocating Internal Combustion Engines in 

the NOx SIP Call States,” E.H. Pechan and Associates, Inc., Springfield, VA, August 11, 2000.

Available on the Internet at http://www.epa.gov/ttn/ecas/regdata/cost/pechan8-11.pdf

2

“Suggested Short List and Evaluation of Point and Area Source Emission Control Measures for 

the Houston-Galveston-Brazoria 8-Hour Ozone Nonattainment Area,” Texas Council on 

Environmental Quality,” Prepared by ENVIRON International Corp. for Lamar Univ. June 15, 

2006. Available on the Internet at http://www.hgac.com/NR/rdonlyres/e4cgpdlu4wd3tiguvvxkg5ziefqy36adm2o5cz5jpm36c67ksxbtfurvvwvgdq

uy362skyhnsel5uh4rdkfz2rusphd/Final+Short+List+%26+Evaluations.pdf.

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. 
5a- 2

Cooling Towers 

There is not a general estimate of control efficiency for this measure; one is to apply a 

continuous flow monitor until VOC emissions have reached a level of 1.7 tons/year for a 

given cooling tower.4

 The annualized cost for a continuous flow monitor is $63,000 – 

this is constant over a variety of cooling tower sizes. 

Wastewater Drains and Separators 

The control efficiency is 65 percent reduction of VOC emissions at a cost of $3,050/ton. 

This is based on actual sampling and cost data for 5 refineries in the Bay Area Air 

Quality Management District (BAAQMD).5

5a.2 Cost Information for EGU sources 

(Full details on controls can be found in Appendix Chapter 3) 

Cost of Controls as a Result of Lower Sub-regional Caps within the MWRPO and OTC and 

other Local Controls outside of these Regions within CAIR 

As previously discussed, the power sector will achieve significant emission reductions under the 

Clean Air Interstate Rule (CAIR) over the next 10 to 15 years. When fully implemented, CAIR 

(in conjunction with NOx SIP Call) will reduce ozone season NOx emissions by over 60 percent 

from 2003 levels within the CAIR states. These reductions will greatly improve air quality and 

will lessen the challenges that some areas face when solving nonattainment issues significantly. 

Power sector impacts analyzed in detail in the Final PM NAAQS RIA 15/35 

(http://www.epa.gov/ttn/ecas/ria.html ) provides the baseline for this RIA. The analysis and 

projections in this section attempt to show the potential impacts of the additional controls applied 

(see section 3.3.3 of this RIA) to facilitate attainment of the more stringent 8-hr ozone standard 

of 0.070 ppm. Generally, the incremental impacts of these controls on the power sector are 

marginal. 

Projected Costs. EPA projects that the annual incremental cost of the proposed new ozone 

standard approach is $0.2billion in 2020. The additional annual costs reflect additional retrofits 

(SCR and SNCR) and generation shifts,. Annualized cost of CAIR is projected to be $6.17 

billion in 2020. The proposed approach applied in this RIA would add $0.2 billion incremental to 

this cost. 

Projected Generation Mix. Coal-fired generation and natural gas/oil-fired generation are 

projected to remain almost unchanged. Installation of approximately 3.7 GWs of SCR and 1.1 

GWs of SNCR incremental to the base case are projected as a result of the lower sub-regional 

caps. There are very small changes in the generation mix. Coal-fired generation increases about 

12 GWh (an increase of approximately 0.25% of the total generation) and gas-fired generation 

4

 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of Regulation 8, 

Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004. 

5

 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of Regulation 8, 

Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004. 
5a- 3

decreases a similar amount. Hydo, nuclear, other, and renewable based generation projected to 

remain the same. Projected retirements of coal units is marginal, accounting to about 0.4 GWs 

compared to the base case approach. 

Projected Nationwide Retail Electricity Prices. Retail electricity prices are projected to change 

marginally, only about 1%. The extension of the cap-and-trade approach in the form of lower 

sub-regional caps allows industry to meet the requirements of CAIR in the most cost-effective 

manner, thereby minimizing the costs passed on to consumers. Retail electricity prices are 

projected to increase less than 1% within the MWRPO and OTC regions, and decrease about 1% 

in the rest of the CAIR region. 

5a.3 Cost information for Onroad and Nonroad Mobile Sources 

(Full details on controls can be found in Appendix Chapter 3) 

Diesel Retrofits and Vehicle Replacement 

To calculate costs for the use of selective catalytic reduction as a retrofit technology, the 

assumption was made that all relevant vehicles would be affected by the control. Therefore, all 

on-road heavy duty diesel vehicles that received a retrofit were assumed to employ selective 

catalytic reduction as a retrofit technology. The average cost of a selective catalytic reduction 

system ranges from $10,000 to $20,000 per vehicle depending on the size of the engine, the sales 

volume, and other factors (Pechan, 2003). For AirControlNET analysis, the average estimated 

cost of this system is $15,000 per heavy duty diesel vehicle. (Source: AirControlNET 

Documentation, III-160). OTAQ conducted an additional assessment of current SCR costs and 

calculated that for the year 2020, the cost of SCRs will be approximately $13,000 per unit. This 

estimate reflects an economy of scale cost reduction of 33%, which is consistent with trends in 

other mobile source control technologies that enter large scale production. 

The rebuild/upgrade kit is applied to nonroad equipment. OTAQ estimates the cost of this kit to 

be $2,000 to $4,000 per vehicle. For this analysis, the average estimated cost is $3,000 per 

vehicle. 

Table 5a.1: Summary of Cost Effectiveness for Rebuild/Upgrade Kit for Various Nonroad 

Vehicles 

Nonroad Vehicle Retrofit

Technology

Range of $/ton NOx 

Emission Reduced 

Range of $/ton HC 

Emission Reduced 

Tractors/Loaders/Backhoes $1,300 $2,200 $9,600 $18,900 

Excavators $1,100 $4,200 $8,100 $43,400 

Crawler Tractor/Dozers $1,100 $4,200 $8,300 $43,500

Skid Steer Loaders $1,000 $1,600 $7,400 $14,800 

Agricultural Tractors 

Rebuild/

Upgrade kit

$1,200 $4,900 $9,300 $34,300 
5a- 4

Table 5a.2: 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/Backhoes $2,900 $5,300 $32,200 $63,700 

Excavators $2,700 $10,400 $27,400 $146,200 

Crawler Tractor/Dozers $2,800 $10,400 $27,900 $146,700 

Skid Steer Loaders $2,600 $4,000 $24,900 $52,100 

Agricultural Tractors 

SCR

$3,000 $7,600 $31,200 $115,500 

Table 5a.3: 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 $5,600 $14,100 $46,900 $126,200 

Class 8b Truck 

SCR $1,100 $2,500 $14,900 $44,600 

Implement Continuous Inspection and Maintenance Using Remote Onboard Diagnostics (OBD) 

Continuous I/M can significantly lower test costs and “convenience” costs of I/M programs. 

Using radio frequency transmission, there is a one-time cost for the Continuous I/M device and 

its installation. In the case of Oregon, this cost is $50. The unit is then good for the life of the 

vehicle. Annual or biennial test fees are not required beyond this initial fee to operate the system 

but there may be additional operational costs to cover data processing, reporting, and oversight. 

For the proposal RIA, we present estimated cost savings of the Continuous I/M program, but do 

not include the cost savings in the overall cost estimates. For the final RIA, we plan to include 

the Continuous I/M cost savings in the overall costs. This will result in a significant reduction of 

overall cost. 

We can compare the costs of periodic testing to Continuous I/M. The cost of data processing, 

reporting and oversight is estimated to be $2 per vehicle per year in the typical I/M area. If we 

assume an average vehicle life span of 14 years, with the first test at 4 years of age, vehicles will 

get 5 inspections in a biennial program and 10 in an annual program (not including additional 

change of ownership inspections, which are required in some areas). Thus, in a Continuous I/M 

program, an additional cost of $10-$20 will be incurred for each vehicle over its life, assuming 

the same costs apply in a Continuous I/M program as in a tailpipe test program. 

In addition to test costs, Continuous I/M avoids most of the convenience costs associated with 

I/M – the time and fuel it takes to drive to the station, get a test, and return home. The one-time 

installation of the transmitter requires a visit to the test station, but no further visits are required 

after that. So, if we assume, conservatively, that the typical test cycle requires a total of two 

hours of time at $20 per hour and a half-gallon of gas (10 miles round trip with an average fuel 

economy of 20 mpg) at $3 per gallon gives us a cost of $41.50. Over the life of the vehicle that 
5a- 5

works out to $207.50 in a biennial program or $415 in an annual program. Compare this to the 

one time trip for Continuous I/M OBD at a cost of $41.50 and substantial savings are realized. 

Application of Continuous I/M resulted in NOx reductions of 4.2 to 6.5 percent (approximately 

10,000 tons), depending on the geographic area, vehicle class, and type of existing I/M program. 

Some areas have no I/M, some I/M programs require annual testing, some require biennial 

testing, and some areas are piloting continuous I/M. We applied continuous I/M reductions only 

to those areas that currently have annual or biennial programs. 

Putting it all together, the table below shows the lifetime inspection and convenience costs of 

Continuous I/M versus periodic I/M (assuming the current mix of annual and biennial testing 

and current test costs). Periodic I/M testing costs about $20 billion over a 10 year lifecycle with 

an additional $25 billion in convenience costs for a total of $45 billion. By contrast, Remote 

OBD has a test and installation cost of $4.3 billion dollars over the same 10 year period, and a 

convenience cost of $2.5 billion for a total of $6.8 billion. Thus, nationwide installation of 

Remote OBD would save the nation’s motorists about $38 billion in inspection and convenience 

costs over a 10 year period. 

Table 5a.4 Lifetime Inspection and Convenience Costs of I/M 

Test/Install 

Cost

Convenience 

Cost

Total

Cost

Continuous

I/M

$20 billion $25 billion $45 billion 

Remote I/M $4.3 billion $2.5 billion $6.8 billion 

Savings $15.7 billion $22.5 billion $38.2 billion 

Given that Continuous I/M will actually reduce the cost of I/M, implementation of this measure 

is highly cost-effective. More information on I/M can be found at 

http://www.epa.gov/otaq/regs/im/im-tsd.pdf and www.epa.gov/obd/regtech/inspection.htm 

Eliminating Long Duration Truck Idling 

For purposes of this RIA, we identified this measure as a no cost strategy i.e. $0/ton NOx. Both 

TSEs and MIRTs have upfront capital costs, but these costs can be fully recovered by the fuel 

savings. The examples below illustrate the potential rate of return on investments in idle 

reduction strategies. 

TSE 

The average price of TSE technology is $11,500 per parking space. The average service life of 

this technology is 15 years. Truck engines at idle consume approximately 1 gallon per hour of 

idle. Current TSE projects are operating in environments where trucks are idling, on average, for 

8 hours per day per space for 365 days per year (or about 2,920 hours per year). Since TSE 

technology can completely eliminate long duration idling at truck spaces (i.e. a 100% fuel 

savings), this translates into 2,920 gallons of fuel saved per year per space. At current diesel 

prices ($2.90/gallon), this fuel savings translates into $8,468. Therefore, an $11,500 capital 
5a- 6

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. 

MIRTs

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 

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 costeffectiveness of this measure.6

 TRB conducted an extensive literature review and then 

synthesized the data to develop comparable estimates of cost-effectiveness of a wide range of 

CMAQ-funded measures. We took the average of the median cost-effectiveness of a sampling 

of CMAQ-funded measures and then applied this number to the overarching commuter reduction 

measure. The CMAQ-funded measures we selected were:

 regional rideshares 

 vanpool programs 

 park-and-ride lots 

 regional transportation demand management 

 employer trip reduction programs 

We felt that these measures were a representative sampling of commuter reduction incentive 

programs. There is a great deal of variability, however, in the type of programs and the level of 

incentives that employers offer which can impact both the amount of emissions reductions and 

the cost of commuter reduction incentive programs. 

6

 Transportation Research Board, National Research Council, 2002. The Congestion Mitigation and Air Quality 

Improvement Program: assessing 10 years of experience, Committee for the Evaluation of the Congestion 

Mitigation and Air Quality Improvement Program. 
5a- 7

We chose to apply the resulting average cost-effectiveness estimate to one pollutant – NOx – in 

order to be able to compare commuter reduction programs to other NOx reduction strategies. 

TRB reported the cost-effectiveness of each measure, however, as a $/ton reduction of both VOC 

and NOx by applying the total cost of the program to a 1:4 weighted sum of VOC and NOx 

[[total emissions reduction = (VOC * 1) + (NOx * 4)). There was not enough information in the 

TRB study to isolate the $/ton cost-effectiveness for just NOx reductions, so we used the 

combined NOx and VOC estimate. The results are presented in Table 5a.5. 

Table 5a.5 Cost-Effectiveness of Best Workplaces for Commuters Type Measures from the 

2002 TRB Study, 

$/ton (2000$) 1:4 VOC:NOx (reported in the RIA as $/ton NOx)

 Low High Median 

Regional Rideshare $1,200 $16,000 $7,400 

Vanpool Programs $5,200 $89,000 $10,500 

Park-and-ride lots $8,600 $70,700 $43,000 

Regional TDM $2,300 $33,200 $12,500 

Employer trip reduction programs $5,800 $175,500 $22,700 

Average of All Measures $4,620 $76,900 $19,200

Cost-Effectiveness of Measure: $19,200/ton NOx 

Reduce Gasoline RVP from 7.8 to 7.0 in Remaining Nonattainment Areas 

Cost-Effectiveness of Measure: Cost per ton will be $5,700 to $36,000 / ton VOC 

For more information on RVP: 

x 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. 

x U.S. EPA. Guide on Federal and State Summer RVP Standards for Conventional 

Gasoline Only. EPA420-B-05-012. November 2005 
6-1

Chapter 6. Incremental Benefits of Attaining Alternative Ozone Standards Relative to the 

Current 8-hour Standard (0.08 ppm) 

Synopsis

Based on projected emissions and air quality modeling, in 2020, 203 counties in the U.S. with 

ozone monitors are estimated to fail to meet an alternative ozone standard of 0.070 ppm for the 

4

th highest maximum 8-hour ozone concentration. This number falls to 82 for an alternative 

standard of 0.075 ppm, and further to 29 for an ozone standard of 0.079 ppm and increases to 

360 for an alternative standard of 0.065 ppm. We estimated the health benefits of attaining these 

alternative ozone standards across the U.S. using the EPA Environmental Benefits Modeling and 

Analysis Program (BenMAP). We performed a two-stage analysis. 

In the first stage we estimated the benefits associated with changes in modeled air quality 

following application of control technologies known to be currently available. These control 

strategies were sufficient to bring some, but not all, areas into attainment with the various 

standard levels. Thus, the benefits computed during this first stage were for partial attainment in 

some areas. In the second stage, we estimated the benefits of fully attaining the standards in all 

areas by using a “rollback” methodology to reduce ozone concentrations at residually 

nonattaining monitors to a level that would just meet the standards. We deviated from this twostage approach when analyzing the 0.075 ppm standard alternative, where we applied an 

interpolation technique that is detailed further in this chapter. To calculate the monetary value of 

the adverse health outcomes potentially avoided due to these reductions in ambient ozone levels, 

we used health impact functions based on published epidemiological studies, and valuation 

functions derived from the economics literature.1

 Key health endpoints analyzed included 

premature mortality, hospital and emergency room visits, school absences, and minor restricted 

activity days. 

There is considerable uncertainty in the magnitude of the association between ozone and 

premature mortality. This analysis presents four alternative estimates for the association based 

upon different functions reported in the scientific literature.. We also note that there are 

uncertainties within each study that are not fully captured by this range of estimates. 

Recognizing that additional research is needed to more fully establish underlying mechanisms by 

which such effects occur, we also consider the possibility that the observed associations between 

ozone and mortality may not be causal in nature. Using the National Morbidity, Mortality and 

Air Pollution Study (NMMAPS) that was used as the primary basis for the risk analysis 

presented in our Staff Paper and reviewed by Clean Air Science Advisory Committee (CASAC), 

we estimated 280 avoided premature deaths annually in 2020 from reducing ozone levels to meet 

a standard of 0.070 ppm, which, when added to the other projected benefits from reduced ozone, 

including 5,600 hospital and emergency room admissions, 780,000 school absences, and over 

2,100,000 minor restricted activity days, leads to an estimated total ozone-related benefit of $2 

1 Health impact functions measure the change in a health endpoint of interest, such as hospital 

admissions, for a given change in ambient ozone or PM concentration
6-2

billion/yr (1999$). Using three studies that synthesize data across a large number of individual 

studies, we estimate between 1,100 and 1,400 avoided premature deaths annually in 2020 from 

reducing ozone to 0.070 ppm, leading to total monetized ozone-related benefits of between $7.4 

and $9.1 billion/yr. Alternatively, if there is no causal relationship between ozone and mortality, 

avoided premature deaths associated with reduced ozone exposure would be zero and total 

monetized ozone-related morbidity benefits would be $190 million/yr.

For a less stringent standard of 0.075 ppm, using the NMMAPS ozone mortality study resulted in 

200 premature deaths avoided and total monetized benefits of $1.6 billion/yr. Using the three 

synthesis studies, estimated premature deaths avoided for the less stringent standard are between 

880 and 1,100, with total monetized ozone benefits between $5.9 and $7.3 billion/yr. 

Alternatively, if there is no causal relationship between ozone and mortality, avoided premature 

deaths associated with reduced ozone exposure would be zero and total monetized ozone-related 

morbidity benefits would be $150 million/yr.

For a less stringent standard of 0.079 ppm, using the NMMAPS ozone mortality study resulted in 

19 premature deaths avoided and total monetized benefits of $140 million/yr. Using the three 

synthesis studies, estimated premature deaths avoided for the less stringent standard are between 

78 and 85, with total monetized ozone benefits between $510 and $560 million/yr. Alternatively, 

if there is no causal relationship between ozone and mortality, avoided premature deaths 

associated with reduced ozone exposure would be zero and total monetized ozone-related 

morbidity benefits would be $12 million/yr. 

For a more stringent standard of 0.065 ppm, using the NMMAPS ozone mortality study resulted 

in 530 premature deaths avoided and total monetized benefits of $3.7 billion/yr. Using the three 

synthesis studies, estimated premature deaths avoided for the more stringent standard are 

between 2,100 and 2,400, with total monetized ozone benefits between $14 and $16 billion/yr.

Alternatively, if there is no causal relationship between ozone and mortality, avoided premature 

deaths associated with reduced ozone exposure would be zero and total monetized ozone-related 

morbidity benefits would be $330 million/yr. These estimates reflect EPA's interim approach to 

characterizing the benefits of reducing premature mortality associated with ozone exposure. 

EPA has requested advice from the National Academy of Sciences on how best to quantify 

uncertainty in the relationship between ozone exposure and premature mortality in the context of 

quantifying benefits associated with alternative ozone control strategies.

In addition to the direct benefits from reduced ozone concentrations, attainment of the standards 

would likely result in health and welfare benefits from the reduction of PM2.5 that would occur as 

ozone precursor emissions (NOx and VOC) are reduced. Using both modeled and extrapolated 

reductions in these precursor emissions, we estimated PM-related co-benefits for the four 

alternative standards. For each alternative standard, we provide a range of estimated benefits 

based on several different PM mortality effect estimates. These effect estimates were derived 

from two different sources: the published epidemiology literature and an expert elicitation study 

conducted by EPA in 2006. For the partial attainment of the 0.070 ppm standard, we estimated 

PM co-benefits including between 220 and 2,200 premature deaths avoided, with total monetized 

PM co-benefits of between $1 and $9.9 billion/yr (3% discount rate, 1999$). For the 2020 
6-3

attainment of the 0.070 ppm alternative, incremental to attainment of the 0.08 ppm standard, we 

estimate total ozone and PM2.5-related co-benefits to be between $2.5 and $33 billion/yr; this 

range encompasses the expert functions and the ozone mortality functions as well as the 

possibility that there is no causal relationship between ozone and mortality. For the 2020 

attainment of the 0.065 ppm alternative, incremental to attainment of the 0.08 ppm standard, we 

estimate total benefits of between $4.3 and $57 billion/yr; this range encompasses the expert 

functions and the ozone mortality functions as well as the possibility that there is no causal 

relationship between ozone and mortality. For the 2020 attainment of the 0.075 ppm alternative, 

incremental to attainment of the 0.08 ppm standard, we estimate total ozone and PM2.5-related

co-benefits to be between $1.5 and $22 billion/yr; this range encompasses the expert functions 

and the ozone mortality functions as well as the possibility that there is no causal relationship 

between ozone and mortality. For the 2020 attainment of the 0.079 ppm alternative, incremental 

to attainment of the 0.08 ppm standard, we estimate total ozone and PM2.5-related co-benefits to 

be between $1.1 and $12 billion/yr; this range encompasses the expert functions and the ozone 

mortality functions as well as the possibility that there is no causal relationship between ozone 

and mortality. 

 6.1. Background

Our purpose for this analysis is to assess the human health benefits of attaining alternative 8-hour 

ozone standards, including 0.075 ppm, 0.070 ppm, and 0.065 ppm, incremental to attainment of 

the current 8-hour ozone standard of 0.08 ppm.2

 We applied a damage function approach similar 

to those used in several recent U.S. EPA regulatory impact analyses, including those for the 2006 

Particulate Matter (PM) NAAQS (U.S. EPA, 2006) and the Clean Air Interstate Rule (U.S. EPA, 

2005). This approach estimates changes in individual health and welfare endpoints (specific 

effects that can be associated with changes in air quality) and assigns values to those changes 

assuming independence of the individual values. Total benefits are calculated simply as the sum 

of the values for all non-overlapping health and welfare endpoints. This analysis largely builds 

off of both the analytical approach used in the 2006 PM NAAQS 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, we refer the 

reader to those documents, as well as to the EPA Guidelines for Economic Analysis.3,4,5

We applied a two-stage approach to estimate the benefits of fully attaining each alternative 

standard. In the first stage, we estimated the benefits associated with changes in modeled air 

2

 This is effectively 0.084 ppm due to current rounding conventions. When calculating benefits 

in this chapter we followed the rounding convention and rounded to 0.084 ppm. 

3

 U.S. EPA. 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards 

for Particle Pollution, Chapter 5. Available at http://www.epa.gov/ttn/ecas/ria.html.

4

 Hubbell, B., A. Hallberg, D.R. McCubbin, and E. Post. 2005. Health-Related Benefits of 

Attaining the 8-Hr Ozone Standard. Environmental Health Perspectives 113:73–82. 

U.S. EPA. 2000. Guidelines for Preparing Economic Analyses. 

http://yosemite1.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/Guidelines.pdf

5

 U.S. EPA. 2000. Guidelines for Preparing Economic Analyses. 

http://yosemite1.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/Guidelines.pdf
6-4

quality following application of control technologies known to be currently available. These 

control strategies were sufficient to bring some, but not all, areas into attainment with the various 

standard levels. Thus, the benefits computed during this first stage were for partial attainment in 

some areas (see Chapter 3 for details on these control technologies and the results of the air 

quality modeling). In the second stage, we estimated the benefits of fully attaining the standards 

in all areas by using a “rollback” methodology to reduce ozone concentrations at residually 

nonattaining monitors to a level that would just meet the standards (see Appendix 6 for details on 

this methodology). We conducted analyses to examine the sensitivity of our results to a number 

of different assumptions about the choice of health effects and effect estimates from published 

epidemiological studies, as well as parameters that affect the economic valuation of health 

effects. A quantitative assessment of non-health benefits, e.g. benefits from reduced ozonerelated crop damage, was outside of the scope of this analysis due to data and resource 

limitations. 

For this assessment, we estimated benefits of changes in ozone and PM co-benefits resulting 

from application of illustrative control strategies on ozone precursor emissions to attain 

alternative ozone NAAQS. With the exception of ozone-related premature mortality, we use 

methods consistent with previous PM and ozone benefits assessments. Specifically, the analysis 

of PM co-benefits uses an approach identical to that used in the 2006 PM NAAQS RIA (U.S. 

EPA, 2006). The ozone benefits analysis for non-mortality endpoints uses an approach nearly 

identical to that for the Clean Air Interstate Rule RIA (U.S. EPA, 2005).6

All ozone and PM2.5 co-benefits estimates in this chapter are incremental to a baseline of national 

full attainment with 0.08 ppm.7

 This baseline incorporates emission reductions projected to be 

achieved as a result of an array of federal rules such as the Clean Air Interstate and Non-Road 

Diesel Rule, as well as ozone and PM2.5 state implementation plans. Moreover, the PM2.5 cobenefits are incremental to an assumption of full attainment of the 2006 PM2.5 NAAQS. A 

complete discussion of the baseline may be found in Chapter 3. The PM co-benefits presented in 

this chapter are incremental to the PM benefits estimated in the 2006 PM NAAQS RIA and 

reflect the PM benefits from NOx reductions associated with each ozone control strategy. 

The remainder of this chapter describes the data and methods used in this analysis, along with 

the results. Additional details of the analysis are provided in Appendix 6 of this RIA. Section 

6.2 discusses the probabilistic framework for the benefits analysis and how key uncertainties are 

addressed in the analysis. Section 6.3 discusses the literature on ozone- and PM-related health 

effects and describes the specific set of health impact functions we used in the benefits analysis.

Section 6.4 describes the economic values selected to estimate the dollar value of ozone- and 

PM- related health impacts. Finally, Section 6.5 presents the results and implications of the 

analysis.

6

 The one exception relates to the use of updated health impact functions for emergency

department visits. These new functions are detailed further in this chapter. 

7

 The PM2.5 benefits presented below reflect the NOx emission reductions from the ozone 

control strategy. Reductions from Ocean-Going Vessels burning residual diesel fuel were 

included both East and West in the baseline PM co-benefits, but not included in the ozone 

baseline for the west. See chapter 3 for more details of this rule and its application. 
6-5

6.2. Characterizing Uncertainty: Moving Toward a Probabilistic Framework for 

Benefits Assessment

The National Research Council (NRC) (2002) highlighted the need for EPA to conduct rigorous 

quantitative analysis of uncertainty in its benefits estimates and to present these estimates to 

decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. In 

response to these comments, EPA’s Office of Air and Radiation (OAR) is developing a 

comprehensive strategy for characterizing the aggregate impact of uncertainty in key modeling 

elements on both health incidence and benefits estimates. Components of that process include 

emissions modeling, air quality modeling, health effects incidence estimation, and valuation.

Two aspects of OAR’s approach that have been used in several recent RIAs are employed 

here.8,9,10 First, we use Monte Carlo methods for estimating characterizing random sampling 

error associated with the concentration response functions from epidemiological studies and 

economic valuation functions. Monte Carlo simulation uses random sampling from distributions 

of parameters to characterize the effects of uncertainty on output variables, such as incidence of 

premature mortality. Specifically, we used Monte Carlo methods to generate confidence intervals 

around the estimated health impact and dollar benefits. Distributions for individual effect 

estimates are based on the reported standard errors in the epidemiological studies. Distributions 

for unit values are described in Table 6-4. 

Second, we use a recently completed expert elicitation of the concentration response function 

describing the relationship between premature mortality and ambient PM2.5 concentration.11 We 

note that incorporating only the uncertainty from random sampling error omits important sources 

of uncertainty (e.g., in the functional form of the model—e..g., whether or not a threshold may 

exist). Use of the expert elicitation and incorporation of the standard errors approaches provide 

insights into the likelihood of different outcomes and about the state of knowledge regarding the 

benefits estimates. Both approaches have different strengths and weaknesses, which are full 

described in Chapter 5 of the PM NAAQS RIA. 

In benefit analyses of air pollution regulations conducted to date, the estimated impact of 

reductions in premature mortality has accounted for 85% to 95% of total benefits. Therefore, in 

characterizing the uncertainty related to the estimates of total benefits it is particularly important 

to attempt to characterize the uncertainties associated with this endpoint. The health impact 

8

 U.S. Environmental Protection Agency, 2004a. Final Regulatory Analysis: Control of 

Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Prepared by Office of Air and 

Radiation. Available at http://www.epa.gov/nonroad-diesel/2004fr/420r04007.pdf

9

 U.S. Environmental Protection Agency, 2005. Regulatory Impact Analysis for the Clean Air 

Interstate Rule. EPA 452/-03-001. Prepared by Office of Air and Radiation. Available at: 

http://www.epa.gov/interstateairquality/tsd0175.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). 
6-6

functions used to estimate avoided premature deaths associated with reductions in ozone have 

associated standard errors that represent the statistical errors around the effect estimates in the 

underlying epidemiological studies.12 In our results, we report credible intervals based on these 

standard errors, reflecting the uncertainty in the estimated change in incidence of avoided 

premature deaths. We also provide multiple estimates, to reflect model uncertainty between 

alternative study designs. In addition, we characterize the uncertainty introduced by the inability 

of existing empirical studies to discern whether the relationship between ozone and pre-mature 

mortality is causal by providing an effect estimate preconditioned on an assumption that the 

effect estimate for pre-mature mortality from ozone is zero. 

For premature mortality associated with exposure to PM, we follow the same approach used in 

the RIA for 2006 PM NAAQS (U.S. EPA, 2006), presenting several empirical estimates of 

premature deaths avoided, and a set of twelve estimates based on results of the expert elicitation 

study.13 Even these multiple characterizations, including confidence intervals, omit the 

contribution to overall uncertainty of uncertainty in air quality changes, baseline incidence rates, 

populations exposed and transferability of the effect estimate to diverse locations. Furthermore, 

the approach presented here does not yet include methods for addressing correlation between 

input parameters and the identification of reasonable upper and lower bounds for input 

distributions characterizing uncertainty in additional model elements. As a result, the reported 

confidence intervals and range of estimates give an incomplete picture about the overall 

uncertainty in the estimates. This information should be interpreted within the context of the 

larger uncertainty surrounding the entire analysis. 

6.3. Health Impact Functions

Health impact functions measure the change in a health endpoint of interest, such as hospital 

admissions, for a given change in ambient ozone or PM concentration. Health impact functions 

are derived from primary epidemiology studies, meta-analyses of multiple epidemiology studies, 

or expert elicitations. A standard health impact function has four components: 1) an effect 

estimate from a particular study; 2) a baseline incidence rate for the health effect (obtained from 

either the epidemiology study or a source of public health statistics such as the Centers for 

Disease Control); 3) the size of the potentially affected population; and 4) the estimated change 

in the relevant ozone or PM summary measures. 

A typical health impact function might look like: 

12 Health impact functions measure the change in a health endpoint of interest, such as hospital 

admissions, for a given change in ambient ozone or PM concentration. 

13 Industrial Economics, Inc. 2006. Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared for 

EPA Office of Air Quality Planning and Standards, September. Available at: 

http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf

'

'

y y e

x

  



0

( ) , 1

E
6-7

where y0 is the baseline incidence (the product of the baseline incidence rate times the potentially 

affected population),  is the effect estimate, and x is the estimated change in the summary 

ozone measure. There are other functional forms, but the basic elements remain the same. 

Chapter 3 described the ozone and PM air quality inputs to the health impact functions. The 

following subsections describe the sources for each of the other elements: size of potentially 

affected populations; effect estimates; and baseline incidence rates. 

6.3.1 Potentially Affected Populations 

The starting point for estimating the size of potentially affected populations is the 2000 U.S. 

Census block level dataset (Geolytics 2002). Benefits Modeling and Analysis Program 

(BenMAP) incorporates 250 age/gender/race categories to match specific populations potentially 

affected by ozone and other air pollutants. The software constructs specific populations 

matching the populations in each epidemiological study by accessing the appropriate agespecific populations from the overall population database. BenMAP projects populations to 

2020 using growth factors based on economic projections (Woods and Poole Inc. 2001).

6.3.2 Effect Estimate Sources 

The most significant monetized benefits of reducing ambient concentrations of ozone and PM 

are attributable to reductions in human health risks. EPA’s Ozone and PM Criteria Documents 

and the World Health Organization’s 2003 and 2004 reports outline numerous health effects 

known or suspected to be linked to exposure to ambient ozone and PM (US EPA, 2006; US 

EPA, 2005; WHO, 2003; Anderson et al., 2004). EPA recently evaluated the PM literature for 

use in the benefits analysis for the 2006 PM NAAQS RIA. 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 

14 U.S. Environmental Protection Agency, 2005. Regulatory Impact Analysis for the PM 

NAAQS. EPA Prepared by Office of Air and Radiation. Available at: 

http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf pp. 5-29. 
6-8

affected population; (3) the lack of an established concentration-response relationship; or 4) the 

inability to appropriately value the effect (for example, changes in forced expiratory volume) in 

economic terms. Table 6-1 lists the human health and welfare effects of pollutants affected by 

the alternate standards. Table 6-2 lists the health endpoints included in this analysis.
6-9

Table 6-1 Human Health and Welfare Effects of Pollutants Affected by the Alternate 

Standards

Pollutant/Effect 

Quantified and Monetized in Base 

Estimatesa

Unquantified Effects - Changes in: 

PM/Healthb

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 

Nonasthma respiratory emergency room visits 

UVb exposure (+/-)e

PM/Welfare 

 Visibility in Southeastern Class I areas 

Visibility in northeastern and Midwestern Class I areas 

Household soiling 

Visibility in western U.S. Class I areas 

Visibility in residential and non-Class I areas 

UVb exposure (+/-)e

Ozone/Healthf

Premature mortality: short-term 

exposures 

Hospital admissions: respiratory 

Emergency room visits for asthma 

Minor restricted-activity days 

School loss days 

Asthma attacks 

Acute respiratory symptoms 

Cardiovascular emergency room visits 

Chronic respiratory damage 

Premature aging of the lungs 

Nonasthma respiratory emergency room visits 

UVb exposure (+/-)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 proposed 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. 
6-10

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. 

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. 

g

 The categorization of unquantified toxic health and welfare effects is not exhaustive. 
6-11

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 

O3 (24-hour avg) 

O3 (24-hour avg) 

O3 (1-hour max) 

O3 (1-hour max) 

Bell et al (2004) (NMMAPS study) 

Meta-analyses:

Bell et al (2005) 

Ito et al (2005) 

Levy et al (2005) 

All ages 

Premature mortality 

—cohort study, allcause 

PM2.5 (annual avg) Pope et al. (2002) 

Laden et al. (2006) 

>29 years 

>25 years 

Premature mortality, 

total exposures 

PM2.5 (annual avg) Expert Elicitation (IEc, 2006) >24 years 

Premature mortality 

— all-cause 

PM2.5 (annual avg) Woodruff et al. (1997) Infant (<1 year) 

Chronic Illness 

Chronic bronchitis PM2.5 (annual avg) Abbey et al. (1995) >26 years 

Nonfatal heart 

attacks 

PM2.5 (24-hour avg) Peters et al. (2001) Adults (>18 years) 

Hospital Admissions 

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) 

>64 years 

O3 (24-hour avg) 

Burnett et al. (2001) <2 years 

PM2.5 (24-hour avg) Pooled estimate:

Moolgavkar (2003)—ICD 490-496 (COPD) 

Ito (2003)—ICD 490-496 (COPD) 

>64 years 

PM2.5 (24-hour avg) Moolgavkar (2000)—ICD 490-496 (COPD) 20–64 years 

PM2.5 (24-hour avg) Ito (2003)—ICD 480-486 (pneumonia) >64 years 

Respiratory 

PM2.5 (24-hour avg) Sheppard (2003)—ICD 493 (asthma) <65 years 

PM2.5 (24-hour avg) Pooled estimate: 

Moolgavkar (2003)—ICD 390-429 (all 

cardiovascular) 

Ito (2003)—ICD 410-414, 427-428 (ischemic heart 

disease, dysrhythmia, heart failure) 

Cardiovascular >64 years 

PM2.5 (24-hour avg) Moolgavkar (2000)—ICD 390-429 (all 

cardiovascular) 

20–64 years 
6-12

Endpoint Pollutant Study Study Population 

Asthma-related ER 

visits 

O3 (8-hour max) 

Pooled estimate:

Jaffe et al (2003) 

Peel et al (2005) 

Wilson et al (2005) 

5–34 years 

All ages 

All ages 

Asthma-related ER 

visits (con’t) 

PM2.5 (24-hour avg) Norris et al. (1999) 0–18 years 

Other Health Endpoints 

Acute bronchitis PM2.5 (annual avg) Dockery et al. (1996) 8–12 years 

Upper respiratory 

symptoms 

PM10 (24-hour avg) Pope et al. (1991) Asthmatics, 9–11 

years

Lower respiratory 

symptoms 

PM2.5 (24-hour avg) Schwartz and Neas (2000) 7–14 years 

Asthma 

exacerbations 

PM2.5 (24-hour avg) Pooled estimate: 

Ostro et al. (2001) (cough, wheeze and shortness of 

breath) 

Vedal et al. (1998) (cough) 

6–18 yearsa

Work loss days PM2.5 (24-hour avg) Ostro (1987) 18–65 years 

School absence 

days O3 (8-hour avg) 

O3 (1-hour max) 

Pooled estimate:

Gilliland et al. (2001) 

Chen et al. (2000) 

5–17 yearsb

Minor Restricted O3 (24-hour avg) Ostro and Rothschild (1989) 18–65 years 

Activity Days 

(MRADs) 

PM2.5 (24-hour avg) Ostro and Rothschild (1989) 18–65 years 

a

 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. 

b

 Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 

11. Based on recent advice from the National Research Council and the EPA SAB-HES, we 

have calculated reductions in school absences for all school-aged children based on the 

biological similarity between children aged 5 to 17.

In selecting epidemiological studies as sources of effect estimates, we applied several criteria to 

develop a set of studies that is likely to provide the best estimates of impacts in the U.S. To 

account for the potential impacts of different health care systems or underlying health status of 

populations, we give preference to U.S. studies over non-U.S. studies. In addition, due to the 

potential for confounding by co-pollutants, we give preference to effect estimates from models 
6-13

including both ozone and PM over effect estimates from single-pollutant models.15,16

A number of endpoints that are not health-related also may significantly contribute to monetized 

benefits. Potential welfare benefits associated with ozone exposure include: increased outdoor 

worker productivity; increased yields for commercial and non-commercial crops; increased 

commercial forest productivity; reduced damage to urban ornamental plants; increased 

recreational demand for undamaged forest aesthetics; and reduced damage to ecosystem 

functions (U.S. EPA 1999, 2006). While we include estimates of the value of increased outdoor 

worker productivity, estimation of other welfare impacts is beyond the scope of this analysis. 

6.3.2.1 Premature Mortality Effects Estimates 

While particulate matter is the criteria pollutant most clearly associated with premature 

mortality, recent research suggests that short-term repeated ozone exposure likely contributes to 

premature death. The 2006 Ozone Criteria Document states: “Consistent with observed ozonerelated increases in respiratory- and cardiovascular-related morbidity, several newer multi-city 

studies, single-city studies, and several meta-analyses of these studies have provided relatively 

strong epidemiologic evidence for associations between short-term ozone exposure and all-cause 

mortality, even after adjustment for the influence of season and PM” (EPA, 2006: E-17). The 

epidemiologic data are also supported by newly available experimental data from both animal 

and human studies which provide evidence suggestive of plausible pathways by which risk of 

respiratory or cardiovascular morbidity and mortality could be increased by ambient ozone. 

With respect to short-term exposure, the ozone Criteria Document concludes: “This overall body 

of evidence is highly suggestive that ozone directly or indirectly contributes to non-accidental 

and cardiopulmonary-related mortality, but additional research is needed to more fully establish 

underlying mechanisms by which such effects occur” (pg. E-18). 

With respect to the time-series studies, the conclusion regarding the relationship between shortterm exposure and premature mortality is based, in part, upon recent city-specific time-series 

studies such as the Schwartz (2004) analysis in Houston and the Huang et al. (2004) analysis in 

Los Angeles.17 This conclusion is also based on recent meta-analyses by Bell et al. (2005), Ito et 

al. (2005), and Levy et al. (2005), and a new analysis of the National Morbidity, Mortality, and 

Air Pollution Study (NMMAPS) data set by Bell et al. (2004), which specifically sought to 

disentangle the roles of ozone, PM, weather-related variables, and seasonality. The 2006 Criteria 

Document states that “the results from these meta-analyses, as well as several single- and 

15 U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the 

Analytical Plan for EPA’s Second Prospective Analysis –Benefits and Costs of the Clean Air 

Act, 1990—2020. EPA-SAB-COUNCIL-ADV-04-004. 

16 National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed 

Air Pollution Regulations. Washington, DC: The National Academies Press. 

17 For an exhaustive review of the city-specific time-series studies considered in the ozone staff 

paper, see: U.S. Environmental Protection Agency, 2007. Review of the National Ambient Air 

Quality Standards for Ozone: Policy Assessment of Scientific and Technical Information. 

Prepared by the Office of Air and Radiation. Available at 

http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_01_ozone_staff_paper.pdf. pp. 5-36.
6-14

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 timeseries 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 nonaccidental and cardiorespiratory mortality for inclusion in the quantitative risk assessment to be 

appropriate.”

Consistent with the methodology used in the ozone risk assessment found in the Characterization 

of Health Risks found in the Review of the National Ambient Air Quality Standards for Ozone: 

Policy Assessment of Scientific and Technical Information, we included ozone mortality in the 

primary health effects analysis, with the recognition that the exact magnitude of the effects 

estimate is subject to continuing uncertainty. We used effect estimates from the Bell et al. 

(2004) NMMAPS analysis, as well as effect estimates from the three meta-analyses. In addition, 

we include the possibility that there is not a causal association between ozone and mortality, i.e., 

that the effect estimate for premature mortality could be zero. 

We estimate the change in mortality incidence and estimated credible interval18 resulting from 

application of the effect estimate from each study and present them separately to reflect 

differences in the study designs and assumptions about causality. However, it is important to 

note that this procedure only captures the uncertainty in the underlying epidemiological work, 

and does not capture other sources of uncertainty, such as uncertainty in the estimation of 

changes in air pollution exposure (Levy et al., 2000). 

6.3.2.2 Respiratory Hospital Admissions Effect Estimates

Detailed hospital admission and discharge records provide data for an extensive body of 

literature examining the relationship between hospital admissions and air pollution. This is 

especially true for the portion of the population aged 65 and older, because of the availability of 

detailed Medicare records. In addition, there is one study (Burnett et al., 2001) providing an 

effect estimate for respiratory hospital admissions in children under two. 

Because the number of hospital admission studies we considered is so large, we used results 

from a number of studies to pool some hospital admission endpoints. Pooling is the process by 

which multiple study results may be combined in order to produce better estimates of the effect 

18 A credible interval is a posterior probability interval used in Bayesian statistics, which is 

similar to a confidence interval used in frequentist statistics. 
6-15

estimate, or ȕ. For a complete discussion of the pooling process, see Abt (2005).19 To estimate 

total respiratory hospital admissions associated with changes in ambient ozone concentrations for 

adults over 65, we first estimated the change in hospital admissions for each of the different 

effects categories that each study provided for each city. These cities included Minneapolis, 

Detroit, Tacoma and New Haven. To estimate total respiratory hospital admissions for Detroit, 

we added the pneumonia and COPD estimates, based on the effect estimates in the Schwartz 

study (1994b). Similarly, we summed the estimated hospital admissions based on the effect 

estimates the Moolgavkar study reported for Minneapolis (Moolgavkar et al., 1997). To estimate 

total respiratory hospital admissions for Minneapolis using the Schwartz study (1994a), we 

simply estimated pneumonia hospital admissions based on the effect estimate. Making this 

assumption that pneumonia admissions represent the total impact of ozone on hospital 

admissions in this city will give some weight to the possibility that there is no relationship 

between ozone and COPD, reflecting the equivocal evidence represented by the different studies.

We then used a fixed-effects pooling procedure to combine the two total respiratory hospital 

admission estimates for Minneapolis. Finally, we used random effects pooling to combine the 

results for Minneapolis and Detroit with results from studies in Tacoma and New Haven from 

Schwartz (1995). As noted above, this pooling approach incorporates both the precision of the 

individual effect estimates and between-study variability characterizing differences across study 

locations.

6.3.2.3 Asthma-Related Emergency Room Visits Effect Estimates 

We used three studies as the source of the concentration-response functions we used to estimate 

the effects of ozone exposure on asthma-related emergency room (ER) visits: Peel et al. (2005); 

Wilson et al. (2005); and Jaffe et al. (2003). We estimated the change in ER visits using the 

effect estimate(s) from each study and then pooled the results using the random effects pooling 

technique (see Abt, 2005). The study by Jaffe et al. (2003) examined the relationship between 

ER visits and air pollution for populations aged five to 34 in the Ohio cities of Cleveland, 

Columbus and Cincinnati from 1991 through 1996. In single-pollutant Poisson regression 

models, ozone was linked to asthma visits. We use the pooled estimate across all three cities as 

reported in the study. The Peel et al. study (2005) estimated asthma-related ER visits for all ages 

in Atlanta, using air quality data from 1993 to 2000. Using Poisson generalized estimating 

equations, the authors found a marginal association between the maximum daily 8-hour average 

ozone level and ER visits for asthma over a 3-day moving average (lags of 0, 1, and 2 days) in a 

single pollutant model. Wilson et al. (2005) examined the relationship between ER visits for 

respiratory illnesses and asthma and air pollution for all people residing in Portland, Maine from 

1998-2000 and Manchester, New Hampshire from 1996-2000. For all models used in the 

analysis, the authors restricted the ozone data incorporated into the model to the months ozone 

levels are usually measured, the spring-summer months (April through September). Using the 

generalized additive model, Wilson et al. (2005) found a significant association between the 

maximum daily 8-hour average ozone level and ER visits for asthma in Portland, but found no 

significant association for Manchester. Similar to the approach used to generate effect estimates 

for hospital admissions, we used random effects pooling to combine the results across the 

19 Abt Associates, Incorporated. Environmental Benefits Mapping and Analysis Program, 

Technical Appendices. May 2005. pp. I-3
6-16

individual study estimates for ER visits for asthma. The Peel et al. (2005) and Wilson et al. 

(2005) Manchester estimates were not significant at the 95 percent level, and thus, the 

confidence interval for the pooled incidence estimate based on these studies includes negative 

values. This is an artifact of the statistical power of the studies, and the negative values in the 

tails of the estimated effect distributions do not represent improvements in health as ozone 

concentrations are increased. Instead these should be viewed as a measure of uncertainty due to 

limitations in the statistical power of the study. Note that we included both hospital admissions 

and ER visits as separate endpoints associated with ozone exposure, because our estimates of 

hospital admission costs do not include the costs of ER visits, and because most asthma ER visits 

do not result in a hospital admission. 

6.3.2.4 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 

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. 
6-17

6.3.2.6 Worker Productivity 

To monetize benefits associated with increased worker productivity resulting from improved 

ozone air quality, we used information reported in Crocker and Horst (1981). Crocker and Horst 

examined the impacts of ozone exposure on the productivity of outdoor citrus workers. The 

study measured productivity impacts. Worker productivity is measuring the value of the loss in 

productivity for a worker who is at work on a particular day, but due to ozone, cannot work as 

hard. It only applies to outdoor workers, like fruit and vegetable pickers, or construction 

workers. Here, productivity impacts are measured as the change in income associated with a 

change in ozone exposure, given as the elasticity of income with respect to ozone concentration. 

The reported elasticity translates a ten percent reduction in ozone to a 1.4 percent increase in 

income. Given the national median daily income for outdoor workers engaged in strenuous 

activity reported by the U.S. Census Bureau (2002), $68 per day (2000$), a ten percent reduction 

in ozone yields about $0.97 in increased daily wages. We adjust the national median daily 

income estimate to reflect regional variations in income using a factor based on the ratio of 

county median household income to national median household income. No information was 

available for quantifying the uncertainty associated with the central valuation estimate. 

Therefore, no uncertainty analysis was conducted for this endpoint. 

6.3.2.7 Visibility Benefits 

Changes in the level of ambient PM2.5 caused by the reduction in emissions associated with the 

proposed standards will change the level of visibility throughout the United States. Increases in 

PM concentrations cause increases in light extinction, a measure of how much the components of 

the atmosphere absorb light. Due to time limitations, this benefits assessment does not consider 

the value of improvements in visibility associated with simulated attainment of alternate ozone 

standards. We anticipate that the benefits assessment supporting the promulgated ozone standard 

will consider this important benefits category. 

6.3.2.8 Other Unquantified Effects 

6.3.2.8.1 Direct Ozone Effects on Vegetation 

The Ozone Criteria Document notes that “current ambient concentrations in many areas of the 

country are sufficient to impair growth of numerous common and economically valuable plant 

and tree species.” (U.S. EPA, 2006, page 9-1). Changes in ground-level ozone resulting from the 

implementation of alternative ozone standards are expected to affect crop and forest yields 

throughout the affected area. Recent scientific studies have also found the ozone negatively 

impacts the quality or nutritive value of crops (U.S. EPA, 2006, page 9-16). 

Well-developed techniques exist to provide monetary estimates of these benefits to agricultural 

producers and to consumers. These techniques use models of planting decisions, yield response 

functions, and the supply of and demand for agricultural products. The resulting welfare 

measures are based on predicted changes in market prices and production costs. Models also 

exist to measure benefits to silvicultural producers and consumers. However, these models have 
6-18

not been adapted for use in analyzing ozone-related forest impacts. Because of resource 

limitations, we are unable to provide agricultural or benefits estimates for the proposed rule. 

An additional welfare benefit expected to accrue as a result of reductions in ambient ozone 

concentrations in the United States is the economic value the public receives from reduced 

aesthetic injury to forests. There is sufficient scientific information available to reliably establish 

that ambient ozone levels cause visible injury to foliage and impair the growth of some sensitive 

plant species (U.S. EPA, 2006, page 9-19). However, present analytic tools and resources 

preclude EPA from quantifying the benefits of improved forest aesthetics. 

Urban ornamentals (floriculture and nursery crops) represent an additional vegetation category 

likely to experience some degree of negative effects associated with exposure to ambient ozone 

levels and likely to affect large economic sectors. In the absence of adequate exposure-response 

functions and economic damage functions for the potential range of effects relevant to these 

types of vegetation, no direct quantitative economic benefits analysis has been conducted. The 

farm production value of ornamental crops was estimated at over $14 billion in 2003 (USDA, 

2004). This is therefore a potentially important welfare effects category. However, information 

and valuation methods are not available to allow for plausible estimates of the percentage of 

these expenditures that may be related to impacts associated with ozone exposure. 

6.3.2.8.2 Nitrogen Deposition

Deposition to Estuarine and Coastal Waters

Excess nutrient loads, especially of nitrogen, cause a variety of adverse consequences to the 

health of estuarine and coastal waters. These effects include toxic and/or noxious algal blooms 

such as brown and red tides, low (hypoxic) or zero (anoxic) concentrations of dissolved oxygen 

in bottom waters, the loss of submerged aquatic vegetation due to the light-filtering effect of 

thick algal mats, and fundamental shifts in phytoplankton community structure (Bricker et al., 

1999). A recent study found that for the period 1990-2002, atmospheric deposition accounted 

for 17 percent of nitrate loadings in the Gulf of Mexico, where severe hypoxic zones have been 

existed over the last two decades (Booth and Campbell, 2007)20

.

Reductions in atmospheric deposition of NOx are expected to reduce the adverse impacts 

associated with nitrogen deposition to estuarine and coastal waters. However, direct functions 

relating changes in nitrogen loadings to changes in estuarine benefits are not available. The 

preferred WTP-based measure of benefits depends on the availability of these functions and on 

estimates of the value of environmental responses. Because neither appropriate functions nor 

sufficient information to estimate the marginal value of changes in water quality exist at present, 

calculation of a WTP measure is not possible.

20 Booth, M.S., and C. Campbell. 2007. Spring Nitrate Flux in the Mississippi River Basin: A Landscape Model 

with Conservation Applications. Environ. Sci. Technol.; 2007; ASAP Web Release Date: 20-Jun-2007; (Article) 

DOI: 10.1021/es070179e 
6-19

Deposition to Agricultural and Forested Land 

Implementation strategies for alternative standards which reduce NOX emissions, will also 

reduce nitrogen deposition on agricultural land and forests. There is some evidence that nitrogen 

deposition may have positive effects on agricultural output through passive fertilization. 

Holding all other factors constant, farmers’ use of purchased fertilizers or manure may increase 

as deposited nitrogen is reduced. Estimates of the potential value of this possible increase in the 

use of purchased fertilizers are not available, but it is likely that the overall value is very small 

relative to other health and welfare effects. The share of nitrogen requirements provided by this 

deposition is small, and the marginal cost of providing this nitrogen from alternative sources is 

quite low. In some areas, agricultural lands suffer from nitrogen over-saturation due to an 

abundance of on-farm nitrogen production, primarily from animal manure. In these areas, 

reductions in atmospheric deposition of nitrogen from PM represent additional agricultural 

benefits.

Information on the effects of changes in passive nitrogen deposition on forests and other 

terrestrial ecosystems is very limited. The multiplicity of factors affecting forests, including other 

potential stressors such as ozone, and limiting factors such as moisture and other nutrients, 

confound assessments of marginal changes in any one stressor or nutrient in forest ecosystems. 

However, reductions in deposition of nitrogen could have negative effects on forest and 

vegetation growth in ecosystems where nitrogen is a limiting factor (US EPA, 1993). Moreover, 

any positive effect that nitrogen deposition has on forest productivity would enhance the level of 

carbon dioxide sequestration as well.21,22,23

On the other hand, there is evidence that forest ecosystems in some areas of the United States 

(such as the western U.S.) are nitrogen saturated (US EPA, 1993). Once saturation is reached, 

adverse effects of additional nitrogen begin to occur such as soil acidification which can lead to 

leaching of nutrients needed for plant growth and mobilization of harmful elements such as 

aluminum. Increased soil acidification is also linked to higher amounts of acidic runoff to 

streams and lakes and leaching of harmful elements into aquatic ecosystems. 

6.3.2.8.3 Ultraviolet Radiation 

Atmospheric ozone absorbs a harmful band of ultraviolet radiation from the sun called UV-B, 

providing a protective shield to the Earth's surface. The majority of this protection occurs in the 

stratosphere where 90% of atmospheric ozone is located. The remaining 10% of the Earth's 

ozone is present at ground level (referred to as tropospheric ozone) (NAS, 1991; NASA). Only a 

portion of the tropospheric fraction of UV-B shielding is from anthropogenic sources (e.g., 

power plants, byproducts of combustion). The portion of ground level ozone associated with 

21 Peter M. Vitousek et. al., “Human Alteration of the Global Nitrogen Cycle: Causes and Consequences” Issues in 

Ecology No. 1 (Spring) 1997. 

22 Knute J. Nadelhoffer et. al., “Nitrogen deposition makes a minor contribution to carbon 

sequestration in temperate forests” Nature 398, 145-148 (11 March 1999)

23 Martin Köchy and Scott D. Wilson, “Nitrogen deposition and forest expansion in the northern Great Plains 

Journal of Ecology Journal of Ecology 89 (5), 807–817 
6-20

anthropogenic sources varies by locality and over time. Even so, it is reasonable to assume that 

reductions in ground level ozone would lead to increases in the same health effects linked to in 

UV-B exposures. These effects include fatal and nonfatal melanoma and non-melanoma skin 

cancers and cataracts. The values of $15,000 per case for non-fatal melanoma skin cancer, 

$5,000 per case for non-fatal non-melanoma skin cancer, and $15,000 per case of cataracts have 

been used in analyses of stratospheric ozone depletion (U.S. EPA, 1999). Fatal cancers are 

valued using the standard VSL estimate, which for 2020 is $6.6 million (1999$). UV-B has also 

been linked to ecological effects including damage to crops and forest. For a more complete 

listing of quantified and unquantified UV-B radiation effects, see Table G-4 and G-7 in the 

Benefits and Costs of the Clean Air Act, 1990-2010 (U.S. EPA, 1999. UV-B related health 

effects are also discussed in the context of stratospheric ozone in a 2006 report by ICF 

Consulting, prepared for the U.S. EPA. 

There are many factors that influence UV-B radiation penetration to the earth’s surface, 

including latitude, altitude, cloud cover, surface albedo, PM concentration and composition, and 

gas phase pollution. Of these, only latitude and altitude can be defined with small uncertainty in 

any effort to assess the changes in UV-B flux that may be attributable to any changes in 

tropospheric O3 as a result of any revision to the O3 NAAQS. Such an assessment of UV-B 

related health effects would also need to take into account human habits, such as outdoor 

activities (including age- and occupation-related exposure patterns), dress and skin care to 

adequately estimate UV-B exposure levels. However, little is known about the impact of these 

factors on individual exposure to UV-B. 

Moreover, detailed information does not exist regarding other factors that are relevant to 

assessing changes in disease incidence, including: type (e.g., peak or cumulative) and time 

period (e.g., childhood, lifetime, current) of exposures related to various adverse health outcomes 

(e.g., damage to the skin, including skin cancer; damage to the eye, such as cataracts; and 

immune system suppression); wavelength dependency of biological responses; and 

interindividual variability in UV-B resistance to such health outcomes. Beyond these well 

recognized adverse health effects associated with various wavelengths of UV radiation, the 

Criteria Document (section 10.2.3.6) also discusses protective effects of UV-B radiation. Recent 

reports indicate the necessity of UV-B in producing vitamin D, and that vitamin D deficiency can 

cause metabolic bone disease among children and adults, and may also increase the risk of many 

common chronic diseases (e.g., type I diabetes and rheumatoid arthritis) as well as the risk of 

various types of cancers. Thus, the Criteria Document concludes that any assessment that 

attempts to quantify the consequences of increased UV-B exposure on humans due to reduced 

ground-level O3 must include consideration of both negative and positive effects. However, as 

with other impacts of UVB on human health, this beneficial effect of UVB radiation has not 

previously been studied in sufficient detail. We will develop approaches for estimating the 

effects of increased UVB exposures resulting from reductions in tropospheric ozone and will 

work to present peer-reviewed quantified estimates for the final rule. 
6-21

6.3.2.8.4 Climate Implications of Tropospheric Ozone 

Although climate and air quality are generally treated as separate issues, they are closely coupled 

through atmospheric processes. Ozone, itself, is a major greenhouse gas and climate directly 

influences ambient concentrations of ozone. 

The concentration of tropospheric ozone has increased substantially since the pre-industrial era 

and has contributed to warming. Tropospheric ozone is (after CO2 and CH4) the third most 

important contributor to greenhouse gas warming. The National Academy of Sciences recently 

stated24 that regulations targeting ozone precursors would have combined benefits for public 

health and climate. As noted in the OAQPS Staff Paper, the overall body of scientific evidence 

suggests that high concentrations of ozone on a regional scale could have a discernible influence 

on climate. However, the Staff Paper concludes that insufficient information is available at this 

time to quantitatively inform the secondary NAAQS process with regard to this aspect of the 

ozone-climate interaction.

Climate change can affect tropospheric ozone by modifying emissions of precursors, chemistry, 

transport and removal.25 Climate change affects the sources of ozone precursors through physical 

response (lightning), biological response (soils, vegetation, and biomass burning) and human 

response (energy generation, land use, and agriculture). Increases in regional ozone pollution are 

expected due to higher temperatures and weaker circulation. Simulations with global climate 

models for the 21st century indicate a decrease in the lifetime of tropospheric ozone due to 

increasing water vapor which could decrease global background ozone concentrations. 

The Intergovernmental Panel on Climate Change (IPCC) recently released a report26 which 

projects, with “virtual certainty,” declining air quality in cities due to warmer and fewer cold 

days and nights and/or warmer/more frequent hot days and nights over most land areas. The 

report states that projected climate change-related exposures are likely to affect the health status 

of millions of people, in part, due to higher concentrations of ground level ozone related to 

climate change. 

24 National Academy of Sciences, “Radiative Forcing of Climate Change: Expanding the 

Concept and Addressing Uncertainties,” October 2005. 

25Denman, K.L., G. Brasseur, A. Chidthaisong, P. Ciais, P.M. Cox, R.E. Dickinson, D. 

Hauglustaine, C. Heinze, E. Holland, D. Jacob, U. Lohmann, S Ramachandran, P.L. da Silva 

Dias, 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 
6-22

The IPCC also reports27 that the current generation of tropospheric ozone models is generally 

successful in describing the principal features of the present-day global ozone distribution.

However, there is much less confidence in the ability to reproduce the changes in ozone 

associated with perturbations of emissions or climate. There are major discrepancies with 

observed long-term trends in ozone concentrations over the 20th century, including after 1970 

when the reliability of observed ozone trends is high. Resolving these discrepancies is needed to 

establish confidence in the models. 

The EPA is currently leading a research effort with the goal of identifying changes in regional 

US air quality that may occur in a future (2050) climate, focusing on fine particles and ozone. 

The research builds first on an assessment of changes in US air quality due to climate change, 

which includes direct meteorological impacts on atmospheric chemistry and transport and the 

effect of temperature changes on air pollution emissions. Further research will result in an 

assessment that adds the emission impacts from technology, land use, demographic changes, and 

air quality regulations to construct plausible scenarios of US air quality 50 years into the future. 

As noted in the Staff Paper, results from these efforts are expected to be available for 

consideration in the next review of the ozone NAAQS.

6.3.3 Baseline Incidence Rates 

Epidemiological studies of the association between pollution levels and adverse health effects 

generally provide a direct estimate of the relationship of air quality changes to the relative risk of

a health effect, rather than estimating the absolute number of avoided cases. For example, a 

typical result might be that a 100 ppb decrease in daily ozone levels might, in turn, decrease 

hospital admissions by 3 percent. The baseline incidence of the health effect is necessary to 

convert this relative change into a number of cases. A baseline incidence rate is the estimate of 

the number of cases of the health effect per year in the assessment location, as it corresponds to 

baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate 

must be multiplied by the corresponding population number. For example, if the baseline 

incidence rate is the number of cases per year per 100,000 people, that number must be 

multiplied by the number of 100,000s in the population. 

Table 6-3 summarizes the sources of baseline incidence rates and provides average incidence 

rates for the endpoints included in the analysis. For both baseline incidence and prevalence data, 

we used age-specific rates where available. We applied concentration-response functions to 

individual age groups and then summed over the relevant age range to provide an estimate of 

total population benefits. In most cases, we used a single national incidence rate, due to a lack of 

more spatially disaggregated data. Whenever possible, the national rates used are national 

averages, because these data are most applicable to a national assessment of benefits. For some 

studies, however, the only available incidence information comes from the studies themselves; in 

these cases, incidence in the study population is assumed to represent typical incidence at the 

national level. Regional incidence rates are available for hospital admissions, and county-level 

27 Denman, et al, 2007: Couplings Between Changes in the Climate System and 

Biogeochemistry. In: Climate Change 2007: The Physical Science Basis. 
6-23

data are available for premature mortality. We have projected mortality rates such that future 

mortality rates are consistent with our projections of population growth (Abt Associates, 2005). 

Table 6-3. National Average Baseline Incidence Rates

Rate per 100 people per year D by Age Group

Endpoint Source Notes <18 18-24 25-34 35-44 45-54 55-64 65+

Mortality

CDC Compressed Mortality File, 

accessed through CDC Wonder (1996-

1998)

nonaccidental 0.025 0.022 0.057 0.150 0.383 1.006 4.937

Respiratory 

Hospital

Admissions. 

1999 NHDS public use data filesB incidence 0.043 0.084 0.206 0.678 1.926 4.389 11.629

Asthma ER visits 2000 NHAMCS public use data filesC

;

1999 NHDS public use data filesB incidence 1.011 1.087 0.751 0.438 0.352 0.425 0.232

Minor Restricted 

Activity Days 

(MRADs)

Ostro and Rothschild (1989, p. 243) incidence – 780 780 780 780 780 –

School Loss Days

National Center for Education 

Statistics (1996) and 1996 HIS 

(Adams et al., 1999, Table 47); 

estimate of 180 school days per year 

all-cause 990.0 – – – – – –

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/pub/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. 
6-24

Table 6-3 National Average Baseline Incidence Rates (continued)

6.4 Economic Values for Health Outcomes 

Reductions in ambient concentrations of air pollution generally lower the risk of future adverse 

health effects for a large population. Therefore, the appropriate economic measure is 

willingness-to-pay (WTP) for changes in risk of a health effect rather than WTP for a health 

effect that would occur with certainty (Freeman, 1993). Epidemiological studies generally 

provide estimates of the relative risks of a particular health effect that is avoided because of a 

reduction in air pollution. We converted those to units of avoided statistical incidence for ease of 

presentation. We calculated the value of avoided statistical incidences by dividing individual 

WTP for a risk reduction by the related observed change in risk. For example, suppose a 

pollution-reduction regulation is able to reduce the risk of premature mortality from 2 in 10,000 

to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then 

the WTP for an avoided statistical premature death is $1 million ($100/0.0001 change in risk). 

WTP estimates generally are not available for some health effects, such as hospital admissions. 

In these cases, we used the cost of treating or mitigating the effect as a primary estimate. These 

cost-of-illness (COI) estimates generally understate the true value of reducing the risk of a health 

effect, because they reflect the direct expenditures related to treatment, but not the value of 

avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987). We provide unit 

values for health endpoints (along with information on the distribution of the unit value) in Table 

6-4. All values are in constant year 2000 dollars, adjusted for growth in real income out to 2020 

using projections provided by Standard and Poor’s. Economic theory argues that WTP for most 

goods (such as environmental protection) will increase if real income increases. Many of the 

valuation studies used in this analysis were conducted in the late 1980s and early 1990s.

Because real income has grown since the studies were conducted, people’s willingness to pay for 

Endpoint Source Notes

Rate per 100 people per 

year 4

Ostro et al. (2001) 

Incidence (and 

prevalence) 

among 

asthmatic 

AfricanAmerican 

children 

Daily wheeze 

Daily cough 

Daily dyspnea 

0.076 (0.173)

0.067 (0.145)

0.037 (0.074)

Asthma Exacerbations 

Vedal et al. (1998) 

Incidence (and 

prevalence) 

among 

asthmatic 

children 

Daily wheeze 

Daily cough 

Daily dyspnea 

0.038 

0.086 

0.045 
6-25

reductions in the risk of premature death and disease likely has grown as well. We did not adjust 

cost of illness-based values because they are based on current costs. Similarly, we did not adjust 

the value of school absences, because that value is based on current wage rates. Table 6.4 

presents the values for individual endpoints adjusted to year 2020 income levels. The discussion 

below provides additional details on ozone related endpoints. For details on valuation estimates 

for PM related endpoints, see the 2006 PM NAAQS RIA. 

6.4.1 Mortality Valuation 

To estimate the monetary benefit of reducing the risk of premature death, we used the “value of 

statistical lives” saved (VSL) approach, which is a summary measure for the value of small 

changes in mortality risk for a large number of people. The VSL approach applies information 

from several published value-of-life studies to determine a reasonable monetary value of 

preventing premature mortality. The mean value of avoiding one statistical death is estimated to 

be roughly $5.5 million at 1990 income levels (2000 $), and $6.6 million at 2020 income levels. 

This represents an intermediate value from a variety of estimates in the economics literature (see 

the 2006 PM NAAQS RIA for more details on the calculation of VSL).

6.4.2 Hospital Admissions Valuation 

In the absence of estimates of societal WTP to avoid hospital visits/admissions for specific 

illnesses, estimates of total cost of illness (total medical costs plus the value of lost productivity) 

typically are used as conservative, or lower bound, estimates. These estimates are biased 

downward, because they do not include the willingness-to-pay value of avoiding pain and 

suffering.

The International Classification of Diseases (ICD-9, 1979) code-specific COI estimates used in 

this analysis consist of estimated hospital charges and the estimated opportunity cost of time 

spent in the hospital (based on the average length of a hospital stay for the illness). We based all 

estimates of hospital charges and length of stays on statistics provided by the Agency for 

Healthcare Research and Quality (AHRQ 2000). We estimated the opportunity cost of a day 

spent in the hospital as the value of the lost daily wage, regardless of whether the hospitalized 

individual is in the workforce. To estimate the lost daily wage, we divided the 1990 median 

weekly wage by five and inflated the result to year 2000$ using the CPI-U “all items.” The 

resulting estimate is $109.35. The total cost-of-illness estimate for an ICD code-specific hospital 

stay lasting n days, then, was the mean hospital charge plus $109 &n.

6.4.3 Asthma-Related Emergency Room Visits Valuation 

To value asthma emergency room visits, we used a simple average of two estimates from the 

health economics literature. The first estimate comes from Smith et al. (1997), who reported 

approximately 1.2 million asthma-related emergency room visits in 1987, at a total cost of 

$186.5 million (1987$). The average cost per visit that year was $155; in 2000$, that cost was 

$311.55 (using the CPI-U for medical care to adjust to 2000$). The second estimate comes from 

Stanford et al. (1999), who reported the cost of an average asthma-related emergency room visit 

at $260.67, based on 1996-1997 data. A simple average of the two estimates yields a (rounded) 
6-26

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, IEc (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 IEc estimate 

of WTP to avoid a minor respiratory restricted activity day is $38.37 (1990$), or about $52 

($2000).

Although Ostro and Rothschild (1989) statistically linked ozone and minor restricted activity 

days, it is likely that most MRADs associated with ozone exposure are, in fact, minor respiratory

restricted activity days. For the purpose of valuing this health endpoint, we used the estimate of 

mean WTP to avoid a minor respiratory restricted activity day. 

6.4.5 School Absences 

To value a school absence, we: (1) estimated the probability that if a school child stays home 

from school, a parent will have to stay home from work to care for the child; and (2) valued the 

lost productivity at the parent’s wage. To do this, we estimated the number of families with 

school-age children in which both parents work, and we valued a school-loss day as the 

probability that such a day also would result in a work-loss day. We calculated this value by 

multiplying the proportion of households with school-age children by a measure of lost wages. 

We used this method in the absence of a preferable WTP method. However, this approach 

suffers from several uncertainties. First, it omits willingness to pay to avoid the symptoms/illness 

that resulted in the school absence; second, it effectively gives zero value to school absences that 

do not result in work-loss days; and third, it uses conservative assumptions about the wages of 

the parent staying home with the child. Finally, this method assumes that parents are unable to 

work from home. If this is not a valid assumption, then there would be no lost wages. 

For this valuation approach, we assumed that in a household with two working parents, the 

female parent will stay home with a sick child. From the Statistical Abstract of the United States 

(U.S. Census Bureau, 2001), we obtained: (1) the numbers of single, married and “other” 

(widowed, divorced or separated) working women with children; and (2) the rates of 

participation in the workforce of single, married and “other” women with children. From these 

two sets of statistics, we calculated a weighted average participation rate of 72.85 percent. 

Our estimate of daily lost wage (wages lost if a mother must stay at home with a sick child) is 

based on the year 2000 median weekly wage among women ages 25 and older (U.S. Census 

Bureau, 2001). This median weekly wage is $551. Dividing by five gives an estimated median 

daily wage of $103. To estimate the expected lost wages on a day when a mother has to stay 

home with a school-age child, we first estimated the probability that the mother is in the 

workforce then multiplied that estimate by the daily wage she would lose by missing a work day: 

72.85 percent times $103, for a total loss of $75. This valuation approach is similar to that used 

by Hall et al. (2003). 
6-27

Table 6-4. Unit Values for Economic Valuation of Health Endpoints (2000$)

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) 

$5,500,000 $6,600,000 Point estimate is the mean of a normal distribution with a 95% confidence interval between 

$1 and $10 million. Confidence interval is based on two meta-analyses of the wage-risk 

VSL literature: $1 million represents the lower end of the interquartile range from the 

Mrozek and Taylor (2002) meta-analysis and $10 million represents the upper end of the 

interquartile range from the Viscusi and Aldy (2003) meta-analysis. The mean of the 

distribution is consistent with the mean estimate from a third meta-analysis (Kochi et al 

2006). The VSL represents the value of a small change in mortality risk aggregated over the 

affected population. 

Chronic Bronchitis (CB) $340,000 $420,000 The WTP to avoid a case of pollution-related CB is calculated as 

WTP WTP e x

x

 

 

13

13 *

E*( )

, where x is the severity of an average CB case, WTP13 is the WTP 

for a severe case of CB, and Қ is the parameter relating WTP to severity, based on the 

regression results reported in Krupnick and Cropper (1992). The distribution of WTP for an 

average severity-level case of CB was generated by Monte Carlo methods, drawing from 

each of three distributions: (1) WTP to avoid a severe case of CB is assigned a 1/9 

probability of being each of the first nine deciles of the distribution of WTP responses in 

Viscusi et al. (1991); (2) the severity of a pollution-related case of CB (relative to the case 

described in the Viscusi study) is assumed to have a triangular distribution, with the most 

likely value at severity level 6.5 and endpoints at 1.0 and 12.0; and (3) the constant in the 

elasticity of WTP with respect to severity is normally distributed with mean = 0.18 and 

standard deviation = 0.0669 (from Krupnick and Cropper [1992]). This process and the 

rationale for choosing it is described in detail in the Costs and Benefits of the Clean Air Act, 

1990 to 2010 (EPA, 1999).

(continued)
6-28

Table 6-4: Unit Values Used for Economic Valuation of Health Endpoints (2000$) (continued) 

Central Estimate of Value Per 

Statistical Incidence 

Health Endpoint 

1990 Income 

Level

2020 Income 

Level Derivation of Distributions of Estimates 

Nonfatal Myocardial 

Infarction (heart attack) 

 3% discount rate

 Age 0–24 

 Age 25–44 

 Age 45–54 

 Age 55–65 

 Age 66 and over 

 7% discount rate

 Age 0–24 

 Age 25–44 

 Age 45–54 

 Age 55–65 

 Age 66 and over 

$66,902 

$74,676 

$78,834 

$140,649 

$66,902 

$65,293 

$73,149 

$76,871 

$132,214 

$65,293 

$66,902 

$74,676 

$78,834 

$140,649 

$66,902 

$65,293 

$73,149 

$76,871 

$132,214 

$65,293 

No distributional information available. Age-specific cost-of-illness values reflect lost 

earnings and direct medical costs over a 5-year period following a nonfatal 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 ($22,331 at 3% discount rate; $21,113 at 7% 

discount rate) 

Hospital Admissions 

Chronic Obstructive 

Pulmonary Disease 

(COPD)

$12,378 $12,378 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 $6,634 $6,634 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 $18,387 $18,387 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+) $18,353 $18,353 No distributions available. The COI point estimates (lost earnings plus direct medical costs) 

are based on ICD-9 code level information (e.g., average hospital care costs, average 

length of hospital stay, and weighted share of total COPD category illnesses) reported in 

Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov). 
6-29

Table 6-4: Unit Values Used for Economic Valuation of Health Endpoints (2000$) (continued) 

Central Estimate of Value Per 

Statistical Incidence 

Health Endpoint 

1990 Income 

Level

2020 Income 

Level Derivation of Distributions of Estimates 

All respiratory (ages 0-2) $7,741 $7,741 No distributions available. The COI point estimates (lost earnings plus direct medical costs) 

are based on ICD-9 code level information (e.g., average hospital care costs, average 

length of hospital stay, and weighted share of total COPD category illnesses) reported in 

Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov). 

Emergency Room Visits 

for Asthma 

$286 $286 No distributional information available. Simple average of two unit COI values: 

(1) $311.55, from Smith et al. (1997) and 

(2) $260.67, from Stanford et al. (1999). 

Respiratory Ailments Not Requiring Hospitalization 

Upper Respiratory 

Symptoms (URS) 

$25 $27 Combinations of the three symptoms for which WTP estimates are available that closely 

match those listed by Pope et al. result in seven different “symptom clusters,” each 

describing a “type” of URS. A dollar value was derived for each type of URS, using midrange estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assuming 

additivity of WTPs. In the absence of information surrounding the frequency with which 

each of the seven types of URS occurs within the URS symptom complex, we assumed a 

uniform distribution between $9.2 and $43.1. 

Lower Respiratory 

Symptoms (LRS) 

$16 $18 Combinations of the four symptoms for which WTP estimates are available that closely 

match those listed by Schwartz et al. result in 11 different “symptom clusters,” each 

describing a “type” of LRS. A dollar value was derived for each type of LRS, using midrange estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assuming 

additivity of WTPs. The dollar value for LRS is the average of the dollar values for the 11 

different types of LRS. In the absence of information surrounding the frequency with which 

each of the 11 types of LRS occurs within the LRS symptom complex, we assumed a 

uniform distribution between $6.9 and $24.46. 

Asthma Exacerbations $42 $45 Asthma exacerbations are valued at $45 per incidence, based on the mean of average 

WTP estimates for the four severity definitions of a “bad asthma day,” described in Rowe 

and Chestnut (1986). This study surveyed asthmatics to estimate WTP for avoidance of a 

“bad asthma day,” as defined by the subjects. For purposes of valuation, an asthma 

exacerbation is assumed to be equivalent to a day in which asthma is moderate or worse as 

reported in the Rowe and Chestnut (1986) study. The value is assumed have a uniform 

distribution between $15.6 and $70.8. 

(continued)
6-30

Table 6-4: Unit Values Used for Economic Valuation of Health Endpoints (2000$) (continued) 

Central Estimate of Value Per 

Statistical Incidence 

Health Endpoint 

1990 Income 

Level

2020 Income 

Level Derivation of Distributions of Estimates 

Acute Bronchitis $360 $380 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. (1994). The low daily estimate of $10 is the sum of the mid-range values 

recommended by IEc (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 $110. 

Work Loss Days (WLDs) Variable (U.S. 

median=$110) 

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. 

Minor Restricted Activity 

Days (MRADs) 

$51 $54 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. 

School Absence Days $75 $75 No distribution available 
6-31

6.5 Results and Implications

Tables 6-5 through 6-28 summarize the reduction in incidence for ozone- and PM-related health 

endpoints for each of the alternative ozone standards evaluated. Tables 6-29 through 6-44 

summarize the ozone-related economic benefits for each of the alternative standards.28 Note that 

incidence and valuation estimates for each standard alternative are broken into two sets of tables. 

The first set of tables summarizes incidence and valuation for simulated national attainment with 

the standard alternative in the East and areas outside of California, and “glidepath” attainment in 

California. The second set of tables present incidence and valuation estimates for California post2020, to account for the additional emission reductions projected to occur as a result of full 

implementation of a series of mobile source rules. In addition to the mean incidence estimates, we 

have included 5th and 95th percentile estimates, except where noted, based on the Monte Carlo 

simulations described above. In the tables presenting the 0.065 ppm and 0.070 ppm estimates, the 

total change in ozone-related incidence from fully attaining the alternative standards is broken out 

into the change in incidence associated with the modeled partial attainment scenario and the sum 

of the change in incidence associated with achieving the partial attainment increment plus the 

residual attainment increment. As described in Appendix 6, to calculate the change in ozone 

concentrations to reach full attainment, we rolled back the ozone monitor data so that the 4th

highest daily maximum 8-hour average just met the level required to attain the alternative standard.

This approach will likely understate the benefits that would occur due to implementation of actual 

controls to reduce ozone precursor emissions because controls implemented to reduce ozone 

concentrations at the highest monitor would likely result in some reductions in ozone 

concentrations at attaining monitors down-wind (i.e. the controls would lead to concentrations 

below the standard in down-wind locations). Therefore, air quality improvements and resulting 

health benefits from full attainment would be more widespread than we have estimated in our 

rollback analyses. The incidence and valuation results for attainment of the 0.075 ppm alternative 

are derived through an interpolation technique described in Appendix 6. As such, these estimates 

are presented as full attainment only. The incidence and valuation estimates for attainment of the 

0.079 ppm alternative are derived through monitor rollbacks alone and thus are presented as full 

attainment only. 

In addition to disaggregating ozone benefits between modeled and rollback for the 0.070 ppm and 

0.065 ppm standard alternatives, we also provide disaggregation by region, with separate benefits 

estimates for the Eastern U.S., California, and the Western U.S. outside of California. The 

estimates of ozone-related mortality and morbidity for California are broken into glidepath and full 

attainment. Certain California projected non-attainment counties are required to meet an ozone 

target above the actual standard (that is, a “glidepath”) by 2020 due to the severity of nonattainment. The estimates in this column reflect the benefits of meeting this target. 

6.5.1 Glidepath incidence and valuation estimates for 0.065 ppm and 0.075 ppm alternatives

This analysis includes an assessment of the benefits of reaching the glidepath targets for each of 

the standard alternatives in 2020 in California. Due to time and resource limitations, we were able 

28 Note that the valuation estimates for ozone benefits are not discounted. Because these are short 

term benefits that occur the same year in which the alternate standard is met, discounting is not 

necessary.
6-32

to perform a full scale benefits analysis of the California glidepath targets for the 0.070 ppm 

alternative only. Thus, we derived the glidepath benefits estimates for the 0.075 ppm and 0.065 

ppm alternatives by applying a scaling factor. This scaling factor represents the ratio of the 

California 0.070 ppm glidepath full attainment benefits to the California 0.070 ppm full attainment 

benefits. This process entailed the following steps: (1) calculate the ratio of the California 0.070 

ppm glidepath target benefits to the California 0.070 ppm full attainment benefits for each 

incidence and valuation estimate; (2) multiply this ratio by the California full attainment 0.075 

ppm and 0.065 ppm incidence and valuation estimate to derive glidepath estimates. Because these 

results are scaled, it was not possible to generate confidence intervals.

While clearly the 2020 glidepath targets for the current and alternative standards vary among the 

standard alternatives, the relative air quality increment between the glidepath base and control 

cases in California is nearly identical among the standard alternatives. As such, we believe this 

scaling approach is a valid technique to develop screening-level estimates of 0.065 ppm and 0.075 

ppm California glidepath benefits. 

6.5.2 PM2.5 co-benefit estimates 

As discussed further below, tables 6-9, 6-10, 6-15, 6-16, 6-21, 6-22, 6-27 and 6-28 present the 

PM2.5 co-benefits associated with full attainment of the 0.065 ppm, 0.070 ppm, 0.075 ppm and 

0.079 ppm alternatives. To derive estimates of incidence and valuation for the PM2.5 related cobenefits of full attainment of each ozone standard alternative, we applied two different scaling 

techniques. To estimate total valuation estimates, we applied benefit per-ton metrics; this 

procedure is detailed further below. Note that the valuation estimates of the PM2.5-related full 

attainment benefits are presented at a 3% discount rate; due to time and resource limitations it was 

not possible to calculate these benefits at a 7% discount rate. Had we performed this calculation, 

we estimate that PM2.5-related full attainment co-benefits would be approximately 15% lower. All 

PM2.5 co-benefit estimates are incremental to the 2006 PM NAAQS RIA. 

To estimate total incidence estimates, we applied a simple scaling factor. To estimate PM2.5-related 

incidence associated with the attainment of each ozone alternative, we calculated a separate scaling 

factor as follows: (1) we calculated the ratio of the full attainment PM2.5 valuation estimate 

(calculated using the benefit per ton metrics described below) to the partial attainment to the partial 

attainment PM2.5 valuation estimate; (2) multiply this scaling ratio against each of the PM2.5 partial 

attainment mortality and morbidity endpoints to generate a scaled estimate of mortality and 

morbidity. While there are clearly substantial uncertainties inherent in this technique, it does 

produce useful screening-level estimates of PM2.5-related incidence 
6-33

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number 

of lives saved are based on an assumption of a normal distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. 

D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns 

E

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.

Table 6-5: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the Incidence of Premature Mortality 

Associated with Ozone Exposure in 2020 (Incremental to Current Ozone Standard)D

Eastern U.S. 

Western U.S. 

Excluding California California 

Modeled Partial 

Attainment Full Attainment

Modeled 

Partial 

Attainment

Full 

Attainment GlidepathE

National 2020 

Benefits

Model or 

AssumptionA Reference

Arithmetic MeanB

(95% Credible Intervals)C

NMMAPS Bell et al. 2004 130

(45--220) 

480

(160—790) 

0.23

(0.08--0.37) 

43

(15-72) 8.5 530

 Bell et al. 2005 540

(260--820) 

1,900 

(930—2,900) 

0.86

(0.42—1.3) 

180

(86—270) 34 2,100 

 Levy et al. 2005 780

(540--1,000) 

2,100 

(1,500—2,800)

31

(22--41)

190

(130—250)

32 2,400 Meta-Analysis 

 Ito et al. 2005 590

(360--820) 

2,100 

(1,300—2,900)

1

(0.6--1.4) 

190

(120—270)

37 2,300 

Assumption that association 

is not causal 

0 0 0 0 0 0 
6-34

A 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. 

B All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns 

C This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.

Table 6-6: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the Incidence of Morbidity Associated 

with Ozone Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parentheses)B

Eastern U.S. 

Western U.S. Excluding 

California California 

Morbidity Endpoint 

Modeled Partial 

Attainment Full Attainment

Modeled Partial 

Attainment Full Attainment

Glidepath 

AttainmentC

National 2020 

Benefits

Hospital Admissions 

(ages 0-1) 

960

(410--1,500) 

2,700 

(1,200—4,300) 

53

(23--83) 

330

(150—520) 

48 3,100 

Hospital Admissions 

(ages 65-99) 

1,100 

(52—2,800) 

3,900 

 (180—9,800) 

3.8 

(0.17--9.4) 

320

(16—790) 

57 4,300 

Emergency Department 

Visits, Asthma-RelatedA

830

(-230--2,500) 

2,500 

(-680—7,700) 

21

(-5.8--66) 

130

(-35—400) 

19 2,600 

School Absences 410,000 

(100,000--1,000,000) 

1,200,000 

(290,000—3,000,000) 

20,000 

(4,900--53,000) 

120,000 

(30,000—310,000) 

19,000 1,300,000 

Minor Restricted Activity 

Days

1,100,000 

(460,000--1,800,000) 

3,200,000 

(1,300,000—5,000,000) 

49,000 

(20,000--78,000) 

310,000 

(130,000—490,000) 

50,000 3,500,000 
6-35

Table 6-7: Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual Reductions in 

the Incidence of Premature Mortality Associated with Ozone Exposure 

(Incremental to Current Ozone Standard)E

Model or 

AssumptionA Reference California GlidepathB

California 

Incremental Post2020 BenefitsC California TotalD

NMMAPS Bell et al. 2004 8.5 95 100

 Bell et al. 2005 34 390 420

Meta-Analysis Levy et al. 2005 32 420 450

 Ito et al. 2005 37 420 450

Assumption that association 

is not causal 

0 0 0

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 Two areas in California have high levels of ozone and are not planning to meet the current standard until after 2020. The estimates in the table reflect a 

progress point in 2020 or "glidepath target" for the two California areas.

C

 Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment. 

D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

E

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-36

Table 6-8: Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure D

Morbidity Endpoint 

California 

GlidepathA

California Incremental 

Post-2020 BenefitsB California TotalC

Hospital Admissions 

(ages 0-1) 48 830 880 

Hospital Admissions 

(ages 65-99) 57 620 670 

Emergency Department Visits, 

Asthma-RelatedA 19 290 310 

School Absences 19,000 320,000 340,000 

Minor Restricted Activity Days 50,000 780,000 830,000 

A Two areas in California have high levels of ozone and are not planning to meet the current standard until after 2020. The estimates in the table 

reflect a progress point in 2020 or "glidepath target" for the two California areas. 

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

D

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns 
6-37

Table 6-9: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of PM Premature Mortality associate with PM co-benefitC

California 

National + 2020 

California Glidepath 

Benefits Glidepath

Incremental 

Post-2020

Benefits Total 

Mortality Impact Functions Derived from Epidemiology Literature

ACS StudyA

1,800 33 160 190 

Harvard Six-City StudyB

4,000 75 360 430 

Woodruff et al 1997 (infant mortality) 4 0.1 0.34 0.41 

Mortality Impact Functions Derived from Expert Elicitation

Expert A 5,500 100 490 590 

Expert B 4,200 78 370 450 

Expert C 4,100 77 370 450 

Expert D 2,900 55 260 320 

Expert E 6,800 130 610 740 

Expert F 3,800 71 340 410 

Expert G 2,400 45 220 260 

Expert H 3,100 58 280 330 

Expert I 4,100 77 370 440 

Expert J 3,300 62 300 360 

Expert K 660 12 59 72 

Expert L 3,000 57 270 330 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which 

has previously been reported as the primary estimate in recent RIAs 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has 

previously been a sensitivity estimate.

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.
6-38

Table 6-10: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with PM Co-benefitA

California 

National + 2020 

California Glidepath 

Benefits Glidepath

Incremental 

Post-2020 

Benefits Total 

Morbidity Impact Functions Derived from Epidemiology Literature

Chronic Bronchitis (age >25 and over) 1,300 25 120 150 

Nonfatal myocardial infarction (age >17) 4,000 74 350 430 

Hospital admissions--respiratory (all ages) 460 9 41 50 

Hospital admissions-- cardiovascular 

(age >17) 930 17 83 100 

Emergency room visits for asthma 

(age <19) 2,000 35 180 210 

Acute bronchitis (age 8-12) 3,500 65 310 380 

Lower respiratory symptoms (age 7-14) 29,000 550 2,600 3,200 

Upper respiratory symptoms (asthmatic 

children age 9-18) 22,000 400 1,900 2,300 

Asthma exacerbation (asthmatic children 

age 6--18) 27,000 500 2,400 2,900 

Work loss days (age 18-65) 190,000 3,500 17,000 20,000 

Minor restricted activity days (age 18-65) 1,100,000 21,000 100,000 120,000 

 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates 

incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were derived through a scaling technique described above.
6-39

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number 

of lives saved are based on an assumption of a normal distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. 

D This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.

E All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns

Table 6-11: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in the Incidence of Premature Mortality 

Associated with Ozone Exposure (Incremental to Current Ozone Standard)E

Eastern U.S. 

Western U.S. Excluding 

California California 

Modeled 

Partial 

Attainment 

Full 

Attainment 

Modeled 

Partial 

Attainment Full Attainment 

Glidepath 

AttainmentD

2020 National 

Attainment 

Model or AssumptionA Reference 

Arithmetic MeanB

(95% Credible Intervals)C

NMMAPS Bell et al. 2004 130

(45--220) 

260

(88--440) 

0.23 

(0.08--0.37) 

11

(3.8--19) 

5.5 

(1.8--9.1) 

280

(93—470) 

Bell et al. 2005 540

(260--820) 

1,100 

(510—1,600) 

0.86 

(0.42--1.3) 

47

(23--71) 

22

(11--34) 

1,100 

(540—1,700) 

Levy et al. 2005 780

(540--1,000) 

1,300 

(900—1,700) 

31

(22--41) 

73

(50--95) 

21

(14--27) 

1,400 

(960—1,800) Meta-Analysis 

Ito et al. 2005 590

(360--820) 

1,200 

(700--1,600) 

1

(0.6--1.4) 

50

(30--70) 

24

(15--34) 

1,200 

(740—1,700) 

Assumption that 

association is not causal 

0 0 0 0 0 0 
6-40

Table 6-12: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in the Incidence of Morbidity Associated with Ozone 

Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parentheses)C

Eastern U.S. 

Western U.S. Excluding 

California California 

Morbidity 

Endpoint 

Modeled Partial 

Attainment Full Attainment 

Modeled Partial 

Attainment Full Attainment Glidepath AttainmentB

2020 National 

Benefits

Hospital Admissions 

(ages 0-1) 

960

(410--1,500) 

1,700 

(720--2,600) 

53

(23--83) 

130

(55--200) 

33

(14--51) 

1,800 

(790—2,900) 

Hospital Admissions 

(ages 65-99) 

1,100 

(52—2,800) 

2,100 

(100--5,400) 

3.8 

(0.17--9.4) 

86

(4.2--210) 

37

(1.8--92) 

2,300 

(110—5,700) 

Emergency

Department Visits, 

Asthma-RelatedA

830

(-230--2,500) 

1,500 

(-400--4,300) 

21

(-5.8--66) 

50

(-13--150) 

13

(-3.5--37) 

1,500 

(-420—4,500) 

School Absences 410,000 

(100,000--1,000,000) 

720,000 

(170,000--1,800,000) 

20,000 

(4,900--53,000) 

47,000 

(11,000--120,000) 

13,000 

(3,100--33,000) 

780,000 

(190,000—1,900,000) 

Minor Restricted 

Activity Days 

1,100,000 

(460,000--1,800,000) 

1,900,000 

(790,000--3,000,000) 

49,000 

(20,000--78,000) 

120,000 

(49,000--190,000) 

34,000 

(14,000--53,000) 

2,100,000 

(850,000—3,300,000) 

A 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. 

B

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.

C All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns.
6-41

Table 6-13: Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual Reductions in 

the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current 

Ozone Standard)E

Model or 

AssumptionA Reference California GlidepathB

California 

Incremental Post2020 BenefitsC California TotalD

NMMAPS Bell et al. 2004 5.5 

(1.8--9.1) 56 62

 Bell et al. 2005 22

(11--34) 230 250

 Levy et al. 2005 21

(14--27) Meta-Analysis 250 280

 Ito et al. 2005 24

(15--34) 250 270

Assumption that association 

is not causal 

0 0 0

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 Two areas in California have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress 

point in 2020 or "glidepath target" for the two California areas.

C

 Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment. 

D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

E

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns 
6-42

Table 6-14: Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to 

Current Ozone Standard, 95% Confidence Intervals in Parentheses)D

Morbidity Endpoint 

California 

GlidepathA

California Incremental 

Post-2020 BenefitsB California TotalC

Hospital Admissions 

(ages 0-1) 

33

(14--51) 520 560 

Hospital Admissions 

(ages 65-99) 

37

(1.8--92) 370 400 

Emergency Department Visits, 

Asthma-RelatedA

13

(-3.5--37) 180 190 

School Absences 13,000

(3,100--33,000) 200,000 210,000 

Minor Restricted Activity Days 34,000

(14,000--53,000) 480,000 520,000 

A Two areas in California have high levels of ozone and not planning to meet the current standard until after 2020. The estimates in the table reflect a progress 

point in 2020 or "glidepath target" for the two California areas.

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

D

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns
6-43

Table 6-15: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of PM Premature Mortality associate with PM co-benefitC

California 

National + 2020 

California Glidepath 

Benefits Glidepath

Incremental 

Post-2020

Benefits Total 

Mortality Impact Functions Derived from Epidemiology Literature

ACS StudyA

1,000 13 120 130 

Harvard Six-City StudyB

2,300 30 270 300 

Woodruff et al 1997 (infant mortality) 2 0 0.3 0.3 

Mortality Impact Functions Derived from Expert Elicitation

Expert A 3,200 41 360 410

Expert B 2,400 31 280 310

Expert C 2,400 31 280 310

Expert D 1,700 22 190 220

Expert E 4,000 51 450 500

Expert F 2,200 28 250 280

Expert G 1,400 18 160 180

Expert H 1,800 23 200 230

Expert I 2,400 31 270 300

Expert J 1,900 25 220 250

Expert K 390 5 44 49

Expert L 1,800 23 200 220

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.
6-44

Table 6-16: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with PM Co-benefitA

California 

National + 2020 

California Glidepath 

Benefits Glidepath

Incremental 

Post-2020 

Benefits Total 

Morbidity Impact Functions Derived from Epidemiology Literature

Chronic Bronchitis (age >25 and over) 780 10 89 99 

Nonfatal myocardial infarction (age >17) 2,300 30 260 290 

Hospital admissions--respiratory (all ages) 270 3 31 34 

Hospital admissions-- cardiovascular 

(age >17) 540 7 62 69 

Emergency room visits for asthma 

(age <19) 1,200 14 130 150 

Acute bronchitis (age 8-12) 2,000 26 230 260 

Lower respiratory symptoms (age 7-14) 17,000 220 2,000 2,200 

Upper respiratory symptoms (asthmatic 

children age 9-18) 13,000 160 1,400 1,600 

Asthma exacerbation (asthmatic children 

age 6--18) 16,000 200 1,800 2,000 

Work loss days (age 18-65) 110,000 1,400 12,000 14,000 

Minor restricted activity days (age 18-65) 650,000 8,300 74,000 82,000 

 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates 

incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were derived through a scaling technique described above.
6-45

Table 6-17: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in the Incidence of Premature 

Mortality Ozone Exposures (Incremental to Current Ozone Standard)D

California 

Eastern U.S. 

Western U.S. 

Excluding 

California Glidepath AttainmentE

2020 National 

Benefits

Model or AssumptionA Reference 

Arithmetic MeanB

(95% Credible Intervals)C

NMMAPS Bell et al. 2004 190 8.9 0 200 

 Bell et al. 2005 840 40 0 880 

Meta-Analysis Levy et al. 2005 1,100 65 0 1,100 

 Ito et al. 2005 920 43 0 960 

Assumption that association 

is not causal 0 0 0

0

0

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number 

of lives saved are based on an assumption of a normal distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible 

intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that precluded us from 

generating such estimates. 

D All estimates rounded to two significant figures. As such, totals will not sum across columns 

E This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-46

A

 Confidence intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that precluded 

us from generating such estimates. 

B All estimates rounded to two significant figures. As such, totals will not sum across columns 

C

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.

Table 6-18: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in the Incidence of Morbidity Associated 

with Ozone Exposure (Incremental to Current Ozone Standard)A,B

California 

Morbidity Endpoint Eastern U.S. 

Western U.S. 

Excluding 

California Glidepath AttainmentC

2020 National 

Benefits

Hospital Admissions (ages 0-1) 1,300 110 0 1,400 

Hospital Admissions (ages 65-99) 1,700 76 0 1,800 

Emergency Department Visits, Asthma-Related 1,200 44 0 1,200 

School Absences 570,000 42,000 0 610,000 

Minor Restricted Activity Days 1,500,000 110,000 0 1,600,000 
6-47

Table 6-19: Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual Reductions in 

the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current 

Ozone Standard)E

Model or 

AssumptionA Reference California GlidepathB

California 

Incremental Post2020 BenefitsC California TotalD

NMMAPS Bell et al. 2004 0 35 35

 Bell et al. 2005 0 140 140

Meta-Analysis Levy et al. 2005 0 150 150

 Ito et al. 2005 0 160 160

Assumption that association 

is not causal 

0 0 0 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 Two areas in California have high levels of ozone and not planning to meet the current standard until after 2020. The estimates in the table reflect a progress 

point in 2020 or "glidepath target" for the two California areas. 

C

 Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment. 

D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

E

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns 
6-48

Table 6-20: Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to 

Current Ozone Standard, 95% Confidence Intervals in Parentheses)D

Morbidity Endpoint 

California 

GlidepathA

California Incremental 

Post-2020 BenefitsB California TotalC

Hospital Admissions 

(ages 0-1) 0 320 320

Hospital Admissions 

(ages 65-99) 0 230 230

Emergency Department Visits, 

Asthma-RelatedA 0 110 110

School Absences 0 120,000 120,000

Minor Restricted Activity Days 0 290,000 290,000

A Two areas in California have high levels of ozone and are not planning to meet the current standard until after 2020. The estimates in the table reflect a 

progress point in 2020 or "glidepath target" for the two California areas.

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

D

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns 
6-49

Table 6-21: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of PM Premature Mortality associate with PM co-benefitC

California 

National + 2020 

California Glidepath 

Benefits Glidepath

Incremental 

Post-2020

Benefits Total 

Mortality Impact Functions Derived from Epidemiology Literature

ACS StudyA 620 0 70 70

Harvard Six-City StudyB 1,400 0 160 160

Woodruff et al 1997 (infant mortality) 1 0 0.2 0.2

Mortality Impact Functions Derived from Expert Elicitation

Expert A 1,900 0 220 220 

Expert B 1,500 0 170 170 

Expert C 1,400 0 160 160 

Expert D 1,000 0 120 120 

Expert E 2,400 0 270 270 

Expert F 1,200 0 150 150 

Expert G 840 0 96 96 

Expert H 1,100 0 120 120 

Expert I 1,400 0 160 160 

Expert J 1,200 0 130 130 

Expert K 230 0 26 26 

Expert L 1,100 0 120 120 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has 

previously been reported as the primary estimate in recent RIAs 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has 

previously been a sensitivity estimate.

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.
6-50

Table 6-22: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with PM Co-benefitA

California 

National + 2020 

California Glidepath 

Benefits Glidepath

Incremental 

Post-2020 

Benefits Total 

Morbidity Impact Functions Derived from Epidemiology Literature

Chronic Bronchitis (age >25 and over) 470 0 53 53 

Nonfatal myocardial infarction (age >17) 1,400 0 160 160 

Hospital admissions--respiratory (all ages) 160 0 18 18 

Hospital admissions-- cardiovascular 

(age >17) 320 0 37 37 

Emergency room visits for asthma 

(age <19) 690 0 78 78 

Acute bronchitis (age 8-12) 1,200 0 140 140 

Lower respiratory symptoms (age 7-14) 10,000 0 1,200 1,200 

Upper respiratory symptoms (asthmatic 

children age 9-18) 7,500 0 850 850 

Asthma exacerbation (asthmatic children 

age 6--18) 9,400 0 1,100 1,100 

Work loss days (age 18-65) 65,000 0

7,400 7,400

Minor restricted activity days (age 18-65) 390,000 0 44,000 44,000 

 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates 

incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were derived through a scaling technique described above.
6-51

Table 6-23: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in the Incidence of Premature 

Mortality Ozone Exposures (Incremental to Current Ozone Standard)D

California 

Eastern U.S. 

Western U.S. 

Excluding 

California Glidepath AttainmentE

2020 National 

Benefits

Model or AssumptionA Reference 

Arithmetic MeanB

(95% Credible Intervals)C

NMMAPS Bell et al. 2004 19

(7.6—31) 0 0 19

(7.6—31) 

 Bell et al. 2005 78

(41—120) 0 0 78

(41—120) 

 Levy et al. 2005 78

(56—100) 0 0 78(56—100) 

Meta-Analysis 

 Ito et al. 2005 85

(55—120) 0 0 85

(55—120) 

Assumption that association 

is not causal 0 0 0 0 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number 

of lives saved are based on an assumption of a normal distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. Credible 

intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that precluded us from 

generating such estimates. 

D All estimates rounded to two significant figures. As such, totals will not sum across columns 

E This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-52

A

 Confidence intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that precluded 

us from generating such estimates. 

B All estimates rounded to two significant figures. As such, totals will not sum across columns 

C

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.

Table 6-24: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in the Incidence of Morbidity Associated 

with Ozone Exposure (Incremental to Current Ozone Standard)B

California 

Morbidity Endpoint Eastern U.S. 

Western U.S. 

Excluding 

California Glidepath Attainment A ,C

2020 National 

Benefits

Hospital Admissions (ages 0-1) 120

(56—180) 0 0 120

(56—180) 

Hospital Admissions (ages 65-99) 160

(7.4—310) 0 0 160

(7.4—310) 

Emergency Department Visits, Asthma-Related 94

(-5.7—250) 0 0 94

(-5.7—250) 

School Absences 50,000 

(15,000—76,000) 0 0 50,000 

(15,000—76,000) 

Minor Restricted Activity Days 130,000 

(58,000—190,000) 0 0 130,000 

(58,000—190,000) 
6-53

Table 6-25: Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual Reductions in 

the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental to Current 

Ozone Standard)E

Model or 

AssumptionA Reference California GlidepathB

California 

Incremental Post2020 BenefitsC California TotalD

NMMAPS Bell et al. 2004 0 8.4 8.4 

 Bell et al. 2005 0 34 34 

Meta-Analysis Levy et al. 2005 0 38 38 

 Ito et al. 2005 0 37 37 

Assumption that association 

is not causal 

0 0 0 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 Two areas of California that have high levels of ozone are not planning to meet the current standard until after 2020. The estimates in the table reflect a 

progress point in 2020 or "glidepath target" for the two California areas. 

C

 Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment. 

D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

E

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns 
6-54

Table 6-26: Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to 

Current Ozone Standard, 95% Confidence Intervals in Parentheses)D

Morbidity Endpoint 

California 

GlidepathA

California Incremental 

Post-2020 BenefitsB California TotalC

Hospital Admissions 

(ages 0-1) 0 80 80 

Hospital Admissions 

(ages 65-99) 0 55 55 

Emergency Department Visits, 

Asthma-RelatedA 0 27 27 

School Absences 0 30,000 30,000 

Minor Restricted Activity Days 0 73,000 73,000 

A Two areas have high levels of ozone and are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in 

2020 or "glidepath target" for the two California areas.

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

D

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns 
6-55

Table 6-27: Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of PM Premature Mortality associate with PM co-benefitC

California 

National + 2020 

California Glidepath 

Benefits Glidepath

Incremental 

Post-2020

Benefits Total 

Mortality Impact Functions Derived from Epidemiology Literature

ACS StudyA

480 0 22 22 

Harvard Six-City StudyB

1,100 0 50 50 

Woodruff et al 1997 (infant mortality) 1

0 0.05 0.05 

Mortality Impact Functions Derived from Expert Elicitation

Expert A 1,500 0 68 68 

Expert B 1,200 0 52 52 

Expert C 1,100 0 51 51 

Expert D 800 0 36 36 

Expert E 1,900 0 84 84 

Expert F 1,000 0 47 47 

Expert G 660 0 30 30 

Expert H 840 0 38 38 

Expert I 1,100 0 51 51 

Expert J 910 0 41 41 

Expert K 180 0 8.2 8.2 

Expert L 830 0 37 37 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has 

previously been reported as the primary estimate in recent RIAs 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has 

previously been a sensitivity estimate.

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.
6-56

Table 6-28: Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with PM Co-benefit (95th

percentile confidence intervals provided in parentheses)A

California 

National + 2020 

California Glidepath 

Benefits Glidepath

Incremental 

Post-2020 

Benefits Total 

Morbidity Impact Functions Derived from Epidemiology Literature

Chronic Bronchitis (age >25 and over) 370 0 17 17 

Nonfatal myocardial infarction (age >17) 1,100 0 49 49 

Hospital admissions--respiratory (all ages) 130 0 5.7 5.7 

Hospital admissions-- cardiovascular 

(age >17) 250 0 12 12 

Emergency room visits for asthma 

(age <19) 540 0 24 24 

Acute bronchitis (age 8-12) 950 0 43 43 

Lower respiratory symptoms (age 7-14) 8,100 0 360 360 

Upper respiratory symptoms (asthmatic 

children age 9-18) 5,900 0 270 270 

Asthma exacerbation (asthmatic children 

age 6--18) 7,300 0 330 330 

Work loss days (age 18-65) 51,000 0 2,300 2,300 

Minor restricted activity days (age 18-65) 310,000 0 14,000 14,000 

 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates 

incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were derived through a scaling technique described above. 
6-57

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number 

of lives saved are based on an assumption of a normal distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. 

D All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns 

E This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.

Table 6-29: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Valuation of Reductions in the Incidence of 

Premature Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard, Millions of 1999$)D

Eastern U.S. 

Western U.S. Excluding 

California California 

Modeled Partial 

Attainment Full Attainment

Modeled 

Partial 

Attainment Full Attainment

Glidepath 

AttainmentE

2020 National 

Benefits

Model or 

AssumptionA Reference

Arithmetic MeanB

(95% Credible Intervals)C

NMMAPS Bell et al. 2004 $850

($120—$1,900) 

$3,100 

($430--$6,800) 

$1.4 

($0.2--$3.2) 

$280

($39--$620) $54 $3,400 

 Bell et al. 2005 $3,500 

($550--$7,500) 

$12,000 

($2,000--$26,000) 

$5.5 

($0.9--$12) 

$1,100 

($180--$2,400) $220 $14,000 

 Levy et al. 2005 $5,000 

($890--$9,600) 

$14,000 

($2,400--$26,000) 

$200

($36--$390) 

$1,200 

($220--$2,400) $200 $15,000 

MetaAnalysis

 Ito et al. 2005 $3,800 

($650--$7,500) 

$13,000 

($2,300--$27,000) 

$6.3 

($1.1--$13) 

$1,200 

 ($210--$2,400) $240 $15,000 

Assumption that association 

is not causal 

0 0 0 0 0 0 
6-58

A All estimates rounded to two significant figures. As such, totals will not sum across columns 

B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.

Table 6-30: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the Incidence of Morbidity Associated 

with Ozone Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parentheses, 

Millions of 1999$)A

Eastern U.S. 

Western U.S. Excluding 

California California 

Morbidity Endpoint 

Modeled Partial 

Attainment

Full 

Attainment

Modeled Partial 

Attainment Full Attainment Glidepath AttainmentB

2020 National 

Benefits

Hospital Admissions 

(ages 0-1) 

$7.1 

($3.1--$11) 

$20

($8.8--$32) 

$0.39 

($0.17--$0.62) 

$2.5 

($1.1--$3.9) $0.4 $23 

Hospital Admissions 

(ages 65-99) 

$19

($0.9--$49) 

$68

($3.2--$170) 

$0.67 

($0.003— 

$0. 2) 

$5.6 

($0.28--$14) $1 $75 

Emergency Department 

Visits, Asthma-Related 

$0.23 

($-0.06--$0.67) 

$0.7 

($-0.2--$2) --

$0.04 

($-0.009--$0.1) -- $0.7 

School Absences $30

($7.2-$72) 

$87

($21--$210) 

$1.5 

($0.35--$3.8) 

$8.9 

($2.1--$22) $1.4 $97 

Worker Productivity $15 $38 $0.38 $3.9 $2.9 $45 

Minor Restricted Activity 

Days

$27

($1.2--$63) 

$79

($3.4--$180) 

$1.2 

($0.05--$2.8) 

$7.6 

($0.3--$18) $1.3 $87 
6-59

Table 6-31: Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental 

to Current Ozone Standard)E

Model or 

AssumptionA Reference California GlidepathB

California 

Incremental Post2020 BenefitsC California TotalD

NMMAPS Bell et al. 2004 $54 $610 $660 

 Bell et al. 2005 $220 $2,500 $2,700 

Meta-Analysis Levy et al. 2005 $200 $2,600 $2,900 

 Ito et al. 2005 $240 $2,700 $2,900 

Assumption that association 

is not causal 0 0 0 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas. 

C

 Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment. 

D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

E

 All estimates rounded to two significant figures. As such, totals will not sum across columns 
6-60

Table 6-32: Illustrative Strategy to Attain 0.065 ppm in California: Estimated Annual 

Valuation of Reductions in the Incidence of Morbidity Associated with Ozone Exposure 

(Incremental to Current Ozone Standard)D

Morbidity Endpoint 

California 

GlidepathA

California Incremental 

Post-2020 BenefitsB California TotalC

Hospital Admissions 

(ages 0-1) $0.4 $6.2 $6.6 

Hospital Admissions 

(ages 65-99) $1 $11 $12 

Emergency Department Visits, 

Asthma-RelatedA -- $0.8 $0.9 

School Absences $1.4 $23 $25 

Worker Productivity $2.9 $26 $29 

Minor Restricted Activity Days $1.3 $19 $21 

A This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas. 

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

D

 All estimates rounded to two significant figures. As such, totals will not sum across columns 
6-61

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the annual number 

of lives saved are based on an assumption of a normal distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist statistics. 

D This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas. 

E All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns

Table 6-33: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of Reductions in the Incidence of Premature 

Mortality Associated with Ozone Exposure (Incremental to Current Ozone Standard, Millions of 1999$)E

Eastern U.S. 

Western U.S. 

Excluding California California 

Modeled Partial 

Attainment Full Attainment 

Modeled 

Partial 

Attainment 

Full 

Attainment Glidepath AttainmentD

2020 National 

Benefits

Model or 

AssumptionA Reference 

Arithmetic MeanB

(95% Credible Intervals)C

NMMAPS Bell et al. 2004 $850

($120—$1,900) 

$1,700 

($240--$3,800) 

$1.4 

($0.2--$3.2) 

$73

($10--$160) 

$35

($5--$78) 

$1,800 

($250--$4,000) 

Bell et al. 2005 $3,500 

($550--$7,500) 

$6,800 

($1,100--$14,000) 

$5.5 

($0.9--$12) 

$300

($48--$630)

$140

($23--$300) 

$7,200 

($1,200--$15,000) 

Levy et al. 2005 $5,000 

($890--$9,600) 

$8,300 

($1,500--$16,000) 

$200

($36--$390) 

$470

($83--$900)

$130

($24--$260) 

$8,900 

($1,600--$17,000) Meta-Analysis 

Ito et al. 2005 $3,800 

($1,300--$9,300) 

$7,400 

($1,300--$15,000) 

$6.3 

($1.1--$13) 

$320

($56--$640)

$150

($27--$310) 

$7,900 

($1,400--$16,000) 

Assumption that 

association is not 

causal

0 0 0 0 0 0 
6-62

A

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.

B All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns 

Table 6-34: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of Reductions in the Incidence of Morbidity 

Associated with Ozone Exposure (Incremental to Current Ozone Standard, 95% Confidence Intervals in Parentheses, Millions 

of 1999$)B

Eastern U.S. 

Western U.S. Excluding 

California California 

Morbidity Endpoint 

Modeled Partial 

Attainment 

Full 

Attainment 

Modeled Partial 

Attainment 

Full 

Attainment Glidepath AttainmentA

2020 National 

Benefits

Hospital Admissions 

(ages 0-1) 

$7.1 

($3.1--$11) 

$12

($5.3--$19) 

$0.39 

($0.17--$0.62) 

$1

($0.41--$1.5) 

$0.24 

($0.11--$0.38) 

$14

($5.9--$21) 

Hospital Admissions 

(ages 65-99) 

$19

($0.9--$49) 

$38

($1.8--$95) 

$0.67 

($0.003— 

$0. 2) 

$1.5 

($0.074--

$3.8) 

$0.65 

($0.32--$1.6) 

$40

($1.9--$100) 

Emergency Department 

Visits, Asthma-Related 

$0.23 

($-0.06--$0.67) 

$0.4 

($-0.1--$1.2) -- -- -- 

$0.5 

(-$0.1--$1.2) 

School Absences $30

($7.2-$72) 

$52

($13--$130) 

$1.5 

($0.35--$3.8) 

$3.4 

($0.8--$8.4) 

$0.93 

($0.2--$2.4) 

$56

($14--$140) 

Worker Productivity $15 $22 $0.38 $1.4 $1.9 $26 

Minor Restricted Activity 

Days

$27

($1.2--$63) 

$47

($2--$110) 

$1.2 

($0.05--$2.8) 

$2.9 

($0.13--$6.8) 

$0.83 

($0.036--$1.9) 

$51

($2.2--$120) 
6-63

Table 6-35: Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental 

to Current Ozone Standard)E

Model or 

AssumptionA Reference California GlidepathB

California 

Incremental Post2020 BenefitsC California TotalD

NMMAPS Bell et al. 2004 $35

($5--$78) $360 $390 

 Bell et al. 2005 $140

($23--$300) $1,500 $1,600 

 Levy et al. 2005 $130

($24--$260) Meta-Analysis $1,600 $1,800 

 Ito et al. 2005 $150

($27--$310) $1,600 $1,700 

Assumption that association 

is not causal 0 0 0 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas. 

C

 Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment. 

D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

E

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns 
6-64

Table 6-36: Illustrative Strategy to Attain 0.070 ppm in California: Estimated Annual Valuation 

of Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to 

Current Ozone Standard, 95% Confidence Intervals in Parentheses)D

Morbidity Endpoint 

California 

GlidepathA

California Incremental 

Post-2020 BenefitsB California TotalC

Hospital Admissions 

(ages 0-1) 

$0.24 

($0.11--$0.38) $3.9 $4.2 

Hospital Admissions 

(ages 65-99) 

$0.65 

($0.32--$1.6) $6.5 $7.2 

Emergency Department Visits, AsthmaRelatedA -- $0.05 $0.05 

School Absences $0.93 

($0.2--$2.4) $15 $15 

Worker Productivity $1.9 $16 $17 

Minor Restricted Activity Days $0.83 

($0.036--$1.9) $12 $13 

A This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas. 

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

D

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns 
6-65

Table 6-37: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Monetary Value of Reductions in the Incidence of 

Mortality Associated with Exposure to Ozone (Millions of 1999$, Incremental to Current Standard)B

California 

Eastern U.S. 

Western U.S. 

Excluding California Glidepath AttainmentE

2020 National 

Benefits

Model or AssumptionA Reference 

Arithmetic MeanB

(95% Credible Intervals)C

NMMAPS Bell et al. 2004 $1,400 $66 0 $1,400 

 Bell et al. 2005 $5,400 $270 0 $5,700 

Meta-Analysis Levy et al. 2005 $6,700 $430 0 $7,100 

 Ito et al. 2005 $5,900 $290 0 $6,200 

Assumption that association 

is not causal 0 0 0 0 

A

 Confidence intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that precluded 

us from generating such estimates. 

B All estimates rounded to two significant figures. As such, totals will not sum across columns 

C

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-66

Table 6-38: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Monetary Value of Reductions in the Incidence of 

Morbidity Associated with Exposure to Ozone (Millions of 1999$, Incremental to Current Standard)A

California 

Morbidity Endpoint Eastern U.S. 

Western U.S. 

Excluding 

California Glidepath AttainmentB

2020 National 

Benefits

Hospital Admissions (ages 0-1) $9.9 $0.9 0 $11 

Hospital Admissions (ages 65-99) $31 $1.4 0 $32 

Emergency Department Visits, Asthma-Related $0.3 $0.013 0 $0.3 

School Absences $41 $3 0 $44 

Worker Productivity $20 $1.3 0 $21 

Minor Restricted Activity Days $38 $2.6 0 $40 

A All estimates rounded to two significant figures. As such, totals will not sum across columns 

B

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-67

Table 6-39: Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental 

to Current Ozone Standard)E

Model or 

AssumptionA Reference California GlidepathB

California 

Incremental Post2020 BenefitsC California TotalD

NMMAPS Bell et al. 2004 0 $220 $220 

 Bell et al. 2005 0 $910 $910 

Meta-Analysis Levy et al. 2005 0 $990 $990 

 Ito et al. 2005 0 $990 $990 

Assumption that association 

is not causal 0 0 0 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.

C

 Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the

baseline, incremental to 2020 glidepath attainment. 

D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

E

 All estimates rounded to two significant figures. As such, totals will not sum across columns 
6-68

Table 6-40: Illustrative Strategy to Attain 0.075 ppm in California: Estimated Annual 

Valuation of Reductions in the Incidence of Morbidity Associated with Ozone Exposure 

(Incremental to Current Ozone Standard)D

Morbidity Endpoint 

California 

GlidepathA

California Incremental 

Post-2020 BenefitsB California TotalC

Hospital Admissions 

(ages 0-1) 0 $2.4 $2.4 

Hospital Admissions 

(ages 65-99) 0 $4 $4 

Emergency Department Visits, 

Asthma-RelatedA 0 $0.03 $0.03 

School Absences 0 $8.7 $8.7 

Worker Productivity 0 $9 $9 

Minor Restricted Activity Days 0 $7.3 $7.3 

A This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas. 

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

D

 All estimates rounded to two significant figures. As such, totals will not sum across columns 
6-69

Table 6-41: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Monetary Value of Reductions in the Incidence of 

Premature Mortality Associated with Exposure to Ozone (Millions of 1999$, Incremental to Current Standard)B

California 

Eastern U.S. 

Western U.S. 

Excluding California Glidepath AttainmentE

2020 National 

Benefits

Model or AssumptionA Reference 

Arithmetic MeanB

(95% Credible Intervals)C

NMMAPS Bell et al. 2004 $120

($18--$280) 0 0 $120($18--$280) 

 Bell et al. 2005 $500

($82--$1,100) 0 0 $500

($82--$1,100) 

 Levy et al. 2005 $500

($89--$960) 0 0 $500

($89--$960) Meta-Analysis 

 Ito et al. 2005 $550

($94--$1,100) 0 0 $550($94--$1,100) 

Assumption that association 

is not causal 0 0 0 0 

A

 Confidence intervals not provided due to the fact that the incidence estimates were derived through an interpolation technique (see Appendix 6) that precluded 

us from generating such estimates. 

B All estimates rounded to two significant figures. As such, totals will not sum across columns 

C

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.
6-70

Table 6-42: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Monetary Value of Reductions in the Incidence of 

Morbidity Associated with Exposure to Ozone (Millions of 1999$, Incremental to Current Standard)A

California 

Morbidity Endpoint Eastern U.S. 

Western U.S. 

Excluding 

California Glidepath AttainmentB

2020 National 

Benefits

Hospital Admissions (ages 0-1) $0.9 

($0.5--$1.3) 0 0 $0.9 ($0.5--$1.3) 

Hospital Admissions (ages 65-99) $2.8 

($0.4--$5.1) 0 0 $2.8 

($0.4--$5.1) 

Emergency Department Visits, Asthma-Related $0.03 

(0--$0.07) 0 0 $0.03 

(0--$0.07) 

School Absences $3.6 

($1.3--$5.3) 0 0 $3.6 

($1.3--$5.3) 

Worker Productivity $1.3 0 0 $1.3 

Minor Restricted Activity Days $3.1 

($0.1--$7.6) 0 0 $3.1 

($0.1--$7.6) 

A All estimates rounded to two significant figures. As such, totals will not sum across columns 

B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels 

of ozone, are not planning to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or 

"glidepath target" for the two California areas.
6-71

Table 6-43: Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure (Incremental 

to Current Ozone Standard)E

Model or 

AssumptionA Reference California GlidepathB

California 

Incremental Post2020 BenefitsC California TotalD

NMMAPS Bell et al. 2004 0 $46 $46 

 Bell et al. 2005 0 $190 $190 

Meta-Analysis Levy et al. 2005 0 $180 $180 

 Ito et al. 2005 0 $200 $200 

Assumption that association 

is not causal 0 0 0 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas.

C

 Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the

baseline, incremental to 2020 glidepath attainment. 

D This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described above. 

E

 All estimates rounded to two significant figures. As such, totals will not sum across columns 
6-72

Table 6-44: Illustrative Strategy to Attain 0.079 ppm in California: Estimated Annual 

Valuation of Reductions in the Incidence of Morbidity Associated with Ozone Exposure 

(Incremental to Current Ozone Standard)D

Morbidity Endpoint 

California 

GlidepathA

California Incremental 

Post-2020 BenefitsB California TotalC

Hospital Admissions 

(ages 0-1) 0 $0.3 $0.3 

Hospital Admissions 

(ages 65-99) 0 $1 $1 

Emergency Department Visits, 

Asthma-RelatedA 0 $0.1 $0.1 

School Absences 0 $1.3 $1.3 

Worker Productivity 0 $0.5 $0.5 

Minor Restricted Activity Days 0 $1.2 $1.2 

A This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table reflect a progress point in 2020 or "glidepath target" for the two California areas. 

B Certain mobile source programs including Tier-2 and Non-Road Diesel are projected to generate NOx emission reductions in California between 2020 and 

2030. The estimates in this column are the benefits of full attainment with the alternate standard post-2020 with mobile source emission reductions in the 

baseline, incremental to 2020 glidepath attainment.

C This column sums the glidepath and incremental post-2020 benefits. The estimates in this column do not include confidence intervals because they were 

derived through a scaling technique described below. 

D

 All estimates rounded to two significant figures. As such, totals will not sum across columns 
6-73

Estimated reductions in ozone mortality incidence provided in Tables 6-5, 6-7, 6-11, 6-13, 6-17, 

6-19, 6-23 and 6-25 represent the number of premature deaths potentially avoided due to 

reductions in ozone exposure in 2020 using warm season functions from the recent ozonemortality NMMAPS analysis of 95 U.S. communities (Bell et al., 2004) and three meta-analyses 

of the available published literature on ozone-mortality effects (Bell et al., 2005; Ito et al., 2005; 

Levy et al., 2005). These same tables also include the possibility that there is not a causal 

association between ozone and mortality, i.e., that the estimate for premature mortality avoided 

could be zero. As noted above, for each standard alternative we break out estimates between 

2020 national glidepath and California post-2020. Model uncertainty, including whether or not 

the relationship is assumed to be causal, is a key source of uncertainty. Although multiple 

estimates are presented in these tables, no attempt was made to quantify the likelihood of a 

causal relationship between short-term ozone exposure and increased mortality or to weigh the 

results of the various models. 

The estimate of central tendency for premature mortality is expressed as the arithmetic mean, 

with the assumption of a normal distribution, and represents the central estimate of the number of 

premature deaths avoided in association with the proposed standard based on each study.

Statistical uncertainty associated with the model estimate for each study is characterized by the 

95% credible interval29 around the mean estimate (i.e., 2.5th and 97.5th percent interval). Of the 

four available studies, the NMMAPS study by Bell et al. (2004) is considered to be the most 

representative for evaluating potential mortality-related benefits associated with the proposed 

standard due to its extensive coverage (examination of 95 large communities across the United 

States over an extended period of time, from 1987 to 2000) and its specific focus on the ozonemortality 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, CASAC30 stated that it seems unlikely that the 

observed associations between short-term ozone concentrations and daily mortality are due 

29 A credible interval is a posterior probability interval used in Bayesian statistics, which is 

similar to a confidence interval used in frequentist statistics. 

30 Clean Air Scientific Advisory Committee's Peer Review of the Agency's 2nd Draft Ozone 

Staff Paper, October 24, 2006. EPA-CASAC-07-001. Available at 

http://www.epa.gov/sab/pdf/casac-07-001.pdf
6-74

solely to ozone itself (i.e., ozone may be serving as a marker for other agents that are 

contributing to the short-term exposure effects on mortality). Even so, CASAC concluded that 

the evidence was strong enough to support a quantitative risk assessment of the relationship 

between short-term exposure to ozone and premature mortality as part of the Staff Paper. EPA 

has asked the National Academy of Sciences31 for their advice on how best to quantify the 

uncertainty about the relationship between ambient ozone exposure and premature mortality 

within the context of quantifying projected benefits of alternative control strategies. 

Using the NMMAPS study that was used as the basis for the risk analysis presented in our Staff 

Paper, we estimate 280 avoided premature deaths annually in 2020 from reducing ozone levels to 

meet a proposed standard of 0.070 ppm, which, when added to the other projected ozone related 

benefits, leads to an estimated total benefit of $1.8 billion/yr. Using three studies that synthesize 

data across a large number of individual studies, we estimate between 1,100 and 1,400 avoided 

premature deaths annually in 2020, leading to total monetized benefits of between $7.2 and $8.9 

billion/yr. Alternatively, if there is no causal relationship between ozone and mortality, avoided 

premature deaths would be zero. For a proposed standard of 0.075 ppm, using the NMMAPS 

ozone mortality study, we estimate 200 premature deaths avoided and total monetized benefits of 

$1.4 billion/yr. Using the three synthesis studies, we estimate premature deaths avoided for the 

less stringent standard to be between 880 and 1,100, with total monetized ozone benefits to be 

between $5.7 and $7.1 billion/yr. For a proposed standard of 0.079 ppm, using the NMMAPS 

ozone mortality study, we estimate 19 premature deaths avoided and total monetized benefits of 

$140 million/yr. Using the three synthesis studies, we estimate premature deaths avoided for the 

less stringent standard to be between 78 and 85, with total monetized ozone benefits to be 

between $510 and $560 million/yr. Because EPA is taking comment on alternatives as low as 

0.065 ppm, we show that a more stringent standard of 0.065 ppm, using the NMMAPS ozone 

mortality study is estimated to result in 530 premature deaths avoided and total monetized 

benefits of $3.4 billion/yr. Using the three synthesis studies, estimated premature deaths avoided 

for the more stringent standard are between 2,100 and 2,400, with total monetized ozone benefits 

between $14 and $15 billion/yr. Including premature mortality in our estimates had the largest 

impact on the overall magnitude of benefits: Premature mortality benefits account for more than 

95 percent of the total benefits we can monetize. We note that these estimates reflect EPA's 

interim approach to characterizing the benefits of reducing premature mortality associated with 

ozone exposure. EPA has requested advice from the NAS on how best to quantify uncertainty 

in the relationship between ozone exposure and premature mortality in the context of quantifying 

benefits associated with alternative ozone control strategies. 

6.5.3 PM2.5 Co-Benefits Resulting from Attainment of 0.070 ppm incremental to 0.08 ppm 

The summary of PM2.5 related co-benefits in the tables above represent the benefits of partially 

attaining 0.070 ppm incremental to a partial attainment of 0.08 ppm. Thus, these estimates 

overstate the benefits of 0.070 ppm partial attainment relative to the actual incremental benefits 

31 National Academy of Sciences (2007) Project Scope. Estimating Mortality Risk Reduction Benefits from

Decreasing Tropospheric Ozone Exposure. Division on Earth and Life Studies, Board on Environmental Studies 

and Toxicology. Available at http://www8.nationalacademies.org/cp/projectview.aspx?key=48768
6-75

of this scenario; this is due to the fact that the benefits estimates in these tables include the 

benefits of NOx reductions that would be required to attain a baseline of 0.08 ppm. Of greater 

analytical value would be an estimate of the PM2.5 co-benefits associated with fully attaining 

0.070 ppm incremental to full attainment of the 0.08 ppm standard. 

To generate such an estimate, we calculated a new PM2.5 baseline that established the PM2.5 air 

quality associated with full attainment of 0.08 ppm. To create such a baseline, EPA utilized 

benefit PM2.5 per-ton estimates. These PM2.5 benefit per-ton estimates provide the total 

monetized human health benefits (the sum of premature mortality and premature morbidity) of 

reducing one ton of PM2.5 from a specified source. EPA has used a similar technique in previous 

Regulatory Impact Analyses.32 These estimates are based on the sum of the valuation of the Pope 

(2002) estimates of mortality (3% discount rate, 1999$) and valuation of the morbidity 

incidence. Readers interested in reviewing the complete methodology for creating the benefit 

per-ton estimates used in this analysis can consult the Technical Support Document 

accompanying this RIA. 

Estimating the PM2.5 benefits that represented the full attainment of both 0.070 ppm incremental 

to full attainment of 0.08 ppm entailed the following four steps:

1. Estimate the number of tons of NOx necessary to attain a baseline of 0.08 ppm. Chapter 3 

described the method used to estimate the extrapolated NOx emissions reductions 

necessary to attain a baseline of 0.08 ppm full attainment. 

2. Calculate the benefits of attaining 0.08 ppm. To estimate the benefits of fully attaining 

0.08 ppm incremental to partial attainment of 0.08 ppm, the relevant benefit per ton is 

simply multiplied by the total number of extrapolated NOx tons abated. 

3. Calculate the benefits of partially attaining 0.070 ppm incremental to full attainment of 

0.08 ppm. 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 PM2.5 benefits of fully attaining 0.070 ppm. Multiplying the estimate of the 

extrapolated NOx tons necessary to attain 0.070 ppm fully (found in chapter 3) produces 

an estimate of the incremental benefits of fully attaining 0.070 ppm incremental to partial 

attainment of 0.070 ppm. By adding this incremental benefit estimate to the benefits 

generated in step 3, we derived a total benefit estimate of attaining 0.070 ppm 

incremental to 0.08 ppm.

The process for estimating the PM2.5 co-benefits of fully attaining 0.065 ppm and 0.075 ppm is 

identical to the steps above, with the following exception; in step four we substituted the number 

of extrapolated tons necessary to attain 0.065 ppm and 0.075 ppm, respectively. Table 5-21 

below provides the inputs to the calculation steps described above. In the example below we 

32 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/chapter10.pdf

[accessed 18 May 2007]. 
6-76

calculate total benefits using the Pope et al. (2002) mortality estimate. However, in subsequent 

tables we present benefits using Laden et al. (2006) as well as the twelve expert functions 

described previously in this document. Note that while our benefit per ton estimates are 

associated with broad source categories (in this case, NOx Electrical Generating Units, Other 

NOx point sources and Mobile NOx sources) the extrapolated tons were not. For this reason we 

simply assumed that the total number of extrapolated NOx tons were evenly distributed between 

these three source types. 

Table 6-45: Estimated PM2.5 Co-Benefits Associated with Full Attainment of 0.070 

ppm incremental to 0.08 ppmA

Calculation Extrapolated NOx Tons 

Benefit per ton 

estimate 

Valuation of PM2.5 

Benefits

(Billions 1999$)B

 

Benefits of attaining 0.08 ppm 

partially and 0.070 ppm partially: --- --- $2.9B 

 

NOx EGU: 45,000 $3,100 

NOx Point: 45,000 $2,800 

Benefits of attaining 0.08 ppm 

from a baseline of 0.08 ppm 

partial attainment: NOx Mobile: 45,000 $4,600 

$0.48B

 

Benefits of attaining 0.070 ppm 

partially, incremental to 

attainment of 0.08 ppm 

--- --- 

 = $2.9B - $0.48B

 

=$2.5 B 

 

NOx EGU: 340,000 $3,100 

NOx Point: 340,000 $2,800 

Benefits of attaining 0.070 ppm in 

2020 incremental to partial 

attainment of 0.070 ppm NOx Mobile: 340,000 $4,600 

$3.5B

Benefits of attaining 0.070 ppm 

incremental to attainment of 0.08 

ppm

 =$2.5B + 3.5B 

 

=$6.0B

A

Numbers have been rounded to two significant figures and therefore summation may not match table estimates. 

PM2.5 benefit estimates do not include confidence intervals because they are derived using benefit per-ton estimates. 

B All estimates derived using the Pope et al. (2002) mortality estimate at a 3% discount rate, in 1999$.

The procedure for calculating the PM2.5 benefits resulting from full attainment of 0.079 ppm, 

0.075 ppm and 0.065 ppm is identical to this example, with the exception of step 4; the PM2.5

benefits of attaining 0.065 ppm, 0.075 ppm and 0.079 ppm incremental to partial attainment of 

0.070 ppm are $7.8B, $1.1B and $0.4B respectively. Thus, the total PM2.5 benefits of attaining 

0.065 ppm, 0.075 ppm and 0.079 ppm are $10.2B, $3.6B and $2.5B respectively. The full 

attainment PM2.5 benefits do not include confidence intervals. Because this full attainment 

estimate was derived by summing the modeled PM2.5 benefits and the benefits derived using the 

benefit per-ton metrics—and these benefit per ton metrics do not include confidence intervals—

the resulting sum of total PM2.5 benefits do not include confidence intervals. 
6-77

6.5.4 Estimate of Full Attainment Benefits 

Tables 6-36 through 6-43 below summarize the estimates of full attainment and 2020 California 

glidepath attainment ozone benefits and PM2.5 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 glidepath ozone benefits and PM2.5 cobenefits. The second table presents California-only glidepath and post-2020 ozone benefits and 

PM2.5 co-benefits. Tables 6-44 through 6-53 summarize the combined ozone and PM2.5 cobenefits. The presentation of combined ozone and PM2.5 co-benefit tables is broken into four 

components for each standard alternative. The first table presents national glidepath benefits. The 

second table presents the California-only glidepath benefits. The third table presents the 

incremental benefits that accrue in California from full attainment of the alternative standard 

after 2020. The last table presents total California benefits—the sum of glidepath benefits and 

post-2020 benefits.
6-78

Table 6-46: Estimate of Total Annual Ozone and PM2.5 Benefits (95% 

Confidence Intervals, Millions of $1999) for the 0.065 ppm Standard 

Alternative: National Glidepath Attainment 

Ozone Mortality and Morbidity Benefits of Attaining 0.065 ppm 

Standard Alternative and 

Model or AssumptionA Ozone Benefits, Arithmetic MeanB

NMMAPS Bell (2004) $3,700 

Bell (2005) $14,000 

Ito (2005) $15,000 MetaAnalysis

Levy (2005) $16,000 

No Causality $330

PM2.5 Mortality and Morbidity Benefits of Attaining 0.065 ppm 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyC $10,000 

Harvard Six-City StudyD $23,000 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $33,000

Expert B $25,000

Expert C $25,000

Expert D $17,000

Expert E $41,000

Expert F $23,000

Expert G $15,000

Expert H $19,000

Expert I $25,000

Expert J $20,000

Expert K $4,000

Expert L $18,000

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist 

statistics. Credible intervals for ozone estimates and confidence intervals for PM2.5 estimates not provided due to the fact that the valuation 

estimates were derived through a scaling technique (see above) that precluded us from generating such estimates. 

C

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported 

in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs 

D

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method 

for incorporating what has previously been a sensitivity estimate. 

E

 All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived

using a 7% discount rate would be approximately 15% lower. 
6-79

Table 6-47: Estimate of Total Annual Ozone and PM2.5 Benefits (95% 

Confidence Intervals, Millions of $1999) for the 0.065 ppm Standard 

Alternative: California Attainment 

Ozone Mortality and Morbidity Benefits of Attaining 0.065 ppm 

Ozone Benefits, Arithmetic MeanB

Standard Alternative and 

Model or AssumptionA Glidepath Incremental Post2020 Benefits Total

NMMAPS Bell (2004) $61 $690 $750

Bell (2005) $230 $2,600 $2,800

Ito (2005) $210 $2,800 $3,000 MetaAnalysis

Levy (2005) $240 $2,700 $3,000

No Causality $6.8 $93 $100 

PM2.5 Mortality and Morbidity Benefits of Attaining 0.065 ppm 

Mortality Impact Functions Derived from Epidemiology Literature 

Glidepath Incremental Post2020 Benefits Total

ACS StudyC $180 $930 $1,100 

Harvard Six-City StudyD $380 $2,000 $2,400 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $570 $3,000 $3,600 

Expert B $430 $2,300 $2,700 

Expert C $430 $2,300 $2,700 

Expert D $300 $1,600 $1,900 

Expert E $710 $3,800 $4,500 

Expert F $390 $2,100 $2,500 

Expert G $250 $1,300 $1,600 

Expert H $320 $1,700 $2,000 

Expert I $420 $2,200 $2,700 

Expert J $340 $1,800 $2,200 

Expert K $68 $360 $430 

Expert L $310 $1,700 $2,000 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist 

statistics. Credible intervals for ozone estimates and confidence intervals for PM2.5 estimates not provided due to the fact that the valuation 

estimates were derived through a scaling technique (see above) that precluded us from generating such estimates. 

C

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported 

in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs 

D

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method 

for incorporating what has previously been a sensitivity estimate. 

E

 All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived

using a 7% discount rate would be approximately 15% lower. 
6-80

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval around the mean estimates of the 

annual number of lives saved are based on an assumption of a normal distribution. Confidence intervals not available for PM2.5 valuation 

estimates due to the fact that they were derived through a scaling technique (see above) that precluded us from generating such estimates. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist 

statistics.

D

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported 

in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs 

E

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method 

for incorporating what has previously been a sensitivity estimate. 

F

 All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived

using a 7% discount rate would be approximately 15% lower. 

Table 6-48: Estimate of Total Annual Ozone and PM2.5 Benefits (95% 

Confidence Intervals, Millions of $1999) for the 0.070 ppm Standard 

Alternative: National Glidepath Attainment 

Ozone Mortality and Morbidity Benefits of Attaining 0.070 ppm 

Standard Alternative and 

Model or AssumptionA

Ozone Benefits, Arithmetic MeanB

(95% Credible Intervals)C

NMMAPS Bell (2004) $2,000 

($300--$4,400) 

Bell (2005) $7,400 

($1,200--$16,000) 

Ito (2005) $8,000 

($1,400--$16,000) 

MetaAnalysis

Levy (2005) $9,100 

($1,600--$18,000) 

No Causality 

$190

($49--$400) 

PM2.5 Mortality and Morbidity Benefits of Attaining 0.070 ppm 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyD

$6,000

Harvard Six-City StudyE

$14,000

Mortality Impact Functions Derived from Expert Elicitation

Expert A $19,000

Expert B $15,000

Expert C $15,000

Expert D $10,000

Expert E $24,000

Expert F $13,000

Expert G $8,500

Expert H $11,000

Expert I $14,000

Expert J $12,000

Expert K $2,300

Expert L $11,000
6-81

Table 6-49: Estimate of Total Annual Ozone and PM2.5 Benefits (95% 

Confidence Intervals, Millions of $1999) for the 0.070 ppm Standard 

Alternative: California Attainment 

Ozone Mortality and Morbidity Benefits of Attaining 0.070 ppm 

Ozone Benefits, Arithmetic MeanB

Standard Alternative and 

Model or AssumptionA Glidepath Incremental Post2020 Benefits Total

NMMAPS Bell (2004) $40

($7.2--$86) $410 $450 

Bell (2005) $150

($25--$310) $1,500 $1,700 

Ito (2005) $160

($29--$320) $1,600 $1,800 MetaAnalysis

Levy (2005) $140

($26--$270) $1,700 $1,800 

No Causality 

$4.5 

($2.3--$8.2) $57 $61 

PM2.5 Mortality and Morbidity Benefits of Attaining 0.070 ppm 

Mortality Impact Functions Derived from Epidemiology Literature 

Glidepath Incremental Post2020 Benefits Total

ACS StudyC

$70 $690 $760 

Harvard Six-City StudyD

$150 $1,500 $1,600 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $230 $2,200 $2,400 

Expert B $170 $1,700 $1,900 

Expert C $170 $1,700 $1,900 

Expert D $120 $1,200 $1,300 

Expert E $280 $2,800 $3,100 

Expert F $160 $1,500 $1,700 

Expert G $100 $980 $1,100 

Expert H $130 $1,200 $1,400 

Expert I $170 $1,700 $1,800 

Expert J $140 $1,300 $1,500 

Expert K $27 $270 $300 

Expert L $120 $1,200 $1,300 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist 

statistics. Credible intervals for ozone estimates and confidence intervals for PM2.5 estimates not provided due to the fact that the valuation 

estimates were derived through a scaling technique (see above) that precluded us from generating such estimates. 

C

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported 

in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs 

D

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method 

for incorporating what has previously been a sensitivity estimate. 

E

 All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived

using a 7% discount rate would be approximately 15% lower. 
6-82

Table 6-50: Estimate of Total Annual Ozone and PM2.5 Benefits (95% 

Confidence Intervals, Millions of $1999) for the 0.075 ppm Standard 

Alternative: National Glidepath Attainment 

Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm 

Standard Alternative and 

Model or AssumptionA Ozone Benefits, Arithmetic MeanB

NMMAPS Bell (2004) $1,600 

Bell (2005) $5,900

Ito (2005) $6,400 MetaAnalysis

Levy (2005) $7,300

No Causality $150 

PM2.5 Mortality and Morbidity Benefits of Attaining 0.075 ppm 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyC $3,600 

Harvard Six-City StudyD $8,600 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $12,000

Expert B $8,800

Expert C $8,700

Expert D $6,100

Expert E $14,000

Expert F $7,900

Expert G $5,100

Expert H $6,500

Expert I $8,600

Expert J $7,000

Expert K $1,400

Expert L $6,300

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean estimates of the annual number of lives saved are based on 

an assumption of a normal distribution. Credible intervals for ozone estimates and confidence intervals for PM2.5 estimates not provided due to 

the fact that the valuation estimates were derived through a scaling technique (see above) that precluded us from generating such estimates.. 

C

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported 

in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs 

D

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method 

for incorporating what has previously been a sensitivity estimate. 

F

 All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived

using a 7% discount rate would be approximately 15% lower. 
6-83

Table 6-51: Estimate of Total Annual Ozone and PM2.5 Benefits (95% 

Confidence Intervals, Millions of $1999) for the 0.075 ppm Standard 

Alternative: California Attainment 

Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm 

Ozone Benefits, Arithmetic MeanB

Standard Alternative and 

Model or AssumptionA Glidepath Incremental Post2020 Benefits Total

NMMAPS Bell (2004) 0 $260 $260 

Bell (2005) 0 $940 $940 

Ito (2005) 0 $1,000 $1,000 MetaAnalysis

Levy (2005) 0 $1,000 $1,000 

No Causality 0 $33 $33 

PM2.5 Mortality and Morbidity Benefits of Attaining 0.075 ppm 

Mortality Impact Functions Derived from Epidemiology Literature 

Glidepath Incremental Post2020 Benefits Total

ACS StudyC 0 $410 $410 

Harvard Six-City StudyD 0 $870 $870 

Mortality Impact Functions Derived from Expert Elicitation

Expert A 0 $1,300 $1,300 

Expert B 0 $1,000 $1,000 

Expert C 0 $990 $990 

Expert D 0 $690 $690 

Expert E 0 $1,600 $1,600 

Expert F 0 $900 $900 

Expert G 0 $580 $580 

Expert H 0 $740 $740 

Expert I 0 $980 $980 

Expert J 0 $790 $790 

Expert K 0 $160 $160 

Expert L 0 $720 $720 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist 

statistics. Credible intervals for ozone estimates and confidence intervals for PM2.5 estimates not provided due to the fact that the valuation 

estimates were derived through a scaling technique (see above) that precluded us from generating such estimates. 

C

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported 

in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs 

D

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method 

for incorporating what has previously been a sensitivity estimate. 

E

 All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived

using a 7% discount rate would be approximately 15% lower. 
6-84

Table 6-52: Estimate of Total Annual Ozone and PM2.5 Benefits (95% 

Confidence Intervals, Millions of $1999) for the 0.079 ppm Standard 

Alternative: National Glidepath Attainment 

Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm 

Standard Alternative and 

Model or AssumptionA Ozone Benefits, Arithmetic MeanB

NMMAPS Bell (2004) 

$140

($22--$300) 

Bell (2005) $510

($86--$1,100) 

Ito (2005) $560

($98--$1,100) 

MetaAnalysis

Levy (2005) $510

($93--$980) 

No Causality 

$12

($3.5--$21)

PM2.5 Mortality and Morbidity Benefits of Attaining 0.075 ppm 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyC $2,800 

Harvard Six-City StudyD $7,000 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $9,100

Expert B $6,900

Expert C $6,800

Expert D $4,800

Expert E $11,000

Expert F $6,200

Expert G $4,000

Expert H $5,100

Expert I $6,800

Expert J $5,500

Expert K $1,100

Expert L $5,000

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean estimates of the annual number of lives saved are based on 

an assumption of a normal distribution. Credible intervals for ozone estimates and confidence intervals for PM2.5 estimates not provided due to 

the fact that the valuation estimates were derived through a scaling technique (see above) that precluded us from generating such estimates.. 

C

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported 

in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs 

D

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method 

for incorporating what has previously been a sensitivity estimate. 

F

 All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived

using a 7% discount rate would be approximately 15% lower. 
6-85

Table 6-53: Estimate of Total Annual Ozone and PM2.5 Benefits (95% 

Confidence Intervals, Millions of $1999) for the 0.079 ppm Standard 

Alternative: California Attainment 

Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm 

Ozone Benefits, Arithmetic MeanB

Standard Alternative and 

Model or AssumptionA Glidepath Incremental Post2020 Benefits Total

NMMAPS Bell (2004) 0 $50 $50 

Bell (2005) 0 $190 $190 

Ito (2005) 0 $210 $210 MetaAnalysis

Levy (2005) 0 $190 $190 

No Causality 0 $4.3 $4.3 

PM2.5 Mortality and Morbidity Benefits of Attaining 0.075 ppm 

Mortality Impact Functions Derived from Epidemiology Literature 

Glidepath Incremental Post2020 Benefits Total

ACS StudyC 0 $130 $130 

Harvard Six-City StudyD 0 $270 $270 

Mortality Impact Functions Derived from Expert Elicitation

Expert A 0 $410 $410 

Expert B 0 $310 $310 

Expert C 0 $310 $310 

Expert D 0 $220 $220 

Expert E 0 $510 $510 

Expert F 0 $280 $280 

Expert G 0 $180 $180 

Expert H 0 $230 $230 

Expert I 0 $310 $310 

Expert J 0 $250 $250 

Expert K 0 $49 $49 

Expert L 0 $220 $220 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence interval used in frequentist 

statistics. Credible intervals for ozone estimates and confidence intervals for PM2.5 estimates not provided due to the fact that the valuation 

estimates were derived through a scaling technique (see above) that precluded us from generating such estimates. 

C

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported 

in Pope et al (2002), which has previously been reported as the primary estimate in recent RIAs 

D

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method 

for incorporating what has previously been a sensitivity estimate. 

E

 All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived

using a 7% discount rate would be approximately 15% lower. 
6-86

Table 6-54: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: National Glidepath 

Attainment 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB

$14,000 $24,000 $25,000 $26,000 $11,000 

Harvard Six-City StudyC

$27,000 $37,000 $38,000 $38,000 $23,000 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $37,000 $47,000 $48,000 $49,000 $33,000 

Expert B $29,000 $39,000 $40,000 $41,000 $26,000 

Expert C $29,000 $39,000 $40,000 $40,000 $25,000 

Expert D $21,000 $32,000 $33,000 $33,000 $18,000 

Expert E $45,000 $55,000 $56,000 $57,000 $42,000 

Expert F $26,000 $37,000 $38,000 $38,000 $23,000 

Expert G $18,000 $29,000 $30,000 $30,000 $15,000 

Expert H $22,000 $33,000 $34,000 $34,000 $19,000 

Expert I $28,000 $39,000 $40,000 $40,000 $25,000 

Expert J $24,000 $34,000 $35,000 $35,000 $20,000 

Expert K $7,700 $18,000 $19,000 $20,000 $4,300 

Expert L $22,000 $32,000 $33,000 $34,000 $19,000 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. 
6-87

Table 6-55: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: California Glidepath 

Attainment 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB

$240 $400 $390 $420 $180

Harvard Six-City StudyC

$440 $600 $590 $620 $380

Mortality Impact Functions Derived from Expert Elicitation

Expert A $630 $790 $780 $810 $570

Expert B $490 $660 $650 $670 $440

Expert C $490 $650 $640 $670 $430

Expert D $360 $520 $510 $540 $300

Expert E $770 $930 $920 $950 $710

Expert F $450 $620 $600 $630 $400

Expert G $310 $480 $460 $490 $260

Expert H $380 $540 $530 $560 $320

Expert I $480 $650 $630 $660 $430

Expert J $400 $570 $550 $580 $350

Expert K $130 $300 $280 $310 $75

Expert L $370 $540 $520 $550 $320

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. 
6-88

Table 6-56: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: Incremental Benefits 

of California Post 2020 Attainment 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB

$1,600 $3,400 $3,600 $3,600 $1,000 

Harvard Six-City StudyC

$2,600 $4,500 $4,700 $4,700 $2,100 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $3,600 $5,500 $5,700 $5,700 $3,100 

Expert B $2,900 $4,800 $5,000 $5,000 $2,400 

Expert C $2,900 $4,800 $5,000 $4,900 $2,400 

Expert D $2,200 $4,100 $4,300 $4,300 $1,700 

Expert E $4,400 $6,200 $6,500 $6,400 $3,800 

Expert F $2,700 $4,600 $4,800 $4,700 $2,200 

Expert G $2,000 $3,800 $4,000 $4,000 $1,400 

Expert H $2,300 $4,200 $4,400 $4,400 $1,800 

Expert I $2,900 $4,700 $5,000 $4,900 $2,300 

Expert J $2,500 $4,300 $4,500 $4,500 $1,900 

Expert K $1,000 $2,900 $3,100 $3,000 $450 

Expert L $2,300 $4,100 $4,400 $4,300 $1,700 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. 
6-89

Table 6-57: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.065 ppm Alternative Standard: Total California 

Benefits of Post 2020 Attainment 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $1,800 $3,800 $4,000 $4,000 $1,200 

Harvard Six-City StudyC $3,100 $5,100 $5,300 $5,300 $2,500 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $4,300 $6,300 $6,500 $6,500 $3,700 

Expert B $3,400 $5,400 $5,700 $5,600 $2,800 

Expert C $3,400 $5,400 $5,600 $5,600 $2,800 

Expert D $2,600 $4,600 $4,800 $4,800 $2,000 

Expert E $5,200 $7,200 $7,400 $7,400 $4,600 

Expert F $3,200 $5,200 $5,400 $5,400 $2,600 

Expert G $2,300 $4,300 $4,500 $4,500 $1,700 

Expert H $2,700 $4,700 $4,900 $4,900 $2,100 

Expert I $3,400 $5,400 $5,600 $5,600 $2,800 

Expert J $2,900 $4,900 $5,100 $5,100 $2,300 

Expert K $1,100 $3,100 $3,400 $3,300 $520 

Expert L $2,700 $4,700 $4,900 $4,900 $2,100 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. 
6-90

Table 6-58: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: National Glidepath 

Attainment 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $7,900 $13,000 $14,000 $15,000 $6,200 

Harvard Six-City StudyC $16,000 $21,000 $22,000 $23,000 $14,000 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $21,000 $27,000 $27,000 $28,000 $19,000 

Expert B $17,000 $22,000 $23,000 $24,000 $15,000 

Expert C $17,000 $22,000 $23,000 $24,000 $15,000 

Expert D $12,000 $18,000 $18,000 $19,000 $10,000 

Expert E $26,000 $31,000 $32,000 $33,000 $24,000 

Expert F $15,000 $21,000 $21,000 $22,000 $13,000 

Expert G $11,000 $16,000 $17,000 $18,000 $8,700 

Expert H $13,000 $18,000 $19,000 $20,000 $11,000 

Expert I $16,000 $22,000 $22,000 $23,000 $15,000 

Expert J $14,000 $19,000 $20,000 $21,000 $12,000 

Expert K $4,300 $9,700 $10,000 $11,000 $2,500 

Expert L $13,000 $18,000 $19,000 $20,000 $11,000 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. 
6-91

Table 6-59: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: California Glidepath 

Attainment 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB

$110 $220 $230 $210 $75

Harvard Six-City StudyC

$190 $300 $310 $290 $150

Mortality Impact Functions Derived from Expert Elicitation

Expert A $270 $370 $380 $360 $230 

Expert B $210 $320 $330 $310 $180 

Expert C $210 $320 $330 $310 $180 

Expert D $160 $270 $280 $260 $120 

Expert E $320 $430 $440 $420 $290 

Expert F $200 $300 $310 $290 $160 

Expert G $140 $250 $260 $240 $100 

Expert H $170 $270 $290 $260 $130 

Expert I $210 $320 $330 $310 $170 

Expert J $180 $280 $300 $270 $140 

Expert K $67 $170 $190 $170 $32 

Expert L $160 $270 $280 $260 $130 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. 
6-92

Table 6-60: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: Incremental Benefits 

of California Post 2020 Attainment 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB

$1,100 $2,200 $2,300 $2,300 $740

Harvard Six-City StudyC

$1,900 $3,000 $3,100 $3,100 $1,500

Mortality Impact Functions Derived from Expert Elicitation

Expert A $2,600 $3,700 $3,800 $3,900 $2,300 

Expert B $2,100 $3,200 $3,300 $3,300 $1,800 

Expert C $2,100 $3,200 $3,300 $3,300 $1,700 

Expert D $1,600 $2,700 $2,800 $2,800 $1,200 

Expert E $3,200 $4,300 $4,400 $4,400 $2,800 

Expert F $1,900 $3,000 $3,100 $3,200 $1,600 

Expert G $1,400 $2,500 $2,600 $2,600 $1,000 

Expert H $1,600 $2,700 $2,900 $2,900 $1,300 

Expert I $2,000 $3,200 $3,300 $3,300 $1,700 

Expert J $1,700 $2,800 $3,000 $3,000 $1,400 

Expert K $650 $1,800 $1,900 $1,900 $320 

Expert L $1,600 $2,700 $2,800 $2,900 $1,300 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. 
6-93

Table 6-61: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.070 ppm Alternative Standard: California Post 2020 

Attainment 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB

$1,200 $2,400 $2,500 $2,300 $810

Harvard Six-City StudyC

$2,100 $3,300 $3,400 $3,400 $1,700

Mortality Impact Functions Derived from Expert Elicitation

Expert A $2,900 $4,100 $4,200 $4,200 $2,500 

Expert B $2,300 $3,500 $3,600 $3,700 $1,900 

Expert C $2,300 $3,500 $3,600 $3,600 $1,900 

Expert D $1,700 $2,900 $3,100 $3,100 $1,300 

Expert E $3,500 $4,700 $4,800 $4,800 $3,100 

Expert F $2,100 $3,300 $3,400 $3,500 $1,700 

Expert G $1,500 $2,700 $2,800 $2,900 $1,100 

Expert H $1,800 $3,000 $3,100 $3,200 $1,400 

Expert I $2,300 $3,500 $3,600 $3,600 $1,900 

Expert J $1,900 $3,100 $3,200 $3,300 $1,500 

Expert K $720 $1,900 $2,100 $2,100 $350 

Expert L $1,800 $3,000 $3,100 $3,100 $1,400 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. 
6-94

Table 6-62: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.075 ppm Alternative Standard: National Glidepath 

Attainment 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $5,100 $9,400 $10,000 $11,000 $3,700 

Harvard Six-City StudyC $10,000 $15,000 $15,000 $16,000 $8,800 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $13,000 $17,000 $18,000 $19,000 $12,000 

Expert B $10,000 $15,000 $15,000 $16,000 $9,000 

Expert C $10,000 $15,000 $15,000 $16,000 $8,900 

Expert D $7,600 $12,000 $13,000 $13,000 $6,200 

Expert E $16,000 $20,000 $21,000 $22,000 $15,000 

Expert F $9,500 $14,000 $14,000 $15,000 $8,100 

Expert G $6,700 $11,000 $12,000 $12,000 $5,200 

Expert H $8,000 $12,000 $13,000 $14,000 $6,600 

Expert I $10,000 $15,000 $15,000 $16,000 $8,800 

Expert J $8,500 $13,000 $13,000 $14,000 $7,100 

Expert K $3,000 $7,300 $7,800 $8,700 $1,500 

Expert L $7,900 $12,000 $13,000 $14,000 $6,500 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. 
6-95

Table 6-63: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.075 ppm Alternative Standard: California Post 2020 

Attainment 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $660 $1,400 $1,400 $1,400 $440 

Harvard Six-City StudyC $1,100 $1,800 $1,900 $1,900 $900 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $1,600 $2,300 $2,300 $2,300 $1,400 

Expert B $1,300 $2,000 $2,000 $2,000 $1,000 

Expert C $1,300 $1,900 $2,000 $2,000 $1,300 

Expert D $950 $1,600 $1,700 $1,700 $950 

Expert E $1,900 $2,600 $2,700 $2,700 $1,900 

Expert F $1,200 $1,900 $1,900 $1,900 $1,200 

Expert G $830 $1,500 $1,600 $1,600 $830 

Expert H $990 $1,700 $1,800 $1,800 $990 

Expert I $1,200 $1,900 $2,000 $2,000 $1,200 

Expert J $1,100 $1,700 $1,800 $1,800 $1,100 

Expert K $410 $1,100 $1,200 $1,200 $410 

Expert L $980 $1,700 $1,700 $1,700 $980 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. 
6-96

Table 6-64: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.079 ppm Alternative Standard: National Glidepath 

Attainment 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $3,100 $4,000 $4,000 $4,000 $3,100 

Harvard Six-City StudyC $7,300 $8,100 $8,200 $8,200 $7,000 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $9,200 $9,600 $9,600 $9,600 $9,100 

Expert B $7,100 $7,400 $7,500 $7,400 $6,900 

Expert C $7,000 $7,400 $7,400 $7,400 $6,900 

Expert D $4,900 $5,300 $5,300 $5,300 $4,800 

Expert E $11,000 $12,000 $12,000 $12,000 $11,000 

Expert F $6,400 $6,700 $6,800 $6,700 $6,200 

Expert G $4,100 $4,500 $4,600 $4,500 $4,000 

Expert H $5,200 $5,600 $5,600 $5,600 $5,100 

Expert I $6,900 $7,300 $7,300 $7,300 $6,800 

Expert J $5,600 $6,000 $6,000 $6,000 $5,500 

Expert K $1,200 $1,600 $1,700 $1,600 $1,100 

Expert L $5,100 $5,500 $5,500 $5,500 $5,000 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. 
6-97

Table 6-65: Combined Estimate of Annual Ozone and PM2.5 Benefits (95% Confidence 

Intervals, Millions of $1999) for the 0.079 ppm Alternative Standard: California Post 2020 

Attainment 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $180 $320 $330 $320 $130 

Harvard Six-City StudyC $320 $460 $480 $460 $280 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $460 $600 $620 $600 $410 

Expert B $360 $500 $520 $500 $320 

Expert C $360 $500 $520 $500 $310 

Expert D $270 $400 $420 $400 $220 

Expert E $560 $700 $720 $700 $520 

Expert F $330 $470 $490 $470 $290 

Expert G $230 $370 $390 $370 $190 

Expert H $280 $420 $440 $420 $230 

Expert I $360 $500 $510 $490 $310 

Expert J $300 $440 $450 $440 $250 

Expert K $100 $240 $260 $240 $54 

Expert L $270 $410 $430 $410 $230 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text on page 63). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to the fact that the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. Estimates derived using benefit per ton estimates discounted at 3%.Estimates derived using a 7% discount rate would be approximately 15% lower. 
6-98

6.5.5 Discussion of Results and Uncertainties

This analysis has estimated the health and welfare benefits of reductions in ambient 

concentrations of ozone and particulate matter resulting from a set of illustrative control 

strategies to reduce emissions of ozone. The results suggest there will be significant additional 

health and welfare benefits arising from reducing emissions from a variety of sources in and 

around projected nonattaining counties in 2020. While 2020 is the expected date that states 

would need to demonstrate attainment with the revised standard, it is expected that benefits (and 

costs) will begin occurring much earlier, as states begin implementing control measures to show 

reasonable progress towards attainment. Using the full range of benefits (including the results of 

the expert elicitation), we estimate that total ozone and PM2.5 benefits would be between and 

$2.5 and $33 billion annually for the 0.070 ppm alternative when the emissions reductions from 

implementing the new standard is fully realized provides additional evidence of the important 

role that implementation of the standards plays in reducing the health risks associated with 

exceeding the standard. 

There are several important factors to consider when evaluating the relative benefits of the 

attainment strategies for each of the alternative ozone standards. 

1. California accounts for a substantial share of the total benefits for each of the evaluated 

standards. Benefits are most uncertain for California given the unique challenge of 

modeling attainment with the standards due to the high levels of ozone, difficulties of 

modeling the impacts of emissions controls on air quality, and the very large proportion 

of California benefits that were derived through extrapolation are very large relative to 

other areas of the U.S. for each standard alternative. On the one hand, these California 

benefits are likely to understate the actual benefits of attainment strategies, because we 

applied an estimation approach that reduced concentrations only at the specific violating 

monitors and not surrounding monitors that did not violate the standards. The magnitude 

of this underestimate is unknown. On the other hand, it is possible that new technologies 

might not meet the specifications, development timelines, or cost estimates provided in 

this analysis, thereby increasing the uncertainty in when and if such benefits would be 

truly achieved. 

2. There are substantial uncertainties associated with the estimated benefits of the 0.065 

ppm, 0.075 ppm, and 0.079 ppm alternatives, which were derived through extrapolation 

and interpolation, respectively. The great majority of benefits estimated for the 0.065 

ppm standard alternative were derived through extrapolation. As noted above, these 

benefits are likely to be more uncertain than the modeled benefits. The 0.075 ppm 

benefits were derived through an interpolation technique (described in Appendix 6) 

which scaled-down the benefits of the 0.070 ppm benefits analysis. A key assumption in 

this approach is that the control strategy to attain 0.075 ppm would share the same 

characteristics of the 0.070 ppm strategy—namely, regional emission controls on 

electrical generating units and emission controls applied to counties within 200km of 

projected non-attainment monitors. To the extent that states utilized fewer regional 

emission controls, total benefits for the 0.075 ppm strategy may be smaller. 
6-99

EPA employed a monitor rollback approach to estimate the benefits of attaining an 

alternative standard of 0.079 ppm nationwide. This approach likely understates the 

benefits that would occur due to implementation of actual controls because controls 

implemented to reduce ozone concentrations at the highest monitor would likely result in 

some reductions in ozone concentrations at attaining monitors down-wind (i.e. the 

controls would lead to concentrations below the standard in down-wind locations).

Therefore, air quality improvements and resulting health benefits from full attainment 

would be more widespread than we have estimated in our rollback analysis. 

EPA calculated 0.075 ppm benefits by interpolating the 0.070 ppm benefits estimates.33

This interpolation approach may overestimate benefits relative to a modeled control 

scenario developed specifically to attain the 0.075 ppm alternative. The interpolation 

method scales down benefits only at the monitors we project to exceed 0.075 ppm—but it 

still captures the benefits achieved by the 0.070 ppm regional control strategy that occur 

outside of these projected non-attainment areas. To the extent that a modeled emission 

control strategy to attain 0.075 ppm does not include these broader regional emission 

reductions, total benefits would be lower than those we have estimated in this RIA. 

Interpolation and monitor rollback methods of benefits estimation are inherently 

different. As described above, for the purposes of reviewing this analysis, the reader 

should understand that the benefits described for attaining a standard of 0.079 ppm are 

likely understated, whereas the estimated benefits of attaining a standard of 0.075 ppm 

are likely overstated. We will develop and present consistent approaches for the 

alternative standards for the final RIA. 

3. There are a variety of uncertainties associated with the health impact functions used in 

this modeling effort. These include: within study variability, which is the precision with 

which a given study estimates the relationship between air quality changes and health 

effects; across study variation, which refers to the fact that different published studies of 

the same pollutant/health effect relationship typically do not report identical findings and 

in some instances the differences are substantial.; the application of C-R functions 

nationwide, which does not account for any relationship between region and health effect, 

to the extent that such a relationship exists; extrapolation of impact functions across 

population, in which we assumed that certain health impact functions applied to age 

ranges broader than that considered in the original epidemiological study; and, finally, 

there are various uncertainties in the C-R function, including causality, the correlation 

among multiple pollutants, the shape of the C-R function and the relative toxicity of PM 

component species, and the lag between exposure and the onset of the health effect.

4. There are a variety of uncertainties associated with the economic valuation of the health 

endpoints estimated in this analysis. Uncertainties specific to the valuation of premature 

mortality include across study variation; the assumption that WTP for mortality risk 

33 This procedure is detailed in Appendix 6A. 
6-100

reduction is linear; assuming that voluntary and involuntary mortality risk will be valued 

equally; assuming that premature mortality from air pollution risk, which tend to involve 

longer periods of time, will be valued the same as short catastrophic events; the 

possibility for self-selection in avoiding risk, which may bias WTP estimates upward. 

5. This analysis includes estimates of PM2.5 co-benefits that were derived through benefit 

per-ton estimates derived from the Pope et. al (2002) mortality estimate. These benefit 

per-ton estimates represent regional averages. As such, they do not reflect any local 

variability in the incremental PM2.5 benefits per ton of NOx abated. As discussed in the 

PM NAAQS RIA (Table 5.5), there are a large number of uncertainties associated with 

these PM benefits. 

6. For the 0.070 ppm alternative, we estimate co-benefits from PM to be between 20% and 

99% of total benefits, depending on the PM2.5 and ozone mortality functions used. In our 

calculation of PM2.5 co-benefits we assume that states will pursue an ozone strategy that 

reduces NOx emissions. As such, these estimates are strongly influenced by the 

assumption that all PM components are equally toxic. We also acknowledge that when 

implementing any new standard, states may elect to pursue a different ozone strategy, 

which would in turn affect the level of PM2.5 co-benefits.

7. Inherent in any analysis of future regulatory programs are uncertainties in projecting 

atmospheric conditions and source-level emissions, as well as population, health 

baselines, incomes, technology, and other factors. In addition, data limitations prevent an 

overall quantitative estimate of the uncertainty associated with estimates of total 

economic benefits. If one is mindful of these limitations, the magnitude of the benefits 

estimates presented here can be useful information in expanding the understanding of the 

public health impacts of reducing ozone precursor emissions. 

8. There are certain unquantified effects not considered in this benefits analysis due to lack 

of data, time and resources. These unquantified endpoints include the direct effects of of 

ozone on vegetation, the deposition of nitrogen to estuarine and coastal waters and 

agricultural and forested land, and the changes in the level of exposure to ultraviolet 

radiation from ground level ozone.

EPA will continue to evaluate new methods and models and select those most appropriate for 

estimating the health benefits of reductions in air pollution. It is important to continue improving 

benefits transfer methods in terms of transferring economic values and transferring estimated 

impact functions. The development of both better models of current health outcomes and new 

models for additional health effects such as asthma, high blood pressure, and adverse birth 

outcomes (such as low birth weight) will be essential to future improvements in the accuracy and 

reliability of benefits analyses (Guo et al., 1999; Ibald-Mulli et al., 2001). Enhanced 

collaboration between air quality modelers, epidemiologists, toxicologists, and economists 

should result in a more tightly integrated analytical framework for measuring health benefits of 
6-101

air pollution policies. Readers interested in a more extensive discussion of the sources of 

uncertainty in human health benefits analyses should consult the PM NAAQS RIA.34

6.5.6 Summary of Total Benefits 

Tables 6-54 presents the total number of estimated ozone and PM2.5-related premature mortalities 

and morbidities avoided nationwide in 2020. Table 6-55 presents these estimates for California, 

post 2020. 

34 U.S. Environmental Protection Agency, 2006. Regulatory Impact Analysis for the PM 

NAAQS. EPA Prepared by Office of Air and Radiation. Available at: 

http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf
6-102

Table 6-66: Summary of Total Number of Annual Ozone and PM2.5-Related Premature 

Mortalities and Premature Morbidity Avoided: 

2020 National Benefits 

Combined Estimate of Mortality

Standard Alternative and 

Model or AssumptionA

Combined Range of Ozone Benefits and 

 PM2.5 Co-Benefits

 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 

NMMAPS Bell (2004) 200 to 1,900 430 to 2,600 670 to 4,300 1,200 to 7,400 

Bell (2005) 260 to 2,000 1,100 to 3,300 1,500 to 5,100 2,800 to 9,000 

Meta-Analysis Ito (2005) 270 to 2,000 1,200 to 3,300 1,600 to 5,200 3,000 to 9,200 

Levy (2005) 260 to 2,000 1,300 to 3,500 1,800 to 5,400 3,000 to 9,200 

No Causality 180 to 1,900 230 to 2,400 390 to 4,000 660 to 6,900

Combined Estimate of Morbidity

Acute Myocardial Infarction 1,100 1,400 2,300 4,000 

Hospital and ER Visits 1,300 5,600 7,600 13,000 

Chronic Bronchitis 370 470 780 1,300 

Acute Bronchitis 950 1,200 2,000 3,500 

Asthma Exacerbation 7,300 9,400 16,000 27,000 

Lower Respiratory Symptoms 8,100 10,000 17,000 29,000 

Upper Respiratory Symptoms 5,900 7,500 13,000 22,000

School Loss Days 50,000 610,000 780,000 1,300,000 

Work Loss Days 51,000 65,000 110,000 190,000 

Minor Restricted Activity Days 430,000 2,000,000 2,700,000 4,700,000 
6-103

Table 6-67: Summary of Total Number of Annual Ozone and PM2.5-Related Premature 

Mortalities and Premature Morbidity Avoided: California Post 2020 Attainment 

Combined Estimate of Mortality

Standard Alternative and 

Model or AssumptionA

Combined Range of Ozone Benefits and 

 PM2.5 Co-Benefits

 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 

NMMAPS Bell (2004) 17 to 93 61 to 310 110 to 570 180 to 840 

Bell (2005) 42 to 120 170 to 410 300 to 760 490 to 1,200 

Meta-Analysis Ito (2005) 45 to 120 180 to 430 320 to 780 530 to 1,200 

Levy (2005) 46 to 120 180 to 430 320 to 780 520 to 1,200 

No Causality 8.2 to 84 26 to 270 49 to 500 72 to 740 

Combined Estimate of Morbidity

Acute Myocardial Infarction 49 160 290 430 

Hospital and ER Visits 200 790 1,400 2,200 

Chronic Bronchitis 17 53 99 150 

Acute Bronchitis 43 140 260 380 

Asthma Exacerbation 330 1,100 2,000 2,900 

Lower Respiratory Symptoms 360 1,200 2,200 3,200 

Upper Respiratory Symptoms 270 850 1,600 2,300 

School Loss Days 30,000 120,000 210,000 340,000 

Work Loss Days 2,300 7,400 14,000 20,000 

Minor Restricted Activity Days 87,000 340,000 600,000 960,000 
6-104

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6a-1

Appendix Chapter 6a: Additional Benefits Information

Summary

This appendix provides additional information regarding the benefits analysis, including (1) 

methods for developing estimate of full attainment air quality; (2) the process for interpolating 

the 0.075 ppm benefits estimate; (3) the partial attainment PM2.5 incidence and valuation 

estimates. 

6a.1 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-2

technique.1

 This procedure creates grid cell level summer season ozone metrics (1 hour 

maximum, 5 hour average, 8 hour maximum, 8 hour average and 24 hour average). 

4. Calculate grid cell-level ratio of each ozone metric to calculated design value. The result 

of this calculation is a grid cell-level ratio of metric to design value that can then be 

subsequently used to scale the calculated design value and thus derive each of the 

metrics. 

After having calculated these scaling ratios we then performed the monitor rollback as follows: 

1. Roll back the calculated 0.08 ppm partial attainment design value to just equal the 0.08 

ppm standard. This process creates a new baseline design value grid. 

2. Scale the design value grid cell values to ozone metric grid cell values by using ratios 

described above.

3. Create new 0.084 ppm baseline air quality grid from grid cell-level ozone metrics. 

4. Roll back the calculate calculated 0.070 ppm and 0.065 ppm partial attainment design 

values at each monitor to just each the 0.070 ppm and 0.065 ppm standards, respectively. 

5. Scale the calculated full attainment design value to grid cell-level ozone metric using 

ratios described above. 

6. Create new 0.070 ppm and 0.065 ppm air quality grids from grid cell-level ozone metrics. 

7. Perform benefits analysis with baseline and control grids.

To develop a 0.075 ppm full attainment air quality grid we performed an interpolation of the 

0.070 ppm full attainment air quality grid, rather than a monitor rollback. This interpolation 

entailed the following steps: 

1. We identified any monitors that were projected to not attain 0.075 ppm alternative in the 

0.084 ppm base case air quality grid. 

2. For these monitors we calculated an adjustment factor that would scale down the air 

quality improvement at that monitor. The purpose of this adjustment was to ensure that 

the improvement in air quality at that monitor reflected the attainment of the 0.075 ppm 

standard. This ratio was calculated by dividing the improvement in the design value 

necessary to attain 0.075 ppm by the improvement in the design value necessary to attain 

0.070 ppm. For example, a monitor whose baseline is 0.084 would receive 2/3 of the air 

quality improvement from attaining 0.075 ppm than they would from attaining 0.070 

ppm. 

3. We then interpolated these monitor-specific ratios to the grid cell-level in BenMAP. 

4. Finally, we used these grid cell-level ratios as the basis for scaling down the grid celllevel estimates of incidence and valuation from the 0.070 ppm analysis. 

6a.2 Partial Attainment PM2.5 Incidence and Valuation Estimates 

Tables 6a.1 through 6a.5 below summarize the estimates of PM2.5 incidence and valuation 

resulting from the 0.070 ppm partial attainment scenario. These estimates provided the basis for 

the full attainment PM2.5 co-benefit estimates found in Chapter 6 of this RIA.

1

 BenMAP Technical Appendices, Abt Associates: May 2005. Page C-12. 
6a-3

Table 6a-1: 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) 

Eastern U.S. 

Western U.S. Excluding 

California California National PM co-benefits 

Mortality Impact Functions Derived from Epidemiology Literature

ACS StudyA 510

(170--840) 

0.17 

(0.06--0.27) 

47

(16—77) 

550

(190--920) 

Harvard Six-City StudyB 1,100 

(570--1,700) 

0.4 

(0.18--0.6)

110

(53--160) 

1,300 

(630--1,900) 

Woodruff et al 1997 (infant mortality) 1.1 

(0.5—1.7) 

0.04 

(0.02--0.06) 

0.14 

(0.07--0.2)

1.3 

(0.6—2)

Mortality Impact Functions Derived from Expert Elicitation 

Expert A 1,600 

(170--3,000) 

77

(8.2--150) 

140

(15--270) 

1,800 

(190--3,400) 

Expert B 1,200 

(140--2,600) 

56

(4--130) 

110

(13--240) 

1,400 

(160--3,000) 

Expert C 1,200 

(140--2,700) 

58

(6.6--130) 

110

(12--250) 

1,400 

(150--3,000) 

Expert D 820

(85--1,400) 

40

(4.1—68) 

76

(7.8--130) 

940

(96—1,600) 

Expert E 2,000 

(890—3,000) 

95

(44--150) 

180

(82--280) 

2,200 

(1,000--3,500) 

Expert F 1,100 

(740--1,600) 

51

(33—74) 

99

(67--150) 

1,200 

(840—1,800) 

Expert G 690

(0—1,300) 

34

(0—65)

63

(0--120) 

790

(0--1,500) 

Expert H 880

(-46--2,100) 

43

(-2.2—100) 

80

(-4.2--200) 

1,000 

(-52—2,400) 

Expert I 1,200 

(60--2,200) 

57

(3--110) 

110

(5.5--200) 

1,300 

(69—2,500) 

Expert J 950

(230--2,200) 

46

(11—110) 

87

(21--200) 

1,100 

(260--2,500) 

Expert K 190

(0--970) 

8.8 

(0—47)

19

(0—95)

220

(0--1,100) 

Expert L 860

(120--1,600) 

33

(0.04--81) 

79

(11--150) 

970

(130—1,900) 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary 

estimate in recent RIAs 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity estimate.

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
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)

Eastern U.S. 

Western U.S. Excluding 

California California National PM co-benefits 

Morbidity Impact Functions Derived from Epidemiology Literature

Chronic Bronchitis (age >25 and over) 380

(42-720)

12

(1.3—21) 

43

(4.6—81) 

440

(47—820)

Nonfatal myocardial infarction (age >17) 1,100

(560—1,700) 

0.4

(0.2—0.6) 

94

(47—140)

1,200

(610—1,800) 

Hospital admissions--respiratory (all ages) 130

(59—200) --

10

(4.4-15) 

140

(63—220)

Hospital admissions-- cardiovascular 

(age >17) 

270

(160—370) --

20

(12—28)

290

(170—400)

Emergency room visits for asthma 

(age <19) 

560

(310—820) --

22

(12—32)

590

(320—850)

Acute bronchitis (age 8-12) 990

(-130—2,100) 

32

(-4.1—67) 

130

(-17—270) 

1,200

(-150—2,400) 

Lower respiratory symptoms (age 7-14) 8,400

(3,600—13,000) 

3.6

(1.6—5.5) 

1,200

(520—1,900) 

9,600

(4,200—15,000) 

Upper respiratory symptoms (asthmatic 

children age 9-18) 

6,100

(1,500—11,000) 

2.6

(0.7—4.6) 

870

(220—1,500) 

7,000

(1,800—12,000) 

Asthma exacerbation (asthmatic children 

age 6--18) 

7,700

(550—24,000) 

3.4

(0.24—11) 

1,100

(77—3,400) 

8,700

(620—28,000) 

Work loss days (age 18-65) 53,000

(46,000—61,000) 

20

(17—22)

7,200

(6,100—8,200) 

61,000

(52,000—69,000) 

Minor restricted activity days (age 18-65) 320,000

(260,000—370,000)

120

(100—140)

42,000

(35,000—49,000) 

360,000

(300,000—420,000)

 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA
6a-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 1999$) 95th Percentile Confidence Intervals Provided in Parentheses 

Eastern U.S. 

Western U.S. Excluding 

California California 

National PM cobenefits

Mortality Impact Functions Derived from Epidemiology Literature

ACS StudyA $2,900 

($410--$6,700) 

$1

($0.14--$2.2) 

$270

($38--$610) 

$3,200 

($450--$7,300) 

Harvard Six-City StudyB $6,600 

($1,100--$14,000) 

$2.1 

($0.35--$4.6) 

$610

($98--$1,300) 

$7,200 

($1,200--$15,000) 

Woodruff et al 1997 (infant mortality) $6.3 

($1--$14) 

$0.2 

($0.03--$0.5) 

$0.8 

($0.12--$1.7) 

$7.3 

($1.1--$16) 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A $9,100 

($810--$23,000) 

$440

($51--$1,100) 

$830

($75--$2,100) 

$10,000 

($930--$26,000) 

Expert B $6,900 

($470--$21,000) 

$320

($16--$1,000) 

$630

($42--$2,000) 

$7,900 

($520--$24,000) 

Expert C $6,800 

($620--$21,000) 

$340

($38--$990) 

$630

($57--$1,900) 

$7,800 

($710--$23,000) 

Expert D $4,800 

($500--$11,000) 

$230

($30--$550) 

$440

($46--$1,000) 

$5,400 

($570--$13,000) 

Expert E $11,000 

($1,800--$25,000) 

$550

($120--$1,200) 

$1,000 

($160--$2,300) 

$13,000 

($2,000--$28,000) 

Expert F $6,200 

($1,100--$14,000) 

$290

($67--$630) 

$570

(100--$1,200) 

$7,100 

($1,300--$15,000) 

Expert G $4,000 

(0--$11,000) 

$200

(0--$530) 

$370

(0--$990) 

$4,600 

(0--$12,000) 

Expert H $5,100 

($11--$16,000) 

$250

(0.7--$770) 

$470

($1--$1,500) 

$5,800 

($12--$18,000) 

Expert I $6,800 

($570--$17,000) 

$330

($35--$840) 

$620

($53--$1,600) 

$7,700 

($650--$19,000) 

Expert J $5,500 

($700--$17,000) 

$270

($44--$810) 

$500

($64--$1,500) 

$6,200 

($790--$19,000) 

Expert K $1,100 

(0--$6,500) 

$51

(0--$320) 

$110

(0--$650) 

$1,300 

(0--$7,500) 

Expert L $5,000 

($440--$13,000) 

$190

($0.2--$640) 

$460

($39--$1,200) 

$5,700 

($480--$15,000) 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American Cancer Society cohort reported in Pope et al (2002), which has previously been 

reported as the primary estimate in recent RIAs 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice on the appropriate method for incorporating what has previously been a sensitivity 

estimate.

C All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA 
6a-6

Table 6a-4: Illustrative Strategy to Partially Attain 0.070 ppm: Estimated Partial Attainment Value of Reductions in PM2.5-Related Premature 

Mortality Associated with PM co-benefit (7 percent discount rate, in millions of 1999$) 95th Percentile Confidence Intervals Provided in Parentheses 

Eastern U.S. 

Western U.S. Excluding 

California California 

National PM cobenefits

Mortality Impact Functions Derived from Epidemiology Literature

ACS StudyA $2,500 

($350--$5,600) 

$0.8 

(0.1--$1.9) 

$230

($32--$520) 

$2,700 

($380--$6,100) 

Harvard Six-City StudyB $5,600 

($890--$12,000) 

$1.8 

($0.3--$3.9) 

$510

($82--$1,100) 

$6,100 

($980--$13,000) 

Woodruff et al 1997 (infant mortality) $5.3 

($0.81--$12) 

$0.2 

($0.03--$0.38) 

$0.7 

($0.1--$1.4) 

$6.2 

($0.9--$14) 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A $7,700 

($880--$19,000) 

$370

($43--$940) 

$700

($81--$1,800) 

$8,700 

($780--$22,000) 

Expert B $5,800 

($510--$18,000) 

$270

($13--$870) 

$530

($46--$1,700) 

$6,600 

($440--$21,000) 

Expert C $5,800 

($660--$17,000) 

$280

($32--$830) 

$530

($61--$1,600) 

$6,600 

($600--$19,400) 

Expert D $4,000 

($520--$9,500) 

$200

($26--$470) 

$370

($48--$880) 

$4,600 

($480--$11,000) 

Expert E $9,500 

($2,000--$21,000) 

$470

($98--$1,000) 

$870

($180--$1,900) 

$11,000 

($1,700--$24,000) 

Expert F $5,300 

($1,300--$11,000) 

$250

($57--$530) 

$480

($120--$1,000) 

$6,000 

($1,100--$13,000) 

Expert G $3,400 

(0--$9,100) 

$160

(0--$440) 

$310

(0--$830) 

$3,800 

(0--$10,00) 

Expert H $4,300 

($12--$13,000) 

$210

($0.6--$650) 

$390

($1.1--$1,200) 

$4,900 

($10--$15,000) 

Expert I $5,900 

($600--$14,000) 

$290

($30--$700) 

$540

($55--$1,300) 

$6,700 

($550--$16,000) 

Expert J $4,600 

($750--$14,000) 

$230

($37--$680) 

$420

($69--$1,300) 

$5,300 

($670--$16,000) 

Expert K $920

(0--$5,500) 

$43

(0--$270) 

$93

(0--$550) 

$1,100 

(0--$6,300) 

Expert L $4,200 

($460--$11,000) 

$160

($0.16--$540) 

$380

($42--$1,000) 

$4,700 

($400--$13,000) 
6a-7

Table 6a-5: Illustrative Strategy to Partially Attain 0.070 ppm: Estimated Partial Attainment Monetary Value of Reductions in Risk of PM2.5-

Related Morbidity Reductions Associated with PM co-benefit (in millions of 1999$) 95th Percentile Confidence Intervals Provided in 

Parentheses

Eastern U.S. 

Western U.S. Excluding 

California California National PM co-benefits 

Morbidity Impact Functions Derived from Epidemiology Literature

Chronic Bronchitis (age >25 and over) $160

($8.7--$720) 

$4.7 

($0.3--$22) 

$17

($1--$80) 

$180

($10--$820) 

Nonfatal myocardial infarction (age >17) $93

($25--$200) 

$0.03 

($0.01--$0.7) 

$8

($2.1--$17) 

$100

($27--$220) 

Hospital admissions--respiratory (all ages) $90

($23--$200) 

$0.03 

($0.01--$0.7) 

$7.7 

($2--$17) 

$98

($25--$220) 

Hospital admissions-- cardiovascular 

(age >17) 

$2.1 

($1--$3.1) ---

$0.2 

($0.08--$0.2) 

$2.3 

($1.1--$3.4) 

Emergency room visits for asthma 

(age <19) 

$5.5 

($3.4--$7.5) ---

$0.4 

($0.3--$0.57) 

$5.9 

($3.7--$8.1) 

Acute bronchitis (age 8-12) $0.2 

($0.04--$0.3) --- --- 

$0.2 

($0.09--$0.26) 

Lower respiratory symptoms (age 7-14) $0.4 

($-0.02--$1) ---

$0.05 

($-0.002--$0.1) 

$0.4 

($-0.02--$1.2) 

Upper respiratory symptoms (asthmatic 

children age 9-18) 

$0.1 

($0.04--$0.27) ---

$0.02 

($0.006--$0.04) 

$0.15 

($0.05--$0.3) 

Asthma exacerbation (asthmatic children 

age 6--18) 

$0.2 

($0.03—0.38) ---

$0.022 

($0.005--$0.05) 

$0.2 

($0.04--$0.44) 

Work loss days (age 18-65) $0.34 

($0.03--$1.3) ---

$0.05 

($0.004--$0.18) 

$0.4 

($0.03--$1.5) 

Minor restricted activity days (age 18-65) $5.3 

($4.6--$6) ---

$0.9 

($0.7--$1) 

$7.4 

($6.4--$8.3) 

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 
6b-1

Appendix Chapter 6b: Health-Based Cost-Effectiveness of Reductions in Ambient 

PM2.5 Associated with Illustrative Ozone NAAQS 0.070ppm Attainment Strategy 

6b.1 Summary 

Health-based cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) have been used 

to analyze numerous health interventions but have not been widely adopted as tools to analyze 

environmental policies. The Office of Management and Budget (OMB) recently issued Circular 

A-4 guidance on regulatory analyses, requiring federal agencies to “prepare a CEA for all major 

rulemakings for which the primary benefits are improved public health and safety to the extent 

that a valid effectiveness measure can be developed to represent expected health and safety 

outcomes.” Environmental quality improvements may have multiple health and ecological 

benefits, making application of CEA more difficult and less straightforward. For the Ozone 

NAAQS, CEA may provide a useful framework for evaluation: non-health benefits are 

substantial, but the majority of quantified benefits come from health effects. Therefore, EPA is 

including in the Ozone NAAQS RIA a preliminary and experimental application of one type of 

CEA—a modified quality-adjusted life-years (QALYs) approach. 

This cost effectiveness analysis considers the PM2.5 benefits resulting from the illustrative ozone 

control strategies only. Estimation of QALY or Morbidity Inclusive Life Year (MILY, discussed 

below) impacts associated with reducing ozone concentrations is difficult for several reasons. 

First, with the exception of premature death, the set of ozone-related health endpoints includes 

only acute diseases and impacts. As discussed below, there are a number of reasons that the 

QALY method is not appropriate for valuing acute health effects. Second, calculation of QALY 

or MILY impacts for premature mortality is complicated by a lack of information, including the 

change in life expectancy associated with the risk reduction (for MILYs and QALYs) and the 

baseline quality of life for individuals experiencing the risk reduction (for QALY calculations). 

The EPA has recently asked the National Academies of Sciences1 for advice on characterizing 

the mortality risk reduction benefits of reducing ozone concentrations. In their evaluation, the 

NAS Committee on Estimating Mortality Risk Reduction Benefits from Decreasing 

Tropospheric Ozone Exposure will provide advice on, among other topics, the adequacy of a 

basis for estimating the likely impact on life expectancy from reductions in short-term daily 

exposures to ozone. If there is an adequate basis, they will, to the extent practicable, estimate the

magnitude and associated uncertainties of this impact. While awaiting the recommendations of 

the NAS committee, EPA is electing to not calculate QALY or MILY impacts for ozone related 

health effects for this proposal RIA. EPA will investigate the feasibility of performing such an 

analysis for the final RIA. As a result, the overall $/MILY estimates for attainment of alternative 

ozone NAAQS reported in this appendix will overstate the expected $/MILY incorporating 

ozone effects. 

1

 National Academy of Sciences (2007) Project Scope. Estimating Mortality Risk 

Reduction Benefits from Decreasing Tropospheric Ozone Exposure. Division on Earth and Life 

Studies, Board on Environmental Studies and Toxicology. Available at: 

http://www8.nationalacademies.org/cp/projectview.aspx?key=48768
6b-2

QALYs were developed to evaluate the effectiveness of individual medical treatments, and EPA 

is still evaluating the appropriate methods for CEA for environmental regulations. Agency 

concerns with the standard QALY methodology include the treatment of people with fewer years 

to live (the elderly); fairness to people with preexisting conditions that may lead to reduced life 

expectancy and reduced quality of life; and how the analysis should best account for non-health 

benefits, such as improved visibility. 

The Institute of Medicine (a member institution of the National Academies of Science) 

established the Committee to Evaluate Measures of Health Benefits for Environmental, Health, 

and Safety Regulation to assess the scientific validity, ethical implications, and practical utility 

of a wide range of effectiveness measures used or proposed in CEA. This committee prepared a 

report titled “Valuing Health for Regulatory Cost-Effectiveness Analysis” which concluded that 

CEA is a useful tool for assessing regulatory interventions to promote human health and safety, 

although not sufficient for informed regulatory decisions (Miller, Robinson, and Lawrence, 

2006). They emphasized the need for additional data and methodological improvements for 

CEA analyses, and urged greater consistency in the reporting of assumptions, data elements, and 

analytic methods. They also provided a number of recommendations for the conduct of 

regulatory CEA analyses. EPA is evaluating these recommendations and will determine a 

response for upcoming analyses. For this analysis, we use the same approach that was applied in 

the CEA that accompanied the RIA’s for the Clean Air Interstate Rule and the PM NAAQS. 

The methodology presented in this appendix is not intended to stand as precedent either for 

future air pollution regulations or for other EPA regulations where it may be inappropriate. It is 

intended solely to demonstrate one particular approach to estimating the cost-effectiveness of 

reductions in ambient PM2.5 in achieving improvements in public health. Reductions in ambient 

PM2.5 likely will have other health and environmental benefits that will not be reflected in this 

CEA. Other EPA regulations affecting other aspects of environmental quality and public health 

may require additional data and models that may preclude the development of similar healthbased CEAs. A number of additional methodological issues must be considered when 

conducting CEAs for environmental policies, including treatment of nonhealth effects, 

aggregation of acute and long-term health impacts, and aggregation of life extensions and 

quality-of-life improvements in different populations. The appropriateness of health-based CEA 

should be evaluated on a case-by-case basis subject to the availability of appropriate data and 

models, among other factors. 

Attainment of the revised Ozone NAAQS is expected to result in substantial reductions in 

potential population exposure to ambient concentrations of PM by 2020. The benefit-cost 

analysis presented in the RIA shows that partial attainment of the revised 0.070 ppm ozone 

standard achieves substantial health benefits whose monetized value is roughly equal to costs 

(net benefits are between -$6B and $4.1B). Despite the risk of oversimplifying benefits, 

cautiously-interpreted cost-effectiveness calculations may provide further evidence of whether 

the costs associated with attainment strategies for the Ozone NAAQS are a reasonable health 

investment for the nation. 

This analysis provides estimates of commonly used health-based effectiveness measures, 

including lives saved, life years saved (from reductions in mortality risk), and QALYs saved 

(from reductions in morbidity risk) associated with the reduction of ambient PM2.5 due to 
6b-3

illustrative attainment strategies for a more stringent annual ozone standard. In addition, we use 

an alternative aggregate effectiveness metric, Morbidity Inclusive Life Years (MILY) to address 

some of the concerns about aggregation of life extension and quality-of-life impacts. It 

represents the sum of life years gained due to reductions in premature mortality and the QALY 

gained due to reductions in chronic morbidity. This measure may be preferred to existing QALY 

aggregation approaches because it does not devalue life extensions in individuals with 

preexisting illnesses that reduce quality of life. However, the MILY measure is still based on life 

years and thus still inherently gives more weight to interventions that reduce mortality and 

morbidity impacts for younger populations with higher remaining life expectancy. This analysis 

focuses on life extensions and improvements in quality of life through reductions in two diseases 

with chronic impacts: chronic bronchitis (CB) and nonfatal acute myocardial infarctions. Monte 

Carlo simulations are used to propagate uncertainty in several analytical parameters and 

characterize the distribution of estimated impacts. While the benefit-cost analysis presented in 

the RIA characterizes mortality impacts using a number of different sources for the PM mortality 

effect estimate, for this analysis, we focus on the mortality results generated using the effect 

estimates derived from the Pope et al. (2002) and Laden et al. (2005) studies. 

Presented in three different metrics, the analysis suggests the following: 

x In 2020 the illustrative attainment strategy for the revised 0.070 ppm standards will result 

in: 

– Between 550 (95% CI: 215 – 890) and 1,300 (95% CI: 680 – 1,800) premature 

deaths avoided using the Pope (2002) and Laden (2006) studies, respectively, or 

– Between 6,100 (95% CI: 2,400 – 9,800) and 14,000 (95% CI: 7,500 – 20,000) 

life years gained (discounted at 3 percent) using the Pope (2002) and Laden 

(2006) studies, respectively, or 

– Between 9,100 (95% CI: 3,100 – 16,000) and 17,000 (95% CI: 8,200 – 27,000) 

MILYs gained (discounted at 3 percent) using the Pope (2002) and Laden (2006) 

studies, respectively. 

x Using a 7 percent discount rate, mean discounted life years gained are between 4,600 and 

10,400 using the Pope (2002) and Laden (2006) studies, respectively; mean MILYs 

gained are 6,800 and 13,000 using the two studies (The estimates of premature deaths 

avoided are not affected by the discount rate.) 

x The associated reductions in CB and nonfatal acute myocardial infarctions will reduce 

medical costs by approximately $140 million based on a 3 or 7 percent discount rate. 

Direct private compliance costs for the 0.070 ppm partial attainment strategy, are $3.9 billion in 

2020. Based on these costs, the incremental cost effectiveness (net of cost of illness and other 

health and visibility benefits) of the 0.070 ppm partial attainment strategy is $430,000/MILY 

using a 3 percent discount rate and $580,000/MILY using a 7 percent discount rate if one 

calculates MILY’s using the Pope (2002) mortality estimate. The incremental cost effectiveness 

(again, net of cost of illness and other health and visibility benefits) of the 0.070 ppm partial 

attainment strategy is $230,000/MILY using a 3 percent discount rate and $310,000/MILY using 
6b-4

a 7 percent discount rate if one calculates MILY’s using the Laden (2006) mortality estimate. 

See Chapters 3 and 5 of this RIA for more discussion of the control strategies and cost estimates. 

6b.2 Introduction 

Analyses of environmental regulations have typically used benefit-cost analysis to characterize 

impacts on social welfare. Benefit-cost analyses allow for aggregation of the benefits of 

reducing mortality risks with other monetized benefits of reducing air pollution, including acute 

and chronic morbidity, and nonhealth benefits such as improved visibility. One of the great 

advantages of the benefit-cost paradigm is that a wide range of quantifiable benefits can be 

compared to costs to evaluate the economic efficiency of particular actions. However, 

alternative paradigms such as CEA and CUA analyses may also provide useful insights. CEA 

involves estimation of the costs per unit of benefit (e.g., lives or life years saved). CUA is a 

special type of CEA using preference-based measures of effectiveness, such as QALYs. 

CEA and CUA are most useful for comparing programs that have similar goals, for example, 

alternative medical interventions or treatments that can save a life or cure a disease. They are 

less readily applicable to programs with multiple categories of benefits, such as those reducing 

ambient air pollution, because the cost-effectiveness calculation is based on the quantity of a 

single benefit category. In other words, we cannot readily convert improvements in nonhealth 

benefits such as visibility to a health metric such as life years saved. For these reasons, 

environmental economists prefer to present results in terms of monetary benefits and net 

benefits. 

However, QALY-based CUA has been widely adopted within the health economics literature 

(Neumann, 2003; Gold et al., 1996) and in the analysis of public health interventions (US FDA, 

2004). QALY-based analyses have not been as accepted in the environmental economics 

literature because of concerns about the theoretical consistency of QALYs with individual 

preferences (Hammitt, 2002), treatment of nonhuman health benefits, and a number of other 

factors (Freeman, Hammitt, and De Civita, 2002). For environmental regulations, benefit-cost 

analysis has been the preferred method of choosing among regulatory alternatives in terms of 

economic efficiency. Recently several academic analyses have proposed the use of life yearsbased benefit-cost or CEAs of air pollution regulations (Cohen, Hammitt, and Levy, 2003; Coyle 

et al., 2003; Rabl, 2003; Carrothers, Evans, and Graham, 2002). In addition, the World Health 

Organization has adopted the use of disability-adjusted life years, a variant on QALYs, to assess 

the global burden of disease due to different causes, including environmental pollution (Murray 

et al., 2002; de Hollander et al., 1999). 

Recently, the U.S. OMB (Circular A-4, 2003) issued new guidance requiring federal agencies to 

provide both CEA and benefit-cost analyses for major regulations. The OMB Circular A-4 

directs agencies to “prepare a CEA for all major rulemakings for which the primary benefits are 

improved public health and safety to the extent that a valid effectiveness measure can be 

developed to represent expected health and safety outcomes.” We are including a CEA for the 

illustrative PM NAAQS attainment strategies to illustrate one potential approach for conducting 

a CEA. EPA is still evaluating the appropriate methods for CEA for environmental regulations 

with multiple outcomes. 
6b-5

The methodology presented in this appendix is not intended to stand as precedent either for 

future air pollution regulations or for other EPA regulations governing water, solid waste, or 

other regulatory objectives. It is intended solely to demonstrate one particular approach to 

estimating the effectiveness of reductions in ambient PM2.5 in achieving improvements in public 

health. This analysis focuses on effectiveness measured by improvements in life expectancy and 

reductions in the incidence of two diseases with chronic impacts on quality of life: CB and 

nonfatal acute myocardial infarctions. Other EPA regulations affecting other aspects of 

environmental quality and public health may require additional data and models that may 

preclude the development of similar QALY-based analyses. The appropriateness of QALYbased CEA should be evaluated on a case-by-case basis subject to the availability of appropriate 

data and models. 

Preparation of a CEA requires identification of an appropriate measure of rule effectiveness. 

Given the significant impact of reductions in ambient PM2.5 on reductions in the risk of 

mortality, lives saved is an important measure of effectiveness. However, one of the ongoing 

controversies in health impact assessment regards whether reductions in mortality risk should be 

reported and valued in terms of statistical lives saved or in terms of statistical life years saved. 

Life years saved measures differentiate among premature mortalities based on the remaining life 

expectancy of affected individuals. In general, under the life years approach, older individuals 

will gain fewer life years than younger individuals for the same reduction in mortality risk during 

a given time period, making interventions that benefit older individuals seem less beneficial 

relative to similar interventions benefiting younger individuals. A further complication in the 

debate is whether to apply quality adjustments to life years lost. Under this approach, 

individuals with preexisting health conditions would have fewer QALYs lost relative to healthy 

individuals for the same loss in life expectancy, making interventions that primarily benefit 

individuals with poor health seem less beneficial to similar interventions affecting primarily 

healthy individuals. 

In addition to substantial mortality risk reduction benefits, strategies for attaining the revised PM 

NAAQS will also result in significant reductions in chronic and acute morbidity. Several 

approaches have been developed to incorporate both morbidity and mortality into a single 

effectiveness metric. The most common of these is the QALY approach, which expresses all 

morbidity and mortality impacts in terms of quality of life multiplied by the duration of time with 

that quality of life. The QALY approach has some appealing characteristics. For example, it can 

account for morbidity effects as well as losses in life expectancy without requiring the 

assignment of dollar values to calculate total benefits. By doing so it provides an alternative 

framework to benefit-cost analysis for aggregating quantitative measures of health impacts. 

While used extensively in the economic evaluation of medical interventions (Gold et al., 1996), 

QALYs have not been widely used in evaluating environmental health regulations. A number of 

specific issues arise with the use of QALYs in evaluating environmental programs that affect a 

broad and heterogeneous population and that provide both health and nonhealth benefits. The 

U.S. Public Health Service report on cost-effectiveness in health and medicine notes the 

following: 

 For decisions that involve greater diversity in interventions and the people to whom 

they apply, cost-effectiveness ratios continue to provide essential information, but 
6b-6

that information must, to a greater degree, be evaluated in light of circumstances and 

values that cannot be included in the analysis. Individuals in the population will 

differ widely in their health and disability before the intervention, or in age, wealth, 

or other characteristics, raising questions about how society values gains for the 

more and less health, for young and old, for rich and poor, and so on. The 

assumption that all QALYs are of equal value is less likely to be reasonable in this 

context. (Gold et al., 1996, p. 11) 

Use of QALYs as a measure of effectiveness for environmental regulations is still developing, 

and while this analysis provides one framework for using QALYs to evaluate environmental 

regulations, there are clearly many issues, both scientific and ethical, that need to be addressed 

with additional research. The Institute of Medicine panel evaluating QALYs and other 

effectiveness measures prepared a report titled “Valuing Health for Regulatory CostEffectiveness Analysis” which concluded that “the QALY is the best measure at present on 

which to standardize Health Adjusted Life Year estimation because of its widespread use, 

flexibility, and relative simplicity” (Miller, Robinson, and Lawrence, 2006). EPA is evaluating 

this recommendation and will determine a response for upcoming analyses. For this analysis, for 

reasons discussed in the text, we use the same MILY approach that was applied in the CEA that 

accompanied the RIA for the Clean Air Interstate Rule. 

This appendix presents cost-effectiveness methodologies for evaluating programs such as 

attainment strategies for the revised Ozone NAAQS that are intended to reduce both ozone and 

PM2.5 precursors, such as NOx and VOCs, starting from the standard QALY literature and 

seeking a parallel structure to benefit-cost analysis in the use of air quality and health inputs (see

Hubbell [2004a] for a discussion of some of the issues that arise in comparing QALY and 

benefit-cost frameworks in analyzing air pollution impacts). For the purposes of this analysis, 

we calculate effectiveness using several different metrics, including lives prolonged, life years 

gained, and modified QALYs. For the life years and QALY-type approaches, we use life table 

methods to calculate the change in life expectancy expected to result from changes in mortality 

risk from PM. We use existing estimates of preferences for different health states to obtain 

QALY weights for morbidity endpoints associated with air pollution. In general, consistent with 

the Gold et al. (1996) recommendations, we use weights obtained from a societal perspective 

when available. We explore several different sources for these weights to characterize some of 

the potential uncertainty in the QALY estimates. We follow many of the principles of the 

reference case analysis as defined in Gold et al. (1996), although in some cases we depart from 

the reference case approach when data limitations require us to do so (primarily in the selection 

of quality-of-life weights for morbidity endpoints). We also depart from the reference case (and 

the recommendations of the IOM report) in the method of combining life expectancy and 

quality-of-life gains. 

Results in most tables are presented only at a discount rate of 3 percent, rather than at both 3 

percent and 7 percent as recommended in EPA and OMB guidance. This is strictly for ease of 

presentation. Aggregate results at 7 percent are presented in the summary, and the impact of 

using a 7 percent discount rate instead of 3 percent rate is summarized in a sensitivity analysis. 

Monte Carlo simulation methods are used to propagate uncertainty in several of the model 

parameters throughout the analysis. We characterize overall uncertainty in the results with 95 
6b-7

percent confidence intervals based on the Monte Carlo simulations. In addition, we examine the 

impacts of changing key parameters, such as the discount rate, on the effectiveness measures and 

the cost-effectiveness metrics. 

The remainder of this appendix provides an overview of the key issues involved in life year- and 

QALY-based approaches for evaluating the health impacts of air pollution regulations, provides 

detailed discussions of the steps required for each type of effectiveness calculation, and presents 

the CEA for the PM NAAQS illustrative attainment strategies. Section G.3 introduces the 

various effectiveness measures and discusses some of the assumptions required for each. Section 

G.4 details the methodology used to calculate changes in life years and quality adjustments for 

mortality and morbidity endpoints. Section G.5 provides the results for the illustrative 

attainment strategies for the revised and more stringent alternative PM NAAQS and discusses 

their implications for cost-effectiveness of these attainment strategies. 

6b.3 Effectiveness Measures 

Three major classes of benefits are associated with reductions in air pollution: mortality, 

morbidity, and nonhealth (welfare). For the purposes of benefit-cost analysis, EPA has presented 

mortality-related benefits using estimates of avoided premature mortalities, representing the 

cumulative result of reducing the risk of premature mortality from long-term exposure to PM2.5

for a large portion of the U.S. population. Morbidity benefits have been characterized by 

numbers of new incidences avoided for chronic diseases such as CB, avoided admissions for 

hospitalizations associated with acute and chronic conditions, and avoided days with symptoms 

for minor illnesses. Nonhealth benefits are characterized by the monetary value of reducing the 

impact (e.g., the dollar value of improvements in visibility at national parks). 

For the purposes of CEA, we focus the effectiveness measure on the quantifiable health impacts 

of the reduction in PM2.5. Treatment of nonhealth benefits is important and is discussed in some 

detail later in this section. If the main impact of interest is reductions in mortality risk from air

pollution, the effectiveness measures are relatively straightforward to develop. Mortality 

impacts can be characterized similar to the benefits analysis, by counting the number of 

premature mortalities avoided, or can be characterized in terms of increases in life expectancy or 

life years.2

 Estimates of premature mortality have the benefit of being relatively simple to 

calculate, are consistent with the benefit-cost analysis, and do not impose additional assumptions 

on the degree of life shortening. However, some have argued that counts of premature 

mortalities avoided are problematic because a gain in life of only a few months would be 

2

 Life expectancy is an ex ante concept, indicating the impact on an entire population’s 

expectation of the number of life years they have remaining, before knowing which individuals 

will be affected. Life expectancy thus incorporates both the probability of an effect and the 

impact of the effect if realized. Life years is an ex post concept, indicating the impact on 

individuals who actually die from exposure to air pollution. Changes in population life 

expectancy will always be substantially smaller than changes in life years per premature 

mortality avoided, although the total life years gained in the population will be the same. This is 

because life expectancy gains average expected life years gained over the entire population, 

while life years gained measures life years gained only for those experiencing the life extension. 
6b-8

considered equivalent to a gain of a many life years, and the true effectiveness of an intervention 

is the gain in life expectancy or life years (Rabl, 2003; Miller and Hurley, 2003). 

Calculations of changes in life years and life expectancy can be accomplished using standard life 

table methods (Miller and Hurley, 2003). However, the calculations require assumptions about 

the baseline mortality risks for each age cohort affected by air pollution. A general assumption 

may be that air pollution mortality risks affect the general mortality risk of the population in a 

proportional manner. However, some concerns have been raised that air pollution affects mainly 

those individuals with preexisting cardiovascular and respiratory disease, who may have reduced 

life expectancy relative to the general population. This issue is explored in more detail below. 

Air pollution is also associated with a number of significant chronic and acute morbidity 

endpoints. Failure to consider these morbidity effects may understate the cost-effectiveness of 

air pollution regulations or give too little weight to reductions in particular pollutants that have 

large morbidity impacts but no effect on life expectancy. The QALY approach explicitly 

incorporates morbidity impacts into measures of life years gained and is often used in health 

economics to assess the cost-effectiveness of medical spending programs (Gold et al., 1996). 

Using a QALY rating system, health quality ranges from 0 to 1, where 1 may represent full 

health, 0 death, and some number in between (e.g., 0.8) an impaired condition. QALYs thus 

measure morbidity as a reduction in quality of life over a period of life. QALYs assume that 

duration and quality of life are equivalent, so that 1 year spent in perfect health is equivalent to 2

years spent with quality of life half that of perfect health. QALYs can be used to evaluate 

environmental rules under certain circumstances, although some very strong assumptions 

(detailed below) are associated with QALYs. The U.S. Public Health Service Panel on Cost 

Effectiveness in Health and Medicine recommended using QALYs when evaluating medical and 

public health programs that primarily reduce both mortality and morbidity (Gold et al., 1996). 

Although there are significant nonhealth benefits associated with air pollution regulations, over 

90 percent of quantifiable monetized benefits are health-related, as is the case with the 

attainment strategies for the PM NAAQS. Thus, it can be argued that QALYs are more 

applicable for these types of regulations than for other environmental policies. However, the 

value of nonhealth benefits should not be ignored. As discussed below, we have chosen to 

subtract the value of nonhealth benefits from the costs in the numerator of the cost-effectiveness 

ratio. 

In the following sections, we lay out a phased approach to describing effectiveness. We begin by 

discussing how the life-extending benefits of air pollution reductions are calculated, and then we 

incorporate morbidity effects using the QALY approach. We also introduce an alternative 

aggregated health metric, Morbidity Inclusive Life Years (MILY) to address some of the ethical 

concerns about aggregating life extension impacts in populations with preexisting disabling 

conditions. 

The use of QALYs is predicated on the assumptions embedded in the QALY analytical 

framework. As noted in the QALY literature, QALYs are consistent with the utility theory that 

underlies most of economics only if one imposes several restrictive assumptions, including 

independence between longevity and quality of life in the utility function, risk neutrality with 

respect to years of life (which implies that the utility function is linear), and constant 

proportionality in trade-offs between quality and quantity of life (Pliskin, Shepard, and 
6b-9

Weinstein, 1980; Bleichrodt, Wakker, and Johannesson, 1996). To the extent that these 

assumptions do not represent actual preferences, the QALY approach will not provide results 

that are consistent with a benefit-cost analysis based on the Kaldor-Hicks criterion.3 Even if the 

assumptions are reasonably consistent with reality, because QALYs represent an average 

valuation of health states rather than the sum of societal WTP, there are no guarantees that the 

option with the highest QALY per dollar of cost will satisfy the Kaldor-Hicks criterion (i.e., 

generate a potential Pareto improvement [Garber and Phelps, 1997]). 

Benefit-cost analysis based on WTP is not without potentially troubling underlying structures as 

well, incorporating ability to pay (and thus the potential for equity concerns) and the notion of 

consumer sovereignty (which emphasizes wealth effects). Table G-1 compares the two 

approaches across a number of parameters. For the most part, WTP allows parameters to be 

determined empirically, while the QALY approach imposes some conditions a priori.

Table 6b-1: Comparison of QALY and WTP Approaches 

Parameter QALY WTP 

Risk aversion Risk neutral Empirically determined 

Relation of duration and quality Independent Empirically determined 

Proportionality of duration/ quality trade-off Constant Variable 

Treatment of time/age in utility function Utility linear in time Empirically determined 

Preferences Community/Individual Individual 

Source of preference data Stated Revealed and stated 

Treatment of income and prices Not explicitly considered Constrains choices 

6b.4 Changes in Premature Death, Life Years, and Quality of Life 

To generate health outcomes, we used the same framework as for the benefit-cost analysis 

described in Chapter 6. For convenience, we summarize the basic methodologies here. For 

more details, see Chapter 6 and the BenMAP user’s manual 

(http://www.epa.gov/ttn/ecas/benmodels.html). 

BenMAP uses health impact functions to generate changes in the incidence of health effects. 

Health impact functions are derived from the epidemiology literature. A standard health impact 

function has four components: an effect estimate from a particular epidemiological study, a 

baseline incidence rate for the health effect (obtained from either the epidemiology study or a 

3

 The Kaldor-Hicks efficiency criterion requires that the “winners” in a particular case be 

potentially able to compensate the “losers” such that total societal welfare improves. In this 

case, it is sufficient that total benefits exceed total costs of the regulation. This is also known as 

a potential Pareto improvement, because gains could be allocated such that at least one person in 

society would be better off while no one would be worse off. 
6b-10

source of public health statistics like CDC), the affected population, and the estimated change in 

the relevant PM summary measure. 

A typical health impact function might look like this: 

'

'

y y e

x

  



0

( ) , 1

E

where y0 is the baseline incidence, equal to the baseline incidence rate times the potentially 

affected population;  is the effect estimate; and x is the estimated change in PM2.5. There are 

other functional forms, but the basic elements remain the same. 

6b.4.1 Calculating Reductions in Premature Deaths 

As in several recent air pollution health impact assessments (e.g., Kunzli et al., 2000; EPA, 

2004), we focus on the prospective cohort long-term exposure studies in deriving the health 

impact function for the estimate of premature mortality. Cohort analyses are better able to 

capture the full public health impact of exposure to air pollution over time (Kunzli et al., 2001; 

NRC, 2002). We selected effects estimate from the extended analysis of the ACS cohort (Pope 

et al., 2002) as well as from the Harvard Six City Study (Laden et al., 2006). Given the focus in 

this analysis on developing a broader expression of uncertainties in the benefits estimates, and 

the weight that was placed on both the ACS and Harvard Six-city studies by experts participating 

in the PM2.5 mortality expert elicitation, we have elected to provide estimates derived from both 

Pope et al. (2002) and Laden et al. (2006). 

This latest re-analysis of the ACS cohort data (Pope et al, 2002) provides additional refinements 

to the analysis of PM-related mortality by (a) extending the follow-up period for the ACS study 

subjects to 16 years, which triples the size of the mortality data set; (b) substantially increasing 

exposure data, including consideration for cohort exposure to PM2.5 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 PM2.5 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 PM2.5 levels. The effect estimate from this study is 0.0058, which is equivalent 

to a relative risk of 1.06 for a 10 g change in PM2.5.

Very recently, a follow up to the Harvard 6-city study was published (Laden et al., 2006), that 

both confirmed the effect size from the first study and provided additional confirmation that 

reductions in PM2.5 directly result in reductions in the risk of premature death. This additional 

evidence stems from the observed reductions in PM2.5 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 the Laden 

et al is 0.0148, which is equivalent to a relative risk of 1.16 for a 10 ȝg/m3 change in PM2.5.
6b-11

Age, cause, and county-specific mortality rates were obtained from CDC for the years 1996 

through 1998. CDC maintains an online data repository of health statistics, CDC Wonder, 

accessible at http://wonder.cdc.gov/. The mortality rates provided are derived from U.S. death 

records and U.S. Census Bureau postcensal population estimates. Mortality rates were averaged 

across 3 years (1996 through 1998) to provide more stable estimates. When estimating rates for 

age groups that differed from the CDC Wonder groupings, we assumed that rates were uniform 

across all ages in the reported age group. For example, to estimate mortality rates for individuals 

ages 30 and up, we scaled the 25- to 34-year old death count and population by one-half and then 

generated a population-weighted mortality rate using data for the older age groups. 

The reductions in incidence of premature mortality within each age group associated with the 

illustrative attainment strategies for the revised and more stringent alternative Ozone NAAQS in 

2020 are summarized in Table G-2. 

6b.4.2 Calculating Changes in Life Years from Direct Reductions in PM2.5-Related Mortality 

Risk 

To calculate changes in life years associated with a given change in air pollution, we used a life 

table approach coupled with age-specific estimates of reductions in premature mortality. We 

began with the complete unabridged life table for the United States in 2000, obtained from CDC 

(CDC, 2002). For each 1-year age interval (e.g., zero to one, one to two) the life table provides 

estimates of the baseline probability of dying during the interval, person years lived in the 

interval, and remaining life expectancy. From this unabridged life table, we constructed an 

abridged life table to match the age intervals for which we have predictions of changes in 

incidence of premature mortality. We used the abridgement method described in CDC (2002). 

Table G-3 presents the abridged life table for 10-year age intervals for adults over 30 (to match 

the Pope et al. [2002] study population). Note that the abridgement actually includes one 5-year 

interval, covering adults 30 to 34, with the remaining age intervals covering 10 years each. This 

is to provide conformity with the age intervals available for mortality rates. 
6b-12

Table 6b-2: Estimated Reduction in Incidence of All-cause Premature Mortality Associated 

with Illustrative Attainment Strategies for the Revised and More Stringent 

Alternative Ozone NAAQS in 2020 

 Reduction in All-Cause Premature Mortality 

(95% CI)

Age Interval Pope (2002) Laden (2006) 

30 – 34 4 

(2 – 6) 

9

(5 – 13) 

35 – 44 12 

(5 – 20) 

28

(15 – 40) 

45 – 54 26 

(10 – 42) 

60

(32 – 87) 

55 – 64 70 

(27 – 110) 

160 

(86 – 230) 

65 – 74 120 

(48 – 200) 

280 

(150 – 400) 

75 – 84 140 

(56 – 230) 

320 

(180 – 470) 

85+ 170 

(68 – 280) 

390 

(210 – 570) 

Total 550 

(220 – 890) 

1,300 

(680 – 1,800) 

From the abridged life table (Table 6b-3), we obtained the remaining life expectancy for each 

age cohort, conditional on surviving to that age. This is then the number of life years lost for an 

individual in the general population dying during that age interval. This information can then be 

combined with the estimated number of premature deaths in each age interval calculated with 

BenMAP (see previous subsection). Total life years gained will then be the sum of life years 

gained in each age interval: 

TotalLife Years LEi i M

i

N

 u

 

¦1

,

where LEi is the remaining life expectancy for age interval i, Mi is the change in incidence of 

mortality in age interval i, and N is the number of age intervals. 

For the purposes of determining cost-effectiveness, it is also necessary to consider the timedependent nature of the gains in life years. Standard economic theory suggests that benefits 

occurring in future years should be discounted relative to benefits occurring in the present. OMB 

and EPA guidance suggest discount rates of three and seven percent. As noted earlier, we 

present gains in future life years discounted at 3 percent. Results based on 7 percent are included 

in the summary and the overall impact of a 7 percent rate is summarized in Table 6b-16. 

Selection of a 3 percent discount rate is also consistent with recommendations from the U.S. 

Public Health Service Panel on Cost Effectiveness in Health and Medicine (Gold et al., 1996). 
6b-13

Table 6b-3: Abridged Life Table for the Total Population, United States, 2000 

Age Interval 

Probability of 

Dying 

Between 

Ages x to 

x+1 

Number 

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 

End

Age qx Ix dx Lx Tx ex

30 35 0.00577 97,696 564 487,130 4,723,539 48.3 

35 45 0.01979 97,132 1,922 962,882 4,236,409 43.6 

45 55 0.04303 95,210 4,097 934,026 3,273,527 34.4 

55 65 0.09858 91,113 8,982 872,003 2,339,501 25.7 

65 75 0.21779 82,131 17,887 740,927 1,467,498 17.9 

75 85 0.45584 64,244 29,285 505,278 726,571 11.3 

85 95 0.79256 34,959 27,707 196,269 221,293 6.3 

95 100 0.75441 7,252 5,471 20,388 25,024 3.5 

100+ 1.00000 1,781 1,781 4,636 4,636 2.6 

Discounted total life years gained is calculated as follows: 

Discounted LY e dt rt

LE

 



3

,

0

where r is the discount rate, equal to 0.03 in this case, t indicates time, and LE is the life 

expectancy at the time when the premature death would have occurred. Life years are further 

discounted to account for the lag between the reduction in ambient PM2.5 and the reduction in 

mortality risk. We use the same 20-year segmented lag structure that is used in the benefit-cost 

analysis (see Chapter 6). 

The most complete estimate of the impacts of PM2.5 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 PM2.5

concentrations averaged over the periods 1979–1983 and 1999–2000. Use of all-cause mortality 

is appropriate if there are no differences in the life expectancy of individuals dying from air 

pollution-related causes and those dying from other causes. The argument that long-term 

exposure to PM2.5 may affect mainly individuals with serious preexisting illnesses is not 

supported by current empirical studies. For example, the Krewski et al. (2000) ACS reanalysis 

suggests that the mortality risk is no greater for those with preexisting illness at time of 
6b-14

enrollment in the study. Life expectancy for the general population in fact includes individuals 

with serious chronic illness. Mortality rates for the general population then reflect prevalence of 

chronic disease, and as populations age the prevalence of chronic disease increases. 

The only reason one might use a lower life expectancy is if the population at risk from air 

pollution was limited solely to those with preexisting disease. Also, note that the OMB Circular 

A-4 notes that “if QALYs are used to evaluate a lifesaving rule aimed at a population that 

happens to experience a high rate of disability (i.e., where the rule is not designed to affect the 

disability), the number of life years saved should not necessarily be diminished simply because 

the rule saves lives of people with life-shortening disabilities. Both analytic simplicity and 

fairness suggest that the estimate number of life years saved for the disabled population should 

be based on average life expectancy information for the relevant age cohorts.” As such, use of a 

general population life expectancy is preferred over disability-specific life expectancies. Our 

primary life years calculations are thus consistent with the concept of not penalizing individuals 

with disabling chronic health conditions by assessing them reduced benefits of mortality risk 

reductions. 

For this analysis, direct impacts on life expectancy are measured only through the estimated 

change in mortality risk based on the Pope et al. (2002) C-R function. The SAB-HES has 

advised against including additional gains in life expectancy due to reductions in incidence of 

chronic disease or nonfatal heart attacks (EPA-SAB-COUNCIL-ADV-04-002). Although 

reductions in these endpoints are likely to result in increased life expectancy, the HES has 

suggested that the cohort design and relatively long follow-up period in the Pope et al. study 

should capture any life-prolonging impacts associated with those endpoints. Impacts of CB and 

nonfatal heart attacks on quality of life will be captured separately in the QALY calculation as 

years lived with improved quality of life. The methods for calculating this benefit are discussed 

below. 

6b.4.2.1 Should Life Years Gained Be Adjusted for Initial Health Status? 

The methods outlined above provide estimates of the total number of life years gained in a 

population, regardless of the quality of those life years, or equivalently, assuming that all life 

years gained are in perfect health. In some CEAs (Cohen, Hammitt, and Levy, 2003; Coyle et 

al., 2003), analysts have adjusted the number of life years gained to reflect the fact that 1) the 

general public is not in perfect health and thus “healthy” life years are less than total life years 

gained and 2) those affected by air pollution may be in a worse health state than the general 

population and therefore will not gain as many “healthy” life years adjusted for quality, from an 

air pollution reduction. This adjustment, which converts life years gained into QALYs, raises a 

number of serious ethical issues. Proponents of QALYs have promoted the nondiscriminatory 

nature of QALYs in evaluating improvements in quality of life (e.g., an improvement from a 

score of 0.2 to 0.4 is equivalent to an improvement from 0.8 to 1.0), so the starting health status 

does not affect the evaluation of interventions that improve quality of life. However, for lifeextending interventions, the gains in QALY will be directly proportional to the baseline health 

state (e.g., an individual with a 30-year life expectancy and a starting health status of 0.5 will 

gain exactly half the QALYs of an individual with the same life expectancy and a starting health 

status of 1.0 for a similar life-extending intervention). This is troubling because it imposes an 

additional penalty for those already suffering from disabling conditions. Brock (2002) notes that 
6b-15

“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 lifeshortening disabilities. Both analytic simplicity and fairness suggest that the 

estimated number of life years saved for the disabled population should be based on 

average life expectancy information for the relevant age cohorts. More generally, 

when numeric adjustments are made for life expectancy or quality of life, analysts 

should prefer use of population averages rather than information derived from 

subgroups dominated by a particular demographic or income group. (p. 13) 

This suggests two adjustments to the standard QALY methodology: one adjusting the relevant 

life expectancy of the affected population, and the other affecting the baseline quality of life for 

the affected population. 

In addition to the issue of fairness, potential measurement issues are specific to the air pollution 

context that might argue for caution in applying quality-of-life adjustments to life years gained 

due to air pollution reductions. A number of epidemiological and toxicological studies link 

exposure to air pollution with chronic diseases, such as CB and atherosclerosis (Abbey et al., 

1995; Schwartz, 1993; Suwa et al., 2002). If these same individuals with chronic disease caused 

by exposure to air pollution are then at increased risk of premature death from air pollution, there 

is an important dimension of “double jeopardy” involved in determining the correct baseline for 

assessing QALYs lost to air pollution (see Singer et al. [1995] for a broader discussion of the 

double-jeopardy argument). 

Analyses estimating mortality from acute exposures that ignore the effects of long-term exposure 

on morbidity may understate the health impacts of reducing air pollution. Individuals exposed to 

chronically elevated levels of air pollution may realize an increased risk of death and chronic 

disease throughout life. If at some age they contract heart (or some other chronic) disease as a 

result of the exposure to air pollution, they will from that point forward have both reduced life 

expectancy and reduced quality of life. The benefit to that individual from reducing lifetime 

exposure to air pollution would be the increase in life expectancy plus the increase in quality of 

life over the full period of increased life expectancy. If the QALY loss is determined based on 

the underlying chronic condition and life expectancy without regard to the fact that the person 

would never have been in that state without long-term exposure to elevated air pollution, then the 

person is placed in double jeopardy. In other words, air pollution has placed more people in the 

susceptible pool, but then we penalize those people in evaluating policies by treating their 

subsequent deaths as less valuable, adding insult to injury, and potentially downplaying the 

importance of life expectancy losses due to air pollution. If the risk of chronic disease and risk 
6b-16

of death are considered together, then there is no conceptual problem with measuring QALYs, 

but this has not been the case in recent applications of QALYs to air pollution (Carrothers, 

Evans, and Graham, 2002; Coyle et al., 2003). The use of QALYs thus highlights the need for a 

better understanding of the relationship between chronic disease and long-term exposure and 

suggests that analyses need to consider morbidity and mortality jointly, rather than treating each 

as a separate endpoint (this is an issue for current benefit-cost approaches as well). 

Because of the fairness and measurement concerns discussed above, for the purposes of this 

analysis, we do not reduce the number of life years gained to reflect any differences in 

underlying health status that might reduce quality of life in remaining years. Thus, we maintain 

the assumption that all direct gains in life years resulting from mortality risk reductions will be 

assigned a weight of 1.0. The U.S. Public Health Service Panel on Cost Effectiveness in Health 

and Medicine recommends that “since lives saved or extended by an intervention will not be in 

perfect health, a saved life year will count as less than 1 full QALY” (Gold et al., 1996). 

However, for the purposes of this analysis, we propose an alternative to the traditional aggregate 

QALY metric that keeps separate quality adjustments to life expectancy and gains in life 

expectancy. As such, we do not make any adjustments to life years gained to reflect the less than 

perfect health of the general population. Gains in quality of life will be addressed as they accrue 

because of reductions in the incidence of chronic diseases. This is an explicit equity choice in 

the treatment of issues associated with quality-of-life adjustments for increases in life expectancy 

that still capitalizes on the ability of QALYs to capture both morbidity and mortality impacts in a 

single effectiveness measure. 

6b.5 Calculating Changes in the Quality of Life Years (Morbidity) 

In addition to directly measuring the quantity of life gained, measured by life years, it may also 

be informative to measure gains in the quality of life. Reducing air pollution also leads to 

reductions in serious illnesses that affect quality of life. These include CB and cardiovascular 

disease, for which we are able to quantify changes in the incidence of nonfatal heart attacks. To 

capture these important benefits in the measure of effectiveness, they must first be converted into 

a life-year equivalent so that they can be combined with the direct gains in life expectancy. 

For this analysis, we developed estimates of the QALYs gained from reductions in the incidence 

of CB and nonfatal heart attacks associated with reductions in ambient PM2.5. In general, QALY 

calculations require four elements: 

1. the estimated change in incidence of the health condition, 

2. the duration of the health condition, 

3. the quality-of-life weight with the health condition, and 

4. the quality-of-life weight without the health condition (i.e., the baseline health state). 

The first element is derived using the health impact function approach. The second element is 

based on the medical literature for each health condition. The third and fourth elements are 

derived from the medical cost-effectiveness and cost-utility literature. In the following two 

subsections, we discuss the choices of elements for CB and nonfatal heart attacks. 
6b-17

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 qualityof-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 preferencebased weights were available. Sources for weights are discussed in more detail below. Table G-4 

summarizes the key inputs for calculating QALYs associated with chronic health endpoints. 

6b.5.1 Calculating QALYs Associated with Reductions in the Incidence of Chronic Bronchitis 

CB is characterized by mucus in the lungs and a persistent wet cough for at least 3 months a year 

for several years in a row. CB affects an estimated 5 percent of the U.S. population (American 

Lung Association, 1999). For gains in quality of life resulting from reduced incidences of PMinduced CB, discounted QALYs are calculated as 

DISCOUNTED QALYGAINED CBi i i i D w w  

CB

i

 u u  ¦'

*

where CBi is the number of incidences of CB avoided in age interval i, wi is the average QALY 

weight for age interval i, wi

CB is the QALY weight associated with CB, Di

*

 is the discounted 

duration of life with CB for individuals with onset of disease in age interval i, equal to 

e d t rt

t

Di 

 

3 1

, where Di is the duration of life with CB for individuals with onset of disease in age 

interval i. 

A limited number of studies have estimated the impact of air pollution on new incidences of CB. 

Schwartz (1993) and Abbey et al. (1995) provide evidence that long-term PM exposure gives 

rise to the development of CB in the United States. Because this analysis focuses on the impacts 

of reducing ambient PM2.5, only the Abbey et al. (1995) study is used, because it is the only 

study focusing on the relationship between PM2.5 and new incidences of CB. The number of 

cases of CB in each age interval is derived from applying the impact function from Abbey et al. 

(1995), to the population in each age interval with the appropriate baseline incidence rate.4

 The 

effect estimate from the Abbey et al. (1995) study is 0.0137, which, based on the logistic 

4

 Prevalence rates for CB were obtained from the 1999 National Health Interview Survey 

(American Lung Association, 2002). Prevalence rates were available for three age groups: 18–

44, 45–64, and 65 and older. Prevalence rates per person for these groups were 0.0367 for 18–

44, 0.0505 for 45–64, and 0.0587 for 65 and older. The incidence rate for new cases of CB 

(0.00378 per person) was taken directly from Abbey et al. (1995). 
6b-18

specification of the model, is equivalent to a relative risk of 1.15 for a 10 g change in PM2.5.

Table G-5 presents the estimated reduction in new incidences of CB associated with the 

illustrative PM NAAQS attainment strategies. 

Table 6b-4: Summary of Key Parameters Used in QALY Calculations for Chronic Disease 

Endpoints 

Parameter Value(s) Source(s) 

Discount rate 0.03 (0.07 

sensitivity 

analysis) 

Gold et al. (1996), U.S. EPA (2000), U.S. OMB (2003) 

Quality of life preference 

score for chronic 

bronchitis 

0.5 – 0.7 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]) 

Duration of acute phase 

of acute myocardial 

infarction (AMI) 

5.5 days – 22 

days 

Uniform distribution with lower bound based on average 

length of stay for an AMI (AHRQ, 2000) and upper bound 

based on Vos (1999b). 

Probability of CHF post 

AMI 

0.2 Vos, 1999a (WHO Burden of Disease Study, based on 

Cowie et al., 1997) 

Probability of angina post 

AMI 

0.51 American Heart Association, 2003 

(Calculated as the population with angina divided by the 

total population with heart disease) 

Quality-of-life preference 

score for post-AMI with 

CHF (no angina) 

0.80 – 0.89 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. 

Quality-of-life preference 

score for post-AMI with 

CHF and angina 

0.76 – 0.85 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. 

Quality-of-life preference 

score for post-AMI with 

angina (no CHF) 

0.7 – 0.89 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). 

Quality-of-life preference 

score for post-AMI (no 

angina, no CHF) 

0.93 Only one value available from the literature. Thus, no 

distribution is specified. Source of value is Kuntz et al. 

(1996). 

CB is assumed to persist for the remainder of an affected individual’s lifespan. Duration of CB 

will thus equal life expectancy conditioned on having CB. CDC has estimated that COPD (of 

which CB is one element) results in an average loss of life years equal to 4.26 per COPD death, 

relative to a reference life expectancy of 75 years (CDC, 2003). Thus, we subtract 4.26 from the 

remaining life expectancy for each age group, up to age 75. For age groups over 75, we apply 

the ratio of 4.26 to the life expectancy for the 65 to 74 year group (0.237) to the life expectancy 
6b-19

for the 75 to 84 and 85 and up age groups to estimate potential life years lost and then subtract 

that value from the base life expectancy. 

Table 6b-5: Estimated Reduction in Incidence of Chronic Bronchitis Associated with Illustrative 

Attainment Strategies for the Revised and More Stringent Alternative PM NAAQS in 

2020

 Reduction in Incidence (95% Confidence Interval) 

Age Interval 070 ppm Partial Attainment Strategy 

25 – 34 74 

(14 – 140) 

35 – 44 85 

(16 – 160) 

45 – 54 82 

(15 – 150) 

55 – 64 88 

(16 – 160) 

65 – 74 62 

(12 – 640) 

75 – 84 31 

(6 – 56) 

85+ 14 

(3 – 25) 

Total 440 

(80 – 790) 

Quality of life with chronic lung diseases has been examined in several studies. In an analysis of 

the impacts of environmental exposures to contaminants, de Hollander et al. (1999) assigned a 

weight of 0.69 to years lived with CB. This weight was based on physicians’ evaluations of 

health states similar to CB. Salomon and Murray (2003) estimated a pooled weight of 0.77 

based on visual analogue scale, time trade-off, standard gamble, and person trade-off techniques 

applied to a convenience sample of health professionals. The Harvard Center for Risk Analysis 

catalog of preference scores reports a weight of 0.40 for severe COPD, with a range from 0.2 to 

0.8, based on the judgments of the study’s authors (Bell et al., 2001). The Victoria Burden of 

Disease (BoD) study used a weight of 0.47 for severe COPD and 0.83 for mild to moderate 

COPD, based on an analysis by Stouthard et al. (1997) of chronic diseases in Dutch populations 

(Vos, 1999a). Based on the recommendations of Gold et al. (1996), quality-of-life weights based 

on community preferences are preferred for CEA of interventions affecting broad populations. 

Use of weights based on health professionals is not recommended. It is not clear from the 

Victoria BoD study whether the weights used for COPD are based on community preferences or 

judgments of health professionals. The Harvard catalog score is clearly identified as based on 

author judgment. Given the lack of a clear preferred weight, we select a triangular distribution 

centered at 0.7 with an upper bound at 0.9 (slightly better than a mild/moderate case defined by 

the Victoria BoD study) and a lower bound at 0.5 based on the Victoria BoD study. We will 

need additional empirical data on quality of life with chronic respiratory diseases based on 

community preferences to improve our estimates. 
6b-20

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 qualityof-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 BallTM software program to develop the distribution of 

QALYs gained per incidence of CB for each age interval.5

 Based on the assumptions defined 

above, the mean 3 percent discounted QALY gained per incidence of CB for each age interval 

along with the 95 percent confidence interval resulting from the Monte Carlo simulation is 

presented in Table G-6. Table G-6 presents both the undiscounted and discounted QALYs 

gained per incidence. 

5

 Monte Carlo simulation uses random sampling from distributions of parameters to characterize 

the effects of uncertainty on output variables. For more details, see Gentile (1998). 
6b-21

Table 6b-6: QALYs Gained per Avoided Incidence of CB 

Age Interval QALYs Gained per Incidence 

Start Age End Age Undiscounted Discounted (3%) 

25 34 12.15 

(4.40-19.95) 

6.52 

(2.36-10.71) 

35 44 9.91 

(3.54-16.10) 

5.94 

(2.12-9.66) 

45 54 7.49 

(2.71-12.34) 

5.03 

(1.82-8.29) 

55 64 5.36 

(1.95-8.80) 

4.03 

(1.47-6.61) 

65 74 3.40 

(1.22-5.64) 

2.84 

(1.02-4.71) 

75 84 2.15 

(0.77-3.49) 

1.92 

(0.69-3.13) 

85+ 0.79 

(0.27-1.29) 

0.77 

(0.26-1.25) 

6b.5.2 Calculating QALYs Associated with Reductions in the Incidence of Nonfatal Myocardial 

Infarctions 

Nonfatal heart attacks, or acute myocardial infarctions, require more complicated calculations to 

derive estimates of QALY impacts. The actual heart attack, which results when an area of the 

heart muscle dies or is permanently damaged because of oxygen deprivation, and subsequent 

emergency care are of relatively short duration. Many heart attacks result in sudden death. 

However, for survivors, the long-term impacts of advanced CHD are potentially of long duration 

and can result in significant losses in quality of life and life expectancy. 

In this phase of the analysis, we did not independently estimate the gains in life expectancy 

associated with reductions in nonfatal heart attacks. Based on recommendations from the SABHES, we assumed that all gains in life expectancy are captured in the estimates of reduced 

mortality risk provided by the Pope et al. (2002) analysis. We only estimate the change in 

quality of life over the period of life affected by the occurrence of a heart attack. This may 

understate the QALY impacts of nonfatal heart attacks but ensures that the overall QALY impact 

estimates across endpoints do not double-count potential life-year gains. 

Our approach adapts a CHD model developed for the Victoria Burden of Disease study (Vos, 

1999b). This model accounts for the lost quality of life during the heart attack and the possible 

health states following the heart attack. Figure G-1 shows the heart attack QALY model in 

diagrammatic form. 

The total gain in QALYs is calculated as: 
6b-22

   

DISCOUNTED AMI QALY GAINED

AMI D w w AMI p D w w i i

AMI

i i

AMI

i j

i j ij

PostAMI

i ij

postAMI

i

 

¦ ¦ u u   u u  ¦ 

' '

* *

1

4

where AMIi is the number of nonfatal acute myocardial infarctions avoided in age interval i,

wi

AMI is the QALY weight associated with the acute phase of the AMI, pj

 is the probability of 

being in the jth post-AMI status, wij

postAMI is the QALY weight associated with post-AMI health 

status j, wi is the average QALY weight for age interval i, D e dt i

AMI rt

t

Di

AMI

*

 



 

3 1

 , the discounted 

value of Di

AMI , the duration of the acute phase of the AMI, and D e dt i

postAMI rt

t

Di

postAMI

*

 



 

3 1

, is the 

discounted value of Dij

PostAMI , the duration of post-AMI health status j.

Figure 6b-1. Decision Tree Used in Modeling Gains in QALYs from Reduced Incidence of 

Nonfatal Acute Myocardial Infarctions 

Nonfatal AMI

Congestive Heart Failure

Yes

No

Angina

Angina

Yes

No

No

Yes

Post AMI QALY with Angina and CHF

Post AMI QALY with CHF without Angina

Post AMI QALY with Angina without CHF

Post AMI QALY without Angina or CHF

Acute Treatment Stage Chronic Post-AMI Follow up Stage

Deleted: <sp>
6b-23

Nonfatal heart attacks have been linked with short-term exposures to PM2.5 in the United States 

(Peters et al., 2001) and other countries (Poloniecki et al., 1997). We used a recent study by 

Peters et al. (2001) as the basis for the impact function estimating the relationship between PM2.5

and nonfatal heart attacks. Peters et al. is the only available U.S. study to provide a specific 

estimate for heart attacks. Other studies, such as Samet et al. (2000) and Moolgavkar (2000), 

show a consistent relationship between all cardiovascular hospital admissions, including for 

nonfatal heart attacks, and PM. Given the lasting impact of a heart attack on longer-term health 

costs and earnings, we chose to provide a separate estimate for nonfatal heart attacks based on 

the single available U.S. effect estimate. The finding of a specific impact on heart attacks is 

consistent with hospital admission and other studies showing relationships between fine particles 

and cardiovascular effects both within and outside the United States. These studies provide a 

weight of evidence for this type of effect. Several epidemiologic studies (Liao et al., 1999; Gold 

et al., 2000; Magari et al., 2001) have shown that heart rate variability (an indicator of how much 

the heart is able to speed up or slow down in response to momentary stresses) is negatively 

related to PM levels. Heart rate variability is a risk factor for heart attacks and other CHDs 

(Carthenon et al., 2002; Dekker et al., 2000; Liao et al., 1997, Tsuji et al., 1996). As such, 

significant impacts of PM on heart rate variability are consistent with an increased risk of heart 

attacks. 

The number of avoided nonfatal AMI in each age interval is derived from applying the impact 

function from Peters et al. (2001) to the population in each age interval with the appropriate 

baseline incidence rate.6

 The effect estimate from the Peters et al. (2001) study is 0.0241, which, 

based on the logistic specification of the model, is equivalent to a relative risk of 1.27 for a 10 g

change in PM2.5. Table 6b-7 presents the estimated reduction in nonfatal AMI associated with 

the illustrative Ozone NAAQS attainment strategies.

6

 Daily nonfatal myocardial infarction incidence rates per person were obtained from the 1999 

National Hospital Discharge Survey (assuming all diagnosed nonfatal AMI visit the hospital). 

Age-specific rates for four regions are used in the analysis. Regional averages for populations 18 

and older are 0.0000159 for the Northeast, 0.0000135 for the Midwest, 0.0000111 for the South, 

and 0.0000100 for the West. 
6b-24

Table 6b-7: Estimated Reduction in Nonfatal Acute Myocardial Infarctions Associated with 

Illustrative Attainment Strategies for the Revised and More Stringent Alternative PM 

NAAQS in 2020 

 Reduction in Incidence*(95% Confidence Interval) 

Age Interval 070 ppm Attainment Strategy 

18 – 24 1 

(1 – 2) 

25 – 34 4 

(3 – 6) 

35 – 44 37 

(20 – 53) 

45 – 54 110 

(61 – 170) 

55 – 64 290 

(160 – 430) 

65 – 74 350 

(190 – 500) 

75 – 84 280 

(150 – 410) 

85+ 150 

(80 – 220) 

Total 1,200 

(660 – 1,800) 

Acute myocardial infarction results in significant loss of quality of life for a relatively short 

duration. The WHO Global Burden of Disease study, as reported in Vos (1999b), assumes that 

the acute phase of an acute myocardial infarction lasts for 0.06 years, or around 22 days. An 

alternative assumption is the acute phase is characterized by the average length of hospital stay 

for an AMI in the United States, which is 5.5 days, based on data from the Agency for 

Healthcare Research and Quality’s Healthcare Cost and Utilization Project (HCUP).7 We 

assumed a distribution of acute phase duration characterized by a uniform distribution between 

5.5 and 22 days, noting that due to earlier discharges and in-home therapy available in the United 

States, duration of reduced quality of life may continue after discharge from the hospital. In the 

period during and directly following an AMI (the acute phase), we assigned a quality of life 

weight equal to 0.605, consistent with the weight for the period in treatment during and 

immediately after an attack (Vos, 1999b). 

During the post-AMI period, a number of different health states can determine the loss in quality 

of life. We chose to classify post-AMI health status into four states defined by the presence or 

absence of angina and congestive heart failure (CHF). This makes a very explicit assumption 

that without the occurrence of an AMI, individuals would not experience either angina or CHF. 

7

 Average length of stay estimated from the HCUP data includes all discharges, including those 

due to death. As such, the 5.5-day average length of stay is likely an underestimate of the 

average length of stay for AMI admissions where the patient is discharged alive. 
6b-25

If in fact individuals already have CHF or angina, then the quality of life gained will be 

overstated. We do not have information about the percentage of the population have been 

diagnosed with angina or CHF with no occurrence of an AMI. Nor do we have information on 

what proportion of the heart attacks occurring due to PM exposure are first heart attacks versus 

repeat attacks. Probabilities for the four post-AMI health states sum to one. 

Given the occurrence of a nonfatal AMI, the probability of congestive heart failure is set at 0.2, 

following the heart disease model developed by Vos (1999b). The probability is based on a 

study by Cowie et al. (1997), which estimated that 20 percent of those surviving AMI develop 

heart failure, based on an analysis of the results of the Framingham Heart Study. 

The probability of angina is based on the prevalence rate of angina in the U.S. population. Using 

data from the American Heart Association, we calculated the prevalence rate for angina by 

dividing the estimated number of people with angina (6.6 million) by the estimated number of 

people with CHD of all types (12.9 million). We then assumed that the prevalence of angina in 

the population surviving an AMI is similar to the prevalence of angina in the total population 

with CHD. The estimated prevalence rate is 51 percent, so the probability of angina is 0.51. 

Combining these factors leads to the probabilities for each of the four health states as follows: 

I. Post AMI with CHF and angina = 0.102 

II. Post AMI with CHF without angina = 0.098 

III. Post AMI with angina without CHF = 0.408 

IV. Post AMI without angina or CHF = 0.392 

Duration of post-AMI health states varies, based in part on assumptions regarding life 

expectancy with post-AMI complicating health conditions. Based on the model used for 

established market economies (EME) in the WHO Global Burden of Disease study, as reported 

in Vos (1999b), we assumed that individuals with CHF have a relatively short remaining life 

expectancy and thus a relatively short period with reduced quality of life (recall that gains in life 

expectancy are assumed to be captured by the cohort estimates of reduced mortality risk). 

Table 6b-8 provides the duration (both discounted and undiscounted) of CHF assumed for postAMI cases by age interval. 
6b-26

Table 6b-8: Assumed Duration of Congestive Heart Failure 

Age Interval Duration of Heart Failure (years) 

Start Age End Age Undiscounted Discounted (3%) 

18 24 7.11 6.51 

25 34 6.98 6.40 

35 44 6.49 6.00 

45 54 5.31 4.99 

55 64 1.96 1.93 

65 74 1.71 1.69 

75 84 1.52 1.50 

85+ 1.52 1.50 

Duration of health states without CHF is assumed to be equal to the life expectancy of 

individuals conditional on surviving an AMI. Ganz et al. (2000) note that “Because patients with 

a history of myocardial infarction have a higher chance of dying of CHD that is unrelated to 

recurrent myocardial infarction (for example, arrhythmia), this cohort has a higher risk for death 

from causes other than myocardial infarction or stroke than does an unselected population.” 

They go on to specify a mortality risk ratio of 1.52 for mortality from other causes for the cohort 

of individuals with a previous (nonfatal) AMI. The risk ratio is relative to all-cause mortality for 

an age-matched unselected population (i.e., general population). We adopted the same ratios and 

applied them to each age-specific all-cause mortality rate to derive life expectancies (both 

discounted and undiscounted) for each age group after an AMI, presented in Table 6b-9. These 

life expectancies are then used to represent the duration of non-CHF post-AMI health states (III 

and IV). 

Table 6b-9: Assumed Duration of Non-CHF Post-AMI Health States 

Age Interval Post-AMI Years of Life Expectancy (non-CHF) 

Start Age End Age Undiscounted Discounted (3%) 

18 24 55.5 27.68 

25 34 46.1 25.54 

35 44 36.8 22.76 

45 54 27.9 19.28 

55 64 19.8 15.21 

65 74 12.8 10.82 

75 84 7.4 6.75 

85+ 3.6 3.47 

For the four post-AMI health states, we used QALY weights based on preferences for the 

combined conditions characterizing each health state. A number of estimates of QALY weights 

are available for post-AMI health conditions. 
6b-27

The first two health states are characterized by the presence of CHF, with or without angina. 

The Harvard Center for Risk Analysis catalog of preference scores provides several specific 

weights for CHF with and without mild or severe angina and one set specific to post-AMI CHF. 

Following the Victoria Burden of Disease model, we assumed that most cases of angina will be 

treated and thus kept at a mild to moderate state. We thus focused our selection on QALY 

weights for mild to moderate angina. The Harvard database includes two sets of community 

preference-based scores for CHF (Stinnett et al., 1996; Kuntz et al., 1996). The scores for CHF 

with angina range from 0.736 to 0.85. The lower of the two scores is based on angina in general 

with no delineation by severity. Based on the range of the scores for mild to severe cases of 

angina in the second study, one can infer that an average case of angina has a score around 0.96 

of the score for a mild case. Applying this adjustment raises the lower end of the range of 

preference scores for a mild case of angina to 0.76. We selected a uniform distribution over the 

range 0.76 to 0.85 for CHF with mild angina, with a midpoint of 0.81. The same two studies in 

the Harvard catalog also provide weights for CHF without angina. These scores range from 

0.801 to 0.89. We selected a uniform distribution over this range, with a midpoint of 0.85. 

The third health state is characterized by angina, without the presence of CHF. The Harvard 

catalog includes five sets of community preference-based scores for angina, one that specifies 

scores for both mild and severe angina (Kuntz et al., 1996), one that specifies mild angina only 

(Pliskin, Stason, and Weinstein, 1981), one that specifies severe angina only (Cohen, Breall, and 

Ho, 1994), and two that specify angina with no severity classification (Salkeld, Phongsavan, and 

Oldenburg, 1997; Stinnett et al., 1996). With the exception of the Pliskin, Stason, and Weinstein 

score, all of the angina scores are based on the time trade-off method of elicitation. The Pliskin, 

Stason, and Weinstein score is based on the standard gamble elicitation method. The scores for 

the nonspecific severity angina fall within the range of the two scores for mild angina 

specifically. Thus, we used the range of mild angina scores as the endpoints of a uniform 

distribution. The range of mild angina scores is from 0.7 to 0.89, with a midpoint of 0.80. 

For the fourth health state, characterized by the absence of CHF and/or angina, there is only one 

relevant community preference score available from the Harvard catalog. This score is 0.93, 

derived from a time trade-off elicitation (Kuntz et al., 1996). Insufficient information is 

available to provide a distribution for this weight; therefore, it is treated as a fixed value. 

Similar to CB, we assumed that the reference weight for the general population without AMI is 

0.95. To allow for uncertainty in this parameter, we assigned a triangular distribution around this 

weight, bounded by 0.9 and 1.0. 

Based on the assumptions defined above, we used Monte Carlo simulation methods as 

implemented in the Crystal BallTM software program to develop the distribution of QALYs 

gained per incidence of nonfatal AMI for each age interval. For the Monte Carlo simulation, all 

distributions were assumed to be independent. The mean QALYs gained per incidence of 

nonfatal AMI for each age interval is presented in Table 6b-10, along with the 95 percent 

confidence interval resulting from the Monte Carlo simulation. Table 6b-10 presents both the 

undiscounted and discounted QALYs gained per incidence. 
6b-28

Table 6b-10: QALYs Gained per Avoided Nonfatal Myocardial Infarction 

Age Interval QALYs Gained per Incidencea

Start Age End Age Undiscounted Discounted (3%) 

18 24 4.18 

(1.24-7.09) 

2.17 

(0.70-3.62) 

25 34 3.48 

(1.09-5.87) 

2.00 

(0.68-3.33) 

35 44 2.81 

(0.88-4.74) 

1.79 

(0.60-2.99) 

45 54 2.14 

(0.67-3.61) 

1.52 

(0.51-2.53) 

55 64 1.49 

(0.42-2.52) 

1.16 

(0.34-1.95) 

65 74 0.97 

(0.30-1.64) 

0.83 

(0.26-1.39) 

75 84 0.59 

(0.20-0.97) 

0.54 

(0.19-0.89) 

85+ 0.32 

(0.13-0.50) 

0.31 

(0.13-0.49) 

a

 Mean of Monte Carlo generated distribution; 95% confidence interval presented in parentheses. 

6b.6 Cost-Effectiveness Analysis 

Given the estimates of changes in life expectancy and quality of life, the next step is to aggregate 

life expectancy and quality-of-life gains to form an effectiveness measure that can be compared 

to costs to develop cost-effectiveness ratios. This section discusses the proper characterization of 

the combined effectiveness measure and the appropriate calculation of the numerator of the costeffectiveness ratio. 

6b.6.1 Aggregating Life Expectancy and Quality-of-Life Gains 

To develop an integrated measure of changes in health, we simply sum together the gains in life 

years from reduced mortality risk in each age interval with the gains in QALYs from reductions 

in incidence of CB and acute myocardial infarctions. The resulting measure of effectiveness 

then forms the denominator in the cost-effectiveness ratio. What is this combined measure of 

effectiveness? It is not a QALY measure in a strict sense, because we have not adjusted lifeexpectancy gains for preexisting health status (quality of life). It is however, an effectiveness 

measure that adds to the standard life years calculation a scaled morbidity equivalent. Thus, we 

term the aggregate measure morbidity inclusive life years, or MILYs. Alternatively, the 

combined measure could be considered as QALYs with an assumption that the community 

preference weight for all life-expectancy gains is 1.0. If one considers that this weight might be 

considered to be a “fair” treatment of those with preexisting disabilities, the effectiveness 

measure might be termed “fair QALY” gained. However, this implies that all aspects of fairness 

have been addressed, and there are clearly other issues with the fairness of QALYs (or other 

effectiveness measures) that are not addressed in this simple adjustment. The MILY measure 
6b-29

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 qualityof-life gains, it merely represents an alternative social weighting that is consistent with the spirit

of the recent OMB guidance on CEA. The guidance notes that “fairness is important in the 

choice and execution of effectiveness measures” (OMB, 2003). The resulting aggregate measure 

of effectiveness will not be consistent with a strict utility interpretation of QALYs; however, it 

may still be a useful index of effectiveness. 

Applying the life expectancies and distributions of QALYs per incidence for CB and AMI to 

estimated distributions of incidences yields distributions of life expectancy and QALYs gained 

due to the Ozone NAAQS illustrative attainment strategies. These distributions reflect both the 

quantified uncertainty in incidence estimates and the quantified uncertainty in QALYs gained per 

incidence. 

For the attainment strategy for the revised 070 ppm standards, Table 6b-11 presents the mean 3 

percent discounted MILYs gained for each age interval, broken out by life expectancy and 

quality-of-life categories. Note that quality-of-life gains occur from age 18 and up, while life 

expectancy gains accrue only after age 29. This is based on the ages of the study populations in 

the underlying epidemiological studies. It is unlikely that such discontinuities exist in reality, but 

to avoid overstating effectiveness, we chose to limit the life-expectancy gains to those occurring 

in the population 30 and over and the morbidity gains to the specific adult populations examined 

in the studies. 

It is worth noting that around a third of mortality-related benefits are due to reductions in 

premature deaths among those 75 and older, while only 7 percent of morbidity benefits occur in 

this age group. This is due to two factors: (1) the relatively low baseline mortality rates in 

populations under 75, and (2) the relatively constant baseline rates of chronic disease coupled 

with the relatively long period of life that is lived with increased quality of life without CB and 

advanced heart disease. 

The relationship between age and the distribution of MILYs gained from mortality and morbidity 

is shown for the 070 ppm attainment strategy in Figure _-2. Because the baseline mortality rate 

is increasing in age at a much faster rate than the prevalence rate for CB, the share of MILYs 

gained accounted for by mortality is proportional to age. At the oldest age interval, avoiding 

incidences of CB leads to only a few MILYs gained, due to the lower number of years lived with 

CB. MILYs gained from avoided premature mortality is low in the youngest age intervals 

because of the low overall mortality rates in these intervals, although the number of MILYs per 

incidence is high. In later years, even though the MILYs gained per incidence avoided is low, 

the number of cases is very high due to higher baseline mortality rates. 
6b-30

Table 6b-11. Estimated Gains in 3 Percent Discounted MILYs Associated with Illustrative 

Attainment Strategies for the Revised Ozone NAAQS (0.070 ppm) in 2020: Pope 

(2002) Estimate of Mortalitya

Age

Life Years Gained 

from Mortality Risk 

Reductions 

(95% CI) 

QALY Gained from 

Reductions in 

Chronic Bronchitis 

(95% CI) 

QALY Gained from 

Reductions in Acute 

Myocardial Infarctions 

(95% CI) 

Total Gain in 

MILYs 

(95% CI) 

18–24 — — 3 

(1 – 5) 

3

(1 – 5) 

25–34 100 

(41 – 170) 

490

(91 – 1,100) 

8

(3 – 15) 

600

(130 – 1,200) 

35–44 310 

(120 – 490) 

500

(93 – 1,100) 

70

(23 – 120) 

870

(240 – 1,700) 

45–54 580 

(230 – 930) 

420

(77 – 890) 

170 

(60 – 320) 

1,200 

(360 – 2,100) 

55–64 1,300 

(500 – 2,100) 

350

(64 – 760) 

330 

(110 – 620) 

2,000 

(680 – 3,400) 

65–74 1,700 

(680 – 2,800) 

180

(33 – 380) 

280 

(100 – 530) 

2,200 

(810 – 3,700) 

75–84 1,400 

(540 – 2,200) 

60 

(11 – 130) 

150 

(54 – 280) 

1,600 

(610 – 2,600) 

85+ 690 

(270 – 1,100) 

10 

(2 – 22) 

43

(16 – 80) 

740

(290 – 1,200) 

Total 6,100 

(2,400 – 9,800) 

2,000 

(370 – 4,300) 

1,100 

(370 – 2,000) 

9,100 

(3,100 – 16,000) 

a

 Note that all estimates have been rounded to two significant digits. 
6b-31

Table 6b-12. Estimated Gains in 3 Percent Discounted MILYs Associated with Illustrative 

Attainment Strategies for the Revised Ozone NAAQS (0.070 ppm) in 2020: Laden 

(2006) Estimate of Mortalitya

Age

Life Years Gained 

from Mortality Risk 

Reductions 

(95% CI) 

QALY Gained from 

Reductions in 

Chronic Bronchitis 

(95% CI) 

QALY Gained from 

Reductions in Acute 

Myocardial Infarctions 

(95% CI) 

Total Gain in 

MILYs 

(95% CI) 

18–24 — — 3 

(1 – 5) 

3

(1 – 5) 

25–34 240 

(130 – 340) 

490

(91 – 1,100) 

8

(3 – 15) 

730

(220 – 1,400) 

35–44 690 

(380 – 1,000) 

500

(93 – 1,100) 

70

(23 – 120) 

1,300 

(490 – 2,200) 

45–54 1,400 

(710 – 1,900) 

420

(77 – 890) 

170 

(60 – 320) 

1,900 

(850 – 3,100) 

55–64 2,900 

(1,600 – 4,200) 

350

(64 – 760) 

330 

(110 – 620) 

3,600 

(1,800 – 5,600) 

65–74 3,900 

(2,100 – 5,700) 

180

(33 – 380) 

280 

(100 – 530) 

4,400 

(2,300 – 6,600) 

75–84 3,100 

(1,700 – 4,600) 

60 

(11 – 130) 

150 

(54 – 280) 

3,300 

(1,800 – 5,000) 

85+ 1,600 

(840 – 2,300) 

10 

(2 – 22) 

43

(16 – 80) 

1,600 

(860 – 2,400) 

Total 14,000 

(7,500 – 20,000) 

2,000 

(370 – 4,300) 

1,100 

(370 – 2,000) 

17,000 

(8,200 – 26,000) 

a

 Note that all estimates have been rounded to two significant digits. 

Summing over the age intervals provides estimates of total MILYs gained for the Ozone 

NAAQS illustrative attainment strategies. The total number of discounted (3 percent) MILYs 

gained for the 070 ppm attainment strategy using the Pope (2002) estimate is 9,100 (95% CI: 

3,100 – 16,000). Using the Laden (2006) estimate, the total number of discounted (3 percent) 

MILYs is 17,000 (95% CI: 8,200 – 26,000). 

6b.6.2 Dealing with Acute Health Effects and Non-health Effects 

Health effects from exposure to particulate air pollution encompass a wide array of chronic and 

acute conditions in addition to premature mortality (EPA, 1996). Although chronic conditions 

and premature mortality generally account for the majority of monetized benefits, acute 

symptoms can affect a broad population or sensitive populations (e.g., asthma exacerbations in 

asthmatic children. In addition, reductions in air pollution may result in a broad set of nonhealth 

environmental benefits, including improved visibility in national parks, increased agricultural 

and forestry yields, reduced acid damage to buildings, and a host of other impacts. QALYs 

address only health impacts, and the OMB guidance notes that “where regulation may yield 

several different beneficial outcomes, a cost-effectiveness comparison becomes more difficult to 

interpret because there is more than one measure of effectiveness to incorporate in the analysis.” 
6b-32

With regard to acute health impacts, Bala and Zarkin (2000) suggest that QALYs are not 

appropriate for valuing acute symptoms, because of problems with both measuring utility for 

acute health states and applying QALYs in a linear fashion to very short duration health states. 

Johnson and Lievense (2000) suggest using conjoint analysis to get healthy-utility time 

equivalences that can be compared across acute effects, but it is not clear how these can be 

combined with QALYs for chronic effects and loss of life expectancy. There is also a class of 

effects that EPA has traditionally treated as acute, such as hospital admissions, which may also 

result in a loss of quality of life for a period of time following the effect. For example, life after 

asthma hospitalization has been estimated with a utility weight of 0.93 (Bell et al., 2001; 

Kerridge, Glasziou, and Hillman, 1995). 

How should these effects be combined with QALYs for chronic and mortality effects? One 

method would be to convert the acute effects to QALYs; however, as noted above, there are 

problems with the linearity assumption (i.e., if a year with asthma symptoms is equivalent to 0.7 

year without asthma symptoms, then 1 day without asthma symptoms is equivalent to 0.0019 

QALY gained). This is troubling from both a conceptual basis and a presentation basis. An 

alternative approach is simply to treat acute health effects like nonhealth benefits and subtract the 

dollar value (based on WTP or COI) from compliance costs in the CEA. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

25-34 35-44 45-54 55-64 65-74 75-84 85+

Age Group

Percent of Total MILY

Morbidity

Mortality

Figure 6b-2. Distribution of Mortality and Morbidity Related MILY Across Age Groups 

for Illustrative Attainment Strategy for the Revised PM NAAQS (3 percent 

Discount Rate)

To address the issues of incorporating acute morbidity and nonhealth benefits, OMB suggests 

that agencies “subtract the monetary estimate of the ancillary benefits from the gross cost 

estimate to yield an estimated net cost.” As with benefit-cost analysis, any unquantified benefits 

and/or costs should be noted and an indication of how they might affect the cost-effectiveness 
6b-33

ratio should be described. We will follow this recommended “net cost” approach in the 

illustrative exercise, specifically in netting out the benefits of health improvements other than 

reduced mortality and chronic morbidity, and the benefits of improvements in visibility at 

national parks (see Chapter 5 for more details on these benefit categories). 

6b.6.3 Cost-Effectiveness Ratios 

Construction of cost-effectiveness ratios requires estimates of effectiveness (in this case 

measured by lives saved, life years gained, or MILYs gained) in the denominator and estimates 

of costs in the numerator. The estimate of costs in the numerator should include both the direct 

costs of the controls necessary to achieve the reduction in ambient PM2.5 and the avoided costs 

(cost savings) associated with the reductions in morbidity (Gold et al., 1996). In general, 

because reductions in air pollution do not require direct actions by the affected populations, there 

are no specific costs to affected individuals (aside from the overall increases in prices that might 

be expected to occur as control costs are passed on by affected industries). Likewise, because 

individuals do not engage in any specific actions to realize the health benefit of the pollution 

reduction, there are no decreases in utility (as might occur from a medical intervention) that need 

to be adjusted for in the denominator. Thus, the elements of the numerator are direct costs of 

controls minus the avoided COI associated with CB and nonfatal AMI. In addition, to account 

for the value of reductions in acute health impacts and nonhealth benefits, we net out the 

monetized value of these benefits from the numerator to yield a “net cost” estimate. For the 

MILY aggregate effectiveness measure, the denominator is simply the sum of life years gained 

from increased life expectancy and the sum of QALYs gained from the reductions in CB and 

nonfatal AMI. 

Avoided costs for CB and nonfatal AMI are based on estimates of lost earnings and medical 

costs.8

 Using age-specific annual lost earnings and medical costs estimated by Cropper and 

Krupnick (1990) and a 3 percent discount rate, we estimated a lifetime present discounted value 

(in 2000$) due to CB of $150,542 for someone between the ages of 27 and 44; $97,610 for 

someone between the ages of 45 and 64; and $11,088 for someone over 65. The corresponding 

age-specific estimates of lifetime present discounted value (in 2000$) using a 7 percent discount 

rate are $86,026, $72,261, and $9,030, respectively. These estimates assumed that 1) lost 

earnings continue only until age 65, 2) medical expenditures are incurred until death, and 3) life 

expectancy is unchanged by CB. 

Because the costs associated with a myocardial infarction extend beyond the initial event itself, 

we consider costs incurred over several years. Using age-specific annual lost earnings estimated 

by Cropper and Krupnick (1990) and a 3 percent discount rate, we estimated a present 

8

 Gold et al. (1996) recommend not including lost earnings in the cost-of-illness estimates, 

suggesting that in some cases, they may be already be counted in the effectiveness measures. 

However, this requires that individuals fully incorporate the value of lost earnings and reduced 

labor force participation opportunities into their responses to time-tradeoff or standard-gamble 

questions. For the purposes of this analysis and for consistency with the way costs-of-illness are 

calculated for the benefit-cost analysis, we have assumed that individuals do not incorporate lost 

earnings in responses to these questions. This assumption can be relaxed in future analyses with 

improved understanding of how lost earnings are treated in preference elicitations. 
6b-34

discounted value in lost earnings (in 2000$) over 5 years due to a myocardial infarction of 

$8,774 for someone between the ages of 25 and 44, $12,932 for someone between the ages of 45 

and 54, and $74,746 for someone between the ages of 55 and 65. The corresponding agespecific estimates of lost earnings (in 2000$) using a 7 percent discount rate are $7,855, $11,578, 

and $66,920, respectively. Cropper and Krupnick (1990) do not provide lost earnings estimates 

for populations under 25 or over 65. Thus, we do not include lost earnings in the cost estimates 

for these age groups. 

Two estimates of the direct medical costs of myocardial infarction are used. The first estimate is 

from Wittels, Hay, and Gotto (1990), which estimated expected total medical costs of MI over 5 

years to be $51,211 (in 1986$) for people who were admitted to the hospital and survived 

hospitalization (there does not appear to be any discounting used). Using the CPI-U for medical 

care, the Wittels estimate is $109,474 in year 2000$. This estimated cost is based on a medical 

cost model, which incorporated therapeutic options, projected outcomes, and prices (using 

“knowledgeable cardiologists” as consultants). The model used medical data and medical 

decision algorithms to estimate the probabilities of certain events and/or medical procedures 

being used. The second estimate is from Russell et al. (1998), which estimated first-year direct 

medical costs of treating nonfatal myocardial infarction of $15,540 (in 1995$), and $1,051 

annually thereafter. Converting to year 2000$, that would be $23,353 for a 5-year period 

(without discounting). 

The two estimates from these studies are substantially different, and we have not adequately 

resolved the sources of differences in the estimates. Because the wage-related opportunity cost 

estimates from Cropper and Krupnick (1990) cover a 5-year period, we used estimates for 

medical costs that similarly cover a 5-year period. We used a simple average of the two 5-year 

estimates, or $65,902, and add it to the 5-year opportunity cost estimate. The resulting estimates 

are given in Table 6b-13. 

Table 6b-13: Estimated Costs Over a 5-Year Period (in 2000$) of a Nonfatal Myocardial Infarction 

Age Group Opportunity Cost Medical Costa

 Total Cost 

0 – 24 $0 $65,902 $65,902 

25-44 $8,774b $65,902 $74,676 

45 – 54 $12,253b $65,902 $78,834 

55 – 65 $70,619b $65,902 $140,649 

>65 $0 $65,902 $65,902 

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 is provided in Table 6b-14. Note that the total avoided COI associated 

with the revised PM NAAQS is $520 million and is $1,200 million for the more stringent 

alternative. Note that this does not include any direct avoided medical costs associated with 

premature mortality. Nor does it include any medical costs that occur more than 5 years from the 

onset of a nonfatal AMI. Therefore, this is likely an underestimate of the true avoided COI 

associated with strategies for attainment of the PM NAAQS. 
6b-35

Table 6b-14: Avoided Costs of Illness Associated with Reductions in Chronic Bronchitis and 

Nonfatal Acute Myocardial Infarctions Associated with Attainment Strategies for the 

0.070 ppm alternative Ozone NAAQS in 2020 

Avoided Cost of Illness 

(in millions of 1999$)

Age

Range Chronic Bronchitis Nonfatal Acute Myocardial Infarction 

18-24 — $0.07 

25-34 $11 $0.3 

35-44 $13 $2.6 

45-54 $7 $8.6 

55-64 $8 $40 

65-74 $0.7 $22 

75-84 $0.3 $18 

85+ $0.1 $9.4 

Total $41 $100 

6b.7 Discount Rate Sensitivity Analysis 

A large number of parameters and assumptions are necessary in conducting a CEA. Where 

appropriate and supported by data, we have included distributions of parameter values that were 

used in generating the reported confidence intervals. For the assumed discount rate, we felt it 

more appropriate to examine the impact of the assumption using a sensitivity analysis rather than 

through the integrated probabilistic uncertainty analysis. 

The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing 

discussion within the academic community. OMB and EPA guidance require using both a 7 

percent rate and a 3 percent rate. In the most recent benefit-cost analyses of air pollution 

regulations, a 3 and 7 percent discount rate have been adopted in the primary analysis. A 3 

percent discount rate reflects a “social rate of time preference” discounting concept. A 3 percent 

discount rate is also consistent with the recommendations of the NAS panel on CEA (Gold et al., 

1996), which suggests that “a real annual (riskless) rate of 3 percent should be used in the 

Reference Case analysis.” We have also calculated MILYs and the implicit cost thresholds using 

a 7 percent rate consistent with an “opportunity cost of capital” concept to reflect the time value 

of resources directed to meet regulatory requirements. Further discussion of this topic appears in 

Chapter 7 of Gold et al. (1996), in Chapter 6 of the EPA Guidelines for Economic Analysis, and 

in OMB Circular A-4. 
6b-36

Table 6b-15: Summary of Results for the Illustrative Partial Attainment Strategies for the 

Alternative Ozone Standard of 0.070 ppm in 2020a

 Result Using 3% Discount Rate (95% Confidence Interval) 

 070 ppm Partial Attainment Strategy 

Life years gained from mortality 

risk reductions 

 Pope et al. (2002) 6,100 

(2,400 – 9,800) 

 Laden et al. (2006) 14,000 

(7,500 – 20,000 

QALY gained from reductions in 

chronic bronchitis 

2,000 

(370 – 4,300) 

QALY gained from reductions in 

acute myocardial infarctions 

1,100 

(370 – 2,000) 

Total gain in MILYs 

 Pope et al. (2002) 9,100 

(3,100 – 16,000) 

 Laden et al. (2006) 17,000 

(8,200 – 26,000) 

Avoided cost of illness 

Chronic bronchitis $41 million 

($7.6 million – $75 million) 

Nonfatal AMI $100 million 

($63 million – $220 million) 

Implementation strategy costsb $3.9 billion 

Net cost per MILY 

 Pope et al. (2002) $410,000 

($220,000 – $1,300,000) 

 Laden et al. (2006) $220,000 

($140,000 – $470,000) 

a

 Consistent with recommendations of Gold et al. (1996), all summary results are reported at a precision level of 

two significant digits to reflect limits in the precision of the underlying elements. 

b

 Costs are the private firm costs of control, as discussed in Chapter 6, and reflect discounting using firm 

specific costs of capital. 

Table 6b-16 presents a summary of results using the 7 percent discount rate and the percentage 

difference between the 7 percent results and the base case 3 percent results. Adoption of a 7 

percent discount rate decreases the estimated life years and QALYs gained from implementing 

the PM NAAQS. Adopting a discount rate of 7 percent results in a 35 percent reduction in the 

estimated total MILYs gained in each year, while the cost per MILY increases by approximately 

60 percent. 
6b-37

Table 6b-16: Impacts of Using a 7 Percent Discount Rate on Cost Effectiveness Analysis for the 

Illustrative Attainment Strategies for the Revised and More Stringent PM NAAQS in 

2020

6b.8 Conclusions 

We calculated the effectiveness of PM NAAQS attainment strategies based on reductions in 

premature deaths and incidence of chronic disease. We measured effectiveness using several 

different metrics, including lives saved, life years saved, and QALYs (for improvements in 

quality of life due to reductions in incidence of chronic disease). We suggested a new metric for 

aggregating life years saved and improvements in quality of life, morbidity inclusive life years 

(MILY) which assumes that society assigns a weight of one to years of life extended regardless 

of preexisting disabilities or chronic health conditions. 

CEA of environmental regulations that have substantial public health impacts may be 

informative in identifying programs that have achieved cost-effective reductions in health 

impacts and can suggest areas where additional controls may be justified. However, the overall 

efficiency of a regulatory action can only be judged through a complete benefit-cost analysis that 

takes into account all benefits and costs, including both health and nonhealth effects. The 

benefit-cost analysis for the PM NAAQS attainment strategies, provided in Chapter 9, shows that 

the attainment strategies we modeled have potentially large net benefits, indicating that 

implementation of the revised PM NAAQS will likely result in improvements in overall public 

welfare. 

Result Using 7 Percent 

Discount Rate

Percentage Change Relative to 

Result Using 3 Percent 

Discount Rate

Life years gained from 

mortality risk reductions 

4,600 –24% 

10,000 -24% 

QALY gained from reductions 

in chronic bronchitis 

1,300 –35% 

QALY gained from reductions 

in acute myocardial infarctions 

830 –21% 

Total gain in MILYs 

 Pope et al. (2002) 6,500 -26% 

 Laden et al. (2006) 12,000 -25% 

Avoided cost of illness 

Chronic bronchitis $27 million –36% 

Nonfatal AMI $111 million +10% 

Net cost per MILY 

 Pope et al. (2002) $550,000 +36% 

 Laden et al. (2006) $300,000 +33% 
6b-38

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Quality Standards for Particulate Matter: Assessment of Scientific and Technical Information. 

Report no. EPA/4521R-96-013. Research Triangle Park, NC: Office of Air Quality Planning 

and Standards. 

U.S. Environmental Protection Agency (EPA). 2000. Guidelines for Preparing Economic 

Analyses. EPA 240-R-00-003. 

U.S. Environmental Protection Agency (EPA). 2004. Final Regulatory Analysis: Control of 

Emissions from Nonroad Diesel Engines. 
6b-43

U.S. Environmental Protection Agency (EPA) Science Advisory Board (SAB). EPA-SABCOUNCIL-ADV-04-002. March 2004. Advisory on Plans for Health Effects Analysis in the 

Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of the Clean Air 

Act, 1990-2020: Advisory by the Health Effects Subcommittee of the Advisory Council on 

Clean Air Compliance Analysis.

U.S. Food and Drug Administration (FDA). 2004. Bar Code Label Requirements for Human 

Drug Products and Biological Products; Final Rule. Federal Register: February 26, 2004 

Volume 69 (38): 9119-9171. 

U.S. Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis, 

September 17, 2003. Available at: http://www.whitehouse.gov/omb/ circulars/a004/a-4.pdf. 

Vos, T. 1999a. Final worksheet VM1 COPD.xls, prepared for Victoria Burden of Disease 

Study. Available at: http://www.dhs.vic.gov.au/phd/bod/daly.htm. 

Vos, T. 1999b. Final worksheet VL2 IHD.xls, prepared for Victoria Burden of Disease Study. 

Available at: http://www.dhs.vic.gov.au/phd/bod/daly.htm. 

Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. 1990. “Medical Costs of Coronary Artery Disease 

in the United States.” American Journal of Cardiology 65(7):432-40. 
6c-1

Appendix Chapter 6-c: Additional Sensitivity Analyses Related To the Benefits Analysis

The analysis presented in Chapter 6 is based on our current interpretation of the scientific and 

economic literature. That interpretation requires judgments regarding the best available data, 

models, and modeling methodologies and the assumptions that are most appropriate to adopt 

in the face of important uncertainties. The majority of the analytical assumptions used to 

develop the primary estimates of benefits have been reviewed and approved by EPA’s SAB.

Both EPA and the SAB recognize that data and modeling limitations as well as simplifying 

assumptions can introduce significant uncertainty into the benefit results and that alternative 

choices exist for some inputs to the analysis, such as the mortality C-R functions. 

This appendix supplements our primary analysis of benefits with three additional sensitivity 

calculations. These supplemental estimates examine sensitivity to both valuation issues (e.g., 

the appropriate income elasticity) and for physical effects issues (e.g., the structure of the 

cessation lag and the sensitivity of the premature mortality estimate to the presence of a 

presumed threshold). These supplemental estimates are not meant to be comprehensive. 

Rather, they reflect some of the key issues identified by EPA or commentors as likely to have 

a significant impact on total benefits. The individual adjustments in the tables should not 

simply be added together because 1) there may be overlap among the alternative assumptions 

and 2) the joint probability among certain sets of alternative assumptions may be low. 

6c.1 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” (EPASAB-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-2

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-ADV04-002, 2004, p. 24). However, they noted that “an important question to be resolved is what 

the relative magnitudes of these segments should be, and how many of the acute effects are 

assumed to be included in the cohort effect estimate” (EPA-SAB-COUNCIL-ADV-04-002, 

2004, p. 24-25). Since the publication of that report in March 2004, EPA has sought 

additional clarification from this committee. In its followup advice provided in December 

2004, this SAB suggested that until additional research has been completed, EPA should 

assume a segmented lag structure characterized by 30 percent of mortality reductions 

occurring in the first year, 50 percent occurring evenly over years 2 to 5 after the reduction in 

PM2.5, and 20 percent occurring evenly over the years 6 to 20 after the reduction in PM2.5

(EPA-COUNCIL-LTR-05-001, 2004). The distribution of deaths over the latency period is 

intended to reflect the contribution of short-term exposures in the first year, cardiopulmonary 

deaths in the 2- to 5-year period, and long-term lung disease and lung cancer in the 6- to 20-

year period. Furthermore, in their advisory letter, the SAB-HES recommended that EPA 

include sensitivity analyses on other possible lag structures. In this appendix, we investigate 

the sensitivity of premature mortality-reduction related benefits to alternative cessation lag 

structures, noting that ongoing and future research may result in changes to the lag structure 

used for the primary analysis. 

In previous advice from the SAB-HES, they recommended an analysis of 0-, 8-, and 15-year 

lags, as well as variations on the proportions of mortality allocated to each segment in the 

segmented lag structure (EPA-SAB-COUNCIL-ADV-00-001, 1999, (EPA-COUNCIL-LTR05-001, 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 

1Although 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-3

Pope et al. and Dockery et al. analyses do not supporting or refute the existence of a lag.

Therefore, any lag structure applied to the avoided incidences estimated from either of these 

studies will be an assumed structure. The 8- and 15-year lags implicitly assume that all 

premature mortalities occur at the end of the study periods (i.e., at 8 and 15 years).

In addition to the simple 8- and 15-year lags, we have added three additional sensitivity 

analyses examining the impact of assuming different allocations of mortality to the 

segmented lag of the type suggested by the SAB-HES. The first sensitivity analysis assumes 

that more of the mortality impact is associated with chronic lung diseases or lung cancer and 

less with acute cardiopulmonary causes. This illustrative lag structure is characterized by 20 

percent of mortality reductions occurring in the first year, 50 percent occurring evenly over 

years 2 to 5 after the reduction in PM2.5, 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 Röösli et al. (2004), which estimates the 

percentage of total mortality impact in each period t as 

(C.1) 

The Röösli 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-1. These estimates are based on the value of statistical 

lives saved approach (i.e., $5.5 million per incidence) and are presented for both a 3 and 7 

percent discount rate over the lag period.

>  @

> @  

%

.

.

Mortality Reduction(t) 

  

  





 

f

¦

RR e

RR e

t

t

t

1 1 1

1 1 1

0 5

0 5

1
6c-4

Table 6c-1. 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

(billion 1999$)a.b

Percent 

Difference from 

Base Estimate 

None Incidences all occur in the first year 

 3% discount rate $3.5 10.4% 

 7% discount rate $3.5 31.2% 

8-year Incidences all occur in the 8th year 

 3% discount rate $2.9 -10.3% 

 7% discount rate $2.2 -18.3% 

15-year Incidences all occur in the 15th year 

 3% discount rate $2.3 -27.0% 

 7% discount rate $1.4 -49.1% 

Alternative 

Segmented

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 $3.1 -3.2% 

 7% discount rate $2.5 -8.7% 

5-Year

Distributed

50 percent of incidences occur in years 1 

and 2 and 50 percent in years 2 to 5 

 3% discount rate $3.4 4.9% 

 7% discount rate $3.1 17.1% 

Exponential Incidences occur at an exponentially 

declining rate following year of change in 

exposure

 3% discount rate $3.4 5.6% 

 7% discount rate $3.1 14.8% 

a

 Dollar values rounded to two significant digits. 
6c-5

The results of this sensitivity analyses demonstrate that because of discounting of delayed 

benefits, the lag structure may also have a large impact on monetized benefits, reducing 

benefits by 30 percent if an extreme assumption that no effects occur until after 15 years is 

applied. However, for most reasonable distributed lag structures, differences in the specific 

shape of the lag function have relatively small impacts on overall benefits. For example, the 

overall impact of moving from the previous 5-year distributed lag to the segmented lag 

recommended by the SAB-HES in 2004 in the primary estimate is relatively modest, 

reducing benefits by approximately 5 percent when a 3 percent discount rate is used and 15 

percent when a 7 percent discount rate is used. If no lag is assumed, benefits are increased 

by around 10 percent relative to the segmented lag with a 3 percent discount rate and 30 

percent with a 7 percent discount rate.

6c. 2 Threshold Sensitivity Analysis 

Chapter 6 presents the results of the PM2.5 premature mortality benefits analysis based on an 

assumed cutpoint in the long-term mortality concentration-response function at 10 μg/m3,

and an assumed cutpoint in the short-term morbidity concentration-response functions at 10 

μg/m3

. There is ongoing debate as to whether there exists a threshold below which there 

would be no benefit to further reductions in PM2.5. Some researchers have hypothesized the 

presence of a threshold relationship. The nature of the hypothesized relationship is the 

possibility that there exists a PM concentration level below which further reductions no 

longer yield premature mortality reduction benefits. EPA’s most recent PM2.5 Criteria 

Document concludes that “the available evidence does not either support or refute the 

existence of thresholds for the effects of PM on mortality across the range of concentrations 

in the studies” (U.S. EPA, 2004b, p. 9-44). EPA’s Science Advisory Board (SAB) that 

provides advice on benefits analysis methods2 has been to model premature mortality 

associated with PM exposure as a non-threshold effect, that is, with harmful effects to 

exposed populations regardless of the absolute level of ambient PM concentrations. 

For these reasons we provide the results of a sensitivity analysis in which we estimate the 

change in reduction in incidence of PM2.5-related premature mortality resulting from 

changes in the presumed threshold. We also provide a corresponding estimate of the 

valuation of these changes in incidence. 

2

 The advice from the 2004 SAB-HES (U.S. EPA-SAB, 2004b) is characterized by the following: “For the 

studies of long-term exposure, the HES notes that Krewski et al. (2000) have conducted the most careful work 

on this issue. They report that the associations between PM2.5 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-6

Table 6c-2: Mortality Threshold Sensitivity Analysis for 0.070 ppm Ozone Scenario (Using 

Pope et al., 2002 Effect Estimate with Slope Adjustment for Thresholds Above 7.5 ug) 90th

Percentile Confidence Intervals Provided in Parentheses a

 East 

Western U.S. 

Excluding CA California Total 

Less Certainty 

That Benefits Are 

at Least as Large 

No

Threshold

570

(230—920) 

28

(11—45) 

53

(21—85) 

650

(260—1,100) 

 Threshold 

at 7.5 μg 

580

(230—930) 

15

(6—24) 

51

(20—85) 

650

(250—1,000) 

 Threshold 

at 10 μg 

510

(200—810) 

0.2

(0.07—0.3) 

47

(18—75) 

550

(220—890) 

 Threshold 

at 12 μg 

100

(40—160) 

0.03

(0.01—0.04) 

42

(16—67) 

140

(56—230) 

More Certainty 

That Benefits are 

at Least as Large 

Threshold

at 14 μg 

--- --- 36 

(14—59) 

36

(14—59) 

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will not 

sum across columns. 
6c-7

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) 90

th Percentile Confidence Intervals 

Provided in Parentheses a

 Eastern U.S. 

Western U.S. 

Excluding CA California

Total Nationwide 

Attainment 

3%

$3,300

($830--

$6,900)

$160

($41--$340) 

$310

($77--$630) 

$3,800

($950--$7,900) 

Less Certain 

that Benefits 

Are at Least 

as Large 

No

Threshold

7%

$2,800

($700--

$5,800)

$140

($34--$280) 

$260

($64--$530) 

$3,200

($800--$6,200) 

3%

$3,400

($840--

$7,000)

$86

($22--$$180) 

$300

($74--$620) 

$3,700

($940--$7,800) 

Threshold at 

7.5 ug 

7%

$2,800

($710--

$5,900)

$72

($18--$150) 

$250

($63--$520) 

$3,200

($790--$6,600) 

3%

$2,900

($730--

$6,100)

$1

($0.2--$2) 

$270

($67--$560) 

$3,200

($800--$6,600) 

Threshold at 

10 ug 

7%

$2,500

($620--

$5,100)

$0.8

($0.2--$1.7) 

$230

($57--$470) 

$2,700

($670--$5,600) 

3%

$590

($150--

$1,200)

$0.2

($0.04--$0.3) 

$240

($61--$500) 

$830

($210--$1,700) 

Threshold at 

12 ug 

7%

$490

($120--

$1,000)

$0.1

($0.03--$0.3) 

$200

($51--$420) 

$700

($180--$1,500) 

3%

-- -- $210 

($53--$440) 

$210

($53--$440) 

More Certain 

that Benefits 

Are at Least 

as Large 

Threshold at 

14 ug 

7%

-- -- $180 

($44--$370) 

$180

($44--$370) 

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded estimates. As such, totals will 

not sum across columns. 
6c-8

6c.3 Income Elasticity of Willingness to Pay 

As discussed in Chapter 6, our estimates of monetized benefits account for growth in real 

GDP per capita by adjusting the WTP for individual endpoints based on the central estimate 

of the adjustment factor for each of the categories (minor health effects, severe and chronic 

health effects, premature mortality, and visibility). We examined how sensitive the estimate 

of total benefits is to alternative estimates of the income elasticities. Table 6c-3 lists the 

ranges of elasticity values used to calculate the income adjustment factors, while Table 6c-4 

lists the ranges of corresponding adjustment factors. The results of this sensitivity analysis, 

giving the monetized benefit subtotals for the four benefit categories, are presented in Table 

6c-5.

Table 6c-4. Ranges of Elasticity Values Used to Account for Projected Real Income 

Growtha

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 

Visibilityb — — 

a

 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 

Growtha

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 — — 

a

 Based on elasticity values reported in Table C-4, U.S. Census population projections, and projections of real GDP per 

capita. 

b

 No range was applied for visibility because no ranges were available in the current published literature.
6c-9

Table 6c-6. Sensitivity of Monetized Benefits to Alternative Income Elasticitiesa

Benefits Incremental to 080 ppm Partial Attainment Strategy 

(Millions of 1999$) 

Benefit Category Ozone Analysis PM Analysis 

Lower

Sensitivity 

Bound

Upper 

Sensitivity 

Bound

Lower

Sensitivity 

Bound

Upper 

Sensitivity 

Bound

Minor Health Effect $64 $72 $9.7 $11 

Severe and Chronic Health Effects -- -- $160 $190 

Premature Mortalityb

$2,000 $3,000 $2,800 $3,600 

Total Benefitsb

$2,000 $3,100 $2,900 $3,800 

a

 All estimates rounded to two significant digits. 

b Using mortality effect estimate from Pope et al (2002) to estimate PM2.5 mortality and a 3 percent discount 

rate and mortality effect estimate from Bell (2004). 

c

 No range was applied for visibility because no ranges were available in the current published literature. 

Consistent with the impact of mortality on total benefits, the adjustment factor for mortality 

has the largest impact on total benefits. The value of mortality in 2020 ranges from 90 

percent to 130 percent of the primary estimate based on the lower and upper sensitivity 

bounds on the income adjustment factor. The effect on the value of minor and chronic health 

effects is much less pronounced, ranging from 98 percent to 105 percent of the primary 

estimate for minor effects and from 93 percent to 106 percent for chronic effects. 

6c.4 References 

Chestnut, L.G. 1997. “Draft Memorandum: Methodology for Estimating Values for 

Changes in Visibility at National Parks.” April 15.

Chestnut, L.G., and R.D. Rowe. 1990a. Preservation Values for Visibility Protection at the 

National Parks: Draft Final Report. Prepared for Office of Air Quality Planning and 

Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC and Air 

Quality Management Division, National Park Service, Denver, CO. 

Chestnut, L.G., and R.D. Rowe. 1990b. “A New National Park Visibility Value Estimates.” 

In Visibility and Fine Particles, Transactions of an AWMA/EPA International Specialty 

Conference, C.V. Mathai, ed. Air and Waste Management Association, Pittsburgh. 
6c-10

Clancy, L., P. Goodman, H. Sinclair, and D.W. Dockery. 2002. “Effect of Air-pollution 

Control on Death Rates in Dublin, Ireland: An Intervention Study.” Lancet Oct 

19;360(9341):1210-4.

Desvousges, W.H., F.R. Johnson, and H.S. Banzhaf. 1998. Environmental Policy Analysis 

With Limited Information: Principles and Applications of the Transfer Method (New 

Horizons in Environmental Economics.) Edward Elgar Pub: London.

EPA-SAB-COUNCIL-ADV-00-001. October 1999. The Clean Air Act Amendments 

(CAAA) Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Health 

and Ecological Effects Subcommittee on Initial Assessments of Health and Ecological 

Effects. Part 2.

EPA-SAB-COUNCIL-ADV-99-012. July 1999. The Clean Air Act Amendments (CAAA) 

Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Health and 

Ecological Effects Subcommittee on Initial Assessments of Health and Ecological Effects.

Part 1.

EPA-SAB-COUNCIL-ADV-01-004. September 2001. Review of the Draft Analytical Plan 

for EPA’s Second Prospective Analysis—Benefits and Costs of the Clean Air Act 1990-2020:

An Advisory by a Special Panel of the Advisory Council on Clean Air Compliance Analysis.

EPA-SAB-COUNCIL-ADV-04-002. March 2004. Advisory on Plans for Health Effects 

Analysis in the Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs 

of the Clean Air Act, 1990-2020: Advisory by the Health Effects Subcommittee of the 

Advisory Council on Clean Air Compliance Analysis.

Kleckner, N., and J. Neumann. June 3, 1999. “Recommended Approach to Adjusting WTP 

Estimates to Reflect Changes in Real Income.” Memorandum to Jim Democker, US 

EPA/OPAR. 

Roosli M, Kunzli N, Braun-Fahrlander C, Egger M. 2005. “Years of life lost attributable to 

air pollution in Switzerland: dynamic exposure-response model.” 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.
7-1

Chapter 7: Discussion of Ozone Secondary Standard 

Exposures to ozone have been associated with a wide array of vegetation and ecosystem 

effects in the published literature. These effects include those that damage or impair the 

intended use of the plant or ecosystem. Such effects are considered adverse to the public 

welfare and can include: reduced plant growth, visible foliar (leaf) injury, reduced plant 

vigor (e.g., increased susceptibility to harsh weather, disease, insect pest infestation, and 

competition), reduced crop yields, and changes in ecosystems and associated ecosystem 

services.

Vegetation effects research has shown that seasonal air quality indices that cumulate 

peak-weighted hourly ozone concentrations are the best candidates for relating exposure 

to plant growth effects. On the basis of this research, as well as other information 

considered in this review (e.g., policy-relevant background (PRB) levels), the Staff Paper 

concluded that the cumulative, seasonal index referred to as “W126” is the most 

appropriate index for relating vegetation response to ambient ozone exposures. Based on 

additional conclusions regarding appropriate diurnal and seasonal exposure windows, the 

Staff Paper concluded that it was appropriate for the Administrator to consider a 

cumulative seasonal secondary standard, expressed as an index of the annual sum of 

weighted hourly concentrations (using the W126 form), set at a level in the range of 7 to 

21 ppm-hours. The index would be cumulated over the 12-hour daylight window (8:00 

a.m. to 8:00 p.m.) during the consecutive 3 month period during the ozone season with 

the maximum index value (hereafter referred to as the 12-hour, maximum 3-month 

W126).

The Staff Paper also considered the extent to which there is overlap between county-level 

air quality measured in terms of the 8-hour average form of the current secondary 

standard and that measured in terms of the 12-hour W126, alternative cumulative, 

seasonal form. These comparisons were done using 3-year averages for both forms, as 

well as using the 3-year average current 8-hour form and the annual W126 county-level 

air quality values. This Staff Paper assessment used 2002-2004 county-level air quality 

data from the AQS sites and the subset of CASTNET sites having the highest ozone 

levels for the counties in which they are located. Since the completion of the Staff Paper, 

this analysis has been updated using the more recent 3-year period of 2003 to 2005. 

Results from the more recent (2003-2005) 3-year average comparisons (see Table 1 

below) showed that after meeting the current 3-year average form of the 0.08-ppm, 8-

hour average standard, the number of counties not meeting a 3-year average W126 form 

ranged from 11 at the upper level of the proposed W126 range (21 ppm-hours) to 76 

counties (W126 of 15 ppm-hours- representing the upper bound of the CASAC 

recommended range), to 221 counties at the lower end of the proposed W126 range (7 

ppm-hours). The degree of overlap is greater when levels within the proposed range 

(0.070-0.075 ppm) for a revised 8-hour average standard are met, specifically the number 

of counties still exceeding a W126 form of a standard range from 0 at a W126 level of 21 

ppm-hours to 25 at a W126 level of 7 ppm-hours. The Staff Paper notes that when 

individual years are compared (e.g., using the annual W126 level) significant variability 

occurs between years in the degree of overlap between the numbers of counties meeting 
7-2

various levels of the 8-hour and W126 forms and, therefore, cautions that the degree of 

protection for vegetation provided by an 8-hour average form in terms of cumulative, 

seasonal exposures would not be expected to be consistent on a year to year basis. 

The Staff Paper also identified additional aspects of this analysis that would suggest 

caution should be used in interpreting these results. First, due to the lack of more 

complete monitor coverage in many rural areas, the Staff Paper concluded that this 

analysis may not be an accurate reflection of the situation in non-monitored, rural 

counties. Because of the lack of monitoring in rural areas where important vegetation 

and ecosystems are located, it remains uncertain as to the extent to which air quality 

improvements designed to reduce 8-hour ozone average concentrations would reduce 

ozone exposures measured by a seasonal, cumulative W126 index. The Staff Paper 

indicated this to be an important consideration because: (1) the biological database 

stresses the importance of cumulative, seasonal exposures in determining plant response; 

(2) plants have not been specifically tested for the importance of daily maximum 8-hour 

ozone concentrations in relation to plant response; and (3) the effects of attainment of a 

8-hour standard in upwind urban areas on rural air quality distributions cannot be 

characterized with confidence due to the lack of monitoring data in rural and remote 

areas.

In addition, though within the range of 8-hour average levels being proposed the numbers 

of counties exceeding mid- to low levels of W126 are greatly reduced, many of these 

counties contain areas of national public interest. For example, at the 8-hour level of 

0.075 ppm, 12 counties would still exceed the W126 level of 15 ppm-hours. Most of 

these counties contain high elevation, rural or remote sites where ozone air quality 

distributions tend to be flatter and the potential for disconnect between 8-hour average 

and cumulative, seasonal forms, greater. Therefore, the Staff Paper notes that additional 

rural high elevation areas important for vegetation that are not currently monitored likely 

experience similar ozone exposure patterns. These factors are important considerations 

in determining whether the current 8-hour form can appropriately provide requisite 

protection for vegetation. 

Due to time and resource limitations, EPA did not calculate the costs and monetized 

benefits of a separate secondary standard. Consideration to these costs and benefits will 

be provided in the final RIA. 
7-3

Table 7.1 Comparison of number of counties exceeding various W126 levels when 

meeting various levels of the 8-hr standard for the 3-year period 2003-20051

1

 The top value in each box represents the number of counties meeting the 8-hour level based on 2003-2005 

data but exceeding the W126 level based on a 3-year W126 average for the 2003-2005 period. The 

numbers in parentheses indicate the range in the number of counties that exceed the W126 level on an 

annual basis in one of the three years—2003, 2004, 2005– based on 1-year W126 values. The range 

indicates significant interannual variability. 

Levels of 12-hr W126 (ppm-hrs) 

8-hr level met >21 >15 >7 

0.084 ppm 11 

(7-27)

76

(23-173)

221

(244-382)

0.075 ppm 0 

(0-7)

11

(3-33)

114

(49-134)

0.070 ppm 0 

(0-1)

2

(1-6)

25

(13-36)
8-1

Chapter 8: Conclusions and Implications of the Illustrative Benefit-Cost Analysis

Synopsis

EPA has performed an illustrative analysis to estimate the costs and human health benefits of 

nationally attaining alternative ozone standards. We have considered 4 alternative standards 

incremental to attaining the current ozone standard: 0.079 ppm, 0.075 ppm, 0.070 ppm, and 

0.065 ppm. This chapter summarizes these results and discusses the implications of the analysis. 

This analysis serves both to satisfy the requirements of E.O. 12866 and to provide the public 

with an estimate of the potential costs and benefits of attaining alternative ozone standards. The 

benefit and cost estimates below are calculated incremental to a 2020 baseline that incorporates 

air quality improvements achieved through the projected implementation of existing regulations 

and full attainment of the current standards for ozone and PM NAAQS (including the 

hypothetical control strategy developed in the RIA for full attainment of the PM NAAQS 15/35 

promulgated in September, 2006). This RIA presents two sets of results: The first reflects full 

attainment in all locations except two areas of California, which are planning to meet the current 

standards after 2020, and so have estimated costs and benefits for the analyzed standards for 

partial attainment in 2020 (their “glidepath” targets).1 The second estimate, for California only, 

presents the additional costs and benefits that might result from California fully attaining the 

standards in a year beyond 2020. Finally, this chapter provides additional context for the RIA 

analysis and a discussion of limitations and uncertainties. In addition, given the technological 

limitations associated with reducing ozone precursors, we provide estimated cost and benefit 

numbers based on both partial attainment (manageable with current technologies) and full 

attainment (manageable in some locations only with hypothetical technologies). 

8.1 Results 

Presentation of Results 

There are two sets of results presented below. The first set of results is for 2020. For analytical 

purposes explained previously, we assume that almost all areas of the country will meet each 

alternative standard in 2020 through the development of technologies at least as effective as the 

hypothetical strategies used in this illustration. It is expected that benefits and costs will begin 

occurring earlier, as states begin implementing control measures to attain earlier or to show 

progress towards attainment. Some areas with very high levels of ozone do not plan to meet even 

the current standard until after 2020; specifically, two California areas have adopted plans for 

post-2020 attainment as noted above. In these locations, we provide estimates of the costs and 

benefits of attaining a “glidepath” target in our 2020 analysis year.2

 The 2020 results thus do 

1

 Because these two areas adopted 8-hour ozone implementation plans calling for post-2020 

attainment after we had completed much of our analysis, these areas are assumed to meet the 

current standard in 2020 and 2021 respectively, somewhat earlier than the date in their plans, 

which results in a steeper glide path, and higher costs and benefits, for all the standards analyzed. 

See chapter 4. 

2

 See footnote 1. 
8-2

not represent a complete “full attainment” scenario for the entire nation, particularly for more 

stringent alternative new standards examined. In order to gain an understanding of the possible 

additional costs and benefits of fully attaining in California, we provide an additional set of 

results focusing on California. 

By the year 2030, various mobile source rules, such as the onroad and nonroad diesel rules, 

among others, would be expected to be fully implemented. Because California will likely not 

have to attain until closer to 2030, it is important to reflect the impact those rules might have on 

the emissions that affect ozone nonattainment. To reflect the emission reductions that are 

expected from these rules, we subtract those tons from our estimates of the emissions reductions 

that might be needed for California to fully attain in 2020, thus making our analysis more 

consistent with full attainment later than 2020. EPA did the analysis this way because to force 

full attainment in California in an earlier year would not be consistent with the CAA, and would 

likely lead to an overstatement of costs because those areas might benefit from these existing 

federal or state programs that would be implemented between 2020 and the attainment year (see 

detail in Chapter 4); because additional new technologies may become available between 2020 

and the attainment year; and/or the cost of existing technologies might fall over time due to 

economic factors such as economies of scale or improvements in the efficiency of installing and 

operating controls (‘learning by doing’). On the other hand, it is also possible that new 

technologies might not meet the specifications, development time lines, or cost estimates 

provided in this analysis. 

It is not appropriate to add together the 2020 national attainment, California glidepath estimate 

and the estimate of California full attainment as an estimate of national full attainment in 2020 It 

is not appropriate to do this because each estimate is based on different baseline conditions for 

emissions and air quality. In addition, both estimates include estimates of California glidepath 

results, leading to the potential for double counting if added together. 

The following set of tables summarizes the costs and benefits of the scenarios analyzed, and 

shows the net benefits for each of the scenarios across a range of modeling assumptions 

concerning the calculation of costs and benefits. Tables 8.1a-c present benefits and costs of 

national attainment in 2020, including the “glidepath” targets for California. Companion Table 

8.2 provides the estimated reductions in premature mortality and morbidity for national 

attainment in 2020, including the “glidepath” targets for California. Tables 8.3a-c present the 

additional costs and benefits of full attainment for California (“glidepath” plus future year 

attainment added together into one total); Table 8.4 is the companion table showing estimated 

reductions in premature mortality and morbidity. 

The individual row estimates for benefits reflect the variability in the functions available for 

estimating the largest source of benefits – avoided ozone premature mortality. Ranges within the 

total benefits column reflect variability in the estimates of PM premature mortality co-benefits 

across the available effect estimates. Ranges in the total costs column reflect different 

assumptions about the extrapolation of costs. The low end of the range of net benefits is 

constructed by subtracting the highest cost from the lowest benefit, while the high end of the 

range is constructed by subtracting the lowest cost from the highest benefit. Following these 
8-3

tables is a discussion of the implications of these estimates, as well as the uncertainties and 

limitations that should be considered in interpreting the estimates. 
8-4

Table 8.1a National Annual Costs and Benefits: 0.079 ppm Standard in 2020 

(including California glidepath ) 

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function or 

Assumption Reference 

Total Benefits* Total Costs** Net Benefits 

NMMAPS Bell et al. 2004 $1.2 to $11 $3 to $3.3 -$2.1 to $8.5 

Bell et al. 2005 $1.6 to $12 $3 to $3.3 -$1.7 to $8.9 

Meta-analysis Ito et al. 2005 $1.7 to $12 $3 to $3.3 -$1.7 to $8.9 

Levy et al. 2005 $1.6 to $12 $3 to $3.3 -$1.7 to $8.9 

Assumption that association is not 

causal*** 

$1.1 to $11 $3 to $3.3 -$2.2 to $8.4 

Table 8.1b National Annual Costs and Benefits: 0.075 ppm Standard in 2020 

(including California glidepath ) 

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function or 

Assumption Reference 

Total Benefits* Total Costs** Net Benefits 

NMMAPS Bell et al. 2004 $3 to $16 $5.5 to $8.8 -$5.8 to $10.5 

Bell et al. 2005 $7.3 to $20 $5.5 to $8.8 -$1.5 to $15 

Meta-analysis Ito et al. 2005 $7.8 to $21 $5.5 to $8.8 -$1. to $15

Levy et al. 2005 $8.7 to $22 $5.5 to $8.8 -$0.1 to $16 

Assumption that association is not 

causal*** 

$1.5 to $15 $5.5 to $8.8 -$7.3 to $9 

Table 8.1c National Annual Costs and Benefits: 0.070 ppm Standard in 2020 

(including California glidepath)

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function or 

Assumption Reference 

Total Benefits* Total Costs** Net Benefits 

NMMAPS Bell et al. 2004 $4.3 to $26 $10 to $22 -$17 to $16 

Bell et al. 2005 $9.7 to $31 $10 to $22 -$12 to $21 

Meta-analysis Ito et al. 2005 $10 to $32 $10 to $22 -$11 to $22 

Levy et al. 2005 $11 to $33 $10 to $22 -$10 to $23 

Assumption that association is not 

causal*** 

$2.5 to $24 $10 to $22 -$20 to $14 
8-5

Table 8.1d National Annual Costs and Benefits : 0.065 ppm Standard in 2020 

(including California glidepath)

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function or 

Assumption Reference 

Total Benefits* Total Costs** Net Benefits 

NMMAPS Bell et al. 2004 $7.7 to $45 $17 to $46 -$38 to $28 

Bell et al. 2005 $18 to $55 $17 to $46 -$28 to $38 

Meta-analysis Ito et al. 2005 $19 to $56 $17 to $46 -$27 to $39 

Levy et al. 2005 $20 to $57 $17 to $46 -$27 to $40 

Assumption that association is not 

causal*** 

$4.3 to $42 $17 to $46 -$42 to $25 

*Includes ozone benefits, and PM 2.5 co-benefits. Range was developed by adding the estimate 

from the ozone premature mortality function to both the lower and upper ends of the range of the 

PM2.5 premature mortality functions characterized in the expert elicitation 

**Range reflects lower and upper bound cost estimates

***Total includes ozone morbidity benefits only 
8-6

Table 8.2: Summary of Total Number of Annual Ozone and PM2.5-Related Premature 

Mortalities and Premature Morbidity Avoided: 2020 National Benefits 

Combined Estimate of Mortality

Standard Alternative and 

Model or Assumption

Combined Range of Ozone Benefits and 

 PM2.5 Co-Benefits

 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 

NMMAPS Bell (2004) 200 to 1,900 430 to 2,600 670 to 4,300 1,200 to 7,400 

Bell (2005) 260 to 2,000 1,100 to 3,300 1,500 to 5,100 2,800 to 9,000 

Meta-Analysis Ito (2005) 270 to 2,000 1,200 to 3,300 1,600 to 5,200 3,000 to 9,200 

Levy (2005) 260 to 2,000 1,300 to 3,500 1,800 to 5,400 3,000 to 9,200 

No Causality 180 to 1,900 230 to 2,400 390 to 4,000 660 to 6,900

Combined Estimate of Morbidity

Acute Myocardial Infarction 1,100 1,400 2,300 4,000 

Hospital and ER Visits 1,300 5,600 7,600 13,000 

Chronic Bronchitis 370 470 780 1,300 

Acute Bronchitis 950 1,200 2,000 3,500 

Asthma Exacerbation 7,300 9,400 16,000 27,000 

Lower Respiratory Symptoms 8,100 10,000 17,000 29,000 

Upper Respiratory Symptoms 5,900 7,500 13,000 22,000

School Loss Days 50,000 610,000 780,000 1,300,000 

Work Loss Days 51,000 65,000 110,000 190,000 

Minor Restricted Activity Days 430,000 2,000,000 2,700,000 4,700,000 
8-7

Table 8.3a California: Annual Costs and Benefits of Attaining 0.079 ppm Standard 

(beyond 2020)* 

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality

Function 

or Assumption Reference 

Total Benefits** Total Costs*** Net Benefits 

NMMAPS Bell et al. 2004 $0.1 to $0.6 $0.3 to $1.7 -$1.6 to $0.2 

Bell et al. 2005 $0.2 to $0.7 $0.3 to $1.7 -$1.5 to $0.4 

Meta-analysis Ito et al. 2005 $0.3 to $0.7 $0.3 to $1.7 -$1.4 to $0.4 

Levy et al. 2005 $0.2 to $0.7 $0.3 to $1.7 -$1.5 to $0.4 

Assumption that association is not 

causal**** 

$0.05 to $0.5 $0.3 to $1.7 -$1.6 to $0.2 

Table 8.3b California: Annual Costs and Benefits of Attaining 0.070 ppm Standard 

(beyond 2020)* 

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality Function

or Assumption Reference 

Total Benefits** Total Costs*** Net Benefits 

NMMAPS Bell et al. 2004 $0.7 to $3.5 $2 to $13 -$12 to $1.5 

Bell et al. 2005 $1.9 to $4.7 $2 to $13 -$11 to $2.7 

Meta-analysis Ito et al. 2005 $2.1 to $4.8 $2 to $13 -$11 to $2.9 

Levy et al. 2005 $2.1 to $4.8 $2 to $13 -$11 to $2.9 

Assumption that association is not 

causal**** 

$0.4 to $3.1 $2 to $13 -$13 to $1.2 

Table 8.3c California: Annual Costs and Benefits of Attaining 0.075 ppm Standard 

(beyond 2020)* 

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality Function

or Assumption Reference 

Total Benefits** Total Costs*** Net Benefits 

NMMAPS Bell et al. 2004 $0.4 to $1.9 $1.1 to $6.2 -$5.8 to $0.8 

Bell et al. 2005 $1.1 to $2.6 $1.1 to $6.2 -$5.1 to $1.5 

Meta-analysis Ito et al. 2005 $1.2 to $2.7 $1.1 to $6.2 -$5.1 to $1.6 

Levy et al. 2005 $1.2 to $2.7 $1.1 to $6.2 -$5 to $1.6 

Assumption that association is not 

causal**** $0.2 to $1.7 $1.1 to $6.2 -$6 to $0.6 
8-8

Table 8.3d California: Annual Costs and Benefits of Attaining 0.065 ppm Standard 

(beyond 2020)*

Premature Mean Total Benefits, in Billions of 1999$ 

Mortality Function

or Assumption Reference 

Total Benefits** Total Costs*** Net Benefits 

NMMAPS Bell et al. 2004 $1.1 to $5.2 $2.9 to $21 -$19 to $2.3 

Bell et al. 2005 $3.1 to $7.2 $2.9 to $21 -$17 to $4.3 

Meta-analysis Ito et al. 2005 $3.4 to $7.4 $2.9 to $21 -$17 to $4.5 

Levy et al. 2005 $3.3 to $7.4 $2.9 to $21 -$17 to $4.5 

Assumption that association is not 

causal**** $0.5 to $4.6 $2.9 to $21 -$20 to $1.7 

* Tables present the total of CA glidepath in 2020, plus the additional increment needed to reach 

full attainment in a year beyond 2020 

** Includes ozone benefits and PM 2.5 co-benefits. Range was developed by adding the estimate 

from the ozone premature mortality function to both the lower and upper ends of the range of the 

PM2.5 premature mortality functions characterized in the expert elicitation 

***Range reflects lower and upper bound cost estimates 

****Total includes ozone morbidity benefits only 
8-9

Table 8.4: Summary of Total Number of Annual Ozone and PM2.5-Related Premature 

Mortalities and Premature Morbidity Avoided: California Post 2020 Attainment 

Combined Estimate of Mortality

Standard Alternative and 

Model or Assumption

Combined Range of Ozone Benefits and 

 PM2.5 Co-Benefits

 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 

NMMAPS Bell (2004) 17 to 93 61 to 310 110 to 570 180 to 840 

Bell (2005) 42 to 120 170 to 410 300 to 760 490 to 1,200 

Meta-Analysis Ito (2005) 45 to 120 180 to 430 320 to 780 530 to 1,200 

Levy (2005) 46 to 120 180 to 430 320 to 780 520 to 1,200 

No Causality 8.2 to 84 26 to 270 49 to 500 72 to 740 

Combined Estimate of Morbidity

Acute Myocardial Infarction 49 160 290 430 

Hospital and ER Visits 200 790 1,400 2,200 

Chronic Bronchitis 17 53 99 150 

Acute Bronchitis 43 140 260 380 

Asthma Exacerbation 330 1,100 2,000 2,900 

Lower Respiratory Symptoms 360 1,200 2,200 3,200 

Upper Respiratory Symptoms 270 850 1,600 2,300 

School Loss Days 30,000 120,000 210,000 340,000 

Work Loss Days 2,300 7,400 14,000 20,000 

Minor Restricted Activity Days 87,000 340,000 600,000 960,000 
8-10

8.2 Discussion of Results

Relative Contribution of PM benefits to total benefits

Because of the relatively strong relationship between PM2.5 concentrations and premature 

mortality, PM co-benefits resulting from reductions in NOx emissions can make up a large 

fraction of total montetized benefits, depending on the specific PM mortality impact function 

used, and on the relative magnitude of ozone benefits, which is dependent on the specific ozone 

mortality function assumed. PM co-benefits based on daily average concentrations are 

calculated over the entire year, while ozone related benefits are calculated only during the 

summer ozone season. Because the control strategies evaluated in this RIA are assumed to 

operate year round rather than only during the ozone season, this means that PM benefits will 

accumulate during both the ozone season and the rest of the year. 

PM co-benefits account for between 13 and 99 percent of co-benefits, depending on the standard 

analyzed and on the choice of ozone and PM mortality functions used. The estimate with the 

lowest fraction from PM co-benefits occurs when ozone mortality is based on the Levy et al 

(2005) study and when PM2.5 mortality is based on the function provided by “Expert K”3 from 

the expert elicitation. The estimate with the highest fraction from PM co-benefits occurs when 

no ozone mortality reductions are included (following the assumption of no causal relationship 

between ozone and mortality) and when PM2.5 mortality is based on the function provided by 

“Expert E”4 from the expert elicitation. 

Impact of Uncertainty in the Magnitude of ozone benefits.

The degree to which net benefits are positive depends largely on the size of the effect estimate 

used for the relationship between premature mortality and ozone and to a lesser extent on the 

cost extrapolation methodology. In the cases where net benefits are negative, the magnitude of 

the economic loss depends largely on the extrapolation method used to calculate the costs of full 

attainment. Because of the high degree of uncertainty in these calculations, overall conclusions 

about the magnitude of net benefits and the likelihood they will be positive or negative for any of 

our evaluated scenarios cannot be drawn with any degree of confidence. As such, we cannot 

conclude that strategies for attainment of a tighter ozone NAAQS would either pass or fail a 

cost-benefit test. In other words, we cannot make an estimate of whether costs will outweigh 

benefits (or vice versa). As we improve our databases of control technologies and refine our 

understanding of the magnitude of the relationships between air pollution and premature 

mortality, our confidence in estimates of costs and benefits will likely improve. 

3

 As discussed in Chapter 6, one way in which we characterize the model uncertainty associated 

with the relationship between particulate matter and premature mortality was to conduct an 

expert elicitation. The elicitation yielded twelve different functions, generated by asking 

12 experts a structured set of questions, leading each to articulate a functional form for the 

relationship, in a probabilistic estimate of uncertainty. Among the twelve experts, Expert K's 

function characterizes the weakest relationship between PM and premature mortality, whereas 

Expert E's function characterizes the strongest relationship. 

4 See above.
8-11

Challenges to Modeling Full Attainment in All Areas

Because of relatively higher ozone levels in several large urban areas (Southern California, 

Chicago, Houston, and the Northeastern urban corridor, including New York and Philadelphia) 

and because of limitations on the available database of currently known emissions control 

technologies, EPA recognized from the outset that known and reasonably anticipated emissions 

controls would likely be insufficient to bring many areas into attainment with either the current 

or alternative, more stringent ozone standards. Therefore, we designed this analysis in two 

stages: the first stage focused on analyzing the air quality improvements that could be achieved 

through application of documented, well-characterized emissions controls, and the costs and 

benefits associated with those controls. The second stage utilized extrapolation methods to 

estimate the costs and benefits of additional emissions reductions needed to bring all areas into 

full attainment with the standards. Clearly, the second stage analysis is a highly speculative 

exercise, as it is based on estimating emission reductions and air quality improvements without 

any information about the specific controls that would be available to do so. 

The structure of the RIA reflects this 2-stage analytical approach. Separate chapters are provided 

for the cost, emissions and air quality impacts of modeled controls and for extrapolated costs and 

air quality impacts. We have used the information currently available to develop reasonable 

approximations of the costs and benefits of the extrapolated portion of the emissions reductions 

necessary to reach attainment. However, due to the high level of uncertainty in all aspects of the 

extrapolation, we judged it appropriate to provide separate estimates of the costs and benefits for 

the modeled stage and the extrapolated stage, as well as an overall estimate for reaching full 

attainment. There is a single chapter on benefits, because the methodology for estimating 

benefits does not change between stages. However, in that chapter, we again provide separate 

estimates of the benefits associated with the modeled and extrapolated portions of the analysis. 

In both stages of the analysis, it should be recognized that all estimates of future costs and 

benefits are not intended to be forecasts of the actual costs and benefits of implementing revised 

standards. Ultimately, states and urban areas will be responsible for developing and 

implementing emissions control programs to reach attainment with the ozone NAAQS, with the 

timing of attainment being determined by future decisions by states and EPA. Our estimates are 

intended to provide information on the general magnitude of the costs and benefits of alternative 

standards, rather than precise predictions of control measures, costs, or benefits. With these 

caveats, we expect that this analysis can provide a reasonable picture of the types of emissions 

controls that are currently available, the direct costs of those controls, the levels of emissions 

reductions that may be achieved with these controls, the air quality impact that can be expected 

to result from reducing emissions, and the public health benefits of reductions in ambinent ozone 

levels. This analysis identifies those areas of the U.S. where our existing knowledge of control 

strategies is not sufficient to allow us to model attainment, and where additional data or research 

may be needed to develop strategies for attainment. EPA plans to address some of these areas in 

the RIA analysis for the final rule through additional research on control technologies, sensitivity 

analyses using air quality models, and refinement of methods for extrapolating the costs and 

benefits of reaching full attainment. 
8-12

In many ways, regulatory impact analyses for proposed actions are a learning process that can 

yield valuable information about the technical and policy issues that are associated with a 

particular regulatory action. This is especially true for RIAs for proposed NAAQS, where we 

are required to stretch our understanding of both science and technology to develop scenarios 

that illustrate how certain we are about how economically feasible the attainment of these 

standards might be regionally. The proposed ozone NAAQS RIA provided great challenges 

when compared to previous RIAs. Why was this so? Primarily because as we tighten standards 

across multiple pollutants with overlapping precursors (e.g. the recent tightening of the PM2.5

standards), we move further down the list of cost-effective known and available controls. As we 

deplete our database of available choices of known controls, we are left with background 

emissions and remaining anthropogenic emissions for which we do not have enough knowledge 

to determine how and at what cost reductions can be achieved in the future when attainment 

would be required. With the more stringent NAAQS, more areas will need to find ways of 

reducing emissions, and as existing technologies are either inadequate to achieve desired 

reductions, or as the stock of low-cost existing technologies is depleted (causing the cost per ton 

of pollution reduced to increase), there will be pressure to develop new technologies to fill these 

needs. While we can speculate on what some of these technologies might look like based on 

current research and development and model programs being evaluated by states and localities, 

the actual technological path is highly uncertain.

Because of the lack of knowledge regarding the development of future emissions control 

technologies, a significant portion of our analysis is based on extrapolating from available data to 

generate the emissions reductions necessary to reach full attainment of an alternative ozone 

NAAQS and the resulting costs and benefits. Studies indicate that it is not uncommon for preregulatory cost estimates to be higher than later estimates, in part because of inability to predict 

technological advances. Over longer time horizons, such as the time allowed for areas with high 

levels of ozone pollution to meet the ozone NAAQS, the opportunity for technical advances is 

greater (See Chapter 5 for detail). Also, due to the nature of the extrapolation method for 

benefits (which focuses on reductions in ozone only at monitors that exceed the NAAQS), we 

generally understate the total benefits that would result from implementing additional emissions 

controls to fully attain the ozone NAAQS (i.e., assuming that the application of control strategies 

would result in ozone reductions both at nonattaining and attaining monitors). On the other 

hand, the possibility also exists that benefits are overestimated, both because it is possible that 

new technologies might not meet the specifications, development time lines, or cost estimates 

provided in this analysis and because the analysis assumes there are quantifiable benefits to 

reducing ambient ozone below each of the alternative standards. 

Estimated benefits and costs may reflect both bias and uncertainty. While we strive to avoid bias 

and characterize uncertainty to the extent possible, we note that in some cases, biased estimates 

were used due to data and/or methodological limitations. In these cases we have tried to identify 

the direction and potential magnitude of the bias." These extrapolated benefits are uncertain, but 

the relative uncertainty compared to the modeled benefits is similar, once the underestimation 

bias has been taken into account. The emissions and cost extrapolations do not have a clear 

directional bias, however, they are much more uncertain relative to the modeled emissions and 

cost estimates, because of the lack of refined information about the relationship between 
8-13

emissions reductions and ozone changes in specific locations, and because of the difficulties in 

extrapolating costs along a marginal cost curve well beyond the observed data without 

accounting for shifts in the cost-curve due to improvements in technology or use of technologies 

over time. Of course, these benefits and costs will only be realized if the emission reductions 

projected in this extrapolated approach actually occur in the future. 

8.3 What did we learn through this analysis? 

1) As in our analysis for the 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 (average $/ton of $4,100) appear cost-effective relative to NOx 

reductions (average $/ton of $3,600). However, when compared on a $/ppb basis, 

NOx reductions (average $/ppb of $36 million) are almost always more costeffective than VOC controls (average $/ppb of $164 million) because of the much 

lower conversion of VOC to ozone. The one exception to this is in urban areas 

which are VOC limited. In those locations, NOx reductions can actually result in 

increases in ozone, and as such, VOC reductions can be cost-effective relative to 

NOx on a $/ppb basis. 

2) Our knowledge of technologies that might achieve NOx and VOC reductions to attain 

alternative ozone NAAQS is insufficient. In some areas of the U.S., our existing 

controls database was insufficient to meet even the current ozone standard. After 

applying existing rules and the hypothetical controls applied in the PM NAAQS 

RIA across the nation (excluding California), we were able to identify controls for 

35 states and DC that reduced overall NOx emissions by 17 percent and VOC by 

4 percent. For California, the percentages were 8 percent for NOx and 10 percent 

for VOC. After these reductions, remaining emissions were still substantial, with 

over 7 million tons of NOx and 9 million tons of VOCs remaining. The large 

remaining inventories of NOx and VOC emissions suggests that additional control 

measures need to be developed, with appropriate consideration of the relative 

effectiveness of NOx and VOC in achieving ozone reductions. 

3) Most of the overall reductions in NOx achieved in our illustrative control strategy 

were from non-EGU point sources. This was due to the fact that: 1) EGUs have 

been heavily controlled under the recent NOx SIP call and Clean Air Interstate 

Rules. The EGU program we included in our strategy for meeting the alternative 

ozone standards was not intended to achieve overall reductions in NOx beyond 

the CAIR caps, but instead to obtain NOx emission reductions in areas where they 

would more effectively reduce ozone concentrations in downwind nonattainment 

areas; and 2) mobile sources are already subject to ongoing emission reduction 

programs through the Tier 2 highway, onroad diesel and nonroad diesel rules. 

Thus, the opportunities for controlling NOx emissions were much greater in the 

non-EGU sector than in the mobile or EGU sectors. However, the remaining 

NOx emissions from EGU and mobile sectors are still greater than non-EGU 

sources, and additional reductions from these sectors may need to be considered 
8-14

in developing strategies to achieve full attainment. We are evaluating 

technologies and programs that might be applied in these sectors in the future. 

Exploratory analyses indicate that there are opportunities to achieve emission 

reductions from EGU peaking units on High Energy Demand Days (HEDD) with 

targeted strategies. Another area under analysis is the energy efficiency/clean 

distributed generation based emission reductions. Potential changes in the 

generation mix as a result of increase in the use of renewables and Renewable 

Portfolio Standards (RPS) are also likely to create changes in emission behavior. 

However, overall regional or national emissions levels stay constant under a given 

cap. 

4) Some EPA existing mobile source programs will help areas reach attainment.

These programs promise to continue to help areas reduce ozone concentrations 

between 2020 and 2030. In California, continued implementation of mobile 

source rules including the onroad and nonroad diesel rules and the locomotive and 

marine engines rule are projected to reduce NOx emissions by an additional 25 

percent and VOC emissions by an additional 11 percent during this time period. 

These additional reductions will significantly reduce the overall cost of attainment 

relative to what California might have needed to reduce from other sectors if 

attainment were to be required in 2020. However, delaying attainment by 10 

years will result in delayed health benefits as well. Based on a simple scaling 

exercise, we estimate that between $0.3 and $1 billion in benefits could have been 

realized from full attainment with the 0.070 ppm alternative each year between 

2020 and 2030. However, the potential for extra costs of up to between $0.3 and 

$4 billion per year suggests that allowing for delayed attainment until 2030 for 

these severe nonattainment areas may make economic sense. We are unable at 

this time to identify controls that would achieve the full attainment in California 

by 2020. 

5) Tightening the ozone standards can provide significant, but not uniform, health 

benefits. The magnitude of the benefits is highly uncertain, and is not expected to 

be uniform throughout the nation. While our illustrative analyses showed that the 

benefits of implementing a tighter standard will likely result in reduced health 

impacts for the nation as a whole, the particular scenarios that we modeled show 

that some areas of the U.S. will see ozone (and PM2.5) levels increase. This is due 

to two reasons. The first reason is that the complexities involved in the 

atmospheric processes which govern the transformation of emissions into ozone 

result in some locations and times when reducing NOx emissions can actually 

increase ozone levels on some days (see Chapter 2 for more discussion). For 

most locations, these days are few relative to the days when ozone levels are 

decreased. However, in some urban areas the net effect of implementing NOx 

controls is to increase overall ozone levels and increase the health effects 

associated with ozone. This same phenomenon results in some areas also seeing 

increases in PM2.5 formation. The second reason is that the particular control 

strategy that we modeled for EGU sources is a modification to controls on sources 

within the overall cap and trade program in the Eastern U.S, established under the 
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CAIR. As with any cap and trade program, changes in requirements at particular 

sources will result in shifts in power generation and emissions at other sources. 

Because under our chosen EGU control scenario the overall emissions cap for the 

CAIR region remains the same, some areas of the country will see a decrease in 

emissions, while others will see an increase. This is not unexpected, and is an 

essential element of the cap and trade program. Our goal in selecting the EGU 

control strategy was to focus the emissions reductions in areas likely to benefit the 

most from EGU NOx emissions reductions, with emissions increases largely 

occurring in areas in attainment with the ozone NAAQS. However, this 

necessarily means that in those areas where emissions increases occurred, ozone 

levels would also be expected to increase, with commensurate increases in health 

impacts. On a national level, however, we expected overall health benefits of the 

modeled EGU strategy to be positive. In addition, our air quality modeling 

analysis showed that while ozone levels did increase in some areas, none of these 

increases resulted in an attaining area moving into nonattainment. Adjustments to 

our control scenario might achieve a pattern of reductions that achieves further air 

quality improvement. 

6) There is uncertainty in Estimating the Benefits of 0.079 ppm and 0.075 ppm 

EPA employed a monitor rollback approach to estimate the benefits of attaining 

an alternative standard of 0.079 ppm nationwide. This approach likely understates 

the benefits that would occur due to implementation of actual controls because 

controls implemented to reduce ozone concentrations at the highest monitor 

would likely result in some reductions in ozone concentrations at attaining 

monitors down-wind (i.e. the controls would lead to concentrations below the 

standard in down-wind locations). Therefore, air quality improvements and 

resulting health benefits from full attainment would be more widespread than we 

have estimated in our rollback analysis. 

EPA calculated 0.075 ppm benefits by interpolating the 0.070 ppm benefits 

estimates.5

 This interpolation approach may overestimate benefits relative to a 

modeled control scenario developed specifically to attain the 0.075 ppm 

alternative. The interpolation method scales down benefits only at the monitors 

we project to exceed 0.075 ppm—but it still captures the benefits achieved by the 

0.070 ppm regional control strategy that occur outside of these projected nonattainment areas. To the extent that a modeled emission control strategy to attain 

0.075 ppm does not include these broader regional emission reductions, total 

benefits would be lower than those we have estimated in this RIA. 

Interpolation and monitor rollback methods of benefits estimation are inherently 

different. As described above, for the purposes of reviewing this analysis, the 

reader should understand that the benefits described for attaining a standard of 

0.079 ppm are likely understated, whereas the estimated benefits of attaining a 

standard of 0.075 ppm are likely overstated. EPA will develop and present 

consistent approaches for the alternative standards for the final RIA. 

5

 This procedure is detailed in Appendix 6A. 
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7) Tightening the ozone standards can incur significant, but uncertain, costs 

An engineering cost comparison demonstrates that the cost of the 0.070 ppm 

Ozone NAAQS control strategy ($3.9 billion per year) is only slightly higher than 

the Clean Air Interstate Rule ($3.6 billion per year) and roughly one and half to 

just over four times higher than the PM NAAQS 15/35 control strategy with 

annual engineering costs of $850 million. It should be noted that for the Ozone 

NAAQS $3.9 billion represent the cost of partial attainment. Full attainment 

using extrapolation methods are expected to increase total costs significantly. For 

example, total costs for the 0.070 ppm standard are significant at $13 to $26 

billion. Yet, the magnitude and distribution of costs across sectors and areas is 

highly uncertain. Our estimates of costs for a set of modeled NOx and VOC 

controls comprise only a small part of the estimated costs of full attainment. 

These estimated costs for the modeled set of controls are still uncertain, but they 

are based on the best available information on control technologies, and have their 

basis in real, tested technologies. Estimating costs of full attainment required 

significant extrapolation of the cost curve for known technologies, and was based 

on generalized relationships between emissions and ozone levels. Based on air 

quality modeling sensitivity analyses, there is clearly significant spatial variability 

in the relationship between local and regional NOx emission reductions and ozone 

levels across urban areas. However, because we were unable to analyze all of the 

urban areas that are expected to need reductions, we used the same ratio of ozone 

to emissions throughout the U.S. This introduces significant uncertainty into the 

calculation of the emissions reductions that might be needed to reach full 

attainment. In addition, because VOCs are generally much less effective than 

NOx in achieving ozone reductions at key monitors (with the exception of 

California), we did not use any VOC control data in the extrapolation to full 

attainment. This meant that in some areas, we assumed the need for more 

expensive NOx controls than might be required if a specific area chose to use a 

combination of NOx and VOC controls. However, VOC controls would have to 

be very inexpensive relative to NOx controls on a per ton basis in order for VOC 

controls to be a cost-effective substitute for NOx reductions. Extrapolating costs 

by applying a cost-curve based on known technologies also introduces 

uncertainties. For some locations, the extrapolation requires only a modest 

reduction beyond known controls. In these cases, the extrapolation is likely 

reasonable and not as prone to uncertainties. However, for areas where the bulk 

of air quality improvements were derived from extrapolated emissions reductions 

that go well beyond the area of the known controls, the increasing marginal costs 

can suggest a cost per ton which stretches credibility. For example, in California, 

extrapolation to full attainment results in a marginal cost for the last ton of NOx 

of $89,645 in Los Angeles and $74,495 in Kern County, which are five to six 

times larger than the marginal cost at the last known cost effective control. 

Economic theory would suggest that as marginal costs rise, research and 

development to produce new, more cost effective technologies will also increase, 

leading to a downward shift in the overall cost curve. We did not assume any 
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shift in the cost curve to reflect technological innovation, instead we provide a 

sensitivity analysis by showing estimates assuming a high and low fixed cost per 

ton. We are likely overstating costs in the future when using the marginal cost 

and high fixed estimates. 

8) Non-EGU point source controls dominate the estimated costs. These costs 

account for about 70 percent of modeled costs. The average cost per ton for these 

reductions is approximately $3,400, and the highest marginal cost for the last cost 

effective control applied is $15,267. Mobile source controls were also significant 

contributors to overall costs, accounting for over 25 percent of total modeled 

costs. 

9) The economic impacts (i.e. social costs) of the cost of these modeled controls 

were not included in this analysis. Incorporating the economic impact of the 

extrapolated portion of the costs was too uncertain to be included as part of these 

estimates, and it was determined best to keep the modeled and extrapolated costs 

on the same basis. However, incorporating any economic impacts would increase 

the total cost of attainment in 2020 for a revised ozone standard. 

10) California costs and benefits are highly uncertain.. California faces large 

challenges in meeting any alternative standard, but their largest challenges may be 

in attaining the existing standard. Because our analysis suggested that all 

available controls would be exhausted in attempting (unsuccessfully) to meet the 

current 0.08 ppm standard (effectively 0.084 ppm) all of the benefits and costs in 

California are based on extrapolation. Both the benefits and the costs associated 

with the assumed NOx and VOC reductions in California are particularly 

uncertain. The costs are uncertain to the point where we have little confidence 

that they represent a meaningful characterization of possible future costs of 

implementation in California. As such, we recommend comparison of costs and 

benefits for the rest of the U.S. as a basis for judging the relative merits of 

implementation. Costs for full attainment in California will clearly be substantial, 

but the level of uncertainty about those costs is simply too great to provide any 

useful conclusions. This is also true for many other areas of the U.S., but the 

uncertainties are magnified in the case of California.

11) Costs and benefits will depend on implementation timeframes. States will 

ultimately select the specific timelines for implementation as part of their State 

Implementation Plans. To the extent that states seek classification as extreme 

nonattainment areas, the timeline for implementation may be extended beyond 

2020, meaning that the amount of emissions reductions that will be required in 

2020 will be less, and costs and benefits in 2020 will also be lowered. 
9-1

Chapter 9: Statutory and Executive Order Impact Analyses 

Synopsis

This chapter summarizes the Statutory and Executive Order (EO) impact analyses relevant for 

the ozone NAAQS RIA. In general, because this RIA analyzes an illustrative attainment strategy 

to meet the revised NAAQS, and because States will ultimately implement the new NAAQS, the 

Statutory and Executive Orders below did not require additional analysis. For each EO and 

Statutory requirement we describe both the requirements and the way in which the RIA 

addresses these requirements. Further analyses of the NAAQS proposal and its impact on these 

statutory and executive orders are found in section VII of the NAAQS preamble. 

9.1 Executive Order 12866: Regulatory Planning and Review 

Under section 3(f)(1) of Executive Order (EO) 12866 (58 FR 51735, October 4, 1993), the ozone 

NAAQS action is an “economically significant regulatory action” because it is likely to have an 

annual effect on the economy of $100 million or more. Accordingly, EPA prepared this 

regulatory impact analysis (RIA) of the potential costs and benefits associated with this action. 

The RIA estimates the costs and monetized human health benefits of attaining three alternative 

ozone NAAQS nationwide. Specifically, the RIA examines the alternatives of 0.075 ppm, 0.070 

ppm, and 0.065 ppm. The RIA contains illustrative analyses that consider a limited number of 

emissions control scenarios that States and Regional Planning Organizations might implement to 

achieve these alternative ozone NAAQS. However, the Clean Air Act (CAA) and judicial 

decisions make clear that the economic and technical feasibility of attaining ambient standards 

are not to be considered in setting or revising NAAQS, although such factors may be considered 

in the development of State plans to implement the standards. Accordingly, although an RIA has 

been prepared, the results of the RIA have not been considered in issuing this rule. 

9.2 Paperwork Reduction Act 

This RIA does not impose an information collection burden under the provisions of the 

Paperwork Reduction Act, 44 U.S.C. 3501 et seq. There are no information collection 

requirements directly associated with revisions to a NAAQS under section 109 of the CAA. 

Burden is defined as the total time, effort, or financial resources expended by persons to 

generate, maintain, retain, or disclose or provide information to or for a Federal agency. This 

includes the time needed to review instructions; develop, acquire, install, and utilize technology 

and systems for the purposes of collecting, validating, and verifying information, processing and 

maintaining information, and disclosing and providing information; adjust the existing ways to 

comply with any previously applicable instructions and requirements; train personnel to be able 

to respond to a collection of information; search data sources; complete and review the collection 

of information; and transmit or otherwise disclose the information. 
9-2

An agency may not conduct or sponsor information collection, and a person is not required to 

respond to a collection of information unless it displays a currently valid OMB control number. 

The OMB control numbers for EPA’s regulations in 40 CFR are listed in 40 CFR part 9. 

9.3 Regulatory Flexibility Act 

The EPA has determined that it is not necessary to prepare a regulatory flexibility analysis in 

connection with this RIA. For purposes of assessing the impacts of today’s rule on small 

entities, small entity is defined as: (1) a small business that is a small industrial entity as defined 

by the Small Business Administration’s (SBA) regulations at 13 CFR 121.201; (2) a small 

governmental jurisdiction that is a government of a city, county, town, school district or special 

district with a population of less than 50,000; and (3) a small organization that is any not-forprofit enterprise which is independently owned and operated and is not dominant in its field. 

After considering the economic impacts of today’s rule on small entities, EPA has concluded that 

this action will not have a significant economic impact on a substantial number of small entities. 

This rule will not impose any requirements on small entities. This rule establishes national 

standards for allowable concentrations of ozone in ambient air, as required by section 109 of the 

CAA. See also ATA I at 1044-45 (NAAQS do not have significant impacts upon small entities 

because NAAQS themselves impose no regulations upon small entities). 

9.4 Unfunded Mandates Reform Act 

Title II of the Unfunded Mandates Reform Act of 1995 (UMRA), Public Law 104-4, establishes 

requirements for Federal agencies to assess the effects of their regulatory actions on State, local, 

and Tribal governments and the private sector. Under section 202 of the UMRA, EPA generally 

must prepare a written statement, including a cost-benefit analysis, for proposed and final rules 

with “Federal mandates” that may result in expenditures to State, local, and Tribal governments, 

in the aggregate, or to the private sector, of $100 million or more in any 1 year. Before 

promulgating an EPA rule for which a written statement is needed, section 205 of the UMRA 

generally requires EPA to identify and consider a reasonable number of regulatory alternatives 

and adopt the least costly, most cost-effective or least burdensome alternative that achieves the 

objectives of the rule. The provisions of section 205 do not apply when they are inconsistent 

with applicable law. Moreover, section 205 allows EPA to adopt an alternative other than the 

least costly, most cost-effective or least burdensome alternative if the Administrator publishes 

with the final rule an explanation why that alternative was not adopted. Before EPA establishes 

any regulatory requirements that may significantly or uniquely affect small governments, 

including Tribal governments, it must have developed under section 203 of the UMRA a small 

government agency plan. The plan must provide for notifying potentially affected small 

governments, enabling officials of affected small governments to have meaningful and timely 

input in the development of EPA regulatory proposals with significant Federal intergovernmental 

mandates, and informing, educating, and advising small governments on compliance with the 

regulatory requirements. 

This proposal contains no Federal mandates (under the regulatory provisions of Title II of the 

UMRA) for State, local, or Tribal governments or the private sector. The rule imposes no new 
9-3

expenditure or enforceable duty on any State, local or Tribal governments or the private sector, 

and EPA has determined that this rule contains no regulatory requirements that might 

significantly or uniquely affect small governments. Furthermore, as indicated previously, in 

setting a NAAQS, EPA cannot consider the economic or technological feasibility of attaining 

ambient air quality standards, although such factors may be considered to a degree in the 

development of State plans to implement the standards. See also ATA I at 1043 (noting that 

because EPA is precluded from considering costs of implementation in establishing NAAQS, 

preparation of a Regulatory Impact Analysis pursuant to the Unfunded Mandates Reform Act 

would not furnish any information which the court could consider in reviewing the NAAQS).

Accordingly, EPA has determined that the provisions of sections 202, 203, and 205 of the 

UMRA do not apply to this final decision. The EPA acknowledges, however, that any 

corresponding revisions to associated SIP requirements and air quality surveillance requirements, 

40 CFR part 51 and 40 CFR part 58, respectively, might result in such effects. Accordingly, 

EPA has addressed unfunded mandates in the notice that announces the revisions to 40 CFR part 

58, and will, as appropriate, address unfunded mandates when it proposes any revisions to 40 

CFR part 51. 

9.5 Executive Order 13132: Federalism 

Executive Order 13132, entitled “Federalism” (64 FR 43255, August 10, 1999), requires EPA to 

develop an accountable process to ensure “meaningful and timely input by State and local 

officials in the development of regulatory policies that have federalism implications.” “Policies 

that have federalism implications” is defined in the Executive Order to include regulations that 

have “substantial direct effects on the States, on the relationship between the national 

government and the States, or on the distribution of power and responsibilities among the various 

levels of government.” 

At the time of this proposal, EPA concludes that the proposed rule would not have substantial 

direct effects on the States, on the relationship between the national government and the States, 

or on the distribution of power and responsibilities among the various levels of government, as 

specified in Executive Order 13132. However, EPA recognized that States would have a 

substantial interest in this rule and any corresponding revisions to associated SIP requirements 

and air quality surveillance requirements, 40 CFR part 51 and 40 CFR part 58, respectively. 

Therefore, in the spirit of Executive Order 13132, and consistent with EPA policy to promote 

communications between EPA and State and local governments, EPA specifically solicits 

comment on the rule from State and local officials at this time. 

9.6 Executive Order 13175: Consultation and Coordination with Indian Tribal 

Governments 

Executive Order 13175, entitled “Consultation and Coordination with Indian Tribal 

Governments” (65 FR 67249, November 9, 2000), requires EPA to develop an accountable 

process to ensure “meaningful and timely input by tribal officials in the development of 

regulatory policies that have tribal implications.” This rule concerns the establishment of ozone 

NAAQS. The Tribal Authority Rule gives Tribes the opportunity to develop and implement 
9-4

CAA programs such as the ozone NAAQS, but it leaves to the discretion of the Tribe whether to 

develop these programs and which programs, or appropriate elements of a program, they will 

adopt.

This proposed rule does not have Tribal implications, as specified in Executive Order 13175. It 

does not have a substantial direct effect on one or more Indian Tribes, since Tribes are not 

obligated to adopt or implement any NAAQS. Thus, Executive Order 13175 does not apply to 

this rule. However, in the spirit of efficaciousness, EPA staff participated in the regularly 

scheduled Tribal Air call sponsored by the National Tribal Air Association during the spring of 

2007 as this proposal was under development. EPA specifically solicits additional comment on 

the proposed NAAQS rule from Tribal officials. 

9.7 Executive Order 13045: Protection of Children from Environmental Health & 

Safety Risks 

Executive Order 13045, “Protection of Children from Environmental Health Risks and Safety 

Risks” (62 FR 19885, April 23, 1997) applies to any rule that: (1) is determined to be 

“economically significant” as defined under Executive Order 12866, and (2) concerns an 

environmental health or safety risk that EPA has reason to believe may have a disproportionate 

effect on children. If the regulatory action meets both criteria, the Agency must evaluate the 

environmental health or safety effects of the rule on children, and explain why the regulation is 

preferable to other potentially effective and reasonably feasible alternatives considered by the 

Agency. This rule is subject to Executive Order 13045 because it is an economically significant 

regulatory action as defined by Executive Order 12866, and we believe that the environmental 

health risk addressed by this action may have a disproportionate effect on children.

The NAAQS constitute uniform, national standards for ozone pollution; these standards are 

designed to protect public health with an adequate margin of safety, as required by CAA section 

109. However, the protection offered by these standards may be especially important for 

children because children, along with other sensitive population subgroups such as the elderly 

and people with existing heart or lung disease, are potentially susceptible to health effects 

resulting from ozone exposure. Because children are considered a potentially susceptible 

population, we have carefully evaluated the environmental health effects of exposure to ozone 

pollution to this sub-population. These effects and the size of the population affected are 

summarized in section 8.7 of the Criteria Document and section 3.6 of the Staff Paper, and the 

results of our evaluation of the effects of ozone pollution on children are discussed in sections 

II.A-C of the NAAQS proposal preamble. 

9.8 Executive Order 13211: Actions that Significantly Affect Energy Supply, 

Distribution or Use 

This proposed rule is not a “significant energy action” as defined in Executive Order 13211, 

“Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or Use” 

(66 FR 28355 (May 22, 2001)) because in the Agency’s judgment it is not likely to have a 

significant adverse effect on the supply, distribution, or use of energy. The purpose of this rule is 
9-5

to establish revised NAAQS for ozone. The rule does not prescribe specific pollution control 

strategies by which these ambient standards will be met. Such strategies will be developed by 

States on a case-by-case basis, and EPA cannot predict whether the control options selected by 

States will include regulations on energy suppliers, distributors, or users. Thus, EPA concludes 

that this rule is not likely to have any adverse energy effects and does not constitute a significant 

energy action as defined in Executive Order 13211. 

9.9 National Technology Transfer Advancement Act 

Section 12(d) of the National Technology Transfer Advancement Act of 1995 (NTTAA), Public 

Law No. 104-113, §12(d) (15 U.S.C. 272 note), directs EPA to use voluntary consensus 

standards in its regulatory activities unless to do so would be inconsistent with applicable law or 

otherwise impractical. Voluntary consensus standards are technical standards (e.g., materials 

specifications, test methods, sampling procedures, and business practices) that are developed or 

adopted by voluntary consensus standards bodies. The NTTAA directs EPA to provide 

Congress, through OMB, explanations when the Agency decides not to use available and 

applicable voluntary consensus standards. Since EPA is not changing any of the monitoring 

requirements as part of this proposal, there are no impacts associated with the NTTAA. 

9.10 Executive Order 12898: Federal Actions to Address Environmental Justice in 

Minority Populations and Low-Income Populations 

Executive Order 12898, “Federal Actions to Address Environmental Justice in Minority 

Populations and Low-Income Populations,” requires Federal agencies to consider the impact of 

programs, policies, and activities on minority populations and low-income populations. 

According to EPA guidance, agencies are to assess whether minority or low-income populations 

face a risk or a rate of exposure to hazards that are significant and that “appreciably exceeds or is 

likely to appreciably exceed the risk or rate to the general population or to the appropriate 

comparison group” (EPA, 1998). 

In accordance with Executive Order 12898, the Agency has considered whether these decisions 

may have disproportionate negative impacts on minority or low-income populations. This rule 

establishes uniform, national ambient air quality standards for ozone, and is not expected to have 

disproportionate negative impacts on minority or low income populations. In this NAAQS 

proposal, the Administrator considered the available information regarding health effects among 

vulnerable and susceptible populations, such as those with preexisting conditions. Thus it 

remains EPA’s conclusion that this rule is not expected to have disproportionate negative 

impacts on minority or low income populations. 
United States Office of Air Quality Planning and Standards Publication No

Environmental Protection Air Quality Strategies and Standards Division July 2007 Agency Research Triangle Park, NC