Regulatory Impact Analysis of the Proposed 

Revisions to the National Ambient Air 

Quality Standards for Ground-Level Ozone
ii

[This page intentionally left blank]
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EPA-452/P-14-006

November 2014

Regulatory Impact Analysis of the Proposed Revisions 

to the National Ambient Air Quality Standards for Ground-Level Ozone

U.S. Environmental Protection Agency

Office of Air and Radiation

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711
iv

CONTACT INFORMATION

This document has been prepared by staff from the Office of Air Quality Planning and 

Standards, U.S. Environmental Protection Agency. Questions related to this document should be 

addressed to Robin Langdon, U.S. Environmental Protection Agency, Office of Air Quality 

Planning and Standards, C439-02, Research Triangle Park, North Carolina 27711 (email: 

langdon.robin@epa.gov). 

ACKNOWLEDGEMENTS

In addition to EPA staff from the Office of Air Quality Planning and Standards, personnel from 

the Office of Atmospheric Programs, the Office of Transportation and Air Quality, the Office of 

Policy Analysis and Review, and the Office of Policy’s National Center for Environmental 

Economics contributed data and analysis to this document. 
v

TABLE OF CONTENTS

LIST OF TABLES.................................................................................................................. xiii

LIST OF FIGURES ...............................................................................................................xxiv

EXECUTIVE SUMMARY ...................................................................................................ES-1

Overview .......................................................................................................................ES-1

ES.1 Overview of Analytical Approach.................................................................ES-3

ES.1.1 Establishing the Baseline ..........................................................................ES-5

ES.1.2 Control Strategies and Emissions Reductions............................................ES-6

ES.1.2.1 Emissions Reductions from Known Controls in 2025 ......................ES-7

ES.1.2.2 Emissions Reductions beyond Known Controls in 2025...................ES-9

ES.1.2.3 Emissions Reductions beyond Known Controls for Post-2025 .........ES-9

ES.1.3 Human Health Benefits...........................................................................ES-10

ES.1.4 Welfare Co-Benefits of the Primary Standard..........................................ES-12

ES.2 Results of Benefit-Cost Analysis.................................................................ES-13

ES.3 References..................................................................................................ES-18

CHAPTER 1: INTRODUCTION AND BACKGROUND...................................................... 1-1

Overview ......................................................................................................................... 1-1

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

1.1.1 NAAQS ...................................................................................................... 1-1

1.1.2 Ozone NAAQS ........................................................................................... 1-2

1.2 Role of this RIA in the Process of Setting the NAAQS .......................................... 1-2

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

1.2.2 Role of Statutory and Executive Orders ...................................................... 1-3

1.2.3 The Need for National Ambient Air Quality Standards ............................... 1-4

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

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

1.3.1 Existing and Revised Ozone National Ambient Air Quality Standards ........ 1-5

1.3.2 Establishing Attainment with the Current Ozone National Ambient Air 

Quality Standard .................................................................................................... 1-6

1.3.3 Establishing the Baseline for Evaluation of Alternative Standards.............. 1-8

1.4 Health and Welfare Benefits Analysis Approach.................................................... 1-9

1.4.1 Health Benefits ........................................................................................... 1-9

1.4.2 Welfare Co-Benefits ..................................................................................1-10

1.5 Cost Analysis Approach........................................................................................1-10

1.6 Organization of this Regulatory Impact Analysis ..................................................1-10

1.7 References............................................................................................................1-11

CHAPTER 2: DEFINING THE OZONE AIR QUALITY PROBLEM................................... 2-1

Overview ......................................................................................................................... 2-1

2.1 Emissions and Atmospheric Chemistry.................................................................. 2-1
vi

2.2 Spatial and Temporal Variations in Ambient Ozone Concentrations ...................... 2-3

2.3 Ozone Monitoring.................................................................................................. 2-5

2.3.1 Ozone Monitoring Network ........................................................................ 2-5

2.3.2 Recent Ozone Monitoring Data and Trends................................................. 2-7

2.4 Background Ozone ...............................................................................................2-10

2.4.1 Seasonal Mean Background Ozone in the U.S............................................2-12

2.4.2 Seasonal Mean Background Ozone in the U.S. as a Proportion of Total 

Ozone ..................................................................................................................2-15

2.4.3 Daily Distributions of Background Ozone within the Seasonal Mean .........2-16

2.5 References............................................................................................................2-19

CHAPTER 3: AIR QUALITY MODELING AND ANALYSIS............................................. 3-1

Overview ......................................................................................................................... 3-1

3.1 Modeling Ozone Levels in the Future .................................................................... 3-2

3.1.1 Selection of Future Analytic Year ............................................................... 3-2

3.1.2 Air Quality Modeling Platform ................................................................... 3-3

3.1.3 Emissions Inventories................................................................................. 3-7

3.1.4 Emissions Sensitivity Simulations..............................................................3-11

3.2 Methods for Calculating Current and Future Year Ozone Design Values ..............3-16

3.2.1 Current Year Ozone Design Value Calculations.........................................3-16

3.2.2 Future Year Ozone Design Value Projections ............................................3-17

3.3 Determining Tons of Emissions Reductions to Meet Various NAAQS Levels.......3-18

3.3.1 Determining Ozone Response from Each Emissions Sensitivity.................3-18

3.3.2 Combining Response from Multiple Sensitivity Runs To Construct 

Baseline And Alternative Standard Scenarios........................................................3-22

3.3.3 Creation of the Baseline Scenario...............................................................3-24

3.3.4 Creation of the 70, 65, and 60 ppb Alternative Standard Level Scenarios...3-25

3.3.5 Monitoring Sites Excluded from Quantitative Analysis..............................3-26

3.4 Creating Spatial Surfaces......................................................................................3-28

3.4.1 BenMap Surfaces.......................................................................................3-28

3.4.2 W126 surfaces ...........................................................................................3-35

3.5 References............................................................................................................3-40

APPENDIX 3: ADDITIONAL AIR QUALITY ANALYSIS AND RESULTS ....................3A-1

3A.1 2011 Model Evaluation for Ozone ................................................................3A-1

3A.2 California Sub-Regions and Areas of Influence...........................................3A-17

3A.3 VOC Impact Areas......................................................................................3A-22

3A.4 Numeric Examples of Calculation Methodology for Changes in Design 

Values ...................................................................................................................3A-23

3A.5 Emissions Reductions Applied to Create Baseline and Alternative Standard 

Level Scenarios ........................................................................................................3A-25

3A.6 Design Values for All Monitors included in the Quantitative Analysis ........3A-27

3A.7 Monitors Excluded from the Quantitative Analysis .....................................3A-51

3A.7.1 Sites without Projections Due to Insufficient Days..................................3A-51

3A.7.2 Winter Ozone..........................................................................................3A-53
vii

3A.7.3 Monitoring Sites in Rural/Remote Areas of the West and Southwest.......3A-54

3A.8 Calculation Methodology for W126 Metric..........................................................3A-63

3A.9 References...........................................................................................................3A-64

CHAPTER 4: CONTROL STRATEGIES AND EMISSIONS REDUCTIONS...................... 4-1

Overview ......................................................................................................................... 4-1

4.1 Control Strategy Analysis Steps............................................................................. 4-3

4.2 Baseline Control Strategy ...................................................................................... 4-5

4.3 Alternative Standard Analyses..............................................................................4-12

4.3.1 Identifying Known Controls Needed to Meet the Alternative Standards.....4-14

4.3.2 Known Control Measures Analyzed...........................................................4-19

4.3.3 Emissions Reductions beyond Known Controls Needed to Meet the 

Alternative Standards............................................................................................4-21

4.3.4 Summary of Emissions Reductions Needed to Meet the Alternative 

Standards..............................................................................................................4-22

4.4 Limitations and Uncertainties ...............................................................................4-23

4.5 References............................................................................................................4-25

APPENDIX 4: CONTROL STRATEGIES AND EMISSIONS REDUCTIONS...................4A-1

Overview .......................................................................................................................4A-1

4A.1 Types of Control Measures ...........................................................................4A-1

4A.2 Application of Control Measures in Geographic Areas..................................4A-1

4A.3 NOx Control Measures for NonEGU Point Sources.......................................4A-4

4A.4 VOC Control Measures for Non-EGU Point Sources ..................................4A-12

4A.5 NOx Control Measures for Nonpoint (Area) and Nonroad Sources..............4A-12

4A.6 VOC Control Measures for Nonpoint (Area) Sources................................4A-123

CHAPTER 5: HUMAN HEALTH BENEFITS ANALYSIS APPROACH AND RESULTS .. 5-1

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

5.2 Overview............................................................................................................... 5-4

5.3 Updated Methodology Presented in this RIA ......................................................... 5-8

5.4 Human Health Benefits Analysis Methods............................................................5-10

5.4.1 Health Impact Assessment .........................................................................5-12

5.4.2 Economic Valuation of Health Impacts......................................................5-14

5.4.3 Estimating Benefits for the 2025 and Post-2025 Scenarios.........................5-16

5.4.4 Benefit-per-ton Estimates for PM2.5 ...........................................................5-17

5.5 Characterizing Uncertainty ...................................................................................5-19

5.5.1 Monte Carlo Assessment............................................................................5-21

5.5.2 Sensitivity Analysis Addressing Both Incidence and Dollar Benefit 

Valuation..............................................................................................................5-22

5.5.3 Supplemental Analyses..............................................................................5-24

5.5.4 Qualitative Assessment of Uncertainty and Other Analysis Limitations .....5-25

5.6 Benefits Analysis Data Inputs...............................................................................5-26

5.6.1 Demographic Data .....................................................................................5-26
viii

5.6.2 Baseline Incidence and Prevalence Estimates.............................................5-27

5.6.3 Effect Coefficients.....................................................................................5-30

5.6.3.1 Ozone Premature Mortality Effect Coefficients ...................................5-38

5.6.3.2 Hospital Admissions and Emergency Department Visits .....................5-48

5.6.3.3 Acute Health Events and School/Work Loss Days...............................5-50

5.6.3.4 Unquantified Human Health Effects....................................................5-52

5.6.4 Economic Valuation Estimates...................................................................5-55

5.6.4.1 Mortality Valuation .............................................................................5-56

5.6.4.2 Hospital Admissions and Emergency Department Valuation ...............5-67

5.6.4.3 Nonfatal Myocardial Infarctions Valuation..........................................5-68

5.6.4.4 Valuation of Acute Health Events........................................................5-70

5.6.4.5 Growth in WTP Reflecting National Income Growth over Time..........5-71

5.7 Benefits Results..................................................................................................5-755

5.7.1 Benefits of the Proposed and Alternative Annual Primary Ozone 

Standards for the 2025 Scenario..........................................................................5-766

5.7.2 Benefits of the Proposed and Alternative Annual Primary Ozone 

Standards for the post-2025 Scenario ..................................................................5-822

5.7.3 Uncertainty in Benefits Results (including Discussion of Sensitivity 

Analyses and Supplemental Analyses) ................................................................5-877

5.7.3.1 Sensitivity Analyses...................................................................................5-89

5.7.3.2 Supplemental Analyses............................................................................5-911

5.8 Discussion ..........................................................................................................5-922

5.9 References..........................................................................................................5-955

APPENDIX 5A: COMPREHENSIVE CHARACTERIZATION OF UNCERTAINTY IN 

OZONE BENEFITS ANALYSIS...................................................................................5A-1

Overview .......................................................................................................................5A-1

5A.1 Description of Classifications Applied in the Uncertainty Characterization ...5A-1

5A.1.1 Direction of Bias.......................................................................................5A-2

5A.1.2 Magnitude of Impact.................................................................................5A-2

5A.1.3 Confidence in Analytic Approach .............................................................5A-3

5A.1.4 Uncertainty Quantification........................................................................5A-4

5A.2 Organization of the Qualitative Uncertainty Table ........................................5A-5

5A.3 References..................................................................................................5A-16

Appendix 5B: ADDITIONAL SENSITIVITY ANALYSES RELATED TO THE OZONE 

HEALTH BENEFITS ANALYSIS ................................................................................5B-1

Overview .......................................................................................................................5B-1

5B.1 Threshold Sensitivity Analysis for Premature Mortality Incidence and 

Benefits from Long-term Exposure to Ozone ..............................................................5B-2

5B.2 Alternative Concentration-Response Functions for PM2.5–Related Mortality.5B-4

5B.3 Income Elasticity of Willingness-to-Pay .......................................................5B-7

5B.4 References....................................................................................................5B-9
ix

APPENDIX 5C: SUPPLEMENTAL ANALYSES RELATED TO THE OZONE HEALTH 

BENEFITS ANALYSIS.................................................................................................5C-1

Overview .......................................................................................................................5C-1

5C.1 Age Group-Differentiated Aspects of Short-Term Ozone Exposure-Related 

Mortality.....................................................................................................................5C-1

5C.2 Evaluation of Mortality Impacts Relative to the Baseline Pollutant 

Concentrations (used in generating those mortality estimates) for both Short-Term 

Ozone Exposure-Related Mortality and Long-Term PM2.5 Exposure-Related 

Mortality.....................................................................................................................5C-6

5C.3 Core Incidence and Dollar Benefits Estimates Reflecting Application of 

Known Controls for the 2025 Scenario ..................................................................... 5C-14

5C.4 References.................................................................................................. 5C-19

APPENDIX 5D: DISCUSSION OF EFFECT ESTIMATES REFLECTED IN THE 

DEVELOPMENT OF DOLLAR-PER-TON VALUES USED IN MODELING PM2.5 

COBENEFITS ...............................................................................................................5D-1

Overview .......................................................................................................................5D-1

5D.1 PM2.5 Premature Mortality Effect Coefficients ..............................................5D-5

5D.2 Hospital Admissions and Emergency Department Visits.............................5D-14

5D.3 Acute Health Events and School/Work Loss Days ......................................5D-16

5D.4 Nonfatal Acute Myocardial Infarctions (AMI) (Heart Attacks)....................5D-19

5D.5 References..................................................................................................5D-21

APPENDIX 5E: INPUTS TO PM2.5 COBENEFITS MODELING......................................... 5E-1

Overview ....................................................................................................................... 5E-1

CHAPTER 6: IMPACTS ON PUBLIC WELFARE OF ATTAINMENT STRATEGIES TO 

MEET PRIMARY AND SECONDARY OZONE NAAQS.............................................. 6-1

Overview ......................................................................................................................... 6-1

6.1 Welfare Benefits of Strategies to Attain Primary and Secondary Ozone Standards. 6-2

6.2 Welfare Benefits of Reducing Ozone ..................................................................... 6-3

6.3 Additional Welfare Benefits of Strategies to Meet the Ozone NAAQS................... 6-6

6.4 Analysis of Commercial Agricultural and Forestry Related Benefits Using the 

Forest and Agricultural Sector Optimization Model – Greenhouse Gas Version 

(FASOMGHG)............................................................................................................. 6-8

6.4.1 Summary of the Analytical Approach ......................................................... 6-8

6.4.2 Summary of FASOMGHG Results ............................................................6-10

6.5 References............................................................................................................6-16

APPENDIX 6A: Methods and Data Used to Develop estimates of Ozone Effects on Crop 

and Forest Productivity ..................................................................................................6A-1

6A.1 Methodology.................................................................................................6A-1
x

6A.1.1 Calculation of Relative Yield Loss............................................................6A-4

6A.1.1.1 Relative Yield Loss for Crops..........................................................6A-5

6A.1.1.2 Relative Yield Loss for Trees ..........................................................6A-7

6A.1.1.3 Calculation of Relative Yield Gain ..................................................6A-7

6A.1.2 Conducting Model Scenarios in FASOMGHG..........................................6A-8

6A.1.3 Data Inputs ...............................................................................................6A-9

6A.1.3.1 Ambient Ozone Concentration Data ..............................................6A-10

6A.1.3.2 Changes in Crop and Forest Yields with Respect to 75 ppb 

Scenario......................................................................................................6A-16

6A.2 Model Results.............................................................................................6A-25

6A.2.1 Agricultural Sector..................................................................................6A-25

6A.2.1.1 Production and Prices....................................................................6A-25

6A.2.1.2 Crop Acreage ................................................................................6A-28

6A.2.2 Forestry Sector........................................................................................6A-30

6A.2.2.1 Production and Prices....................................................................6A-30

6A.2.2.2 Forest Acres Harvested..................................................................6A-33

6A.2.2.3 Forest Inventory ............................................................................6A-34

6A.2.3 Cross-Sectoral Policy Impacts.................................................................6A-35

6A.2.3.1 Land Use .......................................................................................6A-36

6A.2.3.2 Welfare .........................................................................................6A-37

6A.2.3.3 Greenhouse Mitigation Potential....................................................6A-40

6A.3 Summary ....................................................................................................6A-43

6A.4 References..................................................................................................6A-45

CHAPTER 7: ENGINEERING COST ANALYSIS and Economic Impacts........................... 7-1

Overview ......................................................................................................................... 7-1

7.1 Estimating Engineering Compliance Costs............................................................. 7-2

7.1.1 Methods and Data ....................................................................................... 7-2

7.1.2 Compliance Cost Estimates for Known Controls......................................... 7-4

7.2 The Challenge of Estimating Costs for Unknown Controls....................................7-10

7.2.1 Incomplete Characterization of NOx Marginal Abatement Cost Curves .....7-11

7.2.2 Comparison of Baseline Emissions and Controls across Ozone NAAQS 

RIAs from 1997 to 2014 .......................................................................................7-13

7.2.3 Impact of Technological Innovation and Diffusion.....................................7-18

7.2.4 Learning by Doing .....................................................................................7-20

7.2.5 Using Regional NOx Offset Prices to Estimate Costs of Unknown 

Emissions Controls...............................................................................................7-22

7.2.6 Conclusion.................................................................................................7-25

7.3 Compliance Cost Estimates for Unknown Emissions Controls..............................7-26

7.3.1 Methods.....................................................................................................7-26

7.3.2 Unknown Compliance Cost Estimates........................................................7-30

7.4 Total Compliance Cost Estimates..........................................................................7-31

7.5 Updated Methodology Presented in this RIA ........................................................7-33

7.6 Economic Impacts ................................................................................................7-36

7.6.1 Introduction ...............................................................................................7-36

7.6.2 Summary of Market Impacts......................................................................7-37
xi

7.7 Uncertainties and Limitations ...............................................................................7-38

7.8 References............................................................................................................7-40

APPENDIX 7A: ENGINEERING COST ANALYSIS.........................................................7A-8

Overview .......................................................................................................................7A-8

7A.1 Cost of Known Controls in Alternative Standards Analyses ..........................7A-8

7A.2 Alternative Estimates of Costs Associated with Emissions Reductions from 

Unknown Controls......................................................................................................7A-8

CHAPTER 8: COMPARISON OF COSTS AND BENEFITS................................................. 8-1

Overview ......................................................................................................................... 8-1

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

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

8.2.1 Relative Contribution of PM Benefits to Total Benefits............................... 8-9

8.2.2 Developing Future Control Strategies with Limited Data ............................ 8-9

8.3 Framing Uncertainty.............................................................................................8-11

8.4 Key Observations from the Analysis.....................................................................8-14

8.5 References............................................................................................................8-16

CHAPTER 9: STATUTORY AND EXECUTIVE ORDER IMPACT ANALYSIS ................. 9-1

Overview ......................................................................................................................... 9-1

9.1 National Technology Transfer and Advancement Act ............................................ 9-1

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

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

9.4 Unfunded Mandates Reform Act............................................................................ 9-3

9.5 Executive Order 12866: Regulatory Planning and Review ..................................... 9-5

9.6 Executive Order 12898: Federal Actions to Address Environmental Justice in 

Minority Populations and Low-Income Populations...................................................... 9-5

9.7 Executive Order 13045: Protection of Children from Environmental Health & 

Safety Risks.................................................................................................................. 9-7

9.8 Executive Order 13132: Federalism ....................................................................... 9-8

9.9 Executive Order 13175: Consultation and Coordination with Indian Tribal 

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

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

Distribution, or Use .....................................................................................................9-10

APPENDIX 9A: SOCIO-DEMOGRAPHIC CHARACTERISTICS OF POPULATIONS IN 

CORE-BASED STATISTICAL AREAS WITH OZONE MONITORS EXCEEDING 

PROPOSED OZONE STANDARDS.............................................................................9A-1

9A.1 Design of Analysis...............................................................................................9A-1

9A1.1 Demographic Variables Included in Analysis ..................................................9A-3

9A.2 Considerations in Evaluating and Interpreting Results..........................................9A-6

9A.3 Presentation of Results.........................................................................................9A-7
xii

CHAPTER 10: QUALITATIVE DISCUSSION OF EMPLOYMENT IMPACTS OF AIR 

QUALITY ......................................................................................................................10-1

Overview ........................................................................................................................10-1

10.1 Economic Theory and Employment ......................................................................10-1

10.2 Current State of Knowledge Based on the Peer-Reviewed Literature ....................10-6

10.2.1 Regulated Sectors ......................................................................................10-6

10.2.2 Labor Supply Impacts................................................................................10-7

10.3 Employment Related to Installation and Maintenance of NOx Control Equipment 10-8

10.3.1 Employment Resulting from Addition of NOx Controls at EGUs...............10-9

10.3.2 Assessment of Employment Impacts for Individual Industrial, 

Commercial, and Institutional (ICI) Boilers and Cement Kilns............................10-12

10.4 References..........................................................................................................10-15
xiii

LIST OF TABLES

Table ES-1. Summary of Emission Reductions by Sector for Known Controls for 70 ppb 

Proposed Alternative Standard Level for 2025, except California (1,000 tons/year)a..ES-8

Table ES-2. Summary of Emission Reductions by Sector for Known Controls for 65 ppb 

Proposed Alternative Standard Level for 2025 - except California (1,000 tons/year)a.ES-8

Table ES-3. Summary of Emission Reductions by Sector for Known Controls for 60 ppb 

Alternative Standard Level for 2025 - except California (1,000 tons/year)a................ES-8

Table ES-4. Summary of Emissions Reductions by Alternative Standard for Unknown 

Controls for 2025 - except California (1,000 tons/year)a............................................ES-9

Table ES-5. Summary of Emissions Reductions by Alternative Standard Level for 

Unknown Controls for post-2025 - California (1,000 tons/year)a..............................ES-10

Table ES-6. Total Annual Costs and Benefitsafor U.S., except California in 2025 

(billions of 2011$, 7% Discount Rate)b....................................................................ES-14

Table ES-7. Summary of Total Number of Annual Ozone and PM-Related Premature 

Mortalities and Premature Morbidity: 2025 National Benefits a...............................ES-14

Table ES-8. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for 2025 - U.S., except California (billions of 2011$, 7% Discount Rate)a......ES-15

Table ES-9. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

2025 Scenario (nationwide benefits of attaining each alternative standard everywhere 

in the U.S. except California) – Full Attainment a....................................................ES-16

Table ES-10. Total Annual Costs and Benefitsaof Control Strategies Applied in 

California, post-2025 (billions of 2011$, 7% Discount Rate)b..................................ES-17

Table ES-11. Summary of Total Number of Annual Ozone and PM-Related Premature 

Mortalities and Premature Morbidity: Post-2025a....................................................ES-17

Table ES-12. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for post-2025 - California (billions of 2011$, 7% Discount Rate)a..................ES-18

Table ES-13. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

post-2025 Scenario (nationwide benefits of attaining each alternative standard just in 

California) – Full Attainmenta.....................................................................................3-18

Table 3-1. 2011 and 2025 Base Case NOx and VOC Emissions by Sector (thousand 

tons) 11

Table 3-2. List of Emissions Sensitivity Cases that Were Modeled in CAMx to 

Determine Ozone Response Factors............................................................................3-15
xiv

Table 3-3. Anthropogenic NOx and VOC Emissions from the 2025 Base and Explicit 

Control Cases* ...........................................................................................................3-16

Table 3A-1. Daily Maximum 8-hour Ozone Performance Statistics ≥ 60 ppb by Region, 

by Network................................................................................................................3A-6

Table 3A-2. Key Monitoring Sites Used for the Ozone Time Series Analysis .................3A-11

Table 3A-3. Emissions Reductions Applied to Create the Baseline Scenario*.................3A-25

Table 3A-4. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 

70 ppb Scenario .......................................................................................................3A-25

Table 3A-5. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 

65 ppb Scenario .......................................................................................................3A-26

Table 3A-6. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 

60 ppb Scenario .......................................................................................................3A-26

Table 3A-7. Design Values for California Region Monitors............................................3A-27

Table 3A-8. Design Values for Southwest Monitors.......................................................3A-31

Table 3A-9. Design Values for Central Region Monitors................................................3A-35

Table 3A-10. Design Values for Midwest Monitors..........................................................3A-39

Table 3A-11. Design Values for Northeast Monitors........................................................3A-45

Table 3A-12. Monitors Without Projections due to Insufficient High Modeling Days to 

Meet EPA Guidance for Projecting Design Values ..................................................3A-52

Table 3A-13. Monitors Determined to Have Design Values Affected by Winter Ozone 

Events 53

Table 3A-14. Monitors with Limited Response to Regional NOx and National VOC 

Emissions Reductions in the 2025 Baseline .............................................................3A-56

Table 4-1. Controls Applied for the Alternative Standard Analyses Control Strategy ....... 4-7

Table 4-2. Summary of Emission Reductions by Sector for Known Controls Applied to 

Demonstrate Attainment of the Current Standard for the 2025 Baseline - U.S., except 

California (1,000 tons/year)a.......................................................................................4-11

Table 4-3. Summary of Emission Reductions (Known and Unknown Controls) Applied 

to Demonstrate Attainment in California for the post-2025 Baseline (1,000 

tons/year)a..................................................................................................................4-11
xv

Table 4-4. Number of Counties with Exceedances and Number of Additional Counties 

Where Reductions Were Applied for the 2025 Alternative Standards Analyses - U.S., 

except California ........................................................................................................4-18

Table 4-5. Summary of Emission Reductions by Sector for Known Controls Applied to 

Demonstrate Nationwide Attainment with a 70 ppb Ozone Standard in 2025, except 

California (1,000 tons/year)a.......................................................................................4-18

Table 4-6. Summary of Emission Reductions by Sector for Known Controls Applied to 

Demonstrate Nationwide Attainment with a 65 ppb Ozone Standard in 2025 - except 

California (1,000 tons/year)a.......................................................................................4-19

Table 4-7. Summary of Emission Reductions by Sector for Known Controls Applied to 

Demonstrate Nationwide Attainment with a 60 ppb Ozone Standard in 2025 - except 

California (1,000 tons/year)a.......................................................................................4-19

Table 4-8. Summary of Emissions Reductions by Alternative Standard for Unknown 

Controls for 2025 - except California (1,000 tons/year)a.............................................4-21

Table 4-9. Summary of Emissions Reductions by Alternative Level for Unknown 

Controls for post-2025 - California (1,000 tons/year)a.................................................4-21

Table 4-10. Summary of Known and Unknown Emissions Reductions by Alternative 

Standard Levels in 2025, Except California (1,000 tons/year)a....................................4-22

Table 4-11. Summary of Known and Unknown Emissions Reductions by Alternative 

Standard Levels for post-2025 - California (1,000 tons/year)a.....................................4-23

Table 4A-1. Geographic Areas for Application of NOx Controls in the Baseline and 

Alternative Standard Analyses - U.S., except Californiaa...........................................4A-2

Table 4A-2. Geographic Areas for Application of VOC Controls in the Baseline and 

Alternative Standard Analyses - U.S., except Californiaa...........................................4A-3

Table 4A-3. Geographic Areas for Application of NOx Controls in the Baseline and 

Alternative Standard Analyses - Californiaa...............................................................4A-3

Table 4A-4. Geographic Areas for Application of VOC Controls in the Baseline and 

Alternative Standard Analyses - Californiaa...............................................................4A-4

Table 4A-5. NOx Control Measures Applied in the Baseline Analysis ..............................4A-5

Table 4A-6. VOC Control Measures Applied in the Baseline Analysis .............................4A-6

Table 4A-7. NOx Control Measures Applied in the 70 ppb Alternative Standard 

Analysis....................................................................................................................4A-6
xvi

Table 4A-8. VOC Control Measures Applied in the 70 ppb Alternative Standard 

Analysis.....................................................................................................................4A-8

Table 4A-9. NOx Control Measures Applied in the 65 ppb Alternative Standard 

Analysis.................................................................................................................... 4A-8

Table 4A-10. VOC Control Measures Applied in the 65 ppb Alternative Standard 

Analysis...................................................................................................................4A-10

Table 4A-11. NOx Control Measures Applied in the 60 ppb Alternative Standard 

Analysis...................................................................................................................4A-10

Table 4A-12. VOC Control Measures Applied in the 60 ppb Alternative Standard 

Analysis...................................................................................................................4A-12

Table 5-1. Estimated Monetized Benefits of Attainment of the Alternative Ozone 

Standards for the 2025 Scenario (nationwide benefits of attaining each alternative 

standard everywhere in the U.S. except California) – Full Attainment (billions of 

2011$) a....................................................................................................................... 5-3

Table 5-2. Estimated Monetized Benefits of Attainment of the Alternative Ozone 

Standards for the Post-2025 Scenario (nationwide benefits of attaining each 

alternative standard just in California) – Full Attainment (billions of 2011$) a............. 5-3

Table 5-3. Human Health Effects of Pollutants Potentially Affected by Strategies to 

Attain the Primary Ozone Standards (endpoints included in the core analysis are 

identified with a red checkmark).................................................................................. 5-5

Table 5-4. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population ....................................................................................5-29

Table 5-5. Asthma Prevalence Rates...............................................................................5-29

Table 5-6. Criteria Used When Selecting C-R Functions.................................................5-33

Table 5-7. Health Endpoints and Epidemiological Studies Used to Quantify OzoneRelated Health Impacts in the Core Analysis a

............................................................5-35

Table 5-8. Health Endpoints and Epidemiological Studies Used to Quantify PM2.5-

Related Health Impacts in the Core Analysis a............................................................5-36

Table 5-9. Health Endpoints and Epidemiological Studies Used to Quantify OzoneRelated Health Impacts in the Sensitivity Analysis a

...................................................5-37

Table 5-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a..............5-57

Table 5-11. Influence of Applied VSL Attributes on the Size of the Economic Benefits 

of Reductions in the Risk of Premature Death (U.S. EPA, 2006a)...............................5-62
xvii

Table 5-12. Unit Values for Hospital Admissions a...........................................................5-67

Table 5-13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart 

Attacks a.....................................................................................................................5-69

Table 5-14. Estimated Costs Over a 5-Year Period of a Nonfatal Myocardial Infarction 

(in 2011$) a.................................................................................................................5-69

Table 5-15. Elasticity Values Used to Account for Projected Real Income Growth a.........5-74

Table 5-16. Adjustment Factors Used to Account for Projected Real Income Growth a.....5-75

Table 5-17. Population-Weighted Air Quality Change for the Proposed and Alternative 

Annual Primary Ozone Standards Relative to Analytical Baseline for 2025a...............5-77

Table 5-18. Emission Reductions in Illustrative Emission Reduction Strategies for the 

Proposed and Alternative Annual Primary Ozone Standards, by Pollutant and Region 

Relative to Analytical Baseline – Full Attainment (tons)a............................................5-77

Table 5-19. Estimated Number of Avoided Ozone-Only Health Impacts for the Proposed 

and Alternative Annual Ozone Standards (Incremental to the Analytical Baseline) for 

the 2025 Scenario (nationwide benefits of attaining each alternative standard 

everywhere in the U.S. except California) a, b.............................................................5-78

Table 5-20. Total Monetized Ozone-Only Benefits for the Proposed and Alternative 

Annual Ozone Standards (Incremental to the Analytical Baseline) for the 2025 

Scenario (nationwide benefits of attaining each alternative standard everywhere in 

the U.S. except California) (millions of 2011) a, b........................................................5-79

Table 5-21. Estimated Number of Avoided PM2.5-Related Health Impacts for the 

Proposed and Alternative Annual Ozone Standards (Incremental to the Analytical 

Baseline) for the 2025 Scenario (nationwide benefits of attaining each alternative 

standard everywhere in the U.S. except California) a...................................................5-80

Table 5-22. Monetized PM2.5-Related Health Co-Benefits for the Proposed and 

Alternative Annual Ozone Standards (Incremental to Analytical Baseline) for the 

2025 Scenario (nationwide benefits of attaining each alternative standard everywhere 

in the U.S. except California) (Millions of 2011)a,b,c...................................................5-80

Table 5-23. Estimate of Monetized Ozone and PM2.5 Benefits for Proposed and 

Alternative Annual Ozone Standards Incremental to the Analytical Baseline for the 

2025 Scenario (nationwide benefits of attaining each alternative standard everywhere 

in the U.S. except California) – Full Attainment (billions of 2011$) a.........................5-81

Table 5-24. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

2025 Scenario (nationwide benefits of attaining each alternative standard everywhere 

in the U.S. except California) – Full Attainment a.......................................................5-82
xviii

Table 5-25. Population-Weighted Air Quality Change for the Proposed and Alternative 

Annual Primary Ozone Standards Relative to Analytical Baseline for post-2025 a......5-82

Table 5-26. Estimated Number of Avoided Ozone-Only Health Impacts for the Proposed 

and Alternative Annual Ozone Standards (Incremental to the Analytical Baseline) for 

the Post-2025 Scenario (nationwide benefits of attaining each alternative standard 

just in California) a, b...................................................................................................5-83

Table 5-27. Total Monetized Ozone-Only Benefits for the Proposed and Alternative 

Annual Ozone Standards (Incremental to the Analytical Baseline) for the post-2025 

Scenario (nationwide benefits of attaining each alternative standard just in 

California) (millions of 2011) a, b................................................................................5-84

Table 5-28. Estimated Number of Avoided PM2.5-Related Health Impacts for the 

Proposed and Alternative Annual Ozone Standards (Incremental to the Analytical 

Baseline) for the post-2025 Scenario (nationwide benefits of attaining each 

alternative standard just in California) a......................................................................5-85

Table 5-29. Monetized PM2.5-Related Health Co-Benefits for the Proposed and 

Alternative Annual Ozone Standards (Incremental to Analytical Baseline) for the 

post-2025 Scenario (nationwide benefits of attaining each alternative standard just in 

California) (Millions of 2011) a,b.................................................................................5-86

Table 5-30. Estimate of Monetized Ozone and PM2.5 Benefits for Proposed and Alternative 

Annual Ozone Standards Incremental to the Analytical Baseline for the post-2025 

Scenario (nationwide benefits of attaining each alternative standard just in 

California) – Full Attainment (billions of 2011$) a......................................................5-86

Table 5-31. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

post-2025 Scenario (nationwide benefits of attaining each alternative standard just in 

California) – Full Attainment a....................................................................................5-87

Table 5A-1. Summary of Qualitative Uncertainty for Key Modeling Elements in Ozone 

Benefits.....................................................................................................................5A-6

Table 5B-1. Long-term Ozone Mortality Incidence at Various Assumed Thresholds a......5B-4

Table 5B-2. Application of Alternative (Expert Elicitation-Based Effect Estimates) to the 

Modeling of PM2.5 Co-benefit Estimates for PM2.5 (avoided incidence) .....................5B-7

Table 5B-3. Ranges of Elasticity Values Used to Account for Projected Real Income 

Growth a....................................................................................................................5B-8

Table 5B-4. Ranges of Adjustment Factors Used to Account for Projected Real Income 

Growth to 2024 a.......................................................................................................5B-8

Table 5B-5. Sensitivity of Monetized Ozone Benefits to Alternative Income Elasticities 

in 2025 (Millions of 2011$) a.....................................................................................5B-8
xix

Table 5C-1. Potential Reduction in Premature Mortality by Age Range from Attaining 

Alternate Ozone Standards (2025 scenario) a, b...........................................................5C-4

Table 5C-2. Potential Years of Life Gained by Age Range from Attaining Alternate 

Ozone Standards (2025 Scenario) a,b..........................................................................5C-4

Table 5C-3. Estimated Percent Reduction in All-Cause Mortality Attributed to the 

Proposed Primary Ozone Standards (2025 Scenario) a...............................................5C-5

Table 5C-4. Population Exposure in the Baseline Sector Modeling (used to generate the 

benefit-per-ton estimates) Above and Below Various Concentrations Benchmarks in 

the Underlying Epidemiology Studies a.................................................................... 5C-12

Table 5C-5. Fraction of Total Core Benefits Associated with Partial Attainment 

(application of known controls) (2025 Scenario)...................................................... 5C-14

Table 5C-6. Estimated Number of Avoided Ozone-Only Health Impacts for the 

Alternative Annual Primary Ozone Standards (Incremental to the Analytical 

Baseline) for the Partial Attainment of the 2025 Scenario (known controls) a,b......... 5C-15

Table 5C-7. Total Monetized Ozone-Only Benefits for the Alternative Annual Primary 

Ozone Standards (Incremental to the Analytical Baseline) for the Partial Attainment 

of the 2025 Scenario (using known controls) a,b....................................................... 5C-16

Table 5C-8. Estimated Number of Avoided PM2.5-Related Health Impacts for the 

Alternative Annual Primary Ozone Standards (Incremental to the Analytical 

Baseline) for the Partial Attainment of the 2025 Scenario (using known controls) a,b 5C-17

Table 5C-9. Monetized PM2.5-Related Health Co-Benefits for the Alternative Annual 

Primary Ozone Standards (Incremental to Analytical Baseline) for the Partial 

Attainment of the 2025 Scenario (using known controls) a,b,c................................... 5C-18

Table 5C-10. Combined Estimate of Monetized Ozone and PM2.5 Benefits for the 

Alternative Annual Primary Ozone Standards for the Partial Attainment of the 2025 

Scenario (using known controls) (billions of 2011$) a,b............................................ 5C-18

Table 5D-1. Health Endpoints and Epidemiological Studies Used to Quantify PM2.5-

related Health Impacts in the Core Analysis a.............................................................5D-4

Table 5D-2. Summary of Effect Estimates from Associated with Change in Long-Term 

Exposure to PM2.5 in Recent Cohort Studies in North America ................................5D-10

Table 6-1. Welfare Effects of NOx and VOC Emissions.................................................. 6-3

Table 6-2. Change in Consumer and Producer Surplus in the Forestry Sector from 

Attaining Alternative Ozone Standard Levels Compared to Attaining the Current 

Ozone Standard (Million 2011$).................................................................................6-11
xx

Table 6-3. Percent Change in Consumer and Producer Surplus in the Forestry Sector 

from Attaining Alternative Ozone Standard Levels Compared to Attaining the 

Current Ozone Standard..............................................................................................6-11

Table 6-4. Change in Consumer and Producer Surplus in the Agricultural Sector from 

Attaining Alternative Ozone Standard Levels Compared to Attaining the Current 

Ozone Standard (Million 2011$).................................................................................6-11

Table 6-5. Percent Change in Consumer and Producer Surplus in the Agricultural 

Sector from Attaining Alternative Ozone Standard Levels Compared to Attaining the 

Current Ozone Standard..............................................................................................6-12

Table 6-6. Annualized Changes in Consumer and Producer Surplus in Agriculture and 

Forestry from Attaining Alternative Ozone Standard Levels Compared to Attaining 

the Current Ozone Standard, 2010-2040, Million 2011$ (3% Discount Rate)..............6-13

Table 6-7. Annualized Percent Changes in Consumer and Producer Surplus in 

Agriculture and Forestry from Attaining Alternative Ozone Standard Levels 

Compared to Attaining the Current Ozone Standard, 2010-2040, (3% Discount Rate) 6-13

Table 6-8. Annualized Changes in Consumer and Producer Surplus in Agriculture and 

Forestry from Attaining Alternative Ozone Standard Levels Compared to Attaining 

the Current Ozone Standard, 2010-2040, Million 2011$ (7% Discount Rate)..............6-13

Table 6-9. Annualized Percent Changes in Consumer and Producer Surplus in 

Agriculture and Forestry from Attaining Alternative Ozone Standard Levels 

Compared to Attaining the Current Ozone Standard, 2010-2040, (7% Discount Rate) 6-14

Table 6-10. Increase in Carbon Sequestration from Attaining Alternative Ozone Standard 

Levels Compared to Attaining the Current Ozone Standard, MMtCO2e ......................6-15

Table 6-11. Percent Change in Carbon Sequestration from Attaining Alternative Ozone 

Standard Levels Compared to Attaining the Current Ozone Standard..........................6-16

Table 6A-1. Comparison of Total Cropland and Forestland NLCD Area (sq m) ...............6A-2

Table 6A-2. Parameter Values Used for Crops and Tree Species ......................................6A-5

Table 6A-3. Mapping of Ozone Impacts on Crops to FASOMGHG Crops .......................6A-6

Table 6A-4. Mapping of Ozone Impacts on Forests to FASOMGHG Forest Types...........6A-7

Table 6A-5. Forestland W126 Ozone Values under Alternative Scenarios ......................6A-10

Table 6A-6. Cropland W126 Ozone Values under Modeled Scenarios............................6A-11

Table 6A-7. Agricultural Production Fisher Indices (Current conditions =100)...............6A-25

Table 6A-7a. Agricultural Price Fisher Indices (Current Conditions = 100)......................6A-25
xxi

Table 6A-8. Major Crop Acreage, Million Acres............................................................6A-28

Table 6A-9. Forest Products Production, Million Cubic Feet ..........................................6A-30

Table 6A-10. Forest Product Prices, U.S. Dollars per Cubic Foot .....................................6A-31

Table 6A-11. Forest Product Prices and Percentage Change, U.S. Dollars per Cubic Foot 6A-32

Table 6A-12. Forest Acres Harvested, Thousand Acres....................................................6A-33

Table 6A-13. Existing and New Forest Inventory, Million Cubic Feet ..............................6A-34

Table 6A-14. Land Use by Major Category, Thousand Acres ...........................................6A-35

Table 6A-15. Consumer and Producer Surplus in Agriculture, Million 2010 U.S. Dollars.6A-37

Table 6A-16. Consumer and Producer Surplus in Forestry, Million 2010 U.S. Dollars .....6A-38

Table 6A-17. Annualized Changes in Consumer and Producer Surplus in Agriculture and 

Forestry, 2010–2044, Million 2010 U.S. Dollars (3% Discount Rate) ......................6A-39

Table 6A-18. Annualized Changes in Consumer and Producer Surplus in Agriculture and 

Forestry, 2010–2044, Million 2010 U.S. Dollars (7% Discount Rate) ......................6A-39

Table 6A-19. Carbon Storage, MMtCO2e .........................................................................6A-41

Table 6A-20. Forestry Carbon Sequestration, MMtCO2e ..................................................6A-42

Table 7-1. Summary of Known Annualized Control Costs by Sector for 70 ppb for 

2025- U.S., except California (millions of 2011$)........................................................ 7-8

Table 7-2. Summary of Known Annualized Control Costs by Sector for 65 ppb for 

2025 U.S., except California (millions of 2011$) ......................................................... 7-9

Table 7-3. Summary of Known Annualized Control Costs by Sector for 60 ppb for 

2025 U.S., except California (millions of 2011$) ......................................................... 7-9

Table 7-4. Emissions and Cost Information for Major NOx Rules Issued Between 1997 

and 2008.....................................................................................................................7-14

Table 7-5. Comparison of Key Assumptions Used in Developing Estimates of NOx 

Emissions Controls and Costs across Past and Current Ozone NAAQS Regulatory 

Impact Analyses a.......................................................................................................7-16

Table 7-6. Average NOx Offset Prices for Four Areas (2011$) a......................................7-24

Table 7-7. Annualized NOx Offset Prices for Four Areas (2011$)a.................................7-25
xxii

Table 7-8. Extrapolated Control Costs in 2025 by Alternative Standard for 2025 -- U.S., 

except California (millions of 2011$) .........................................................................7-31

Table 7-9. Extrapolated Control Costs in 2025 by Alternative Standard for Post-2025 --

California (millions of 2011$) ....................................................................................7-31

Table 7-10. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for 2025 - U.S., except California (millions of 2011$, 7% Discount Rate)a........7-33

Table 7-11. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for post-2025 - California (millions of 2011$, 7% Discount Rate)a....................7-33

Table 7A-1. Costs for Known NOx Controls in the 70 ppb Analysis (millions, 2011$)a....7A-1

Table 7A-2. Costs for Known VOC Controls in the 70 ppb Analysis (millions, 2011$) a...7A-2

Table 7A-3. Costs for Known NOx Controls in the 65 ppb Analysis (millions, 2011$) a....7A-3

Table 7A-4. Costs for Known VOC Controls in the 65 ppb Analysis (millions, 2011$) a...7A-4

Table 7A-5. Costs for Known NOx Controls in the 60 ppb Analysis (millions, 2011$) a...7A-5

Table 7A-6. Costs for Known VOC Controls in the 60 ppb Analysis (millions, 2011$) a...7A-6

Table 7A-7. Extrapolated Control Costs in 2025 by Alternative Standard for 2025 U.S., 

except California, using Alternative Average Cost Assumptions (millions of 2011$).7A-7

Table 7A-8. Extrapolated Control Costs in 2025 by Alternative Standard for Post-2025 

California, using Alternative Average Cost Assumptions (millions of 2011$)............7A-7

Table 7A-9. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for 2025 - U.S. using Alternative Cost Assumption for Extrapolated Costs, 

except California (millions of 2011$)a.......................................................................7A-7

Table 7A-10. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for Post-2025 California - U.S. using Alternative Cost Assumption for 

Extrapolated Costs (millions of 2011$)a.....................................................................7A-8

Table 8-1. Total Costs, Total Monetized Benefits, and Net Benefits in 2025 for U.S., 

except California (billions of 2011$)a.......................................................................... 8-4

Table 8-2. Total Costs, Total Monetized Benefits, and Net Benefits of Control 

Strategies Applied in California, Post-2025 (billions of 2011$)a.................................. 8-4

Table 8-3. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for 2025 - U.S., except California (billions of 2011$, 7% Discount Rate)a.......... 8-5

Table 8-4. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for post-2025 - California (billions of 2011$, 7% Discount Rate)a...................... 8-5
xxiii

Table 8-5. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

2025 Scenario (nationwide benefits of attaining each alternative standard everywhere 

in the U.S. except California) – Full Attainment a........................................................ 8-5

Table 8-6. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

post-2025 Scenario (nationwide benefits of attaining each alternative standard just in 

California) – Full Attainment a..................................................................................... 8-6

Table 8-7. Human Health Effects of Pollutants Potentially Affected by Strategies to 

Attain the Primary Ozone Standards............................................................................ 8-6

Table 8-8. Relevant Factors and Their Potential Implications for Attainment ..................8-12

Table 9-1. Summary of Population Totals and Demographic Categories for Areas of Interest 

and National Perspective ............................................................................................. 9-6

Table 9-2. Summary of Population Totals and Demographic Categories for Areas of Interest 

and National Perspective ............................................................................................. 9-6

Table 10-1. Summary of Direct Labor Impacts for SCR Installation at EGUs.................10-11

Table 10-2. Key Assumptions in Labor Analysis for EGUs ............................................10-12

Table 10-3. Summary of Direct Labor Impacts for Individual ICI Boilers.......................10-14

Table 10-4. Estimated Direct Labor Impacts for Individual SNCR Applied to a MidSized Cement Kiln (125-208 tons clinker/hr)............................................................10-15
xxiv

LIST OF FIGURES

Figure ES-1. Analytical Flowchart for Primary Standards Analyses...................................ES-5

Figure ES-2. Projected Ozone Design Values in the 2025 Baseline Scenario .....................ES-7

Figure ES-3. Projected Ozone Design Values in the post-2025 Baseline Scenario............ES-10

Figure 2-1. Map of U.S. Ambient O3 Monitoring Sites Reporting Data to EPA During 

the 2009-2013 Period................................................................................................... 2-6

Figure 2-2. Trend in U.S. Annual 4th Highest Daily Maximum 8-hour Ozone 

Concentrations in ppb, 2000 to 2013. Solid center line represents the median value 

across monitoring sites, dashed lines represent 25th and 75th percentile values, and 

top/bottom lines represent 10th and 90th percentile values........................................... 2-8

Figure 2-3. Map of 8-hour Ozone Design Values in ppb, Averaged Across the 2009-

2011, 2010-2012, and 2011-2013 Periods.................................................................... 2-8

Figure 2-4. Trend in U.S. Annual W126 Concentrations in ppm-hrs, 2000 to 2013. Solid 

center line represents the median value across monitoring sites, dashed lines 

represent 25th and 75th percentile values, and top/bottom lines represent 10th and 

90th percentile values. ................................................................................................2-10

Figure 2-5. Map of 3-year Average W126 Values in ppm-hrs, Averaged Across the 

2009-2011, 2010-2012, and 2011-2013 Periods..........................................................2-10

Figure 2-6. Map of 2007 CMAQ-estimated Seasonal Mean of 8-hour Daily Maximum 

Ozone from Natural Background (ppb) based on Zero-Out Modeling .........................2-13

Figure 2-7. Map of 2007 CMAQ-estimated Seasonal Mean of 8-hour Daily Maximum 

Ozone from North American Background (ppb) based on Zero-out Modeling.............2-14

Figure 2-8. Map of 2007 CMAQ-estimated Seasonal Mean of 8-hour Daily Maximum 

Ozone from United States Background (ppb) based on Zero-Out Modeling ................2-15

Figure 2-9. Map of Site-Specific Ratios of U.S. Background to Total Seasonal Mean 

Ozone based on 2007 CMAQ Zero-Out Modeling ......................................................2-17

Figure 2-10. Map of Site-Specific Ratios of Apportionment-Based U.S. Background to 

Seasonal Mean Ozone based on 2007 CAMx Source Apportionment Modeling..........2-18

Figure 2-11. Distributions of Absolute Estimates of Apportionment-Based U.S. 

Background (all site-days), Binned by Modeled MDA8 from the 2007 Source 

Apportionment Simulation..........................................................................................2-18
xxv

Figure 2-12. Distributions of the Relative Proportion of Apportionment-Based U.S. 

Background to Total Ozone (all site-days), Binned by Modeled MDA8 from the 

2007 Source Apportionment Simulation .....................................................................2-19

Figure 3-1. Map of the CAMx Modeling Domain Used for Ozone NAAQS RIA .............. 3-4

Figure 3-2. Map of Counties for Which Explicit Emissions Controls Were Identified and 

Modeled in CAMx (shaded in orange) and Counties that Contained One or More 

Monitor Projected above 70 ppb in the 2025 Base Case Modeling (shaded in blue). ...3-13

Figure 3-3. Five U.S. Regions Used to Create Across-the-Board Emissions Reduction 

and Combination Cases ..............................................................................................3-15

Figure 3-4. Map of VOC Impact Areas Applied in the Evaluation of a 60 ppb 

Alternative Standard Level .........................................................................................3-22

Figure 3-5. Projected post-2025 Baseline Scenario May-September Mean of 8-hr Daily

Maximum Ozone (ppb)...............................................................................................3-32

Figure 3-6. Change in May-September Mean of 8-hr daily Maximum Ozone (ppb) 

between the post-2025 Baseline Scenario and the post-2025 70 ppb Scenario .............3-33

Figure 3-7. Change in May-September Mean of 8-hr daily Maximum Ozone (ppb) 

between the post-2025 Baseline Scenario and the post-2025 65 ppb Scenario .............3-34

Figure 3-8. Change in May-September Mean of 8-hr Daily Maximum Ozone (ppb) 

between the post-2025 Baseline Scenario and the post-2025 60 ppb Scenario .............3-35

Figure 3-9. Projected post-2025 Baseline Scenario W126 Values (ppm-hrs) ....................3-37

Figure 3-10. Projected post-2025 70 ppb Scenario W126 Values (ppm-hrs) .......................3-38

Figure 3-11. Projected post-2025 65 ppb Scenario W126 Values (ppm-hrs) .......................3-39

Figure 3-12. Projected post-2025 60 ppb Scenario W126 Values (ppm-hrs) .......................3-40

Figure 3A-1. Distribution of observed and predicted MDA8 ozone by month for the 

period May through September for the Northeast subregion, (a) AQS network and 

(b) CASTNet network. [symbol = median; top/bottom of box = 75th/25th 

percentiles; top/bottom line = max/min values]..........................................................3A-6

Figure 3A-2. Distribution of observed and predicted MDA8 ozone by month for the 

period May through September for the Southeast subregion, (a) AQS network and 

(b) CASTNet network................................................................................................3A-7

Figure 3A-3. Distribution of observed and predicted MDA8 ozone by month for the 

period May through September for the Midwest subregion, (a) AQS network and (b) 

CASTNet network.....................................................................................................3A-7
xxvi

Figure 3A-4. Distribution of observed and predicted MDA8 ozone by month for the 

period May through September for the Central states, (a) AQS network and (b) 

CASTNet network.....................................................................................................3A-8

Figure 3A-5. Distribution of observed and predicted MDA8 ozone by month for the 

period May through September for the West, (a) AQS network and (b) CASTNet 

network. ....................................................................................................................3A-8

Figure 3A-6. Mean Bias (ppb) of MDA8 ozone greater than 60 ppb over the period MaySeptember 2011 at AQS and CASTNet monitoring sites in 12-km U.S. modeling 

domain.. ....................................................................................................................3A-9

Figure 3A-7. Normalized Mean Bias (%) of MDA8 ozone greater than 60 ppb over the 

period May-September 2011 at AQS and CASTNet monitoring sites in 12-km U.S. 

modeling domain .......................................................................................................3A-9

Figure 3A-8. Mean Error (ppb) of MDA8 ozone greater than 60 ppb over the period MaySeptember 2011 at AQS and CASTNet monitoring sites in 12-km U.S. modeling 

domain.. ..................................................................................................................3A-10

Figure 3A-9. Normalized Mean Error (%) of MDA8 ozone greater than 60 ppb over the 

period May-September 2011 at AQS and CASTNet monitoring sites in 12-km U.S. 

modeling domain .....................................................................................................3A-10

Figure 3A-10a. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 60195001 in Fresno Co., California ........................3A-11

Figure 3A-10b. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 60710005 in San Bernardino Co., California...........3A-12

Figure 3A-10c. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 80350004 in Douglas Co., Colorado .......................3A-12

Figure 3A-10d. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 484392003 in Tarrant Co., Texas............................3A-13

Figure 3A-10e. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 480391004 in Brazoria Co., Texas..........................3A-13

Figure 3A-10f. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 551170006 in Sheboygan Co., Wisconsin ...............3A-14

Figure 3A-10g. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 261630019 in Wayne Co., Michigan.......................3A-14

Figure 3A-10h. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 211110067 in Jefferson Co., Kentucky ...................3A-15
xxvii

Figure 3A-10i. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 420031005 in Allegheny Co., Pennsylvania ............3A-15

Figure 3A-10j . Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 240210037 in Frederick Co., Maryland...........3A-16

Figure 3A-10k. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 240251001 in Harford Co., Maryland .....................3A-16

Figure 3A-10l. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 360810124 in Queens, New York ...........................3A-17

Figure 3A-10m. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 361030002 in Suffolk County, New York...............3A-17

Figure 3A-11. a) Depiction of governing wind patterns and topography. b) California 

Air Basins and sub-regions used for this analysis. Northern sub-region is outlined in 

pink and southern sub-region is outlined in blue. .....................................................3A-19

Figure 3A-12. Impact of 50% anthropogenic California NOx cuts (ppb) on 8-hr daily 

average ozone concentrations on three days in 2011 ................................................3A-20

Figure 3A-13. Downwind California receptor regions for Northern California (green) 

and Southern California (purple)..............................................................................3A-21

Figure 3A-14. Impact of 50% US anthropogenic VOC cuts (ppb) on 8-hr daily average 

ozone concentrations on three days in 2011 .............................................................3A-22

Figure 3A-15. Change in July average of 8-hr daily maximum ozone concentration (ppb) 

due to 50% cut in US anthropogenic VOC emissions...............................................3A-23

Figure 3A-16. Projected change in 2025 ozone design values with an additional 75% 

regional NOx control (Southwest region; stars represent sites identified in Table 3A14).......... ..............................................................................................................3A-55

Figure 3A-17. Location of sites identified in Table 3A-14 .............................................3A-58

Figure 3A-18. Projected change in 2025 ozone design values with an additional 90% 

California NOx control (Southwest region; stars represent sites identified in Table 

3A-14)...................................................................................................................3A-60

Figure 3A-19. Daily 8-hr maximum ozone values at ozone monitor in Weminuche 

Wilderness area in La Plata County Colorado from 2009-2013. Horizontal line 

provided at 65 ppb. ..................................................................................................3A-62

Figure 4-1. Counties Projected to Exceed the Baseline Level of the Current Ozone 

Standard (75 ppb) in 2025 Base Case........................................................................... 4-9
xxviii

Figure 4-2. Counties Where Emissions Reductions Were Applied to Demonstrate 

Attainment of the Current Standard for the Baseline Analysis.....................................4-10

Figure 4-3. Projected Ozone Design Values in the 2025 Baseline Scenario ......................4-13

Figure 4-4. Projected Ozone Design Values in the post-2025 Baseline Scenario...............4-14

Figure 4-5. Counties Where Emissions Reductions Were Applied to Demonstrate 

Attainment with a 70 ppb Ozone Standard in the 2025 Analysis .................................4-16

Figure 4-6. Counties Where Emissions Reductions Were Applied to Demonstrate 

Attainment with 65 and 60 ppb Ozone Standards in the 2025 Analyses.......................4-17

Figure 5-1. Illustration of BenMAP-CE Approach ...........................................................5-14

Figure 5-2. Data Inputs and Outputs for the BenMAP-CE Program..................................5-16

Figure 5-3. Procedure for Generating Benefits Estimates for the 2025 and Post-2025 

Scenarios....................................................................................................................5-17

Figure 5C-1. Premature Ozone-related Deaths Avoided for the Alternative Standards 

(2025 scenario) According to the Baseline Ozone Concentrations..............................5C-7

Figure 5C-2. Cumulative Probability Plot of Premature Ozone-related Deaths Avoided for 

the Alternative Standards (2025 scenario) According to the Baseline Ozone 

Concentrations...........................................................................................................5C-8

Figure 5C-3. Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure 

in the Baseline Sector Modeling (used to generate the benefit-per-ton estimates)* ... 5C-12

Figure 5C-4. Cumulative Distribution of Adult Population (age 30+) by Annual Mean 

PM2.5 Exposure in the Baseline Sector Modeling (used to generate the benefit-per-ton 

estimates)* .............................................................................................................. 5C-13

Figure 6A-1. Cropland Area (sq m) by County according to NLCD 2006 Data ..................6A-2

Figure 6A-2. Cropland Area (sq m) by County according to NLCD 2011 Data ..................6A-3

Figure 6A-3. Forest Area (sq m) by County according to NLCD 2006 Data.......................6A-3

Figure 6A-4. Forest Area (sq m) by County according to NLCD 2011 Data.......................6A-4

Figure 6A-5. FASOMGHG Modeling Flowchart ...............................................................6A-9

Figure 6A-6. Ozone Reductions with Respect to 75 ppb under Alternative Scenarios.......6A-14

Figure 6A-7. Percentage Changes in Corn RYGs with Respect to the 75 ppb Scenario ....6A-16

Figure 6A-8. Percentage Changes in Cotton RYGs with Respect to the 75 ppb Scenario..6A-17
xxix

Figure 6A-9. Percentage Changes in Potato RYGs with Respect to the 75 ppb Scenario ..6A-18

Figure 6A-10. Percentage Changes in Sorghum RYGs with Respect to the 75 ppb 

Scenario..................................................................................................................6A-19

Figure 6A-11. Percentage Changes in Soybean RYGs with Respect to the 75 ppb 

Scenario........................................................................................................ ........6A-20

Figure 6A-12. Percentage Changes in Winter Wheat RYGs with Respect to the 75 ppb 

Scenario..................................................................................................................6A-21

Figure 6A-13. Percentage Changes in Softwood RYGs with Respect to the 75 ppb 

Scenario..................................................................................................................6A-22

Figure 6A-14. Percentage Changes in Hardwood RYGs with Respect to the 75 ppb 

Scenario..................................................................................................................6A-23

Figure 6A-15. Carbon Storage in Forestry Sector, MMtCO2e.........................................6A-41

Figure 7-1. Marginal Costs for Known NOx Controls for All Source Sectors (EGU, nonEGU Point, Nonpoint, and Nonroad) ........................................................................... 7-5

Figure 7-2. Observed but incomplete MACC (solid line) based on known controls 

identified by current tools and complete MACC (dashed line) where gaps indicate 

abatement not identified by current tools ....................................................................7-12

Figure 10-1. Size Distribution of 145 Existing Coal-Fired EGU Units without SCR NOx 

Controls....................................................................................................................10-11
ES-1

EXECUTIVE SUMMARY

Overview

In setting primary national ambient air quality standards (NAAQS), the EPA’s 

responsibility under the law is to establish standards that protect public health. The Clean Air Act 

(the Act) requires the 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 the EPA to base this decision on health considerations only; economic factors cannot be 

considered. The prohibition against considering cost in the setting of the primary air quality 

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

The Agency believes that consideration of costs and benefits is an essential decision-making tool 

for the efficient implementation of these standards. The impacts of costs, benefits, and efficiency 

are considered by the States when they make decisions regarding what timelines, strategies, and 

policies are appropriate for their circumstances.

The EPA is proposing to revise the level of the ozone NAAQS to within a range of 65

ppb to 70 ppb and is soliciting comment on alternative standard levels below 65 ppb, as low as 

60 ppb. The EPA is also proposing to revise the level of the secondary standard to within the 

range of 65 ppb to 70 ppb to provide increased protection against vegetation-related effects on 

public welfare.1 The EPA performed an illustrative analysis of the potential costs, human health 

benefits, and welfare co-benefits of nationally attaining primary alternative ozone standard levels 

and did not estimate any incremental costs and benefits associated with attaining a revised 

secondary standard. Per Executive Order 12866 and the guidelines of OMB Circular A-4, this 

Regulatory Impact Analysis (RIA) presents the analyses of the following alternative standard 

levels -- 70 ppb, 65 ppb, and 60 ppb. The cost and benefit estimates below are calculated 

incremental to a 2025 baseline that incorporates air quality improvements achieved through the 

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

 

1 As an initial matter, the EPA is proposing that ambient ozone concentrations in terms of a three-year average 

W126 index value within the range from 13 parts per million-hours (ppm-hours) to 17 ppm-hours would provide the 

requisite protection against known or anticipated adverse effects to the public welfare, which data analyses indicate 

would provide air quality in terms of three-year average W126 index values of a range at or below 13 ppm-hours to 

17 ppm-hours. Data analyses also indicate that actions taken to attain a standard in the range of 65 ppb to 70 ppb 

would also improve air quality as measured by the W126 metric.
ES-2

NAAQS (75 ppb). The 2025 baseline reflects, among other existing regulations, the Mercury 

and Air Toxics Standard, the Clean Air Interstate Rule, the Tier 3 Motor Vehicle Emission and 

Fuel Standards, and adjustments for the Clean Power Plan, all of which will help many areas 

move toward attainment of the existing ozone standard (see Chapter 3, Section 3.1.3 for 

additional information). 

In this RIA we present the primary costs and benefits estimates for 2025. We assume 

that potential nonattainment areas everywhere in the U.S., excluding California, will be 

designated such that they are required to reach attainment by 2025, and we developed our 

projected baselines for emissions, air quality, and populations for 2025. 

The EPA will likely finalize designations for a revised ozone NAAQS in late 

2017. Depending on the precise timing of the effective date of those designations, nonattainment 

areas classified as Marginal will likely have to attain in either late 2020 or early 

2021. Nonattainment areas classified as Moderate will likely have to attain in either late 2023 or 

early 2024. If a Moderate nonattainment area qualifies for two 1-year extensions, the area may 

have as late as 2026 to attain. Lastly, Serious nonattainment areas will likely have to attain in 

late 2026 or early 2027. We selected 2025 as the primary year of analysis because most areas of 

the U.S. will likely be required to meet a revised ozone standard by 2025 and because it provided 

a good representation of the remaining air quality concerns that Moderate nonattainment areas 

would face; states with areas classified as Moderate and higher are required to develop 

attainment demonstration plans for those nonattainment areas.

In estimating the incremental costs and benefits of potential alternative standards, we 

recognize that there are several areas that are not required to meet the existing ozone standard by 

2025. The Clean Air Act allows areas with more significant air quality problems to take 

additional time to reach the existing standard. Several areas in California are not required to 

meet the existing standard by 2025 and may not be required to meet a revised standard until

sometime between 2032 and 2037.

2

 We were not able to project emissions and air quality 

 

2 The EPA will likely finalize designations for a revised ozone NAAQS in late 2017. Depending on the precise 

timing of the effective date of those designations, nonattainment areas classified as Severe 15 will likely have to 
ES-3

beyond 2025 for California, however, we adjusted baseline air quality to reflect mobile source 

emissions reductions for California that would occur between 2025 and 2030; these emissions 

reductions were the result of mobile source regulations expected to be fully implemented by 

2030. While there is uncertainty about the precise timing of emissions reductions and related 

costs for California, we assume costs occur through the end of 2037 and beginning of 2038. In 

addition, we model benefits for California using projected population demographics for 2038. 

Because of the different timing for incurring costs and accruing benefits and for ease of 

discussion throughout the analyses, we refer to the different time periods for potential attainment 

as 2025 and post-2025 to reflect that (1) we did not project emissions and air quality for any year 

other than 2025; (2) for California, emissions controls and associated costs are assumed to occur 

through the end of 2037 and beginning of 2038; and (3) for California benefits are modeled using 

population demographics in 2038. It is not straightforward to discount the post-2025 results for 

California to compare with or add to the 2025 results for the rest of the U.S. While we estimate 

benefits using 2038 information, we do not have good information on precisely when the costs of 

controls will be incurred. Because of these differences in timing related to California attaining a 

revised standard, the separate costs and benefits estimates for post-2025 should not be added to 

the primary estimates for 2025.

ES.1 Overview of Analytical Approach

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

of ambient ozone (Chapter 2 – Defining the Air Quality Problem); estimates of current and 

future emissions of relevant precursors that contribute to the problem; air quality analyses of 

baseline and alternative control strategies (Chapter 3 – Air Quality Modeling and Analysis); 

development of illustrative control strategies to attain the primary alternative standard levels 

(Chapter 4 – Control Strategies and Emissions Reductions); estimates of the incremental benefits 

of attaining the primary alternative standard levels (Chapter 5 – Human Health Benefits); a 

qualitative discussion of the welfare co-benefits of attaining the primary alternative standard 

levels (Chapter 6 – Welfare Co-Benefits of the Primary Standard); estimates of the incremental 

 

attain sometime between late 2032 and early 2033 and nonattainment areas classified as Extreme will likely have to 

attain by December 31, 2037. 
ES-4

costs of attaining the primary alternative standard levels (Chapter 7 – Engineering Cost Analysis 

and Economic Impacts); a comparison and discussion of the benefits and costs (Chapter 8 –

Comparison of Costs and Benefits); an analysis of the impacts of the relevant statutory and 

executive orders (Chapter 9 – Statutory and Executive Order Impact Analysis); and a discussion 

of the theoretical framework used to analyze regulation-induced employment impacts, as well as 

information on employment related to installation of NOx controls on coal and gas-fired electric 

generating units, industrial boilers, and cement kilns (Chapter 10 – Qualitative Discussion of 

Employment Impacts of Air Quality).

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 a revised standard. The goal of this RIA is to provide 

estimates of the costs and benefits of the illustrative attainment strategies to the meet each

alternative standard level. The flowchart below (Figure ES-1) outlines the analytical steps taken 

to illustrate attainment with the potential alternative standard levels, and the following 

discussion, by primary flowchart section, describes the steps taken. 
ES-5

Figure ES-1. Analytical Flowchart for Primary Standards Analyses

ES.1.1 Establishing the Baseline

The future year base case reflects emissions projected from 2011 to 2025 and 

incorporates current state and federal programs, including the Tier 3 Motor Vehicle Emission 

and Fuel Standards (U.S. EPA, 2014a) (see Chapter 3, Section 3.1.3 for a discussion of the rules 

included in the base case). The base case does not include control programs specifically for the 

purpose of attaining the existing ozone standard (75 ppb). The baseline builds on the future year 

base case and reflects the additional emissions reductions needed to reach attainment of the 

current ozone standard (75 ppb), as well as adjustments for the Clean Power Plan (U.S. EPA, 

2014b).

We performed a national scale air quality modeling analysis to estimate ozone 

concentrations for the future base case year of 2025. To accomplish this, we modeled multiple 
ES-6

emissions cases for 2025, including the 2025 base case and twelve 2025 emissions sensitivity 

simulations. The twelve emissions sensitivity simulations were used to develop ozone sensitivity 

factors (ppb/ton) from the modeled response of ozone to changes in NOx and VOC emissions 

from various sources and locations. These ozone sensitivity factors were then used to determine 

the amount of emissions reductions needed to reach the 2025 baseline and evaluate potential 

alternative standard levels of 70, 65, and 60 ppb incremental to the baseline. We used the 

estimated emissions reductions needed to reach each of these standard levels to analyze the costs 

and benefits of alternative standard levels. 

ES.1.2 Control Strategies and Emissions Reductions

The EPA analyzed illustrative control strategies that areas across the U.S. might employ 

to attain alternative revised primary ozone standard levels of 70, 65, and 60 ppb. The EPA 

analyzed the impact that additional emissions control technologies and measures, across 

numerous sectors, would have on predicted ambient ozone concentrations incremental to the 

baseline. These control measures, also referred to as known controls, are based on information 

available at the time of this analysis and include primarily end-of-pipe control technologies. In 

addition, to attain some of the alternative primary standard levels analyzed, some areas needed 

additional emissions reductions beyond the known controls, and we refer to these as unknown 

controls (see Chapter 7, Section 7.2 for additional information).

Using average ozone response factors, we estimated the portion of the emissions 

reductions required to meet the baseline, including any additional emissions reductions beyond 

known controls. We then estimated the emissions reductions incremental to the baseline that 

were needed to meet the alternative standard levels of 70, 65, and 60 ppb. Costs of controls 

incremental to (i.e., over and above) the baseline emissions reductions are attributed to the costs 

of meeting the alternative standard levels. These emissions reductions can come from both 

specific known controls, as well as unknown controls. The baseline shows that by 2025, while 

ozone air quality would be significantly better than today under current requirements, depending 

on the alternative standard level analyzed, several areas in the Eastern, Central, and Western U.S. 

would need to develop and adopt additional controls to attain alternative standard levels (see 

Chapter 4, Section 4.3). 
ES-7

ES.1.2.1 Emissions Reductions from Known Controls in 2025

Figure ES-2 shows the counties projected to exceed the alternative standard levels 

analyzed for 2025 for areas other than California. For the 70 ppb alternative standard level, 

emissions reductions were required for monitors in the Central and Northeast regions. For the 65 

and 60 ppb alternative standard levels, emissions reductions were applied in all regions with 

projected baseline design values (DVs) above these levels.3 For the 60 ppb alternative standard 

level, additional VOC emissions reductions were identified in Chicago because some sites in that 

area experienced NOx disbenefits, meaning that the regional NOx emissions reductions resulted 

in ozone increases from below 60 ppb to above 60 ppb. Tables ES-1 through ES-3 show the 

emissions reductions from known controls for the alternative standard levels analyzed. 

Figure ES-2. Projected Ozone Design Values in the 2025 Baseline Scenario

 

3 A design value is a statistic that describes the air quality status of a given area relative to the level of the NAAQS. 

Design values are typically used to classify nonattainment areas, assess progress toward meeting the NAAQS, and 

develop control strategies.
ES-8

Table ES-1. Summary of Emission Reductions by Sector for Known Controls for 70 ppb 

Proposed Alternative Standard Level for 2025, except California (1,000 

tons/year)a

Geographic Area Emissions Sector NOx VOC

East

EGU 25 -

Non-EGU Point 210 0.98

Nonpoint 260 54

Nonroad 5 -

Total 490 55

West

EGU - -

Non-EGU Point - -

Nonpoint - -

Nonroad - -

Total - -

a Estimates are rounded to two significant figures. 

Table ES-2. Summary of Emission Reductions by Sector for Known Controls for 65 ppb 

Proposed Alternative Standard Level for 2025 - except California (1,000 

tons/year)a

Geographic Area Emissions Sector NOx VOC

East

EGU 170 -

Non-EGU Point 410 3.6

Nonpoint 420 95

Nonroad 12 -

Total 1,000 99

West

EGU 36 -

Non-EGU Point 38 0.47

Nonpoint 37 6.6

Nonroad 1.3 -

Total 110 7

a Estimates are rounded to two significant figures.

Table ES-3. Summary of Emission Reductions by Sector for Known Controls for 60 ppb 

Alternative Standard Level for 2025 - except California (1,000 tons/year)a

Geographic Area Emissions Sector NOx VOC

East

EGU 170 -

Non-EGU Point 410 4.2

Nonpoint 420 99

Nonroad 12 -

Total 1,000 100

West

EGU 62 -

Non-EGU Point 48 0.47

Nonpoint 39 6.6

Nonroad 1.3 -

Total 150 7

a Estimates are rounded to two significant figures. 
ES-9

ES.1.2.2 Emissions Reductions beyond Known Controls in 2025

There were several areas where known controls did not achieve enough emissions 

reductions to attain the proposed alternative standard levels of 70 and 65 as well as the more 

stringent alternative standard level of 60 ppb. To complete the analysis, the EPA then estimated 

the additional emissions reductions beyond known controls needed to reach attainment (i.e., 

unknown controls). Table ES-4 shows the emissions reductions needed from unknown controls 

in 2025 for the U.S., except California, for the alternative standard levels analyzed.

Table ES-4. Summary of Emissions Reductions by Alternative Standard for Unknown 

Controls for 2025 - except California (1,000 tons/year)a

Region NOx VOC

Proposed Alternative Standard Levels

70 ppb East

West

150

-

-

-

65 ppb East

West

750

-

-

-

Alternative Standard Level

60 ppb East

West

1,900

350

41

-

ES.1.2.3 Emissions Reductions beyond Known Controls for Post-2025

Figure ES-3 shows the counties projected to exceed the alternative standard levels 

analyzed for the post-2025 analysis for California. For the California post-2025 alternative 

standard level analyses, all known controls were applied in the baseline, so incremental 

emissions reductions are from unknown controls. Table ES-5 shows the emissions reductions 

needed from unknown controls for post-2025 for California for the alternative standard levels 

analyzed.
ES-10

Figure ES-3. Projected Ozone Design Values in the post-2025 Baseline Scenario

Table ES-5. Summary of Emissions Reductions by Alternative Standard Level for 

Unknown Controls for post-2025 - California (1,000 tons/year)a

Region NOx VOC

Proposed Alternative Standard Levels

70 ppb CA 53 -

65 ppb CA 110 -

Alternative Standard Level

60 ppb CA 140 -

a Estimates are rounded to two significant figures. 

ES.1.3 Human Health Benefits

To estimate benefits, we follow a “damage-function” approach in calculating total 

benefits of the modeled changes in environmental quality. This approach estimates changes in 

individual health endpoints (specific effects that can be associated with changes in air quality) 

and assigns values to those changes assuming independence of the values for those individual 

endpoints. Total benefits are calculated as the sum of the values for all non-overlapping health 

endpoints. The “damage-function” approach is the standard method for assessing costs and 

benefits of environmental quality programs and has been used in several recent published 

analyses (Levy et al., 2009; Fann et al., 2012a; Tagaris et al., 2009).
ES-11

To assess economic values in a damage-function framework, the changes in 

environmental quality must be translated into effects on people or on the things that people 

value. In some cases, the changes in environmental quality can be directly valued, as is the case 

for changes in visibility. In other cases, such as for changes in ozone and PM, an impact analysis 

must first be conducted to convert air quality changes into effects that can be assigned dollar 

values. For the purposes of this RIA, the health impacts analysis is limited to those health effects 

that are directly linked to ambient levels of air pollution and specifically to those linked to ozone 

and PM2.5.

Benefits estimates for ozone were generated using the damage function approach outlined 

above wherein potential changes in ambient ozone levels (associated with future attainment of 

alternative standard levels) were explicitly modeled and then translated into reductions in the 

incidence of specific health endpoints. In generating ozone benefits estimates for the two 

attainment timeframes considered in the RIA (2025 and post-2025), we used three distinct 

benefits simulations including one completed for 2025 and two completed for 2038. The way in 

which these three benefits simulations were used to generate estimates for the two timeframes is 

detailed in Chapter 5, Section 5.4.3. 

In contrast to ozone, we used a benefit-per-ton approach in modeling PM2.5 co-benefits. 

With this approach, we use the results of previous benefits analysis simulations focusing on 

PM2.5 to derive benefits-per-ton estimates for NOx.4 We then combine these dollar-per-ton 

estimates with projected reductions in NOx associated with meeting a given alternative standard 

level to project cobenefits associated with PM2.5. We acknowledge increased uncertainty 

associated with the dollar-per-ton approach for PM2.5, relative to explicitly modeling benefits 

using gridded PM2.5 surfaces specific to the baseline and alternative standard levels (see 

Appendix 5A, Table 5A-1 for additional discussion). 

In addition to ozone and PM2.5 benefits, implementing emissions controls to reach some of 

the alternative ozone standard levels would reduce other ambient pollutants. However, because 

the methods used in this analysis to simulate attainment do not account for changes in ambient 

 

4

In addition to dollar-per-ton estimates for NOx, we also used incidence-per-ton values (also for NOx) for specific 

health endpoints to generate incidence reduction estimates associated with the dollar benefits. 
ES-12

concentrations of other pollutants, we were not able to quantify the co-benefits of reduced 

exposure to these pollutants. In addition, due to data and methodology limitations, we were 

unable to estimate some anticipated health benefits associated with exposure to ozone and PM2.5.

ES.1.4 Welfare Co-Benefits of the Primary Standard

Section 109 of the Clean Air Act defines welfare effects to include any non-health effects, 

including direct economic damages in the form of lost productivity of crops and trees, indirect 

damages through alteration of ecosystem functions, indirect economic damages through the loss 

in value of recreational experiences or the existence value of important resources, and direct 

damages to property, either through impacts on material structures or by soiling of surfaces (42 

U.S.C. 7409). Ozone can affect ecological systems, leading to changes in the ecological 

community and influencing the diversity, health, and vigor of individual species (U.S. EPA, 

2013). Ozone causes discernible injury to a wide array of vegetation (U.S. EPA, 2013). In terms 

of forest productivity and ecosystem diversity, ozone may be the pollutant with the greatest 

potential for region-scale forest impacts (U.S. EPA, 2013). Studies have demonstrated repeatedly 

that ozone concentrations observed in polluted areas can have substantial impacts on plant 

function (De Steiguer et al,. 1990; Pye, 1988).

In this RIA, we are able to quantify only a small portion of the welfare impacts associated 

with reductions in ozone concentrations to meet alternative ozone standards. Using a model of 

commercial agriculture and forest markets, we are able to analyze the effects on consumers and 

producers of forest and agricultural products of changes in the W126 index resulting from 

meeting alternative standards within the proposed range of 70 to 65 ppb, as well as a lower 

standard level of 60 ppb. We also assess the effects of those changes in commercial agricultural 

and forest yields on carbon sequestration and storage. This analysis provides limited quantitative 

information on the welfare co-benefits of meeting these alternative standards, focused only on 

one subset of ecosystem services. Commercial and non-commercial forests provide a number of 

additional services, including medicinal uses, non-commercial food and fiber production, arts 

and crafts uses, habitat, recreational uses, and cultural uses for Native American tribes. A more 

complete discussion of these additional ecosystem services is provided in the final Welfare Risk 

and Exposure Assessment for Ozone (U.S. EPA, 2014c).
ES-13

ES.2 Results of Benefit-Cost Analysis

Below in Table ES-6, we present the primary costs and benefits estimates for 2025 for all 

areas except California. In addition, Tables 5-1 and 5-23 in Chapter 5 provide a breakdown of 

ozone-only and PM2.5-only benefits, as well as total benefits at 3 percent. We anticipate that 

benefits and costs will likely begin occurring earlier, as states begin implementing control 

measures to show progress towards attainment. In these tables, ranges within the total benefits 

rows reflect variability in the studies upon which the estimates associated with premature 

mortality were derived. PM2.5 co-benefits account for approximately two-thirds to three-quarters 

of the estimated benefits, depending on the standard analyzed and on the choice of ozone and 

PM mortality functions used. In addition for 2025, Table ES-7 presents the numbers of 

premature deaths avoided for the alternative standard levels analyzed, as well as the other health 

effects avoided. Table ES-8 provides information on the costs by geographic region for the U.S., 

except California in 2025, and Table ES-9 provides a regional breakdown of benefits for 2025.

In the RIA we provide estimates of costs of emissions reductions to attain the proposed 

standards in three regions -- California, the rest of the western U.S., and the eastern U.S. In 

addition, we provide estimates of the benefits that accrue to each of these three regions resulting 

from (i) control strategies applied within the region, (ii) reductions in transport of ozone 

associated with emissions reductions in other regions, and (iii) the control strategies for which 

the regional cost estimates are generated. These benefits are not directly comparable to the costs 

of control strategies in a region because the benefits include benefits not associated with those 

control strategies.

The net benefits of emissions reductions strategies in a specific region would be the 

benefits of the emissions reductions occurring both within and outside of the region minus the 

costs of the emissions reductions. Because the air quality modeling is done the national level, we 

do not estimate separately the nationwide benefits associated with the emissions reductions 

occurring in any specific region.5 As a result, we are only able to provide net benefits estimates 

at the national level. The difference between the costs for a specific region and the benefits 

 

5 For California, we provide separate estimates of the costs and nationwide estimates of benefits, so it is appropriate 

to calculate net benefits. As such, we provide net benefits for the post-2025 California analysis.
ES-14

accruing to that region is not an estimate of net benefits of the emissions reductions in that 

region.

Table ES-6. Total Annual Costs and Benefitsafor U.S., except California in 2025 (billions 

of 2011$, 7% Discount Rate)b

Proposed Alternative Standard Levels Alternative Standard 

Level

70 ppb 65 ppb 60 ppb

Total Costs (7%) $3.9 $15 $39

Total Health Benefits (7%)c$6.4 to $13.0 $19 to $38 $34 to $70

Net Benefits (7%) $2.5 to $9.1 $4 to $23 ($5) to $31

a Benefits are nationwide benefits of attainment everywhere except California.

b EPA believes that providing comparisons of social costs and social benefits at 3 and 7 percent is appropriate. 

Estimating multiple years of costs and benefits is not possible for this RIA due to data and resource limitations. As 

a result, we provide a snapshot of costs and benefits in 2025, using the best available information to approximate 

social costs and social benefits recognizing uncertainties and limitations in those estimates.

c The benefits range reflects the LOW and UPPER core estimates of short-term ozone and long-term PM mortality.

EPA believes that providing comparisons of social costs and social benefits at 3 and 7 

percent is appropriate. Ideally, streams of social costs and social benefits over time would be 

estimated and the net present values of each would be compared to determine net benefits of the 

illustrative attainment strategies. The three different uses of discounting in the RIA – (i) 

construction of annualized engineering costs, (ii) adjusting the value of mortality risk for lags in 

mortality risk decreases, and (iii) adjusting the cost of illness for non-fatal heart attacks to adjust 

for lags in follow up costs -- are all appropriate. Our estimates of net benefits are the 

approximations of the net value (in 2025) of benefits attributable to emissions reductions needed 

to attain just for the year 2025.

Table ES-7. Summary of Total Number of Annual Ozone and PM-Related Premature 

Mortalities and Premature Morbidity: 2025 National Benefits a 

Proposed Alternative Standard Levels

(95th percentile confidence intervals)b

Alternative Standard 

Level

(95th percentile 

confidence intervals)

70 ppb 65 ppb 60 ppb

Short-term exposure-related 

premature deaths avoided (all ages) 

(Ozone – 2 studies)

200 to 340

(97 to 300) 

(180 to 490)

630 to 1,000

(310 to 940)

(560 to 1,500)

1,100 to 1,900

(560 to 1,700)

(1,000 to 2,800)

Long-term exposure-related 

premature deaths avoided (age 

30+) (PM – 2 studies)

O3: 680

(230 to 1,100)

PM2.5: 510 to 1,100c

O3: 2,100

(710 to 3,500)

PM2.5: 1,400 to 3,300c

O3: 3,900

(1,300 to 6,400)

PM2.5: 2,600 to 6,000c

Other health effects avoidedd

Non-fatal heart attacks (age 18-99) (5 

studies) PM 64 to 600 180 to 1,700 330 to 3,100
ES-15

Proposed Alternative Standard Levels

(95th percentile confidence intervals)b

Alternative Standard 

Level

(95th percentile 

confidence intervals)

70 ppb 65 ppb 60 ppb

Respiratory hospital admissions (age 

0-99)O3, PM 510 1,500 2,900

Cardiovascular hospital admissions 

(age 18-99) PM 180 530 950

Asthma emergency department visits 

(age 0-99) O3, PM 1,400 4,300 8,000

Acute bronchitis (age 8-12) PM 790 2,300 4,100

Asthma exacerbation (age 6-18) O3, PM 320,000 960,000 1,800,000

Lost work days (age 18-65) PM 65,000 180,000 340,000

Minor restricted activity days (age 

18-65) O3, PM 1,300,000 4,000,000 7,300,000

Upper & lower respiratory symptoms 

(children 7-14) PM 24,000 70,000 130,000

School loss days (age 5-17) O3 330,000 1,000,000 1,900,000

a Nationwide benefits of attainment everywhere except California. 

b We present a confidence interval in parentheses for each study on short-term or long-term ozone-related mortality.

c These estimates were generated using benefit-per-ton estimates and confidence intervals are not available. In 

general, the 95th percentile confidence interval for the health impact function alone ranges from + 30 percent for 

mortality incidence based on Krewski et al. (2009) and + 46 percent based on Lepeule et al. (2012).

d See Table 5-19 in Chapter 5 for detailed information on confidence intervals related to ozone-related morbidity 

incidence estimates. The PM2.5 morbidity incidence estimates were generated using benefit-per-ton estimates and 

confidence intervals are not available.

Table ES-8. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for 2025 - U.S., except California (billions of 2011$, 7% Discount Rate)a

Alternative Level Geographic Area Total Control Costs (Known and 

Extrapolated)

70 ppb

East 3.9

West -

Total $3.9

65 ppb

East 15

West 0.40

Total $15

60 ppb

East 33

West 5.8

Total $39

a All values are rounded to two significant figures. Extrapolated costs are based on the average-cost methodology.
ES-16

Table ES-9. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

2025 Scenario (nationwide benefits of attaining each alternative standard 

everywhere in the U.S. except California) – Full Attainment a

Region Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

East b 99% 96% 92%

California 0% 0% 0%

Rest of West 1% 4% 7%

a Because we use benefit-per-ton estimates to calculate the PM2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits.

b

Includes Texas and those states to the north and east. Several recent rules such as Tier 3 will have substantially 

reduced ozone concentrations by 2025 in the East, thus few additional controls would be needed to reach 70 ppb. 

To understand possible additional costs and benefits of fully attaining in California in a 

post-2025 timeframe, we provide separate results for California in Table ES-10. In addition, 

Tables 5-2 and 5-30 in Chapter 5 provide a breakdown of ozone-only and PM2.5-only benefits, as 

well as total benefits at 3 percent. Relative to the primary cost and benefits estimates, the 

California cost estimates are between 5 and 20 percent and the benefits estimates are between 8 

and 15 percent of the national estimates. Because of the differences in the timing of achieving 

needed emissions reductions, incurring costs, and accruing benefits for California, the separate

costs and benefits estimates for post-2025 should not be added to the primary estimates for 2025. 

For the post-2025 timeframe, Table ES-11 presents the numbers of premature deaths avoided for 

the alternative standard levels analyzed, as well as the other health effects avoided. Table ES-12

provides information on the costs for California for post-2025, and Table ES-13 provides a 

regional breakdown of benefits for post-2025.

The EPA presents separate costs and benefits results for California because forcing 

attainment in an earlier year than would be required under the Clean Air Act would likely lead to 

an overstatement of costs because California might benefit from some existing federal or state 

programs that would be implemented between 2025 and the ultimate attainment years; because 

additional new technologies may become available between 2025 and the attainment years; and 

because the cost of existing technologies might fall over time. As such, we use the best available 

data to estimate costs and benefits for California in a post-2025 timeframe, but because of data 

limitations and additional uncertainty associated with not projecting emissions and air quality 

beyond 2025, we recognize that the estimates of costs and benefits for California in a post-2025 
ES-17

timeframe are likely to be relatively more uncertain than the national attainment estimates for 

2025. 

Table ES-10. Total Annual Costs and Benefitsaof Control Strategies Applied in California, 

post-2025 (billions of 2011$, 7% Discount Rate)b

Proposed Alternative Standard Levels Alternative Standard 

Level

70 ppb 65 ppb 60 ppb

Total Costs (7%) $0.80 $1.6 $2.2

Total Health Benefits (7%)c

$1.1 to $2 $2.2 to $4.1 $3.2 to $5.9

Net Benefits (7%) $0.3 to $1.2 $0.60 to $2.5 $1 to $3.7

a Benefits are nationwide benefits of attainment in California.

b EPA believes that providing comparisons of social costs and social benefits at 3 and 7 percent is appropriate. 

Estimating multiple years of costs and benefits is not possible for this RIA due to data and resource limitations. As 

a result, we provide a snapshot of costs and benefits in 2025, using the best available information to approximate 

social costs and social benefits recognizing uncertainties and limitations in those estimates.

c The benefits range reflects the LOW and UPPER core estimates of short-term ozone and long-term PM mortality.

Table ES-11. Summary of Total Number of Annual Ozone and PM-Related Premature 

Mortalities and Premature Morbidity: Post-2025a 

Proposed Alternative Standard Levels

(95th percentile confidence intervals)b

Alternative Standard 

Level

(95th percentile 

confidence intervals)

70 ppb 65 ppb 60 ppb

Short-term exposure-related 

premature deaths avoided (all ages) 

(Ozone – 2 studies)

65 to 110

(31 to 97)

(57 to 160)

140 to 230

(68 to 210)

(120 to 340)

210 to 350

(100 to 320)

(190 to 510)

Long-term exposure-related 

premature deaths avoided (age 

30+) (PM – 2 studies)

O3: 260

(88 to 430)

PM2.5: 45 to 100c

O3: 560

(190 to 930)

PM2.5: 89 to 200c

O3: 840

(290 to 1,400)

PM2.5: 120 to 280c

Other health effects avoidedd

Non-fatal heart attacks (age 18-99) (5 

studies) PM 6 to 54 11 to 110 16 to 140

Respiratory hospital admissions (age 

0-99)O3, PM 130 290 430

Cardiovascular hospital admissions 

(age 18-99) PM 16 32 45

Asthma emergency department visits 

(age 0-99) O3, PM 340 740 1,100

Acute bronchitis (age 8-12) PM 67 130 180

Asthma exacerbation (age 6-18) O3, PM 99,000 210,000 320,000

Lost work days (age 18-65) PM 5,500 11,000 15,000

Minor restricted activity days (age 

18-65) O3, PM 320,000 690,000 1,000,000

Upper & lower respiratory symptoms 

(children 7-14) PM 2,100 4,100 5,600

School loss days (age 5-17) O3 110,000 230,000 350,000

a Nationwide benefits of attainment in California. 

b We present a confidence interval in parentheses for each study on short-term or long-term ozone-related mortality.
ES-18

c These estimates were generated using benefit-per-ton estimates and confidence intervals are not available. In 

general, the 95th percentile confidence interval for the health impact function alone ranges from + 30 percent for 

mortality incidence based on Krewski et al. (2009) and + 46 percent based on Lepeule et al. (2012).

d See Table 5-26 in Chapter 5 for detailed information on confidence intervals related to ozone-related morbidity 

incidence estimates. The PM2.5 morbidity incidence estimates were generated using benefit-per-ton estimates and 

confidence intervals are not available.

Table ES-12. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for post-2025 - California (billions of 2011$, 7% Discount Rate)a

Alternative Level Geographic Area

Total Control Costs 

(Known and 

Extrapolated)

70 ppb California $0.80

65 ppb California $1.6

60 ppb California $2.2

a All values are rounded to two significant figures. Extrapolated costs are based on the average-cost methodology.

Table ES-13. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

post-2025 Scenario (nationwide benefits of attaining each alternative standard 

just in California) – Full Attainmenta

Region Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

East 0% 0% 0%

California 93% 94% 94%

Rest of West 6% 6% 6%

a Because we use benefit-per-ton estimates to calculate the PM2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits.

Despite uncertainties inherent in any complex, quantitative analysis, the overall underlying 

analytical methods used in this RIA have been peer-reviewed. For a detailed discussion on 

uncertainty associated with developing illustrative control strategies to attain the alternative 

standard levels, see Chapter 4, Section 4.4. For a description of the key assumptions and 

uncertainties related to the modeling of ozone benefits, see Chapter 5, Section 5.7.3, and for an 

additional qualitative discussion of sources of uncertainty associated with both the modeling of 

ozone-related benefits and PM2.5-related co-benefits, see Appendix 5A. For a discussion of the 

limitations and uncertainties in the engineering cost analyses, see Chapter 7, Section 7.7. For a 

discussion about generally framing uncertainty, see Chapter 8, Section 8.3. 

ES.3 References

De Steiguer, J., Pye, J., Love, C. 1990. Air Pollution Damage to U.S. Forests. Journal of Forestry, 88(8), 17-22.
ES-19

Fann N, Lamson A, Wesson K, Risley D, Anenberg SC, Hubbell BJ. 2012a. “Estimating the National Public Health 

Burden Associated with Exposure to Ambient PM2.5 and ozone. Risk Analysis,” Risk Analysis 32(1): 81-95.

Levy JI, Baxter LK, Schwartz J. 2009. “Uncertainty and variability in health-related damages from coal-fired power 

plants in the United States.” Risk Analysis 29(7) 1000-1014.

Pye, J.M. 1988. Impact of ozone on the growth and yield of trees: A review. Journal of Environmental Quality, 17, 

347-360.

Tagaris E, Liao KJ, Delucia AJ, et al. 2009. “Potential impact of climate change on air-pollution related human 

health effects.” Environmental Science & Technolology 43: 4979–4988.

U.S. Environmental Protection Agency (U.S. EPA). 2013. Integrated Science Assessment of Ozone and Related 

Photochemical Oxidants (Final Report). EPA-600/R-10/076F. February. Available on the Internet at 

http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492.

U.S. Environmental Protection Agency (U.S. EPA). 2014a. Control of Air Pollution from Motor Vehicles: Tier 3 

Motor Vehicle Emission and Fuel Standards. Office of Transportation and Air Quality. Available at 

http://www.epa.gov/otaq/tier3.htm.

U.S. Environmental Protection Agency (U.S. EPA). 2014b. Proposed Carbon Pollution Guidelines for Existing 

Power Plants and Emission Standards for Modified and Reconstructed Power Plants. Available at 

http://www2.epa.gov/sites/production/files/2014-06/documents/20140602ria-clean-power-plan.pdf

U.S. Environmental Protection Agency. 2014c. Policy Assessment for the Review of the Ozone NAAQS, Final 

Report. August. Available on the Internet at 

http://www.epa.gov/ttn/naaqs/standards/ozone/data/20140829pa.pdf.
1-1

CHAPTER 1: INTRODUCTION AND BACKGROUND

Overview

The EPA Administrator is proposing to revise the level of the ozone National Ambient 

Air Quality Standards (NAAQS) to within a range of 65 to 70 ppb and is soliciting comment on 

alternative standard levels below 65 ppb, as low as 60 ppb. This chapter summarizes the purpose 

and background of this Regulatory Impact Analysis (RIA). In the RIA we estimate the human 

health and welfare benefits and costs of alternative standards of 65 ppb and 70 ppb, which 

represent the lower and upper bounds of the range of proposed levels, as well as a more stringent 

alternative level of 60 ppb. According to the Clean Air Act (“the Act”), the Environmental 

Protection Agency (EPA) must use health-based criteria in setting the NAAQS and cannot 

consider estimates of compliance cost. The EPA is producing this RIA both to provide the public 

a sense of the benefits and costs of meeting a revised ozone NAAQS and to meet the 

requirements of Executive Orders 12866 and 13563.

1.1 Background

1.1.1 NAAQS

Two sections of the Act govern the establishment and revision of NAAQS. Section 108 

(42 U.S.C. 7408) directs the Administrator to identify pollutants that “may reasonably be 

anticipated to endanger public health or welfare” and to issue air quality criteria for them. These 

air quality criteria are intended to “accurately reflect the latest scientific knowledge useful in 

indicating the kind and extent of all identifiable effects on public health or welfare which may be 

expected from the presence of [a] pollutant in the ambient air.” Ozone is one of six pollutants for 

which the EPA has developed air quality criteria. 

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

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

109(b)(1) defines a primary standard as an ambient air quality standard “the attainment and 

maintenance of which in the judgment of the Administrator, based on [the] criteria and allowing 

an adequate margin of safety, [is] requisite to protect the public health.” A secondary standard, as 

defined in section 109(b)(2), must “specify a level of air quality the attainment and maintenance 

of which in the judgment of the Administrator, based on [the] criteria, is requisite to protect the 
1-2

public welfare from any known or anticipated adverse effects associated with the presence of 

[the] pollutant in the ambient air.” Welfare effects as defined in section 302(h) [42 U.S.C. 

7602(h)] include but are not limited to “effects on soils, water, crops, vegetation, manmade 

materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of 

property, and hazards to transportation, as well as effects on economic values and on personal 

comfort and well-being.” 

Section 109(d) of the Act directs the Administrator to review existing criteria and 

standards at 5-year intervals. When warranted by such review, the Administrator is to retain or 

revise the NAAQS. After promulgation or revision of the NAAQS, the standards are 

implemented by the states.

1.1.2 Ozone NAAQS

The EPA initiated the current ozone NAAQS review in September 2008. Between 2008 

and 2014, the EPA prepared draft and final versions of the Integrated Science Assessment, the 

Health and Welfare Risk and Exposure Assessments, and the Policy Assessment. Multiple drafts 

of these documents were available for public review and comment, and as required by the Clean 

Air Act, were peer-reviewed by the Clean Air Scientific Advisory Committee (CASAC), the 

Administrator’s independent advisory committee established by the CAA. The final documents 

reflect the EPA staff’s consideration of the comments and recommendations made by CASAC 

and the public on draft versions of these documents.

1.2 Role of this RIA in the Process of Setting the NAAQS

1.2.1 Legislative Roles

The EPA Administrator is proposing to revise the level of the ozone National Ambient 

Air Quality Standards (NAAQS) to within a range of 65 to 70 ppb and is soliciting comment on 

alternative standard levels below 65 ppb, as low as 60 ppb. The EPA Administrator is also 

proposing to revise the level of the current secondary standard to within the range of 65 ppb to 

70 ppb. As such, the RIA analyzes a range of potential alternative primary standard levels. In 

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

establish standards that protect public health, regardless of the costs of implementing those

standards. The Act requires the EPA, for each criteria pollutant, to set standards that protect 
1-3

public health with “an adequate margin of safety.” As interpreted by the Agency and the courts, 

the Act requires the EPA to create standards based on health considerations only. 

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

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

or should be ignored. The Agency believes that consideration of costs and benefits is essential to 

making efficient, cost-effective decisions for implementing these standards. The impact of cost 

and efficiency is considered by states during 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 new standards are implemented, but it is not 

relevant to establishing the standards themselves.

1.2.2 Role of Statutory and Executive Orders

This RIA is separate from the NAAQS decision-making process, but several statutes and 

executive orders still apply to any public documentation. The analysis required by these statutes 

and executive orders is presented in Chapter 9. 

The EPA presents this RIA pursuant to Executive Orders 12866 and 13563 and the 

guidelines of Office of Management and Budget (OMB) Circular A-4 (U.S. OMB, 2003). In 

accordance with these guidelines, the RIA analyzes the benefits and costs associated with 

emissions controls to attain the upper and lower bounds of the proposed 8-hour ozone standard 

of 65 parts per billion (ppb) to 70 ppb in ambient air, incremental to a baseline of attaining the 

existing standard (8-hour ozone standard of 75 ppb). OMB Circular A-4 requires analysis of one 

potential alternative standard level more stringent than the proposed range and one less stringent 

than the proposed range. In this RIA, we analyze a more stringent alternative standard level of 

60 ppb. The existing standard of 75 ppb represents the less stringent alternative standard and the 

costs and benefits of this standard were presented in the 2008 ozone NAAQS RIA (U.S. EPA, 

2008a). The available scientific evidence and quantitative exposure and risk information 

indicate that reducing ambient ozone concentrations will reduce the occurrence of harmful health 

effects. As discussed in the Notice, this evidence and information provide strong support for 

considering alternative standard levels from 65 to 70 ppb, but do not identify a bright line within 

this range that indicates exactly where to set a standard. Similarly, the available scientific 
1-4

information does not provide a basis for identifying any specific standard level between 70 and 

75 ppb for analysis in the RIA. 

The control strategies presented in this RIA are illustrative and represent one set of control 

strategies states might choose to implement in order to meet the final standards. As a result, 

benefit and cost estimates provided in the RIA are not additive to benefits and costs from other 

regulations, and, further, the costs and benefits identified in this RIA will not be realized until 

specific controls are mandated by State Implementation Plans (SIPs) or other federal regulations.

1.2.3 The Need for National Ambient Air Quality Standards

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

be issued is to address existing “externalities.” A market failure or externality occurs when one 

party’s actions impose uncompensated costs on another party. Environmental problems are a 

classic case of an externality. Setting and implementing primary and secondary air quality 

standards is one way the government can address an externality and thereby increase air quality 

and improve overall public health and welfare. 

1.2.4 Illustrative Nature of the Analysis

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

number of emissions control scenarios that states might implement to achieve revised 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. 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 rule, nor 

does it attempt to model the specific actions that any state would take to implement a revised 

standard. This analysis attempts to estimate the costs and human and welfare benefits of costeffective implementation strategies that 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 NAAQS. Because states—not the EPA—will 
1-5

implement any revised NAAQS, they will ultimately determine appropriate emissions control 

scenarios. SIPs would likely vary from the 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. 

1.3 Overview and Design of the RIA

The RIA evaluates the costs and benefits of hypothetical national control strategies to 

attain three alternative ozone standard levels of 60, 65 and 70 ppb.

1.3.1 Existing and Revised Ozone National Ambient Air Quality Standards

The EPA is proposing to retain the indicator, averaging time and form of the existing 

primary ozone standard and is proposing to revise the level of that standard to within the range of 

65 ppb to 70 ppb. The EPA is proposing this revision to increase public health protection, 

including for “at-risk” populations such as children, older adults, and people with asthma or 

other lung diseases, against an array of ozone-related adverse health effects. For short-term 

ozone exposures, these effects include decreased lung function, increased respiratory symptoms 

and pulmonary inflammation, effects that result in serious indicators of respiratory morbidity, 

such as emergency department visits and hospital admissions, and all-cause (total nonaccidental) mortality. For long-term ozone exposures, these health effects include a variety of 

respiratory morbidity effects and respiratory mortality. In recognition that levels as low as 60 

ppb could potentially be supported, but would place very little weight on the uncertainties in the 

health effects evidence and exposure/risk information, the EPA is also soliciting comment on 

alternative standard levels below 65 ppb, as low as 60 ppb. In addition, the EPA is taking 

comment on the option of retaining the current 8-hour primary ozone standard of 75 ppb.

The EPA is proposing to revise the level of the secondary standard to within the range of 

65 ppb to 70 ppb to provide increased protection against vegetation-related effects on public 

welfare. As an initial matter, the EPA is proposing that ambient ozone concentrations in terms of 

a three-year average W126 index value within the range from 13 parts per million-hours (ppmhours) to 17 ppm-hours would provide the requisite protection against known or anticipated 

adverse effects to the public welfare, which data analyses indicate would provide air quality in 
1-6

terms of three-year average W126 index values of a range at or below 13 ppm-hours to 17 ppmhours. Data analyses also indicate that actions taken to attain a standard in the range of 65 ppb to 

70 ppb would also improve air quality as measured by the W126 metric. The quantitative 

analysis assesses the welfare benefits of strategies to attain the proposed secondary standard 

levels of 65 to 70 ppb. 

1.3.2 Establishing Attainment with the Current Ozone National Ambient Air Quality Standard

The RIA is intended to evaluate the costs and benefits of reaching attainment with 

alternative ozone standard levels. To develop and evaluate control strategies for attaining a more 

stringent primary standard, it is important to first estimate ozone levels in the future after 

attaining the current NAAQS (75 ppb) and taking into account projections of future air quality 

reflecting on-the-books Federal regulations, enforcement actions, state regulations, and 

population and economic growth. This allows us to then estimate the incremental costs and 

benefits of attaining alternative primary standard levels.

Attaining 75 ppb reflects emissions reductions already achieved as a result of national 

regulations, emissions reductions expected prior to 2025 from recently promulgated national 

regulations (i.e., reductions that were not realized before promulgation of the previous standard,

but are expected prior to attainment of the existing ozone standard), and reductions from 

additional controls that the EPA estimates need to be included to attain the existing standard (75 

ppb). Emissions reductions achieved as a result of state and local agency regulations and 

voluntary programs are reflected to the extent that they are represented in emissions inventory 

information submitted to the EPA by state and local agencies. We took two steps to develop the

baseline reflecting attainment of 75 ppb. First, national ozone concentrations were projected 

based on population and economic growth and the application of emissions controls resulting 

from national rules promulgated prior to this analysis, as well as state programs and enforcement 

actions. Second, we apply an illustrative control strategy to estimate emissions reductions for the 

current standard of 75 ppb, also referred to as the baseline. 

Below is a list of some of the national rules reflected in the baseline. For a more complete 

list, please see the Technical Support Document: Preparation of Emissions Inventories for the 

Version 6.1, 2011 Emissions Modeling Platform (US EPA, 2014a).
1-7

 Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility 

Generating Units (U.S. EPA, 2014b)

 Tier 3 Motor Vehicle Emission and Fuel Standards (U.S. EPA, 2014c)

 Mercury and Air Toxics Standards (U.S. EPA, 2011)

 Reciprocating Internal Combustion Engines (RICE) NESHAPs (U.S. EPA, 2010)

 Hospital/Medical/Infectious Waste Incinerators: New Source Performance Standards and 

Emission Guidelines: Final Rule Amendments (U.S. EPA, 2009)

 C3 Oceangoing Vessels (U.S. EPA, 2010)

 Emissions Standards for Locomotives and Marine Compression-Ignition Engines (U.S. 

EPA, 2008b)

 Control of Emissions for Nonroad Spark Ignition Engines and Equipment (U.S. EPA, 

2008c)

 Regional Haze Regulations and Guidelines for Best Available Retrofit Technology 

Determinations (U.S. EPA, 2005b)

 NOx Emission Standard for New Commercial Aircraft Engines (U.S. EPA, 2005)

 Clean Air Nonroad Diesel Rule (U.S. EPA, 2004)

 Heavy Duty Diesel Rule (U.S. EPA, 2000)

 Light-Duty Vehicle Tier 2 Rule (U.S. EPA, 1999)

The baseline for this analysis does not assume emissions controls that might be 

implemented to meet the other NAAQS for PM2.5, NO2, or SO2. We did not conduct this analysis 

incremental to controls applied as part of previous NAAQS analyses because the data and 

modeling on which these previous analyses were based are now considered outdated and are not 

compatible with the current ozone NAAQS analysis.

6

In addition, all control strategies analyzed 

in NAAQS RIAs are hypothetical. This analysis presents one scenario that states may employ 

but does not prescribe how attainment must be achieved.

 

6 There were no additional NOx controls applied in the PM2.5 NAAQS RIA, and therefore there would be little to no 

impact on the controls selected as part of this analysis. In addition, the only geographic areas that exceed the 

alternative ozone standard levels analyzed in this RIA and in the 2012 PM2.5 NAAQS RIA are in California. The 

attainment dates for a new PM2.5 NAAQS would likely precede attainment dates for a revised ozone NAAQS. 

While the 2012 PM2.5 NAAQS RIA concluded that controls on directly emitted PM2.5 were the most costeffective on a $/ug basis, states may choose to adopt different control options. These options could include NOx 

controls. It is difficult to determine the impact on costs and benefits for this RIA because it is highly dependent 

upon the control measures that would be chosen and the costs of these measures.
1-8

1.3.3 Establishing the Baseline for Evaluation of Alternative Standards

The RIA evaluates, to the extent possible, the costs and benefits of attaining the proposed 

and alternative ozone standards incremental to attaining the existing ozone standard and 

implementing existing and expected regulations. We assume that potential nonattainment areas 

everywhere in the U.S., excluding California, will be designated such that they are required to 

reach attainment by 2025, and we developed our projected baselines for emissions, air quality, 

and populations for 2025. 

The EPA will likely finalize designations for a revised ozone NAAQS in late 

2017. Depending on the precise timing of the effective date of those designations, nonattainment 

areas classified as Marginal will likely have to attain in either late 2020 or early 

2021. Nonattainment areas classified as Moderate will likely have to attain in either late 2023 or 

early 2024. If a Moderate nonattainment area qualifies for two 1-year extensions, the area may 

have as late as early 2026 to attain. Lastly, Serious nonattainment areas will likely have to attain 

in late 2026 or early 2027. We selected 2025 as the primary year of analysis because it provided 

a good representation of the remaining air quality concerns that moderate nonattainment areas 

would face and because most areas of the U.S. will likely be required to meet a revised ozone 

standard by 2025. States with areas classified as Moderate and higher are required to develop 

attainment demonstration plans for those nonattainment areas. In this RIA we present the 

primary costs and benefits estimates for 2025.

In estimating the incremental costs and benefits of potential alternative standards, we 

recognize that there are several areas that are not required to meet the existing ozone standard by 

2025. The Clean Air Act allows areas with more significant air quality problems to take 

additional time to reach the existing standard. Several areas in California are not required to 

meet the existing standard by 2025 and may not be required to meet a revised standard until

sometime between 2032 and 2037.

7

 We were not able to project emissions and air quality 

 

7 The EPA will likely finalize designations for a revised ozone NAAQS in late 2017. Depending on the precise 

timing of the effective date of those designations, nonattainment areas classified as Severe 15 will likely have to 

attain sometime between late 2032 and early 2033 and nonattainment areas classified as Extreme will likely have 

to attain sometime between late 2037 and early 2038cember 31,. 
1-9

beyond 2025 for California, however, we adjusted baseline air quality to reflect mobile source 

emissions reductions for California that would occur between 2025 and 2030; these emissions 

reductions were the result of mobile source regulations expected to be fully implemented by 

2030. While there is uncertainty about the precise timing of emissions reductions and related 

costs for California, we assume costs occur through the end of 2037 and beginning of 2038. In 

addition, we model benefits for California using projected population demographics for 2038. 

Because of the different timing for incurring costs and accruing benefits and for ease of 

discussion throughout the analyses, we refer to the different time periods for potential attainment 

as 2025 and post-2025 to reflect that (1) we did not project emissions and air quality for any year 

other than 2025; (2) for California, emissions controls and associated costs are assumed to occur 

through the end of 2037 and beginning of 2038; and (3) for California benefits are modeled using 

population demographics in 2038. It is not straightforward to discount the post-2025 results for 

California to compare with or add to the 2025 results for the rest of the U.S. While we estimate 

benefits using 2038 information, we do not have good information on precisely when the costs of 

controls will be incurred. Because of these differences in timing related to California attaining a 

revised standard, the separate costs and benefits estimates for post-2025 should not be added to 

the primary estimates for 2025.

1.4 Health and Welfare Benefits Analysis Approach

1.4.1 Health Benefits

The EPA estimated human health (e.g., mortality and morbidity effects) under both 

partial and full attainment of the three alternative ozone standards. We considered an array of 

health impacts attributable to changes in ozone and PM 2.5 exposure and estimated these benefits 

using the BenMAP tool (US EPA, 2014), which has been used in many recent RIAs (e.g., U.S. 

EPA, 2006, 2011a, 2011b), and The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. 

EPA, 2011c). The EPA has incorporated an array of policy and technical updates to the benefits 

analysis approach applied in this RIA, including incorporation of the most recent epidemiology 

studies evaluating mortality and morbidity associated with ozone and PM2.5 exposure, and an 

expanded uncertainty assessment. Each of these updates is fully described in the health benefits 

chapter (Chapter 5). In addition, unquantified health benefits are also discussed in Chapter 5.
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1.4.2 Welfare Co-Benefits

Even though the primary standards are designed to protect against adverse effects to 

human health, the emissions reductions would have welfare co-benefits in addition to the direct 

human health benefits. The term welfare co-benefits covers both environmental and societal 

benefits of reducing pollution. Welfare co-benefits of the primary ozone standard include 

reduced vegetation effects resulting from ozone exposure, reduced ecological effects from 

particulate matter deposition and from nitrogen emissions, reduced climate effects, and changes 

in visibility. Both welfare co-benefits are discussed further in Chapter 6.

1.5 Cost Analysis Approach

The EPA estimated total costs under partial and full attainment of the three alternative 

ozone standards. These cost estimates reflect only engineering costs, which generally include the 

costs of purchasing, installing, and operating the referenced control technologies. The 

technologies and control strategies selected for analysis 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 the EPA anticipates that state and local governments will consider programs that 

are best suited for local conditions.

The partial-attainment cost analysis reflects the engineering costs associated with 

applying end-of-pipe controls, or known controls. Costs for full attainment include estimates for 

the costs associated with the additional emissions reductions that are needed beyond known

controls, referred to as unknown controls. The EPA recognizes that the portion of the cost 

estimates from unknown controls reflects substantial uncertainty about which sectors and which 

technologies might become available for cost-effective application in the future.

1.6 Organization of this Regulatory Impact Analysis

This RIA includes the following ten chapters:

 Chapter 1: Introduction and Background. This chapter introduces the purpose of the 

RIA.

 Chapter 2: Defining the Ozone Air Quality Problem. This chapter characterizes the 

nature, scope, and magnitude of the current-year ozone problem.
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 Chapter 3: Air Quality Modeling and Analysis. The data, tools, and methodology used for 

the air quality modeling are described in this chapter, as well as the post-processing 

techniques used to produce a number of air quality metrics for input into the analysis of 

costs and benefits.

 Chapter 4: Control Strategies. This chapter presents the hypothetical control strategies, 

the geographic areas where controls were applied, and the results of the modeling that 

predicted ozone concentrations in 2025 after applying the control strategies.

 Chapter 5: Human Health Benefits Analysis. This chapter quantifies the health-related 

benefits of the ozone-related air quality improvements associated with several alternative 

standards.

 Chapter 6: Welfare Co-Benefits of the Primary Standard. This chapter quantifies and 

monetizes selected other welfare effects, including vegetation effects from ozone 

exposure, ecological effects from nitrogen and sulfur emissions, changes in visibility, 

materials damage, ecological effects from PM deposition, ecological effects from 

mercury deposition, and climate effects.

 Chapter 7: Engineering Cost Analysis. This chapter summarizes the data sources and 

methodology used to estimate the engineering costs of partial and full attainment of 

several alternative standards.

 Chapter 8: Comparison of Benefits and Costs. This chapter compares estimates of the 

total benefits with total costs and summarizes the net benefits of several alternative 

standards.

 Chapter 9: Statutory and Executive Order Impact Analyses. This chapter summarizes the 

Statutory and Executive Order impact analyses.

 Chapter 10: Qualitative Discussion of Employment Impacts of Air Quality. This chapter 

provides a discussion of employment impacts of reducing emissions of ozone precursors.

1.7 References

U.S. Environmental Protection Agency (U.S. EPA). 1999. Control of Air Pollution from New Motor Vehicles: Tier 

2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements. Office of Transportation and Air 

Quality. Available at http://www.epa.gov/tier2/frm/fr-t2reg.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2000. Heavy-Duty Engine and Vehicle Standards and Highway 

Diesel Fuel Sulfur Control Requirements. Office of Transportation and Air Quality. Available at 

http://www.epa.gov/otaq/highway-diesel/index.htm.

U.S. Environmental Protection Agency (U.S. EPA). 2004. Control of Emissions of Air Pollution from Nonroad 

Diesel Engines and Fuel. Office of Transportation and Air Quality. Available at 

http://www.regulations.gov/search/Regs/contentStreamer?objectId=09000064800be203&disposition=attachment&c

ontentType=pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2005. New Emission Standards for New Commercial Aircraft 

Engines. Office of Transportation and Air Quality. Available at 

http://www.epa.gov/oms/regs/nonroad/aviation/420f05015.htm.
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U.S. Environmental Protection Agency (U.S. EPA). 2005b. Regional Haze Regulations and Guidelines for Best 

Available Retrofit Technology Determinations. Office of Air Quality Planning and Standards. Available at 

http://www.epa.gov/fedrgstr/EPA-AIR/2005/July/Day-06/a12526.pdf and 

http://www.epa.gov/visibility/fs_2005_6_15.html.

U.S. Environmental Protection Agency (U.S. EPA). 2005. Clean Air Interstate Rule. Office of Atmospheric 

Programs, Washington, D.C. Available at http://edocket.access.gpo.gov/2005/pdf/05-5723.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2006. Air Quality Criteria for Ozone and Related 

Photochemical Oxidants (Final). EPA/600/R-05/004aF-cF. Office of Research and Development, Research Triangle 

Park, NC. Available at http://cfpub.epa.gov/ncea/CFM/recordisplay.cfm?deid=149923.

U.S. Environmental Protection Agency (U.S. 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. Office of Air Quality 

Planning and Standards, Research Triangle Park, NC. Available at 

http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Final Ozone NAAQS Regulatory Impact Analysis, US 

EPA, Office of Air Quality Planning and Standards, Research Triangle Park, NC, EPA-452/R-08-003. Available at 

http://www.epa.gov/ttnecas1/regdata/RIAs/452_R_08_003.pdf.

U.S. Environmental Protection Agency (U.S. EPA).2008b. Emissions Standards for Locomotives and Marine 

Compression-Ignition Engines. Office of Transportation and Air Quality. Available at 

http://www.epa.gov/otaq/regs/nonroad/420f08004.pdf. 

U.S. Environmental Protection Agency (U.S. EPA). 2008c. Control of Emissions for Nonroad Spark Ignition 

Engines and Equipment. Office of Transportation and Air Quality. Available at 

http://www.epa.gov/otaq/regs/nonroad/marinesi-equipld/bondfrm.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2009. Standards of Performance for New Stationary Sources 

and Emission Guidelines for Existing Sources: Hospital/Medical/Infectious Waste Incinerators. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. Available at 

http://www.epa.gov/ttn/atw/129/hmiwi/fr06oc09.pdf.

U.S. Environmental Protection Agency (U.S. EPA), 2009f. Regulatory Impact Analysis: Control of Emissions of Air 

Pollution from Category 3 Marine Diesel Engines. Office of Transportation and Air Quality, Assessment and 

Standards Division, Ann Arbor, MI. Report No. EPA-420-R-09-019. Available at: 

http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09019.pdf

U.S. Environmental Protection Agency (U.S. EPA). 2010. Control of Emissions from New Marine CompressionIgnition Engines at or Above 30 Liters per Cylinder. Office of Transportation and Air Quality. Available at 

http://www.regulations.gov/search/Regs/contentStreamer?objectId=0900006480ae43a6&disposition=attachment&c

ontentType=pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2010. National Emission Standards for Hazardous Air 

Pollutants for Reciprocating Internal Combustion Engines. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. Available at http://www.epa.gov/ttn/atw/icengines/fr/fr20au10.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2011. Cross-State Air Pollution Rule. Office of Atmospheric 

Programs, Washington, D.C. Available at http://www.gpo.gov/fdsys/pkg/FR-2011-08-08/pdf/2011-17600.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2011. Regulatory Impact Analysis for the Final Mercury and 

Air Toxics Standards. Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-11-

011. Available at http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2011, The Benefits and Costs of the Clean Air Act from 1990 to 

2020. Office of Air and Radiation, Washington, D.C. Available at: 

http://www.epa.gov/cleanairactbenefits/feb11/fullreport_rev_a.pdf.
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U.S. Environmental Protection Agency (U.S. EPA). 2011. Regulatory Impact Analysis for the Federal 

Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter and Ozone in 27 States; Correction 

of SIP Approvals for 22 States. Available at http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf.

U.S. Environmental Protection Agency, 2014. Policy Assessment for the Review of the Ozone National Ambient 

Air Quality Standards, US EPA, OAQPS, 2014, RTP, NC, EPA-452/R-14-006.

U.S. Environmental Protection Agency, 2014a. Preparation of Emissions Inventories for the Version 6.1, 2011 

Emissions Modeling Platform. Available at: http://www.epa.gov/ttn/chief/emch/index.html#2011.

U.S. Environmental Protection Agency, 2014b. Regulatory Impact Analysis for the Proposed Carbon Pollution 

Guidelines for Existing Power Plants and Emission Standards for Modified and Reconstructed Power Plants, US 

EPA, OAQPS, 2014, RTP, NC, EPA-452/R-14-002.

U.S. Environmental Protection Agency, 2014c. Control of Air Pollution from Motor Vehicles: Tier 3 Motor Vehicle 

Emissions and Fuel Standards Rule, US EPA, OTAQ, 2014, Washington, DC, EPA-420-R-14-005.

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

http://www.whitehouse.gov/omb/circulars/a004/-.pdf.
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CHAPTER 2: DEFINING THE OZONE AIR QUALITY PROBLEM

Overview

This section provides overviews of ozone precursor emissions and atmospheric chemistry 

(section 2.1); ambient ozone concentrations (section 2.2); ambient ozone monitoring in the U.S. 

(section 2.3); and available evidence and information related to background ozone (section 2.4).

2.1 Emissions and Atmospheric Chemistry

Ozone is formed through photochemical reactions of precursor gases and is not directly 

emitted from specific sources. In the stratosphere, ozone occurs naturally and provides protection 

against harmful solar ultraviolet radiation. In the troposphere, near ground level, ozone forms 

through atmospheric reactions involving two main classes of precursor pollutants: volatile 

organic compounds (VOCs) and nitrogen oxides (NOX). Carbon monoxide (CO) and methane 

(CH4) are also important for ozone formation over longer time periods (US EPA, 2013, section 

3.2.2).

Emissions of ozone precursor compounds can be divided into anthropogenic and natural 

source categories, with natural sources further divided into biogenic emissions (from vegetation, 

microbes, and animals) and abiotic emissions (from biomass burning, lightning, and geogenic 

sources). Anthropogenic sources, including mobile sources and power plants, account for the 

majority of NOX and CO emissions. Anthropogenic sources are also important for VOC 

emissions, though in some locations and at certain times of the year (e.g., southeastern states 

during summer) the majority of VOC emissions comes from vegetation (US EPA, 2013, section 

3.2.1).

Rather than varying directly with emissions of its precursors, ozone changes in a nonlinear 

fashion with the concentrations of its precursors. NOX emissions lead to both the formation and 

destruction of ozone, depending on the local quantities of NOX, VOC, free radicals, and sunlight. 

In areas dominated by fresh emissions of NOX, radicals are removed, which lowers the ozone

formation rate. In addition, the scavenging of ozone by reaction with NO is called “titration” and 

is often found in downtown metropolitan areas, especially near busy streets and roads, as well as 

in power plant plumes. This short-lived titration results in localized areas in which ozone

concentrations are suppressed compared to surrounding areas, but which contain NO2 that 
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contributes to subsequent ozone formation further downwind. The NOx titration effect is most 

pronounced in urban core areas that have a high volume of mobile source NOx emissions from 

vehicles. In areas with relatively low NOX concentrations, such as those found in remote 

continental areas and rural and suburban areas downwind of urban centers, ozone production 

typically responds linearly to NOX concentrations (e.g., ozone decreases with decreasing NOX

emissions). Consequently, ozone response to reductions in NOX emissions is complex and may 

include ozone decreases at some times and locations and increases of ozone at other times and 

locations. As a general rule, as NOx emissions reductions occur, you can expect lower ozone

values to increase while the higher ozone values would be expected to decrease. NOx reductions 

are expected to result in a compressed ozone distribution, relative to current conditions (EPA, 

2014a).

The formation of ozone from precursor emissions is also affected by meteorological 

parameters such as the intensity of sunlight and atmospheric mixing. Major episodes of high

ground-level ozone concentrations in the eastern United States are often associated with slowmoving high pressure systems. High pressure systems during the warmer seasons are associated

with the sinking of air, resulting in warm, generally cloudless skies, with light winds. The 

sinking of air results in the development of stable conditions near the surface that inhibit or 

reduce the vertical mixing of ozone precursors. The combination of inhibited vertical mixing and 

light winds minimizes the dispersal of pollutants, allowing their concentrations to build up. In 

addition, in some parts of the United States (e.g., in Los Angeles), mountain barriers limit mixing 

and result in a higher frequency and duration of days with elevated ozone concentrations. 

Photochemical activity involving precursors is enhanced during warmer seasons because of the 

greater availability of sunlight and higher temperatures (US EPA, 2013, section 3.2).

Elevated wintertime ozone concentrations have recently been measured in mountain 

valleys in the Western U.S. (Schnell et al, 2009; Rappengluck et al., 2014; Helmig et al., 2014). 

Hourly ozone concentrations during these winter events have been observed to reach 160 ppb. 

This phenomenon is believed to result from the combination of several factors: 1) strong 

wintertime inversions or “cold pools”, which trap air in a shallow layer close to the ground, 2) 

substantial emissions of NOx and VOC from nearby oil and gas operations, 3) high albedo of 

deep snow, which leads to enhanced UV intensity and photochemical activity, and 4) possible 
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uncharacterized sources of radicals. These wintertime ozone events have currently only been 

observed in a limited number of locations in Wyoming, Utah, and Colorado. Events can last for 

multiple days and can occur several times a year, but do not occur every winter in these 

locations.

Ozone concentrations in a region are affected both by local formation and by transport of 

ozone and its precursors from upwind areas. Ozone transport occurs on many spatial scales 

including local transport between cities, regional transport over large regions of the U.S. and 

international/long-range transport. In addition, ozone can be transferred into the troposphere 

from the stratosphere, which is rich in ozone, through stratosphere-troposphere exchange (STE).

These intrusions usually occur behind cold fronts, bringing stratospheric air with them and 

typically affect ozone concentrations in higher elevation areas (e.g. > 1500 m) more than areas at 

lower elevations (U.S. EPA, 2013, section 3.4.1.1). The role of long-range transport of ozone and 

other elements of ozone background are discussed in more detail in Section 2.4.

2.2 Spatial and Temporal Variations in Ambient Ozone Concentrations

Because ozone is a secondary pollutant formed in the atmosphere from precursor 

emissions, concentrations are generally more regionally homogeneous than concentrations of 

primary pollutants emitted directly from stationary and mobile sources (US EPA, 2013, section 

3.6.2.1). However, variation in local emissions characteristics, meteorological conditions, and 

topography can result in daily and seasonal temporal variability in ambient ozone concentrations, 

as well as local and national-scale spatial variability.

Temporal variation in ambient ozone concentrations results largely from daily and seasonal 

patterns in sunlight, precursor emissions, atmospheric stability, wind direction, and temperature 

(US EPA, 2013, section 3.7.5). On average, ambient ozone concentrations follow wellrecognized daily and seasonal patterns, particularly in urban areas. Specifically, daily maximum 

1-hour ozone concentrations in urban areas tend to occur in mid-afternoon, with more 

pronounced peaks in the warm months of the ozone season than in the colder months (US EPA, 

2013, Figures 3-54, 3-156 to 3-157). Rural sites also follow this general pattern, though it is less 

pronounced in colder months (US EPA, 2013, Figure 3-55). With regard to day-to-day 

variability, median maximum daily 8-hour average (MDA8) ozone concentrations in U.S. cities 
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from 2007 through 2009 were approximately 47 ppb, with typical ranges between 35 to 60 ppb 

and the highest MDA8 concentrations above 100 ppb in several U.S. cities (as noted further 

below).

In addition to temporal variability, there is considerable spatial variability in ambient 

ozone concentrations within cities and across different cities in the United States. With regard to 

spatial variability within a city, local emissions characteristics, geography, and topography can 

have important impacts. For example, as noted above, fresh NO emissions from motor vehicles 

titrate ozone present in the urban background air, resulting in an ozone gradient around roadways

with ozone concentrations increasing as distance from the road increases (US EPA, 2013, section 

3.6.2.1). Measured ozone concentrations are relatively uniform and well-correlated within some 

cities (e.g., Atlanta) while they are more variable in others (e.g., Los Angeles) (US EPA, 2013, 

section 3.6.2.1 and Figures 3-28 to 3-36). 

Ozone concentrations also vary considerably across cities. Several cities had very high 

measured ozone concentrations in 2007 through 2009 when the maximum recorded MDA8 was 

137 ppb in Los Angeles, and was near or above 120 ppb in Atlanta, Baltimore, Dallas, New York 

City, Philadelphia, and St. Louis (US EPA, 2013, Table 3-10). These same cities also had high 

98th percentile ozone concentrations, with Los Angeles recording the highest 98th percentile 

concentration (91 ppb) and many eastern and southern cities reporting 98th percentile 

concentrations near or above 75 ppb. In contrast, somewhat lower 98th percentile ozone 

concentrations were recorded in cities in the western United States outside of California (US 

EPA, 2013, Table 3-10).

Rural sites can be affected by transport of ozone or ozone precursors from upwind urban 

areas and by local anthropogenic sources such as motor vehicles, power generation, biomass 

combustion, or oil and gas operations (US EPA, 2013, section 3.6.2.2). In addition, ozone tends 

to persist longer in rural than in urban areas due to lower rates of chemical scavenging in nonurban environments. At higher elevations, increased ozone concentrations can also result from 

stratospheric intrusions (US EPA, 2013, sections 3.4, 3.6.2.2). As a result, ozone concentrations 

measured in some rural sites can be higher than those measured in nearby urban areas (US EPA, 

2013, section 3.6.2.2).
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2.3 Ozone Monitoring

2.3.1 Ozone Monitoring Network

To monitor compliance with the National Ambient Air Quality Standards (NAAQS), state 

and local environmental agencies operate ozone monitoring sites at various locations, depending 

on the population of the area and typical peak ozone concentrations.8 All of the state and local 

monitoring stations that report data to the EPA Air Quality System (AQS) use ultraviolet (UV) 

Federal Equivalent Methods (FEMs). In 2013, there were over 1,300 state, local, and tribal ozone

monitors reporting concentrations to EPA. The “State and Local Monitoring Stations” (SLAMS)

minimum monitoring requirements to meet the ozone design criteria are specified in 40 CFR Part 

58, Appendix D. The requirements are both population and design value based.

9 The minimum 

number of ozone monitors required in a Metropolitan Statistical Area (MSA) ranges from zero 

for areas with a population of at least 50,000 and under 350,000 with no recent history of an 

ozone design value greater than 85 percent of the NAAQS, to four for areas with a population 

greater than 10 million and an ozone design value greater than 85 percent of the NAAQS. At

least one site for each MSA, or Combined Statistical Area (CSA), must be sited to record the 

maximum concentration for that particular metropolitan area. Since highest ozone concentrations 

tend to be associated with particular seasons for various locations, EPA requires ozone 

monitoring during specific ozone monitoring seasons, which vary by state.

10

Figure 2-1 shows the locations of the U.S. ambient ozone monitoring sites reporting data to 

EPA at any time during the 2009-2013 period. The gray dots that make up over 80% of the 

ozone monitoring network are SLAMS monitors, which are operated by state and local 

governments to meet regulatory requirements and provide air quality information to public health 

agencies. Thus, the SLAMS monitoring sites are largely focused on urban and suburban areas. 

The blue dots highlight two important subsets of monitoring sites within the SLAMS network: 

 

8 The minimum ozone monitoring network requirements for urban areas are listed in Table D-2 of Appendix D to 40 

CFR Part 58.

9 A design value is a statistic that describes the air quality status of a given area relative to the level of the NAAQS. 

Design values are typically used to classify nonattainment areas, assess progress towards meeting the NAAQS, 

and develop control strategies. See http://epa.gov/airtrends/values.html (U, 2010, 677582) for guidance on how 

these values are defined.

10 The required ozone monitoring seasons for each state are listed in Table D-3 of Appendix D to 40 CFR Part 58. 

Revised monitoring seasons are being proposed along with the proposed revision of the ozone NAAQS level.
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the “National Core” (NCore) multi-pollutant monitoring network and the “Photochemical 

Assessment Monitoring Stations” (PAMS) network.

While the existing U.S. ozone monitoring network has a largely urban focus, to address 

ecosystem impacts of ozone, such as biomass loss and foliar injury, it is equally important to 

focus on ozone monitoring in rural areas. The green dots in Figure 2-1 represent the Clean Air 

Status and Trends Network (CASTNET) monitors, which are located in rural areas. There were 

about 80 CASTNET sites operating in 2013, with sites in the eastern U.S. being operated by 

EPA and sites in the western U.S. being operated by the National Park Service (NPS).11 In total, 

there were about 120 rural ozone monitoring sites operating in the U.S. in 2013.

Figure 2-1. Map of U.S. Ambient O3 Monitoring Sites Reporting Data to EPA During the 

2009-2013 Period

 

11 Additionally, the black dots represent “Special Purpose Monitoring Stations” (SPMS), which include about 20 

rural monitors as part of the “Portable O3 Monitoring System” (POMS) network operated by the NPS.
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2.3.2 Recent Ozone Monitoring Data and Trends

To determine whether or not the ozone NAAQS has been met at an ambient monitoring 

site, a statistic commonly referred to as a “design value” must be calculated based on three 

consecutive years of data collected from that site. The form of the existing ozone NAAQS design 

value (DV) statistic is the 3-year average of the annual 4th highest daily maximum 8-hour ozone 

concentration in parts per billion (ppb), with decimal digits truncated. The existing primary and 

secondary ozone NAAQS are met at an ambient monitoring site when the DV is less than or 

equal to 75 ppb.12 In counties or other geographic areas with multiple monitoring sites, the areawide DV is defined as the DV at the highest individual monitoring site, and the area is said to 

have met the NAAQS only if all monitoring sites in the area are meeting the NAAQS.

Figure 2-2 shows the trend in the annual 4th highest daily maximum 8-hour ozone 

concentrations in ppb based on 910 “trends” sites with complete data records over the 2000 to 

2013 period. The center line in this figure represents the median value across the trends sites, 

while the dashed lines represent the 25th and 75th percentiles, and the bottom and top lines 

represent the 10th and 90th percentiles. Figure 2-3 shows a map of the ozone DVs (in ppb) 

averaged across the 2009-2011, 2010-2012, and 2011-2013 periods at all monitoring sites in the 

contiguous U.S.13 The trend figure shows that the annual 4th highest daily maximum values 

decreased for the vast majority of monitoring sites in the U.S. between 2000 and 2013. The 

decreasing trend is especially sharp from 2002 to 2004, when EPA implemented the “NOX SIP 

Call”, a program designed to reduce summertime emissions of NOX in the eastern U.S., but has 

continued to decrease since then, in part due to ongoing reductions in mobile source NOx 

emissions. Within the overall downward trend, there are periodic short-term increases. These 

variations from the overall trend are the result of inter-annual variability in meteorological 

conditions.

 

12 For more details on the data handling procedures used to calculate design values for the existing ozone NAAQS, 

see 40 CFR Part 50, Appendix P.

13 All monitoring sites in Alaska, Hawaii, and Puerto Rico had DVs below 60 ppb.
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Figure 2-2. Trend in U.S. Annual 4th Highest Daily Maximum 8-hour Ozone 

Concentrations in ppb, 2000 to 2013. Solid center line represents the median 

value across monitoring sites, dashed lines represent 25th and 75th percentile 

values, and top/bottom lines represent 10th and 90th percentile values.

Figure 2-3. Map of 8-hour Ozone Design Values in ppb, Averaged Across the 2009-2011, 

2010-2012, and 2011-2013 Periods
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In addition to the DV described above, another ozone metric of interest is the W126 index 

value, which has been found to correlate with ozone-related damage to plants and ecosystems 

(EPA, 2014c). The W126 metric is a seasonal aggregate of daytime (8:00 AM to 8:00 PM) 

hourly ozone concentrations designed to measure the cumulative effects of ozone exposure on 

plant and tree species, with units in parts per million-hours (ppm-hrs). The W126 metric uses a 

logistic weighting function to place less emphasis on exposure to low hourly ozone 

concentrations and more emphasis on exposure to high hourly ozone concentrations (Lefohn et 

al, 1988). 

Figure 2-4 shows the trend in annual W126 concentrations in ppm-hrs based on 900 

“trends” sites with complete data records over the 2000 to 2013 period. The center line in this 

figure represents the median value across the trends sites, while the dashed lines represent the 

25th and 75th percentiles, and the bottom and top lines represent the 10th and 90th percentiles. 

Figure 2-5 shows a map of the 3-year average annual W126 concentrations in ppm-hrs averaged 

across the 2009-2011, 2010-2012, and 2011-2013 periods at all monitoring sites in the 

contiguous U.S. The general patterns seen in these figures are similar to those seen in the DV 

metric for the existing standard.
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Figure 2-4. Trend in U.S. Annual W126 Concentrations in ppm-hrs, 2000 to 2013. Solid 

center line represents the median value across monitoring sites, dashed lines 

represent 25th and 75th percentile values, and top/bottom lines represent 10th 

and 90th percentile values.

Figure 2-5. Map of 3-year Average W126 Values in ppm-hrs, Averaged Across the 2009-

2011, 2010-2012, and 2011-2013 Periods

2.4 Background Ozone 

One of the aspects of ozone that is unusual relative to the other pollutants with NAAQS is 

that, periodically, in some locations, an appreciable fraction of the observed ozone results from 

sources or processes other than local and domestic regional anthropogenic emissions of ozone 

precursors (Fiore et al., 2002). Any ozone formed by processes other than the chemical 

conversion of local or regional ozone precursor emissions is generically referred to as 

“background” ozone. Background ozone can originate from natural sources of ozone and ozone

precursors, as well as from manmade international emissions of ozone precursors. Natural 

sources of ozone precursor emissions such as wildfires, lightning, and vegetation can lead to 

ozone formation by chemical reactions with other natural sources. Another important component 
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of background is ozone that is naturally formed in the stratosphere through interactions of 

ultraviolet light with molecular oxygen. Stratospheric ozone can mix down to the surface at high 

concentrations in discrete events called intrusions, especially at higher-altitude locations. The 

manmade portion of the background includes any ozone formed due to anthropogenic sources of 

ozone precursors emitted far away from the local area (e.g., international emissions). Finally, 

both biogenic and international anthropogenic emissions of methane, which can be chemically 

converted to ozone over relatively long time scales, can also contribute to global background 

ozone levels. Away from the surface, ozone can have an atmospheric lifetime on the order of 

weeks. As a result, background ozone can be transported long distances in the upper troposphere 

and, when meteorological conditions are favorable, be available to mix down to the surface and 

add to the ozone loading from non-background sources. 

The definition of background ozone can vary depending upon context, but it generally 

refers to ozone that is formed by sources or processes that cannot be influenced by actions within 

the jurisdiction of concern. In the Policy Assessment for the Review of the Ozone National 

Ambient Air Quality Standards (US EPA, 2014c), EPA identified three specific definitions of 

background ozone: natural background (NB), North American background (NAB), and United 

States background (USB). Natural background is the narrowest definition of background, and it 

is defined as the ozone that would exist in the absence of any manmade ozone precursor 

emissions. The other two definitions of background are based on a presumption that the U.S. has 

little influence over anthropogenic emissions outside either our continental or domestic borders.

North American background is defined as that ozone that would exist in the absence of any 

manmade ozone precursor emissions from North America. U.S. background is defined as that 

ozone that would exist in the absence of any manmade emissions inside the United States.

Modeling studies have estimated what background levels would be in the absence of 

certain sets of emissions by simply assessing the remaining ozone in a simulation in which 

certain emissions were removed (Zhang et al. (2011), Emery et al. (2012), US EPA (2014c)).

This basic approach is often referred to as “zero-out” modeling or “emissions perturbation” 

modeling. While the zero-out approach has traditionally been used to estimate natural 

background, North American background, and U.S. background, the methodology has an
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acknowledged limitation. It cannot answer the question of how much of the existing observed 

ozone results from background sources or processes.

A separate modeling technique can be used to estimate the contribution of background

ozone and other contributing source terms to total ozone within a model. This approach, referred 

to as “source apportionment” modeling, has been described and evaluated in the peer-reviewed 

literature (Dunker et al., 2002; Kemball-Cook et al., 2009). Source apportionment modeling has 

frequently been used in other regulatory settings to estimate the “contribution” to ozone of 

certain sets of emissions (EPA 2005, EPA 2011). The source apportionment technique provides a 

means of estimating the contributions of each user-identified source category to ozone formation 

in a single model simulation. This is achieved by using multiple tracer species to track the fate of 

ozone precursor emissions (VOC and NOX) and the ozone formation resulting from these 

emissions. The methodology is designed so that all ozone and precursor concentrations are 

tracked and apportioned to the selected source categories at all times without perturbing the 

inherent chemistry. The primary limitation of the source apportionment modeling is that its 

estimations of background ozone are explicitly linked to the emissions scenarios modeled and 

would change with different emissions scenarios. 

2.4.1 Seasonal Mean Background Ozone in the U.S.

The ISA (US EPA 2013, section 3.4) previously established that background ozone

concentrations vary spatially and temporally and that simulated mean background concentrations 

are highest at high-elevation sites within the western U.S. Background levels typically are 

greatest over the U.S. in the spring and early summer. EPA modeling presented in the Policy 

Assessment for the Review of the Ozone National Ambient Air Quality Standards (US EPA, 

2014c) focused on the months from April to October for 2007 (note that the 2007 modeling is 

separate from the 2011 modeling described in Chapter 3 and used as the basis of cost and benefit 

numbers in this RIA). Emissions and model set-up for the 2007 analysis are described in more 

detail in the Policy Assessment. Briefly, the emissions for 2007 were derived from the 2008 

National Emissions Inventory but included 2007 year-specific emissions where available. 

Wildfire emissions were based on a multi-year climatological average as this analysis was meant 

to capture seasonal mean background and typical ranges rather than explicitly quantify 

background ozone on specific days. Figure 2-6 displays the spatial patterns of seasonal mean 
2-13

natural background ozone as estimated by a 2007 zero-out scenario using the Community 

Multiscale Air Quality (CMAQ) model. Seasonal means are computed over those seven months. 

This figure shows the average daily maximum 8-hour ozone concentration that would exist in the 

absence of any anthropogenic ozone precursor emissions at monitor locations. As shown, 

seasonal mean NB levels range from approximately 15-35 ppb (i.e., +/- 1 standard deviation) 

with the highest values at higher-elevation sites in the western U.S. The median value over these

locations is 24.2 ppb, and more than 50 percent of the locations have natural background levels 

of 20-25 ppb. The highest modeled estimate of seasonal average, natural background, 8-hr daily 

maximum ozone is 34.3 ppb at the high-elevation CASTNET site (Gothic) in Gunnison County, 

CO. Natural background ozone levels are higher at these high-elevation locations primarily 

because of natural stratospheric ozone impacts and international transport impacts that increase 

with altitude (where ozone lifetimes are longer).

Figure 2-6. Map of 2007 CMAQ-estimated Seasonal Mean of 8-hour Daily Maximum 

Ozone from Natural Background (ppb) based on Zero-Out Modeling

Figures 2-7 and 2-8 show the same information for the NAB and USB scenarios. In these 

model runs, all anthropogenic ozone precursor emissions were removed from the U.S., Canada, 

and Mexico portions of the modeling domain (NAB scenario) and then only from the U.S. (USB 

scenario). The figures show that there is not a large difference between the NAB and USB 

scenarios. Seasonal mean NAB and USB ozone levels range from 25-50 ppb, with the most 

frequent values estimated in the 30-35 ppb range. The median seasonal mean background levels 
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are 31.5 and 32.7 ppb (NAB and USB, respectively). Again, the highest levels of seasonal mean 

background ozone are predicted over the intermountain western U.S. Locations with NAB and 

USB concentrations greater than 40 ppb are confined to Colorado, Nevada, Utah, Wyoming, 

northern Arizona, eastern California, and parts of New Mexico. The 2007 EPA modeling 

suggests that seasonal mean USB concentrations are on average 1-3 ppb higher than NAB 

background. These results were similar to those reported by Wang et al. (2009). From a seasonal 

mean perspective, background ozone levels are below the NAAQS thresholds.

Figure 2-7. Map of 2007 CMAQ-estimated Seasonal Mean of 8-hour Daily Maximum 

Ozone from North American Background (ppb) based on Zero-out Modeling
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Figure 2-8. Map of 2007 CMAQ-estimated Seasonal Mean of 8-hour Daily Maximum 

Ozone from United States Background (ppb) based on Zero-Out Modeling

2.4.2 Seasonal Mean Background Ozone in the U.S. as a Proportion of Total Ozone

Another informative way to assess the importance of background ozone as part of

seasonal mean ozone levels across the U.S. is to consider the ratios of NB, NAB, and USB to 

total modeled ozone at each monitoring location. Considering the proportional impact of 

background ozone allows for an initial assessment of the relative importance of background and 

non-background sources. Because ozone chemistry is non-linear, one should not assume that 

individual perturbations (e.g., zero-out runs) are additive in all locations. Figures 2-9 and 2-10 

show the ratio of U.S. background to total ozone using the metric of the seasonal mean 8-hr daily 

maximum ozone concentrations as estimated by both the zero-out and source apportionment 

modeling methodologies. Recall that the terms NB, NAB, and USB are explicitly linked to the 

zero-out modeling approach. For comparison, in Figure 2-10 we are extending the definition of 

USB to also include the source apportionment model estimates of the ozone that are attributable 

to sources other than U.S. anthropogenic emissions. To preserve the original definition of USB, 

this second term will be hereafter referred to as “apportionment-based USB”. As noted earlier, 

the advantage of the source apportionment modeling is that all of the modeled ozone is attributed 

to various source terms without perturbing the inherent chemistry. Thus, this approach is not 

affected by the confounding occurrences of background ozone values exceeding the base ozone

values as can happen in the zero-out modeling (i.e., background proportions > 100%).
2-16

Consequently, one would expect the fractional background levels to be lower in the source 

apportionment methodology as a result of removing this artifact.

When averaged over all sites, ozone from sources other than U.S. anthropogenic 

emissions is estimated to comprise 66 (zero-out) and 59 (source apportionment) percent of the 

total seasonal ozone mean. The spatial patterns of USB and apportionment-based USB are 

similar across the two modeling exercises. Background ozone is a relatively larger percentage 

(e.g., 70-80%) of the total seasonal mean ozone in locations within the intermountain western 

U.S. and along the U.S. border. In locations where ozone levels are generally higher, like 

California and the eastern U.S., the seasonal mean background fractions are relatively smaller 

(e.g., 40-60%). The additional 2007 modeling confirms that background ozone, while generally 

not approaching levels of the ozone standard, can comprise a considerable fraction of total 

seasonal mean ozone across the U.S (EPA, 2014c).

2.4.3 Daily Distributions of Background Ozone within the Seasonal Mean

As a first-order understanding, it is valuable to be able to characterize seasonal mean 

levels of background ozone. However, it is well established that background levels can vary 

substantially from day-to-day within the seasonal mean. From an implementation perspective, 

the values of background ozone on possible exceedance days are a more meaningful 

consideration. The Policy Assessment for the Review of the Ozone National Ambient Air 

Quality Standards (US EPA, 2014c) concluded that “anthropogenic sources within the U.S. are 

largely responsible for 4th highest 8-hour daily maximum O3 concentrations” based on modeling 

using a 2007 base year and the two distinct modeling methodologies described above. Figure 2-

11 and 2-12 show the distribution of daily MDA8 apportionment-based USB levels (absolute 

magnitudes and relative fractions, respectively) from the CAMx simulation. The 2007 modeling 

shows that the days with highest ozone levels have similar distributions (i.e., means, interquartile ranges) of background ozone levels as days with lower values, down to approximately 

40 ppb. As a result, the proportion of total ozone that has background origins is smaller on high 

ozone days (e.g., days > 60 ppb) than on the more common lower ozone days that tend to drive 

seasonal means. Figure 2-11 also indicates that there are cases in which the model predicts much 

larger background proportions, as shown by the upper outliers in the figure. These infrequent 

episodes usually occur in relation to a specific event, and occur more often in specific 
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geographical locations, such as at high elevations or wildfire prone areas during the local dry 

season.

It should be noted here that EPA has policies for treatment of air quality monitoring data 

affected by these types of events. EPA’s exceptional events policy allows exclusion of certain air 

quality monitoring data from regulatory determinations if a State adequately demonstrates that an 

exceptional event has caused the exceedance or violation of a NAAQS. In addition, Section 

179B of the Clean Air Act (CAA) also provides for treatment of air quality data from 

international transport when an exceedance or violation of a NAAQS would not have occurred 

but for the emissions emanating from outside of the United States. Finally, CAA section 182(h) 

authorizes the EPA Administrator to determine that an area designated nonattainment can be 

treated as a “rural transport area”. In accordance with the statute, a nonattainment area may 

qualify for this distinction if it meets the following criteria: 1) the area does not contain 

emissions sources that make a significant contribution to monitored ozone concentrations in the 

area, or in other areas; and 2) the area does not include and is not adjacent to an MSA. More 

information regarding how background ozone is addressed in Clean Air Act implementation is 

provided in Section VII.F of the notice of proposed rulemaking.

Figure 2-9. Map of Site-Specific Ratios of U.S. Background to Total Seasonal Mean 

Ozone based on 2007 CMAQ Zero-Out Modeling
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Figure 2-10. Map of Site-Specific Ratios of Apportionment-Based U.S. Background to 

Seasonal Mean Ozone based on 2007 CAMx Source Apportionment Modeling

Figure 2-11. Distributions of Absolute Estimates of Apportionment-Based U.S. 

Background (all site-days), Binned by Modeled MDA8 from the 2007 Source 

Apportionment Simulation
2-19

Figure 2-12. Distributions of the Relative Proportion of Apportionment-Based U.S. 

Background to Total Ozone (all site-days), Binned by Modeled MDA8 from the 

2007 Source Apportionment Simulation

2.5 References

Camalier, L.; Cox, W.; Dolwick, P. (2007). The Effects of Meteorology on Ozone in Urban Areas and their use in 

Assessing Ozone Trends. Atmos Environ 41: 7127-7137. 

Dunker, A.M.; Yarwood, G; Ortmann, J.P.; Wilson, G.M. (2002). Comparison of source apportionment and source 

sensitivity of ozone in a three-dimensional air quality model. Environmental Science & Technology 36: 2953-

2964.

Emery, C; Jung, J; Downey, N; Johnson, J; Jimenez, M; Yarwood, G; Morris, R. (2012). Regional and global 

modeling estimates of policy relevant background ozone over the United States. Atmos Environ 47: 206-217. 

http://dx.doi.org/10.1016/j.atmosenv.2011.11.012.

Helmig, D., Thompson, C.R., Evans, J., Boylan, P., Hueber, J., Park, J.H. (2014). Highly elevated atmospheric 

levels of volatile organic compounds in the Uintah Basin, Utah, Environmental Science & Technology, 48, 4707-

4715.

Henderson, B.H., Possiel, N., Akhtar, F., Simon, H.A. (2012). Regional and Seasonal Analysis of North American 

Background Ozone Estimates from Two Studies. Available on the Internet at: 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_td.html

Kemball-Cook, S.; Parrish, D.; Ryerson, T; Nopmongcol, U.; Johnson, J.; Tai, E.; Yarwood, G. (2009). 

Contributions of regional transport and local sources to ozone exceedances in Houston and Dallas: Comparison of 

results from a photochemical grid model to aircraft and surface measurements. Journal of Geophysical ResearchAtmospheres, 114: D00F02. DOI: 10.1029/2008JD010248.
2-20

Lapina K., Henze D.K., Milford J.B., Huang M., Lin M., Fiore A.M., Carmichael G., Pfister G.G., Bowman K. 

(2014). Assessment of source contributions to seasonal vegetative exposure to ozone in the U.S. Journal of 

Geophysical Research-Atmospheres, 119: DOI: 10.1002/2013JD020905.

Lefohn, A. S.; Laurence, J. A.; Kohut, R. J. (1988). A comparison of indices that describe the relationship between 

exposure to ozone and reduction in the yield of agricultural crops. Atmos. Environ. 22: 1229-1240. 

Rappengluck, B., Ackermann, L., Alvarez, S., Golovko, J., Buhr, M., Field, R.A., Soltis, J., Montague, D.C., Hauze, 

B., Adamson, S., Risch, D., Wilkerson, G., Bush, D., Stoeckenius, T., Keslar, C. (2014). Strong wintertime ozone 

events in the Upper Green River basin, Wyoming, Atmospheric Chemistry and Physics, 14, 4909-4934.

Schnell, R.C., Oltmans, S.J., Neely, R.R., Endres, M.S., Molenar, J.V., White, A.B. (2009) Rapid photochemical 

production of ozone at high concentrations in a rural site during winter, Nature Geoscience, 2, 120-122.

U.S. Environmental Protection Agency (2005). Technical Support Document for the Final Clean Air Interstate Rule 

Air Quality Modeling. Office of Air Quality Planning and Standards, Research Triangle Park, NC, 285pp. 

http://www.epa.gov/cair/technical.html.

U.S. Environmental Protection Agency (2011). Air Quality Modeling Final Rule Technical Support Document. 

Office of Air Quality Planning and Standards, Research Triangle Park, NC, 363pp. 

http://www.epa.gov/airtransport/CSAPR/techinfo.html.

U.S. Environmental Protection Agency. (2013). Integrated Science Assessment for Ozone and Related 

Photochemical Oxidants, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA/600/R10/076.

U.S. Environmental Protection Agency (2014a): Health Risk and Exposure Assessment for Ozone, final report. US 

EPA, OAQPS, 2014, RTP, NC, EPA-452/R-14-004a

U.S. Environmental Protection Agency. (2014b). Welfare Risk and Exposure Assessment for Ozone, final, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. EPA-452/P-14-005a.

U.S. Environmental Protection Agency. (2014c). Policy Assessment for the Review of the Ozone National Ambient 

Air Quality Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA 452/R-14-006.

Wang, HQ; Jacob, DJ; Le Sager, P; Streets, DG; Park, RJ; Gilliland, AB; van Donkelaar, A. (2009). Surface ozone 

background in the United States: Canadian and Mexican pollution influences. Atmos Environ 43: 1310-1319. 

http://dx.doi.org/10.1016/j.atmosenv.2008.11.036.

Zhang, L; Jacob, DJ; Downey, NV; Wood, DA; Blewitt, D; Carouge, CC; Van donkelaar, A; Jones, DBA; Murray, 

LT; Wang, Y. (2011). Improved estimate of the policy-relevant background ozone in the United States using the 

GEOS-Chem global model with 1/2° × 2/3° horizontal resolution over North America. Atmos Environ 4.
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CHAPTER 3: AIR QUALITY MODELING AND ANALYSIS

Overview

This regulatory impacts analysis (RIA) evaluates the costs as well as the health and 

environmental impacts associated with complying with alterative National Ambient Air Quality 

Standards (NAAQS) for ozone. For this purpose, we use air quality modeling to project ozone 

concentrations into the future. This chapter describes the data, tools and methodology used for 

the analysis, as well as the post-processing techniques used to produce a number of ozone

metrics necessary for this analysis. 

Throughout this chapter, the base year modeling refers to model simulations conducted 

for 2011 while the 2025 base case simulation refers to a photochemical model run conducted 

with emissions projected to the year 2025 assuming all current on-the-books federal regulations 

will apply14. A series of 2025 emissions sensitivity cases are created to determine ozone 

response to emissions changes incremental to the 2025 base case. Finally, a set of four scenarios 

are developed based on the 2025 base case and emissions sensitivity cases: the baseline scenario 

(a scenario which applies additional controls to the 2025 base case that would be required to 

meet the current standard of 75 ppb), and 3 alternative standard scenarios which represent 

incremental emissions reductions beyond the baseline to meet potential standard levels of 70, 65, 

and 60 ppb. 

Section 3.1 describes the air quality modeling simulations, section 3.2 describes how 

current and future ozone design values are calculated, section 3.3 describes the methodology for 

determining necessary emissions reductions for meeting various alternative NAAQS levels, and 

section 3.4 describes the creation of spatial surfaces that act as inputs to health and welfare 

benefits calculations.

 

14 The 2012 PM NAAQS is not included in the 2025 base case because the scenarios modeled in PM NAAQS RIA 

did not reflect any NOx emissions reductions (US EPA, 2012)
3-2

3.1 Modeling Ozone Levels in the Future

A national scale air quality modeling analysis was performed to estimate ozone 

concentrations for the future year of 2025. Ozone sensitivity factors were developed using the 

modeled response of ozone to changes in NOx and VOC emissions from various sources and 

locations. The sensitivity factors were used to calculate ratios of changes in ozone to changes in 

emissions in order to determine the amount of emissions reductions needed to reach the baseline 

and evaluate potential alternative standard levels of 70, 65, and 60 ppb incremental to the 

baseline. The resulting emissions reductions were then used to estimate how health- and welfarerelated ozone concentration metrics would change under each scenario. The metrics were used as 

inputs to the calculation of expected costs and benefits associated with the precursor emissions 

and ozone concentration changes resulting from just attaining the alternative ozone standards. 

As described in section 3.2, air quality modeling was used in a relative sense to project 

future concentrations of ozone. As part of this approach, ozone predictions from the 2011 base 

year simulation are coupled with predictions from the 2025 modeling to calculate the relative 

change (between 2011 and 2025) in concentrations. These relative response factors (RRFs) were 

applied to the corresponding measured design values15 (DVs) to predict future DVs. Multiple 

emissions cases were modeled for 2025 including a 2025 base case and twelve 2025 emissions 

sensitivity simulations. Details on the 2011-based air quality modeling platform, the 2025 base 

case and emissions sensitivity simulations, along with the methods and results for attaining these 

NAAQS levels are provided below. 

3.1.1 Selection of Future Analytic Year

The RIA evaluates, to the extent possible, the costs and benefits of attaining the proposed 

alternative ozone standards, incremental to attaining the existing 75 ppb ozone standard and 

implementing existing and expected regulations. We selected 2025 as the primary year of 

analysis because most areas of the U.S. will likely be required to meet a revised ozone standard 

by 2025. We assumed that potential nonattainment areas everywhere in the U.S., excluding 

 

15 The design value is the metric that is compared to the standard level to determine whether a monitor is violating 

the NAAQS. The ozone design value is described in more detail in section 3.2.
3-3

California, will be designated such that they are required to attain by 2025, and we developed our 

projected baselines for emissions, ozone, and populations for 2025. 

In estimating the incremental costs and benefits of potential alternative standards, we 

recognize that there are several areas that are not required to meet the existing ozone standard of 

75 ppb set in 2008 by the year 2025. The Clean Air Act allows areas with more significant air 

quality problems to take additional time to reach the existing standard. Several areas in 

California are not required to meet the existing standard by 2025 and may not be required to 

meet a revised standard until sometime between 2032 and 2037.

We projected emissions for and modeled a single future base case year (2025), however for 

California we adjusted the future baseline ozone concentrations to reflect the effects of mobile 

source emissions reductions that will occur in California between 2025 and 2030 as described in 

Section 3.3. While there is uncertainty about the precise timing of emissions reductions and

related costs for California, we assume costs occur through the end of 2037 and beginning of 

2038. In addition, as described in Chapter 5, we model benefits for California using projected 

population demographics for 2038. 

Because of the different timing for incurring costs and accruing benefits in California and 

for ease of discussion throughout the analyses, we refer to the different time periods for potential 

attainment as 2025 and post-2025, to reflect that (1) we did not project emissions and air quality 

for any year other than 2025; (2) costs in California are assumed to be incurred starting in 2032 

and later; and (3) benefits from attainment of alternative standards in California are modeled 

using population demographics in 2038.

3.1.2 Air Quality Modeling Platform

The 2011-based air quality modeling platform was used to provide emissions, 

meteorology and other inputs to the 2011 and 2025 air quality model simulations. This platform 

was chosen because it represents the most recent, complete set of base year emissions 

information currently available for national-scale modeling. 

We use the Comprehensive Air Quality Model with Extensions (CAMx version 6.1) for 

photochemical model simulations performed for the RIA. CAMx is a three-dimensional grid-
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based Eulerian air quality model designed to estimate the formation and fate of oxidant 

precursors, primary and secondary particulate matter concentrations, and deposition over 

regional and urban spatial scales (e.g., over the contiguous U.S.) (Environ, 2014). Consideration 

of the different processes (e.g., transport and deposition) that affect primary (directly emitted) 

and secondary (formed by atmospheric processes) pollutants at the regional scale in different 

locations is fundamental to understanding and assessing the effects of emissions control 

measures that affect air quality concentrations. Because it accounts for spatial and temporal 

variations as well as differences in the reactivity of emissions, CAMx is useful for evaluating the 

impacts of the control strategies on ozone concentrations. CAMx is applied with the carbon-bond 

6 revision 2 (CB6r2) gas-phase chemistry mechanism (Ruiz and Yarwood, 2013).

Figure 3-1. Map of the CAMx Modeling Domain Used for Ozone NAAQS RIA
3-5

Figure 3-1 shows the geographic extent of the modeling domain that was used for air 

quality modeling in this analysis. The domain covers the 48 contiguous states along with the 

southern portions of Canada and the northern portions of Mexico. This modeling domain 

contains 25 vertical layers with a top at about 17,600 meters, or 50 millibars (mb), and horizontal 

resolution of 12 km x 12 km. The model simulations produce hourly air quality concentrations 

for each 12 km grid cell across the modeling domain.

CAMx requires a variety of input files that contain information pertaining to the 

modeling domain and simulation period. These include gridded, hourly emissions estimates and 

meteorological data, and initial and boundary conditions. Separate emissions inventories were

prepared for the 2011 base year, the 2025 base case, and the 2025 emissions sensitivity 

simulations. All other inputs (i.e. meteorological fields, initial conditions, and boundary 

conditions) were specified for the 2011 base year model application and remained unchanged for 

each future-year modeling simulation. The assumption of constant meteorology and boundary 

conditions was applied for two reasons: 1) this allows us to isolate the impacts of U.S. emissions 

changes, and 2) there is considerable uncertainty in the direction and magnitude in any changes 

in these parameters. EPA recognizes that changes in climate and international emissions may 

impact these model inputs. Specifically, climate change may lead to temperature increases, 

higher stagnation frequency, and increased wildfire activity, all of which could lead to higher 

ozone concentrations. In the western U.S. over the last 15 years, increasing wildfires have 

already been observed (Dennison et al., 2014). Potential future elevated ozone concentrations 

could, in turn, necessitate more stringent emissions reductions. However, there are significant 

uncertainties regarding the precise location and timing of climate change impacts on ambient air 

quality. Generally, climate projections are most robust for periods at least several decades in the 

future because the forcing mechanisms that drive near-term natural variability in climate patterns 

(e.g., El Nino, North American Oscillation) have substantially larger signals over short time 

spans than the driving forces related to long-term climate change. Boundary conditions, which 

are impacted by international emissions and may also influence future ozone concentrations, are 

held constant in this analysis based on a similar rationale regarding the significant uncertainty in 

estimating future levels. 
3-6

CAMx requires detailed emissions inventories containing temporally allocated (i.e., 

hourly) emissions for each grid-cell in the modeling domain for a large number of chemical 

species that act as primary pollutants and precursors to secondary pollutants. The annual 

emission inventories, described in Section 3.1.3, were preprocessed into CAMx-ready inputs 

using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (Houyoux et al., 

2000). 

Meteorological inputs reflecting 2011 conditions across the contiguous U.S. were derived 

from Version 3.4 of the Weather Research Forecasting Model (WRF) (Skamarock, 2008). These 

inputs included hourly-varying horizontal wind components (i.e., speed and direction), 

temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical 

layer. Details of the annual 2011 meteorological model simulation and evaluation are provided in 

a separate technical support document (US EPA, 2014a).

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, GEOS-Chem (Yantosca, 2004) standard 

version 8-03-02 with 8-02-01 chemistry. 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-5; additional information available at: 

http://gmao.gsfc.nasa.gov/GEOS/ and http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS5). This model was run for 2011 with a grid resolution of 2.0 degrees x 2.5 degrees (latitudelongitude). The predictions were used to provide one-way dynamic boundary conditions at onehour intervals and an initial concentration field for the CAMx simulations. A model evaluation 

was conducted to validate the appropriateness of this version and model configuration of GEOSChem for predicting selected measurements relevant to their use as boundary conditions for 

CAMx. This evaluation included using satellite retrievals paired with GEOS-Chem grid cell 

concentrations (Henderson, 2014). More information is available about the GEOS-Chem model 

and other applications using this tool at: http://www-as.harvard.edu/chemistry/trop/geos.

An operational model performance evaluation for ozone was performed to estimate the 

ability of the CAMx modeling system to replicate 2011 measured concentrations. This 

evaluation focused on statistical assessments of model predictions versus observations paired in 
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time and space depending on the sampling period of measured data. Details on the evaluation 

methodology and the calculation of performance statistics are provided in Appendix 3A. Overall, 

the model performance statistics for ozone from the CAMx 2011 simulation are within or close 

to the ranges found in other recent peer-reviewed applications (Simon et al, 2012). These model 

performance results give us confidence that our application of CAMx using this 2011 modeling 

platform provides a scientifically credible approach for assessing ozone concentrations for the 

purposes of the RIA.

3.1.3 Emissions Inventories

The 2011 base year and 2025 base case emissions inventories are described in the 

Technical Support Document: Preparation of Emissions Inventories for the Version 6.1, 2011 

Emissions Modeling Platform (US EPA, 2014b). Section 4 of the technical support document 

(TSD) summarizes the control and growth assumptions by source type that were used to create 

the U.S. 2025 base case emissions inventory, and includes a table of such assumptions for each 

major source sector. Below we summarize the characteristics of the 2025 base case emissions 

for each major source category. 

The 2025 electric generating unit (EGU) projected inventory represents demand growth, 

fuel resource availability, generating technology cost and performance, and other economic 

factors affecting power sector behavior. The EGU emissions were developed using the Integrated 

Planning Model (IPM) version 5.13 

(http://www.epa.gov/powersectormodeling/BaseCasev513.html). IPM is a multiregional, 

dynamic, deterministic linear programming model of the U.S. electric power sector. IPM reflects 

the expected 2025 emissions accounting for the effects of environmental rules and regulations, 

consent decrees and settlements, plant closures, units built, control devices installed, and forecast 

unit construction through the calendar year 2025. In this analysis, the projected EGU emissions 

include impacts from the Final Mercury and Air Toxics Standard (MATS) announced on 

December 21, 2011 and the Clean Air Interstate Rule (CAIR) issued March 10, 200516. 

 

16 A sensitivity case described in Section 3.1.4 also included a representation of EPA’s proposed carbon pollution 

guidelines under section 111(d) of the Clean Air Act (CAA)
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Projections for most stationary emission sources other than EGUs (i.e., non-EGUs) were 

developed by using the EPA Control Strategy Tool (CoST) to create future year inventories. 

CoST is described at http://www.epa.gov/ttnecas1/cost.htm. The 2025 base case non-EGU 

stationary source emissions inventory includes all enforceable national rules and programs 

including the Reciprocating Internal Combustion Engines (RICE) and cement manufacturing 

National Emissions Standards for Hazardous Air Pollutants (NESHAPs) and Boiler Maximum 

Achievable Control Technology (MACT) reconsideration reductions. Projection factors and 

percent reductions for non-EGU point sources reflect comments previously received by EPA, 

along with emissions reductions due to national and local rules, control programs, plant closures, 

consent decrees and settlements. Projection approaches for corn ethanol and biodiesel plants, 

refineries and upstream impacts represent the Energy Independence and Security Act (EISA) 

renewable fuel standards mandate in the Renewable Fuel Standards Program (RFS2). Airportspecific terminal area forecast (TAF) data were used for aircraft to account for projected changes 

in landing/takeoff activity.

Regional projection factors for point and nonpoint oil and gas emissions were developed 

by product type using Annual Energy Outlook (AEO) 2013 projections to year 2025 

(http://www.eia.gov/forecasts/aeo/). Stationary engine criteria air pollutant (CAP) co-benefit 

reductions (i.e., from the RICE NESHAP) and New Source Performance Standards (NSPS) VOC 

controls are reflected for oil and gas sources. 

Projection factors for livestock are based on expected changes in animal population from 

2005 Department of Agriculture data, updated according to EPA experts in July 2012; fertilizer 

application NH3 emissions projections include upstream impacts representing EISA. Area 

fugitive dust projection factors for categories related to livestock estimates are based on expected 

changes in animal population and upstream impacts from EISA. Residential Wood Combustion 

(RWC) projection factors reflect assumed growth of wood burning appliances based on sales 

data, equipment replacement rates and change outs. These changes include growth in loweremitting stoves and a reduction in higher emitting stoves. Projection factors for the remaining 

nonpoint sources such as stationary source fuel combustion, industrial processes, solvent 

utilization, and waste disposal, implement comments received on the projection of these sources 

as a result of recent rulemakings and outreach to states on emission inventories, and they also 
3-9

include emission reductions due to control programs. Portable fuel container (PFC) projection 

factors reflect the impact of the final Mobile Source Air Toxics (MSAT2) rule. Upstream 

impacts from EISA, including post-2011 cellulosic ethanol plants are also reflected.

For onroad, nonroad, and commercial marine vessel mobile sources, all national 

measures for which data were available at the time of modeling have been included. The Tier 3 

standards finalized in March, 2014 (see http://www.epa.gov/otaq/tier3.htm) are represented in 

the onroad and nonroad emissions. The 2011 and 2025 onroad mobile source emissions were 

developed using emissions factors derived from the Tier 3 FRM version of the MOtor Vehicle 

Emission Simulator (MOVES; http://www.epa.gov/otaq/models/moves/). The emissions factors 

for year 2025 were developed using the same meteorology and procedures used to produce the 

2011 emission factors. The onroad mobile source emissions were computed by using SMOKE 

to combine the county-, vehicle type-, and temperature-specific emission factors with vehicle 

miles traveled and vehicle population activity data, while taking into account hourly gridded 

temperature data. 

The MOVES-based 2025 onroad emissions account for changes in activity data and the 

impact of on-the-books national rules including: the Tier 3 Vehicle Emission and Fuel Standards 

Program, the Light-Duty Vehicle Tier 2 Rule, the Heavy Duty Diesel Rule, the Mobile Source 

Air Toxics Rule, the Renewable Fuel Standard (RFS2), the Light Duty Green House 

Gas/Corporate Average Fuel Efficiency (CAFÉ) standards for 2012-2016, the Heavy-Duty 

Vehicle Greenhouse Gas Rule, the 2017 and the Later Model Year Light-Duty Vehicle 

Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; Final Rule (LD 

GHG). The MOVES-based 2025 emissions also include state rules related to the adoption of 

LEV standards, inspection and maintenance programs, Stage II refueling controls, and local fuel 

restrictions. For California, the base case emissions included most of this state’s on-the-books 

regulations, such as those for idling of heavy-duty vehicles, chip reflash, public fleets, track 

trucks, drayage trucks, and heavy duty trucks and buses. The California emissions do not reflect 

the impacts of the GHG/Smartway regulation, nor do they reflect state GHG regulations for the 

projection of other emission sectors because that information was not included in the provided 

inventories. 
3-10

The nonroad mobile 2025 emissions, including railroads and commercial marine vessel 

emissions also include all national control programs. These control programs include the 

Locomotive-Marine Engine rule, the Nonroad Spark Ignition rule and the Class 3 commercial 

marine vessel “ECA-IMO” program. For California, the 2025 emissions for these categories 

reflect the state’s Off-Road Construction Rule for “In-Use Diesel”, cargo handling equipment 

rules in place as of 2011 (see http://www.arb.ca.gov/ports/cargo/cargo.htm), and state rules 

through 2011 related to Transportation Refrigeration Units, the Spark-Ignition Marine Engine 

and Boat Regulations adopted on July 24, 2008 for pleasure craft, and the 2007 and 2010 

regulations to reduce emissions from commercial harbor craft. For ocean-going vessels, the 

emissions data reflect the 2005 voluntary Vessel Speed Reduction (VSR) within 20 nautical 

miles, the 2007 and 2008 auxiliary engine rules, the 40 nautical mile VSR program, the 2009 

Low Sulfur Fuel regulation, the 2009-2018 cold ironing regulation, the use of 1% sulfur fuel in 

the Emissions Control Area (ECA) zone, the 2012-2015 Tier 2 NOx controls, the 2016 0.1% 

sulfur fuel regulation in ECA zone, and the 2016 International Marine Organization (IMO) Tier 

3 NOx controls. Control and growth-related assumptions for 2025 came from the Emissions 

Modeling Platform and are described in more detail in EPA (2014b). Non-U.S. and U.S. 

category 3 commercial marine emissions were projected to 2025 using consistent methods that 

incorporated controls based on ECA and IMO global NOx and SO2 controls.

All modeled 2011 and 2025 emissions cases use the 2006 Canada emissions data. Note 

that 2006 is the latest year for which Canada had provided data at the time the modeling was 

performed, and no accompanying future-year projected base case inventories were provided in a 

form suitable for this analysis. For Mexico, 2012 and 2018 projections of the 1999 Mexico 

National Emissions Inventory were used as described in the Development of Mexico National 

Emissions Inventory Projections for 2008, 2012, and 2030 (ERG, 2009) and the associated 

technical memorandum titled Mexico 2018 Emissions Projections for Point, Area, On-Road 

Motor Vehicle and Nonroad Mobile Sources (ERG, 2009). Mexico emissions were held at 2018 

levels because no 2025 projected emissions were available. Offshore oil platform emissions for 

the United States represent the year 2008 because 2011 emissions were not available as of the 

time of the modeling. Biogenic and fire emissions were held constant for all emissions cases and 

were based on 2011-specific data. Table 3-1 shows the modeled 2011 and 2025 NOX and VOC 
3-11

emissions by sector. Additional details on the emissions by state are given in the Emissions 

Modeling TSD.

Table 3-1. 2011 and 2025 Base Case NOx and VOC Emissions by Sector (thousand tons)

Sector 2011 NOx 2025 NOx 2011 VOC 2025 VOC

EGU-point 1,948 1,508 33 42

NonEGU-point 1,768 1,803 872 881

Point oil and gas 17 22 88 107

Wild and Prescribed Fires 347 347 5,175 5,175

Nonpoint oil and gas 653 874 2,273 2,551

Residential wood 

combustion 36 42 447 489

Other nonpoint 832 856 3,793 3,605

Nonroad 1,630 796 2,025 1,188

Onroad 5,592 1,492 2,738 1,060

C3 Commercial marine 

vessel (CMV) 125 105 5 8

Locomotive and C1/C2 

CMV 1,046 666 48 24

Biogenics 1,018 1,018 40,696 40,696

TOTAL 15,012 9,530 58,192 55,826

3.1.4 Emissions Sensitivity Simulations 

A total of 12 emissions sensitivity runs were conducted to determine ozone response to 

emissions reductions of NOx and VOC in different locations (Table 3-2). We determined that 

this was an efficient and flexible approach that allowed us to evaluate impacts from multiple 

source regions and levels of emission reductions simultaneously. All emissions sensitivity 

simulations were incremental to the 2025 base case emissions described in section 3.1.3. There 

were three types of emissions cases that were modeled in these sensitivity runs:

1) Explicit emissions control cases

2) Across-the-board reductions in anthropogenic emissions for different pollutants and 

locations

3) Combination runs that included both explicit emissions controls and across the board 

reductions.

Explicit Emissions Controls: Four explicit emissions control sensitivity cases were created. 

First, we modeled a case that represented one possible implementation of the EPA’s proposed 

carbon pollution guidelines under section 111(d) of the Clean Air Act (CAA) (i.e., option 1 state;
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hereafter referred to as the 111(d) sensitivity). Emissions for this simulation are described in the 

regulatory impact analysis for that proposed rule (EPA, 2014c). Second, we modeled three 

additional emissions cases that included NOx emissions controls applied to specific sources 

centered around the three regions of the country projected to have nonattainment monitors above 

70 ppb in the 2025 base case: California, Texas, and the Northeastern U.S. Figure 3-2 shows the 

three areas for which the explicit emissions controls were identified and modeled. CoST was 

used to determine potential controls in these areas. NOx controls were identified for all nonpoint, 

non-EGU point, and nonroad sources that emitted more than 50 tons of NOx per year and which 

had available known controls that could be applied for less than $15,000/ton (see chapter 7 for 

additional discussion). These emissions cases are referred to as “explicit control cases” because 

they represent the impact of specific controls rather than sensitivities to all emissions within a 

region. The extent of the area was determined by creating 200 km buffers around all monitors 

projected above 70 ppb in 2025. All counties that fell completely within the buffer were targeted 

for controls. In Texas and California these buffers were restricted to state boundaries. In the 

Northeast, buffers were restricted to states/counties that are currently under the jurisdiction of the 

Ozone Transport Commission (OTC). More details on the specific emissions controls identified 

for these emissions cases are provided in Chapter 4.
3-13

Figure 3-2. Map of Counties for Which Explicit Emissions Controls Were Identified and 

Modeled in CAMx (shaded in orange) and Counties that Contained One or 

More Monitor Projected above 70 ppb in the 2025 Base Case Modeling (shaded 

in blue).17 

Across-the-board Emissions Reductions: Areas of the U.S. projected to contain monitors 

with ozone design values greater than 60 ppb were split into 5 regions for the purpose of 

determining ozone response to emissions reductions (Figure 3-3). Three emissions sensitivity 

cases with across-the-board cuts in emissions from the 2025 base case were created and 

modeled: 

1) 50% cut in all anthropogenic NOx in the Southwest region, 

2) 50% cut in all anthropogenic NOx in the Midwest region, and 

 

17 Note that no buffer was created for the Sheboygan, WI area because emissions reductions from the proposed 

carbon pollution guidelines under section 111(d) of the CAA are projected to be sufficient to bring that location 

down to 70 ppb in 2025.
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3) 50% cut in all U.S. anthropogenic VOC emissions across the 48 contiguous states.

Combination Emissions Sensitivities: Five additional emissions sensitivity cases were 

created and modeled that combined the explicit emissions controls with across-the-board 

reductions. For all combination emissions sensitivity cases, the area over which emissions 

reductions were applied in the explicit emissions control runs was a subset of the full area for 

which across-the-board NOx reductions were applied. These runs included two cases for 

California: explicit emissions controls in California + additional 50% cut in all California 

anthropogenic NOx emissions and explicit emissions controls in California + additional 90% cut 

in all California anthropogenic NOx emissions. Two more emission sensitivities were 

investigated for the Northeast region: explicit emissions controls in the Northeast + additional 

50% cut in all Northeast region anthropogenic NOx emissions and explicit emissions controls in 

the Northeast + additional 90% cut in all Northeast region anthropogenic NOx emissions. We 

identified California and the Northeast as the two regions most likely to need NOx reductions 

beyond 50% to reach one or more of the alternative standard levels considered based on a 

previous EPA analysis (EPA, 2014d). Therefore both a 50% and a 90% NOx cut were performed 

for each of these regions to better capture nonlinearities in ozone response to large NOx 

emissions changes. Finally, a single emissions sensitivity was created for the Central region: 

explicit emissions controls in Texas + additional 50% cut in all Central region anthropogenic 

NOx emissions. A summary of Anthropogenic NOx and VOC emissions that were considered for 

controls in this analysis is given in Table 3-3 by region from the 2025 base case and explicit 

emissions control cases. In other words, the emissions summarized in Table 3-3 only include 

sectors for which emissions reductions were considered to meet various levels of the ozone 

standard. Conversely, Table 3-1 summarizes all U.S. emissions that were included in the 

modeling simulation including sources which contribute to background ozone.
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Figure 3-3. Five U.S. Regions Used to Create Across-the-Board Emissions Reduction and 

Combination Cases

Table 3-2. List of Emissions Sensitivity Cases that Were Modeled in CAMx to Determine 

Ozone Response Factors

Emissions 

Sensitivity 

Case

Region Pollutant Emissions Change

1 National All 111(d) option 1 state

2 National VOC 50% VOC cut

3 California NOx CA explicit emissions control case

4 California NOx CA explicit emissions control case + 50% NOx cut

5 California NOx CA explicit emissions control case + 90% NOx cut

6 Southwest NOx 50% NOx cut

7 Texas NOx TX explicit emissions control case

8 Central NOx TX explicit emissions control case + 

50% NOx cut (central)

9 Midwest NOx 50% NOx cut

10 Northeast NOx Northeast explicit emissions control case

11 Northeast NOx Northeast explicit emissions control case + 

50% NOx cut

12 Northeast NOx Northeast explicit emissions control case + 

90% NOx cut
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Table 3-3. Anthropogenic NOx and VOC Emissions from the 2025 Base and Explicit 

Control Cases*

Region

NOx emissions in 

2025 base case 

(thousand tons)

NOx emissions in 2025 

explicit control cases 

(thousand tons)

VOC emissions in 2025 

base case

(thousand tons)

Northeast 1,185 1,074 1,345

Midwest 1,771 ---- 1,803

Central 2,176 2,073 3,066

Southwest 713 ---- 1,016

California 446 416 478

Other states 1,840 ---- 2,264

Total contiguous US 8,130 ---- 9,971

*Note that unlike Table 3-1, these numbers do not include tribal, biogenic or fire emissions.

3.2 Methods for Calculating Current and Future Year Ozone Design Values

3.2.1 Current Year Ozone Design Value Calculations 

As described in chapter 2, hourly ozone concentrations are used to calculate a statistic 

referred to as a “design value” (DV) which is then compared to the standard level to determine 

whether a monitor is above or below the NAAQS level in question. For ozone, the DV is 

calculated as the 3-year average of the annual 4th highest daily maximum 8-hour ozone 

concentration in parts per billion (ppb), with decimal digits truncated. For the purpose of this 

analysis, the data handling and data completeness criteria used are those being proposed for the 

new NAAQS in the proposed appendix U to 40 CFR Part 50 – Interpretation of the Primary and 

Secondary National Ambient Air Quality Standards for ozone. A standard level of 60 ppb was 

used when determining data completeness criteria as this results in the most inclusive set of 

monitoring site DVs for analysis. For the purpose of this analysis, ozone DVs came from data 

reported in EPA’s air quality system (AQS) for the years 2009-2013. The current-year DVs 

were calculated as the average of 3 consecutive DVs (2009-2011, 2010-2012, and 2011-2013) 

which creates a 5-year weighted average DV. The 5-year weighted average DV is used as the 

base from which to project a future year DV as is recommended by the EPA in its SIP modeling 

guidance (US EPA, 2014e) because it stabilizes year-to-year meteorologically driven variability 

in ozone DVs given that the future year meteorology is unknown. For cases in which there are 

fewer than five years of valid monitoring data at a site, the current year DV was calculated only 

when there was at least three years of consecutive valid data (i.e., at least one complete DV). If a 

monitor had less than three consecutive years of data, then no current year DV was calculated for 

that site and the monitor was not used in this analysis. 
3-17

3.2.2 Future Year Ozone Design Value Projections

Future year ozone design values were calculated at monitor locations using the Model 

Attainment Test Software (MATS) program (Abt Associates, 2014). MATS calculates the 5-

year weighted average DV based on observed data and projects future year values using the 

relative response predicted by the model as described below. Equation (3-1) describes the 

recommended model attainment test in its simplest form, as applied for monitoring site i:

(DVF)i = (RRF)i × (DVB)i Equation 3-1

DVFiis the estimated design value for the future year in which attainment is required at 

monitoring site i; RRFiis the relative response factor at monitoring site i; and DVBiis the base 

design value monitored at site i. The relative response factor for each monitoring site (RRF)iis 

the fractional change of the DV in the vicinity of the monitor that is simulated on high ozone 

days due to emissions changes between the base and future years. The recently released draft 

version of EPA’s ozone and PM2.5 photochemical modeling guidance (US EPA, 2014e) includes 

updates to the recommended ozone attainment test used to calculate future year design values for 

attainment demonstrations. The guidance recommends calculating RRFs based on the highest 10 

modeled ozone days in the ozone season near each monitor location. Given the similar goal of 

this analysis relative to an attainment demonstration, we are using the recommended modeling 

guidance attainment test approach for the analyses. Specifically, the RRF was only calculated

based on the 10 highest days in the base year modeling at the monitor location when the base 8-

hr daily maximum ozone values were greater than or equal to 60 ppb for that day. In cases for 

which the base model simulation did not have 10 days with ozone values greater than or equal to 

60 ppb at a site, we used all days where ozone >= 60 ppb, as long as there are at least 5 days that 

meet that criteria. At monitor locations with less than 5 days with ozone >= 60 ppb, no RRF or

DVF was calculated for the site and the monitor in question was not included in this analysis. 

In determining the ozone RRF we considered model response in grid cells immediately 

surrounding the monitoring site along with the grid cell in which the monitor is located, as is 

currently recommended by the EPA in its SIP modeling guidance (US EPA, 2014e). The RRF 

was based on a 3 x 3 array of 12 km grid cells centered on the location of the grid cell containing 
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the monitor. The grid cell with the highest base ozone value in the 3 x 3 array was used for both 

the base and future components of the RRF calculation. 

3.3 Determining Tons of Emissions Reductions to Meet Various NAAQS Levels 

The following section describes how projected ozone DVs from the 2025 base case and 

12 emissions sensitivity cases were used to determine the expected emissions reductions needed 

to attain the current and potential alternative ozone NAAQS. The scenario for which all U.S. 

ozone monitors are projected to meet the current ozone NAAQS of 75 ppb is referred to as the 

“2025 baseline” scenario. The costs and benefits for meeting 70, 65, and 60 ppb standards will 

be determined incrementally from this baseline. Note that the 2025 baseline is different from the 

2025 base case, which is the emissions scenario described in section 3.1.3 and represents the 

ozone concentrations that are projected to occur in 2025 if there were no distinct reductions 

made for the purpose of meeting the current or alternative ozone NAAQS.

3.3.1 Determining Ozone Response from Each Emissions Sensitivity

Section 3.2.2 describes, in general terms, how the 2025 projections for ozone DVs were 

computed. This procedure was followed for the 2025 base case modeling and for each of the 12 

emissions sensitivity cases. Using the projected DVs and corresponding emissions changes, a 

unique ppb per ton response factor was calculated for each ozone monitor and for each emissions 

sensitivity case based on equation 3-2:

Ri,j =

DVi,j−DV2025base,j

∆Ei

Equation 3-2

In equation 3-2, Ri,j represents the response at monitor j to emissions changes in emissions 

sensitivity case i, DVi,j represents the DV at monitor j in emissions sensitivity i, DV2025base,j

represents the DV at monitor j in the 2025 base case and ΔEi represents the difference in NOx or 

VOC emissions (tons) between the 2025 base case and emissions sensitivity case i. In cases for 

which emissions reductions in sensitivity i, were incremental to emissions reductions in another 

case (k), the following equation was used:

Ri,j =

DVi,j−DVk,j

∆Eik

Equation 3-3
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in which ΔEik represents the difference in NOx or VOC emissions (tons) between the emissions 

case k and emission case i. Thus at each monitoring site in regions with multiple emissions 

sensitivity cases, we determined a set of incremental DV responses per ton of emissions 

reductions. The modeled impacts from the individual cases were then combined in a linear 

manner to estimate the net impacts from multiple cases. For example, in the Northeast, we would 

use the following equation to determine the DVs that would result from a 75% reduction in 

Northeast emissions beyond the explicit emissions control case:

DV75%NE,j = DV2025,j + (RNE_explicitcontrol,j × ∆ENE_explicitcontrol) + (RNE50NOx,j ×

∆E50NOx) + (RNE90NOX,j × (

25

40) ∆E90NOX) Equation 3-4

In equation 3-4, ∆ENE_explicitcontrol represents the difference in NOx emissions between 

the 2025 base case and the 2025 Northeast explicit emissions control case, ∆E50NOx represents 

the difference in NOx emissions between the 2025 Northeast explicit emissions control case and 

the combined Northeast explicit control case with 50% Northeast NOx cuts and ∆E90NOX

represents the difference in NOx emissions between the combined Northeast explicit control case 

with 50% Northeast NOx cuts and the combined Northeast explicit control case with 90% 

Northeast NOx cuts. The 25

40

multiplier represents the ratio of required to modeled emissions (i.e., 

the difference between the 50% and 90% NOx cut emissions sensitivities represent emissions 

equivalent to 40% of the Northeast explicit emissions control case, while in the example above, 

we only require an addition 25% emission reduction beyond the 50% NOx cut simulation). 

In two cases, we determined it was appropriate to compute response factors for smaller 

geographic areas than were modeled in the emissions sensitivity simulations described in section 

3.1.4. One of the cases pertains to splitting the responses between different air basins in 

California and the other case involves the geographic scale of ozone impacts associated with 

emissions reductions of VOC. Both of these case are described below.

In California, 2025 base case ozone DVs were substantially higher in the South Coast Air 

Basin located in the southern portion of the state than in the San Joaquin Valley and in areas 

further north. Additionally, the Transverse Mountain Ranges in Southern California generally 

isolate the air masses in the South Coast Air Basin from those in the San Joaquin Valley. 
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Consequently, it is unrealistic to force emissions reductions in locations in Northern California to 

bring Southern California ozone DVs into attainment with the current or alternative levels of the 

NAAQS. Therefore, when applying the results of the 50% and 90% California NOx emissions 

reduction sensitivities, we made a simplifying assumption that the ozone responses predicted in 

the San Joaquin Valley and areas of California further north are solely due to emissions changes 

in those areas. We made a similar assumption about the response of ozone to emissions changes 

for the southern portion of California. Using this approach we created distinct response factors 

based on changes in ozone DVs and emissions from each of the two California sub-regions. In 

general, this assumption seems reasonable based on the topography and wind patterns in these 

areas and the mountain ranges that separate Los Angeles from the San Joaquin Valley. This 

approach may lead to either some underestimation or overestimation of the air quality impacts of 

emissions reductions in the Central and Northern California locations depending on the extent to 

which emissions reductions in Southern California actually impact ozone in the San Joaquin 

Valley or vice versa. A more complete description of how these areas were delineated and the 

rational is provided in Appendix 3A.

We followed a conceptually similar approach for geographically allocating the response of 

ozone DVs to the 50% reduction in US anthropogenic VOC emissions. Past work has shown that 

impacts of anthropogenic VOC emissions on ozone DVs in the U.S. tend to be localized (Jin et 

al., 2008; Nopmongcol et al., 2014) and so consistent with past analyses (US EPA, 2008) we 

have made the assumption that VOC reductions do not impact ozone at distances more than 

100km from the emissions source. Consequently, we created a series of VOC impact regions for 

urban areas in which ozone is responsive to VOC emissions reductions. These VOC impact 

regions were only created for the urban areas with the highest projected 2025 base case ozone 

DVs in each region: New York City, Pittsburgh, and Baltimore in the Northeast; Detroit, 

Chicago, and Louisville in the Midwest; Houston and Dallas in the Central region; Denver in the 

Southwest; and Northern and Southern California. VOC impact regions were delineated by 

creating a 100km buffer around counties containing violating monitors. Since the counties with 
3-21

violating monitors differed at each standard level, a separate set of VOC impact regions was 

developed for standard levels of 70, 65, and 60 ppb.18 

In addition, VOC impact regions were constrained by state boundaries except in cases 

where a current nonattainment area straddled multiple states (for instance New Jersey and 

Connecticut counties that are included in the New York City nonattainment area were also 

included in the New York City VOC impact region). The in-state constraint was also waived for 

the Chicago area since it is well established that emissions from Chicago and Milwaukee are 

often advected over Lake Michigan where they photochemically react and then impact locations 

in Wisconsin, Illinois, Indiana, and Michigan that border the lake (Dye et al., 1995). In cases

where a county fell within two adjacent overlapping buffer areas (i.e. Easton County which lies 

in both the greater Chicago and Detroit buffers) the county was assigned to the VOC impact area 

that is most likely to be upwind based on prevailing wind patterns (i.e. Detroit). Finally, for 

California, the VOC impact areas were delineated identically to the Northern and Southern NOx

sub-regions described above but were restricted to counties included in the explicit NOx control 

cases. For each monitoring site within a VOC impact area, an ozone DV response factor (Ri,j) 

was calculated using the VOC emissions reductions that occurred within that area based on the 

U.S. 50% VOC sensitivity simulation. Figure 3-4 shows the VOC impact areas that were 

developed for the 60 ppb standard. Maps for the 65 and 70 ppb VOC impact areas look very 

similar to the map in Figure 3-4, but in some cases they include fewer counties around the 

outside of the region.

 

18 The 70 ppb VOC impact areas were also used to construct the baseline (75 ppb) scenario.
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Figure 3-4. Map of VOC Impact Areas Applied in the Evaluation of a 60 ppb Alternative 

Standard Level

3.3.2 Combining Response from Multiple Sensitivity Runs To Construct Baseline And 

Alternative Standard Scenarios

Ozone DVs were calculated for the baseline scenario as well as the proposed range of 65-

70ppb and a more stringent alternative standard of 60ppb by applying response factors described 

in section 3.3.1 and the emissions reductions from multiple modeled sensitivity scenarios using 

Equation 3-5:. 

DVj = DV2025,j + (R1,j × ∆E1) + (R2,j × ∆E2) + (R3,j × ∆E3) + ⋯ Equation 3-5
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For the baseline as well as the three alternative standards analyzed, we determine the least 

amount of emissions reductions (tons) needed to bring the ozone DVs at all monitors down to the 

particular NAAQS level. Note that the emissions reductions were applied on a region-specific 

basis for most monitors. That is, given the construct of the analytic approach, we did not account 

for the co-benefits of inter-regional transport except for monitors that are located near the border 

to two regions. For instance, to determine the requisite tons of NOx and VOC reductions 

necessary to bring monitors in the Central region down to 65 ppb, only emissions in the central 

region were considered (i.e., emissions from the Texas explicit emissions control case, the 50% 

Central region NOx reductions sensitivity case, and the VOC emissions from the Houston and 

Dallas VOC impact areas). This constraint was applied with the assumption that most states 

would not take into account emissions reductions from upwind states when designing their State 

Implementation Plans but also acknowledging that there has been a history of some states 

cooperating with neighboring states in the same region to determine multi-state pollution 

reduction plans.19 This approach avoids the complexity of determining the order in which 

emissions reductions should be considered across the multiple regions modeled for this analysis. 

The result of this constraint is that the amount of emissions reductions estimated for attainment 

in this analysis may be larger than necessary because downwind states may benefit from upwind 

reductions that are not accounted for when determining the regional emissions reduction needed 

to attain. There were two cases where emissions reductions from two regions were applied to 

given monitors in a border area: monitors in the Illinois suburbs of St. Louis (Midwest Region) 

that are clearly affected by emissions in neighboring Missouri (Central Region), and monitors in 

Pittsburgh, PA and in Buffalo, NY (Northeast Region) that are substantially impacted by 

emissions in Ohio (Midwest Region). 

There are several assumptions inherent in this methodology. First, when applying 

responses from the across-the-board emissions reduction sensitivities we do not have any 

information about how ozone DVs respond differently to emissions from different locations 

within the region. Therefore, for the most part, we assume that every ton of NOx or VOC 

 

19 For instance the Ozone Transport Commission (OTC) is an organization made up of states in the eastern U.S. 

which is responsible for “developing and implementing regional solutions to the ground-level ozone problem in the 

Northeast and Mid-Atlantic regions” (www.otcair.org)
3-24

reduced within the region (or VOC impact area) results in the same ozone response regardless of 

where the emissions reductions are identified. In locations for which we have both explicit 

emissions control case reductions and regional across-the-board reductions, it is possible to make 

some more distinctions as described below, but we are still not able to fully account for variable 

response to emissions from different locations within the region. Where possible, we try to 

locate emissions reductions closer to the highest DV monitors with the understanding that 

emissions reductions are likely to have lower impact when they occur further from the monitor 

location. A second assumption is that NOx and VOC responses are additive. In the case of 

monitors impacted by emissions from multiple regions, we also assume that the responses from 

multiple regions are additive. Again, we do not have any more refined information that would 

allow us to account for nonlinear interactions of these emissions. Third, we assume that ozone 

response within each of these sensitivity simulations is linear (i.e., the first ton of NOx reduced 

results in the same ozone response as the last ton of NOx reduced). In cases for which we have 

multiple levels of emissions reductions (i.e., California, the Central region, and the Northeast) we 

assume linearity within each simulation but are able to capture discrete shifts in ozone response 

for each sensitivity simulation (i.e., one response for explicit emissions control case reductions, 

another response level up to 50% NOx reductions beyond the explicit emissions control case 

emissions, and a third level of response between 50% and 90% NOx reductions beyond the 

explicit emissions control case). For the Central states there are only two discrete response 

levels, while in California and the Northeast there are three. Finally, for regions without 90% 

NOx cut emissions sensitivity scenarios (Southwest, Central, and Midwest), response to NOx

reductions greater than 50% must be extrapolated beyond the modeled emissions reductions. 

3.3.3 Creation of the Baseline Scenario

Computing the response of DVs to emissions reductions from each emissions sensitivity 

simulation allowed us to determine what emissions reductions would be needed in each region to 

create the baseline scenario (i.e. to reach 75 ppb at every monitor location). We determine how 

those emissions reductions could be achieved by applying controls in the following order: (1) 

emissions changes from the 111(d) sensitivity, (2) known controls of NOx emissions from 

nonpoint, non-EGU point, and nonroad sources greater than 50 tons per year (explicit control 

cases), (3) mobile source emissions changes between 2025 and 2030 (California only), (4) 
3-25

known controls of VOC emissions, and (5) additional NOx controls. All reductions identified 

from these sources were above and beyond reductions from on-the-books regulations that were 

included in the 2025 base case modeling. The emissions changes from the 111(d) sensitivity 

were applied throughout the entire U.S. in creating the baseline scenario. Other emissions 

changes were only applied in the areas projected to have DVs greater than 75 ppb in the 2025 

base case scenario: California and Texas. In California, all five types of emissions reductions 

were needed to meet the current 75 ppb standard while in Texas only the 111(d) emissions 

changes and a portion of the explicit modeled controls were needed. The 2025 to 2030 mobile 

source changes were applied in California because, as discussed at the beginning of this chapter, 

many locations in California will likely have attainment dates substantially further out than 2025. 

Although emissions projections for those years were not generally available, the state of 

California did provide emissions projections for mobile sources in the year 2030. Emissions of 

both VOC and NOx were available for onroad, nonroad, locomotive, and C1/C2 commercial 

marine vessel sectors by county. There were both increases and decreases between 2025 and 

2030 depending on the county and sector, but overall these mobile source changes resulted in 

VOC emissions that were 1% less than those modeled in the California explicit emissions control 

case and NOx emissions that were 4% less than those modeled in the California explicit 

emissions control case in the Northern California sub-region and 3% less than those modeled in 

the Southern California sub-region. The NOx and VOC mobile source emissions changes were 

applied to create the baseline scenario in California using the response ratios developed from the 

50% California NOx cut and the 50% U.S. VOC cut sensitivity simulations. Summaries of the 

emissions reductions are presented by region in Appendix 3A. In addition, resulting ozone DVs 

at all evaluated monitors are provided in Appendix 3A.

3.3.4 Creation of the 70, 65, and 60 ppb Alternative Standard Level Scenarios

To create the scenarios for the three alternative standard levels (i.e. 70, 65, and 60 ppb), we 

started with the baseline and then identified additional controls for each region from the five 

categories listed in section 3.3.3. Not all types of emissions reductions were required in each 

region for each scenario. For regions that contained a NOx explicit emissions control case buffer, 

only the known controls within the buffer were applied before the known VOC controls. In 

those regions, after explicit emissions control case reductions and the VOC known controls were 
3-26

applied, then additional NOx controls were considered. In those regions, an additional constraint 

was also applied that forced the tons of additional NOx reductions applied within and outside the 

explicit emissions control case area to be applied proportionally to the starting NOx emissions 

within and outside the explicit emissions control case buffer areas (e.g., if 40% of the starting 

emissions in the explicit control scenario simulation were located within the buffer, then 40% of 

the emissions reductions also had to come from within the buffer). This constraint was applied 

because the monitors with the highest DVs were located within the buffers and the response 

factors were based on an average ppb/ton across the region. Thus, the constraint ensured some 

measure of spatial equivalence between the location of the modeled emissions reductions and 

those applied to construct the scenario. In some cases, this constraint also resulted in including 

unknown controls to create the scenario even when known controls were still available within the 

region but outside of the explicit control case buffer. In regions without an explicit emissions 

control case buffer area, all known controls of NOx emissions from nonpoint, non-EGU point, 

and nonroad sources greater than 50 tons per year were applied throughout the entire region 

before any VOC emissions reductions were applied. A numeric example of the calculation 

methodology is provided in Appendix 3A. Summaries of the emissions reductions are presented 

by region in Appendix 3A and by source category in Chapter 4. In addition, ozone DVs at all 

evaluated monitors are provided for each scenario in Appendix 3A.

3.3.5 Monitoring Sites Excluded from Quantitative Analysis

There were 1219 ozone monitors with complete ozone data for at least one DV period 

covering the years 2009-2013. Of those sites, we quantitatively analyzed 1150 (94%) in this 

analysis. In determining the necessary tons of emissions reductions for each of the four 

scenarios, there were three types of sites that were not treated quantitatively, i.e. emissions 

reductions necessary to reach the alternative standard levels at these sites were not quantified. 

First, tons of emissions reductions were not determined for 36 sites that did not have a valid 

projected 2025 base case DV due to less than 5 modeled days above 60 ppb in the 2011 CAMx 

simulation as required to project a DV in the EPA SIP modeling guidance (US EPA, 2014e). It 

is unlikely that these sites would have any substantial impact on resulting costs and benefits, 

since the reason that projections could not be made is that they have no more than 4 modeled 
3-27

days above 60 ppb, in which case they would likely already be meeting all standard levels 

evaluated in this analysis using the current year data. These sites are listed in appendix 3A.

Second, 7 sites for which the DVs were influenced by wintertime ozone episodes were 

not included because the modeling tools are not currently sufficient to properly characterize 

ozone formation during wintertime ozone episodes. It is not appropriate to apply the modelbased response (RRF) developed based on summertime conditions to a wintertime ozone event, 

which is driven by different types of chemistry and meteorology. Since there was no technically 

feasible method for projecting DVs at these sites, these sites were not included in determining 

required reductions in NOx and VOCs to meet current or alternative standard levels. Wintertime 

ozone events tend to be very localized phenomena driven by local emissions from oil and gas 

operations (Schnell et al, 2009; Rappengluck et al., 2014; Helmig et al., 2014). Consequently, 

the emissions reductions needed to lower wintertime ozone levels would likely be different from 

those targeted for summertime ozone events. It follows that there could be additional emissions 

reductions required to lower ozone at these locations and thus potential additional costs and 

benefits that are not quantified in this analysis. Appendix 3A includes a list of sites influenced 

by wintertime ozone and the methodology used to identify those sites. 

Finally, while the majority of the sites had projected ozone exceedances primarily caused 

by local and regional emissions, there were a set of 26 relatively remote, rural sites in the 

Western U.S. with projected 2025 base case DVs between 62 and 69 ppb20 that showed limited 

response to the regional NOx emission and national VOC emission sensitivities in our modeling. 

Air agencies responsible for these locations may choose to pursue one or more of the Clean Air 

Act provisions that offer varying degrees of regulatory relief. Regulatory relief may include:

 Relief from designation as a nonattainment area (through exclusion of data affected by 

exceptional events)

 Relief from the more stringent requirements of higher nonattainment area classifications 

(through treatment as a rural transport area; through exclusion of data affected by 

exceptional events; or through international transport provisions)

 

20 Except in California where sites had projected 2025 base case DVs up to 75 ppb. The California sites all had 

estimated ozone DVs below 70 ppb in the post-2025 baseline scenario.
3-28

 Relief from adopting more than reasonable controls to demonstrate attainment (through 

international transport provisions) 

In addition, some of these sites could potentially benefit from the CAA’s interstate transport 

provisions found in sections 110(a)(2)(D) and 126. Appendix 3A provides additional detail on 

the treatment of these sites.

3.4 Creating Spatial Surfaces

The emissions reductions for attainment of the alternative NAAQS levels were used to 

create spatial fields of ozone concentrations (i.e., spatial surfaces) for input to the calculation of 

the benefits associated with attainment of each NAAQS level, incremental to the baseline. The 

spatial surfaces used to calculate health-related benefits with the BenMap tool (Chapter 5) are 

described below in section 3.4.1. Spatial surfaces used to calculate welfare-related benefits with 

the FASOMGHG model (Chapter 6) are described below in section 3.4.2.

3.4.1 BenMap Surfaces

Two ozone metrics are used to evaluate health benefits associated with meeting different 

ozone standard levels. These metrics and the studies that they are derived from are described in 

more detail in Chapter 5. Briefly, ozone surfaces for the baseline and each alternative NAAQS 

level were created for the following metrics: May-Sep seasonal mean of 8-hr daily maximum 

ozone and Apr-Sep seasonal mean of 1-hr daily maximum ozone. For each metric, surfaces were 

created for a total of 11 scenarios. These scenarios include: 

 2025 baseline

 post-2025 baseline

 2025 70 ppb partial attainment

 2025 70 ppb full attainment

 post-2025 70 ppb full attainment

 2025 65 ppb partial attainment

 2025 65 ppb full attainment

 post-2025 65 ppb full attainment

 2025 60 ppb partial attainment

 2025 60 ppb full attainment
3-29

 post-2025 60 ppb full attainment

The surfaces created for the 2025 scenarios represent all continental U.S. monitors outside 

of California attaining the standard being evaluated while the surfaces for the post-2025 

scenarios represent all continental U.S. monitors including California meeting the standard being 

evaluated. The effects due only to California meeting the standard are isolated in Chapter 5 

through a series of BenMap simulations using these surfaces and varying assumptions about 

population demographics. In addition, for the 2025 scenarios we include “partial” and “full” 

attainment in which the partial attainment scenarios only include emissions reductions identified 

from known control measures while the full attainment scenarios include emissions reductions 

necessary to attain the standard from both known and unknown controls.

The ozone surfaces were created using the following steps. Each step is described in more 

detail below:

 Step 1: Aggregate gridded hourly modeled concentrations into relevant seasonal 

ozone metrics

o Inputs: Hourly gridded model concentrations for 2011, 2025 base case, and 

12 2025 emissions sensitivity simulations detailed in Section 3.1.4

o Outputs: Seasonal ozone metrics for 2011, 2025 base case, and 12 2025 

emissions sensitivity simulations

 Step 2: Calculate response factors for each seasonal ozone metric from each 

emissions sensitivity simulation

o Inputs: Seasonal ozone metrics for 2011, 2025 base case, and 12 2025 

emissions sensitivity simulations; Amount of emissions reductions (tons) 

modeled in each emissions case

o Outputs: Gridded ppb/ton response factor for each seasonal ozone metric 

from each emissions sensitivity simulation

 Step 3: Create gridded field for each attainment scenario and each seasonal ozone 

metric 

o Inputs: Gridded ppb/ton response factor for each seasonal ozone metric 

from each emissions sensitivity simulation; Amount of emissions reductions 

from each region described in Appendix 3A.

o Outputs: Gridded seasonal ozone metrics for each attainment scenario

 Step 4: Create 2011 enhanced Voronoi Neighbor Averaging (eVNA) fused surface 

of modeled and observed values for each seasonal ozone metric
3-30

o Inputs: 2010-2012 observed ozone values (seasonal ozone metrics at each 

monitor location); 2011 modeled ozone (seasonal ozone metrics at each grid 

cell)

o Outputs: 2011 fused modeled/monitored surfaces for each seasonal ozone 

metric

 Step 5: Create eVNA fused modeled/monitored surface for each attainment 

scenario and each seasonal ozone metric

o Inputs: 2011 fused model/obs surfaces for each seasonal ozone metric; 

modeled seasonal ozone metrics (gridded fields) for 2011 and each 

attainment scenario

o Outputs: Fused modeled/monitored surface for each attainment scenario and 

each seasonal ozone metric

Step 1:

Gridded hourly ozone modeled concentrations were aggregated to the relevant metric for 

the 2011, 2025 base case, and each of the 12 emissions sensitivity simulations. This step 

resulted in 15 ozone fields for each of the two metrics. 

Step 2:

A gridded ppb/ton response factor was determined for each metric and for each emissions 

sensitivity simulation. 

Step 3:

Based on the emissions reductions provided in appendix 3A, the response factors were 

multiplied by the relevant tons of emissions reductions for each sensitivity and then summed to 

create a gridded field representing the scenario in question (Equation 3-6)21

O3xy,s,m = O3xy,2025,m + (Rxy,1,m × ∆E1,s) + (Rxy,2,m × ∆E2,s) + (Rxy,2,m × ∆E3,s) + ⋯

Equation 3-6

 

21 An extra 3,500 tons of VOC reductions available in Northern California outside of the N California sub-region 

was mistakenly applied in creating all surfaces. This lead to absolute changes in gridded ozone concentrations of 

less than 0.01 ppb. Since this error was carried through all surfaces, the incremental changes in ozone between 

surfaces were not impacted.
3-31

In equation 3-6, ozonexy,s,m represents the ozone concentrations at grid cell x,y, for 

scenario s, and using metric, m. Similarly ozonexy,2025,m represents the modeled ozone from the 

2025 base simulation at grid cell x,y aggregated to metric m. Rxy,1,m represents the response 

factor (ppb/ton) in grid cell x,y using metric m, for the sensitivity simulation #1. Finally ΔE1,s 

represents the amount of emissions reductions from sources modeled in sensitivity #1 determined 

necessary for scenario s. Partial attainment surfaces at each standard level were created by first 

starting with the full attainment surface and then subtracting off impacts from emissions 

reductions that were identified from unknown controls. For the 70 ppb scenario in which all 

unknown controls were located in the explicit emissions control case buffer areas, the ppb/ton 

response ratios from the explicit emissions control case sensitivity simulations were applied to 

back out impacts from unknown controls. For the 65 and 60 ppb scenarios, unknown controls 

were located both within and outside explicit emissions control case buffer areas. We did not 

have any response ratios that represent emissions from outside the explicit emissions control case 

buffer areas alone. Therefore, for the 65 and 60 ppb scenarios, we applied the response ratios 

from the regional 50% NOx reduction sensitivity simulations. This leads to additional 

uncertainty in the 65 and 60 pp partial attainment surfaces since the relative proportions of 

unknown emissions reductions within and outside the buffer areas were different from the 

relative proportions of emissions reductions within and outside of the buffer areas that were 

applied in the 50% regional NOx reduction simulations.

Step 4:

The MATS tool was used to create a fused gridded 2011 field using both ambient and 

modeled data using the eVNA technique (Abt, 2014). This method essentially takes an 

interpolated field of observed data and adjusts it up or down based on the modeled spatial 

gradients. For this purpose, the 2010-2012 ambient data was interpolated and fused with the 

2011 model data. One “fused” eVNA surface was created for each of the two seasonal ozone 

metrics.

Step 5:
3-32

The 22 model-based surfaces (i.e., 11 scenarios and 2 metrics) were used as inputs in the 

MATS tool along with the gridded 2011 eVNA surfaces. For each metric and each scenario a 

gridded RRF field was created by dividing the gridded ozone field for scenario s by the gridded 

2011 model field. This RRF field was then multiplied by the 2011 eVNA field to create a 

gridded eVNA field for each scenario. 

Results of this process for the May-September 8-hr daily maximum ozone metric are

shown in Figures 3-5, 3-6, 3-7, 3-8. These figures show the post-2025 baseline and the changes 

in ozone between the post-2025 baseline and each of the post-2025 scenarios for lower standard 

levels: 70, 65, and 60 ppb. The post-2025 baseline represents the case where all continental US 

monitors meet the current 75 ppb standard and similarly the post-2025 alternative standard 

scenarios represent the case where all continental US monitors meet the standard level being 

evaluated. 

Figure 3-5. Projected post-2025 Baseline Scenario May-September Mean of 8-hr Daily 

Maximum Ozone (ppb)
3-33

Figure 3-6. Change in May-September Mean of 8-hr daily Maximum Ozone (ppb) 

between the post-2025 Baseline Scenario and the post-2025 70 ppb Scenario
3-34

Figure 3-7. Change in May-September Mean of 8-hr daily Maximum Ozone (ppb) 

between the post-2025 Baseline Scenario and the post-2025 65 ppb Scenario
3-35

Figure 3-8. Change in May-September Mean of 8-hr Daily Maximum Ozone (ppb) 

between the post-2025 Baseline Scenario and the post-2025 60 ppb Scenario

3.4.2 W126 surfaces

This section describes the creation of ozone surfaces aggregated using the W126 metric. 

The general methodology for calculating the W126 metric is provided in appendix 3A. Ozone 

surfaces aggregated to the W126 metric were created for eight scenarios: 

 2025 baseline 

 post-2025 baseline

 2025 70 ppb full attainment

 post-2025 70 ppb
3-36

 2025 65 ppb full attainment

 post-2025 65 ppb

 2025 60 ppb full attainment

 post-2025 60 ppb 

Several steps were followed to create these surfaces. First, as was done with the projected 

ozone DVs and the ozone surfaces for health benefits, ppb/ton response factors was determined 

for each sensitivity simulation. In this case, the response factors were created based on hourly 

ozone data in the 2025 base case and 12 emissions sensitivity simulations. Therefore, six months 

of gridded hourly response factors were created for each emission sensitivity simulation. Then, 

based on the emissions reductions described in Appendix 3A, the hourly response factors were 

multiplied by the relevant tons of emissions reductions from each emissions sensitivity and then 

summed to create a gridded field representing the scenario in question (Equation 3-6). For the 

W126 calculations, the metric in equation 3-6 is hourly ozone. At the end of this step, there were 

eight sets of hourly gridded ozone fields, one for each scenario. These gridded hourly ozone 

fields, along with the 2011 modeled hourly ozone field, were then aggregated into the W126 

metric, which is described in more detail in Chapter 2. The MATS tool was used to project 

W126 values at each monitor location. The set-up was similar to the approach for projecting 

DVs. In essence the 2011 and eight W126 scenarios gridded fields were used to create an RRF 

for each monitor location using the 3x3 matrix of grid cells surrounding the monitor location as 

described in section 3.2.2. These model-based RRFs were multiplied by the three year average 

(2010-2012) of the measured W126 at each monitor. At the end of this step, there were a set of 

W126 values at all monitor locations for each scenario. Finally, a gridded field of W126 values 

was created for each scenario by spatially interpreting the projected monitor values using an 

inverse distance weighted Voronoi Neighbor Averaging (VNA) technique (Gold, 1997; Chen et 

al, 2004). This is similar to how W126 gridded fields have been created for previous EPA 

analyses (EPA, 2014f). Figure 3-9 shows the W126 gridded field for the post-2025 baseline 

scenarios. Figures 3-10, 3-11, and 3-12 show the W126 gridded field of the post-2025 scenarios 

for 70, 65, and 60.
3-37

Figure 3-9. Projected post-2025 Baseline Scenario W126 Values (ppm-hrs)
3-38

Figure 3-10. Projected post-2025 70 ppb Scenario W126 Values (ppm-hrs)
3-39

Figure 3-11. Projected post-2025 65 ppb Scenario W126 Values (ppm-hrs)
3-40

Figure 3-12. Projected post-2025 60 ppb Scenario W126 Values (ppm-hrs)

3.5 References

Abt Associates, 2014. User’s Guide: Modeled Attainment Test Software. 

http://www.epa.gov/scram001/modelingapps_mats.htm

Chen, J., Zhao, R., Li, Z. (2004). Voronoi-based k-order neighbor relations for spatial analysis. ISPRS J 

Photogrammetry Remote Sensing, 59(1-2), 60-72.

Dennison, P.E., Brewer, S.C., Arnold, J.D., Moritz, M.A. (2014) Large wildfire trends in the western United States, 

1984-2011. Geophysical Research Letters, 41, 2928-2933.

Dye T.S., Roberts P.T., Korc, M.E. (1995) Observations of transport processes for ozone and ozone precursors 

during the 1991 Lake Michigan ozone study, J. Appl. Met., 34 (8), 1877–1889

ENVIRON, 2014. User's Guide Comprehensive Air Quality Model with Extensions version 6.1, www.camx.com. 

ENVIRON International Corporation, Novato, CA.

ERG, 2009. Development of Mexico National Emissions Inventory Projections for 2008, 2012, and 2030. Available 

at: http://www.azdeq.gov/environ/air/plan/download/dmneip.pdf.
3-41

Gold, C. (1997). Voronoi methods in GIS. In: Algorithmic Foundation of Geographic Information Systems (va 

Kereveld M., Nievergelt, J., Roos, T., Widmayer, P., eds). Lecture Notes in Computer Science, Vol 1340. Berlin: 

Springer-Verlag, 21-35.

Helmig, D., Thompson, C.R., Evans, J., Boylan, P., Hueber, J., Park, J.H. (2014). Highly elevated atmospheric 

levels of volatile organic compounds in the Uintah Basin, Utah, Environmental Science & Technology, 48, 4707-

4715.

Henderson, B.H., Akhtar, F., Pye, H.O.T., Napelenok, S.L., Hutzell, W.T. (2014). A database and tool for boundary 

conditions for regional air quality modeling: description and evaluations, Geoscientific Model Development, 7, 

339-360.

Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S. (2000), Emissions inventory 

development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project, Journal of 

Geophysical Research – Atmospheres, 105(D7), 9079-9090.

Jin, L., Tonse, S., Cohan, D. S., Mao, X. L., Harley, R. A., Brown, N. J. (2008) Sensitivity analysis of ozone 

formation and transport for a central California air pollution episode. Environ. Sci. Technol., 42, 3683–3689.

Nopmongcol, U., Emery, C., Sakulyanontvittaya, T., Jung, J., Knipping, E., Yarwood, G. (2014) A modeling 

analysis of alternative primary and secondary US ozone standards in urban and rural areas, Atmospheric 

Environment, Available online 28 September 2014, ISSN 1352-2310, 

http://dx.doi.org/10.1016/j.atmosenv.2014.09.062.

Rappengluck, B., Ackermann, L., Alvarez, S., Golovko, J., Buhr, M., Field, R.A., Soltis, J., Montague, D.C., Hauze, 

B., Adamson, S., Risch, D., Wilkerson, G., Bush, D., Stoeckenius, T., Keslar, C. (2014). Strong wintertime ozone 

events in the Upper Green River basin, Wyoming, Atmospheric Chemistry and Physics, 14, 4909-4934.

Ruiz, L.H., Yarwood, G., 2013. Interactions between Organic Aerosol and NOy: Influence on Oxidant Production. 

http://aqrp.ceer.utexas.edu/projectinfoFY12_13/12-012/12-012%20Final%20Report.pdf.

Schnell, R.C., Oltmans, S.J., Neely, R.R., Endres, M.S., Molenar, J.V., White, A.B. (2009) Rapid photochemical 

production of ozone at high concentrations in a rural site during winter, Nature Geoscience, 2, 120-122.

Simon, H., Baker, K.R., Phillips, S. (2012) Compilation and interpretation of photochemical model performance 

statistics published between 2006 and 2012, Atmospheric Environment, 61, 124-139.

U.S. Environmental Protection Agency (2008) Final ozone NAAQS regulatory impact analysis, US EPA, OAQPS, 

RTP, NC, EPA-452/R-08-003 

U.S. Environmental Protection Agency (2012) Regulatory Impact Analysis for the Final Revision to the National 

Ambient Air Quality Standards for Particulate Matter, US EPA, OAQPS, 2012, RTP, NC, EPA-452/R-12-005. 

http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf

U.S. Environmental Protection Agency (2014a) Meteorological Model Performance for Annual 2011 Simulation 

WRF v3.4, US EPA, OAQPS, 2014, RTP, NC http://www.epa.gov/scram001/

U.S. Environmental Protection Agency (2014b) Preparation of Emissions Inventories for the Version 6.1, 2011 

Emissions Modeling Platform (http://www.epa.gov/ttn/chief/emch)

U.S. Environmental Protection Agency (2014c) Regulatory Impact Analysis for the Proposed Carbon Pollution 

Guidelines for Existing Power Plants and Emission Standards for Modified and Reconstructed Power Plants, US 

EPA, OAQPS, 2014, RTP, NC, EPA-542/R-14-002 
3-42

U.S. Environmental Protection Agency (2014d) Health Risk and Exposure Assessment for Ozone, final report. US 

EPA, OAQPS, 2014, RTP, NC, EPA-452/R-14-004a

U.S. Environmental Protection Agency (2014e) Draft Modeling Guidance for demonstrating attainment of air 

quality goals for ozone, PM2.5, and regional haze. September 2014, U.S. Environmental Protection Agency, 

Research Triangle Park, NC, 27711.

U.S. Environmental Protection Agency (2014f) Welfare Risk and Exposure Assessment for Ozone, final report. US

EPA, OAQPS, 2014, RTP, NC, EPA-452/R-14-005a.
3A-1

APPENDIX 3: ADDITIONAL AIR QUALITY ANALYSIS AND RESULTS

3A.1 2011 Model Evaluation for Ozone

An operational model evaluation was conducted for the 2011 base year CAMx annual 

model simulation performed for the 12-km U.S. modeling domain.22 The purpose of this 

evaluation was to examine the ability of the Ozone NAAQS RIA air quality modeling platform 

to replicate the magnitude and spatial and temporal variability of measured (i.e., observed) ozone 

concentrations within the modeling domain. The model evaluation for ozone was based upon 

comparisons of model predicted 8-hour daily maximum concentrations to the corresponding 

observed data at monitoring sites in the EPA Air Quality System (AQS) and the Clean Air Status 

and Trends Network (CASTNet). Included in the evaluation are statistical measures of model 

performance based upon model-predicted versus observed concentrations that were paired in 

space and time on an hourly basis. 

Model performance statistics were calculated for several spatial scales and temporal 

periods. Statistics were calculated for individual monitoring sites and for each of five regions of 

the 12-km U.S. modeling domain. The regions include the Northeast, Midwest, Southeast, and 

Central and Western states which are defined based upon the states contained within the 

Regional Planning Organizations (RPOs)23. For maximum daily average 8-hour (MDA8) ozone, 

the statistics for each site and region were calculated for the May through September ozone 

season.24 In addition to the performance statistics, we prepared several graphical presentations of 

 

22 See Chapter 3, section 3.1.2 of the RIA document for a description of the 12-km U.S. modeling domain.

23 The subregions are defined by States where: Midwest is IL, IN, MI, OH, and WI; Northeast is CT, DE,

MA, MD, ME, NH, NJ, NY, PA, RI, and VT; Southeast is AL, FL, GA, KY, MS, NC, SC, TN, VA, and

WV; Central is AR, IA, KS, LA, MN, MO, NE, OK, and TX; West is AK, CA, OR, WA, AZ, NM, CO, UT, WY,

SD, ND, MT, ID, and NV.

24 In calculating the ozone season statistics we limited the data to those observed and predicted pairs with

observations that exceeded 60 ppb in order to focus on concentrations at the upper portion of the distribution of

values.
3A-2

model performance for MDA8 ozone which is the key pollutant for the Ozone NAAQS Rule. 

These graphical presentations include:

(1) regional maps which show the mean bias and error as well as normalized mean bias and 

error calculated for MDA8 ≥ 60 ppb for May through September at individual monitoring sites,

(2) bar and whisker plots which show the distribution of the predicted and observed data 

by month (May through September) and by region, and

(3) time series plots (May through September) of observed and predicted concentrations 

for 13 representative high ozone sites in the urban areas with the highest projected ozone levels 

in each region from the 2025 base case CAMx simulation. 

The Atmospheric Model Evaluation Tool (AMET) was used to calculate the model 

performance statistics used in this document (Gilliam et al., 2005). For this analysis and 

summary of the 2011 model evaluation for ozone, we have selected the mean bias, mean error, 

normalized mean bias, and normalized mean error to characterize model performance which are 

consistent with the recommendations in Simon et al. (2012) and the draft SIP modeling guidance 

(US EPA 2014). As noted above, we calculated the performance statistics by the May through 

September ozone season.

Mean bias (MB) is used as average of the difference (predicted – observed) divided by the 

total number of replicates (n). Mean bias is given in units of ppb and is defined as:

MB = 1

n

∑ (P − O)

n

1

, where P = predicted and O = observed concentrations. 

Mean error (ME) calculates the absolute value of the difference (predicted - observed) 

divided by the total number of replicates (n). Mean error is given in units of ppb and is defined 

as: 

ME = 1

n

∑ |P − O|

n

1

Normalized mean bias (NMB) is used as a normalization to facilitate a range of 

concentration magnitudes. This statistic averages the difference (predicted - observed) over the 
3A-3

sum of observed values. NMB is a useful model performance indicator because it avoids over 

inflating the observed range of values, especially at low concentrations. Normalized mean bias is 

given in units of % and is defined as:

NMB = ∑ (P−O)

n

1

∑ (O)

n

1

∗ 100

Normalized mean error (NME) is also similar to NMB, where the performance statistic is 

used as a normalization of the mean error. NME calculates the absolute value of the difference 

(predicted - observed) over the sum of observed values. Normalized mean error is given in units 

of % and is defined as:

NME = ∑ |P−O|

n

1

∑ (O)

n

1

∗ 100

In general, the model performance statistics indicate that the 8-hour daily maximum ozone 

concentrations predicted by the 2011 CAMx modeling platform closely reflect the corresponding 

8-hour observed ozone concentrations in space and time in each region of the 12-km U.S. 

modeling domain. The acceptability of model performance was judged by considering the 2011 

CAMx performance results in light of the range of performance found in recent regional ozone 

model applications (NRC, 2002; Phillips et al., 2007; Simon et al., 2011; US EPA, 2005; US 

EPA, 2009; US EPA, 2011). These other modeling studies represent a wide range of modeling 

analyses which cover various models, model configurations, domains, years and/or episodes, 

chemical mechanisms, and aerosol modules. Overall, the ozone model performance results for 

the 2011 CAMx simulations performed for the Ozone NAAQS are within the range found in 

other recent applications. The model performance results, as described in this document, 

demonstrate that the predictions from the Ozone NAAQS modeling platform closely replicate the 

corresponding observed concentrations in terms of the magnitude, temporal fluctuations, and 

spatial differences for 8-hour daily maximum ozone. 

Consistent with EPA’s guidance for attainment demonstration modeling, we have applied 

the model predictions performed as part of the Ozone NAAQS in a relative manner for 

projecting future concentrations of ozone. The National Research Council (NRC, 2002) states 

that using air quality modeling in a relative manner “may help reduce the bias introduced by 
3A-4

modeling errors and, therefore, may be more accurate than using model results directly (absolute 

values) to estimate future pollutant levels”. Thus, the results of this evaluation together with the 

manner in which we are applying model predictions gives us confidence that our air quality 

model applications using the CAMx 2011 modeling platform provides a scientifically credible 

approach for assessing ozone for the Ozone NAAQS Rule.

The 8-hour ozone model performance bias and error statistics by network for the ozone 

season (May-September average) for each region are provided in Table 3A-1. The statistics 

shown were calculated using data pairs on days with observed 8-hour ozone of ≥ 60 ppb. The 

distributions of observed and predicted 8-hour ozone by month in the 5-month ozone season for 

each region are shown in Figures 3A-1 through 3A-5. Spatial plots of the mean bias and error as 

well as the normalized mean bias and error for individual monitors are shown in Figures 3A-6 

and 3A-9. The statistics shown in these two figures were calculated over the ozone season using 

data pairs on days with observed 8-hour ozone of ≥ 60 ppb. Time series plots of observed and 

predicted 8-hour ozone during the ozone season at the 13 representative high ozone monitoring 

sites are provided in Figure 3A-10a-m. These sites are listed in Table 3A-2.

As indicated by the statistics in Table 3A-1, bias and error for 8-hour daily maximum 

ozone are relatively low in each region. Generally, MB for 8-hour ozone ≥ 60 ppb during the 

ozone season is less than 5 ppb except in the Western region and at rural (CASTNET) sites in the 

central region for which ozone is somewhat under-predicted. The monthly distribution of 8-hour 

daily maximum ozone during the ozone season generally corresponds well with that of the 

observed concentrations, as indicated by the graphics in Figures 3A-1 through 3A-5. The 

predicted concentrations tend to be close to the observed 25th percentile, median and 75th 

percentile values for each region, although there is a small persistent overestimation bias for 

these metrics. The CAMx model also has a tendency to under-predict the highest observational 

concentrations at both the AQS and CASTNet network sites.

Figures 3A-6 through 3A-9 show the spatial variability in bias and error at monitor 

locations. Mean bias, as seen from Figure 3A-6, is less than 6 ppb at most of the sites across the 

modeling domain. Figure 3A-7 indicates that the normalized mean bias for days with observed 8-

hour daily maximum ozone greater than or equal to 60 ppb is within ± 10 percent at the vast 
3A-5

majority of monitoring sites across the modeling domain. There are regional differences in model 

performance, where the model tends to over-predict from the Southeast into the Mid-Atlantic 

States and generally under predict in the Central and Western U.S. Model performance in the 

Midwest states shows both under and over predictions. 

Model error, as seen from Figure 3A-8, is 10 ppb or less at most of the sites across the 

modeling domain. Figure 3A-9 indicates that the normalized mean error for days with observed 

8-hour daily maximum ozone greater than or equal to 60 ppb is within 10 percent at the vast 

majority of monitoring sites across the modeling domain. Somewhat greater error is evident at 

sites in several areas most notably along portions of the Northeast Corridor and in portions of 

Florida, North Dakota, Illinois, Ohio, North Carolina, and the western most part of the modeling 

domain.

In addition to the above analysis of overall model performance, we also examine how well 

the modeling platform replicates day to day fluctuations in observed 8-hour daily maximum 

concentrations at 13 high ozone monitoring sites. For this site specific analysis we present the 

time series of observed and predicted 8-hour daily maximum concentrations by site over the 

ozone season, May through September. These monitors were chosen as representative high 

ozone sites in urban areas with the highest projected ozone levels in the 2025 base case 

simulation. The results, as shown in Figures A-10a through m, indicate that the modeling 

platform replicates the day-to-day variability in ozone during this time period. For example, 

several of the sites not only have minimal bias but also accurately capture both the seasonal and 

day-to-day variability in the observations: Alleghany County, PA; Frederick County, MD; 

Wayne County, MI; Jefferson County, KY. Many additional sites generally track well and 

capture day-to-day variability but underestimate some of the peak ozone days: Tarrant County, 

TX; Brazoria County, TX; Harford County, MD; Queens County, NY; Suffolk County, NY; 

Sheboygan County, WI; Douglas County, CO. Finally, the daily modeled ozone at the two 

California sites evaluated correlates well with observations but has a persistent low bias. Looking 

across all 13 sites indicates that the modeling platform is able to capture the site to site 

differences in the short-term variability of ozone concentrations. 
3A-6

Table 3A-1. Daily Maximum 8-hour Ozone Performance Statistics ≥ 60 ppb by Region, 

by Network

Network Subregion

No. of 

Obs MB ME

NMB 

(%)

NME 

(%)

AQS

Northeast 3,746 0.6 7.3 0.9 10.7

Mid-West 4,240 -0.7 7.8 -1.0 11.5

Central 6,087 -4.4 8.2 -6.4 11.9

South 6,736 2.2 7.1 3.3 10.6

West 13,568 -6.6 9.2 -9.6 13.4

CASTNet

Northeast 264 1.1 5.9 1.7 8.7

Mid-West 240 -4.2 6.6 -6.3 9.8

Central 216 -8.2 8.7 -12.4 13.1

South 443 -0.7 5.7 -1.1 8.8

West 905 -11.0 11.5 -16.0 16.7

Figure 3A-1. Distribution of observed and predicted MDA8 ozone by month for the period 

May through September for the Northeast subregion, (a) AQS network and (b) 

CASTNet network. [symbol = median; top/bottom of box = 75th/25th 

percentiles; top/bottom line = max/min values]

a b
3A-7

Figure 3A-2. Distribution of observed and predicted MDA8 ozone by month for the period 

May through September for the Southeast subregion, (a) AQS network and (b) 

CASTNet network

Figure 3A-3. Distribution of observed and predicted MDA8 ozone by month for the period 

May through September for the Midwest subregion, (a) AQS network and (b) 

CASTNet network

a b

a b
3A-8

Figure 3A-4. Distribution of observed and predicted MDA8 ozone by month for the period 

May through September for the Central states, (a) AQS network and (b) 

CASTNet network

Figure 3A-5. Distribution of observed and predicted MDA8 ozone by month for the period 

May through September for the West, (a) AQS network and (b) CASTNet 

network

b

a

a b
3A-9

Figure 3A-6. Mean Bias (ppb) of MDA8 ozone greater than 60 ppb over the period MaySeptember 2011 at AQS and CASTNet monitoring sites in 12-km U.S. modeling 

domain

Figure 3A-7. Normalized Mean Bias (%) of MDA8 ozone greater than 60 ppb over the 

period May-September 2011 at AQS and CASTNet monitoring sites in 12-km 

U.S. modeling domain
3A-10

Figure 3A-8. Mean Error (ppb) of MDA8 ozone greater than 60 ppb over the period MaySeptember 2011 at AQS and CASTNet monitoring sites in 12-km U.S. modeling 

domain

Figure 3A-9. Normalized Mean Error (%) of MDA8 ozone greater than 60 ppb over the 

period May-September 2011 at AQS and CASTNet monitoring sites in 12-km 

U.S. modeling domain
3A-11

Table 3A-2. Key Monitoring Sites Used for the Ozone Time Series Analysis

County State Monitoring

Site ID

Fresno California 60195001

San Bernardino California 60710005

Tarrant Texas 484392003

Brazoria Texas 480391004

Allegheny Pennsylvania 420031005

Frederick Maryland 240210037

Harford Maryland 240251001

Queens New York 360810124

Suffolk New York 361030002

Sheboygan Wisconsin 551170006

Wayne Michigan 261630019

Jefferson Kentucky 211110067

Douglas Colorado 80350004

Figure 3A-10a. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 60195001 in Fresno Co., California
3A-12

Figure 3A-10b. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 60710005 in San Bernardino Co., 

California

Figure 3A-10c. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 80350004 in Douglas Co., Colorado
3A-13

Figure 3A-10d. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 484392003 in Tarrant Co., Texas

Figure 3A-10e. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 480391004 in Brazoria Co., Texas
3A-14

Figure 3A-10f. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 551170006 in Sheboygan Co., Wisconsin

Figure 3A-10g. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 261630019 in Wayne Co., Michigan
3A-15

Figure 3A-10h. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 211110067 in Jefferson Co., Kentucky

Figure 3A-10i. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 420031005 in Allegheny Co., Pennsylvania
3A-16

Figure 3A-10j. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 240210037 in Frederick Co., Maryland

Figure 3A-10k. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 240251001 in Harford Co., Maryland
3A-17

Figure 3A-10l. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 360810124 in Queens, New York

Figure 3A-10m. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 361030002 in Suffolk County, New York.

3A.2 California Sub-Regions and Areas of Influence

As discussed in chapter 3 of the ozone RIA, we performed air quality modeling to gauge 

the sensitivity of ozone to 50% and 90% cuts in NOx emissions statewide in California. When 

applying the results of these model simulations to estimate emissions reductions to attain the 

current and alternative NAAQS, we made a simplifying assumption that the model-predicted 

ozone response at locations in the San Joaquin Valley and areas of California further north are 
3A-18

solely due to emissions changes in these areas. That is, we associated the predicted ozone 

changes in northern California to emissions changes in this portion of the state even though the 

air quality model simulation included reductions statewide. We made a similar assumption about 

the response of ozone to emissions changes for the southern portion of California. The northern 

and southern source regions were identified for the purpose of determining which emissions 

reductions would be considered to impact design values at specific monitors. In calculating the 

impact of emissions changes on design values only monitors within each source regions were 

assumed to be impacted from emissions from within that source region. For VOC emissions 

reductions from available known controls, the source regions were defined to include only those 

portions of the sub-regions that also fell within the NOx buffer area for which known NOx 

controls were applied (see figure 3-4 from chapter 3 of the RIA). Therefore, when determining 

the tons of emissions reductions needed to meet various standard levels, the impacts of emissions

reductions within each source region were applied to just those monitors located within that 

regions. In addition, we determined the areas outside of California that are expected to be most 

affected by emissions reductions from each of the two California sub-regions (i.e., downwind 

impact areas) (Figure 3A-14). When creating the BenMap and FASOMGHG surfaces described 

in section 3.4 of the main chapter, we determined the impact on ozone in these downwind areas 

due to emissions reductions from within each of the two California sub-regions.

Several considerations were considered when delineating these two California sub-regions 

for the various steps in this analysis. The spatial extent of the two California NOx source regions 

were based on the geographic boundaries of California air basins as defined by the California Air 

Resources Board (CARB). CARB designates Air Basins for the “purpose of managing air 

resources” in areas with “generally . . . similar meteorology and geographic conditions 

throughout” (http://www.arb.ca.gov/ei/maps/statemap/abmap.htm). The various Californian Air 

Basins were then combined into two larger sub-regions for this analysis. The geographic 

groupings were based on general air flow patterns which are governed by mountain topography 

and onshore/offshore wind flows. The Air Basins, sub-regions, and predominant California wind 

patterns are shown in Figure 3A-11. One county, Kern County, was split between Air Basins 

that were assigned to different sub-regions: San Joaquin Valley Air Basin (Northern sub-region) 

and Mojave Desert Air Basin (Southern sub-region). Since emissions inventories are categorized 
3A-19

by county, we assigned all Kern County emissions to the Northern sub-region because 

Bakersfield, the most populated area of Kern County, is located in the Northern sub-region.

Figure 3A-11.a) Depiction of governing wind patterns and topography. b) California Air 

Basins and sub-regions used for this analysis. Northern sub-region is outlined in 

pink and southern sub-region is outlined in blue. 

The downwind receptor regions outside of California were determined by examining the 

spatial patterns of ozone impacted by the the 50% cut California in NOx. Ozone changes due to 

the state-wide emissions reductions appear to follow fairly distinct widespread plumes. From 

this analysis it was determined that impacts of emissions reductions from the Northern subregion were generally limited to Oregon, Washington, and a few Nevada counties near Carson 

City. Conversely, emissions reductions from the Southern sub-region appear to have widespread 

impacts across much of the Southwestern and Central US. One caveat is that the geographic 

extent of these downwind regions are based on general transport patterns, as determined by 

examining model outputs from a single year of meteorology (i.e., 2011) and are not intended to 

fully represent the downwind transport of ozone from California emissions on all days and at all 

times. However, this approach was necessary in order to match the Northern and Southern 

California sub-region emissions reductions to ozone impacts in downwind areas outside this 

state. Figure 3A-12 shows examples of the impact on 8-hr daily maximum ozone from 50% 

California NOx cuts on a few representative days. Figure 3A-13 shows the downwind receptor 

regions that were applied in this analysis. Note that VOC impacts were only applied within the 
3A-20

source regions consistent with how VOC reductions were treated for other areas of the country. 

Figure 3A-14 shows that ozone impacts from a 50% cut in US anthropogenic VOC emissions 

were localized within the two California sub-regions and do not appear to impact downwind 

states.

Figure 3A-12.Impact of 50% anthropogenic California NOx cuts (ppb) on 8-hr daily 

average ozone concentrations on three days in 2011
3A-21

Figure 3A-13.Downwind California receptor regions for Northern California (green) and 

Southern California (purple)
3A-22

Figure 3A-14.Impact of 50% US anthropogenic VOC cuts (ppb) on 8-hr daily average 

ozone concentrations on three days in 2011

3A.3 VOC Impact Areas

As described in chapter 3, we defined VOC impact regions for the following urban areas: 

New York City, Pittsburgh, Baltimore, Detroit, Chicago, Louisville, Houston, Dallas, Denver, 

Northern California and Southern California. Not only did these areas have the highest design 

values in each region, but ozone in these areas was also sensitive to VOC emissions reductions in 

our modeling. Figure 3A-15 shows the impact of 50% US anthropogenic VOC cuts on July 

monthly average 8-hour daily maximum ozone concentrations across the US. Ozone in each of 
3A-23

the areas listed above is shown to have at least 0.2 ppb response to VOC emissions cuts.

Figure 3A-15.Change in July average of 8-hr daily maximum ozone concentration (ppb) 

due to 50% cut in US anthropogenic VOC emissions

3A.4 Numeric Examples of Calculation Methodology for Changes in Design Values

In this section we use the data for two monitoring sites to demonstrate how changes in 

design values were calculated, as described in section 3.3. For each monitor, numerical 

examples are given for calculating the emissions reductions necessary to attain the current 75 

ppb NAAQS (i.e., the baseline scenario) as well as the 65 ppb scenario, which is incremental to 

the baseline. Note that design values are truncated when they are compared to a standard level, 

so a calculated design value of 75.9 is truncated to 75 ppb and, therefore, meets the current 75 

ppb standard. Similarly, a design value of 65.9 would meet an alternative standard level of 65. 

For each monitor, we start with the base case design value, then account for ozone changes 

simulated in the 111(d) sensitivity simulation and then apply equation 3-5 from chapter 3. 

DVj = DV2025,j + (R1,j × ∆E1) + (R2,j × ∆E2) + (R3,j × ∆E3) + ⋯ Eq 3-5

Example 1. Fresno California monitor 60195001 (baseline):
3A-24

DV60195001,baseline

= 83⏟.5

DV60195001,2025

+ −⏟0.7

∆DV60195001,111d

⏞ 

NOx+VOC

+ ( −9.9 × 10 ⏟ − 5

R60195001,CAcontrl,NCA

× 15⏟ ,000 

∆ECAcontrl,NCA

)

⏞ 

NOx

+ ( −1.3 × 10 ⏟ − 4

R60195001,CAcontrol+50NOx,NCA

× 8100 ⏟ + 32 , 000 

∆Emobile,2030+∆E

)

⏞ 

NOx

+ ( −9.9 × 10 ⏟ − 6

R60195001,VOC_50,NCA

× 3200 ⏟ + 22 ,000 

∆Emobile,2030+∆E

)

⏞ 

VOC

= 75.9 ppb

Example 2. Fresno California monitor 60195001 (65 ppb scenario):

DVj,65 = 75⏟.9

DVj,baseline

+ ( −1.3 × 10 ⏟ − 4

R60195001,CAcontrol+50NOx,NCA

× 61⏟ , 000 

∆E

)

⏞ 

NOx up to 50% of CA modeled control sensitivity

+ ( −2.0 × 10 ⏟ − 4

R60195001,CAcontrol50−90NOx,NCA

× 12⏟ ,000 

∆E

)

⏞ 

NOx beyond 50% of CA modeled control sensitivity

= 65.6 ppb

Example 3. Dallas monitor 484392003 (baseline):

DV484392003,baseline = 77⏟.1

DV484392003,2025

+ −⏟1.0

∆DV484392003,111d

⏞ 

NOx+VOC

+ ( −1.6 × 10 ⏟ − 5

R484392003,TXcontrol

× 44⏟ ,0 00 

∆E

)

⏞ 

NOx

= 75.4 ppb

Example 4. Dallas monitor 484392003 (65 ppb scenario):
3A-25

DV484392003,65 = 75⏟.4

DV484392003,baseline

+ ( −1.6 × 10 ⏟ − 5

R484392003,TXcontrol

× 56⏟ ,000 

∆E

)

⏞ 

NOx

+ ( −1.1 × 10 ⏟ − 5

R484392003,TXcontrol+50centralNOx

× 770 ⏟ ,00 0

∆E

)

⏞ 

NOx

+ ( −9.2 × 10 ⏟ − 6

R484392003,VOC_50,Dallas

× 17⏟ ,000 

∆E

)

⏞ 

VOC

= 65.9 ppb

3A.5 Emissions Reductions Applied to Create Baseline and Alternative Standard Level 

Scenarios

The following tables present emissions reductions applied in each region to create the 

baseline and alternative standard level scenarios. These emissions reductions were determined 

using the methodology described in section 3.3.2 of the main RIA and demonstrated in section 

3A.4 of this appendix. Sector-specific controls used for these reductions are discussed in more 

detail in chapter 4 of the RIA. These emissions reductions were used to create the ozone 

surfaces described in section 3.4 of the RIA.

Table 3A-3. Emissions Reductions Applied to Create the Baseline Scenario*

Emissions reductions (thousand tons) applied from

2025-2030 

California mobile 

source changes

NOx reductions 

from one of the 

explicit control 

cases

VOC reductions 

identified from 

maxcontrol CoST 

run

Additional NOx 

reductions

Northeast N/A N/A N/A N/A

Midwest N/A N/A N/A N/A

Central N/A

45 (TX explicit 

emissions control 

case)

N/A N/A

Southwest N/A N/A N/A N/A

California 14 (NOx) 

6 (VOC)

29 (CA explicit 

emissions control 

case)

51 (in N and S CA 

buffer region)

32 (N California); 

130 (S California)

*These emission are in addition to changes modeled in the simulation representing option 

1(state) of the proposed carbon pollution guidelines under section 111(d) of the CAA.

Table 3A-4. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 70 

ppb Scenario

Emissions reductions (thousand tons) applied from
3A-26

NOx reductions from 

one of the explicit 

control cases*

VOC reductions 

identified from 

maxcontrol CoST run

Additional NOx reductions

Northeast 110 31 (NY area); 

6 (Baltimore area)

130 (98 within NE buffer; 31 

outside the NE buffer)

Midwest N/A N/A N/A

Central 58 (TX explicit emissions 

control case) 18 (Houston area) 350 (95 within TX buffer; 260 outside of TX buffer)

Southwest N/A N/A N/A

California Exhausted in baseline 

scenario

Exhausted in baseline 

scenario

38 (N California); 

15 (S California)

Table 3A-5. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 65 

ppb Scenario

Emissions reductions (thousand tons) applied from

NOx reductions from one of 

the explicit control cases*

VOC reductions 

identified from 

maxcontrol CoST 

run

Additional NOx reductions

Northeast 110 36 (NY area) 400 (300 within NE buffer;

97outside the NE buffer)

Midwest 250 28 (Chicago area) 180

Central 58 (TX explicit emissions 

control case)

18 (Houston area); 

17 (Dallas area)

770 (210 within TX buffer; 

560 outside of TX buffer)

Southwest 77 7 (Denver area) 36

California Exhausted in baseline scenario Exhausted in baseline 

scenario

73 (N California); 

32 (S California)

*In regions without a modeled explicit control case (Southwest and Midwest) this represents 

equivalent emissions reductions that would have been identified in an explicit control case that 

covered the entire region

Table 3A-6. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 60 

ppb Scenario

Emissions reductions (thousand tons) applied from

NOx reductions from one 

of the explicit control 

cases*

VOC reductions 

identified from 

maxcontrol CoST run

Additional NOx reductions

Northeast 110 36 (NY area); 

6 (Baltimore area)

610 (470 within NE buffer; 

150 outside the NE buffer)

Midwest 250

32 (Chicago area);

41 (additional Chicago 

area VOC control)

620

Central 58 (TX explicit emissions 

control case)

18 (Houston area);

18 (Dallas area)

1,200 (340 within TX buffer; 

910 outside of TX buffer)

Southwest 77 7 (Denver area) 240

California Exhausted in baseline 

scenario

Exhausted in baseline 

scenario

97 (N California); 

48 (S California)
3A-27

*In regions without a modeled explicit control case (Southwest and Midwest) this represents 

equivalent emissions reductions that would have been identified in an explicit control case that 

covered the entire region

3A.6 Design Values for All Monitors included in the Quantitative Analysis 

Table 3A-7. Design Values for California Region Monitors

Site ID Lat long State County Base Case Baseline 70 65 60

60010007 37.69 -121.78 California Alameda 66 61 57 53 48

60010009 37.74 -122.17 California Alameda 47 44 42 40 36

60010011 37.81 -122.28 California Alameda 46 43 41 39 35

60012001 37.65 -122.03 California Alameda 54 51 48 45 41

60050002 38.34 -120.76 California Amador 60 54 50 46 43

60070007 39.71 -121.62 California Butte 63 58 54 50 47

60070008 39.76 -121.84 California Butte 53 49 46 43 40

60090001 38.20 -120.68 California Calaveras 63 57 53 49 46

60111002 39.20 -122.02 California Colusa 53 49 46 43 41

60130002 37.94 -122.03 California Contra Costa 65 60 57 53 48

60131002 38.01 -121.64 California Contra Costa 65 59 56 51 47

60131004 37.96 -122.36 California Contra Costa 51 47 45 42 38

60170010 38.73 -120.82 California El Dorado 67 60 55 50 46

60170012 38.81 -120.03 California El Dorado 61 59 57 56 55

60170020 38.89 -121.00 California El Dorado 69 62 57 52 47

60190007 36.71 -119.74 California Fresno 82 74 69 64 59

60190011 36.79 -119.77 California Fresno 81 73 68 63 58

60190242 36.84 -119.87 California Fresno 81 74 70 65 60

60192009 36.63 -120.38 California Fresno 64 59 55 52 49

60194001 36.60 -119.50 California Fresno 76 69 65 60 56

60195001 36.82 -119.72 California Fresno 83 75 70 65 60

60210003 39.53 -122.19 California Glenn 56 52 49 46 44

60250005 32.68 -115.48 California Imperial 69 62 61 60 59

60254003 33.03 -115.62 California Imperial 64 54 53 51 50

60254004 33.21 -115.55 California Imperial 63 53 52 50 48

60290007 35.35 -118.85 California Kern 81 74 70 65 60

60290008 35.05 -119.40 California Kern 72 67 63 59 55

60290011 35.05 -118.15 California Kern 71 58 57 55 53

60290014 35.36 -119.04 California Kern 77 71 66 61 56

60290232 35.44 -119.02 California Kern 77 71 66 61 57

60295002 35.24 -118.79 California Kern 74 68 64 59 55

60296001 35.50 -119.27 California Kern 73 67 63 59 55

60311004 36.31 -119.64 California Kings 74 67 63 59 55

60333001 39.03 -122.92 California Lake 48 45 43 41 38
3A-28

Site ID Lat long State County Base Case Baseline 70 65 60

60370002 34.14 -117.92 California Los Angeles 75 55 52 48 43

60370016 34.14 -117.85 California Los Angeles 88 65 61 56 51

60370113 34.05 -118.46 California Los Angeles 62 49 46 43 39

60371002 34.18 -118.32 California Los Angeles 73 53 49 46 42

60371103 34.07 -118.23 California Los Angeles 63 48 45 41 37

60371201 34.20 -118.53 California Los Angeles 83 62 59 55 51

60371302 33.90 -118.21 California Los Angeles 57 52 51 49 47

60371602 34.01 -118.07 California Los Angeles 66 55 52 48 44

60371701 34.07 -117.75 California Los Angeles 80 62 58 54 50

60372005 34.13 -118.13 California Los Angeles 74 55 51 47 43

60374002 33.82 -118.19 California Los Angeles 55 50 49 47 45

60376012 34.38 -118.53 California Los Angeles 88 64 60 56 51

60379033 34.67 -118.13 California Los Angeles 79 60 57 54 51

60390004 36.87 -120.01 California Madera 70 64 60 56 52

60392010 36.95 -120.03 California Madera 73 67 63 59 55

60410001 37.97 -122.52 California Marin 47 44 41 39 36

60430006 37.55 -119.84 California Mariposa 65 61 58 55 53

60470003 37.28 -120.43 California Merced 71 65 61 57 53

60530002 36.50 -121.73 California Monterey 50 40 39 37 36

60530008 36.21 -121.13 California Monterey 51 41 40 39 37

60531003 36.70 -121.64 California Monterey 46 36 35 33 32

60550003 38.31 -122.30 California Napa 53 49 46 43 40

60570005 39.23 -121.06 California Nevada 63 58 54 50 47

60570007 39.32 -120.85 California Nevada 61 56 52 48 45

60590007 33.83 -117.94 California Orange 60 49 47 44 42

60591003 33.67 -117.93 California Orange 58 48 46 44 41

60592022 33.63 -117.68 California Orange 60 45 42 40 37

60595001 33.93 -117.95 California Orange 66 54 51 48 45

60610003 38.94 -121.10 California Placer 69 62 57 52 47

60610004 39.10 -120.95 California Placer 61 55 51 47 44

60610006 38.75 -121.27 California Placer 70 63 58 53 48

60650004 34.01 -117.52 California Riverside 77 60 57 53 50

60650008 33.74 -115.82 California Riverside 56 47 46 44 43

60650009 33.45 -117.09 California Riverside 60 46 43 41 39

60650012 33.92 -116.86 California Riverside 85 64 60 56 52

60650016 33.58 -117.08 California Riverside 64 48 45 43 40

60651016 33.95 -116.83 California Riverside 87 65 61 58 53

60652002 33.71 -116.22 California Riverside 74 59 57 55 52

60655001 33.85 -116.54 California Riverside 81 63 60 57 54

60656001 33.79 -117.23 California Riverside 78 58 54 51 47
3A-29

Site ID Lat long State County Base Case Baseline 70 65 60

60658001 34.00 -117.42 California Riverside 89 68 64 60 55

60658005 34.00 -117.49 California Riverside 85 65 61 57 53

60659001 33.68 -117.33 California Riverside 74 55 52 49 45

60659003 33.61 -114.60 California Riverside 60 54 53 52 51

60670002 38.71 -121.38 California Sacramento 65 59 54 49 45

60670006 38.61 -121.37 California Sacramento 66 60 55 50 45

60670010 38.56 -121.49 California Sacramento 61 55 51 47 42

60670011 38.30 -121.42 California Sacramento 62 56 52 48 44

60670012 38.68 -121.16 California Sacramento 77 69 64 58 52

60670014 38.65 -121.51 California Sacramento 60 54 50 46 42

60675003 38.49 -121.21 California Sacramento 72 65 60 54 49

60690002 36.84 -121.36 California San Benito 54 42 40 38 36

60690003 36.49 -121.16 California San Benito 61 48 46 45 43

60710001 34.90 -117.02 California San Bernardino 69 57 55 53 51

60710005 34.24 -117.27 California San Bernardino 99 75 70 65 60

60710012 34.43 -117.56 California San Bernardino 85 65 62 58 54

60710306 34.51 -117.33 California San Bernardino 77 60 57 54 50

60711004 34.10 -117.63 California San Bernardino 91 71 66 62 56

60711234 35.76 -117.40 California San Bernardino 65 60 59 59 58

60712002 34.10 -117.49 California San Bernardino 96 74 69 64 59

60714001 34.42 -117.29 California San Bernardino 88 68 64 60 55

60714003 34.06 -117.15 California San Bernardino 97 73 68 64 59

60719002 34.07 -116.39 California San Bernardino 82 66 63 61 58

60719004 34.11 -117.27 California San Bernardino 91 69 64 60 55

60730001 32.63 -117.06 California San Diego 60 54 53 52 51

60730003 32.79 -116.94 California San Diego 61 49 47 45 43

60730006 32.84 -117.13 California San Diego 62 50 49 47 45

60731001 32.95 -117.26 California San Diego 57 48 46 45 44

60731002 33.13 -117.08 California San Diego 58 44 42 40 38

60731006 32.84 -116.77 California San Diego 69 55 52 50 47

60731008 33.22 -117.40 California San Diego 56 44 43 41 39

60731010 32.70 -117.15 California San Diego 55 49 48 47 46

60731016 32.85 -117.12 California San Diego 59 47 46 44 42

60731201 33.36 -117.09 California San Diego 58 45 43 41 39

60732007 32.55 -116.94 California San Diego 54 48 47 46 45

60771002 37.95 -121.27 California San Joaquin 59 54 50 46 42

60773005 37.68 -121.44 California San Joaquin 70 65 61 56 52

60790005 35.63 -120.69 California San Luis Obispo 55 46 44 43 42

60792006 35.26 -120.67 California San Luis Obispo 47 38 37 36 35

60793001 35.37 -120.84 California San Luis Obispo 46 39 38 38 37
3A-30

Site ID Lat long State County Base Case Baseline 70 65 60

60794002 35.03 -120.50 California San Luis Obispo 50 41 40 39 37

60798001 35.49 -120.67 California San Luis Obispo 53 44 43 42 40

60798005 35.64 -120.23 California San Luis Obispo 67 55 53 52 50

60798006 35.35 -120.04 California San Luis Obispo 65 53 51 49 47

60811001 37.48 -122.20 California San Mateo 53 50 49 46 42

60830008 34.46 -120.03 California Santa Barbara 51 44 43 42 41

60830011 34.43 -119.69 California Santa Barbara 49 42 41 40 39

60831008 34.95 -120.44 California Santa Barbara 43 35 34 33 32

60831013 34.73 -120.43 California Santa Barbara 54 45 44 42 41

60831014 34.54 -119.79 California Santa Barbara 58 49 48 47 45

60831018 34.53 -120.20 California Santa Barbara 49 43 43 42 41

60831021 34.40 -119.46 California Santa Barbara 58 49 48 47 46

60831025 34.49 -120.05 California Santa Barbara 60 52 50 49 48

60832004 34.64 -120.46 California Santa Barbara 47 40 39 38 37

60832011 34.45 -119.83 California Santa Barbara 49 42 41 41 40

60833001 34.61 -120.08 California Santa Barbara 52 44 43 41 40

60834003 34.60 -120.63 California Santa Barbara 54 47 46 45 44

60850002 37.00 -121.57 California Santa Clara 58 53 50 46 42

60850005 37.35 -121.89 California Santa Clara 56 52 49 45 41

60851001 37.23 -121.98 California Santa Clara 59 54 51 47 43

60852006 37.08 -121.60 California Santa Clara 62 57 53 49 45

60852009 37.32 -122.07 California Santa Clara 56 52 49 45 41

60870007 36.98 -121.99 California Santa Cruz 47 36 34 32 30

60890004 40.55 -122.38 California Shasta 52 47 44 41 38

60890007 40.45 -122.30 California Shasta 58 53 49 46 43

60890009 40.69 -122.40 California Shasta 60 54 51 47 44

60893003 40.54 -121.57 California Shasta 58 55 53 51 50

60950004 38.10 -122.24 California Solano 53 49 46 43 39

60950005 38.23 -122.08 California Solano 58 53 50 46 43

60953003 38.36 -121.95 California Solano 58 53 50 46 43

60970003 38.44 -122.71 California Sonoma 39 36 34 32 31

60990005 37.64 -120.99 California Stanislaus 66 60 56 52 48

60990006 37.49 -120.84 California Stanislaus 76 69 64 59 55

61010003 39.14 -121.62 California Sutter 55 50 47 43 41

61010004 39.21 -121.82 California Sutter 64 59 55 52 49

61030004 40.26 -122.09 California Tehama 65 60 56 53 50

61030005 40.18 -122.24 California Tehama 63 58 55 51 49

61070009 36.49 -118.83 California Tulare 79 72 68 64 60

61072002 36.33 -119.29 California Tulare 71 64 60 56 52

61072010 36.03 -119.06 California Tulare 74 68 64 60 56
3A-31

Site ID Lat long State County Base Case Baseline 70 65 60

61090005 37.98 -120.38 California Tuolumne 62 58 54 51 48

61110007 34.21 -118.87 California Ventura 64 49 47 44 42

61110009 34.40 -118.81 California Ventura 66 51 49 46 44

61111004 34.45 -119.23 California Ventura 67 56 55 54 52

61112002 34.28 -118.68 California Ventura 72 55 52 50 46

61113001 34.25 -119.14 California Ventura 55 44 43 42 40

61130004 38.53 -121.77 California Yolo 57 52 49 45 42

61131003 38.66 -121.73 California Yolo 60 54 51 47 43

*The design value from the monitor(s) with the highest projected ozone in each scenario is 

shown in bold blue text

Table 3A-8. Design Values for Southwest Monitors

Site ID lat long State County Base Case Baseline 70 65 60

40051008 35.21 -111.65 Arizona Coconino 63 63 63 62 60

40070010 33.65 -111.11 Arizona Gila 62 62 62 60 52

40130019 33.48 -112.14 Arizona Maricopa 65 65 65 62 51

40131004 33.56 -112.07 Arizona Maricopa 66 66 66 62 51

40131010 33.45 -111.73 Arizona Maricopa 58 58 58 55 46

40132001 33.57 -112.19 Arizona Maricopa 62 62 62 58 48

40132005 33.71 -111.86 Arizona Maricopa 62 62 62 59 50

40133002 33.46 -112.05 Arizona Maricopa 62 62 62 59 48

40133003 33.48 -111.92 Arizona Maricopa 63 63 63 60 50

40134003 33.40 -112.08 Arizona Maricopa 64 64 64 60 50

40134004 33.30 -111.88 Arizona Maricopa 61 61 61 57 48

40134005 33.41 -111.93 Arizona Maricopa 59 59 59 55 46

40134008 33.82 -112.02 Arizona Maricopa 62 62 62 59 49

40134010 33.64 -112.34 Arizona Maricopa 58 58 58 55 46

40134011 33.37 -112.62 Arizona Maricopa 56 56 56 54 47

40137003 33.29 -112.16 Arizona Maricopa 60 60 60 57 49

40137020 33.49 -111.86 Arizona Maricopa 62 62 62 58 48

40137021 33.51 -111.76 Arizona Maricopa 63 63 63 60 50

40137022 33.47 -111.81 Arizona Maricopa 60 60 60 57 48

40137024 33.51 -111.84 Arizona Maricopa 61 61 61 58 48

40139508 33.98 -111.80 Arizona Maricopa 59 59 59 56 48

40139702 33.55 -111.61 Arizona Maricopa 62 62 62 59 49

40139704 33.61 -111.73 Arizona Maricopa 62 62 62 59 49

40139706 33.72 -111.67 Arizona Maricopa 61 61 61 58 49

40139997 33.50 -112.10 Arizona Maricopa 64 64 64 61 50

40170119 34.82 -109.89 Arizona Navajo 61 61 61 58 55

40190021 32.17 -110.74 Arizona Pima 62 62 62 57 51
3A-32

Site ID lat long State County Base Case Baseline 70 65 60

40191011 32.20 -110.88 Arizona Pima 57 57 57 53 47

40191018 32.43 -111.06 Arizona Pima 58 58 58 56 50

40191020 32.05 -110.77 Arizona Pima 60 60 60 55 48

40191028 32.30 -110.98 Arizona Pima 57 57 57 54 48

40191030 31.88 -111.00 Arizona Pima 60 60 60 55 50

40191032 32.17 -110.98 Arizona Pima 56 56 56 52 46

40191034 32.38 -111.13 Arizona Pima 56 56 56 53 48

40213001 33.42 -111.54 Arizona Pinal 61 61 61 58 49

40213003 32.95 -111.76 Arizona Pinal 59 59 59 57 51

40213007 32.51 -111.31 Arizona Pinal 61 61 61 59 55

40217001 33.08 -111.74 Arizona Pinal 60 60 60 58 51

40218001 33.29 -111.29 Arizona Pinal 64 64 64 61 52

40258033 34.55 -112.48 Arizona Yavapai 63 63 63 62 60

80013001 39.84 -104.95 Colorado Adams 61 61 61 58 48

80050002 39.57 -104.96 Colorado Arapahoe 66 66 66 63 53

80050006 39.64 -104.57 Colorado Arapahoe 62 62 62 58 50

80130011 39.96 -105.24 Colorado Boulder 61 61 61 58 48

80310014 39.75 -105.03 Colorado Denver 58 58 58 55 46

80310025 39.70 -105.00 Colorado Denver 58 58 58 55 46

80350004 39.53 -105.07 Colorado Douglas 68 68 68 65 55

80410013 38.96 -104.82 Colorado El Paso 65 65 65 63 58

80410016 38.85 -104.90 Colorado El Paso 67 67 67 65 60

80450012 39.54 -107.78 Colorado Garfield 62 62 62 60 55

80590002 39.80 -105.10 Colorado Jefferson 58 58 58 55 46

80590005 39.64 -105.14 Colorado Jefferson 64 64 64 61 51

80590006 39.91 -105.19 Colorado Jefferson 67 67 67 63 53

80590011 39.74 -105.18 Colorado Jefferson 66 66 66 63 52

80590013 39.54 -105.30 Colorado Jefferson 62 62 62 59 49

80677001 37.14 -107.63 Colorado La Plata 64 64 64 63 60

80677003 37.10 -107.87 Colorado La Plata 63 63 63 62 59

80690007 40.28 -105.55 Colorado Larimer 64 64 64 61 53

80690011 40.59 -105.14 Colorado Larimer 68 68 68 64 54

80690012 40.64 -105.28 Colorado Larimer 61 61 61 58 49

80691004 40.58 -105.08 Colorado Larimer 60 60 60 57 47

80770020 39.13 -108.31 Colorado Mesa 63 63 63 62 57

80810002 40.51 -107.89 Colorado Moffat 61 61 61 59 55

80830006 37.35 -108.59 Colorado Montezuma 61 61 61 60 57

80830101 37.20 -108.49 Colorado Montezuma 60 60 60 59 55

81030005 40.04 -107.85 Colorado Rio Blanco 60 60 60 58 55

81230009 40.39 -104.74 Colorado Weld 67 67 67 64 54
3A-33

Site ID lat long State County Base Case Baseline 70 65 60

320010002 39.47 -118.78 Nevada Churchill 53 53 53 53 52

320030022 36.39 -114.91 Nevada Clark 61 61 61 59 55

320030023 36.81 -114.06 Nevada Clark 57 57 57 56 55

320030043 36.11 -115.25 Nevada Clark 67 67 67 65 57

320030071 36.17 -115.26 Nevada Clark 66 66 66 64 57

320030073 36.17 -115.33 Nevada Clark 66 66 66 64 57

320030075 36.27 -115.24 Nevada Clark 65 65 65 63 56

320030538 36.14 -115.06 Nevada Clark 61 61 61 60 53

320030540 36.14 -115.08 Nevada Clark 61 61 61 60 53

320030601 35.98 -114.85 Nevada Clark 65 65 65 64 60

320031019 35.79 -115.36 Nevada Clark 66 66 66 64 60

320032002 36.19 -115.12 Nevada Clark 61 61 61 60 53

320190006 39.60 -119.25 Nevada Lyon 61 61 61 60 59

320310016 39.53 -119.81 Nevada Washoe 60 60 60 59 58

320310020 39.47 -119.78 Nevada Washoe 61 61 61 60 59

320310025 39.40 -119.74 Nevada Washoe 60 60 60 60 58

320311005 39.54 -119.75 Nevada Washoe 61 61 61 60 59

320312002 39.25 -119.96 Nevada Washoe 55 55 55 55 55

320312009 39.65 -119.84 Nevada Washoe 61 61 61 60 58

325100002 39.17 -119.73 Nevada Carson City 61 61 61 60 60

350010023 35.13 -106.59 New 

Mexico Bernalillo 60 60 60 58 54

350010024 35.06 -106.58 New 

Mexico Bernalillo 61 61 61 59 55

350010027 35.15 -106.70 New 

Mexico Bernalillo 64 64 64 62 59

350010029 35.02 -106.66 New 

Mexico Bernalillo 61 61 61 60 55

350010032 35.06 -106.76 New 

Mexico Bernalillo 58 58 58 57 52

350011012 35.19 -106.51 New 

Mexico Bernalillo 64 64 64 63 59

350011013 35.19 -106.61 New 

Mexico Bernalillo 61 61 61 59 55

350130008 31.93 -106.63 New 

Mexico Dona Ana 61 61 61 60 59

350130023 32.32 -106.77 New 

Mexico Dona Ana 60 60 60 59 58

350171003 32.69 -108.12 New 

Mexico Grant 61 61 61 61 59

350250008 32.73 -103.12 New 

Mexico Lea 60 60 60 60 58

350290003 32.26 -107.72 New 

Mexico Luna 59 59 59 58 57
3A-34

Site ID lat long State County Base Case Baseline 70 65 60

350431001 35.30 -106.55 New 

Mexico Sandoval 56 56 56 55 52

350439004 35.62 -106.72 New 

Mexico Sandoval 59 59 59 59 57

350450009 36.74 -107.98 New 

Mexico San Juan 58 58 58 57 52

350450018 36.81 -107.65 New 

Mexico San Juan 64 64 64 62 57

350451005 36.80 -108.47 New 

Mexico San Juan 56 56 56 54 50

350451233 36.81 -108.70 New 

Mexico San Juan 55 55 55 53 48

350490021 35.62 -106.08 New 

Mexico Santa Fe 60 60 60 60 58

350610008 34.81 -106.74 New 

Mexico Valencia 58 58 58 57 52

490030003 41.49 -112.02 Utah Box Elder 59 59 59 57 49

490037001 41.95 -112.23 Utah Box Elder 60 60 60 59 55

490050004 41.73 -111.84 Utah Cache 59 59 59 57 54

490071003 39.61 -110.80 Utah Carbon 64 64 64 60 56

490110004 40.90 -111.88 Utah Davis 61 61 61 58 52

490131001 40.21 -110.84 Utah Duchesne 63 63 63 62 59

490352004 40.74 -112.21 Utah Salt Lake 65 65 65 62 54

490353006 40.74 -111.87 Utah Salt Lake 65 65 65 61 53

490450003 40.54 -112.30 Utah Tooele 64 64 64 61 53

490490002 40.25 -111.66 Utah Utah 64 64 64 62 57

490495008 40.43 -111.80 Utah Utah 59 59 59 57 53

490495010 40.14 -111.66 Utah Utah 63 63 63 61 57

490570002 41.21 -111.98 Utah Weber 64 64 64 61 54

490571003 41.30 -111.99 Utah Weber 64 64 64 61 53

560050123 44.65 -105.29 Wyoming Campbell 60 60 60 58 53

560050456 44.15 -105.53 Wyoming Campbell 60 60 60 58 53

560070100 41.39 -107.62 Wyoming Carbon 60 60 60 58 56

560130232 43.08 -107.55 Wyoming Fremont 61 61 61 60 58

560210100 41.18 -104.78 Wyoming Laramie 61 61 61 59 54

560350700 42.49 -110.10 Wyoming Sublette 60 60 60 59 58

560370077 41.16 -108.62 Wyoming Sweetwater 60 60 60 58 55

560370200 41.68 -108.02 Wyoming Sweetwater 60 60 60 57 53

560370300 41.75 -109.79 Wyoming Sweetwater 61 61 61 60 56

560410101 41.37 -111.04 Wyoming Uinta 58 58 58 57 54

*The design value from the monitor(s) with the highest projected ozone in each scenario is 

shown in bold blue text
3A-35

Table 3A-9. Design Values for Central Region Monitors

Site ID lat long State County Base Case Baseline 70 65 60

50199991 34.18 -93.10 Arkansas Clark 56 54 52 48 44

50350005 35.20 -90.19 Arkansas Crittenden 64 63 63 61 60

51010002 35.83 -93.21 Arkansas Newton 57 55 54 49 46

51130003 34.45 -94.14 Arkansas Polk 65 63 60 56 53

51190007 34.76 -92.28 Arkansas Pulaski 55 52 51 44 39

51191002 34.84 -92.26 Arkansas Pulaski 58 55 53 47 42

51191008 34.68 -92.33 Arkansas Pulaski 57 54 52 46 42

51430005 36.18 -94.12 Arkansas Washington 62 60 58 54 50

200910010 38.84 -94.75 Kansas Johnson 60 59 55 51 46

201030003 39.33 -94.95 Kansas Leavenworth 59 58 54 50 44

201070002 38.14 -94.73 Kansas Linn 60 58 55 51 47

201619991 39.10 -96.61 Kansas Riley 63 62 60 57 55

201730001 37.78 -97.34 Kansas Sedgwick 55 54 52 49 46

201730010 37.70 -97.31 Kansas Sedgwick 64 63 61 57 53

201730018 37.90 -97.49 Kansas Sedgwick 63 62 59 55 51

201770013 39.02 -95.71 Kansas Shawnee 63 62 59 57 53

201910002 37.48 -97.37 Kansas Sumner 66 65 63 59 55

201950001 38.77 -99.76 Kansas Trego 67 67 65 62 60

202090021 39.12 -94.64 Kansas Wyandotte 56 55 51 47 42

220050004 30.23 -90.97 Louisiana Ascension 63 62 58 54 49

220150008 32.54 -93.75 Louisiana Bossier 69 67 61 55 49

220170001 32.68 -93.86 Louisiana Caddo 67 64 59 53 47

220190002 30.14 -93.37 Louisiana Calcasieu 67 67 64 61 57

220190008 30.26 -93.28 Louisiana Calcasieu 61 61 57 55 51

220190009 30.23 -93.58 Louisiana Calcasieu 66 64 61 57 53

220330003 30.42 -91.18 Louisiana East Baton Rouge 67 67 63 58 53

220330009 30.46 -91.18 Louisiana East Baton Rouge 64 63 59 56 51

220330013 30.70 -91.06 Louisiana East Baton Rouge 59 59 55 52 48

220470009 30.22 -91.32 Louisiana Iberville 63 62 58 54 49

220470012 30.21 -91.13 Louisiana Iberville 65 65 61 57 52

220511001 30.04 -90.28 Louisiana Jefferson 63 62 59 55 50

220550007 30.22 -92.05 Louisiana Lafayette 60 59 57 54 50

220570004 29.76 -90.77 Louisiana Lafourche 61 61 57 52 47

220630002 30.31 -90.81 Louisiana Livingston 62 61 57 53 49

220710012 29.99 -90.10 Louisiana Orleans 60 58 55 51 47

220730004 32.51 -92.05 Louisiana Ouachita 59 59 54 51 46

220770001 30.68 -91.37 Louisiana Pointe Coupee 62 61 58 54 50

220870004 29.94 -89.92 Louisiana St. Bernard 61 59 57 53 50

220890003 29.98 -90.41 Louisiana St. Charles 59 58 55 51 47
3A-36

Site ID lat long State County Base Case Baseline 70 65 60

220930002 29.99 -90.82 Louisiana St. James 58 57 53 49 45

220950002 30.06 -90.61 Louisiana St. John the Baptist 62 61 57 53 48

221030002 30.43 -90.20 Louisiana St. Tammany 63 62 59 56 52

221210001 30.50 -91.21 Louisiana West Baton Rouge 60 59 55 52 47

280010004 31.56 -91.39 Mississippi Adams 56 55 53 51 49

280110001 33.75 -90.72 Mississippi Bolivar 63 62 60 57 53

280330002 34.82 -89.99 Mississippi DeSoto 58 57 57 54 52

280450003 30.30 -89.40 Mississippi Hancock 54 51 51 47 44

280470008 30.39 -89.05 Mississippi Harrison 58 53 55 47 44

280490010 32.39 -90.14 Mississippi Hinds 50 49 47 44 41

280590006 30.38 -88.53 Mississippi Jackson 57 55 55 51 48

280750003 32.36 -88.73 Mississippi Lauderdale 52 51 51 48 47

280810005 34.26 -88.77 Mississippi Lee 51 51 50 49 48

281619991 34.00 -89.80 Mississippi Yalobusha 53 52 51 49 48

290030001 39.95 -94.85 Missouri Andrew 60 59 55 51 46

290190011 39.08 -92.32 Missouri Boone 57 56 53 50 46

290270002 38.71 -92.09 Missouri Callaway 55 55 52 49 46

290370003 38.76 -94.58 Missouri Cass 58 57 53 50 45

290390001 37.69 -94.04 Missouri Cedar 63 61 58 54 50

290470003 39.41 -94.27 Missouri Clay 63 62 58 53 48

290470005 39.30 -94.38 Missouri Clay 62 61 57 52 47

290470006 39.33 -94.58 Missouri Clay 64 63 58 54 48

290490001 39.53 -94.56 Missouri Clinton 64 63 58 54 48

290770036 37.26 -93.30 Missouri Greene 57 56 53 49 46

290770042 37.32 -93.20 Missouri Greene 59 58 55 51 47

290970004 37.24 -94.42 Missouri Jasper 66 62 60 54 49

290990019 38.45 -90.40 Missouri Jefferson 64 64 60 57 52

291130003 39.04 -90.86 Missouri Lincoln 63 62 59 56 53

291370001 39.48 -91.79 Missouri Monroe 58 58 55 53 50

291570001 37.70 -89.70 Missouri Perry 61 62 59 58 56

291831002 38.87 -90.23 Missouri Saint Charles 68 67 63 58 53

291831004 38.90 -90.45 Missouri Saint Charles 65 64 61 57 53

291860005 37.90 -90.42 Missouri Sainte Genevieve 60 60 57 54 50

291890005 38.49 -90.71 Missouri Saint Louis 59 58 55 51 47

291890014 38.71 -90.48 Missouri Saint Louis 66 65 61 58 53

292130004 36.71 -93.22 Missouri Taney 58 56 54 50 46

295100085 38.66 -90.20 Missouri St. Louis City 64 63 59 55 49

400019009 35.75 -94.67 Oklahoma Adair 66 62 61 55 51

400159008 35.11 -98.25 Oklahoma Caddo 64 62 60 55 50

400170101 35.48 -97.75 Oklahoma Canadian 62 62 58 54 49
3A-37

Site ID lat long State County Base Case Baseline 70 65 60

400219002 35.85 -94.99 Oklahoma Cherokee 66 61 61 54 49

400270049 35.32 -97.48 Oklahoma Cleveland 64 62 59 55 50

400310651 34.63 -98.43 Oklahoma Comanche 66 65 62 59 55

400370144 36.11 -96.36 Oklahoma Creek 65 62 60 54 48

400430860 36.16 -98.93 Oklahoma Dewey 66 65 63 60 56

400719010 36.96 -97.03 Oklahoma Kay 64 62 60 56 53

400871073 35.16 -97.47 Oklahoma McClain 63 61 58 54 50

400892001 34.48 -94.66 Oklahoma McCurtain 62 60 59 55 52

400979014 36.23 -95.25 Oklahoma Mayes 68 63 63 55 50

401090033 35.48 -97.49 Oklahoma Oklahoma 66 65 61 58 53

401090096 35.48 -97.30 Oklahoma Oklahoma 65 64 60 57 52

401091037 35.61 -97.48 Oklahoma Oklahoma 67 66 62 59 54

401159004 36.92 -94.84 Oklahoma Ottawa 64 61 60 54 50

401210415 34.90 -95.78 Oklahoma Pittsburg 65 63 60 55 50

401359021 35.41 -94.52 Oklahoma Sequoyah 63 61 59 54 50

401430137 36.36 -96.00 Oklahoma Tulsa 67 64 62 55 50

401430174 35.95 -96.00 Oklahoma Tulsa 65 61 60 52 46

401430178 36.13 -95.76 Oklahoma Tulsa 66 63 60 54 48

401431127 36.20 -95.98 Oklahoma Tulsa 67 64 62 55 49

480271047 31.09 -97.68 Texas Bell 64 62 60 57 54

480290032 29.52 -98.62 Texas Bexar 69 68 65 62 58

480290052 29.63 -98.56 Texas Bexar 71 69 66 63 59

480290059 29.28 -98.31 Texas Bexar 62 60 58 54 51

480391004 29.52 -95.39 Texas Brazoria 77 75 70 65 60

480391016 29.04 -95.47 Texas Brazoria 65 63 61 57 54

480610006 25.89 -97.49 Texas Cameron 58 57 56 54 52

480850005 33.13 -96.79 Texas Collin 72 70 65 61 56

481130069 32.82 -96.86 Texas Dallas 71 70 65 61 56

481130075 32.92 -96.81 Texas Dallas 72 71 66 62 57

481130087 32.68 -96.87 Texas Dallas 71 69 65 60 55

481210034 33.22 -97.20 Texas Denton 74 72 68 63 58

481211032 33.41 -96.94 Texas Denton 72 70 66 61 56

481390016 32.48 -97.03 Texas Ellis 67 66 62 58 54

481391044 32.18 -96.87 Texas Ellis 63 60 57 53 50

481410029 31.79 -106.32 Texas El Paso 58 58 58 57 56

481410055 31.75 -106.40 Texas El Paso 63 63 62 61 60

481410057 31.67 -106.29 Texas El Paso 63 62 62 61 60

481671034 29.25 -94.86 Texas Galveston 70 69 66 62 59

481830001 32.38 -94.71 Texas Gregg 73 67 62 54 48

482010024 29.90 -95.33 Texas Harris 74 73 68 64 59
3A-38

Site ID lat long State County Base Case Baseline 70 65 60

482010026 29.80 -95.13 Texas Harris 71 69 65 61 56

482010029 30.04 -95.67 Texas Harris 72 71 67 63 59

482010046 29.83 -95.28 Texas Harris 70 69 64 60 56

482010047 29.83 -95.49 Texas Harris 70 69 64 60 55

482010051 29.62 -95.47 Texas Harris 71 70 65 60 55

482010055 29.70 -95.50 Texas Harris 72 71 66 61 56

482010062 29.63 -95.27 Texas Harris 70 69 64 59 54

482010066 29.72 -95.50 Texas Harris 69 68 63 59 54

482010070 29.74 -95.32 Texas Harris 69 68 63 59 54

482010075 29.75 -95.35 Texas Harris 71 69 64 60 55

482010416 29.69 -95.29 Texas Harris 70 69 64 59 54

482011015 29.76 -95.08 Texas Harris 68 67 63 59 54

482011034 29.77 -95.22 Texas Harris 75 73 68 63 58

482011035 29.73 -95.26 Texas Harris 72 71 66 61 56

482011039 29.67 -95.13 Texas Harris 76 74 70 65 60

482011050 29.58 -95.02 Texas Harris 72 71 67 63 59

482030002 32.67 -94.17 Texas Harrison 67 63 58 52 47

482150043 26.23 -98.29 Texas Hidalgo 56 55 54 52 51

482151048 26.13 -97.94 Texas Hidalgo 55 55 53 52 50

482210001 32.44 -97.80 Texas Hood 67 66 62 58 54

482311006 33.15 -96.12 Texas Hunt 62 61 57 54 50

482450009 30.04 -94.07 Texas Jefferson 66 64 61 56 52

482450011 29.90 -93.99 Texas Jefferson 66 65 61 57 53

482450022 29.86 -94.32 Texas Jefferson 64 62 59 55 51

482450101 29.73 -93.89 Texas Jefferson 70 69 66 62 58

482450102 29.94 -94.00 Texas Jefferson 63 62 58 54 50

482450628 29.87 -93.96 Texas Jefferson 65 63 60 56 52

482451035 29.98 -94.01 Texas Jefferson 65 63 59 55 51

482510003 32.35 -97.44 Texas Johnson 70 68 64 60 56

482570005 32.56 -96.32 Texas Kaufman 64 61 58 53 50

483091037 31.65 -97.07 Texas McLennan 65 63 60 55 52

483390078 30.35 -95.43 Texas Montgomery 68 67 63 59 55

483491051 32.03 -96.40 Texas Navarro 64 61 58 53 50

483550025 27.77 -97.43 Texas Nueces 66 64 62 59 56

483550026 27.83 -97.56 Texas Nueces 66 64 62 58 55

483611001 30.09 -93.76 Texas Orange 66 64 61 57 52

483611100 30.19 -93.87 Texas Orange 63 60 58 53 49

483670081 32.87 -97.91 Texas Parker 70 68 64 61 57

483739991 30.70 -94.67 Texas Polk 62 61 59 56 54

483970001 32.94 -96.46 Texas Rockwall 68 66 62 59 54
3A-39

Site ID lat long State County Base Case Baseline 70 65 60

484230007 32.34 -95.42 Texas Smith 67 64 61 56 52

484390075 32.99 -97.48 Texas Tarrant 73 71 66 62 57

484391002 32.81 -97.36 Texas Tarrant 71 70 65 61 56

484392003 32.92 -97.28 Texas Tarrant 77 75 70 65 60

484393009 32.98 -97.06 Texas Tarrant 75 73 69 64 59

484393011 32.66 -97.09 Texas Tarrant 72 70 66 61 57

484530014 30.35 -97.76 Texas Travis 65 64 61 57 54

484530020 30.48 -97.87 Texas Travis 63 61 59 55 52

484690003 28.84 -97.01 Texas Victoria 63 60 57 53 50

484790016 27.51 -99.52 Texas Webb 60 59 57 56 54

*The design value from the monitor(s) with the highest projected ozone in each scenario is 

shown in bold blue text

Table 3A-10. Design Values for Midwest Monitors

Site ID lat long State County Base Case Baseline 70 65 60

170010007 39.92 -91.34 Illinois Adams 57 56 56 55 54

170190007 40.24 -88.19 Illinois Champaign 58 58 58 55 53

170191001 40.05 -88.37 Illinois Champaign 60 59 59 57 54

170230001 39.21 -87.67 Illinois Clark 58 58 58 54 50

170310001 41.67 -87.73 Illinois Cook 63 62 62 58 54

170310032 41.76 -87.55 Illinois Cook 60 59 59 60 60

170310064 41.79 -87.60 Illinois Cook 55 54 54 55 55

170310076 41.75 -87.71 Illinois Cook 63 62 62 58 54

170311003 41.98 -87.79 Illinois Cook 52 51 51 53 54

170311601 41.67 -87.99 Illinois Cook 63 63 63 59 54

170314002 41.86 -87.75 Illinois Cook 57 56 56 56 56

170314007 42.06 -87.86 Illinois Cook 49 49 49 50 51

170314201 42.14 -87.80 Illinois Cook 56 56 56 58 59

170317002 42.06 -87.67 Illinois Cook 54 54 54 57 59

170436001 41.81 -88.07 Illinois DuPage 59 58 58 54 50

170491001 39.07 -88.55 Illinois Effingham 57 57 57 54 51

170650002 38.08 -88.62 Illinois Hamilton 62 63 63 60 56

170831001 39.11 -90.32 Illinois Jersey 62 62 62 61 60

170859991 42.29 -90.00 Illinois Jo Daviess 58 57 57 56 55

170890005 42.05 -88.27 Illinois Kane 61 60 60 56 52

170971007 42.47 -87.81 Illinois Lake 58 58 58 59 60

171110001 42.22 -88.24 Illinois McHenry 60 60 60 55 51

171132003 40.52 -89.00 Illinois McLean 58 56 56 53 51

171150013 39.87 -88.93 Illinois Macon 59 58 58 56 53
3A-40

Site ID lat long State County Base Case Baseline 70 65 60

171170002 39.40 -89.81 Illinois Macoupin 57 56 56 55 54

171190008 38.89 -90.15 Illinois Madison 64 63 63 62 49

171191009 38.73 -89.96 Illinois Madison 62 61 61 60 58

171193007 38.86 -90.11 Illinois Madison 63 62 62 61 49

171199991 38.87 -89.62 Illinois Madison 60 59 59 58 57

171430024 40.69 -89.61 Illinois Peoria 54 51 51 48 45

171431001 40.75 -89.59 Illinois Peoria 61 58 58 55 52

171570001 38.18 -89.79 Illinois Randolph 58 57 57 55 53

171613002 41.51 -90.52 Illinois Rock Island 49 48 48 47 45

171630010 38.61 -90.16 Illinois Saint Clair 62 61 61 60 59

171670014 39.83 -89.64 Illinois Sangamon 58 57 57 56 55

171971011 41.22 -88.19 Illinois Will 55 54 54 50 46

172012001 42.33 -89.04 Illinois Winnebago 57 56 56 53 50

180030002 41.22 -85.02 Indiana Allen 56 56 56 53 49

180030004 41.09 -85.10 Indiana Allen 57 56 56 53 50

180110001 40.00 -86.40 Indiana Boone 60 60 60 56 51

180150002 40.54 -86.55 Indiana Carroll 58 57 57 54 50

180190008 38.39 -85.66 Indiana Clark 65 65 65 59 53

180350010 40.30 -85.25 Indiana Delaware 55 54 54 51 47

180390007 41.72 -85.83 Indiana Elkhart 56 55 55 51 48

180431004 38.31 -85.83 Indiana Floyd 63 63 63 58 52

180550001 38.99 -86.99 Indiana Greene 68 67 67 62 57

180570006 40.07 -85.99 Indiana Hamilton 57 57 57 53 49

180590003 39.94 -85.84 Indiana Hancock 53 53 53 49 45

180630004 39.76 -86.40 Indiana Hendricks 56 55 55 52 48

180690002 40.96 -85.38 Indiana Huntington 54 54 54 51 48

180710001 38.92 -86.08 Indiana Jackson 57 57 57 52 47

180810002 39.42 -86.15 Indiana Johnson 57 57 57 53 48

180839991 38.74 -87.49 Indiana Knox 65 65 65 60 55

180890022 41.61 -87.30 Indiana Lake 54 54 54 52 50

180890030 41.68 -87.49 Indiana Lake 58 58 58 56 54

180892008 41.64 -87.49 Indiana Lake 58 58 58 56 54

180910005 41.72 -86.91 Indiana LaPorte 66 66 66 62 59

180910010 41.63 -86.68 Indiana LaPorte 59 59 59 56 52

180950010 40.00 -85.66 Indiana Madison 54 54 54 50 46

180970050 39.86 -86.02 Indiana Marion 60 60 60 56 51

180970057 39.75 -86.19 Indiana Marion 59 58 58 54 50

180970073 39.79 -86.06 Indiana Marion 59 59 59 55 50

180970078 39.81 -86.11 Indiana Marion 59 59 59 55 50

181090005 39.58 -86.48 Indiana Morgan 56 56 56 52 47
3A-41

Site ID lat long State County Base Case Baseline 70 65 60

181230009 38.11 -86.60 Indiana Perry 65 65 65 60 54

181270024 41.62 -87.20 Indiana Porter 56 56 56 54 52

181270026 41.51 -87.04 Indiana Porter 54 54 54 51 47

181290003 38.01 -87.72 Indiana Posey 62 62 62 58 53

181410010 41.55 -86.37 Indiana St. Joseph 52 51 51 48 45

181410015 41.70 -86.21 Indiana St. Joseph 58 57 57 53 49

181411007 41.74 -86.11 Indiana St. Joseph 53 53 53 49 45

181450001 39.61 -85.87 Indiana Shelby 60 60 60 56 51

181630013 38.11 -87.54 Indiana Vanderburgh 64 63 63 59 54

181630021 38.01 -87.58 Indiana Vanderburgh 63 63 63 59 54

181670018 39.49 -87.40 Indiana Vigo 55 55 55 51 47

181670024 39.56 -87.31 Indiana Vigo 55 55 55 51 47

181699991 40.82 -85.66 Indiana Wabash 61 60 60 57 53

181730008 38.05 -87.28 Indiana Warrick 63 63 63 59 54

181730009 38.19 -87.34 Indiana Warrick 63 62 62 58 53

181730011 37.95 -87.32 Indiana Warrick 64 64 64 59 54

210130002 36.61 -83.74 Kentucky Bell 52 52 52 49 46

210150003 38.92 -84.85 Kentucky Boone 56 56 56 51 46

210190017 38.46 -82.64 Kentucky Boyd 60 60 60 54 49

210290006 37.99 -85.71 Kentucky Bullitt 60 60 60 56 51

210373002 39.02 -84.47 Kentucky Campbell 64 64 64 58 52

210430500 38.24 -82.99 Kentucky Carter 57 56 56 52 47

210470006 36.91 -87.32 Kentucky Christian 62 61 61 58 55

210590005 37.78 -87.08 Kentucky Daviess 69 68 68 64 58

210610501 37.13 -86.15 Kentucky Edmonson 57 57 57 54 50

210670012 38.07 -84.50 Kentucky Fayette 58 58 58 54 50

210890007 38.55 -82.73 Kentucky Greenup 60 60 60 54 49

210910012 37.94 -86.90 Kentucky Hancock 65 65 65 60 54

210930006 37.71 -85.85 Kentucky Hardin 58 58 58 53 49

211010014 37.87 -87.46 Kentucky Henderson 68 68 68 63 58

211110027 38.14 -85.58 Kentucky Jefferson 65 65 65 60 54

211110051 38.06 -85.90 Kentucky Jefferson 68 68 68 62 57

211110067 38.23 -85.65 Kentucky Jefferson 70 70 70 64 58

211130001 37.89 -84.59 Kentucky Jessamine 57 59 59 55 51

211390003 37.16 -88.39 Kentucky Livingston 58 62 62 58 54

211451024 37.06 -88.57 Kentucky McCracken 58 63 63 60 57

211759991 37.92 -83.07 Kentucky Morgan 60 60 60 55 50

211850004 38.40 -85.44 Kentucky Oldham 66 66 66 61 55

211930003 37.28 -83.21 Kentucky Perry 62 62 62 57 52

211950002 37.48 -82.54 Kentucky Pike 63 63 63 57 52
3A-42

Site ID lat long State County Base Case Baseline 70 65 60

211990003 37.10 -84.61 Kentucky Pulaski 53 53 53 49 45

212130004 36.71 -86.57 Kentucky Simpson 54 53 53 50 46

212218001 36.78 -87.85 Kentucky Trigg 57 57 57 53 49

212219991 36.78 -87.85 Kentucky Trigg 58 58 58 54 50

212270008 37.04 -86.25 Kentucky Warren 50 50 50 47 44

212299991 37.70 -85.05 Kentucky Washington 56 57 57 53 48

260050003 42.77 -86.15 Michigan Allegan 69 69 69 64 58

260190003 44.62 -86.11 Michigan Benzie 61 61 61 56 52

260210014 42.20 -86.31 Michigan Berrien 68 68 68 63 58

260270003 41.90 -86.00 Michigan Cass 63 62 62 58 54

260370001 42.80 -84.39 Michigan Clinton 57 56 56 52 49

260490021 43.05 -83.67 Michigan Genesee 61 60 60 57 53

260492001 43.17 -83.46 Michigan Genesee 60 59 59 55 52

260630007 43.84 -82.64 Michigan Huron 61 61 61 57 54

260650012 42.74 -84.53 Michigan Ingham 57 56 56 53 49

260770008 42.28 -85.54 Michigan Kalamazoo 60 59 59 56 52

260810020 42.98 -85.67 Michigan Kent 60 60 60 56 51

260810022 43.18 -85.42 Michigan Kent 59 58 58 54 50

260910007 42.00 -83.95 Michigan Lenawee 61 60 60 57 53

260990009 42.73 -82.79 Michigan Macomb 66 65 65 61 57

260991003 42.51 -83.01 Michigan Macomb 68 68 68 64 60

261010922 44.31 -86.24 Michigan Manistee 60 60 60 55 51

261050007 43.95 -86.29 Michigan Mason 61 60 60 56 52

261130001 44.31 -84.89 Michigan Missaukee 58 57 57 54 51

261210039 43.28 -86.31 Michigan Muskegon 66 65 65 60 55

261250001 42.46 -83.18 Michigan Oakland 66 66 66 62 57

261390005 42.89 -85.85 Michigan Ottawa 63 62 62 58 53

261470005 42.95 -82.46 Michigan St. Clair 64 63 63 60 56

261530001 46.29 -85.95 Michigan Schoolcraft 60 60 60 56 52

261579991 43.61 -83.36 Michigan Tuscola 58 57 57 54 50

261610008 42.24 -83.60 Michigan Washtenaw 62 62 62 58 54

261619991 42.42 -83.90 Michigan Washtenaw 61 60 60 56 53

261630001 42.23 -83.21 Michigan Wayne 61 61 61 58 55

261630019 42.43 -83.00 Michigan Wayne 69 68 68 64 60

261659991 44.18 -85.74 Michigan Wexford 56 55 55 52 49

390030009 40.77 -84.05 Ohio Allen 61 61 61 57 53

390071001 41.96 -80.57 Ohio Ashtabula 62 62 62 57 52

390090004 39.31 -82.12 Ohio Athens 57 57 57 53 48

390170004 39.38 -84.54 Ohio Butler 66 65 65 60 55

390170018 39.53 -84.39 Ohio Butler 66 65 65 60 54
3A-43

Site ID lat long State County Base Case Baseline 70 65 60

390179991 39.53 -84.73 Ohio Butler 64 63 63 59 54

390230001 40.00 -83.80 Ohio Clark 61 60 60 56 51

390230003 39.86 -84.00 Ohio Clark 60 60 60 55 50

390250022 39.08 -84.14 Ohio Clermont 62 62 62 57 51

390271002 39.43 -83.79 Ohio Clinton 62 61 61 56 51

390350034 41.56 -81.58 Ohio Cuyahoga 59 58 58 58 57

390350060 41.49 -81.68 Ohio Cuyahoga 52 51 51 51 51

390350064 41.36 -81.86 Ohio Cuyahoga 56 56 56 55 54

390355002 41.54 -81.46 Ohio Cuyahoga 58 58 58 57 57

390410002 40.36 -83.06 Ohio Delaware 59 59 59 55 51

390479991 39.64 -83.26 Ohio Fayette 57 57 57 52 48

390490029 40.08 -82.82 Ohio Franklin 66 66 66 61 56

390490037 39.97 -82.96 Ohio Franklin 61 61 61 56 52

390490081 40.09 -82.96 Ohio Franklin 58 58 58 54 49

390550004 41.52 -81.25 Ohio Geauga 60 60 60 56 52

390570006 39.67 -83.94 Ohio Greene 58 57 57 53 48

390610006 39.28 -84.37 Ohio Hamilton 68 68 68 62 56

390610010 39.21 -84.69 Ohio Hamilton 64 64 64 58 53

390610040 39.13 -84.50 Ohio Hamilton 66 66 66 60 54

390810017 40.37 -80.62 Ohio Jefferson 61 60 60 57 53

390830002 40.31 -82.69 Ohio Knox 59 59 59 55 51

390850003 41.67 -81.42 Ohio Lake 59 59 59 59 58

390850007 41.73 -81.24 Ohio Lake 53 53 53 52 52

390870011 38.63 -82.46 Ohio Lawrence 55 55 55 50 45

390870012 38.51 -82.66 Ohio Lawrence 60 60 60 54 49

390890005 40.03 -82.43 Ohio Licking 59 58 58 54 49

390930018 41.42 -82.10 Ohio Lorain 54 53 53 53 53

390950024 41.64 -83.55 Ohio Lucas 56 55 55 54 53

390950027 41.49 -83.72 Ohio Lucas 58 58 58 54 51

390950034 41.68 -83.31 Ohio Lucas 61 61 61 59 56

390970007 39.79 -83.48 Ohio Madison 59 58 58 54 49

390990013 41.10 -80.66 Ohio Mahoning 57 57 57 53 50

391030004 41.06 -81.92 Ohio Medina 57 57 57 53 49

391090005 40.08 -84.11 Ohio Miami 59 59 59 54 50

391130037 39.79 -84.13 Ohio Montgomery 62 62 62 57 52

391219991 39.94 -81.34 Ohio Noble 52 51 51 48 44

391331001 41.18 -81.33 Ohio Portage 56 56 56 52 48

391351001 39.84 -84.72 Ohio Preble 59 58 58 55 51

391510016 40.83 -81.38 Ohio Stark 61 61 61 57 52

391510022 40.71 -81.60 Ohio Stark 58 58 58 53 49
3A-44

Site ID lat long State County Base Case Baseline 70 65 60

391514005 40.93 -81.12 Ohio Stark 59 58 58 54 50

391530020 41.11 -81.50 Ohio Summit 59 59 59 55 50

391550009 41.45 -80.59 Ohio Trumbull 57 56 56 53 49

391550011 41.24 -80.66 Ohio Trumbull 61 61 61 57 53

391650007 39.43 -84.20 Ohio Warren 62 62 62 57 51

391670004 39.43 -81.46 Ohio Washington 60 59 59 55 50

391730003 41.38 -83.61 Ohio Wood 60 60 60 56 52

470010101 35.97 -84.22 Tennessee Anderson 56 56 56 52 47

470090101 35.63 -83.94 Tennessee Blount 61 61 61 56 51

470090102 35.60 -83.78 Tennessee Blount 53 53 53 49 44

470259991 36.47 -83.83 Tennessee Claiborne 49 48 48 45 42

470370011 36.21 -86.74 Tennessee Davidson 51 51 51 47 43

470370026 36.15 -86.62 Tennessee Davidson 54 54 54 50 46

470419991 36.04 -85.73 Tennessee DeKalb 55 55 55 51 48

470651011 35.23 -85.18 Tennessee Hamilton 57 56 56 53 49

470654003 35.10 -85.16 Tennessee Hamilton 57 56 56 53 49

470890002 36.11 -83.60 Tennessee Jefferson 59 59 59 54 50

470930021 36.09 -83.76 Tennessee Knox 54 54 54 50 45

470931020 36.02 -83.87 Tennessee Knox 56 56 56 52 47

471050109 35.72 -84.34 Tennessee Loudon 59 58 58 54 49

471210104 35.29 -84.95 Tennessee Meigs 56 56 56 53 50

471490101 35.73 -86.60 Tennessee Rutherford 52 52 52 48 44

471550101 35.70 -83.61 Tennessee Sevier 59 58 58 55 51

471550102 35.56 -83.50 Tennessee Sevier 58 58 58 56 53

471570021 35.22 -90.02 Tennessee Shelby 63 62 62 57 53

471570075 35.15 -89.85 Tennessee Shelby 63 63 63 58 54

471571004 35.38 -89.83 Tennessee Shelby 61 59 59 56 52

471632002 36.54 -82.42 Tennessee Sullivan 62 62 62 57 52

471632003 36.58 -82.49 Tennessee Sullivan 61 61 61 56 52

471650007 36.30 -86.65 Tennessee Sumner 59 58 58 54 50

471650101 36.45 -86.56 Tennessee Sumner 55 54 54 51 47

471870106 35.95 -87.14 Tennessee Williamson 54 54 54 49 45

471890103 36.06 -86.29 Tennessee Wilson 55 55 55 51 47

540030003 39.45 -77.96 West Virginia Berkeley 56 55 55 53 50

540110006 38.42 -82.43 West Virginia Cabell 60 59 59 54 49

540219991 38.88 -80.85 West Virginia Gilmer 53 53 53 48 43

540250003 37.91 -80.63 West Virginia Greenbrier 55 55 55 51 47

540291004 40.42 -80.58 West Virginia Hancock 63 63 63 59 56

540390010 38.35 -81.63 West Virginia Kanawha 63 63 63 57 51

540610003 39.65 -79.92 West Virginia Monongalia 63 62 62 57 52
3A-45

Site ID lat long State County Base Case Baseline 70 65 60

540690010 40.11 -80.70 West Virginia Ohio 61 60 60 56 52

540939991 39.09 -79.66 West Virginia Tucker 56 56 56 52 48

541071002 39.32 -81.55 West Virginia Wood 57 56 56 52 47

550090026 44.53 -87.91 Wisconsin Brown 59 58 58 55 51

550210015 43.32 -89.11 Wisconsin Columbia 56 55 55 52 50

550250041 43.10 -89.36 Wisconsin Dane 55 54 54 52 49

550270001 43.47 -88.62 Wisconsin Dodge 62 61 61 58 54

550290004 45.24 -86.99 Wisconsin Door 63 63 63 59 54

550350014 44.76 -91.14 Wisconsin Eau Claire 52 51 51 50 49

550390006 43.69 -88.42 Wisconsin Fond du Lac 61 60 60 57 53

550410007 45.56 -88.81 Wisconsin Forest 54 53 53 51 48

550550002 43.00 -88.82 Wisconsin Jefferson 58 57 57 55 52

550590019 42.50 -87.81 Wisconsin Kenosha 60 59 59 60 60

550610002 44.44 -87.51 Wisconsin Kewaunee 63 63 63 59 54

550630012 43.78 -91.23 Wisconsin La Crosse 54 53 53 52 51

550710007 44.14 -87.62 Wisconsin Manitowoc 66 65 65 60 55

550730012 44.71 -89.77 Wisconsin Marathon 54 53 53 51 49

550790010 43.02 -87.93 Wisconsin Milwaukee 56 55 55 53 51

550790026 43.06 -87.91 Wisconsin Milwaukee 60 60 60 57 54

550790085 43.18 -87.90 Wisconsin Milwaukee 65 64 64 61 57

550870009 44.31 -88.40 Wisconsin Outagamie 59 58 58 56 53

550890008 43.34 -87.92 Wisconsin Ozaukee 66 66 66 62 58

550890009 43.50 -87.81 Wisconsin Ozaukee 62 62 62 58 53

551010017 42.71 -87.80 Wisconsin Racine 57 57 57 57 56

551050024 42.51 -89.06 Wisconsin Rock 59 58 58 55 52

551110007 43.44 -89.68 Wisconsin Sauk 54 53 53 51 49

551170006 43.68 -87.72 Wisconsin Sheboygan 71 70 70 65 60

551199991 45.21 -90.60 Wisconsin Taylor 54 53 53 52 50

551270005 42.58 -88.50 Wisconsin Walworth 59 58 58 55 52

551330027 43.02 -88.22 Wisconsin Waukesha 57 57 57 53 50

*The design value from the monitor(s) with the highest projected ozone in each scenario is 

shown in bold blue text

Table 3A-11. Design Values for Northeast Monitors

Site ID lat long State County Base 

Case Baseline 70 65 60

90010017 41.00 -73.59 Connecticut Fairfield 70 70 66 61 56

90011123 41.40 -73.44 Connecticut Fairfield 67 67 63 58 54

90013007 41.15 -73.10 Connecticut Fairfield 73 73 68 63 58

90019003 41.12 -73.34 Connecticut Fairfield 74 74 69 65 59

90031003 41.78 -72.63 Connecticut Hartford 63 63 59 54 50
3A-46

Site ID lat long State County Base 

Case Baseline 70 65 60

90050005 41.82 -73.30 Connecticut Litchfield 58 58 54 50 46

90070007 41.55 -72.63 Connecticut Middlesex 65 65 61 56 52

90090027 41.30 -72.90 Connecticut New Haven 64 64 60 56 51

90099002 41.26 -72.55 Connecticut New Haven 72 72 67 63 57

90110124 41.35 -72.08 Connecticut New London 67 67 63 59 54

90131001 41.98 -72.39 Connecticut Tolland 63 63 59 55 50

90159991 41.84 -72.01 Connecticut Windham 58 58 55 51 47

100010002 38.98 -75.56 Delaware Kent 59 59 55 51 48

100031007 39.55 -75.73 Delaware New Castle 59 59 55 51 48

100031010 39.82 -75.56 Delaware New Castle 61 61 56 52 48

100031013 39.77 -75.50 Delaware New Castle 62 62 57 53 49

100032004 39.74 -75.56 Delaware New Castle 60 60 55 51 48

100051002 38.64 -75.61 Delaware Sussex 61 61 58 54 51

100051003 38.78 -75.16 Delaware Sussex 64 64 60 57 53

110010041 38.90 -76.95 D.C D.C. 59 59 55 50 46

110010043 38.92 -77.01 D.C. D.C. 62 62 58 53 49

230010014 43.97 -70.12 Maine Androscoggin 51 51 48 44 40

230052003 43.56 -70.21 Maine Cumberland 58 58 55 50 46

230090102 44.35 -68.23 Maine Hancock 59 59 56 53 50

230090103 44.38 -68.26 Maine Hancock 56 56 53 49 46

230112005 44.23 -69.79 Maine Kennebec 52 52 49 45 42

230130004 43.92 -69.26 Maine Knox 56 56 53 49 46

230173001 44.25 -70.86 Maine Oxford 46 46 45 43 42

230194008 44.74 -68.67 Maine Penobscot 48 48 45 42 39

230230006 44.01 -69.83 Maine Sagadahoc 50 50 47 44 40

230290019 44.53 -67.60 Maine Washington 50 50 48 45 43

230290032 44.96 -67.06 Maine Washington 47 47 45 43 41

230310038 43.66 -70.63 Maine York 50 50 47 44 41

230310040 43.59 -70.88 Maine York 53 53 50 47 44

230312002 43.34 -70.47 Maine York 60 60 57 52 48

240030014 38.90 -76.65 Maryland Anne Arundel 64 64 60 55 51

240051007 39.46 -76.63 Maryland Baltimore 65 65 60 55 51

240053001 39.31 -76.47 Maryland Baltimore 66 66 61 56 51

240090011 38.54 -76.62 Maryland Calvert 63 63 59 54 50

240130001 39.44 -77.04 Maryland Carroll 60 60 57 54 51

240150003 39.70 -75.86 Maryland Cecil 65 65 61 57 53

240170010 38.50 -76.81 Maryland Charles 62 62 58 54 51

240199991 38.45 -76.11 Maryland Dorchester 60 60 56 51 47

240210037 39.42 -77.38 Maryland Frederick 63 63 59 57 54
3A-47

Site ID lat long State County Base 

Case Baseline 70 65 60

240230002 39.71 -79.01 Maryland Garrett 60 60 59 58 58

240251001 39.41 -76.30 Maryland Harford 73 73 68 62 57

240259001 39.56 -76.20 Maryland Harford 63 63 58 53 49

240290002 39.31 -75.80 Maryland Kent 62 62 57 53 49

240313001 39.11 -77.11 Maryland Montgomery 61 61 56 52 48

240330030 39.06 -76.88 Maryland

Prince 

George's 61 61 57 52 48

240338003 38.81 -76.74 Maryland

Prince 

George's 64 64 59 55 50

240339991 39.03 -76.82 Maryland

Prince 

George's 62 62 58 53 49

240430009 39.57 -77.72 Maryland Washington 59 59 57 55 54

245100054 39.33 -76.55 Maryland

Baltimore 

(City) 62 62 57 52 48

250010002 41.98 -70.02 Massachusetts Barnstable 60 60 57 53 48

250034002 42.64 -73.17 Massachusetts Berkshire 58 58 55 52 49

250051002 41.63 -70.88 Massachusetts Bristol 60 60 57 53 49

250070001 41.33 -70.79 Massachusetts Dukes 65 65 61 57 53

250092006 42.47 -70.97 Massachusetts Essex 58 58 56 53 50

250094005 42.81 -70.82 Massachusetts Essex 57 57 54 51 47

250095005 42.77 -71.10 Massachusetts Essex 57 57 54 50 46

250130008 42.19 -72.56 Massachusetts Hampden 60 60 57 52 48

250150103 42.40 -72.52 Massachusetts Hampshire 53 53 50 46 43

250154002 42.30 -72.33 Massachusetts Hampshire 58 58 54 50 47

250170009 42.63 -71.36 Massachusetts Middlesex 55 55 52 49 45

250171102 42.41 -71.48 Massachusetts Middlesex 55 55 52 48 45

250213003 42.21 -71.11 Massachusetts Norfolk 59 59 56 53 50

250250041 42.32 -70.97 Massachusetts Suffolk 57 57 55 52 48

250250042 42.33 -71.08 Massachusetts Suffolk 50 50 48 46 43

250270015 42.27 -71.88 Massachusetts Worcester 56 56 53 50 46

250270024 42.10 -71.62 Massachusetts Worcester 56 56 53 49 46

330012004 43.57 -71.50 New Hampshire Belknap 52 52 50 47 45

330050007 42.93 -72.27 New Hampshire Cheshire 51 51 48 46 43

330074001 44.27 -71.30 New Hampshire Coos 58 58 57 56 55

330074002 44.31 -71.22 New Hampshire Coos 51 51 50 49 48

330090010 43.63 -72.31 New Hampshire Grafton 50 50 48 46 44

330111011 42.72 -71.52 New Hampshire Hillsborough 54 54 51 48 45

330115001 42.86 -71.88 New Hampshire Hillsborough 58 58 55 51 49

330131007 43.22 -71.51 New Hampshire Merrimack 53 53 50 47 45

330150014 43.08 -70.75 New Hampshire Rockingham 55 55 52 48 44

330150016 43.05 -70.71 New Hampshire Rockingham 55 55 52 48 45
3A-48

Site ID lat long State County Base 

Case Baseline 70 65 60

330150018 42.86 -71.38 New Hampshire Rockingham 56 56 53 49 46

340010006 39.46 -74.45 New Jersey Atlantic 61 61 57 54 50

340030006 40.87 -73.99 New Jersey Bergen 64 64 59 56 52

340071001 39.68 -74.86 New Jersey Camden 67 67 62 58 53

340110007 39.42 -75.03 New Jersey Cumberland 58 58 54 50 46

340130003 40.72 -74.19 New Jersey Essex 64 64 60 55 52

340150002 39.80 -75.21 New Jersey Gloucester 68 68 63 58 54

340170006 40.67 -74.13 New Jersey Hudson 64 64 59 55 51

340190001 40.52 -74.81 New Jersey Hunterdon 63 63 58 55 51

340210005 40.28 -74.74 New Jersey Mercer 65 65 60 56 52

340219991 40.31 -74.87 New Jersey Mercer 62 62 58 54 50

340230011 40.46 -74.43 New Jersey Middlesex 66 66 61 56 52

340250005 40.28 -74.01 New Jersey Monmouth 66 66 61 56 51

340273001 40.79 -74.68 New Jersey Morris 60 60 56 53 50

340290006 40.06 -74.44 New Jersey Ocean 67 67 62 57 53

340315001 41.06 -74.26 New Jersey Passaic 61 61 56 53 49

340410007 40.92 -75.07 New Jersey Warren 52 52 48 45 43

360010012 42.68 -73.76 New York Albany 57 57 52 49 46

360050133 40.87 -73.88 New York Bronx 64 64 60 56 52

360130006 42.50 -79.32 New York Chautauqua 62 62 61 59 58

360130011 42.29 -79.59 New York Chautauqua 62 62 60 59 57

360150003 42.11 -76.80 New York Chemung 57 57 55 53 52

360270007 41.79 -73.74 New York Dutchess 58 58 54 50 46

360290002 42.99 -78.77 New York Erie 61 61 60 60 60

360310002 44.37 -73.90 New York Essex 57 57 56 55 54

360310003 44.39 -73.86 New York Essex 57 57 55 55 54

360410005 43.45 -74.52 New York Hamilton 57 57 55 54 52

360430005 43.69 -74.99 New York Herkimer 55 55 54 53 52

360450002 44.09 -75.97 New York Jefferson 62 62 60 59 58

360530006 42.73 -75.78 New York Madison 55 55 53 51 49

360551007 43.15 -77.55 New York Monroe 59 59 58 57 56

360610135 40.82 -73.95 New York New York 64 64 61 58 55

360631006 43.22 -78.48 New York Niagara 64 64 63 62 58

360650004 43.30 -75.72 New York Oneida 53 53 52 50 49

360671015 43.05 -76.06 New York Onondaga 60 60 58 56 54

360715001 41.52 -74.22 New York Orange 56 56 52 48 44

360750003 43.28 -76.46 New York Oswego 58 58 56 55 53

360790005 41.46 -73.71 New York Putnam 58 58 54 50 46

360810124 40.74 -73.82 New York Queens 71 71 67 65 60
3A-49

Site ID lat long State County Base 

Case Baseline 70 65 60

360830004 42.78 -73.46 New York Rensselaer 57 57 53 50 47

360850067 40.60 -74.13 New York Richmond 71 71 67 63 58

360870005 41.18 -74.03 New York Rockland 62 62 58 53 49

360910004 43.01 -73.65 New York Saratoga 56 56 52 49 47

360930003 42.80 -73.94 New York Schenectady 54 54 51 48 45

361010003 42.09 -77.21 New York Steuben 57 57 55 54 53

361030002 40.75 -73.42 New York Suffolk 75 75 70 65 60

361030004 40.96 -72.71 New York Suffolk 67 67 63 58 52

361030009 40.83 -73.06 New York Suffolk 72 72 67 62 57

361099991 42.40 -76.65 New York Tompkins 58 58 56 55 54

361111005 42.14 -74.49 New York Ulster 59 59 56 53 50

361173001 43.23 -77.17 New York Wayne 56 56 55 53 52

361192004 41.05 -73.76 New York Westchester 63 63 58 54 50

420010002 39.93 -77.25 Pennsylvania Adams 57 57 53 51 49

420019991 39.92 -77.31 Pennsylvania Adams 59 59 55 52 50

420030008 40.47 -79.96 Pennsylvania Allegheny 67 67 65 62 60

420030010 40.45 -80.02 Pennsylvania Allegheny 65 65 63 60 58

420030067 40.38 -80.17 Pennsylvania Allegheny 65 65 63 62 60

420031005 40.61 -79.73 Pennsylvania Allegheny 71 71 68 65 58

420050001 40.81 -79.56 Pennsylvania Armstrong 64 64 61 59 57

420070002 40.56 -80.50 Pennsylvania Beaver 63 63 62 62 56

420070005 40.68 -80.36 Pennsylvania Beaver 67 67 65 63 57

420070014 40.75 -80.32 Pennsylvania Beaver 64 64 63 61 60

420110006 40.51 -75.79 Pennsylvania Berks 59 59 54 51 48

420110011 40.38 -75.97 Pennsylvania Berks 62 62 57 54 51

420130801 40.54 -78.37 Pennsylvania Blair 66 66 61 58 55

420170012 40.11 -74.88 Pennsylvania Bucks 66 66 61 57 53

420210011 40.31 -78.92 Pennsylvania Cambria 62 62 58 55 52

420270100 40.81 -77.88 Pennsylvania Centre 64 64 60 57 55

420279991 40.72 -77.93 Pennsylvania Centre 65 65 61 58 55

420290100 39.83 -75.77 Pennsylvania Chester 61 61 55 51 47

420334000 41.12 -78.53 Pennsylvania Clearfield 65 65 61 58 56

420430401 40.25 -76.85 Pennsylvania Dauphin 59 59 54 51 48

420431100 40.27 -76.68 Pennsylvania Dauphin 63 63 57 53 50

420450002 39.84 -75.37 Pennsylvania Delaware 61 61 57 52 49

420479991 41.60 -78.77 Pennsylvania Elk 56 56 54 52 51

420490003 42.14 -80.04 Pennsylvania Erie 60 60 60 59 59

420550001 39.96 -77.48 Pennsylvania Franklin 56 56 53 51 49

420590002 39.81 -80.27 Pennsylvania Greene 59 59 58 57 57
3A-50

Site ID lat long State County Base 

Case Baseline 70 65 60

420630004 40.56 -78.92 Pennsylvania Indiana 67 67 63 60 57

420690101 41.48 -75.58 Pennsylvania Lackawanna 60 60 56 53 50

420692006 41.44 -75.62 Pennsylvania Lackawanna 58 58 54 51 49

420710007 40.05 -76.28 Pennsylvania Lancaster 66 66 57 53 50

420710012 40.04 -76.11 Pennsylvania Lancaster 64 64 57 53 50

420730015 41.00 -80.35 Pennsylvania Lawrence 61 61 59 57 55

420750100 40.34 -76.38 Pennsylvania Lebanon 63 63 58 54 51

420770004 40.61 -75.43 Pennsylvania Lehigh 62 62 57 53 50

420791100 41.21 -76.00 Pennsylvania Luzerne 55 55 51 47 44

420791101 41.27 -75.85 Pennsylvania Luzerne 54 54 51 47 44

420810100 41.25 -76.92 Pennsylvania Lycoming 57 57 53 51 48

420850100 41.22 -80.48 Pennsylvania Mercer 62 62 61 60 59

420859991 41.43 -80.15 Pennsylvania Mercer 55 55 54 53 52

420890002 41.08 -75.32 Pennsylvania Monroe 54 54 50 47 45

420910013 40.11 -75.31 Pennsylvania Montgomery 63 63 58 55 51

420950025 40.63 -75.34 Pennsylvania Northampton 61 61 56 53 49

420958000 40.69 -75.24 Pennsylvania Northampton 57 57 52 49 46

420990301 40.46 -77.17 Pennsylvania Perry 59 59 55 53 51

421010004 40.01 -75.10 Pennsylvania Philadelphia 55 55 51 48 44

421010024 40.08 -75.01 Pennsylvania Philadelphia 69 69 65 60 56

421011002 40.04 -75.00 Pennsylvania Philadelphia 67 67 62 58 54

421119991 39.99 -79.25 Pennsylvania Somerset 55 55 53 51 50

421174000 41.64 -76.94 Pennsylvania Tioga 60 60 57 54 52

421250005 40.15 -79.90 Pennsylvania Washington 61 61 59 57 56

421250200 40.17 -80.26 Pennsylvania Washington 60 60 59 57 56

421255001 40.45 -80.42 Pennsylvania Washington 61 61 60 59 58

421290006 40.43 -79.69 Pennsylvania Westmoreland 63 63 60 57 55

421290008 40.30 -79.51 Pennsylvania Westmoreland 61 61 58 55 53

421330008 39.97 -76.70 Pennsylvania York 61 61 53 50 46

421330011 39.86 -76.46 Pennsylvania York 62 62 55 51 47

440030002 41.62 -71.72 Rhode Island Kent 61 61 57 52 48

440071010 41.84 -71.36 Rhode Island Providence 61 61 58 54 50

440090007 41.50 -71.42 Rhode Island Washington 64 64 60 56 51

500030004 42.89 -73.25 Vermont Bennington 54 54 51 48 46

500070007 44.53 -72.87 Vermont Chittenden 52 52 51 50 49

510030001 38.08 -78.50 Virginia Albemarle 54 54 52 50 49

510130020 38.86 -77.06 Virginia Arlington 65 65 60 55 51

510330001 38.20 -77.38 Virginia Caroline 57 57 53 49 45

510360002 37.34 -77.26 Virginia Charles 61 61 56 51 46
3A-51

Site ID lat long State County Base 

Case Baseline 70 65 60

510410004 37.36 -77.59 Virginia Chesterfield 58 58 52 48 44

510590030 38.77 -77.10 Virginia Fairfax 65 65 60 55 51

510610002 38.47 -77.77 Virginia Fauquier 50 50 48 45 43

510690010 39.28 -78.08 Virginia Frederick 54 54 52 51 50

510719991 37.33 -80.56 Virginia Giles 51 51 50 49 49

510850003 37.61 -77.22 Virginia Hanover 58 58 53 49 45

510870014 37.56 -77.40 Virginia Henrico 61 61 55 50 46

511071005 39.02 -77.49 Virginia Loudoun 60 60 56 53 49

511130003 38.52 -78.44 Virginia Madison 59 59 57 56 56

511390004 38.66 -78.50 Virginia Page 55 55 54 53 52

511479991 37.17 -78.31 Virginia Prince Edward 52 52 48 46 44

511530009 38.85 -77.63 Virginia

Prince 

William 58 58 55 52 49

511611004 37.28 -79.88 Virginia Roanoke 53 53 52 49 47

511630003 37.63 -79.51 Virginia Rockbridge 51 51 50 48 47

511650003 38.48 -78.82 Virginia Rockingham 55 55 53 53 52

511790001 38.48 -77.37 Virginia Stafford 57 57 53 48 44

511970002 36.89 -81.25 Virginia Wythe 57 57 56 55 55

515100009 38.81 -77.04 Virginia

Alexandria 

City 63 63 59 54 49

516500008 37.10 -76.39 Virginia Hampton City 58 58 55 51 47

518000004 36.90 -76.44 Virginia Suffolk City 59 59 57 53 49

518000005 36.67 -76.73 Virginia Suffolk City 56 56 54 52 50

*The design value from the monitor(s) with the highest projected ozone in each scenario is 

shown in bold blue text

3A.7 Monitors Excluded from the Quantitative Analysis

There were 1219 ozone monitors with complete ozone data for at least one DV period 

covering the years 2009-2013. Of those sites, we quantitatively analyzed 1150 in this analysis. 

As discussed in chapter 3, 69 sites were excluded from the quantitative analysis of emissions 

reductions needed to reach alternative standard levels. These sites fall into one of three 

categories, as discussed in more detail in the following three subsections.

3A.7.1 Sites without Projections Due to Insufficient Days

Some monitors were excluded from the analysis because no future design value could be 

projected at the site. This occurred when there were not enough modeled high ozone days (4 or 
3A-52

fewer) at the site to compute a design value according to EPA SIP modeling guidance. A list of 

the 36 sites falling into this category is given in Table 3A-12.

Table 3A-12. Monitors Without Projections due to Insufficient High Modeling Days to 

Meet EPA Guidance for Projecting Design Values

Site ID lat long State County

60231004 40.78 -124.18 California Humboldt

60450008 39.15 -123.20 California Mendocino

60750005 37.77 -122.40 California San Francisco

60932001 41.73 -122.63 California Siskiyou

160230101 43.46 -113.56 Idaho Butte

230031100 46.70 -68.03 Maine Aroostook

260330901 46.49 -84.36 Michigan Chippewa

270052013 46.85 -95.85 Minnesota Becker

270177416 46.71 -92.52 Minnesota Carlton

270750005 47.95 -91.50 Minnesota Lake

270834210 44.44 -95.82 Minnesota Lyon

271370034 48.41 -92.83 Minnesota Saint Louis

300298001 48.51 -114.00 Montana Flathead

300490004 46.85 -111.99 Montana Lewis and Clark

311079991 42.83 -97.85 Nebraska Knox

380070002 46.89 -103.38 North Dakota Billings

380130004 48.64 -102.40 North Dakota Burke

380150003 46.83 -100.77 North Dakota Burleigh

380171004 46.93 -96.86 North Dakota Cass

380250003 47.31 -102.53 North Dakota Dunn

380530002 47.58 -103.30 North Dakota McKenzie

380570004 47.30 -101.77 North Dakota Mercer

380650002 47.19 -101.43 North Dakota Oliver

410170122 44.02 -121.26 Oregon Deschutes

410290201 42.23 -122.79 Oregon Jackson

410591003 45.83 -119.26 Oregon Umatilla

460110003 44.35 -96.81 South Dakota Brookings

530090013 48.30 -124.62 Washington Clallam

530330080 47.57 -122.31 Washington King

530530012 46.78 -121.74 Washington Pierce

530570020 48.40 -122.50 Washington Skagit

530730005 48.95 -122.55 Washington Whatcom

550030010 46.60 -90.66 Wisconsin Ashland

551250001 46.05 -89.65 Wisconsin Vilas

560390008 43.67 -110.60 Wyoming Teton
3A-53

Site ID lat long State County

560391011 44.56 -110.40 Wyoming Teton

3A.7.2 Winter Ozone

As discussed in Chapter 2 of the RIA, high winter ozone concentrations that have been 

observed in mountain valleys in the Western U.S. are believed to result from the combination of

strong wintertime inversions, large NOx and VOC emissions from nearby oil and gas operations, 

increased UV intensity due to reflection off of snow surfaces and potentially still uncharacterized 

sources of free radicals. Current modeling tools are not sufficient to properly characterize ozone 

formation for these winter ozone episodes due to 1) the challenging task of capturing complex 

local “cold pool” meteorology using a model resolution that is optimized to capture regional and 

synoptic scale process, 2) uncertainties in quantifying the local emissions from oil and gas 

operations and 3) uncertainties in the chemistry that occurs both in the atmosphere and on snow 

surfaces during these episodes. Therefore, it was not appropriate to project ozone design values 

at monitors impacted by winter events. To identify sites impacted by winter events, we examined 

the ambient data that went into creating the 2009-2013 5-year weighted design value in locations 

known to have conditions favorable for winter ozone formation (i.e. all sites in Wyoming, Utah, 

and Colorado). At these sites, we evaluated the four highest 8-hr daily maximum ozone values in 

each year from 2009-2013 to identify wintertime ozone episodes. A site was categorized as 

having a design value impacted by wintertime ozone if at least 20% of the days examined (4 out 

20) had ozone values greater than or equal to 75 ppb and occurred during a “winter” month 

(November-March). The seven sites identified as being affected by wintertime ozone events are 

listed in Table 3A-13.

Table 3A-13. Monitors Determined to Have Design Values Affected by Winter Ozone 

Events

Site ID lat long State County

# of 

summer 

DV 

days* 

>= 75

# of winter 

DV days*

>= 75

highest 

winter 8-

hr daily 

max

2009-

2013 

DV

081030006 40.09 -108.76 Colorado Rio 

Blanco 0 7 106 71

560130099 42.53 -108.72 Wyoming Fremont 1 4 93 67

560350097 42.98 -110.35 Wyoming Sublette 0 3 83 64
3A-54

560350099 42.72 -109.75 Wyoming Sublette 0 4 123 77

560350100 42.79 -110.06 Wyoming Sublette 0 4 84 67

560350101 42.87 -109.87 Wyoming Sublette 0 4 89 66

560351002 42.37 -109.56 Wyoming Sublette 0 4 94 68

*DV days defined here are the days with the 4 highest 8-hr daily maximum ozone values in each year from 2009-

2013 (20 days).

3A.7.3 Monitoring Sites in Rural/Remote Areas of the West and Southwest

As mentioned in chapter 3 of the RIA, model-predicted ozone concentrations at 26 sites in 

rural/remote areas in the West and Southwest were excluded from the quantitative analysis (see 

list of sites in Table 3A-14). All of these 26 monitoring sites have 2025 baseline concentrations 

below 70 ppb, which is the upper end of the NAAQS range being proposed by the EPA. 

Therefore, no emissions reductions would be required for these sites for a primary standard of 70 

ppb. Furthermore, only 15 of these sites would exceed a standard of 65 ppb. The remaining 11 

would only exceed a standard of 60 ppb, which is not within the EPA’s proposed range. 

These 26 sites have two common characteristics. First, they have small modeled response 

to large regional NOx and VOC reductions in 2025 compared to other sites in the region. 

Second, these monitors would have DVs that remain above the standard after applying 

reductions needed to bring large urban areas in the region into attainment. Figure 3A-16 shows 

the response of design values at all sites in the Southwest region to a 75% NOx reduction in this 

region. Although design values at many urban sites drop by more than 10 ppb beyond the 2025 

base case values, the response at the more remote and rural sites to modeled NOx reductions is 

relatively small. Many of these sites do show response between 2009-2013 DVs and base case 

2025 DVs (up to 15 ppb decreases) suggesting that national on-the-books controls and proposed 

EPA rules could lower ozone DVs in these areas. However, modeling of additional NOx 

reductions within the region provide little incremental benefit suggesting that most of the 

regional anthropogenic sources impacting ozone at these locations have already been accounted 

for in the 2025 base case scenario.
3A-55

Figure 3A-16.Projected change in 2025 ozone design values with an additional 75% 

regional NOx control (Southwest region; stars represent sites identified in Table 

3A-14)

A variety of influences including transport from California and other regional transport, 

cross-border pollution from Mexico, and exceptional events (e.g., wildfires and stratospheric 

intrusions) could contribute to ozone concentrations at the 26 sites. Each of these contributors is 

described further below, along with the Clean Air Act provisions that offer varying degrees of 

regulatory relief. 

We have qualitatively characterized the predominant ozone influence for each site in Table 

3A-14. These qualitative characterizations are based on the modeled response to large regional 

NOx reductions in 2025, proximity to the Mexican border (i.e., potential influence from transborder pollution) and altitude (e.g., potential influence of ozone transported from the free 

troposphere: stratospheric intrusions or long range transport of international anthropogenic 

ozone). Figure 3A-17 shows the location of all sites listed in Table 3A-14 and for demonstrative 

purposes assigns each site to a category based on the predominant source of ozone in that 

location. As the table and figure indicate, all 26 sites have 2025 baseline design values below 70 
3A-56

ppb, 15 sites have design values between 65-70 ppb, and 11 sites have design values between 60-

65 ppb. Of the 26 sites, 12 sites are characterized as border sites, 8 sites are characterized as 

being strongly influenced by California emissions, and 6 sites are influenced by other ozone 

sources. 

Table 3A-14. Monitors with Limited Response to Regional NOx and National VOC 

Emissions Reductions in the 2025 Baseline

Name Site ID State County Altitude 

(m)

Monitor 

Type

Predominant 

O3 Sources

2009-

2013 

DV

Baseline

DV

Chiricahua NM 40038001 Arizona Cochise 1570 CASTNET Mexican border 72 67

Grand Canyon NP 40058001 Arizona Coconino 2152 CASTNET California +

Other sources 71 66

Alamo Lake 40128000 Arizona La Paz 376 SLAMS California 71 65

Yuma Supersite 40278011 Arizona Yuma 51 SLAMS Mexican border 

+ California 75 67

El Centro-9

th st 60251003 California Imperial - SLAMS California + 

Mexican Border 81 66

Death Valley NM 60270101 California Inyo 125

Non-EPA 

Federal 

(NPS)

California + 

Other sources 71 66

Yosemite NP 60430003 California Mariposa 5265 CASTNET California + 

Other sources 77 68

Sequoia and Kings 

Canyon NP 61070006 California Tulare 1890

Non-EPA 

Federal 

(NPS)

California + 

Other sources 81 67

Gothic 80519991 Colorado Gunnison 2926 CASTNET Other sources 66 64

Weminuche 

Wilderness Area 80671004 Colorado La Plata 2367

Non-EPA 

Federal 

(USFS)

Southwest 

region + 

Other sources

72 68

Great Basin NP 320330101 Nevada White Pine 2060 CASTNET California + 

Other sources 72 66

Sunland Park City 

Yard 350130017 New Mexico Dona Ana - SLAMS Central region + Mexican border 66 63

3 Miles N of El 

Paso

350130020 New 

Mexico Dona Ana 1250 SLAMS

Central region + 

Mexican border 

+ Other sources

67 62
3A-57

Name Site ID State County Altitude 

(m)

Monitor 

Type

Predominant 

O3 Sources

2009-

2013 

DV

Baseline

DV

2MI from MT 

Cristo Rey 350130021 New Mexico Dona Ana 1219 SLAMS Central region + Mexican border 71 67

US-Mexico Border 

Crossing 350130022 New Mexico Dona Ana 1280 SLAMS Central region + Mexican border 70 66

BLM land near 

Carlsbad 350151005 New Mexico Eddy 780 SLAMS

Central region + 

Southwest 

region + 

Mexican border

70 66

Big Bend NP 480430101 Texas Brewster 1052 CASTNET Mexican border 70 69

El Paso UTEP 481410037 Texas El Paso 1158 SLAMS Central region + 

Mexican border 71 67

Skyline Park 481410044 Texas El Paso 1158 SLAMS Central region + 

Mexican border 69 65

El Paso Chamizal 481410058 Texas El Paso 1201 SLAMS Central region + 

Mexican border 69 65

BLM 

Land/Carlsbad 483819991 Texas Randall 780 SLAMS

Central region + 

Mexican border 

+ Other sources

73 66

Canyonlands NP 490370101 Utah San Juan 1814 CASTNET Other sources 68 64

North Lava Flow 

Dr 490530006 Utah Washington 846 SLAMS California 68 62

Zion NP 490530130 Utah Washington 1213

Non-EPA 

Federal 

(NPS)

California + 

Other sources 71 65

Centennial 560019991 Wyoming Albany 3178 CASTNET Other sources 69 65

Pinedale 560359991 Wyoming Sublette 2388 CASTNET

Southwest 

region + Other 

sources

65 62
3A-58

Figure 3A-17.Location of sites identified in Table 3A-14 

In Figure 3A-17, the colored dots categorize sites by the predominant source of ozone. 

Many sites may be influenced by more than one source but are placed in a single category for 

illustrative purposes in the Figure. All ozone monitoring sites categorized as not substantially 

affected by natural or transported influences in Table 3A-14 are shown as small diamonds. Gray 

diamonds represent sites that had DVs less than or equal to 60 ppb in the 2025 baseline (or post2025 baseline for California sites). Black diamonds represent sites that had DVs greater than 60 

ppb in the 2025 baseline (or post-2025 baseline for California sites). 

In this section we look at examples of how these sites might leverage various Clean Air 

Act provisions to comply with requirements for lower alternative standard levels including: 

interstate transport provisions, exceptional events demonstrations, rural transport designations, 

and requirements for nonattainment areas in international border areas (i.e., section 179B of the 

Clean Air Act).
3A-59

Clean Air Act sections 110 and 126 have provisions designed to reduce significant 

transport contributions from upwind areas to downwind nonattainment areas. Although the RIA 

accounted for the impacts of regional NOx reductions it was not able to account for the impacts 

of emissions reductions in California on ozone at downwind sites in other states since California 

is likely to have an attainment date that is later than that of other western states. As discussed in 

the main RIA, many areas of California will not be required to meet the current ozone standard 

until after 2025 (i.e., 2027 or later) and may not be required to meet a new ozone standard until 

sometime between 2032 and 203.25 Although California will likely implement some of the 

emissions reductions necessary to meet these standards prior to their attainment date, there is a 

considerable uncertainty about how to quantify the portion of the emissions reductions that may 

occur by 2025. Therefore, we did not account for the benefits of California emissions reductions 

on design values in downwind states.

 However, it is very likely that reductions in California emissions would lead to 

substantial reductions in DVs at some monitoring locations in downwind states. For example, as 

shown in Figure 3A-18, a 90% NOx reduction in California has the potential to substantially 

improve ozone concentrations at downwind receptors in Nevada, Utah, and Arizona. Given the 

number of monitors in California projected to violate a revised ozone standard in 2025, it is quite 

likely that the state will adopt substantial local and regional controls to reach attainment. These 

controls would benefit areas outside of California as well. In addition, California and other 

states may have obligations to reduce emissions if those emissions are contributing substantially 

to interstate transport, as required under sections 110 and 126 of the Clean Air Act. Although no 

assessment of state-receptor linkages has been completed for alternative levels of the NAAQS 

(70, 65, and 60 ppb) this figure indicates that it is possible that California (and other Western 

states) could potentially have significant benefit toward attainment at downwind receptors which 

might then be subject to interstate transport provisions of the Clean Air Act.

 

25 The EPA will likely finalize designations for a revised ozone NAAQS in late 2017. Depending on the precise 

timing of the effective date of those designations, nonattainment areas classified as Severe 15 will likely have to 

attain sometime between late 2032 and early 2033 and nonattainment areas classified as Extreme will likely have to 

attain sometime between late 2037 and early 2038.
3A-60

Figure 3A-18.Projected change in 2025 ozone design values with an additional 90% 

California NOx control (Southwest region; stars represent sites identified in 

Table 3A-14)

An air agency can request and the EPA can agree to exclude data associated with eventinfluenced exceedances or violations of a NAAQS provided the event meets the statutory 

requirements in section 319 of the CAA: 

 The event “affects air quality.”

 The event “is not reasonably controllable or preventable.”

 The event is “caused by human activity that is unlikely to recur at a particular location or 

[is] a natural event.”26

The EPA’s implementing regulations, the 2007 Exceptional Events Rule, further specify that 

states must provide evidence that: 27

 

26 A natural event is further described in 40 CFR 50.1(k) as “an event in which human activity 

plays little or no direct causal role.”

27 See 72 Federal Register 13560 (March 22, 2007), 40 CFR Part 50.1, 40 CFR Part 50.14 and 40 

CFR Part 51.930.
3A-61

 “There is a clear causal relationship between the measurement under consideration and 

the event that is claimed to have affected the air quality in the area;”

 “The event is associated with a measured concentration in excess of normal historical 

fluctuations, including background;” and

 “There would have been no exceedance or violation but for the event.”

Once an air agency requests data exclusion by flagging the subject data and submitting 

supporting documentation showing that the data have been affected by exceptional events (e.g., 

stratospheric intrusions or wildfires) and the EPA concurs with this request, the event-influenced 

data would be excluded from the data set used in regulatory decisions, including determining 

whether or not an area is attaining or violating a NAAQS. As an example, Figure 3A-18 shows 

five years of daily ozone values at Weminuche Wilderness area in La Plata County, CO. This 

figure shows evidence of both episodic and persistent high ozone at this monitoring site between 

2009 and 2013. Several short periods of elevated ozone in springtime (March and April) could 

potentially be due to stratospheric intrusion, although more analysis would be required to 

definitively determine the source(s) contributing to these high concentrations. This figure also 

shows more prolonged periods of ozone values above 65 ppb which are unlikely to qualify for 

exclusion as exceptional events. In cases where design values are only 1-2 ppb above an 

alternative NAAQS level, excluding data from one or two days may be enough to show 

compliance with the standard even if there are still some periods with high ozone values that do 

not qualify. This is especially true because the standard is based on a three-year average. As can 

be seen in Figure 3A-19, some years (2009, 2012 and 2013) have substantially fewer days above 

65 ppb at this site than others (2010 and 2011). Eliminating a few high ozone days in 2010 and 

2011 could potentially bring this site’s projected design value below a threshold level when 

averaged with ozone concentrations from one or more low-ozone years.
3A-62

Figure 3A-19.Daily 8-hr maximum ozone values at ozone monitor in Weminuche 

Wilderness area in La Plata County Colorado from 2009-2013. Horizontal line 

provided at 65 ppb.

Other CAA provisions that could provide regulatory relief for air agencies and potential 

regulated entities include designation as a rural transport area (182(h)) or a determination that the 

area would have attained but for the contribution of international emissions (179B). Rural 

transport areas must show that the area does not contain emissions sources that substantially 

impact monitored ozone concentrations in the area or in other areas and that they are not in or 

adjacent to a Metropolitan Statistical Area (MSA). Section 179B demonstrations are used for 

areas that can demonstrate they would have attained the standard but for emissions emanating 

from outside the U.S. The EPA has used section 179B authority previously to approve attainment 

plans for Mexican border areas in El Paso, TX (O3, PM10, and CO plans); Nogales, AZ (PM10 

plan); and Imperial Valley, CA (PM10 plan). The 1-hour O3 attainment plan for El Paso, TX was 

approved by EPA as sufficient to demonstrate attainment of the NAAQS by the Moderate 

classification deadline of November 15, 1996, taking into account “but for” international 

emissions sources in Ciudad Juárez, Mexico (69 FR 32450, June 10, 2004). The state’s 
3A-63

demonstration included airshed modeling using only the U.S. emissions data because emissions 

data from Ciudad Juárez were not available.

3A.8 Calculation Methodology for W126 Metric

Calculation of the W126 metric occurs in several steps. The first step is to sum the 

weighted hourly ozone concentrations within each calendar month, resulting in monthly index 

values. Since plant and tree species are not photosynthetically active during nighttime hours, 

only ozone concentrations observed during daytime hours (defined as 8:00 AM to 8:00 PM local 

time) are included in the summations. The monthly W126 index values are calculated from the 

hourly ozone concentration data as follows:

Monthly W126 = ∑ ∑

Cdh

1+4403∗exp(−126∗Cdh)

19

h=8

N

d=1 Equation (3A-1)

where N is the number of days in the month, d is the day of the month (d = 1, 2, ..., N), h is the 

hour of the day (h = 0, 1, ..., 23), and Cdh is the hourly ozone concentration observed on day d, 

hour h, in parts per million.

Next, the monthly W126 index values are adjusted for missing data. If Nm is defined as the 

number of daytime ozone concentrations observed during month m (i.e. the number of terms in 

the monthly index summation), then the monthly data completeness rate is Vm = Nm / 12 * N. 

The monthly index values are adjusted by dividing them by their respective Vm. Monthly index 

values are not computed if the monthly data completeness rate is less than 75 percent (Vm < 

0.75). 

Finally, the annual W126 index values are computed as the maximum sum of their 

respective adjusted monthly index values occurring in three consecutive months (i.e., January–

March, February–April, etc.). Three-month periods spanning across two years (i.e., November–

January, December–February) are not considered, because the seasonal nature of ozone makes it 

unlikely for the maximum values to occur at that time of year. The annual W126 concentrations 

are considered valid if the data meet the annual data completeness requirements for the existing 

standard. Three-year W126 index values are calculated by taking the average of annual W126 

index values in the same three-month period in three consecutive years.
3A-64

3A.9 References

Gilliam, R. C., W. Appel, and S. Phillips. The Atmospheric Model Evaluation Tool (AMET): Meteorology Module. 

Presented at 4th Annual CMAS Models-3 Users Conference, Chapel Hill, NC, September 26 - 28, 2005. 

(http://www.cmascenter.org/)

National Research Council (NRC), 2002. Estimating the Public Health Benefits of Proposed Air Pollution 

Regulations, Washington, DC: National Academies Press.

Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform: Air 

Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.

Simon, H., Baker, K., Phillips, S., 2012: Compilation and interpretation of photochemical model performance 

statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139. 

U.S. Environmental Protection Agency (US EPA), 2005; Technical Support Document for the Final Clean Air 

Interstate Rule: Air Quality Modeling; Office of Air Quality Planning and Standards; RTP, NC; (CAIR 

Docket OAR-2005-0053-2149).

U.S. Environmental Protection Agency (US EPA) 2009; Technical Support Document for the Proposal to Designate 

an Emissions Control Area for Nitrogen Oxides, Sulfur Oxides, and Particulate Matter: EPA-420-R-007, 

329pp. (http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09007.pdf)

U.S. Environmental Protection Agency (US EPA) 2014; Draft Modeling Guidance for demonstrating attainment of 

air quality goals for ozone, PM2.5, and regional haze. September 2014, U.S. Environmental Protection 

Agency, Research Triangle Park, NC, 27711. 
4-1

CHAPTER 4: CONTROL STRATEGIES AND EMISSIONS REDUCTIONS

Overview

In order to estimate the costs and benefits of alternative ozone standards, the EPA has 

analyzed hypothetical control strategies that areas across the country might employ to attain 

alternative revised primary ozone standards of 70, 65, and 60 ppb. This chapter documents the 

emission control measures EPA applied to simulate attainment with these alternative ozone 

standards and the projected emission reductions associated with the measures.

This chapter is organized into four sections. Section 4.1 provides a summary of the steps 

used to conduct the control strategy analysis. Section 4.2 describes the emission reductions by 

sector in analyzing the baseline controls to meet the current (75 ppb) ozone standard. Section 4.3 

discusses control measures and emission reductions applied as part of the alternative standard 

analyses. Section 4.4 lists the key limitations and uncertainties associated with the control 

strategy analysis. And finally, Section 4.5 lists the references for the chapter. 

For the purposes of this discussion, it will be helpful to define some terminology. These 

definitions are specific to this analysis:

 Base Case - Emissions projected to the year 2025 reflecting current state and federal 

programs28. This does not include control programs specifically for the purpose of 

attaining the current ozone standard (75 ppb).

 Baseline - For all areas of the U.S. except California, the base case plus additional 

emissions reductions needed to reach attainment of the current ozone standard (75 

ppb) as well as emissions resulting from the Clean Power Plan (U.S. EPA, 2014b). 

Several areas in California are not required to meet the existing standard by 2025 and 

may not be required to meet a revised standard until 2037, thus we conducted 

analyses of attainment in California for post-2025. We explain the baseline treatment 

for California in more detail later in this chapter.

 Alternative Standard Analysis - Emissions reductions and associated hypothetical 

controls needed to reach attainment of the alternative standards. These reductions and 

controls are incremental to the baseline. 

 Design Value - A metric that is compared to the level of the National Ambient Air 

Quality Standard (NAAQS) to determine compliance. Design values are typically 

 

28 A complete list of programs included in the 2025 base case emissions is included in the Technical Support 

Document: Preparation of Emissions Inventories for the Version 6.1, 2011 Emissions Modeling Platform (U.S. 

EPA, 2014d).
4-2

used to classify nonattainment areas, assess progress towards meeting the NAAQS, 

and develop control strategies. The design value for the 8-hour ozone standard is 

calculated as the 3-year average of the 4th highest 8-hour daily maximum 

concentration recorded at each monitoring site.

The EPA analyzed the impact that additional emissions control measures, across 

numerous sectors, would have on predicted ambient ozone concentrations incremental to the 

baseline. These control measures are based on information available at the time of this analysis, 

and include primarily end of pipe controls. Additional emission abatement strategies such as 

fuel switching, energy efficiency, and process changes may also be employed to reach emission 

reduction targets. The potential impact of some of these types of strategies is discussed in 

Chapter 7. Note that we did not conduct this analysis incremental to controls applied as part of 

previous NAAQS analyses (e.g., NOx or PM2.5 because the data and modeling on which these 

previous analyses were based are now considered outdated and are not compatible with the 

current ozone NAAQS analysis. In addition, there were no incremental NOx controls applied in 

the PM2.5 NAAQS and therefore there would be little to no impact on the controls selected to 

meet the alternative ozone standards analyzed.29 Thus, the analysis for the alternative standards 

focuses specifically on incremental improvements beyond the current standard and other existing 

and proposed rules. The selection of control strategies is based on a least cost approach selecting 

from those controls for which we have adequate information on costs, effectiveness, and 

applicability. The hypothetical control strategies presented in this RIA represent illustrative 

options for emissions reductions that achieve national attainment of the alternative standards. 

The hypothetical control strategies are not recommendations or requirements for how a revised 

ozone standard should be implemented, and states will make all final decisions regarding 

implementation strategies for a revised ozone NAAQS.

 

29 There were no additional NOx controls applied in the PM2.5 NAAQS RIA, and therefore there would be little to 

no impact on the controls selected as part of this analysis. In addition, the only geographic areas that exceed the 

alternative ozone standard levels analyzed in this RIA and in the 2012 PM2.5 NAAQS RIA are in California. The 

attainment dates for a new PM2.5 NAAQS would likely precede attainment dates for a revised ozone NAAQS. While 

the 2012 PM2.5 NAAQS RIA concluded that controls on directly emitted PM2.5 were the most cost-effective on 

a$/ug basis, states may choose to adopt different control options. These options could include NOx controls. It is 

difficult to determine the impact on costs and benefits for this RIA because it is highly dependent upon the control 

measures that would be chosen and the costs of these measures.
4-3

The NOx control measures dataset for non-electric generating unit (EGU) point sources 

applied in these control strategies reflects a number of revisions that EPA made since the 

completion of the previous ozone NAAQS RIA. These changes include:

 Removal of incorrect links between control measures and SCCs,

 Updates to cost equations where more recent data was available to improve their 

accuracy,

 Inclusion of information that has recently become available concerning known and 

emerging technologies for reducing NOx emissions, and

 Revising costs and control efficiencies from control measures in the dataset based on 

recently obtained information from industry and multi-jurisdictional organizations 

(e.g., Ozone Transport Commissions and Lake Michigan Air Directors Consortium).

These revisions made the NOx control measures dataset for non-EGUs more accurate, 

defensible, and up to date. These improvements in this dataset will improve our control strategy 

and cost analyses not only for this RIA, but also for other potential rulemakings where control of 

NOx from non-EGUs is an important concern.

4.1 Control Strategy Analysis Steps

The primary year of analysis for analyzing the incremental costs and benefits of meeting 

a revised ozone standard is 2025. The analysis year was chosen because most areas of the U.S. 

will be required to meet a revised ozone standard by 2025. In estimating the incremental costs 

and benefits of potential alternative standards, we recognize that there are several areas that are 

not required to meet the existing ozone standard by 2025. The Clean Air Act allows areas with 

more significant air quality problems to take additional time to reach the existing standard. 

Several areas in California are not required to meet the existing standard by 2025 and may not be 

required to meet a revised standard until December 31, 2037. Depending on how areas in 

California are eventually designated, some areas may have attainment dates earlier than 2037, 

but for simplicity we are not distinguishing unique attainment years for different locations within 

California. To reflect these differences in required attainment dates, we conducted analyses of 

attainment in California for the period post-2025. 
4-4

While our goal for the California analysis was to reflect 2038, we were not able to project 

emissions and air quality beyond 2025 for California. However, we were able to adjust baseline

air quality to reflect mobile source emissions reductions for California that would occur between 

2025 and 2030; these emissions reductions were the result of state and federal mobile source 

regulations expected to be fully implemented by 2030. For ease of discussion throughout the 

analyses we refer to the time periods for potential attainment in California and in other areas of 

the U.S. as post-2025 and 2025, respectively. Because we estimate incremental emissions 

reductions, costs, and benefits for these two distinct time periods, it is not appropriate to add the 

estimates together or to directly compare the estimates.

To conduct the control strategy analyses, we require information on (i) control costs, (ii) 

control effectiveness in terms of NOx or VOC emissions reduced, (iii) the sensitivity of ozone 

design values to the NOx and VOC emissions reductions, and (iv) design value targets for each 

area. For the air quality modeling, the EPA prepared one control scenario for an alternative 

standard level of 70 ppb (Step 2 below) because we did not expect to have sufficient known 

controls for all locations to reach attainment for all of the alternative standards analyzed. The 

control scenario is not really designed to model how areas reach an alternative standard level of 

70 ppb, instead it sets up a process for developing, and applying, a list of potentially available 

known controls ordered by cost. To develop a sufficient amount of controls for the 65 and 60 

ppb alternative standard levels, we also worked to identify known controls in areas or regions 

expected to contribute to nonattainment for these alternative standards.

The following steps were taken by the EPA to analyze the impacts and costs of the 

control scenario incremental to the base case air quality modeling:

1. Identify geographic areas in the U.S. projected to exceed the alternative standard of 

70 ppb in the year 2025 in the base case air quality modeling.

2. Develop a hypothetical control scenario for these areas and generate a control case 

2025 emissions inventory for all areas except California; for California develop a 

control case post-2025 emissions inventory.

3. Perform air quality modeling to assess the air quality impacts of the hypothetical 

control scenario. Additionally, perform a series of emissions sensitivity simulations to 

develop average ozone response to across-the-board NOx and VOC emissions 

reductions in different areas (see Chapter 3).
4-5

4. Calculate the portion of the hypothetical control scenario emission reductions that are 

attributed to meeting the baseline. Estimate any additional emissions reductions 

beyond the known controls that are needed to meet the current standard based on 

average ozone response factors. These are the baseline emission reductions.

5. Estimate the additional emissions reductions incremental to the baseline that are 

needed to meet the alternative standards of 70, 65, and 60 ppb. Costs of controls 

incremental to (i.e., over and above) the baseline reductions are attributed to the costs 

of meeting the alternative standards. These emissions reductions can come from 

specific known controls or emission reductions needed beyond known controls, also 

referred to as unknown controls. Potential controls may be categorized as unknown 

because these needed emissions reductions come from sectors for which we have not 

sufficiently explored emissions abatement opportunities or sectors that might require 

non-traditional abatement through measures like energy efficiency or process 

changes.

The following sections will discuss in more detail the analysis steps presented above.

4.2 Baseline Control Strategy

Establishing the baseline allows us to estimate the incremental costs and benefits of 

attaining the alternative standards. Three steps were used to develop the baseline. First, we 

estimated 2025 base case emissions and air quality, reflecting “on the books” regulations (see 

Section 3.1.3 Emissions Inventories for a discussion of the rules included in the Base Case for 

this analysis).30 Second, we accounted for changes in ozone predicted to occur due to one 

potential approach for implementing the Clean Power Plan. Third, we identified additional 

controls that could be applied to demonstrate attainment of the current ozone standard of 75 ppb. 

Additional control measures were used in three sectors to meet the current ozone standard 

in establishing the baseline:31 Non-Electric Generating Unit Point Sources (Non-EGUs), NonPoint (Area) Sources, and Nonroad Mobile Sources. See Table 4-1 for a summary of controls 

 

30 Among others factors, the baseline for this analysis is also affected by the choice of the future year -- a year 

farther into the future allows for more time for federal measures to work and to attain. This baseline is also affected 

by the air quality starting point, potentially reducing the amount of emissions reductions required for attainment –

this analysis started from a standard of 75 ppb, where the analysis in 2008 started from a standard of 84 ppb. In 

addition, we have identified additional “known” controls to apply in this analysis, controls which are less expensive 

per ton than unknown controls.

31 In establishing the baseline, the U.S. EPA selected a set of cost-effective controls to simulate attainment of the 

current ozone standard. These control sets are hypothetical as states will ultimately determine controls as part of 

the SIP process.
4-6

applied in the baseline analysis. There were several areas in California that did not reach 

attainment of the current standard with known controls. For these geographic areas, we estimated 

the additional emissions reductions needed beyond those achieved by identified known controls 

for NOx and VOC to attain the current standard.
4-7

Table 4-1. Controls Applied for the Alternative Standard Analyses Control Strategy

Sector NOx VOC

Non-EGU Point LEC (Low Emission Combustion) Solvent Recovery System

SCR (Selective Catalytic Reduction) Work Practices, and Material 

Reformulation/Substitution

SNCR (Selective Non-Catalytic Reduction) Low-VOC materials Coatings and AddOn Controls

NSCR (Non-Selective Catalytic Reduction) Low VOC Adhesives and Improved 

Application Methods

LNB (Low NOx Burner Technology) Permanent Total Enclosure (PTE)

LNB + SCR Solvent Substitution, Non-Atomized 

Resin Application Methods

LNB + SNCR Petroleum Wastewater Treatment 

Controls

OXY-Firing Incineration (Thermal, Catalytic, etc) to 

Reduce VOC Emissions

Biosolid Injection Technology

LNB + Flue Gas Recirculation

LNB + Over Fire Air

Ignition Retard

Natural Gas Reburn

Ultra LNB

NonPoint NSCR (Non-Selective Catalytic Reduction) Process Modification to Reduce Fugitive 

VOC Emissions

LEC (Low Emission Combustion) Reformulation to Reduce VOC Content

LNB (Low NOx Burner Technology) Incineration (Thermal, Catalytic, etc) to 

Reduce VOC Emissions

LNB Water Heaters Low Pressure/Vacuum (LPV) Relief 

Valves in Gasoline Storage Tanks

Reduced Solvent Utilization

Gas Recovery in Landfills

Nonroad Diesel Retrofits & Engine Rebuilds

A map of the country is presented in Figure 41, which shows the counties projected to 

exceed the current ozone standard of 75 ppb in the 2025 base case scenario. This includes 8 

projected exceeding counties in California and 3 exceeding counties in Texas. NOx control 
4-8

measures were applied in these 11 counties in the baseline analysis to meet the current ozone

standard. In addition, NOx control measures were applied to 40 California counties and 52 Texas 

counties adjacent to exceeding counties in order to address transport coming from these adjacent 

counties. A map of the areas where control measures were applied to demonstrate attainment of 

the current standard and establish the baseline is presented in Figure 4-2.

To construct the post-2025 baseline, we included mobile source NOx and VOC emissions 

changes that are projected to occur in California from 2025 - 2030 as a result of California’s 

current mobile source control programs and projected changes in vehicle miles traveled and 

nonroad activity levels. These changes were included because they would result in emission 

reductions that would contribute toward attainment of the current ozone standard. No emission 

projections were available for other sectors for this time period, and no mobile source emissions 

projections were available beyond 2030. Additionally, VOC controls were applied in California 

counties highlighted in Figure 4-2. Even with the above mentioned controls, some areas in 

California did not reach attainment with known controls. For these areas, we estimated the 

additional NOx emission reductions needed beyond identified known controls to attain the 

standard. 
4-9

Figure 4-1. Counties Projected to Exceed the Baseline Level of the Current Ozone 

Standard (75 ppb) in 2025 Base Case 
4-10

Figure 4-2. Counties Where Emissions Reductions Were Applied to Demonstrate 

Attainment of the Current Standard for the Baseline Analysis

Tables 4-2 and 4-3 summarize the NOx and VOC emission reductions needed to 

demonstrate attainment of the current ozone standard (75 ppb). 
4-11

Table 4-2. Summary of Emission Reductions by Sector for Known Controls Applied to 

Demonstrate Attainment of the Current Standard for the 2025 Baseline - U.S., 

except California (1,000 tons/year)a

Geographic Area b Emissions Sector NOx VOC

East EGU - -

Non-EGU Point 18 -

Nonpoint 26 -

Nonroad 0.83 -

Onroad - -

Total 45 -

West EGU - -

Non-EGU Point - -

Nonpoint - -

Nonroad - -

Onroad - -

Total - -

a Estimates are rounded to two significant figures. 

b For the control strategy and cost analysis, “East” includes the Northeast, Midwest, and Central regions, and “West” 

includes the Southwest region. See Chapter 3 for a description of these regions.

Table 4-3. Summary of Emission Reductions (Known and Unknown Controls) Applied to 

Demonstrate Attainment in California for the post-2025 Baseline (1,000 

tons/year)a

Emissions Sector NOx VOC

Known Controls EGU - -

Non-EGU Point 14 0.61

Nonpoint 14 47

Nonroad 4.2 -

Onroad - -

Total 31 48

Unknown Controls All 160 -

Total 190 48

a Emission reduction estimates are rounded to two significant figures. 

The 2025 baseline for this analysis presents one scenario of future year air quality based 

upon specific control measures, additional emission reductions beyond known controls, 

promulgated federal rules such as Tier 3, and specific years of initial values for air quality 

monitoring and emissions data. This analysis presents one illustrative strategy relying on the 

identified federal measures and other strategies that states may employ. States may ultimately 
4-12

employ other strategies and/or other federal rules may be adopted that would also help in 

achieving attainment with the current standard. 

4.3 Alternative Standard Analyses

After identifying the controls in the baseline scenario, additional controls needed to meet 

the alternative standards were identified in four sectors: Electric Generating Units (EGUs), NonElectric Generating Unit Point Sources (Non-EGUs), Non-Point (Area) Sources, and Nonroad 

Mobile Sources. Onroad mobile source controls were not applied because they are largely 

addressed in existing rules such as the recent Tier 3 rule. Controls applied for the alternative 

standard analyses were the same as were applied for the baseline analysis (see Table 4-1 for a 

summary of controls applied in the baseline) with the addition of Selective Catalytic Reduction 

applied to EGUs. Other than the addition of EGU controls, the primary difference between the 

controls applied for the alternative standards versus the baseline was the geographic areas to 

which they were applied.

The EPA performed a national scale air quality modeling analysis to estimate ozone 

concentrations for the future base case year of 2025. To accomplish this, we modeled multiple 

emissions cases for 2025, including the 2025 base case and twelve 2025 emissions sensitivity 

simulations. The twelve emissions sensitivity simulations were used to develop ozone sensitivity 

factors (ppb/ton) from the modeled response of ozone to changes in NOx and VOC emissions 

from various sources and locations. These ozone sensitivity factors were then used to determine 

the amount of emissions reductions needed to reach the 2025 baseline and evaluate potential 

alternative standard levels of 70, 65, and 60 ppb incremental to the baseline. As mentioned 

previously, only a subset of known controls were included in the modeled control scenarios. 

Therefore, any additional emissions reductions may include both known and unknown controls. 

In areas of the country outside of California, Texas, and the northeast, total emissions reductions 

needed beyond the baseline were based entirely on response factors, i.e., not based on the 

hypothetical control scenario used in the air quality modeling. 

Figure 4-3 shows the counties projected to exceed the alternative standards analyzed for 

the 2025 baseline for areas other than California. For the 70 ppb scenario, emissions reductions 

were required for monitors in the Central and Northeast regions (see Chapter 3, Figure 3-3 for a 
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depiction of the regions). For the 65 and 60 ppb scenarios, emissions reductions were applied in 

all regions with projected baseline DVs above these levels. For the 60 ppb scenario, additional 

VOC tons were identified in Chicago because some sites in that area experienced NOx

disbenefits meaning that the regional NOx reductions resulted in ozone DV increases from below 

60 ppb to above 60 ppb. Therefore it was not possible to identify a scenario in which regional 

NOx reductions and maximum known VOC controls alone resulted in all Midwest monitors 

meeting a 60 ppb standard. An iterative approach was used determine a combination of NOx and 

VOC emissions reductions that would not lead to over-control at either the NOx-limited monitor 

with the highest design value or the VOC-limited monitor with the highest design value. 

Because of the regional approach we used for locations other than Texas, we were not able to 

geographically fine tune the control strategies, and thus there is some uncertainty in the actual 

amounts of emissions reductions estimated for attaining the alternative standards.

Figure 4-3. Projected Ozone Design Values in the 2025 Baseline Scenario
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Figure 4-4 shows the counties projected to exceed the alternative standards analyzed for 

the post-2025 baseline analysis for California. For the California post-2025 alternative standard 

analyses, all known controls were applied in the baseline so incremental reductions are from 

unknown controls.

Figure 4-4. Projected Ozone Design Values in the post-2025 Baseline Scenario

4.3.1 Identifying Known Controls Needed to Meet the Alternative Standards

For the 2025 alternative control strategy analyses of 70, 65 and 60 ppb, known NOx

controls for four sectors were used: EGUs, non-EGU point, nonpoint, and nonroad mobile 

sources. In a smaller number of geographic areas, VOC controls were applied to non-EGU point 

and nonpoint sources. 
4-15

For Texas, reductions were first applied to NOx sources in the areas surrounding Dallas 

and Houston. Additional reductions then were applied to VOC sources in the area surrounding 

Houston, to NOx sources in other parts of Texas, and to sources in the surrounding states. For the 

Northeast, Midwest, and Southwest areas of the country, reductions were applied to NOx sources 

within the regions. For regions where additional reductions were needed, controls were applied 

to VOC sources in urban areas with the highest ozone design values in the region.

32

 

For California, all known controls were applied in the baseline analysis so there were no 

known controls available to apply toward the incremental emissions reductions needed for the 

alternative analysis levels for post-2025. Maps of the areas where control measures were applied 

to demonstrate attainment of the alternative analysis levels are presented in Figures 4-5 and 4-6. 

Note that we do not account for between region transport of ozone, and therefore, especially for 

the 65 and 60 ppb alternative standard levels, we may be overstating the amount of emissions 

reductions needed for attainment.

 

32 Texas, California, and the northeast were included in the hypothetical control scenario for an alternative standard 

of 70 ppb. Other regions were modeled as part of the 2025 emissions sensitivity simulations. 
4-16

Figure 4-5. Counties Where Emissions Reductions Were Applied to Demonstrate 

Attainment with a 70 ppb Ozone Standard in the 2025 Analysis
4-17

Figure 4-6. Counties Where Emissions Reductions Were Applied to Demonstrate 

Attainment with 65 and 60 ppb Ozone Standards in the 2025 Analyses

Table 4-4 shows the number of exceeding counties and the number of adjacent counties 

to which controls were applied for the alternative standards. For a complete list of geographic 

areas for the alternative standards see Appendix 4.A. 
4-18

Table 4-4. Number of Counties with Exceedances and Number of Additional Counties 

Where Reductions Were Applied for the 2025 Alternative Standards Analyses -

U.S., except California

Alternative Standard Number of Counties with 

Exceedances

Number of Additional Counties Where Reductions 

Were Applied

70 ppb 15 479

65 ppb 115 1,925

60 ppb 289 1,791a

a Number of additional counties where reductions are applied declined for 60 ppb analysis because the 

number of overall counties in the analysis remained the same while the number of exceeding counties increased.

Tables 4-5 through 4-7 show the emissions reductions from known controls for the 

alternative standards analyzed. No exceedances were projected for the West region, outside of 

California, for the 70 ppb alternative standard. For the lower alternative standards of 65 and 60 

ppb, similar controls were applied as were used in the 70 ppb analysis, but the geographic area in 

which they were applied increased. The largest emission reductions were in the non-EGU point 

source and nonpoint sectors. For details regarding emission reductions by control measure see 

Appendix 4.A

Table 4-5. Summary of Emission Reductions by Sector for Known Controls Applied to 

Demonstrate Nationwide Attainment with a 70 ppb Ozone Standard in 2025, 

except California (1,000 tons/year)a

Geographic 

Area

Emissions 

Sector

Baseline Emissions Emission Reductions

NOx VOC NOx VOC

East EGU 884 25 -

Non-EGU Point 1,485 210 0.98

Nonpoint 1,487 260 54

Nonroad 1,235 5 -

Onroad 1,135 - -

Total 6,226 490 55

West EGU 159 - -

Non-EGU Point 226 - -

Nonpoint 193 - -

Nonroad 219 - -

Onroad 197 - -

Total 1,025 - -

a Emission reduction estimates are rounded to two significant figures. 
4-19

Table 4-6. Summary of Emission Reductions by Sector for Known Controls Applied to 

Demonstrate Nationwide Attainment with a 65 ppb Ozone Standard in 2025 -

except California (1,000 tons/year)a

Geographic Area Emissions Sector NOx VOC

East EGU 170 -

Non-EGU Point 410 3.6

Nonpoint 420 95

Nonroad 12 -

Total 1,000 99

West EGU 36 -

Non-EGU Point 38 0.47

Nonpoint 37 6.6

Nonroad 1.3 -

Total 110 7

a Emission reduction estimates are rounded to two significant figures.

Table 4-7. Summary of Emission Reductions by Sector for Known Controls Applied to 

Demonstrate Nationwide Attainment with a 60 ppb Ozone Standard in 2025 -

except California (1,000 tons/year)a

Geographic Area Emissions Sector NOx VOC

East EGU 170 -

Non-EGU Point 410 4.2

Nonpoint 420 99

Nonroad 12 -

Total 1,000 100

West EGU 62 -

Non-EGU Point 48 0.47

Nonpoint 39 6.6

Nonroad 1.3 -

Total 150 7

a Emission reduction estimates are rounded to two significant figures. 

4.3.2 Known Control Measures Analyzed 

Known control measures were applied to electric generating units (EGU), non-EGU 

point, nonpoint (area), and nonroad mobile sources for demonstration of attainment with the 

current and alternative standards. The applied control measures were identified using the EPA’s 

Control Strategy Tool (CoST) (U.S. EPA, 2014c), Integrated Planning Model (IPM), and 

NONROAD Model. CoST models emissions reductions and engineering costs associated with 

control strategies applied to point, area, and mobile sources of air pollutant emissions by 

matching control measures to emissions sources using algorithms such as "maximum emissions 
4-20

reduction", "least cost", and "apply measures in series". For this analysis, we applied the 

maximum emissions reduction algorithm. These controls are described further in Appendix 4.A. 

Specific controls were applied in the air quality modeling only for a portion of the analysis (for 

California, Texas, and the northeast, areas projected to exceed an alternative standard level of 70 

ppb). A majority of the emission reductions needed were identified using the ozone sensitivity 

factors developed from the twelve emissions sensitivity simulations (see Chapter 3, Section 3.1 

for a discussion of the development of the ozone sensitivity factors).

Nonpoint and nonroad mobile source emissions data are generated at the county level, 

and therefore controls for these emissions sectors were applied at the county level. EGU and 

non-EGU point source controls are applied to individual point sources. Control measures were 

applied to point and nonpoint sources of NOx, including: industrial boilers, commercial and 

institutional boilers, reciprocating internal combustion engines in the oil and gas industry and 

other industries, glass manufacturing furnaces, and cement kilns. The analysis for nonroad 

mobile sources applied NOx controls to diesel engines.

In a portion of the geographic areas where NOx controls were applied, the EPA also 

applied control measures to sources of VOC including surface coating, solvents, and fuel storage 

tanks. VOC reductions were analyzed in the urban areas with the highest ozone design values in 

each region: northern and southern California; Denver in the Southwest; Houston and Dallas in 

the Central region; Chicago, Detroit, and Louisville in the Midwest; New York, Baltimore, and 

Pittsburgh in the Northeast. Even among these areas, in some cases NOx reductions necessary to 

bring the very highest urban areas into attainment made the VOC reductions unnecessary in other 

high ozone urban areas within the region. 

To more accurately depict available controls, the EPA employed a decision rule in which 

controls were not applied to any non-EGU or nonpoint sources with less than 25 tons/year of 

emissions per pollutant. This decision rule is more inclusive of sources than the rule we 

employed in the previous O3 and PM2.5 NAAQS RIAs where we applied a minimum of 50 

tons/year for each pollutant. We modified the decision rule for this NAAQS analysis to 

recognize the potential for emissions reductions in the large number of sources emitting in the 

25-50 tons/year range in order to devise control strategies for full, or closer to full, attainment. 
4-21

Historically, the reason for not applying controls to sources emitting less than 25 tons/year has 

been that many point sources with emissions below this level already have controls in place. For

the analysis, we applied best engineering judgement to apply controls to select sources.

4.3.3 Emissions Reductions beyond Known Controls Needed to Meet the Alternative Standards

There were several areas where known controls did not achieve enough emissions

reductions to attain the alternative standards of 70, 65, and 60 ppb. To complete the analysis, the 

EPA then estimated the additional emissions reductions beyond known controls needed to reach 

attainment, also referred to as unknown controls. For information on the methodology used to 

develop the emission reductions estimates, see Chapter 3. Table 4-8 shows the emissions

reductions needed from unknown controls in 2025 for the U.S., except California, for the 

alternative standards analyzed. Table 4-9 shows the reductions needed from unknown controls 

for California for the post-2025 analysis.

Table 4-8. Summary of Emissions Reductions by Alternative Standard for Unknown 

Controls for 2025 - except California (1,000 tons/year)a

Alternative Standard Region NOx VOC

70 ppbb East

West

150

-

-

-

65 ppbc East

West

750

-

-

-

60 ppbd East

West

1,900

350

41

-

a Estimates are rounded to two significant figures. 

b Unknown controls for the 70 ppb alternative standard are needed in the Northeast and Central regions (see Chapter 

3 for a description of these regions). 

c Unknown controls for the 65 ppb alternative standard are needed in the Northeast, Central, and Midwest regions 

(see Chapter 3 for a description of these regions). 

d Unknown controls for the 60 ppb alternative standard are needed in the Northeast, Central, Midwest, and 

Southwest regions (see Chapter 3 for a description of these regions). 

Table 4-9. Summary of Emissions Reductions by Alternative Level for Unknown Controls 

for post-2025 - California (1,000 tons/year)a

Alternative Standard Region NOx VOC

70 ppb CA 53 -

65 ppb CA 110 -
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60 ppb CA 140 -

a Estimates are rounded to two significant figures. 

4.3.4 Summary of Emissions Reductions Needed to Meet the Alternative Standards

Table 4-10 summarizes the known and unknown emissions reductions needed to meet the 

alternative standard levels in 2025 for the East and West, except California. In the East for 2025, 

the unknown NOx reductions needed as percentage of the total rises from 23 percent to 66 

percent as the alternative standard level decreases from 70 ppb to 60 ppb. Meanwhile, no 

unknown VOC reductions are needed in the East for the 70 ppb and 65 ppb levels. In the West 

(except California) for 2025, unknown NOx reductions are not needed until the 60 ppb level, 

when the unknown tons constitute about 70 percent of the total reductions needed. No unknown 

VOC reductions are needed in the West (except California) for 2025 for any of the alternative 

standard levels.

Table 4-10. Summary of Known and Unknown Emissions Reductions by Alternative 

Standard Levels in 2025, Except California (1,000 tons/year)a

Alternative Standard

Geographic Area Emissions Reductions 70 ppb 65 ppb 60 ppb

East NOx Known 490 1,000 1,000

NOx Unknown 150 750 1,900

% NOx Unknown 23% 43% 66%

VOC Known 55 99 100

VOC Unknown 0 0 41

% VOC Unknown 0% 0% 29%

West NOx Known 0 110 150

NOx Unknown 0 0 350

% NOx Unknown N/A 0% 70%

VOC Known 0 7 7

VOC Unknown 0 0 0

% VOC Unknown N/A 0% 0%

a Estimates are rounded to two significant figures. 

Table 4-11 shows again that there were no known NOx emissions reductions identified 

for meeting the alternative standard levels for post-2025 California and that 100 percent of the 

NOx tons needed were unknown. Meanwhile, no unknown VOC reductions are needed for the 

any of the alternative standard levels for post-025 California.
4-23

Table 4-11. Summary of Known and Unknown Emissions Reductions by Alternative 

Standard Levels for post-2025 - California (1,000 tons/year)a

Alternative Standard

Geographic Area Emissions Reductions 70 ppb 65 ppb 60 ppb

California NOx Known 0 0 0

NOx Unknown 53 110 140

% NOx Unknown 100% 100% 100%

VOC Known 0 0 0

VOC Unknown 0 0 0

% VOC Unknown N/A N/A N/A

a Estimates are rounded to two significant figures. 

4.4 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 

engineering studies of air pollution controls and has set up what it believes is the most reasonable 

modeling framework for analyzing the cost, emissions changes, and other impacts of regulatory 

controls. However, the estimates of emissions reductions associated with our control strategies 

above are subject to important limitations and uncertainties. In the following, we discuss the

limitations and uncertainties that are most significant.

 Illustrative control strategy: A control strategy is the set of actions that States 

may take to meet a standard, such as which industries should be required to install 

end-of-pipe controls or certain types of equipment and technology. The illustrative 

control strategy analysis in this RIA presents only one potential pathway to 

attainment. The control strategies are not recommendations for how a revised ozone 

standard should be implemented, and States will make all final decisions regarding 

implementation strategies for the revised NAAQS. We do not presume that the 

control strategies presented in this RIA are an exhaustive list of possibilities for 

emissions reductions.

 Emissions Inventories and Air Quality Modeling: These serve as a foundation 

for the projected ozone values, control strategies and costs in this analysis and thus 

limitations and uncertainties for these inputs impact the results, especially for 
4-24

issues such as future year emissions projections and information on controls 

currently in place at sources. Limitations and uncertainties for these inputs are 

discussed in previous chapters devoted to these subject areas. In addition, there are 

factors that affect emissions, such as economic growth and the makeup of the 

economy (e.g., growth in the oil and natural gas sector), that introduce additional 

uncertainty. 

 Projecting level and geographic scope of exceedances: Estimates of the 

geographic areas that would exceed revised alternative levels of the standard in a 

future year, and the level to which those areas would exceed, are approximations 

based on a number of factors. The actual nonattainment determinations that would 

result from a revised standard will likely depend on the consideration of local 

issues, changes in source operations between the time of this analysis and 

implementation of a new standard, and changes in control technology over time.

 Assumptions about the baseline: There is significant uncertainty about the 

illustration of the impact of rules, especially the Clean Power Plan because it is a 

proposal and because it contains significant flexibility for states to determine how 

to choose measures to comply with the standard. 

 Applicability of control measures: The applicability of a control measure to a 

specific source varies depending on a number of process equipment factors such as 

age, design, capacity, fuel, and operating parameters. These can vary considerably 

from source to source and over time. This analysis makes assumptions across broad 

categories of sources nationwide.

 Control measure advances over time: The control measures applied do not reflect 

potential effects of technological change that may be available in future years and 

the effects of “learning by doing” or “learning by researching” are not accounted 

for in the emissions reduction estimates. Thus, all estimates of impacts associated 

with control measures applied reflect our current knowledge, and not projections, 

of the measures’ effectiveness. In our analysis, we do not have the necessary data 

for cumulative output, fuel sales, or emissions reductions for all sectors included in 

order to properly generate control costs that reflect learning-curve impacts or the 
4-25

impacts of technological change. We believe the effect of including these impacts 

would be to lower our estimates of costs for our projected year control strategies. 

 Pollutants to be targeted: Local knowledge of atmospheric chemistry in each 

geographic area may result in a different prioritization of pollutants (VOC and 

NOx) for control. For the baseline in this analysis, we included only promulgated 

or proposed rules, but that there may be additional regulations promulgated in the 

future that reduce NOx or VOC emissions. These regulations could reduce the 

current baseline levels of emissions. 

4.5 References

U.S. Environmental Protection Agency (U.S. EPA). 2014a. Control of Air Pollution from Motor Vehicles: Tier 3 

Motor Vehicle Emission and Fuel Standards. Office of Transportation and Air Quality. Available at 

http://www.epa.gov/otaq/tier3.htm.

U.S. Environmental Protection Agency (U.S. EPA). 2014b. Proposed Carbon Pollution Guidelines for Existing 

Power Plants and Emission Standards for Modified and Reconstructed Power Plants. Available at 

http://www2.epa.gov/sites/production/files/2014-06/documents/20140602ria-clean-power-plan.pdf

U.S. Environmental Protection Agency (U.S. EPA). 2014c. Control Strategy Tool (CoST) Documentation Report. 

Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at 

http://www.epa.gov/ttnecas1/cost.htm.

U.S. Environmental Protection Agency (2014d) Preparation of Emissions Inventories for the Version 6.1, 2011 

Emissions Modeling Platform (http://www.epa.gov/ttn/chief/emc
4A-1

APPENDIX 4: CONTROL STRATEGIES AND EMISSIONS REDUCTIONS

Overview

Chapter 4 describes the approach that EPA used in applying control measures to 

demonstrate attainment of alternative ozone standard levels of 70, 65, and 60 ppb and estimating 

the resulting emissions reductions. This Appendix contains more detailed information about the 

control strategy analyses, including the control measures that were applied and the geographic 

areas in which they were applied.

4A.1 Types of Control Measures

Several types of control measures were applied in the analyses for the baseline and 

alternative standard levels. These can be grouped into the following classes:

 Max NOx Reductions – NOx control measures for nonEGU point, nonpoint, and nonroad 

sources. For each of these sources, we identified the most effective control (i.e., control 

with the highest percent reduction) that could be applied to the source, given the 

following constraints:

 the source must emit at least 50 tons/yr of NOx (see description of controls on smaller 

sources below);

 any control for nonEGU point sources must result in a reduction of NOx emissions of 

at least 5 tons/yr; and

 any replacement control (i.e., a more effective control replacing an existing control) 

must achieve at least 10% more reduction than the existing control (e.g., we would 

not replace a 60% control with a 65% control).

 NOx Reductions from EGU SCRs – SCRs applied to coal-fired EGUs where no SCR is 

currently in place.

 NOx 25-50 TPY Source Reductions – Similar to the Max NOx Reductions above, except 

for smaller sources in the 25-50 ton/year NOx emissions range.

 Max VOC Reductions – Similar to Max NOx Reductions described above, except this 

includes only VOC controls.

4A.2 Application of Control Measures in Geographic Areas

Control measures were applied to geographic areas including or adjacent to areas that 

were projected to exceed the baseline and alternative standards. See Tables 4A-1 to 4A-4 for a 

listing of the NOx and VOC control groups and geographic areas to which they were applied.
4A-2

Table 4A-1. Geographic Areas for Application of NOx Controls in the Baseline and 

Alternative Standard Analyses - U.S., except Californiaa

Geographic Areas and Control Groups Baseline 70 ppb 65 ppb 60 ppb

EAST

Central Region

Max NOx Reductions within TX buffer x x x x 

NOx EGU SCR within TX buffer x x x 

NOx 25-50 tpy Source controls within TX buffer x x x 

Unknown control NOx Reductions within TX buffer U U U 

Max NOx Reductions outside of TX buffer x x x 

NOx EGU SCR outside of TX buffer x x 

NOx 25-50 tpy Source controls outside of TX buffer x x 

Unknown control NOx Reductions outside of TX buffer U U 

Northeast Region

Max NOx Reductions within Northeast buffer x x x 

NOx EGU SCR within Northeast buffer x x x 

NOx 25-50 tpy Source controls within Northeast buffer x x x 

Unknown control NOx Reductions within Northeast buffer U U U 

Max NOx Reductions outside of Northeast buffer x x x 

NOx EGU SCR outside of Northeast buffer x x 

NOx 25-50 tpy Source controls outside of Northeast buffer x x 

Unknown control NOx Reductions outside NE buffer U U 

Midwest Region

Max NOx Reductions in Midwest Region x x 

NOx EGU SCR in Midwest Region x x 

NOx 25-50 tpy Source controls in Midwest Region x x 

Unknown control NOx Reductions in MW Region U U 

WEST

Southwest Region

Max NOx Reductions in Southwest Region x x 

NOx EGU SCR in Southwest Region x x 

NOx 25-50 tpy Source controls in Southwest Region x 

Unknown control NOx Reductions in SW Region U 

a

“x” indicates known controls were applied; “U” indicates unknown control reductions.
4A-3

Table 4A-2. Geographic Areas for Application of VOC Controls in the Baseline and 

Alternative Standard Analyses - U.S., except Californiaa

Geographic Areas and Control Groups Baseline 70 ppb 65 ppb 60 ppb

EAST

Central Region

Max VOC Reductions within Houston buffer x x x 

Max VOC Reductions within Dallas buffer x x 

Northeast Region

Max VOC Reductions within CT-NJ-NY buffer x x x 

Max VOC Reductions within Baltimore buffer x 

Midwest Region

Max VOC Reductions in Chicago buffer x x 

Unknown control VOC Reductions in Chicago U 

WEST

Southwest Region

Max VOC Reductions in Denver buffer x x 

a

“x” indicates known controls were applied; “U” indicates unknown control reductions.

Table 4A-3. Geographic Areas for Application of NOx Controls in the Baseline and 

Alternative Standard Analyses - Californiaa

Geographic Areas and Control Groups Baseline 70 ppb 65 ppb 60 ppb

California

Max NOx Reductions within California (CA) buffer x 

NOx 25-50 tpy Source controls within N. CA buffer x 

Unknown Control NOx Reductions within N. CA buffer U U U U 

NOx 25-50 tpy Source controls within S. CA buffer x 

Unknown Control NOx Reductions within S. CA buffer U U U U 

a

“x” indicates known controls were applied; “U” indicates unknown control reductions.
4A-4

Table 4A-4. Geographic Areas for Application of VOC Controls in the Baseline and 

Alternative Standard Analyses - Californiaa

Geographic Areas and Control Groups Baseline 70 ppb 65 ppb 60 ppb

California

Max VOC Reductions within N. California buffer x 

Max VOC Reductions within S. California buffer x 

a

“x” indicates known controls were applied.

4A.3 NOx Control Measures for NonEGU Point Sources

Several types of NOx control technologies exist for non-EGU point sources: selective 

catalytic reduction (SCR), selective noncatalytic reduction (SNCR), natural gas reburn (NGR), 

coal reburn, and low-NOx burners (LNB). 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 point source categories covered in this RIA 

include petroleum refineries, kraft pulp mills, cement kilns, stationary internal combustion 

engines, glass manufacturing, combustion turbines, and incinerators. NOx control measures 

available for petroleum refineries, particularly process heaters at these plants, include LNB, 

SNCR, FGR, and SCR along with combinations of these technologies. NOx control measures 

available for kraft pulp mills include those available to industrial boilers, namely LNB, SCR, 

SNCR, along with water injection. 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 plus steam injection (SI) are available measures for 

combustion turbines. Finally, SNCR is an available control technology at incinerators. 

Tables 4A-5 through 4A-12 contain lists of the NOx and VOC control measures applied in 

these analyses for non-EGU point sources,EGUs, nonpoint sources, and nonroad sources. The 
4A-5

table also presents the associated emission reductions for the baseline and alternative standard 

analyses. The number of geographic areas in which they were applied expanded as the level of 

the alternative standard analyzed became more stringent.

Table 4A-5. NOx Control Measures Applied in the Baseline Analysis

NOx Control Measure

Reductions 

(tons/yr) 

Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 694

Biosolid Injection Technology - Cement Kilns 1,021

Episodic Ban - Open Burning 570

Ignition Retard - IC Engines 30

Low Emission Combustion - Gas Fired Lean Burn IC Engines 1,552

Low NOx Burner - Commercial/Institutional Boilers & IC Engines 6,699

Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 165

Low NOx Burner - Industrial Combustion 270

Low NOx Burner - Lime Kilns 309

Low NOx Burner - Natural Gas-Fired Turbines 4,735

Low NOx Burner - Residential Furnaces 5,381

Low NOx Burner - Residential Water Heaters & Space Heaters 6,605

Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 36

Low NOx Burner and Flue Gas Recirculation - Iron & Steel Mills - Reheating 42

Low NOx Burner and SCR - Coal-Fired ICI Boilers 1,174

Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 5,286

Natural Gas Reburn - Natural Gas-Fired EGU Boilers 79

Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 4,998

Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 20,008

OXY-Firing - Glass Manufacturing 5,429

Selective Catalytic Reduction (SCR) - Cement Kilns 2,982

Selective Catalytic Reduction (SCR) - Coal Fired EGU Boilers 76

Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 945

Selective Catalytic Reduction (SCR) - IC Engines, Diesel 2,041

Selective Catalytic Reduction (SCR) - ICI Boilers 3,218

Selective Catalytic Reduction (SCR) - Industrial Incinerators 1,062

Selective Catalytic Reduction (SCR) - Petroleum Refinery Gas-Fired Process Heaters 456

Selective Catalytic Reduction (SCR) - Sludge Incineration 366

Selective Catalytic Reduction (SCR) - Utility Boilers 55

Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 16

Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 130

Selective Non-Catalytic Reduction (SNCR) - Utility Boilers 158
4A-6

Table 4A-6. VOC Control Measures Applied in the Baseline Analysis

VOC Control Measure

Reductions 

(tons/yr) 

Control of Fugitive Releases - Oil & Natural Gas Production 39

Control Technology Guidelines - Wood Furniture Surface Coating 777

Gas Recovery - Municipal Solid Waste Landfill 2,064

Improved Work Practices, Material Substitution, Add-On Controls - Cleaning Solvents 155

Improved Work Practices, Material Substitution, Add-On Controls - Printing 77

Incineration - Other 181

Incineration - Surface Coating 6,362

Low-VOC Coatings and Add-On Controls - Surface Coating 945

LPV Relief Valve - Underground Tanks 353

Permanent Total Enclosure (PTE) - Surface Coating 100

Process Modification - Oil and Natural Gas Production 4,311

RACT - Graphic Arts 3,423

Reduced Solvent Utilization - Surface Coating 342

Reformulation - Aerosol Paints 12

Reformulation - Architectural Coatings 1,912

Reformulation - Industrial Adhesives 5,076

Reformulation-Process Modification - Automobile Refinishing 4,665

Reformulation-Process Modification - Cold Cleaning 5,877

Reformulation-Process Modification - Cutback Asphalt 6,478

Reformulation-Process Modification - Open Top Degreasing 47

Reformulation-Process Modification - Surface Coating 4,225

Wastewater Treatment Controls- POTWs 237

Table 4A-7. NOx Control Measures Applied in the 70 ppb Alternative Standard Analysis

NOx Control Measure

Reductions 

(tons/yr) 

Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 8,091

Biosolid Injection Technology - Cement Kilns 1,315

Episodic Ban - Open Burning 2,086

Ignition Retard - IC Engines 486

Low Emission Combustion - Gas Fired Lean Burn IC Engines 83,046

Low NOx Burner - Coal Cleaning 255

Low NOx Burner - Commercial/Institutional Boilers & IC Engines 20,004

Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 2,290

Low NOx Burner - Industrial Combustion 495

Low NOx Burner - Lime Kilns 1,870

Low NOx Burner - Natural Gas-Fired Turbines 14,408

Low NOx Burner - Residential Furnaces 12,056

Low NOx Burner - Residential Water Heaters & Space Heaters 18,843

Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 350
4A-7

NOx Control Measure

Reductions 

(tons/yr) 

Low NOx Burner and Flue Gas Recirculation - Iron & Steel Mills - Reheating 388

Low NOx Burner and Over Fire Air - Utility Boilers 178

Low NOx Burner and SCR - Coal-Fired ICI Boilers 7,197

Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 22,632

Low NOx Burner and SNCR - Industr/Commercial/Institutional Boilers 153

Low Sulfur Fuel - Miscellaneous 2,892

Natural Gas Reburn - Natural Gas-Fired EGU Boilers 262

Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 4,984

Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 189,563

Non-Selective Catalytic Reduction (NSCR) - Nitric Acid Mfg 659

OXY-Firing - Glass Manufacturing 17,155

SCR and Flue Gas Recirculation - Fluid Catalytic Cracking Units 163

SCR and Flue Gas Recirculation - ICI Boilers 307

Selective Catalytic Reduction (SCR) - Ammonia Mfg 4,476

Selective Catalytic Reduction (SCR) - Cement Kilns 18,260

Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 2,995

Selective Catalytic Reduction (SCR) - IC Engines, Diesel 1,675

Selective Catalytic Reduction (SCR) - ICI Boilers 6,049

Selective Catalytic Reduction (SCR) - Industrial Combustion 1,111

Selective Catalytic Reduction (SCR) - Industrial Incinerators 717

Selective Catalytic Reduction (SCR) - Petroleum Refinery Gas-Fired Process Heaters 4,757

Selective Catalytic Reduction (SCR) - Process Heaters 19

Selective Catalytic Reduction (SCR) - Sludge Incineration 7,991

Selective Catalytic Reduction (SCR) - Utility Boilers 30,482

Selective Non-Catalytic Reduction (SNCR) - Coke Mfg 1,543

Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 1,082

Selective Non-Catalytic Reduction (SNCR) - ICI Boilers 154

Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 482

Selective Non-Catalytic Reduction (SNCR) - Miscellaneous 31

Selective Non-Catalytic Reduction (SNCR) - Municipal Waste Combustors 304

Ultra-Low NOx Burner - Process Heaters 229
4A-8

Table 4A-8. VOC Control Measures Applied in the 70 ppb Alternative Standard Analysis

VOC Control Measure

Reductions 

(tons/yr) 

Control of Fugitive Releases - Oil & Natural Gas Production 16

Control Technology Guidelines - Wood Furniture Surface Coating 1,184

Flare - Petroleum Flare 110

Gas Recovery - Municipal Solid Waste Landfill 242

Improved Work Practices, Material Substitution, Add-On Controls - Cleaning Solvents 10

Improved Work Practices, Material Substitution, Add-On Controls - Printing 148

Incineration - Other 350

Incineration - Surface Coating 14,071

Low-VOC Coatings and Add-On Controls - Surface Coating 209

LPV Relief Valve - Underground Tanks 4,011

Permanent Total Enclosure (PTE) - Surface Coating 446

RACT - Graphic Arts 3,054

Reduced Solvent Utilization - Surface Coating 2,541

Reformulation - Architectural Coatings 17,678

Reformulation - Industrial Adhesives 1,793

Reformulation-Process Modification - Automobile Refinishing 3,209

Reformulation-Process Modification - Cold Cleaning 1,600

Reformulation-Process Modification - Cutback Asphalt 817

Reformulation-Process Modification - Surface Coating 3,571

Solvent Recovery System - Printing/Publishing 31

Wastewater Treatment Controls- POTWs 217

Table 4A-9. NOx Control Measures Applied in the 65 ppb Alternative Standard Analysis

NOx Control Measure

Reductions 

(tons/yr) 

Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 27,057

Biosolid Injection Technology - Cement Kilns 6,423

Episodic Ban - Open Burning 4,423

Ignition Retard - IC Engines 761

Low Emission Combustion - Gas Fired Lean Burn IC Engines 174,033

Low NOx Burner - Coal Cleaning 518

Low NOx Burner - Commercial/Institutional Boilers & IC Engines 40,691

Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 4,226

Low NOx Burner - Industrial Combustion 1,578

Low NOx Burner - Lime Kilns 5,273

Low NOx Burner - Miscellaneous Sources 21

Low NOx Burner - Natural Gas-Fired Turbines 26,982

Low NOx Burner - Residential Furnaces 16,660

Low NOx Burner - Residential Water Heaters & Space Heaters 57,314

Low NOx Burner - Steel Foundry Furnaces 294

Low NOx Burner - Surface Coating Ovens 26

Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 420
4A-9

NOx Control Measure

Reductions 

(tons/yr) 

Low NOx Burner and Flue Gas Recirculation - Iron & Steel Mills - Reheating 892

Low NOx Burner and Over Fire Air - Utility Boilers 333

Low NOx Burner and SCR - Coal-Fired ICI Boilers 30,790

Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 37,948

Low NOx Burner and SNCR - Industr/Commercial/Institutional Boilers 263

Low Sulfur Fuel - Miscellaneous 3,194

Natural Gas Reburn - Natural Gas-Fired EGU Boilers 480

Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 12,863

Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 323,763

Non-Selective Catalytic Reduction (NSCR) - Nitric Acid Mfg 927

OXY-Firing - Glass Manufacturing 29,546

SCR and Flue Gas Recirculation - Fluid Catalytic Cracking Units 185

SCR and Flue Gas Recirculation - ICI Boilers 318

SCR and Flue Gas Recirculation - Process Heaters 548

Selective Catalytic Reduction (SCR) - Ammonia Mfg 5,151

Selective Catalytic Reduction (SCR) - Cement Kilns 36,013

Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 4,108

Selective Catalytic Reduction (SCR) - IC Engines, Diesel 8,905

Selective Catalytic Reduction (SCR) - ICI Boilers 18,284

Selective Catalytic Reduction (SCR) - Industrial Combustion 4,428

Selective Catalytic Reduction (SCR) - Industrial Incinerators 1,006

Selective Catalytic Reduction (SCR) - Iron Ore Processing 1,195

Selective Catalytic Reduction (SCR) - Petroleum Refinery Gas-Fired Process Heaters 7,691

Selective Catalytic Reduction (SCR) - Process Heaters 19

Selective Catalytic Reduction (SCR) - Sludge Incineration 9,007

Selective Catalytic Reduction (SCR) - Space Heaters 272

Selective Catalytic Reduction (SCR) - Utility Boilers 211,200

Selective Non-Catalytic Reduction (SNCR) - Coke Mfg 2,399

Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 1,260

Selective Non-Catalytic Reduction (SNCR) - ICI Boilers 170

Selective Non-Catalytic Reduction (SNCR) - Industrial Combustion 69

Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 1,502

Selective Non-Catalytic Reduction (SNCR) - Miscellaneous 132

Selective Non-Catalytic Reduction (SNCR) - Municipal Waste Combustors 1,351

Selective Non-Catalytic Reduction (SNCR) - Utility Boilers 329

Ultra-Low NOx Burner - Process Heaters 300
4A-10

Table 4A-10. VOC Control Measures Applied in the 65 ppb Alternative Standard Analysis

VOC Control Measure

Reductions 

(tons/yr) 

Control of Fugitive Releases - Oil & Natural Gas Production 31

Control Technology Guidelines - Wood Furniture Surface Coating 1,928

Flare - Petroleum Flare 110

Gas Recovery - Municipal Solid Waste Landfill 332

Improved Work Practices, Material Substitution, Add-On Controls - Cleaning Solvents 245

Improved Work Practices, Material Substitution, Add-On Controls - Printing 564

Incineration - Other 379

Incineration - Surface Coating 25,785

Low VOC Adhesives and Improved Application Methods - Industrial Adhesives 223

Low-VOC Coatings and Add-On Controls - Surface Coating 1,267

LPV Relief Valve - Underground Tanks 7,317

Permanent Total Enclosure (PTE) - Surface Coating 1,554

Petroleum and Solvent Evaporation - Surface Coating Operations 159

RACT - Graphic Arts 5,988

Reduced Solvent Utilization - Surface Coating 2,796

Reformulation - Architectural Coatings 39,057

Reformulation - Industrial Adhesives 1,698

Reformulation-Process Modification - Automobile Refinishing 5,264

Reformulation-Process Modification - Cutback Asphalt 3,058

Reformulation-Process Modification - Surface Coating 6,913

Solvent Recovery System - Printing/Publishing 842

Solvent Substitution and Improved Application Methods - Fiberglass Boat Mfg 14

Wastewater Treatment Controls- POTWs 242

Table 4A-11. NOx Control Measures Applied in the 60 ppb Alternative Standard Analysis

NOx Control Measure

Reductions 

(tons/yr) 

Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 27,547

Biosolid Injection Technology - Cement Kilns 6,423

Episodic Ban - Open Burning 4,561

Ignition Retard - IC Engines 821

Low Emission Combustion - Gas Fired Lean Burn IC Engines 178,146

Low NOx Burner - Coal Cleaning 518

Low NOx Burner - Commercial/Institutional Boilers & IC Engines 40,876

Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 4,319

Low NOx Burner - Industrial Combustion 1,578

Low NOx Burner - Lime Kilns 5,273

Low NOx Burner - Miscellaneous Sources 35

Low NOx Burner - Natural Gas-Fired Turbines 29,155

Low NOx Burner - Residential Furnaces 17,103

Low NOx Burner - Residential Water Heaters & Space Heaters 57,726

Low NOx Burner - Steel Foundry Furnaces 294
4A-11

NOx Control Measure

Reductions 

(tons/yr) 

Low NOx Burner - Surface Coating Ovens 26

Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 420

Low NOx Burner and Flue Gas Recirculation - Iron & Steel Mills - Reheating 892

Low NOx Burner and Over Fire Air - Utility Boilers 333

Low NOx Burner and SCR - Coal-Fired ICI Boilers 30,817

Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 38,350

Low NOx Burner and SNCR - Industr/Commercial/Institutional Boilers 263

Low Sulfur Fuel - Miscellaneous 3,194

Natural Gas Reburn - Natural Gas-Fired EGU Boilers 502

Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 12,863

Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 325,380

Non-Selective Catalytic Reduction (NSCR) - Nitric Acid Mfg 958

OXY-Firing - Glass Manufacturing 29,546

SCR and Flue Gas Recirculation - Fluid Catalytic Cracking Units 185

SCR and Flue Gas Recirculation - ICI Boilers 318

SCR and Flue Gas Recirculation - Process Heaters 548

Selective Catalytic Reduction (SCR) - Ammonia Mfg 5,151

Selective Catalytic Reduction (SCR) - Cement Kilns 36,013

Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 4,135

Selective Catalytic Reduction (SCR) - IC Engines, Diesel 9,288

Selective Catalytic Reduction (SCR) - ICI Boilers 18,284

Selective Catalytic Reduction (SCR) - Industrial Combustion 4,428

Selective Catalytic Reduction (SCR) - Industrial Incinerators 1,006

Selective Catalytic Reduction (SCR) - Iron Ore Processing 1,195

Selective Catalytic Reduction (SCR) - Petroleum Refinery Gas-Fired Process Heaters 7,691

Selective Catalytic Reduction (SCR) - Process Heaters 19

Selective Catalytic Reduction (SCR) - Sludge Incineration 9,007

Selective Catalytic Reduction (SCR) - Space Heaters 335

Selective Catalytic Reduction (SCR) - Utility Boilers 236,736

Selective Non-Catalytic Reduction (SNCR) - Coke Mfg 2,399

Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 1,260

Selective Non-Catalytic Reduction (SNCR) - ICI Boilers 170

Selective Non-Catalytic Reduction (SNCR) - Industrial Combustion 92

Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 1,502

Selective Non-Catalytic Reduction (SNCR) - Miscellaneous 132

Selective Non-Catalytic Reduction (SNCR) - Municipal Waste Combustors 1,351

Selective Non-Catalytic Reduction (SNCR) - Utility Boilers 329

Ultra-Low NOx Burner - Process Heaters 300
4A-12

Table 4A-12. VOC Control Measures Applied in the 60 ppb Alternative Standard Analysis

VOC Control Measure

Reductions 

(tons/yr) 

Control of Fugitive Releases - Oil & Natural Gas Production 33

Control Technology Guidelines - Wood Furniture Surface Coating 2,063

Flare - Petroleum Flare 110

Gas Recovery - Municipal Solid Waste Landfill 372

Improved Work Practices, Material Substitution, Add-On Controls - Cleaning Solvents 265

Improved Work Practices, Material Substitution, Add-On Controls - Printing 564

Incineration - Other 379

Incineration - Surface Coating 26,109

Low VOC Adhesives and Improved Application Methods - Industrial Adhesives 237

Low-VOC Coatings and Add-On Controls - Surface Coating 1,523

LPV Relief Valve - Underground Tanks 7,610

Permanent Total Enclosure (PTE) - Surface Coating 1,857

Petroleum and Solvent Evaporation - Surface Coating Operations 237

RACT - Graphic Arts 6,273

Reduced Solvent Utilization - Surface Coating 2,886

Reformulation - Architectural Coatings 40,866

Reformulation - Industrial Adhesives 1,698

Reformulation-Process Modification - Automobile Refinishing 5,633

Reformulation-Process Modification - Cutback Asphalt 3,571

Reformulation-Process Modification - Surface Coating 7,072

Solvent Recovery System - Printing/Publishing 888

Solvent Substitution and Improved Application Methods - Fiberglass Boat Mfg 14

Wastewater Treatment Controls- POTWs 242

4A.4 VOC Control Measures for Non-EGU Point Sources

VOC controls were applied to a number of non-EGU point sources. Some examples are 

permanent total enclosures (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 they 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). Another control is petroleum and solvent evaporation applied to printing 

and publishing sources as well as to surface coating operations.

4A.5 NOx Control Measures for Nonpoint (Area) and Nonroad Sources

The nonpoint source sector of the emissions inventory is composed of sources that are 

generally too small and/or numerous to estimate emissions on an individual source basis (e.g.,
4A-13

dry cleaners, residential furnaces, woodstoves, fireplaces, backyard waste burning, etc). Instead, 

we estimate their emissions for each county as a whole, often using an emissions factor that is 

applied to a surrogate of activity such as population or number of houses. 

Control measures for nonpoint sources are also applied at the county level, i.e., to the 

county level emissions as a whole. Several control measures were applied to NOx emissions from 

nonpoint sources. One is low NOx burner technology to reduce NOx emissions. This control is 

applied to industrial oil, natural gas, and coal combustion sources. Other nonpoint source 

controls include the installation of low-NOx space heaters and water heaters in commercial and 

institutional sources, and episodic bans on open burning. The open burning control measure 

applied to yard waste and land clearing debris. It consists of periodic daily bans on burning such 

waste, as the predicted ozone levels indicate that such burning activities should be postponed. 

This control measure is not applied to any prescribed burning activities.

Retrofitting diesel nonroad equipment can provide NOx and HC benefits. The retrofit 

strategies included in the RIA nonroad retrofit measure are:

• Installation of emissions after-treatment devices called selective catalytic reduction 

(“SCRs”)

• Rebuilding engines (“rebuild/upgrade kit”)

We chose to focus on these strategies due to their high NOx emissions reduction potential 

and widespread application.

4A.6 VOC Control Measures for Nonpoint (Area) Sources

Some VOC controls for nonpoint sources are for the use of low or no VOC materials for 

graphic art sources. Other controls involve the application of limits for adhesive and sealant 

VOC content in wood furniture and solvent source categories. The OTC solvent cleaning rule 

establishes hardware and operating requirements for specified vapor cleaning machines, as well 

as solvent volatility limits and operating practices for cold cleaners. The Low Pressure/Vacuum 

Relief Valve control measure is the addition of low pressure/vacuum (LP/V) relief valves to 

gasoline storage tanks at service stations with Stage II control systems. LP/V relief valves 
4A-14

prevent breathing emissions from gasoline storage tank vent pipes. Another control based on a 

California South Coast Air Quality Management District (SQAQMD) establishes VOC content 

limits for metal coatings along with application procedures and equipment requirements. 

Switching to Emulsified Asphalts is a generic control measure replacing VOC-containing 

cutback asphalt with VOC-free emulsified asphalt. The Reformulation control measures include 

switching to and/or encouraging the use of low-VOC materials.
5-1

CHAPTER 5: HUMAN HEALTH BENEFITS ANALYSIS APPROACH AND RESULTS

5.1 Synopsis

This chapter presents the estimated human health benefits for the proposed range of 

National Ambient Air Quality Standards (NAAQS) for ozone. In this chapter, we quantify the 

health-related benefits of the ozone air quality improvements resulting from the illustrative 

emission control scenarios that reduce emissions of the ozone precursor pollutants nitrogen 

oxides (NOx) and volatile organic compounds (VOCs) to reach the set of alternative ozone 

NAAQS levels being considered. This chapter also estimates the health co-benefits of the fine 

particulate matter (PM2.5)-related air quality improvements that would occur as a result of 

reducing NOx emissions.33 The EPA Administrator is proposing to revise the level of the 

primary ozone standard to within a range of 65 to 70 ppb and is soliciting comment on 

alternative standard levels below 65 ppb, and as low as 60 ppb. In the Regulatory Impact 

Analysis (RIA) we analyze the following alternative standard levels: 70, 65 and 60 ppb.

We selected 2025 as the primary year of analysis because the Clean Air Act requires 

most areas of the U.S. to meet a revised ozone standard by 2025. Benefits are estimated 

incremental to attainment of the existing standard of 75 ppb. In estimating the incremental costs 

and benefits of potential alternative standards, we recognize that there are several areas that the 

Act does not require to meet the existing ozone standard of 75 ppb by 2025. The Clean Air Act 

provides areas with more significant air quality problems with additional time to reach the 

existing standard. Several areas in California are not expected to meet the existing standard by 

2025 and may not be required to meet a revised standard until December 31, 2037. 

We estimated the benefits of California attaining a revised standard in 2038 to account 

for the fact that many locations in this state must attain a revised standard at a later date than the 

rest of the U.S. We assume that projected nonattainment areas everywhere in the U.S. excluding 

California will be designated such that they attain a revised standard by 2025, and we develop 

our projected baseline emissions, air quality, and population estimates for 2025. We also assume 

 

33 VOC reductions associated with simulated attainment of alternative ozone standards also have the potential to 

impact PM2.5 concentrations, but we are not able to model those effects at this time.
5-2

that the projected nonattainment areas in California will be designated such that they reach 

attainment by approximately 2038. 

We were not able to project baseline emissions and air quality levels beyond 2025 for 

California for sectors other than mobile sources. We account for changes in mobile source 

precursor emissions expected to occur between 2025 and 2030. While there is uncertainty about 

the precise timing of emissions reductions and related costs for California, we assume costs 

occur through 2038. We also model benefits for California accounting for population growth to 

2038 (see section 5.6.1). Because we were unable to account for the change in emissions from 

other sectors our projected baseline may under- or over-estimate the post-2025 ozone levels in 

California. In this analysis, we refer to estimates of nationwide benefits of attaining an 

alternative standard everywhere in the U.S. except California as the 2025 scenario. The post2025 scenario refers to estimates of nationwide benefits of attaining an alternative standard just 

in California. 

Because we estimate incremental costs and benefits for these two distinct scenarios 

reflecting attainment in different years, it is not appropriate to either sum, or directly compare, 

the estimates. Consequently, in presenting both incidence and dollar benefit estimates in this 

chapter, we present and discuss the 2025 scenario and post-2025 scenario in separate sections 

(see sections 5.7.1 and 5.7.2, respectively). 

Benefits estimated for the 2025 and post-2025 scenarios are relative to an analytical 

baseline in which the nation attains the current primary ozone standard (i.e., 4th highest daily 

maximum 8-hour ozone concentration of 75 ppb) and incorporates promulgated national 

regulations and illustrative emission controls to simulate attainment with 75 ppb. Table 5-1 

summarizes the estimated monetized benefits (total and ozone only) of attaining alternative 

ozone standards of 70 ppb, 65 ppb, and 60 ppb for the 2025 scenario (i.e., nationwide benefits of 

attaining everywhere in the U.S. but California). Table 5-2 presents the same types of benefit 

estimates for the post-2025 scenario (i.e., nationwide benefits of attaining just in California). 

These estimates reflect the sum of the economic value of estimated morbidity and mortality 

effects related to changes in exposure to ozone and fine particulate matter (PM2.5). However, it is 

important to emphasize that it is not appropriate to compare the ozone-only benefits to total 
5-3

costs. There are additional unquantified benefits which are described in Section 5.2. The 

estimated benefits for attaining the proposed standards are incremental to the substantial benefits 

estimated for several recent implementation rules (e.g., U.S. EPA, 2011e, 2014a). 

Table 5-1. Estimated Monetized Benefits of Attainment of the Alternative Ozone Standards 

for the 2025 Scenario (nationwide benefits of attaining each alternative standard 

everywhere in the U.S. except California) – Full Attainment (billions of 2011$) a

Discount 

Rate 70 ppb 65 ppb 60 ppb

Total Benefits 3% $6.9 to $14 +B $20 to $41 +B $37 to $75 +B

7% $6.4 to $13 +B $19 to $38 +B $34 to $70 +B

Ozone-only Benefits (range 

reflects Smith et al., 2009 and 

Zanobetti and Schwartz, 2008)

b $2.0 to $3.4 +B $6.4 to $11 +B $12 to $20 +B

PM2.5 Co-benefits (range reflects 

Krewski et al., 2009 and Lepeule 

et al., 2012)

3% $4.8 to $11 $14 to $31 $25 to $56

7% $4.3 to $9.7 $12 to $28 $22 to $50

a Rounded to two significant figures. It was not possible to quantify all benefits in this analysis due to data 

limitations. “B” is the sum of all unquantified health and welfare benefits. These estimates reflect the economic 

value of avoided morbidities and premature deaths using risk coefficients from the studies noted.

b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as 

ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category. 

Table 5-2. Estimated Monetized Benefits of Attainment of the Alternative Ozone Standards 

for the Post-2025 Scenario (nationwide benefits of attaining each alternative 

standard just in California) – Full Attainment (billions of 2011$) a

Discount 

Rate 70 ppb 65 ppb 60 ppb

Total Benefits 3% $1.1 to $2.0 +B $2.3 to $4.2 +B $3.4 to $6.2 +B

7% $1.1 to $2.0 +B $2.2 to $4.1 +B $3.2 to $5.9 +B

Ozone-only Benefits (range reflects 

Smith et al., 2009 and Zanobetti and 

Schwartz, 2008)

b $0.66 to $1.1 $1.4 to $2.4 $2.1 to $3.6

PM2.5 Co-benefits (range reflects 

Krewski et al., 2009 and Lepeule et 

al., 2012)

3% $0.42 to $0.95 $0.83 to $1.9 $1.1 to $2.6

7% $0.38 to $0.86 $0.75 to $1.7 $1.0 to $2.3

a Rounded to two significant figures. It was not possible to quantify all benefits in this analysis due to data 

limitations. “B” is the sum of all unquantified health and welfare benefits. These estimates reflect the economic 

value of avoided morbidities and premature deaths using risk coefficients from the studies noted.

b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as 

ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category. 

In addition to ozone and PM2.5 benefits, implementing emissions controls to reach some 

of the alternative ozone standards would reduce other ambient pollutants, such as VOCs and 
5-4

NO2. However, because the method used in this analysis to simulate attainment does not account 

for changes in ambient concentrations of other pollutants, we were not able to quantify the cobenefits of reduced exposure to these pollutants. In addition, due to data and methodology 

limitations, we were unable to estimate some anticipated health benefits associated with 

exposure to ozone and PM2.5.

5.2 Overview

This chapter presents estimated health benefits for three alternative ozone standards (70, 

65 and 60ppb) that the EPA could quantify, given the available resources, data and methods. 

Separate set of benefits are presented for the 2025 scenario, representing nationwide benefits of 

attaining an alternative standard everywhere in the U.S. but California in 2025 and the post-2025 

scenario, representing nationwide benefits of attaining an alternative standard just in California

in 2037. This chapter characterizes the benefits of implementing new ozone standards by 

answering three key questions:

1. What health effects are avoided by reducing ambient ozone levels to attain a 

revised ozone standard?

2. What is the economic value of these effects?

3. What are the co-benefits of reductions in ambient PM2.5 associated with 

reductions in emissions of ozone precursors (specifically NOx)?

In this analysis, we quantify an array of adverse health impacts attributable to ozone and 

PM2.5. The Integrated Science Assessment for Ozone and Related Photochemical Oxidants

(“ozone ISA”) (U.S. EPA, 2013a) identifies the human health effects associated with ozone 

exposure, which include premature death and a variety of illnesses associated with acute (dayslong) and chronic (months to years-long) exposures. Similarly, the Integrated Science 

Assessment for Particulate Matter (“PM ISA”) (U.S. EPA, 2009b) identifies the human health 

effects associated with ambient particles, which include premature death and a variety of 

illnesses associated with acute and chronic exposures. Air pollution can affect human health in a 

variety of ways, and in Table 5-3 we summarize the “categories” of effects and describe those 

that we could quantify in our “core” benefits estimates and those we were unable to quantify 

because we lacked the data, time or techniques. 
5-5

This list of unquantified benefit categories is not exhaustive and we are not always able 

to quantify each effect completely. Endpoints that the ozone and PM ISAs classified as causal or 

likely causal we quantified with confidence. We excluded from quantification effects not 

identified as having at least a causal or likely causal relationship with the affected pollutants. 

Selecting endpoints in this way should not imply that these pollutants are unrelated to other 

human health and environmental effects. Following this criterion, we excluded some effects that 

were identified in previous lists of unquantified benefits in other RIAs (e.g., UVb exposure), but 

are not identified in the most recent ISA as having a causal or likely causal relationship with 

ozone. In designing this benefits analysis, including the identification of endpoints to include in 

the core estimate, we also considered the design of the Health Risk and Exposure Assessment 

(HREA) completed as part of this ozone NAAQS review (USEPA, 2014b). The design and 

implementation of the HREA was subjected to rigorous peer review by the Science Advisory 

Board’s (SAB’s) Clean Air Scientific Advisory Committee (CASAC) with the results of that 

review being presented in letter form (Frey, and Samet 2012 for the first draft and Frey, 2014 for 

the second draft of the HREA). The overall design of the HREA, including the health endpoints 

selected for modeling was supported by the CASAC.34 

This benefits analysis relies on an array of data inputs—including emissions estimates, 

modeled ozone air quality, health impact functions and valuation estimates among others—

which are themselves subject to uncertainty and may in turn contribute to the overall uncertainty 

in this analysis. We employ several techniques to characterize this uncertainty, which are 

described in detail in sections 5.5 and 5.7.3.

Table 5-3. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain 

the Primary Ozone Standards (endpoints included in the core analysis are identified 

with a red checkmark)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Improved Human Health

 

34 The CASAC expressed their support for the overall design of the HREA, including endpoints selected and 

epidemiological studies used in supplying the effect estimates used to model those endpoints (Samet and Frey, 2012, 

p. 15 and Frey, 2014, p. 9).
5-6

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Reduced incidence 

of premature 

mortality from 

exposure to ozone

Premature mortality based on short-term 

exposure (all ages)

 

Section 5.6

Premature respiratory mortality based on 

long-term exposure (age 30–99)

 a

Reduced incidence 

of morbidity from 

exposure to ozone

Hospital admissions—respiratory causes 

(age > 65)

 

Emergency department visits for asthma 

(all ages)

 

Asthma exacerbation (age 6-18)  

Minor restricted-activity days (age 18–65)  

School absence days (age 5–17)  

Decreased outdoor worker productivity 

(age 18–65)

b b

Other respiratory effects (e.g., 

medication use, pulmonary 

inflammation, decrements in lung 

functioning)

— —

ozone ISA d

Cardiovascular (e.g., hospital admissions, 

emergency department visits)

— —

Reproductive and developmental effects 

(e.g., reduced birthweight, restricted 

fetal growth)

— —

Reduced incidence 

of premature 

mortality from 

exposure to PM2.5

Adult premature mortality based on 

cohort study estimates and expert 

elicitation estimates (age >25 or age >30)

 

See section 

5.6 and 

Appendix 5D

Infant mortality (age <1)  

Reduced incidence 

of morbidity from 

exposure to PM2.5

Non-fatal heart attacks (age > 18)  

Hospital admissions—respiratory (all 

ages)

 

Hospital admissions—cardiovascular (age 

>20)

 

Emergency department visits for asthma 

(all ages)

 

Acute bronchitis (age 8–12)  

Lower respiratory symptoms (age 7–14)  

Upper respiratory symptoms (asthmatics 

age 9–11)

 

Asthma exacerbation (asthmatics age 6–

18)

 

Lost work days (age 18–65)  

Minor restricted-activity days (age 18–65)  

Chronic Bronchitis (age >26) — —

Emergency department visits for 

cardiovascular effects (all ages)

— —

Strokes and cerebrovascular disease (age 

50–79)

— —

Other cardiovascular effects (e.g., other 

ages)

— — PM ISA c
5-7

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Other respiratory effects (e.g., pulmonary 

function, non-asthma ER visits, nonbronchitis chronic diseases, other ages 

and populations)

— —

Reproductive and developmental effects 

(e.g., low birth weight, pre-term births, 

etc.)

— —

PM ISA c,d

Cancer, mutagenicity, and genotoxicity 

effects

— —

Reduced incidence 

of morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — —

NO2 ISA e

Chronic lung disease hospital admissions 

(age > 65)

— —

Respiratory emergency department visits 

(all ages)

— —

Asthma exacerbation (asthmatics age 4–

18)

— —

Acute respiratory symptoms (age 7–14) — —

Premature mortality — —

NO2 ISA c,d

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, 

lung function, other ages and 

populations)

— —

a Due to concerns over translating incidence estimates into dollar benefits, for long-term ozone exposure-related 

respiratory mortality, we included estimates of reduced incidence as part of the core analysis, but included 

associated dollar benefits as a sensitivity analysis (see section 5.3).

b We are in the process of considering an update to the worker productivity analysis for ozone based on more recent 

literature (see section 5.6.3.4). As noted in section 5.6.3.4 we are requesting public comment on the approach we 

present for modeling this endpoint and will consider that input in determining whether to proceed with an updated 

simulation of this endpoint.

c We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.

d We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other 

significant concerns over the strength of the association.

e We assess these benefits qualitatively due to time and resource limitations for this analysis.

As described in Chapter 1 of this RIA, there are important differences worth noting in the 

design and analytical objectives of NAAQS RIAs compared to RIAs for rules that implement 

technology standards, such as Tier 3 (U.S. EPA, 2014a). The NAAQS RIAs illustrate the 

potential costs and benefits of attaining a revised air quality standard nationwide. These analyses 

simulate an array of strategies to reduce emissions at different sources and may model wellestablished emission control technologies for sectors and emission controls for which the control 

technology has not yet been developed (i.e., “unknown” controls). This type of RIA accounts for 

existing regulations and controls needed to attain the current standards and so estimated benefits 

and costs are incremental to attaining the current standard. In short, NAAQS RIAs hypothesize, 
5-8

but do not predict, the emission reduction strategies that States may enact when implementing a 

revised NAAQS. Setting a NAAQS does not result directly in costs or benefits. By contrast, the 

emission reductions from implementation rules are generally for specific, well-characterized 

sources, such as the recent MATS rule addressing emissions from coal and oil-fired electricity 

generating units (U.S. EPA, 2011e). In general, the EPA is more confident in the magnitude and 

location of the emission reductions for implementation rules. As such, emission reductions 

achieved under promulgated implementation rules such as MATS have been reflected in the 

baseline of this NAAQS analysis (the full set of rules reflected in baseline are presented in 

section 3.1.3). Subsequent implementation rules will be reflected in the baseline for the next 

ozone NAAQS review. For this reason, the benefits estimated provided in this RIA and all other 

NAAQS RIAs should not be added to the benefits estimated for implementation rules.

5.3 Updated Methodology Presented in this RIA

The benefits analysis presented in this chapter incorporates an array of policy and 

technical changes that the Agency has adopted since the previous review of the ozone standards 

in 2008 and the proposed reconsideration in 2010. Below we note the aspects of this analysis that 

differ from the reconsideration RIA (U.S. EPA, 2010d):

1. The population demographic data in BenMAP-CE (U.S. EPA, 2014d) reflects the 2010 

Census and future projections based on economic forecasting models developed by 

Woods and Poole, Inc. (Woods and Poole, 2012). These data replace the earlier 

demographic projection data from Woods and Poole (2007). This update was introduced 

in the final PM NAAQS RIA (U.S. EPA, 2012b).

2. The baseline incidence rates used to quantify air pollution-related hospital admissions 

and emergency department visits and the asthma prevalence rates were updated to replace 

the earlier rates. This update was introduced in the final CSAPR (U.S. EPA, 2011d). 

3. We updated the median wage data in the cost-of-illness studies. This update was 

introduced in the final PM NAAQS RIA (U.S. EPA, 2012b).

4. Updates for ozone-related effects:

a. Incorporated new mortality studies. We include two new multi-city studies to 

estimate deaths attributable to short-term exposure for the core analysis (Smith et al., 

2009 and Zanobetti and Schwartz 2008). We also estimate long-term respiratory 

deaths using Jerrett et al. (2009). While we believe the evidence supports including 

long-term respiratory deaths in the core analysis, limitations in our ability to specify a 

lag between exposure and the onset of death (i.e. the cessation lag that is required for 
5-9

valuing these deaths) prevents us from estimating dollar benefits in the core analysis. 

Both the new short-term and long-term mortality studies were included in the HREA 

completed in support of this NAAQS review with the overall design of that HREA 

(including inclusion of these new studies) being subjected to rigorous review by 

CASAC (Frey, and Samet 2012; Frey, 2014).

b. Incorporated new morbidity studies. The ozone ISA (U.S. EPA, 2013a) identifies 

several new epidemiological studies examining the association between short-term 

ozone exposure and respiratory hospitalizations, respiratory emergency department 

visits, and exacerbated asthma. Upon carefully evaluating this new literature, we 

added several new studies to our health impact assessment. Several of these studies 

were also included in the HREA, which as noted earlier, underwent rigorous review 

by CASAC.

c. Expanded uncertainty assessment. We added a comprehensive, qualitative assessment 

of the various uncertain parameters and assumptions within the benefits analysis and 

expanded the evaluation of air quality benchmarks for ozone-related mortality. We 

introduce this expanded assessment in this RIA (see sections 5.5 and 5.7.3).

5. Updates for PM2.5-related effects

a. Incorporated new mortality studies. We updated the American Cancer Society cohort 

study to Krewski et al. (2009) and updated the Harvard Six Cities cohort study to 

Lepeule et al. (2012). The effect coefficient for Krewski et al. (2009) is identical to 

the previous coefficient, and the Lepeule et al. (2012) is roughly similar to the 

previous coefficient. Both studies show narrower confidence intervals. The update for 

the American Cancer Society cohort was introduced in the proposal RIA for the PM 

NAAQS review (U.S. EPA, 2012b) and the update for the Harvard Six Cities cohort 

was introduced in the final RIA for the PM NAAQS review (U.S. EPA, 2012b).

b. Incorporated new morbidity studies. The epidemiological literature has produced 

several recent studies examining the association between short-term PM2.5 exposure 

and respiratory and cardiovascular hospitalizations, respiratory and cardiovascular 

emergency department visits, and stroke. Upon careful evaluation of new literature in 

the PM ISA and Provisional Assessment, we added several new studies and health 

endpoints to our health impact assessment. These updates were introduced in the 

proposal (U.S. EPA, 2012) and final RIAs for the PM NAAQS review (U.S. EPA, 

2012b).

c. Updated the survival rates for non-fatal acute myocardial infarctions. Based on recent 

data from Agency for Healthcare Research and Quality’s Healthcare Utilization 

Project National Inpatient Sample database (AHRQ, 2009), we identified death rates 

for adults hospitalized with acute myocardial infarction stratified by age. These rates 

replaced the survival rates from Rosamond et al. (1999). This update was introduced 

in the final RIA for the PM NAAQS review (U.S. EPA, 2012b).
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d. Expanded uncertainty assessment. We clarified the comprehensive assessment of the 

various uncertain parameters and assumptions within the benefits analysis and 

expanded the evaluation of air quality benchmarks. This update was introduced in the 

proposed CSAPR RIA (U.S. EPA, 2010g) and refined in the final PM NAAQS RIA 

(U.S. EPA, 2012b).

Although the list above identifies the major changes implemented since the 2010 ozone 

reconsideration RIA, the EPA has also updated several additional components of the benefits 

analysis since the 2008 ozone NAAQS RIA (U.S. EPA, 2008a), which were reflected in the 

reconsideration RIA. In the Portland Cement NESHAP proposal RIA (U.S. EPA, 2009a), the 

Agency no longer assumed a concentration threshold in the concentration-response function for 

PM2.5-related health effects and began estimating the benefits derived from the two major cohort 

studies of PM2.5 and mortality as the core benefits estimates, while still including a range of 

sensitivity estimates based on the EPA’s PM2.5 mortality expert elicitation. In the NO2 NAAQS 

proposal RIA (U.S. EPA, 2009a), we revised the estimate used for the value-of-a-statistical life 

to be consistent with Agency guidance. 

5.4 Human Health Benefits Analysis Methods

We follow a “damage-function” approach in calculating total benefits of the modeled 

changes in environmental quality.35 This approach estimates changes in individual health 

endpoints (specific effects that can be associated with changes in air quality) and assigns values 

to those changes assuming independence of the values for those individual endpoints. Total 

benefits are calculated simply as the sum of the values for all non-overlapping health endpoints. 

The “damage-function” approach is the standard method for assessing costs and benefits of 

environmental quality programs and has been used in several recent published analyses (Levy et 

al., 2009; Fann et al., 2012a; Tagaris et al., 2009).

To assess economic values in a damage-function framework, the changes in 

environmental quality must be translated into effects on people or on the things that people 

value. In some cases, the changes in environmental quality can be directly valued, as is the case 

for changes in visibility. In other cases, such as for changes in ozone and PM, an impact analysis 

 

35 The damage function approach is a more comprehensive method of estimating total benefits than the hedonic 

price approach applied to housing prices, which requires homebuyers to be knowledgeable of the full magnitude 

of health risks associated with their home purchase. 
5-11

must first be conducted to convert air quality changes into effects that can be assigned dollar 

values. For the purposes of this RIA, the health impacts analysis (HIA) is limited to those health 

effects that are directly linked to ambient levels of air pollution and specifically to those linked to 

ozone and PM2.5.

We note at the outset that the EPA rarely has the time or resources to perform extensive 

new research to measure directly either the health outcomes or their values for regulatory 

analyses. Thus, similar to Kunzli et al. (2000) and other, more recent health impact analyses, our 

estimates are based on the best available methods of benefits transfer. Benefits transfer is the 

science and art of adapting primary research from similar contexts to obtain the most accurate 

measure of benefits for the environmental quality change under analysis. Adjustments are made 

for the level of environmental quality change, the socio-demographic and economic 

characteristics of the affected population, and other factors to improve the accuracy and 

robustness of benefits estimates. 

Benefits estimates for ozone were generated using the damage function approach outlined 

above wherein potential changes in ambient ozone levels (associated with future attainment of 

alternative standard levels) were explicitly modeled and then translated into reductions in the 

incidence of specific health endpoints. In generating ozone benefits estimates for the two 

scenarios considered in the RIA (2025 and post-2025), we actually utilized three distinct benefits 

simulations including one completed for 2025 and two completed for 2038. The way in which 

these three benefits simulations were used to generate estimates for the two time periods reported 

in the RIA (2025 and post-2035) is described in section 5.4.3. 

In contrast to ozone, we used a benefit-per-ton (reduced form) approach in modeling PM2.5

co-benefits (see section 5.4.4 for additional detail). With this approach, we utilize the results of 

previous benefits analysis simulations focusing on PM2.5 to derive benefits-per-ton estimates for 

NOx.36 We then combine these dollar-per-ton estimates with projected reductions in NOx 

associated with meeting a given alternative standard to project co-benefits associated with PM2.5. 

We acknowledge increased uncertainty associated with the dollar-per-ton approach for PM2.5, 

 

36 In addition to dollar-per-ton estimates for NOx, we also utilized incidence-per-ton values (also for NOx) for 

specific health endpoints in order to generate incidence reduction estimates associated with the dollar benefits.
5-12

relative to explicitly modeling benefits using gridded PM2.5 surfaces specific to the baseline and 

alternative scenarios being considered in this review (see sections 5.4.4 and 5.7.3 and Appendix 

5A, Table 5A-1 for additional discussion). 

Sections 5.4.1 and 5.4.2 describe respectively, the underlying basis for the health and 

economic valuation estimates. Section 5.4.3 describes the procedure used to combine the three 

benefits simulations referenced above in order to generate benefits for the two time periods 

considered in the RIA (2025 and post-2025). Finally, section 5.4.4 provides an overview of the 

benefit-per-ton estimates used to estimate the PM2.5 co-benefits from NOx emission reductions in 

this RIA.

5.4.1 Health Impact Assessment

The health impact assessment (HIA) quantifies the changes in the incidence of adverse 

health impacts resulting from changes in human exposure to PM2.5 and ozone air quality. HIAs 

are a well-established approach for estimating the retrospective or prospective change in adverse 

health impacts expected to result from population-level changes in exposure to pollutants (Levy 

et al., 2009). PC-based tools such as the environmental Benefits Mapping and Analysis Program 

– Community Edition (BenMAP-CE) can systematize health impact analyses by applying a 

database of key input parameters, including health impact functions and population projections—

provided that key input data are available, including air quality estimates and risk coefficients 

(U.S. EPA, 2014d). Analysts have applied the HIA approach to estimate human health impacts 

resulting from hypothetical changes in pollutant levels (Hubbell et al., 2005; Tagaris et al., 2009; 

Fann et al., 2012a). The EPA and others have relied upon this method to predict future changes 

in health impacts expected to result from the implementation of regulations affecting air quality 

(e.g., U.S. EPA, 2014d). For this assessment, the HIA is limited to those health effects that are 

directly linked to ambient ozone and PM2.5 concentrations. There may be other indirect health 

impacts associated with implementing emissions controls, such as occupational health exposures.
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The HIA approach used in this analysis involves three basic steps: (1) utilizing 

projections of ozone air quality37 and estimating the change in the spatial distribution of the 

ambient air quality; (2) determining the subsequent change in population-level exposure; (3) 

calculating health impacts by applying concentration-response relationships drawn from the 

epidemiological literature to this change in population exposure (Hubbell et al., 2009).

A typical health impact function might look as follows:

∆y = 1 − (e

β∙∆x

)yo ∙ Pop (5.1)

where y0 is the baseline incidence rate for the health endpoint being quantified (for 

example, a health impact function quantifying changes in mortality would use the baseline, or 

background, mortality rate for the given population of interest); Pop is the population affected by 

the change in air quality; x is the change in air quality; and β is the effect coefficient drawn 

from the epidemiological study. Figure 5-1 provides a simplified overview of this approach.

 

37 Projections of ambient ozone concentrations for this analysis were generated by applying emissions reductions 

described in chapters 3 and 4 to gridded surfaces of recent-year ozone concentrations. The full methodology 

which incorporates information both from ambient measurements and from photochemical modeling simulations 

is described in section 3.4.1 of this RIA.
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Figure 5-1. Illustration of BenMAP-CE Approach

5.4.2 Economic Valuation of Health Impacts

After quantifying the change in adverse health impacts, the final step is to estimate the 

economic value of these avoided impacts. The appropriate economic value for a change in a 

health effect depends on whether the health effect is viewed ex ante (before the effect has 

occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air 

pollution generally lower the risk of future adverse health effects by a small amount for a large 

population. The appropriate economic measure is therefore ex ante willingness to pay (WTP) for 

changes in risk. Epidemiological studies generally provide estimates of the relative risks of a 

particular health effect for a given increment of air pollution (often per 10 ppb ozone). These 

relative risks can be used to develop risk coefficients that relate a unit reduction in ozone or 

PM2.5 to changes in the incidence of a health effect. In order to value these changes in incidence, 

WTP for changes in risk need to be converted into WTP per statistical incidence. This measure is 

Baseline Air Quality Post-Policy Scenario Air Quality

Incremental Air 

Quality

Improvement

ozone

Reduction

Population

Ages 30 -99

Background

Incidence

Rate

Effect

Estimate

Mortality 

Reduction
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calculated by dividing individual WTP for a risk reduction by the related observed change in 

risk.

For some health effects, such as hospital admissions, WTP estimates are generally not 

available. In these cases, we use the costs of treating or mitigating the effect, which generally 

understate the true value of reductions in risk of a health effect because they exclude the value of 

avoided pain and suffering from the health effect.

We use the BenMAP-CE version 1.0.8 (U.S. EPA, 2014d) to estimate the health impacts 

and monetized health benefits for the proposed standard range. Figure 5-2 shows the data inputs 

and outputs for the BenMAP-CE program.

38

 

38 The environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) is an opensource PC-based tool that quantifies the number and economic value of air pollution-related deaths and illnesses. As 

compared to the version that it replaces, BenMAP v4, the BenMAP-CE tool uses the same computational algorithms 

and input data to calculate incidence counts and dollar values—for a given air quality change, both versions report 

the same estimates, within rounding. BenMAP-CE differs from the legacy version of BenMAP in two important 

ways: (1) it is open-source and the uncompiled code is available to the public; (2) it is written in C#, which is both 

more broadly used and modern than the code it replaces (Delphi). BenMAP-CE was last used to support the Ozone 

Health Risk and Exposure Assessment completed in support of the current review.

Modeled Baseline 

and Post-Control 

Ambient Ozone

Ozone Health 

Functions

Economic 

Valuation 

Functions

2025 Population 

Projections

Ozone Incremental 

Air Quality Change

Ozone-Related 

Health Impacts

Woods & Poole 

Population 

Projections

Background 

Incidence and 

Prevalence Rates

Monetized Ozonerelated Benefits

Blue identifies a user-selected input within the BenMAP-CE program

Green identifies a data input generated outside of the BenMAP-CE program

Census 

Population Data
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Figure 5-2. Data Inputs and Outputs for the BenMAP-CE Program

5.4.3 Estimating Benefits for the 2025 and Post-2025 Scenarios

As described in section 5.1, we estimated benefit for two scenarios: 2025 and post-2025. 

The need for these two distinct time periods reflects the fact that, while most of the U.S. will 

have attained both the current and any alternative standard by 2025, there are portions of the 

country with more significant air quality problems (including several areas in California) that 

may not be required to meet an alternative standard until as late as December 31, 2037. 

Consequently, for each alternative standard we model a 2025 scenario reflecting the nationwide 

benefits of attaining that standard everywhere in the U.S. except California. We then model a 

post-2025 scenario, which represents nationwide benefits from attaining that same standard in 

California. Due to the temporal disconnect between these two scenarios, we do not attempt to 

sum these two estimates, but instead present each estimate in separate sections of this document 

(sections 5.7.1 and 5.7.2, respectively).

Our approach for estimating the benefits of attaining alternate ozone standards post-2025 

is illustrated in Figure 5-3; in this figure, Simulation A represents our approach for estimating the 

benefits of attaining alternate ozone standards in every state except California in 2025. We first 

estimated the benefits occurring in 2038 from all areas (including California) attaining each 

alternative standard (Simulation C). Next, we simulated the nationwide benefits of attaining each 

alternate ozone standard in 2038 for every state except California (Simulation B). Subtracting 

Simulation B from Simulation C calculates the benefits of attaining each alternate ozone 

standard after 2025— that is, the nationwide benefits from California alone attaining the standard 

in 2038. There are important caveats associated with this approach mentioned in Section 5.1 and 

discussed further in section 5.7.3. 
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Figure 5-3. Procedure for Generating Benefits Estimates for the 2025 and Post-2025 

Scenarios

5.4.4 Benefit-per-ton Estimates for PM2.5

We used a “benefit-per-ton” approach to estimate the PM2.5 co-benefits in this RIA. EPA 

has applied this approach in several previous RIAs (e.g., U.S. EPA, 2014a). These benefit-perton estimates provide the total monetized human health co-benefits (the sum of premature 

mortality), of reducing one ton of NOx (as a PM2.5 precursor) from a specified source.39 In 

general, these estimates apply the same benefits methods (e.g., health impact assessment then 

economic valuation), which are described further below, for all PM2.5 impacts attributable to a 

sector, and these benefits are then divided by the tons of a PM2.5 precursor (e.g., NOx) from that 

sector. As discussed below, we acknowledge that this approach has greater uncertainty relative to 

explicitly modeling benefits for PM2.5 based on application of gridded surfaces specifically 

 

39 In generating these estimates, we first use incidence-per-ton values to generate estimates of reductions in 

morbidity and mortality incidence for core endpoints (see Table 5-3). Then in estimating dollar values associated 

with these reductions in incidence, we use dollar-per-ton values for mortality only, noting that this is likely to 

provide coverage for upwards of 97% of the total dollar benefits (i.e., morbidity endpoints provide less than 3% of 

total benefits – see RIA from last PM review, USEPA, 2012, Table 5-19).
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generated for the baseline and alternative standard levels being considered in this ozone NAAQS 

review. However, resource and time constraints prevented us from completing detailed PM2.5 

modeling as part of this review.

We used a method to calculate the regional benefit-per-ton estimates that is a slightly 

modified version of the national benefit-per-ton estimates described in the TSD: Estimating the 

Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors (U.S. EPA, 2013b). The national 

estimates used in this NAAQS review were derived using the approach published in Fann et al. 

(2012c), but they have since been updated to reflect the epidemiology studies and Census 

population data first applied in the final PM NAAQS RIA (U.S. EPA, 2012b). The approach in 

Fann et al. (2012c) is similar to the work previously published by Fann et al. (2009), but the 

newer study includes improvements that provide more refined estimates of PM2.5-related health 

benefits for emissions reductions in the various sectors. Specifically, the air quality modeling 

data reflect industrial sectors that are more narrowly defined. In addition, the updated air quality 

modeling data reflects more recent emissions data -- a 2005 baseline projected to 2016 rather 

than 2001 baseline projected to 2015 -- and has higher spatial resolution (12 km rather than 36 

km grid cells).40

In Section 5.6 below, we describe all of the data inputs used in deriving the dollar-per-ton 

values for each sector, including the demographic data, baseline incidence, and valuation 

functions. The specification of effect estimates (including selection of epidemiology studies) 

used in the derivation of the benefit-per-ton values for PM2.5 is described in detail in Appendix 

5D. The benefit-per-ton estimates (by sector) that resulted from this modeling as well as the NOx 

reductions for each alternative standard level used in generating the PM2.5 cobenefit estimates are 

presented in Appendix 5E. Additional information on the source apportionment modeling for 

each of the sectors can be found in Fann et al. (2012c) and the TSD (U.S. EPA, 2013b). 

 

40 Sector-level estimates of PM2.5 are modeled using CAMx version 5.30. Specifically, the particulate source 

apportionment technology (PSAT) incorporated into CAMx generates estimates of the contribution from specific 

emission source groups to primary emitted and secondarily formed PM2.5, PSAT uses reactive tracers in generating 

these fractional estimates in order to capture nonlinear formation and removal processes related to PM2.5. 

Contributions from each sector are modeled at the 12km level, while boundary conditions are represented using a 

36km grid resolution (additional detail on modeling can be found in Fann et al., 2012) 
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Specifically for this analysis, we applied the benefit-per-ton estimates for 2025 and 2030 in 

generating PM2.5 co-benefit estimates for the 2025 and post-2025 time periods, respectively (both 

sets of benefit-per-ton estimates are presented in Appendix 5E).

41

As discussed in greater detail in section 5.7.3 and Appendix 5A, Table 5A-1, we recognize 

uncertainty associated with application of the benefit-per-ton approach used in modeling PM2.5 

cobenefits. The benefit-per-ton estimates used here reflect specific geographic patterns of 

emissions reductions and specific air quality and benefits modeling assumptions associated with 

the derivation of those estimates. Consequently, these estimates may not reflect local variability 

in factors associated with PM2.5-realted health impacts (e.g., population density, baseline health 

incidence rates) since air quality modeling that could have shed light on local conditions was not 

performed for this RIA. Therefore, use of these benefit-per-ton values to estimate co-benefits 

may lead to higher or lower benefit estimates than if co-benefits were calculated based on direct 

air quality modeling.

5.5 Characterizing Uncertainty

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty. This analysis is no exception. As outlined both 

in this and preceding chapters, this analysis includes many data sources as inputs, including 

emission inventories, air quality data from models (with their associated parameters and inputs), 

population data, population estimates, health effect estimates from epidemiology studies, 

economic data for monetizing benefits, and assumptions regarding the future state of the world 

(i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain and 

would affect the benefits estimate. When the uncertainties from each stage of the analysis are 

compounded, even small uncertainties can have large effects on the total quantified benefits.

After reviewing the EPA’s approach, the National Research Council (NRC) (2002, 2008), 

which is part of the National Academies of Science, concluded that the EPA’s general 

methodology for calculating the benefits of reducing air pollution is reasonable and informative 

in spite of inherent uncertainties. The NRC also highlighted the need to conduct rigorous 

 

41 We do not have benefit-per-ton estimates for 2038. The last year available is 2030, which is an underestimate of 

the 2038 benefits because the population grows and ages over time.
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quantitative analyses of uncertainty and to present benefits estimates to decision makers in ways 

that foster an appropriate appreciation of their inherent uncertainty. Since the publication of these 

reports, the EPA has continued work to improve the characterization of uncertainty in both 

health incidence and benefits estimates. In response to these recommendations, we have 

expanded our previous analyses to incorporate additional quantitative and qualitative 

characterizations of uncertainty. Although we have not yet been able to make as much progress 

towards a full, probabilistic uncertainty assessment as envisioned by the NAS as we had hoped, 

we have added a number of additional quantitative and qualitative analyses to highlight the 

impact that uncertain assumptions may have on the benefits estimates. These additional analyses 

focus primarily on uncertainty related to the mortality endpoint (for both ozone and PM2.5) since 

mortality is the driver for dollar benefits. In addition, for some inputs into the benefits analysis, 

such as the air quality data, it is difficult to address uncertainty probabilistically due to the 

complexity of the underlying air quality models and emission inputs. Therefore, we decline to 

construct alternative assumptions simply for the purpose of probabilistic uncertainty 

characterization when there is no scientific literature to support those alternate assumptions.

To characterize uncertainty and variability, we follow an approach that combines 

elements from two recent analyses by the EPA (U.S. EPA, 2010b; 2011b), and uses a tiered 

approach developed by the World Health Organization (WHO) for characterizing uncertainty 

(WHO, 2008). We present this tiered assessment as well as an assessment of the potential impact 

and magnitude of each aspect of uncertainty in Appendix 5A (results of these assessments are 

summarized in section 5.7.3). Data limitations prevent us from treating each source of 

uncertainty quantitatively and from reaching a full-probabilistic simulation of our results, but we 

were able to consider the influence of uncertainty in the risk coefficients and economic valuation 

functions by incorporating several quantitative analyses described in more detail below:

1. A Monte Carlo assessment that accounts for random sampling error and between study 

variability in the epidemiological and economic valuation studies for ozone-related health 

effects. See section 5.5.1 for additional detail on the Monte Carlo assessment.

2. A series of sensitivity analyses primarily focused on the mortality endpoint (for both 

ozone and PM2.5). We focus on mortality in conducting sensitivity analyses reflecting the 

important role that this endpoint plays in driving both ozone-related and PM2.5 (cobenefit) related dollar benefits. These sensitivity analyses address factors related to (a) 

estimating incidence (e.g., multiple epidemiology studies providing alternative effect 
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estimates, shape of the C-R function including potential for thresholds) and (b) estimating 

associated dollar benefits (e.g., income elasticity related to willingness to pay functions 

and uncertainty in specifying lag structures for long-term exposure-related mortality). See 

section 5.5.2 for additional detail on the set of sensitivity analyses completed for this 

RIA.

3. Supplemental analyses which allow us to consider additional factors related to the 

benefits analysis. These include an assessment of the age-related differentiation of shortterm ozone exposure-related mortality (including life year saved and how estimates of 

avoided mortality are distributed across age ranges). In addition, we also looked at the 

relationship between estimates of mortality and the underlying baseline ambient air levels 

used in their derivation. These analyses allow us to consider which range of baseline 

levels drive the benefits estimates. Finally, we considered the fraction of benefit estimates 

(for the 2025 scenario) which are associated with application of (higher-confidence) 

known emissions controls. See section 5.5.3 for additional detail on the supplemental 

analyses completed for this RIA.

5.5.1 Monte Carlo Assessment

Similar to other recent RIAs, we used Monte Carlo methods for characterizing random

sampling error associated with the concentration response functions from epidemiological 

studies and random effects modeling to characterize both sampling error and variability across 

the economic valuation functions. The Monte Carlo simulation in the BenMAP-CE software 

randomly samples from a distribution of incidence and valuation estimates to characterize the 

effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to 

generate confidence intervals around the estimated health impact and monetized benefits. The 

reported standard errors in the epidemiological studies determined the distributions for individual 

effect estimates for endpoints estimated using a single study. For endpoints estimated using a 

pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and 

the variance across studies. The confidence intervals around the monetized benefits incorporate 

the epidemiology standard errors as well as the distribution of the valuation function. These 

confidence intervals do not reflect other sources of uncertainty inherent within the estimates, 

such as baseline incidence rates, populations exposed and transferability of the effect estimate to 

diverse locations. As a result, the reported confidence intervals and range of estimates give an 

incomplete picture about the overall uncertainty in the benefits estimates.
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In this RIA, we provide confidence intervals for ozone-related benefits, but we are unable 

to provide confidence intervals for PM2.5-related co-benefits due to the use of benefit-per-ton 

estimates.

5.5.2 Sensitivity Analysis Addressing Both Incidence and Dollar Benefit Valuation

We assign the greatest economic value to the reduction in mortality risk. Therefore, it is 

particularly important to characterize to a reasonable extent the uncertainties associated with 

reductions in premature mortality, including both incidence estimation and the translation of 

reduced mortality into equivalent dollar benefits. Each of the sensitivity analyses completed for 

this RIA are briefly described below. The reader is referred to section 5.7.3.1 for discussion of 

the results and observations stemming from these sensitivity analyses. 

 Alternative C-R functions for short-term ozone exposure-related mortality:

Alternative concentration-response functions are useful for assessing uncertainty beyond 

random statistical error, including uncertainty in the functional form of the model or 

alternative study designs. For ozone we have included two multi-city studies (Smith et 

al., 2009 and Zanobetti and Schwartz 2008) in our core estimate of the range for shortterm exposure-related mortality. For the sensitivity analysis addressing this endpoint, we 

have included additional multi-city and meta-analysis studies utilized in RIAs completed 

for previous ozone NAAQS reviews (Bell et al., 2004 and 2005, Huang, 2005, Ito et al., 

2005 and Levy et al., 2005), as well as alternative model specifications from the Smith et 

al (2009) study. The selection of studies for the core and sensitivity analyses, reflects 

consideration for recommendations made both by the NAS in relation to modeling ozone 

benefits (p. 80, NRC, 2008) and the CASAC in their review of the HREA completed in 

support of this ozone NAAQS review. We also considered information and 

recommendations provided in the latest ozone ISA. Additional detail on the selection and 

use of studies in modeling short-term exposure-related mortality for the core analysis is 

presented in section 5.6.3.1.

 Impact of potential thresholds on the modeling of long-term ozone exposure-related 

respiratory mortality: Consistent with the HREA, we estimate counts of respiratory 

deaths from long-term exposure to ozone in our core analysis. As discussed in detail in 

section 5.6.3.1, the Jerrett et al., 2009 study from which the mortality effect estimate was 

derived included an exploration of potential thresholds in the concentration-response 

function. To provide a more comprehensive picture of potential benefits associated with 

long term ozone exposures, we use the results of the threshold analysis conducted by 

Jerrett et al, 2009 to conduct a sensitivity analysis evaluating models with a range of 

potential thresholds in addition to a non-threshold model (see section 5.7.3.1 and 

Appendix 5B, section 5B.1). 
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 Considering alternative C-R functions in estimating long-term PM2.5 exposurerelated mortality: In modeling co-benefits related to reductions in long-term exposure to 

PM2.5, our dollar-per-ton approach relies on estimates based on two studies (Krewski et 

al., 2009 and Lepeule et al., 2012 – see Appendix 5D, section 5D.1). To better understand 

the concentration-response relationship between PM2.5 exposure and premature mortality, 

the EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 2006).42 In 

general, the results of the expert elicitation support the conclusion that the benefits of 

PM2.5 control are very likely to be substantial. Using alternate relationships between 

PM2.5 and premature mortality supplied by experts, higher and lower benefits estimates 

are plausible, but most of the expert-based estimates of the mean PM2.5 effect on 

mortality fall between the two epidemiology-based estimates (Roman et al., 2008). 

Application of the expert elicitation-based effect estimates (as part of characterizing 

uncertainty) is covered in section 5.7.3.1 and Appendix 5B, section 5B.2. In addition to 

these studies, we have included a discussion of other recent multi-state cohort studies 

conducted in North America, but we have not estimated benefits using the effect 

coefficients from these studies (see Appendix 5D, section 5D.1).

 Specifying the cessation lag for long-term PM2.5 exposure-related respiratory 

mortality: As discussed in section 5.1 and 5.6.4.1, uncertainty in projecting the cessation 

lag for long-term PM2.5 exposure-related respiratory mortality prevents us from 

estimating dollar benefits associated with projected reductions in mortality. In the 

absence of clear evidence pointing to a particular lag structure, we have decided to use 

two lag structures (the 20 year segmented lag used for PM2.5 and an assumption of zero 

lag – see section 5.6.4.1). The range of dollar benefits that result have been included as 

sensitivity analyses and not in the core analysis.

 Income elasticity in the specification of willingness to pay (WTP) functions used for 

mortality and morbidity endpoints: There is uncertainty in specifying the degree to 

which the WTP function used in valuing mortality and some morbidity endpoints tracks 

projected increase in income over time (i.e., the income elasticity for WTP). We 

completed a sensitivity analysis to evaluate the potential impact of this factor on dollar 

estimates generated for mortality (and a subset of morbidity endpoints – see section 

5.6.4.1).

Even these multiple estimates (including confidence intervals in the case of estimates 

generated for ozone) cannot account for the role of other input variables in contributing to 

overall uncertainty, including emissions and air quality modeling, baseline incidence rates, and 

population exposure estimates. 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 

 

42 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually 

of multiple experts, are obtained (Ayyub, 2002).
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elements. As a result, the reported confidence intervals and range of estimates give an 

incomplete picture about the overall uncertainty in the estimates. Thus, confidence intervals 

reported for individual endpoints and for total benefits should be interpreted within the context of 

the larger uncertainty surrounding the entire analysis.

5.5.3 Supplemental Analyses

We have also conducted a number of supplemental analyses designed to provide 

additional perspectives on the core mortality estimates generated for this RIA. These analyses 

(the results of which are described in section 5.7.3.2) include:

 Age group-differentiated aspects of short-term ozone exposure-related mortality:

We examined several risk metrics intended to characterize how mortality risk reductions 

are distributed across different age ranges. These include (a) estimated reduction in life 

years lost, (b) distribution of mortality incidence reductions across age ranges and (c) 

estimated reductions in baseline mortality incidence rates by age group.

 Analysis of baseline ozone levels used in modeling short-term ozone exposurerelated mortality: We assess the relationship between short-term exposure-related 

mortality for ozone and the distribution of baseline (i.e., reflecting attainment of the 

current standard) 8hr max daily values used in deriving those estimates (see section 

5.7.3.2 and Appendix 5C, section 5C.2). This analysis allows us to explore how estimates 

of ozone- attributable mortality are distributed with regard to projected ambient ozone 

levels, including the fraction of overall mortality that falls within specific ozone ranges. 

We note that, while the latest ozone ISA did not provide support for a threshold in 

relation to short-term exposure-related mortality, it did note that there is reduced 

confidence in specifying the nature of the concentration-response function at lower ozone 

levels (in the range of 20ppb and below) (ozone ISA, U.S. EPA, 2013a, section 2.5.4.4). 

We use the distribution of short-term mortality across ozone levels to determine the 

fraction of mortality reductions (i.e., benefits) that fall within this lower confidence 

range.43

 Analysis of baseline PM2.5 levels used in modeling short-term ozone exposurerelated mortality: We also include a similar plot of the baseline annual PM2.5 levels used 

in modeling long-term PM2.5 exposure-related mortality (see section 5.7.3.2 and 

Appendix 5C, section 5C.2). However, we are using a reduced form dollar-per-ton 

 

43 However, care must be taken in interpreting this range of reduced confidence since benefits estimates are based on 

the average daily 8hr max across the ozone season and not on a true daily time series of 8hr metrics within each grid 

cell. The use of a seasonal mean 8hr max (rather than the more temporally differentiated daily time series) has been 

shown to generate nearly identical benefit estimates at the national-level due to underlying linearity in the benefits 

model being used. However, the use of the seasonal average 8hr metric, rather than a full daily time series, does 

decrease overall temporal variability in a plot of mortality versus ozone level which introduces uncertainty into the 

interpretation of these plots.
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approach in modeling PM2.5 cobenefits, we do not have spatially differentiated PM2.5

values (and associated mortality estimates) with which to derive this type of distributional 

plot specifically for this RIA and consequently, we have reproduced a plot from the 

earlier analysis used to generate the benefit-per-ton values.

 Fraction of core ozone and PM2.5 (cobenefit) estimates associated with application of 

known emissions controls for the 2025 scenario: This analysis estimates the fraction of 

core incidence and associated dollar benefits that are associated with application of the 

set of known (higher confidence) emissions control measures. Note that this analysis is 

only completed for the 2025 scenario since application of controls in California 

(associated with the post-2025 scenario) exclusively involves application of unknown 

controls. 

5.5.4 Qualitative Assessment of Uncertainty and Other Analysis Limitations

Although we strive to incorporate as many quantitative assessments of uncertainty as 

possible, there are several aspects we are only able to address qualitatively. These aspects are 

important factors to consider when evaluating the relative benefits of the emission reduction 

strategies for the proposed and alternative standards.

The total monetized benefits presented in this chapter are based on our interpretation of 

the best available scientific literature and methods and supported by the EPA’s independent SAB 

(Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis) 

(SAB-HES) (U.S. EPA- SAB, 2010a) and the National Academies of Science (NAS) (NRC, 

2002, 2008). The benefits estimates are subject to a number of assumptions and uncertainties. 

To more fully address all these uncertainties including those we cannot quantify, we 

apply a four-tiered approach using the WHO uncertainty framework (WHO, 2008), which 

provides a means for systematically linking the characterization of uncertainty to the 

sophistication of the underlying risk assessment. The EPA has applied similar approaches in 

previous analyses (U.S. EPA, 2010b, 2011b, U.S. EPA, 2012a – the HREA). Using this 

framework, we summarize the key uncertainties in the health benefits analysis, including our 

assessment of the direction of potential bias, magnitude of impact on the monetized benefits, 

degree of confidence in our analytical approach, and our ability to assess the source of 

uncertainty. More information on this approach and the uncertainty characterization are available 

in section 5.7.3 and Appendix 5A. 
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As previously described, we strive to monetize as many of the benefits anticipated from 

the proposed and alternative standards as possible given data and resource limitations, but the 

monetized benefits estimated in this RIA inevitably only reflect a portion of the total health 

benefits. Data and methodological limitations prevented the EPA from quantifying or monetizing 

the benefits from several important health benefit categories from emission reduction strategies 

to attain the alternative ozone standards analyzed in this RIA, including potential co-benefits 

from reducing NO2 exposure (see section 5.6.3.6 for more information) and reductions in VOC 

exposures. 

5.6 Benefits Analysis Data Inputs

In Figure 5-2 above, we summarized the key data inputs to the health impact and 

economic valuation estimate. Below we summarize the data sources for each of these inputs, 

including demographic projections, incidence and prevalence rates, effect coefficients, and 

economic valuation. We indicate where we have updated key data inputs since the benefits 

analysis conducted for the 2008 ozone NAAQS RIA (U.S. EPA, 2008a) and the 2010 ozone 

NAAQS Reconsideration RIA (U.S. EPA, 2010d). 

5.6.1 Demographic Data

Quantified and monetized human health impacts depend on the demographic 

characteristics of the population, including age, location, and income. We use population 

projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods 

and Poole, 2012). The Woods and Poole (WP) database contains county-level projections of 

population by age, sex, and race out to 2040, relative to a baseline using the 2010 Census data; 

the 2008 proposal RIA incorporated WP projections relative to a baseline using 2000 Census 

data. Projections in each county are determined simultaneously with every other county in the 

United States to take into account patterns of economic growth and migration. The sum of 

growth in county-level populations is constrained to equal a previously determined national 

population growth, based on Bureau of Census estimates (Hollman et al., 2000). According to 

WP, linking county-level growth projections together and constraining to a national-level total 

growth avoids potential errors introduced by forecasting each county independently. County 

projections are developed in a four-stage process:
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 First, national-level variables such as income, employment, and populations are 

forecasted.

 Second, employment projections are made for 179 economic areas defined by the Bureau 

of Economic Analysis (U.S. BEA, 2004), using an “export-base” approach, which relies 

on linking industrial-sector production of non-locally consumed production items, such 

as outputs from mining, agriculture, and manufacturing with the national economy. The 

export-based approach requires estimation of demand equations or calculation of 

historical growth rates for output and employment by sector.

 Third, population is projected for each economic area based on net migration rates 

derived from employment opportunities and following a cohort-component method based 

on fertility and mortality in each area.

 Fourth, employment and population projections are repeated for counties, using the 

economic region totals as bounds. The age, sex, and race distributions for each region or 

county are determined by aging the population by single year of age by sex and race for 

each year through 2040 based on historical rates of mortality, fertility, and migration.

5.6.2 Baseline Incidence and Prevalence Estimates

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide a direct estimate of the relationship of air quality changes to the relative 

risk of a health effect, rather than estimating the absolute number of avoided cases. For example, 

a typical result might be that a 5 ppb decrease in 8hr max daily ozone levels might be associated 

with a decrease in hospital admissions of three percent. The baseline incidence of the health 

effect is necessary to convert this relative change into a number of cases. A baseline incidence 

rate is the estimate of the number of cases of the health effect per year in the assessment location, 

as it corresponds to baseline pollutant levels in that location. To derive the total baseline 

incidence per year, this rate must be multiplied by the corresponding population number. For 

example, if the baseline incidence rate is the number of cases per year per million people, that 

number must be multiplied by the millions of people in the total population.

Table 5-4 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 
5-28

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. County, state and regional incidence rates are available for 

hospital admissions, and county-level data are available for premature mortality. 

We projected mortality rates such that future mortality rates are consistent with our 

projections of population growth (Abt Associates, 2012). To perform this calculation, we began 

first with an average of 2004–2006 cause-specific mortality rates. Using Census Bureau 

projected national-level annual mortality rates stratified by age range, we projected these 

mortality rates to 2050 in 5-year increments (Abt Associates, 2012; U.S. Bureau of the Census 

2002).

The baseline incidence rates for hospital admissions and emergency department visits 

reflect the updated rates first applied in the CSAPR RIA (U.S. EPA, 2011d). In addition, we 

have updated the baseline incidence rates for acute myocardial infarction. These updated rates 

(AHRQ, 2007) provide a better representation of the rates at which populations of different ages, 

and in different locations, visit the hospital and emergency department for air pollution-related 

illnesses. Also, the new baseline incidence rates are more spatially refined. For many locations 

within the U.S., these data are resolved at the county- or state-level, providing a better 

characterization of the geographic distribution of hospital and emergency department visits than 

the previous national rates. Lastly, these rates reflect unscheduled hospital admissions only, 

which represents a conservative assumption that most air pollution-related visits are likely to be 

unscheduled. If air pollution-related hospital admissions are scheduled, this assumption would 

underestimate these benefits.

For the set of endpoints affecting the asthmatic population, in addition to baseline 

incidence rates, prevalence rates of asthma in the population are needed to define the applicable 

population. Table 5-5 lists the prevalence rates used to determine the applicable population for 

asthma symptoms. Note that these reflect current asthma prevalence and assume no change in 

prevalence rates in future years. We updated these rates in the CSAPR RIA (U.S. EPA, 2011d).
5-29

Table 5-4. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population

Endpoint Parameter

Rates

Value Source

Mortality Daily or annual mortality 

rate projected to 2025 a

Age-, cause-, and 

county-specific rate

CDC WONDER (2004–2006)

U.S. Census bureau, 2000

Hospitalizations Daily hospitalization rate Age-, region-, state-, 

county- and causespecific rate

2007 HCUP data files b

ER Visits Daily ER visit rate for asthma 

and cardiovascular events

Age-, region-, state-, 

county- and causespecific rate

2007 HCUP data files b

Nonfatal Myocardial 

Infarction (heart 

attacks)

Daily nonfatal myocardial 

infarction incidence rate per 

person, 18+

Age-, region-, state-, 

and county-specific 

rate

2007 HCUP data files b adjusted by 

0.93 for probability of surviving 

after 28 days (Rosamond et al., 

1999)

Asthma Exacerbations Incidence among asthmatic 

African-American children

daily wheeze

daily cough

daily shortness of breath

0.173

0.145

0.074

Ostro et al. (2001) 

Acute Bronchitis Annual bronchitis incidence 

rate, children

0.043 American Lung Association (2002, 

Table 11)

Lower Respiratory 

Symptoms

Daily lower respiratory 

symptom incidence among 

children c

0.0012 Schwartz et al. (1994, Table 2)

Upper Respiratory 

Symptoms

Daily upper respiratory 

symptom incidence among 

asthmatic children

0.3419 Pope et al. (1991, Table 2)

Work Loss Days Daily WLD incidence rate per 

person (18–65)

Aged 18–24

Aged 25–44

Aged 45–64

0.00540

0.00678

0.00492

1996 HIS (Adams, Hendershot, and 

Marano, 1999, Table 41); U.S. 

Census Bureau (2000)

School Loss Days Rate per person per year, 

assuming 180 school days 

per year

9.9 National Center for Education 

Statistics (1996) and 1996 HIS 

(Adams et al., 1999, Table 47); 

Minor RestrictedActivity DaysDaily MRAD incidence rate 

per person

0.02137 Ostro and Rothschild (1989, 

p. 243)

a Mortality rates are only available at 5-year increments.

b Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level 

hospital and emergency department discharges for a variety of ICD codes (AHRQ, 2007).

c Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze.

Table 5-5. Asthma Prevalence Rates 

Population Group

Asthma Prevalence Rates

Value Source

All Ages 0.0780 American Lung Association (2010, Table 7)

< 18 0.0941
5-30

5–17 0.1070

18–44 0.0719

45–64 0.0745

65+ 0.0716

African American, 5–17 0.1776 American Lung Association (2010, Table 9)

African American, <18 0.1553 American Lung Association a

a Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008).

5.6.3 Effect Coefficients

The modeling of incidence and benefits for each pollutant employ distinct and separate 

sets of effect estimates since these are obtained from epidemiological studies specific to a given 

endpoint/pollutant combination. In the case of PM2.5, the dollar-per-ton approach being 

employed reflects application of effect estimates (for all endpoints) which have been used by the 

EPA in previous RIA’s (e.g., PM NAAQS, U.S. EPA, 2012b). Consequently, while we identify 

the studies and effect estimates reflected in the dollar-per-ton values used for PM2.5 (see Table 5-

8), we do not present a detailed discussion of those effect estimates in this section and instead 

present that more detailed discussion in Appendix 5D. By contrast, with ozone, we conducted

detailed benefits modeling using an updated set of effect estimates that reflects a combination of 

values used in previous RIAs together with updated values. For that reason, we provide a 

detailed discussion of effect estimates for ozone (including the rationale for their selection) in 

this section. 

The first step in selecting effect coefficients is to identify the health endpoints to be 

quantified. We base our selection of health endpoints on consistency with the EPA’s ISAs 

(which replace previous “Criteria Documents”), with input and advice from the HES, a scientific 

review panel specifically established to provide advice on the use of the scientific literature in 

developing benefits analyses for the EPA’s Report to Congress on The Benefits and Costs of the 

Clean Air Act 1990 to 2020 (U.S. EPA, 2011a). In addition, we have included more recent 

epidemiology studies from the ozone ISA (U.S. EPA, 2013a), PM ISA (U.S. EPA, 2009b), and 

the PM Provisional Assessment (U.S. EPA, 2012c).44 In selecting health endpoints for ozone, we 

also considered the suite of endpoints included in core modeling for the HREA, which was 

supported by CASAC (Frey, and Samet 2012; Frey, 2014). In general, we follow a weight of 

evidence approach, based on the biological plausibility of effects, availability of concentration-

 

44 The peer-reviewed studies in the Provisional Assessment have not yet undergone external review by the SAB. 
5-31

response functions from well conducted peer-reviewed epidemiological studies, cohesiveness of 

results across studies, and a focus on endpoints reflecting public health impacts (like hospital 

admissions) rather than physiological responses (such as changes in clinical measures like 

Forced Expiratory Volume [FEV1]).

There are several types of data that can support the determination of types and magnitude 

of health effects associated with air pollution exposures. These sources of data include 

toxicological studies (including animal and cellular studies), human clinical trials, and 

observational epidemiology studies. All of these data sources provide important contributions to 

the weight of evidence surrounding a particular health impact. However, only epidemiology 

studies provide direct concentration-response relationships that can be used to evaluate 

population-level impacts of reductions in ambient pollution levels in a health impact assessment.

For the data-derived estimates, we relied on the published scientific literature to ascertain 

the relationship between ozone and PM2.5 and adverse human health effects. We evaluated 

epidemiological studies using the selection criteria summarized in Table 5-6. These criteria 

include consideration of whether the study was peer-reviewed, the match between the pollutant 

studied and the pollutant of interest, the study design and location, and characteristics of the 

study population, among other considerations. In general, the use of concentration-response 

functions from more than a single study can provide a more representative distribution of the 

effect estimate. However, there are often differences between studies examining the same 

endpoint, making it difficult to pool the results in a consistent manner. For example, studies may 

examine different pollutants or different age groups. For this reason, we consider very carefully 

the set of studies available examining each endpoint and select a consistent subset that provides a 

good balance of population coverage and match with the pollutant of interest. In many cases, 

either because of a lack of multiple studies, consistency problems, or clear superiority in the 

quality or comprehensiveness of one study over others, a single published study is selected as the 

basis of the effect estimate.
5-32

When several effect estimates for a pollutant and a given health endpoint (with the 

exception of mortality)45 have been selected, they are quantitatively combined or pooled to 

derive a more robust estimate of the relationship. The BenMAP Manual Technical Appendices 

for an earlier version of the program provides details of the procedures used to combine multiple 

impact functions (Abt Associates, 2012). In general, we used fixed or random effects models to 

pool estimates from different single city studies of the same endpoint. Fixed effect pooling 

simply weights each study’s estimate by the inverse variance, giving more weight to studies with 

greater statistical power (lower variance). Random effects pooling accounts for both within-study 

variance and between-study variability, due, for example, to differences in population 

susceptibility. We used the fixed effect model as our null hypothesis and then determined 

whether the data suggest that we should reject this null hypothesis, in which case we would use 

the random effects model.46 Pooled impact functions are used to estimate hospital admissions 

and asthma exacerbations. When combining evidence across multi-city studies (e.g., 

cardiovascular hospital admission studies), we use equal weights pooling. The effect estimates 

drawn from each multi-city study are themselves pooled across a large number of urban areas. 

For this reason, we elected to give each study an equal weight rather than weighting by the 

inverse of the variance reported in each study. For more details on methods used to pool 

incidence estimates, see the BenMAP Manual Appendices (Abt Associates, 2012).

Effect estimates selected for a given health endpoint were applied consistently across all 

locations nationwide. This applies to both impact functions defined by a single effect estimate 

and those defined by a pooling of multiple effect estimates. Although the effect estimate may, in 

fact, vary from one location to another (e.g., because of differences in population susceptibilities 

or differences in the composition of PM), location-specific effect estimates are generally not 

available.

 

45 In the case of mortality, when we have multiple studies providing effect estimates for the core analysis, we 

include the range of the resulting incidence and dollar benefit estimates rather than pooling them in order to provide 

additional characterization of overall confidence associated with this key endpoint. However, for morbidity 

endpoints we do pool estimates as described here.

46 EPA recently changed the algorithm BenMAP uses to calculate study variance, which is used in the pooling 

process. Prior versions of the model calculated population variance, while the version used here calculates sample 

variance. 
5-33

Table 5-6. Criteria Used When Selecting C-R Functions

Consideration Comments

Peer-Reviewed 

Research

Peer-reviewed research is preferred to research that has not undergone the peerreview process.

Study Type Prospective vs. cohort: Among studies that consider chronic exposure (e.g., over a year 

or longer), prospective cohort studies are preferred over ecological studies because 

they control for important individual-level confounding variables that cannot be 

controlled for in ecological studies. 

Multi-city vs. pooled/meta-analysis: In recommending approaches for modeling ozonerelated mortality, the NAS notes a number of advantages multi-city time series studies 

as compared to meta-analyses. Multi-city studies utilize a consistent model structure 

and can include factors that explain differences between effect estimates among the 

cities. By contrast, with meta-analyses given the aggregation of large sets of studies, the 

construct definitions can become imprecise and the results difficult to interpret. In 

addition, meta-analyses can suffer from publication bias which can result in high-biased 

effect estimates. Ultimately, the NAS recommends that the greatest emphasis be placed 

on estimates based on systematic new multi-city analyses without excluding 

consideration of meta-analyses (NRC, 2008). Reflecting these observations by the NAS, 

in modeling ozone benefits, we have included several newer multi-city studies in the 

core analysis and a combination of multi-city and meta analyses in the accompanying 

sensitivity analysis. Placing emphasis on these two multi-city studies was also supported 

by the CASAC in the context of the HREA (Frey, and Samet 2012; Frey, 2014).

Study Period Studies examining a relatively longer period of time (and therefore having more data) 

are preferred, because they have greater statistical power to detect effects. Studies that 

are more recent are also preferred because of possible changes in pollution mixes, 

medical care, and lifestyle over time. However, when there are only a few studies 

available, studies from all years will be included.

Seasonality While the measurement of PM is typically collected across the full year, ozone 

monitoring seasons can vary substantially across different regions of the country. Given 

modeling constraints, we were not able to consider variation in ozone seasons in 

modeling benefits and instead, had to select a standard ozone season for the entire 

country (May 1st through September 31st). Consequently, in selecting effect estimates, 

we favored those values that reflected ozone seasons close to this fixed ozone season 

(i.e., we did not include effect estimates based on a full year of ozone monitoring data). 

Population Attributes The most technically appropriate measures of benefits would be based on impact 

functions that cover the entire sensitive population but allow for heterogeneity across 

age or other relevant demographic factors. In the absence of effect estimates specific to 

age, sex, preexisting condition status, or other relevant factors, it may be appropriate to 

select effect estimates that cover the broadest population to match with the desired 

outcome of the analysis, which is total national-level health impacts. When available, 

multi-city studies are preferred to single city studies because they provide a more 

generalizable representation of the concentration-response function.

Study Size Studies examining a relatively large sample are preferred because they generally have 

more power to detect small magnitude effects. A large sample can be obtained in 

several ways, including through a large population or through repeated observations on 

a smaller population (e.g., through a symptom diary recorded for a panel of asthmatic 

children).
5-34

Consideration Comments

Study Location U.S. studies are more desirable than non-U.S. studies because of potential differences in 

pollution characteristics, exposure patterns, medical care system, population behavior, 

and lifestyle. National estimates are most appropriate when benefits are nationally 

distributed; the impact of regional differences may be important when benefits only 

accrue to a single area.

Pollutants Included in 

Model

An important factor affecting the specification of co-pollutant models for ozone and PM 

is sampling frequency. While ozone is typically measured every hour of each day during 

the ozone season for a specific location, PM is typically measured every 3rd or 6th day. 

For this reason, when modeling the PM effect, epidemiological models specifying copollutants are preferred because this approach controls for the potential ozone effect 

while not diminishing the effective sample size available for specifying the PM effect. 

However, when modeling the ozone effect, the use of copollutants modeling (with PM) 

can substantially reduce sample size (by 1/3 to 1/6) since only days with both ozone and 

PM can be used. While these copollutants models may control for potential PM effects, 

they also result in a substantially less robust characterization of the ozone effect due to 

the reduced number of ozone measurements. For this reason, while we favor 

copollutants models in modeling PM benefits, for ozone we favor single pollutant 

models for the core estimate and reserve copollutants models for sensitivity analyses. 

Measure of PM For this analysis, impact functions based on PM2.5 are preferred to PM10 because of the 

focus on reducing emissions of PM2.5 precursors, and because air quality modeling was 

conducted for this size fraction of PM. Where PM2.5 functions are not available, PM10

functions are used as surrogates, recognizing that there will be potential downward 

(upward) biases if the fine fraction of PM10 is more (less) toxic than the coarse fraction. 

Economically Valuable 

Health Effects

Some health effects, such as forced expiratory volume and other technical 

measurements of lung function, are difficult to value in monetary terms. These health 

effects are not quantified in this analysis.

Non-overlapping 

Endpoints

Although the benefits associated with each individual health endpoint may be analyzed 

separately, care must be exercised in selecting health endpoints to include in the overall 

benefits analysis because of the possibility of double-counting of benefits. 

The specific studies from which effect estimates for the core analysis related to ozone 

exposure are drawn are included in Table 5-7. Table 5-8 identifies studies reflected in the dollarper-ton analysis for PM2.5. We highlight in red those studies that have been added since the 

benefits analysis conducted for the ozone reconsideration (U.S. EPA, 2010d) or the ozone 

NAAQS RIA (U.S. EPA, 2008a). In all cases where effect estimates are drawn directly from 

epidemiological studies, standard errors are used as a partial representation of the uncertainty in 

the size of the effect estimate. Table 5-9 summarizes those health endpoints and studies we have 

included as sensitivity analyses for ozone.
5-35

Table 5-7. Health Endpoints and Epidemiological Studies Used to Quantify Ozone-Related 

Health Impacts in the Core Analysis a

Endpoint Study Study 

Population

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE, respectively)

Premature Mortality

Premature 

mortality—short-term

Smith et al. (2009) All ages β = 0.00032 (0.00008)

Zanobetti and Schwartz 

(2008) β = 0.00051 (0.00012)

Premature respiratory 

mortality-long-term 

(incidence only)

Jerrett et al. (2009) >29 years β = 0.003971 (0.00133) 

Hospital Admissions

Respiratory Pooled estimate:

 Katsouyanni et al. (2009) > 65 years

β = 0.00061 (0.00041) natural splines

β = 0.00064 (0.00040) penalized splines

Asthma-related 

emergency 

department visits

Pooled estimate:

 Glad et al. (2012)

0-99 years

β = 0.00306 (0.00117)

 Ito et al. (2007) β = 0.00521 (0.00091)

 Mar and Koenig (2010) 

 

β = 0.01044 (0.00436) (0-17 yr olds)

β = 0.00770 (0.00284) (18-99 yr olds)

 Peel et al. (2005) β = 0.00087 (0.00053)

 Sarnat et al. (2013) β = 0.00111 (0.00028)

 Wilson et al. (2005) RR = 1.022 (0.996 – 1.049) per 25 

Other Health Endpoints

Asthma exacerbations 

Pooled estimate: b

Mortimer et al. (2002)

6–18 yearsc

β = 0.00929 (0.00387)

O’Connor et al. (2008) β = 0.00097 (0.00299)

Schildcrout et al. (2006) β = 0.00222 (0.00282)

School loss days Pooled estimate:

 Chen et al. (2000) 5-17 years

β = 0.015763 (0.004985)

 Gilliland et al. (2001) β = 0.007824 (0.004445)

Acute respiratory 

symptoms (MRAD) Ostro and Rothschild (1989) 18–65 years

β = 0.002596 (0.000776)

a Studies highlighted in red represent updates incorporated since the 2008 ozone NAAQS RIA (U.S. EPA, 2008a).

b As discussed in sections 5.3 and 5.6.3.1, while we believe that available evidence supports inclusion of estimates of 

long-term exposure-related respiratory mortality incidence in the core analysis, uncertainty in specifying a lag 

structure for this endpoint (a key factor in valuation) prevents us from including dollar benefits in the core analysis 

and instead, these are included as part of the sensitivity analysis exploring this endpoint.

c The original study populations were 5 to 12yrs for the O’Conner et al., (2008) and Schildcrout et al., (2006) and 5-

9yrs for the Mortimer et al., (2002) study. Based on advice from the SAB-HES, we extended the applied population 

to 6-18yrs for all three studies, reflecting the common biological basis for the effect in children in the broader age 

group. See: U.S. EPA-SAB (2004a) and NRC (2002)
5-36

Table 5-8. Health Endpoints and Epidemiological Studies Used to Quantify PM2.5-Related 

Health Impacts in the Core Analysis a

Endpoint Study

Study 

Population

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE, respectively)

Premature Mortality

Premature mortality—

cohort study, all-cause

Krewski et al. (2009) > 29 years

> 24 years

RR = 1.06 (1.04–1.06) per 10 μg/m3

Lepeule et al. (2012) RR = 1.14 (1.07–1.22) per 10 μg/m3

Premature mortality—

all-cause

Woodruff et al. (1997) Infant (< 1 

year)

OR = 1.04 (1.02–1.07) per 10 μg/m3

Chronic Illness

Nonfatal heart attacks Peters et al. (2001) Adults (> 18 

years)

OR = 1.62 (1.13–2.34) per 20 μg/m3

Pooled estimate:

Pope et al. (2006) β = 0.00481 (0.00199)

Sullivan et al. (2005) β = 0.00198 (0.00224)

Zanobetti et al. (2009) β = 0.00225 (0.000591)

Zanobetti and Schwartz (2006) β = 0.0053 (0.00221)

Hospital Admissions 

Respiratory Zanobetti et al. (2009)—ICD 

460-519 (All respiratory)

> 64 years β=0.00207 (0.00446)

Kloog et al. (2012)—ICD 460-

519 (All Respiratory

β=0.0007 (0.000961)

Moolgavkar (2000)—ICD 490–

496 (Chronic lung disease)

18–64 years 1.02 (1.01–1.03) per 36 μg/m3

Babin et al. (2007)—ICD 493 

(asthma)

< 19 years β=0.002 (0.004337)

Sheppard (2003)—ICD 493 

(asthma) < 18 RR = 1.04 (1.01–1.06) per 11.8 μg/m3

Cardiovascular Pooled estimate:

Zanobetti et al. (2009)—ICD 

390-459 (all cardiovascular)

> 64 years

β=0.00189 (0.000283)

Peng et al. (2009)—ICD 426-

427; 428; 430-438; 410-414; 

429; 440-449 (Cardio-, cerebroand peripheral vascular disease)

β=0.00068

(0.000214)

Peng et al. (2008)—ICD 426-

427; 428; 430-438; 410-414; 

429; 440-449 (Cardio-, cerebroand peripheral vascular disease)

β=0.00071

(0.00013)

Bell et al. (2008a)—ICD 426-

427; 428; 430-438; 410-414; 

429; 440-449 (Cardio-, cerebroand peripheral vascular disease)

β=0.0008

(0.000107)

Moolgavkar (2000)—ICD 390–

429 (all cardiovascular)

20–64 years RR=1.04 (t statistic: 4.1) per 10 μg/m3

Asthma-related 

emergency department 

visits

Pooled estimate:

Mar et al. (2010)

All ages RR = 1.04 (1.01–1.07) per 7 μg/m3

Slaughter et al. (2005) RR = 1.03 (0.98–1.09) per 10 μg/m3

Glad et al. (2012) β=0.00392 (0.002843)

Other Health Endpoints
5-37

Acute bronchitis Dockery et al. (1996) 8–12 years OR = 1.50 (0.91–2.47) per 14.9 μg/m3

Asthma exacerbations Pooled estimate:

Ostro et al. (2001) (cough, 

wheeze, shortness of breath) b

6–18 years b OR = 1.03 (0.98–1.07) 

OR = 1.06 (1.01–1.11) 

OR = 1.08 (1.00–1.17) per 30 μg/m3

Mar et al. (2004) (cough, 

shortness of breath)

RR = 1.21 (1–1.47) per 

RR = 1.13 (0.86–1.48) per 10 μg/m3

 Work loss days Ostro (1987) 18–65 years β=0.0046 (0.00036)

Acute respiratory 

symptoms (MRAD)

Ostro and Rothschild (1989) 

(Minor restricted activity days) 18–65 years β=0.00220 (0.000658)

Upper respiratory 

symptoms

Pope et al. (1991) Asthmatics, 

9–11 years 1.003 (1–1.006) per 10 μg/m3

Lower respiratory 

symptoms

Schwartz and Neas (2000) 7–14 years OR = 1.33 (1.11–1.58) per 15 μg/m3

a Studies highlighted in red represent updates incorporated since the ozone NAAQS RIA (U.S. EPA, 2008a). These 

updates were introduced in the PM NAAQS RIA (U.S. EPA, 2012b).

b The original study populations were 8 to 13 for the Ostro et al. (2001) study and 7 to 12 for the Mar et al. (2004) 

study. Based on advice from the SAB-HES, we extended the applied population to 6-18, reflecting the common 

biological basis for the effect in children in the broader age group. See: U.S. EPA-SAB (2004a,b) and NRC (2002).

Table 5-9. Health Endpoints and Epidemiological Studies Used to Quantify Ozone-Related 

Health Impacts in the Sensitivity Analysis a

Endpoint Study

Study 

Population

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE, respectively)

Premature Mortality

Premature respiratory 

mortality—long-term

Jerrett et al. (2009)-based models:

> 29 years

- non-threshold ozone only (86 

cities) β=0.002664 (0.000969)

- non-threshold ozone only (96 

cities) β=0.00286 (0.000942)

- threshold 40 ppbb β=0.00312 (0.00096)

- threshold 45 ppb β=0.00336 (0.001)

- threshold 50 ppb β=0.00356 0.00106) 

- threshold 55 ppb β=0.00417 (0.00118)

- threshold 56 ppb β=0.00432 (0.00121)

- threshold 60 ppb β=0.00402 (0.00137)

Premature 

mortality—short-term

Smith et al. (2009) (copollutant 

model with PM10)

All ages

β=0.00026 (0.00017)

Bell et al. (2005) β=0.00080 (0.00021)

Levy et al. (2005) β=0.00112 (0.00018)

Bell et al. (2004) β=0.00026 (0.00009)

Ito et al. (2005) β=0.00117 (0.00024)

Schwartz et al. (2005) β=0.00043 (0.00015)

Huang et al. (2005) 

(cardiopulmonary) β=0.00026 (0.00009)

a Studies highlighted in red represent updates incorporated since the 2008 ozone NAAQS RIA (U.S. EPA, 2008b).

b All threshold models are ozone-only and based on the full 96 city dataset.
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5.6.3.1 Ozone Premature Mortality Effect Coefficients

Core Mortality Effect Coefficients for Short-term ozone Exposure. The overall body 

of evidence indicates that there is likely to be a causal relationship between short-term ozone 

exposure and premature death. The 2013 ozone ISA states that:

“The evaluation of new multi-city studies that examined the association between 

short-term ozone exposure and mortality found evidence which supports the 

conclusions of the 2006 ozone AQCD. These new studies reported consistent 

positive associations between short-term ozone exposure and all-cause (nonaccidental) mortality, with associations persisting or increasing in magnitude during 

the warm season, and provide additional support for associations between ozone 

exposure and cardiovascular and respiratory mortality” (ozone ISA section 6.6.3, 

USEPA 2013). 

The ISA concludes by stating that, “Although some uncertainties still remain, the collective body 

of evidence is sufficient to conclude there is likely to be a causal relationship between short-term 

ozone exposure and total mortality.” (ozone ISA section 6.6.3, USEPA 2013a). Regarding 

potential confounding of the ozone mortality effect by PM, the ISA states, “Overall, across 

studies, the potential impact of PM indices on ozone-mortality risk estimates tended to be much 

smaller than the variation in ozone-mortality risk estimates across cities suggesting that ozone 

effects are independent of the relationship between PM and mortality.” (ozone ISA section 6.3.3, 

USEPA 2013a). However, the ISA does note that the interpretation of the potential confounding 

effects of PM on ozone-mortality risk estimates requires caution. This caution reflects in part, the 

every-3

rd

- and every-6th-day PM sampling schedule (in most cities) which limits the overall 

sample size available for evaluating potential confounding of the ozone effect by PM. (ozone 

ISA section 6.3.3, USEPA 2013a). 

In their review of the HREA, the SAB’s CASAC Ozone Review Panel expressed support 

for epidemiological studies and corresponding concentration-response functions used in the 

HREA, which included effect estimates obtained from Smith et al., (2009) and Zanobetti and 

Schwartz (2008b) (Frey and Samet, 2012, p. 17-18 and Frey, 2014, p, 9). Furthermore, the 

CASAC specifically noted support for the use of multi-city studies, where available in modeling 
5-39

the health endpoints included in the analysis (Frey and Samet, 2012, p. 15). In addition, they 

expressed support for modeling total risk, and de-emphasized the importance of estimating risk 

associated with ozone concentrations above the lowest measure level (LML) from contributing 

epidemiological studies (Frey and Samet, 2012, p. 10 and 18). 

In 2006, the EPA requested an NAS study to answer four key questions regarding ozonerelated mortality: (1) how did the epidemiological literature to that point improve our 

understanding of the size of the ozone-related mortality effect? (2) How best can EPA quantify 

the level of ozone-related mortality impacts from short-term exposure? (3) How might EPA 

estimate the change in life expectancy? (4) What methods should EPA use to estimate the 

monetary value of changes in ozone-related mortality risk and life expectancy?

In 2008, the NAS (NRC, 2008) issued a series of recommendations to the EPA regarding 

the quantification and valuation of ozone-related short-term mortality. Chief among these was 

that “...short-term exposure to ambient ozone is likely to contribute to premature deaths” and the 

committee recommended that “ozone-related mortality be included in future estimates of the 

health benefits of reducing ozone exposures...” The NAS also recommended that “...the greatest 

emphasis be placed on the multi-city and NMMAPS studies without exclusion of the metaanalyses” (NRC, 2008). In addition, NAS recommended that EPA “should give little or no 

weight to the assumption that there is no causal association between estimated reductions in 

premature mortality and reduced ozone exposure” (NRC, 2008). In 2010, the Health Effects 

Subcommittee of the Advisory Council on Clean Air Compliance Analysis, while reviewing 

EPA’s The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a), also 

confirmed the NAS recommendation to include ozone mortality benefits (U.S. EPA-SAB, 

2010a).

In view of the findings of the ozone ISA, the NAS panel, the HES panel, and the CASAC 

panel, we include ozone-related premature mortality for short-term exposure in the core health 

effects analysis using effect coefficients from the Smith et al. (2009) NMMAPS analysis and the 

Zanobetti and Schwartz (2008) multi-city study. As discussed below, we also include several 

additional studies as sensitivity analyses. This approach with an emphasis on newer multi-city 

studies is consistent with recommendations provided by the NAS in their ozone mortality report 
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(NRC, 2008), “The committee recommends that the greatest emphasis be placed on estimates 

from new systematic multi-city analyses that use national databases of air pollution and 

mortality, such as in the NMMAPS, without excluding consideration of meta-analyses of 

previously published studies.” In selecting the Smith et al. (2009) and Zanobetti and Schwartz 

(2008b) studies, we point both to CASAC support for the use of these two studies in the context 

of the HREA completed for this NAAQS review (Samet and Frey, 2012, p. 15 and Frey, 2014, p. 

9) and the fact that both of these studies are multi-city studies published more recently (as 

compared with other multi-city studies or meta-analyses included in the sensitivity analyses – see 

discussion below).

The Smith et al., (2009) study is a reanalysis of the NMMAPS data set focused on 

evaluating the relationship between short-term ozone exposure and mortality. While this 

reproduces the core national-scale estimates presented in Bell et al., (2004), it also explores the 

sensitivity of the mortality effect to different model specifications including (a) regional versus 

national Bayes-based adjustment,47 (b) co-pollutants models considering PM10, (c) all-year 

versus ozone-season based estimates, and (d) consideration of a range of ozone metrics, 

including the daily 8hr max, which is of particular interest in the context of the RIA given that is 

the metric that is used in the form of the ozone standard. In addition, the Smith et al. (2009) 

study does not use the trimmed mean approach employed in the Bell et al. (2004) study in 

preparing ozone monitor data, which is another advantage.48 In selecting effect estimates from 

Smith et al. (2009) for use in the core analysis, we focused on an ozone-only estimate for nonaccidental mortality based on the 8hr max metric for the warmer ozone season. In addition, for 

the sensitivity analysis, we included a copollutants model (ozone and PM10) from Smith et al. 

(2009) for all-cause mortality which also used the 8hr max ozone metric for the ozone season. As 

 

47 In Bayesian modeling, effect estimates are “updated” from an assumed prior value using observational data. In 

the Smith et al (2009) approach, the prior values are either a regional or national mean of the individual effect 

estimates obtained for each individual city. The Bayesian adjusted city-specific effect estimates are then 

calculated by updating the selected prior value based on the relative precision of each city-specific estimate and 

the variation observed across all city-specific individual effect estimates. City-specific estimates are pulled 

towards the prior value if they have low precision and/or there is low overall variation across estimates. Cityspecific estimates are given less adjustment if they are precisely estimated and/or there is greater overall 

variation across estimates.

48 There are a number of concerns regarding the trimmed mean approach including (1) the potential loss of temporal 

variation in the data when the approach is used (this could impact the size of the effect estimate) and (2) a lack of 

complete documentation for the approach which prevents a full reviewing or replication of the technique.
5-41

noted in Table 5-6, the decision to use a single pollutant model for the core analysis and reserve 

the copollutants model for the sensitivity analysis reflects our concern that the reduced sampling 

frequency for days with copollutants measurements (1/3 and 1/6) can impact characterization of 

the ozone effect which is the focus of this assessment. In addition, as noted earlier in this section, 

the latest ozone ISA states that ozone effects are likely to be independent of the relationship 

between PM and mortality, which further supports the approach of favoring single pollutant 

models in the core analysis.

The Zanobetti and Smith (2008) study evaluated the relationship between ozone exposure 

(using an 8hr mean metric for the warm season June-August) and all-cause mortality in 48 U.S. 

cities using data collected between 1989 and 2000. The study presented single pollutant 

concentration-response functions based on shorter (0-3 day) and longer (0-20) day lag structures, 

with the comparison of effects based on these different lag structures being a central focus of the 

study. For the core analysis, we used the 0-3 day lag based concentration-response function since 

this had the strongest effect and tighter confidence interval. Note that, because the RIA utilizes 

the 8hr max ozone metric, we had to covert the effect estimate from Zanobetti and Smith (2008) 

which is based on an 8hr mean metric to an equivalent effect estimate based on an 8hr max. To 

do this, we used the ozone metric approach wherein the original effect estimate (and standard 

error) is multiplied by the appropriate ozone metric adjustment ratio.49

Core Mortality Effect Coefficient for Long-term ozone Exposure. We also estimated 

long-term ozone exposure-related respiratory mortality incidence in the core analysis. The 

available evidence did not allow us to characterize how long-term exposure to ozone related to

the year of onset of mortality (i.e., a cessation lag), which is a necessary input to quantifying the 

discounted dollar benefits. For this reason, we report the dollar benefits of avoided long-term 

exposure-related respiratory deaths as a sensitivity analyses (see section 5.7.3.1). Support for 

 

49 These adjustment ratios are created by (a) obtaining summary air quality (composite monitor values) for each 

urban study area/ozone season combination reflected in the original epidemiology study, (b) calculating the ratio of 

the 8hr max to the study-specific air metric (for each of the urban study areas), and (c) taking the average of these 

urban-study area ratios. Ratio adjustment of the effect estimate does introduce uncertainty into the benefits estimates 

generated using these adjusted effect estimates, however, adjustments of relatively similar metrics (e.g., 8hr max and 

8hr mean), as is the case with the Zanobetti and Schwartz (2008) study, are likely to introduce less uncertainty than

adjustments for more disparate ratios (e.g., 24hr or 1hr max ratios to 8hr max equivalents).
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modeling long-term exposure-related mortality incidence comes from the final ozone ISA as well 

as recommendations provided by CASAC in their review of the HREA completed for the current 

ozone NAAQS review (Frey, 2014, p. 3 and 9).

The final ozone ISA references long-term respiratory mortality in section 7.2.1 (USEPA 

2013a) where they state, “The positive results from various designs and locations support a 

relationship between long-term exposure to ambient ozone concentrations and respiratory health 

effects and mortality.” Later in that chapter, the ISA states that: “The strongest evidence for an 

association between long-term exposure to ambient ozone concentrations and mortality is 

derived from associations reported in the Jerrett et al. (2009) study for respiratory mortality that 

remained robust after adjusting for PM2.5 concentrations.” (Section 7.7.1, USEPA, 2013a). In that 

same section, the authors also state that:

“Coherence and biological plausibility for this observation [the association between 

long-term exposure and respiratory mortality] is provided by evidence from 

epidemiologic, controlled human exposure, and animal toxicological studies for the 

effects of short- and long-term exposure to ozone on respiratory effects (see Sections 

6.2 and 7.2). Respiratory mortality is a relatively small portion of total mortality 

[about 7.6% of all deaths in 2010 were due to respiratory causes (Murphy et al., 

2012)], thus it is not surprising that the respiratory mortality signal may be difficult 

to detect in studies of cardiopulmonary or total mortality.” 

While the ozone ISA concludes that evidence is suggestive of a causal association between total 

mortality and long-term ozone exposure (section 7.7.1), specifically with regard to respiratory 

health effects (including mortality), the ISA concludes that there is likely to be a causal 

association (section 7.2.8).

In their review of the HREA completed for the ozone NAAQS review and specifically 

modeling of the long-term exposure-related respiratory mortality endpoint, the CASAC states 

that, “The basis for estimating long-term mortality (respiratory) risks relies on a single study, 

Jerrett et al. (2009), and the HREA should acknowledge the uncertainty and confidence in 

modeling results from the use of a single study, albeit a good one.” And, while they comment on 

the size of the chronic obstructive pulmonary disease (COPD) mortality effect attributable to 
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ozone (page 7-68) they go on to state that, “the CASAC concurs that Jerrett et al. (2009) is an 

appropriate study to use at this time as the basis for the long-term mortality risk estimates given 

its adequacy and the lack of alternative data.” (Frey, C., 2014). This advice supersedes previous 

advice provided by the HES to include long-term ozone exposure-related mortality only as a 

sensitivity analysis.

The Jerrett et al. (2009) study was the first to explore the relationship between long-term 

ozone exposure and respiratory mortality (rather than focusing on cardiopulmonary mortality). 

Jerrett et al. (2009) exhibits a number of strengths including (a) the study is based on the 1.2 

million participant American Cancer Society cohort drawn from all 50 states, DC, and Puerto 

Rico (included ozone data from 1977 [5 years before enrollment in the cohort began] to 2000); 

(b) it includes copollutants models that controlled for PM2.5; and (c) it explored the potential for 

a threshold concentration associated with the long-term mortality endpoint. However there are 

attributes to this study that affect how we interpret the long-term exposure-related respiratory 

mortality estimates. First, while CASAC notes that Jerrett et al. (2009) is well designed, it is a 

single study—and so provides the only quantitative basis for estimating this endpoint. By 

comparison, we estimate short-term exposure-related mortality risk using several studies. 

There is also the potential existence and location of a threshold in the C-R function 

relating mortality and long-term ozone concentrations. That uncertainty could greatly influence 

our quantitative risk estimates (we address the potential for a threshold in a sensitivity analysis 

below). The CASAC did address the use of the zero threshold versus threshold based models in 

the context of the HREA, stating that, “The EPA examined the threshold analysis contained in 

Jerrett et al. (2009) and found that the mortality model including a threshold at 56 ppb had the 

lowest log likelihood value of all models examined. However, it is not clear whether the 56 ppb 

threshold model is a better predictor of respiratory mortality than when using a linear model for 

the Jerrett et al. data. Different, but valid statistical tests produced different conclusions about the 

threshold versus linear models. The less stringent test judged the 56 ppb threshold model to be 

superior to the linear model, but the confidence interval indicates the threshold could occur 

anywhere from 0 to 60 ppb. Using the more stringent statistical test, none of the threshold 

models produce better predictions than the linear model. Given these results, the CASAC 

concurs with the EPA’s planned approach [as stated in the 2nd draft HREA] to conduct a 
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sensitivity analysis evaluating potential thresholds in the C-R functions that relate long-term 

ozone exposures with respiratory mortality and to not make the threshold models the core 

analytical procedure in the PA.” (Frey, 2014, p. 13-14) Here, the CASAC clearly states their 

support for inclusion of the zero threshold (linear model) as the core approach, while treating the 

threshold models as sensitivities. 

Reflecting this advice provided by CASAC, we have generated the core benefit estimate 

for long-term exposure-related respiratory mortality using a non-threshold co-pollutant model 

(with PM2.5) obtained from Jerrett et al. (2009) (see Tables 5-7 and 5-9). Using a co-pollutant 

model is consistent with the fact that this study applied seasonal average metrics that are 

insensitive to co-pollutant monitoring for PM2.5; we explore the influence of co-pollutant models 

and thresholds in a sensitivity analysis below.50 The effect estimates used to model long-term 

ozone-attributable mortality are calculated using a seasonal average of peak (1-hr maximum) 

measurements. These long-term exposure metrics can be viewed as long-term exposures to daily 

peak ozone over the warmer months, as compared with annual average levels such as are used in 

long-term PM exposure calculations. This increases the need for care in attempting to combine 

estimates of long-term ozone-attributable mortality and short-term ozone-attributable mortality 

estimates, in order to avoid double counting. It is also important to keep in mind that our 

estimates of short-term ozone- attributable mortality are for all-causes, while estimates of longterm ozone-attributable mortality are focused on respiratory-related mortality. This further limits 

the ability to compare estimates of long-term and short-term exposure related mortality.

 

50 See Table 5-6 for additional discussion of the issue involving reduced sampling frequency and modeling of shortterm ozone exposure-related mortality.
5-45

Sensitivity Analysis: Alternate Mortality Effect Coefficients for Short-term Ozone 

Exposure. In addition to the ozone-related studies we use for the core estimates, we also 

evaluate several alternative studies to characterize uncertainty in the core estimates. These 

alternative studies include a mix of meta-analyses and multi-city studies which have been 

included in RIAs completed for previous ozone NAAQS reviews, including the review 

completed in 2008 as well as the Reconsideration completed in 2010 (USEPA, 2008a and 

USEPA 2010d, respectively). The decision to include a mixture of meta-analyses and multi-city 

studies in the sensitivity analysis reflects the recommendation from the NRC reference earlier, 

that in modeling short-term exposure-related mortality for ozone, emphasis be placed on more 

recent multi-city studies without excluding consideration for meta-analyses.

In selecting effect estimates from each study, we followed the criteria presented in 

Table 5-6. Consequently, we favored effect estimates reflecting the warmer ozone monitoring 

period, if available. We also favored effect estimates based on the 8hr max air metric if available. 

And finally, while we considered multi-pollutant models (providing some coverage for potential 

confounding by PM), we placed primary emphasis on single-pollutant models since these would 

typically have a significantly larger dataset with which to specify the ozone mortality effect. The 

decision to emphasize single-pollutant models and deemphasize copollutants models 

(specifically in modeling short-term endpoints) reflected observations in the current ozone ISA. 

In relation to short-term mortality, the authors note limitations of co-pollutant models due to the 

reduced sampling frequency association with PM in most cities. However, they also note that, 

“Together, these co-pollutant-adjusted findings across respiratory endpoints provide support for 

the independent effects of short-term exposures to ambient ozone.” (ozone ISA section 6.2.9, 

USEPA, 2013a). The set of multi-city and meta-analysis studies selected for inclusion in the 

sensitivity analysis (including effect estimates and standard errors) is presented in Table 5-9. 

Studies and effect estimates selected for the core analysis are also included in the table for 

completeness. Detailed discussions of each of these studies can be found in the current ozone 

ISA as well as the RIA for the ozone NAAQS completed in 2008.

Threshold-Based Effect Coefficients for Long-term Ozone Exposure. As discussed in 

the HREA completed as part of this NAAQS review (U.S. EPA, 2014b), the exploration of 
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potential thresholds for long-term exposure-related respiratory mortality (as discussed in the 

Jerrett et al., 2009 study) deserves additional discussion.51 In their memo clarifying the results of 

their study (see Sasser, 2014), the authors note that in terms of goodness of fit, long-term health 

risk models including ozone clearly performed better than models without ozone, indicating the 

improved predictions of respiratory mortality when ozone is included. In exploring different 

functional forms, they report that the model including a threshold at 56 ppb had the lowest loglikelihood value of all models evaluated (i.e., linear models and models including thresholds 

ranging from 40-60 ppb), and thus provided the best overall statistical fit to the data. However, 

they also note that it is not clear whether the 56 ppb threshold model is a better predictor of 

respiratory mortality than when using a linear (no-threshold) model for this dataset. Using one 

statistical test, the model with a threshold at 56 ppb was determined to be statistically superior to 

the linear model. Using another, more stringent test, none of the threshold models considered 

were statistically superior to the linear model. Under the less stringent test, although the 

threshold model produces a statistically superior prediction than the linear model, there is 

uncertainty about the specific location of the threshold, if one exists. This is because the 

confidence intervals on the model predictions indicate that a threshold could exist anywhere 

from 0 to 60 ppb. The authors conclude that considerable caution should be exercised in using 

any specific threshold, particularly when the more stringent statistical test indicates there is no 

significantly improved prediction. 

Based on this additional information from the authors (Sasser, 2014), we have chosen to 

reflect the uncertainty about the existence and location of a potential threshold by estimating 

mortality attributable to long-term ozone exposures using a range of threshold-based effect 

estimates as sensitivity analyses (see section 5.7.3.1 and Appendix 5B, section 5B.1). 

Specifically, we generate additional long-term risk results using unique risk models that include 

 

51 The approach we developed to explore the potential for thresholds related to long-term exposure-related mortality 

was presented in a memorandum to CASAC which was also released to the public (Sasser, 2014). That 

memorandum describes additional data obtained from the authors of Jerrett et al. (2009) to support modeling of 

potential thresholds and also lays out our proposed approach for exploring the impact of potential thresholds on 

estimates of long-term exposure-related mortality (including presentation of the threshold-based results as a 

sensitivity analysis and inclusion of the non-threshold model-based results as the core analysis). This plan, including 

these details related to presentation of the threshold and non-threshold based estimates were supported by CASAC 

(Frey, 2014, p. 13-14).
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a range of thresholds from 40 ppb to 60 ppb in 5 ppb increments, while also including a model 

with a threshold equal to 56 ppb, which had the lowest log likelihood value for all models 

examined.52 In addition, to exploring the impact of potential thresholds, as part of the sensitivity 

analysis we also explore the impact of using ozone-only (non-threshold) models in estimating 

long-term exposure-related respiratory mortality.53

Ozone Exposure Metric. Both the NMMAPS analysis and the individual time series studies 

upon which the meta-analyses were based use the 24-hour average or 1-hour maximum ozone 

concentrations as exposure metrics. The 24-hour average is not the most relevant ozone exposure 

metric to characterize population-level exposure. Given that the majority of the people tend to be 

outdoors during the daylight hours and concentrations are highest during the daylight hours, the 

24-hour average metric is not appropriate. Moreover, the 1-hour maximum metric uses an 

exposure window different than that used for the current ozone NAAQS. Together, this means 

that the most biologically relevant metric, and the one used in the ozone NAAQS since 1997 is 

the maximum daily 8-hour average ozone. Thus, we have converted ozone mortality health 

impact functions that use a 24-hour average or 1-hour maximum ozone metric to maximum 8-

hour average ozone concentration using standard conversion functions.

This practice is consistent with the form of the current ozone standard. This conversion 

also does not affect the relative magnitude of the health impact function from a mathematical 

standpoint. An equivalent change in the 24-hour average, 1-hour maximum and 8-hour 

maximum will provide the same overall change in incidence of a health effect.54 The conversion 

ratios are based on observed relationships between the 24-hour average and 8-hour maximum 

 

52 There is a separate effect estimate (and associated standard error) for each of the fitted threshold models estimated 

in Jerrett et al. (2009). As a result, the sensitivity of estimated mortality attributable to long-term ozone 

concentrations is affected by both the assumed threshold level (below which there is no effect of ozone) and the 

effect estimate applied to ozone concentrations above the threshold.

53 The set of ozone-only non-threshold effect estimates include (a) a value based on the 86 cities for which there are 

copollutants monitoring data for both ozone and PM2.5 (this best compared with the core estimate based on the 

copollutants non-threshold model) and (b) a value based on the 96 cities for which there is PM2.5 data (these 96 

cities were used in developing the threshold-based effect estimates used in the analysis). 

54 However, it is important to note that different ozone metrics may not be well-correlated (from either a spatial or 

temporal standpoint) within a given geographic area which means that application of ratio-converted effect estimates 

for the same endpoint can result in different incidence estimates for the same location under certain conditions. This 

introduces uncertainty into the use of these ratio-adjusted effect estimates (see Appendix 5A).
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ozone values. For example, in the Bell et al., 2004 analysis of ozone-related premature mortality, 

the authors found that the relationship between the 24-hour average, the 8-hour maximum, and 

the 1-hour maximum was 2:1.5:1, so that the derived health impact effect estimate based on the 

1-hour maximum should be half that of the effect estimate based on the 24-hour values (and the 

8-hour maximum three-quarters of the 24-hour effect estimate).

In the sensitivity analyses for this benefits analysis, we apply national effect estimates 

based on the pooled multi-city results reported in Bell et al (2004) and the three meta-analysis 

studies. Bell et al (2004), Bell et al (2005), Levy et al (2005), and Ito et al (2005) all provide 

national conversion ratios between daily average and 8-hour and 1-hour maxima, based on 

national data. 

5.6.3.2 Hospital Admissions and Emergency Department Visits

Because of the availability of detailed hospital admission and discharge records, there is 

an extensive body of literature examining the relationship between hospital admissions and air 

pollution. For this reason, we pool together the incidence estimates using several different 

studies for many of the hospital admission endpoints. In addition, some studies have examined 

the relationship between air pollution and emergency department (ED) visits. Since most 

emergency department visits do not result in an admission to the hospital (i.e., most people going 

to the emergency department are treated and return home), we treat hospital admissions and 

emergency department visits separately, taking account of the fraction of emergency department 

visits that are admitted to the hospital. Specifically, within the baseline incidence rates, we parse 

out the scheduled hospital visits from unscheduled ones as well as the hospital visits that 

originated in the emergency department.

With regard to short-term hospital admissions and ED visits, the current ozone ISA states 

that, “[c]ompared with studies reviewed in the 2006 ozone AQCD, a larger number of recent 

studies examined hospital admissions and ED visits for specific respiratory outcomes. Although 

limited in number, both single- and multi-city studies consistently found positive associations 

between short-term ozone exposures and asthma and COPD hospital admissions and ED visits, 

with more limited evidence for pneumonia. Consistent with the conclusions of the 2006 ozone 

AQCD, in studies that conducted seasonal analyses, risk estimates were elevated in the warm 
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season compared to cold season or all-season analyses, specifically for asthma and COPD.” 

(ozone ISA section 6.2.9, USEPA, 2013a). In this same section, the ISA also addresses potential 

thresholds in effect: “Although the C-R relationship has not been extensively examined, 

preliminary examinations found no evidence of a threshold between short term ozone exposure 

and asthma hospital admissions and pediatric asthma ED visits...” Regarding the potential for 

confounding by other pollutants including PM, the ISA observes that, “Several epidemiologic 

studies of respiratory morbidity and mortality evaluated the potential confounding effects of 

copollutants, in particular, PM10, PM2.5, or NO2. In most cases, effect estimates remained robust 

to the inclusion of copollutants.” (ozone ISA section 6.2.9, USEPA 2013a).

Based on consideration for these observation from the ISA, and a thorough review of 

available epidemiological studies, for the core analysis, we model respiratory hospital 

admissions (for 65-99yr olds) using effect estimates obtained from Katsouyanni et al., 2009 and

asthma-related emergency room visits (for all ages) using several single-city studies. The 

Katsouyanni et al., 2009 study is for all respiratory hospital admissions, and thus to avoid double 

counting, we do not provide separate estimates for specific subcategories of respiratory 

admissions such as asthma. The Katsouyanni et al., 2009 study provides effect estimates specific 

to the summer season, which is an advantage, however it also utilizes the 1hr max metric, which 

required adjustment using air metric ratios to generate equivalent 8hr max effect estimates for 

use in the RIA.55 The study provides summer season single pollutant effect estimates based both 

on natural and penalized splines. We used both of these effect estimates and pooled the results 

using equal-weight averaging. It is also important to note that, while the Katsouyanni et al., 2009 

study did include a set of effect estimates based on copollutants modeling (with PM10), these 

were based on the full year rather than the summer season. Given our focus on warmer ozone 

season-based models, we only considered the single pollutant models in the RIA.

A number of studies are available to model respiratory ED visits. However, at this time 

we do not have a valuation function for this endpoint. Since we do have a valuation function 

available for the narrower category of asthma-related ED visits, for the core estimate, we have 

 

55 Given that the Katsouyanni et al., 2009 study included a larger number of cities (14), rather than constructing an 

air metric adjustment ratio based on this set of urban study areas, we used a national ratio to adjust effect estimates 

to represent the 8hr metric used in the RIA. 
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focused on asthma-related ED visits (for which we can estimate the economic value), using a set 

of single city studies together with random-effects pooling to generate a single pooled estimate. 

The set of single city studies used in this calculation include: Peel et al., (2005) and Sarnat et al., 

(2013) both for Atlanta, Wilson et al., (2005) and Mar and Koenig (2009) for Seattle, Wilson et 

al., (2005) for Portland ME, Ito et al., (2007) for New York City, and Glad et al., (2012) for 

Pittsburgh. We note that of these single city studies, only the Ito et al., (2007) study included a 

co-pollutant model (for PM2.5).56 In addition, two of the studies required adjustments of their 

betas to reflect the 8hr max air metric. Specifically, Glad et al., (2012) utilizes the 1hr max air 

metric, while Sarnat et al., 2013 utilized the 24hr average metric. Each required the use of air 

metric ratios to adjust their betas. In generating a single pooled benefit estimate for this endpoint, 

we used random/fixed effects pooling to combine estimates across these single city studies. 

5.6.3.3 Acute Health Events and School/Work Loss Days

In addition to mortality, chronic illness, and hospital admissions, a number of acute 

health effects not requiring hospitalization are associated with exposure to ozone and PM2.5. The 

sources for the effect estimates used to quantify these effects are described below.

Asthma exacerbations. For this RIA, we have followed the SAB-HES recommendations 

regarding asthma exacerbations in developing the core estimate (U.S. EPA-SAB, 2004a). 

Although certain studies of acute respiratory events characterize these impacts among only 

asthmatic populations, others consider the full population, including both asthmatics and nonasthmatics. For this reason, incidence estimates derived from studies focused only on asthmatics 

cannot be added to estimates from studies that consider the full population—to do so would 

double-count impacts. To prevent such double-counting, we estimated the exacerbation of 

asthma among children and excluded adults from the calculation. Asthma exacerbations 

occurring in adults are assumed to be captured in the general population endpoints such as work 

loss days and minor restricted activity days (MRADs). Finally, we note the important distinction 

 

56 While we have included copollutants models as sensitivity analyses for mortality, given the reduced role of 

morbidity endpoints in driving overall dollar benefits, we have not included separate copollutants models for any of 

the morbidity endpoints as sensitivity analyses. In the case of the Ito et al., (2007) study, we do note, that while the 

copollutants model does result in a somewhat smaller effect estimate for asthma ED visits as compared with the 

single pollutant (ozone-only) model, the SE for the copollutants model is also larger (as would be expected). 
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between the exacerbation of asthma among asthmatic populations, and the onset of asthma 

among populations not previously suffering from asthma; in this RIA, we quantify the 

exacerbation of asthma among asthmatic populations and not the onset of new cases of asthma.

Based on advice from the SAB-HES (EPA-SAB 2004a), regardless of the age ranges 

included in the source epidemiology studies, we extend the applied population to ages 6 to 18, 

reflecting the common biological basis for the effect in children in the broader age group. This 

age range expansion is also supported by NRC (2002, pp. 8, 116).

To characterize asthma exacerbations in children from exposure to ozone, we selected 

three multi-city studies (Mortimer et al., 2002, O’Connor et al., 2008, and Schildcrout et al., 

2006). Of these three, one of the studies (O’Connor et al., 2008) only included a multi-pollutant 

model (for PM2.5 and NO2) and consequently, that effect estimate was used in the core analysis. 

All three of these studies required the application of air metric ratios to adjust effect estimates to 

represent the 8hr metric used in the RIA.57 To combine these three estimates into a single pooled 

estimate, we used equal weights. 

Acute Respiratory Symptoms. We estimate one type of acute respiratory symptom related to 

ozone exposure - MRAD. Minor restricted activity days result when individuals reduce most 

usual daily activities and replace them with less strenuous activities or rest, yet not to the point of 

missing work or school. For example, a mechanic who would usually be doing physical work 

most of the day will instead spend the day at a desk doing paper work and phone work because 

of difficulty breathing or chest pain.

For ozone, we modeled MRADs using Ostro and Rothschild (1989). This study provides 

a copollutants model (with PM2.5) based on a national sample of 18-64yr olds. The original study 

used a 24hr average metric and included control for PM2.5, which necessitated the use of an air 

metric ratio to convert the effect estimate to an 8hr max equivalent. 

 

57 Mortimer et al., (2002) had effect estimates based on an 8hr mean metric, O’Connor et al., (2008) utilized a 24hr 

metric and Schildcrout et al., (2006) was based on a 1hr max metric. Consequently, all three studies required the 

application of air metric ratios to produce effect estimates reflecting an 8hr max metric. 
5-52

School loss days (absences). 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 advice from the National Research Council (NRC, 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 pooled the results using the random effects 

pooling procedure.

5.6.3.4 Unquantified Human Health Effects

The illustrative emission reduction strategies to reach the proposed and alternative 

standards described in Chapter 4 would reduce emissions of NOx and VOCs. Although we have 

quantified many of the health benefits associated with reducing exposure to ozone and PM2.5, as 

shown in Table 5-3, we are unable to quantify the health benefits associated with reducing the 

potential for NO2 or VOC exposures due to the absence of air quality modeling data for these 

pollutants in this analysis. In addition, we are unable to quantify the effects of VOC reductions 

on ambient PM2.5 and associated health effects. Although the method we applied simulated the 

impact of attaining the proposed and alternative standards on ambient levels of ozone, this 

method does not simulate how the illustrative emission reductions would affect ambient levels of 

NO2 or VOC. Below we provide a qualitative description of these health benefits. In general, 

previous analyses have shown that the monetized value of these additional health benefits is 

much smaller than ozone and PM2.5-related benefits (U.S. EPA, 2010a, 2010c, 2010d). 
5-53

Epidemiological researchers have associated NO2 exposure with adverse health effects in 

numerous toxicological, clinical and epidemiological studies, as described in the Integrated 

Science Assessment for Oxides of Nitrogen—Health Criteria (NO2 ISA) (U.S. EPA, 2008b). The 

NO2 ISA provides a comprehensive review of the current evidence of health and environmental 

effects of NO2. The NO2 ISA concluded that the evidence “is sufficient to infer a likely causal 

relationship between short-term NO2 exposure and adverse effects on the respiratory system.” 

These epidemiologic and experimental studies encompass a number of endpoints including 

emergency department visits and hospitalizations, respiratory symptoms, airway 

hyperresponsiveness, airway inflammation, and lung function. Effect estimates from 

epidemiologic studies conducted in the United States and Canada generally indicate a 2–

20 percent increase in risks for ED visits and hospital admissions and higher risks for respiratory 

symptoms. The NO2 ISA concluded that the relationship between short-term NO2 exposure and 

premature mortality was “suggestive but not sufficient to infer a causal relationship” because it is 

difficult to attribute the mortality risk effects to NO2 alone. Although the NO2 ISA stated that 

studies consistently reported a relationship between NO2 exposure and mortality, the effect was 

generally smaller than that for other pollutants such as PM. We did not quantify these benefits 

due to data constraints.

The EPA last quantified the value of ozone-related worker productivity in the final 

Regulatory Impact Analysis supporting the Transport Rule (USEPA, 2011d). That analysis 

applied information reported in Crocker and Horst (1981) to relate changes in ground-level 

ozone to changes in the productivity of outdoor citrus workers. That study found that a 10 

percent reduction in ozone translated to a 1.4 increase in income among outdoor citrus 

workers. Concerned that this study might not adequately characterize the relationship between 

ground-level ozone and the productivity of agricultural workers because of the vintage of the 

underlying data, the Agency subsequently omitted this endpoint.

In 2012, Graff Zivin and Neidell published “The impact of pollution on worker 

productivity” in the American Economic Review. That study combined data on individual-level 

daily harvest rates for Outdoor Agricultural Workers (OWAs) with ground-level ozone pollution 

to characterize changes in worker productivity. The authors used data on harvest rates from a 

500-acre farm in the Central Valley of California. That farm produced three crops (blueberries 
5-54

and two types of grapes) and the harvesting laborers were paid through piece rate contracts. The 

analyses in the paper were based on 2009 and 2010 California growing seasons. The analyses 

were not affected by: (i) endogenous ozone exposure (because there were limited local sources of 

ozone precursors); (ii) avoidance behavior (because the work has to be performed outdoors); and 

(iii) shirking (due to the nature of the piece rate contract).

Table 3 in Graff Zivin and Neidell (2012) reports the main result: A 10 ppb increase in 

work-day ozone concentration (represented by hourly measurements averaged between 6am and 

3pm) will result in a decline of 0.143 (with a standard error of 0.068) in standardized hourly 

pieces collected on a given work day. The standardized hourly pieces were “the average hourly 

productivity minus the minimum number of pieces per hour required to reach the piece rate 

regime, divided by the standard deviation of productivity for each crop” (Graff Zivin and 

Neidell, 2012; p. 3665). The range of ozone concentrations in the sample was between 10.50 ppb 

and 86.0 ppb (Table 1 in Graff Zivin and Neidell, 2012). This result is significant and robust 

under different model specifications designed to test modeling assumptions. Based on the effect 

estimate and individual-level information in their dataset, the authors estimated the effect of an 

increase in ozone concentration on the worker productivity, as measured by the average number 

of pieces collected per hour during a given work day (rather than by standardized hourly piece 

rate that was used in regression modeling). They found a decline of 5.5% in worker productivity 

due to a 10 ppb increase in average work-day ozone concentration.

While Graff Zivin and Neidell (2012) report the information needed to quantify ozonerelated worker productivity, we are still evaluating whether and how to most appropriately apply 

the limited evidence from this study in a national benefits assessment. An important issue is the 

generalizability of the results to the appropriate population. We are considering the 

appropriateness of applying the results of this study to estimate the benefits of increased worker 

productivity as part of the final ozone RIA, and seek public comment on this approach as an 

important input to our consideration.
5-55

5.6.4 Economic Valuation Estimates

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 changes in risk 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 measure 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 mortality amounts to $1 million ($100/0.0001 

change in risk). Using this approach, the size of the affected population is automatically taken 

into account by the number of incidences predicted by epidemiological studies applied to the 

relevant population. The same type of calculation can produce values for statistical incidences of 

other health endpoints.

WTP estimates generally are not available for some health effects, such as hospital 

admissions. In these cases, we instead used the cost of treating or mitigating the effect to 

estimate the economic value. Cost-of-illness (COI) estimates generally (although not necessarily 

in all cases) 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 5-10.58 All values are in constant year 2011$, adjusted for growth in 

real income for WTP estimates out to 2024 using projections provided by Standard and Poor’s, 

 

58 We note that a number of the endpoints included in Table 5-10 and discussed in this section were only modeled 

for PM2.5 and consequently could be moved to Appendix 5C (as was done with the discussion of effect estimates 

specific to PM2.5). However, given that many of the valuation functions discussed are shared between the two 

pollutants and given the relatively shorter length of discussions associated with these valuation functions, we have 

included coverage of all valuation functions (for ozone and PM2.5-related endpoints) in this section.
5-56

which is discussed in further detail below.

59 Economic theory argues that WTP for most goods 

(such as environmental protection) will increase if real income increases. Several of the valuation 

studies used in this analysis were conducted in the late 1980s and early 1990s, and we are in the 

process of reviewing the literature to update these unit values. The discussion below provides 

additional details on valuing specific PM2.5-related related endpoints.

5.6.4.1 Mortality Valuation

Following the advice of the SAB’s Environmental Economics Advisory Committee 

(SAB-EEAC), the EPA currently uses the value of statistical life (VSL) approach in calculating 

the core estimate of mortality benefits, because we believe this calculation provides the most 

reasonable single estimate of an individual’s willingness to trade off money for reductions in 

mortality risk (U.S. EPA-SAB, 2000). The VSL approach is a summary measure for the value of 

small changes in mortality risk experienced by a large number of people. For a period of time 

(2004–2008), the Office of Air and Radiation (OAR) valued mortality risk reductions using a 

VSL estimate derived from a limited analysis of some of the available studies. OAR arrived at a 

VSL using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of the 

wage-risk literature. The $1 million value represented the lower end of the interquartile range 

from the Mrozek and Taylor (2002) meta-analysis of 33 studies. The $10 million value 

represented the upper end of the interquartile range from the Viscusi and Aldy (2003) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$) was also consistent with the 

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the 

Agency neither changed its official guidance on the use of VSL in rule-makings nor subjected 

the interim estimate to a scientific peer-review process through SAB or other peer-review group.

 

59 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 

estimates use income growth only through 2024 and are therefore likely underestimates. Currently, BenMAP does 

not have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and calculated the benefitper-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton estimates to 2011$ using the Consumer 

Price Index (CPI-U, all items). This approach slightly underestimates the inflation for medical index and wage index 

between 2010 and 2011, which affects COI estimates and wage-based estimates. 
5-57

Table 5-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a

Health Endpoint

Central Estimate of Value Per Statistical Incidence

1990 Income Level 2024 Income Level Derivation of Distributions of Estimates

Premature Mortality (Value of a 

Statistical Life)

$8,300,000 $10,000,000 The EPA currently recommends a central VSL of $4.8 million (1990$, 

1990 income) based on a Weibull distribution fitted to 26 published 

VSL estimates (5 contingent valuation and 21 labor market studies). 

The underlying studies, the distribution parameters, and other useful 

information are available in Appendix B of the EPA’s Guidelines for 

Preparing Economic Analyses (U.S. EPA, 2010e).

Nonfatal Myocardial Infarction 

(heart attack)

3% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–64

Age 65 and over

7% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–64

Age 65 and over

$100,000

$110,000

$120,000

$210,000

$100,000

$100,000

$110,000

$120,000

$190,000

$100,000

$100,000

$110,000

$120,000

$210,000

$100,000

$100,000

$110,000

$120,000

$190,000

$100,000

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 in 2000$:

age of onset: at 3% at 7%

25–44 $9,000 $8,000

45–54 $13,000 $12,000

55–65 $77,000 $69,000

Direct medical expenses (2000$): An average of:

1. Wittels et al. (1990) ($100,000—no discounting)

2. Russell et al. (1998), 5-year period ($22,000 at 3% discount rate; 

$21,000 at 7% discount rate)

(continued)
5-58

Table 5-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a(continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence

2000 Income Level 2024 Income Level Derivation of Distributions of Estimates

Hospital Admissions 

Chronic Lung Disease (18–64) $22,000 $22,000 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 chronic lung illnesses) 

reported in Agency for Healthcare Research and Quality (2007) 

(www.ahrq.gov). 

Asthma Admissions (0–64) $16,000 $16,000 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 (2007) 

(www.ahrq.gov). 

All Cardiovascular

Age 18–64

Age 65–99

$44,000

$42,000

$44,000

$42,000

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 

(2007) (www.ahrq.gov). 

All respiratory (ages 65+) $37,000 $37,000 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 respiratory category illnesses) reported 

in Agency for Healthcare Research and Quality, 2007 

(www.ahrq.gov).

Emergency Department Visits 

for Asthma

$440 $440 No distributional information available. Simple average of two unit 

COI values (2000$):

(1) $310, from Smith et al. (1997) and

(2) $260, from Stanford et al. (1999).

(continued)
5-59

Table 5-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a(continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence

2000 Income Level 2024 Income Level Derivation of Distributions of Estimates

Respiratory Ailments Not Requiring Hospitalization

Upper Respiratory Symptoms 

(URS)

$35 $32 Combinations of the three symptoms for which WTP estimates are 

available that closely match those listed by Pope et al. result in seven 

different “symptom clusters,” each describing a “type” of URS. A 

dollar value was derived for each type of URS, using mid-range 

estimates of WTP (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 $43 (2000$).

Lower Respiratory Symptoms 

(LRS)

$22 $21 Combinations of the four symptoms for which WTP estimates are 

available that closely match those listed by Schwartz et al. result in 

11 different “symptom clusters,” each describing a “type” of LRS. A 

dollar value was derived for each type of LRS, using mid-range 

estimates of WTP (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 $25 (2000$).

Asthma Exacerbations $56 $60 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 to have a uniform 

distribution between $16 and $71 (2000$).

(continued)
5-60

Table 5-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a(continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence

2000 Income Level 2024 Income Level Derivation of Distributions of Estimates

Respiratory Ailments Not Requiring Hospitalization (continued)

Acute Bronchitis $460 $500 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 $110 (2000$). 

Work Loss Days (WLDs) Variable 

(U.S. median = $150)

Variable 

(U.S. median = $150)

No distribution available. Point estimate is based on county-specific 

median annual wages divided by 52 and then by 5—to get median 

daily wage. U.S. Year 2000 Census, compiled by Geolytics, Inc. 

(Geolytics, 2002)

School Loss Days $98 $98 No distribution available. Based on (1) 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) the value of the parent’s lost 

productivity.

Minor Restricted Activity Days 

(MRADs)

$64 $68 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 $52 (2000$). 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) 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.

a All estimates are rounded to two significant digits. Unrounded estimates in 2000$ are available in the Appendix J of the BenMAP user manual (Abt Associates, 

2012). Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 estimates use income growth only through 

2024 and are therefore likely underestimates. Currently, BenMAP does not have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and calculated the benefit-per-ton estimates in 2010$. We then 

adjusted the resulting benefit-per-ton estimates to 2011$ using the Consumer Price Index (CPI-U, all items). This approach slightly underestimates the inflation for medical index and wage index between 2010 and 2011, which 

affects COI estimates and wage-based estimates. 
5-61

During this time, the Agency continued work to update its guidance on valuing mortality 

risk reductions, including commissioning a report from meta-analytic experts to evaluate 

methodological questions raised by the EPA and the SAB on combining estimates from the 

various data sources. In addition, the Agency consulted several times with the SAB-EEAC on the 

issue. With input from the meta-analytic experts, the SAB-EEAC advised the Agency to update 

its guidance using specific, appropriate meta-analytic techniques to combine estimates from 

unique data sources and different studies, including those using different methodologies (i.e., 

wage-risk and stated preference) (U.S. EPA-SAB, 2007).

Until updated guidance is available, the Agency determined that a single, peer-reviewed 

estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, the 

Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines 

for Preparing Economic Analyses (U.S. EPA, 2000)60 while the Agency continues its efforts to 

update its guidance on this issue. This approach calculates a mean value across VSL estimates 

derived from 26 labor market and contingent valuation studies published between 1974 and 

1991. The mean VSL across these studies is $4.8 million (1990$) or $6.3 million (2000$).61 The 

Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing 

mortality risk reductions and has made significant progress in responding to the SAB-EEAC’s 

specific recommendations. In the process, the Agency has identified a number of important 

issues to be considered in updating its mortality risk valuation estimates. These are detailed in a 

white paper on “Valuing Mortality Risk Reductions in Environmental Policy,” which underwent 

review by the SAB-EEAC. A meeting with the SAB on this paper was held on March 14, 2011 

and formal recommendations were transmitted on July 29, 2011 (U.S. EPA-SAB, 2011). EPA is 

taking SAB’s recommendations under advisement.

The economics literature concerning the appropriate method for valuing reductions in 

premature mortality risk is still developing. The adoption of a value for the projected reduction in 

 

60 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA retained the VSL endorsed 

by the SAB with the understanding that further updates to the mortality risk valuation guidance would be 

forthcoming in the near future. 

61 In this analysis, we adjust the VSL to account for a different currency year (2011$) and to account for income 

growth to 2024. After applying these adjustments to the $6.3 million value, the VSL is $10 million. Income 

growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 estimates 

use income growth only through 2024 and are therefore likely underestimates.
5-62

the risk of premature mortality is the subject of continuing discussion within the economics and 

public policy analysis community. The EPA strives to use the best economic science in its 

analyses. Given the mixed theoretical finding and empirical evidence regarding adjustments to 

VSL for risk and population characteristics (e.g., Smith et al., 2004; Alberini et al., 2004; Aldy 

and Viscusi, 2008), we use a single VSL for all reductions in mortality risk.

Although there are several differences between the labor market studies the EPA uses to 

derive a VSL estimate and the ozone and PM2.5 air pollution context addressed here, those 

differences in the affected populations and the nature of the risks imply both upward and 

downward adjustments. Table 5-11 lists some of these differences and the expected effect on the 

VSL estimate for air pollution-related mortality. In the absence of a comprehensive and balanced 

set of adjustment factors, the EPA believes it is reasonable to continue to use the $4.8 million 

(1990$) value adjusted for inflation and income growth over time while acknowledging the 

significant limitations and uncertainties in the available literature.

Table 5-11. Influence of Applied VSL Attributes on the Size of the Economic Benefits of 

Reductions in the Risk of Premature Death (U.S. EPA, 2006a)

Attribute Expected Direction of Bias

Age Uncertain, perhaps overestimate

Life Expectancy/Health Status Uncertain, perhaps overestimate

Attitudes Toward Risk Underestimate

Income Uncertain

Voluntary vs. Involuntary Uncertain, perhaps underestimate

Catastrophic vs. Protracted Death Uncertain, perhaps underestimate

The SAB-EEAC has reviewed many potential VSL adjustments and the state of the 

economics literature. The SAB-EEAC advised the EPA to “continue to use a wage-risk-based 

VSL as its primary estimate, including appropriate sensitivity analyses to reflect the uncertainty 

of these estimates,” and that “the only risk characteristic for which adjustments to the VSL can 

be made is the timing of the risk” (U.S. EPA-SAB, 2000). In developing our core estimate of the 

benefits of premature mortality reductions, we have followed this advice. 

For PM2.5-related premature mortality, we assume that there is a “cessation” lag between 

exposures and the total realization of changes in health effects. For PM2.5, we assumed that some 

of the incidences of premature mortality related to PM2.5 exposures occur in a distributed fashion 
5-63

over the 20 years following exposure and discounted over the period between exposure and 

premature mortality. Although the structure of the lag is uncertain, the EPA follows the advice of 

the SAB-HES to assume a segmented lag structure characterized by 30 percent of mortality 

reductions in the first year, 50 percent over years 2 to 5, and 20 percent over the years 6 to 20 

after the reduction in PM2.5 (U.S. EPA-SAB, 2004c). To take this into account in the valuation of 

reductions in premature mortality, we discount the value of premature mortality occurring in 

future years using rates of 3 percent and 7 percent.62 Changes in the cessation lag assumptions do 

not change the total number of estimated deaths but rather the timing of those deaths. As such, 

the monetized PM2.5 co-benefits using a 7 percent discount rate are only approximately 10 

percent less than the monetized benefits using a 3 percent discount rate. Further discussion of 

this topic appears in the EPA’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e).

For ozone, we acknowledge substantial uncertainty associated with specifying the lag for 

long-term respiratory mortality. As stated earlier, it is this uncertainty related to specifying a lag 

structure which prevents us from including this endpoint as a monetary benefit estimate within 

the core analysis.63 In presenting dollar benefit estimates as part of the sensitivity analysis, we 

include both an assumption of zero lag and a lag structure matching that used for the core PM2.5

estimate (the SAB 20 year segmented lag). Inclusion of the zero lag reflects consideration for the 

possibility that the long-term respiratory mortality estimate captures primarily, an accumulation 

of short-term mortality effects across the ozone season.64 The use of the 20 year segmented lag 

 

62 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the 

federal government. To comply with OMB Circular A-4, EPA provides monetized benefits using discount rates of 

3% and 7% (OMB, 2003). A 3% discount reflects reliance on a “social rate of time preference” discounting 

concept. A 7% rate is consistent with an “opportunity cost of capital” concept to reflect the time value of 

resources directed to meet regulatory requirements. 

63 Recall however, that we consider the estimate of reduced incidence of respiratory mortality associated with longterm ozone exposure to have sufficient support in the literature to be included as part of the core estimate. 

64 In presenting risk estimates associated with modeling long-term ozone-related respiratory mortality in the HREA, 

we noted that: “The effect estimates used in modeling long-term O3-attributable mortality, utilize a seasonal average 

of peak (1-hr maximum) measurements. These long-term exposure metrics can be viewed as long-term exposures to 

daily peak O3 over the warmer months, as compared with annual average levels such as are used in long-term PM 

exposure calculations. This increases the need for care in interpreting these long-term O3-attributable mortality 

estimates together with the short-term O3-attributable mortality estimates, in order to avoid double counting.” 

(USEPA, 2014b). This statement was included in the 2nd draft of the HREA that CASAC commented extensively 

on (p. 7-21) (Frey 2014). While the CASAC made recommendations on specific aspects of our approach for 

modeling this endpoint (specifically the need to include consideration for potential thresholds as a sensitivity 

analysis), they did not criticize this observation regarding the potential that this metric could actually represent an 
5-64

reflects consideration for advice provided by the HES (USEPA-SAB, 2010a), where they state 

that, “[i]f Alternative Estimates are derived using cohort mortality evidence, there is no evidence 

in the literature to support a different cessation lag between ozone and particulate matter. The 

HES therefore recommends using the same cessation lag structure and assumptions as for 

particulate matter when utilizing cohort mortality evidence for ozone.” Dollar benefit estimates 

generated using both lag assumptions are presented as sensitivity analyses (see section 5.7.3.1). 

Uncertainties Specific to Premature Mortality Valuation. The economic benefits 

associated with reductions in the risk of premature mortality are the largest category of 

monetized benefits in this RIA. In addition, in prior analyses, the EPA identified valuation of 

mortality-related benefits as the largest contributor to the range of uncertainty in monetized 

benefits (Mansfield et al., 2009).65 Because of the uncertainty in estimates of the value of 

reducing premature mortality risk, it is important to adequately characterize and understand the 

various types of economic approaches available for valuing reductions in mortality risk. Such an 

assessment also requires an understanding of how alternative valuation approaches reflect that 

some individuals may be more susceptible to air pollution-induced mortality or reflect 

differences in the nature of the risk presented by air pollution relative to the risks studied in the 

relevant economics literature.

The health science literature on air pollution indicates that several human characteristics 

affect the degree to which mortality risk affects an individual. For example, some age groups 

appear to be more susceptible to air pollution than others (e.g., the elderly and children). Health 

status prior to exposure also affects susceptibility. An ideal benefits estimate of mortality risk 

reduction would reflect these human characteristics, in addition to an individual’s WTP to 

improve one’s own chances of survival plus WTP to improve other individuals’ survival rates. 

 

accumulation of short-term daily peak exposures and that care needed to be taken to avoid double-counting of 

mortality incidence. Our inclusion of a zero lag reflects the potential that the estimate of long-term exposure-related 

respiratory mortality could (to a significant extent) capture an accumulation of short-term effects. Note, that we 

include the zero threshold model together with a 20 year segmented lag model in order to capture a potential range 

of lag effect and both are given equal coverage in generating the dollar benefit estimates included as a sensitivity 

analysis for this endpoint.

65 This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological effect 

estimates and economic valuation estimates. Additional sources of model error such as those examined in the 

PM2.5 mortality expert elicitation (Roman et al., 2008) may result in different conclusions about the relative 

contribution of sources of uncertainty.
5-65

The ideal measure would also take into account the specific nature of the risk reduction 

commodity that is provided to individuals, as well as the context in which risk is reduced. To 

measure this value, it is important to assess how reductions in air pollution reduce the risk of 

dying from the time that reductions take effect onward and how individuals value these changes. 

Each individual’s survival curve, or the probability of surviving beyond a given age, should shift 

as a result of an environmental quality improvement. For example, changing the current 

probability of survival for an individual also shifts future probabilities of that individual’s 

survival. This probability shift will differ across individuals because survival curves depend on 

such characteristics as age, health state, and the current age to which the individual is likely to 

survive.

Although a survival curve approach provides a theoretically preferred method for valuing 

the benefits of reduced risk of premature mortality associated with reducing air pollution, the 

approach requires a great deal of data to implement. The economic valuation literature does not 

yet include good estimates of the value of this risk reduction commodity. As a result, in this 

study we value reductions in premature mortality risk using the VSL approach.

Other uncertainties specific to premature mortality valuation include the following:

 Across-study variation: There is considerable uncertainty as to whether the available 

literature on VSL provides adequate estimates of the VSL for risk reductions from air 

pollution reduction. Although there is considerable variation in the analytical designs and 

data used in the existing literature, the majority of the studies involve the value of risks to 

a middle-aged working population. Most of the studies examine differences in wages of 

risky occupations, using a hedonic wage approach. Certain characteristics of both the 

population affected and the mortality risk facing that population are believed to affect the 

average WTP to reduce the risk. The appropriateness of a distribution of WTP based on 

the current VSL literature for valuing the mortality-related benefits of reductions in air 

pollution concentrations therefore depends not only on the quality of the studies (i.e., how 

well they measure what they are trying to measure), but also on the extent to which the 

risks being valued are similar and the extent to which the subjects in the studies are 

similar to the population affected by changes in pollution concentrations.

 Level of risk reduction: The transferability of estimates of the VSL from the wage-risk 

studies to the context of this analysis rests on the assumption that, within a reasonable 

range, WTP for reductions in mortality risk is linear in risk reduction. For example, 

suppose a study provides a result that the average WTP for a reduction in mortality risk 

of 1/100,000 is $50, but that the actual mortality risk reduction resulting from a given 

pollutant reduction is 1/10,000. If WTP for reductions in mortality risk is linear in risk 
5-66

reduction, then a WTP of $50 for a reduction of 1/100,000 implies a WTP of $500 for a 

risk reduction of 1/10,000 (which is 10 times the risk reduction valued in the study). 

Under the assumption of linearity, the estimate of the VSL does not depend on the 

particular amount of risk reduction being valued. This assumption has been shown to be 

reasonable provided the change in the risk being valued is within the range of risks 

evaluated in the underlying studies (Rowlatt et al., 1998).

 Voluntariness of risks evaluated: Although job-related mortality risks may differ in 

several ways from air pollution-related mortality risks, the most important difference may 

be that job-related risks are incurred voluntarily, or generally assumed to be, whereas air 

pollution-related risks are incurred involuntarily. Some evidence suggests that people will 

pay more to reduce involuntarily incurred risks than risks incurred voluntarily (e.g., 

Lichtenstein and Slovic, 2006). If this is the case, WTP estimates based on wage-risk 

studies may understate WTP to reduce involuntarily incurred air pollution-related 

mortality risks.

 Sudden versus protracted death: A final important difference related to the nature of 

the risk may be that some workplace mortality risks tend to involve sudden, catastrophic 

events, whereas air pollution-related risks tend to involve longer periods of disease and 

suffering prior to death. Some evidence suggests that WTP to avoid a risk of a protracted 

death involving prolonged suffering and loss of dignity and personal control is greater 

than the WTP to avoid a risk (of identical magnitude) of sudden death (e.g., Tsuge et al., 

2005; Alberini and Scasny, 2011). To the extent that the mortality risks addressed in this 

assessment are associated with longer periods of illness or greater pain and suffering than 

are the risks addressed in the valuation literature, the WTP measurements employed in 

the present analysis would reflect a downward bias.

 Self-selection and skill in avoiding risk: Recent research (Shogren and Stamland, 2002) 

suggests that VSL estimates based on hedonic wage studies may overstate the average 

value of a risk reduction. This is based on the fact that the risk-wage trade-off revealed in 

hedonic studies reflects the preferences of the marginal worker (i.e., that worker who 

demands the highest compensation for his risk reduction for a given job). This worker 

must have either a higher workplace risk than the average worker in a given occupation, a 

lower risk tolerance than the average worker in that occupation, or both. Conversely, the 

marginal worker should have a higher risk tolerance than workers employed in less-risky 

sectors. However, the risk estimate used in hedonic studies is generally based on average 

risk, so the VSL may be biased, in an ambiguous direction, because the wage differential 

and risk measures do not match.

 Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) 

finds that because individuals reevaluate their baseline risk of death as they age, the 

marginal value of risk reductions does not decline with age as predicted by some lifetime 

consumption models. This research supports findings in recent stated preference studies 

that suggest only small reductions in the value of mortality risk reductions with 

increasing age (e.g., Alberini et al., 2004).
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5.6.4.2 Hospital Admissions and Emergency Department Valuation

In the absence of estimates of societal WTP to avoid hospital visits/admissions for 

specific illnesses, we derive COI estimates for use in the benefits analysis. The International 

Classification of Diseases (ICD) (WHO, 1977) 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’s Healthcare Utilization Project National Inpatient Sample (NIS) database (AHRQ, 

2007). 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 median weekly wage reported by the 2007 American Community 

Survey (ACS) by five and deflated the result to the correct currency year using the CPI-U “all 

items” (Abt Associates, 2012). The resulting national average lost daily wage is $150 (2011$). 

The total cost-of-illness estimate for an ICD code-specific hospital stay lasting n days, then, was 

the mean hospital charge plus daily lost wage multiplied by n. In general, the mean length of stay 

has decreased since the 2000 database used in the previous version of BenMAP, while the mean 

hospital charge has increased. We provide the rounded unit values in 2011$ for the COI 

functions used in this analysis in Table 5-12.

Table 5-12. Unit Values for Hospital Admissions a

End Point ICD Codes

Age Range Mean Hospital 

Charge 

(2011$)

Mean 

Length of 

Stay (days)

Total Cost of 

Illness (unit 

value in 2011$) min. max.

HA, Chronic Lung Disease 490–496 18 64 $20,000 3.9 $22,000

HA, Asthma 493 0 64 $15,000 3.0 $16,000

HA, All Cardiovascular 390–429 18 64 $41,000 4.1 $44,000

HA, All Cardiovascular 390–429 65 99 $38,000 4.9 $42,000

HA, All Respiratory 460–519 65 99 $32,000 6.1 $37,000

a All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in Appendix J of the 

BenMAP user manual (Abt Associates, 2012).

To value asthma emergency department 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 department visits in 1987, at a 

total cost of $186 million (1987$). The average cost per visit that year was $155; in 2011$, that 

cost was $480 (using the CPI-U for medical care to adjust to 2011$). The second estimate comes 
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from Stanford et al. (1999), who reported the cost of an average asthma-related emergency 

department visit based on 1996–1997 data at $400 (using the CPI-U for medical care to adjust to 

2011$). A simple average of the two estimates yields a unit value of $440 (2011$).

5.6.4.3 Nonfatal Myocardial Infarctions Valuation

We were not able to identify a suitable WTP value for reductions in the risk of nonfatal 

heart attacks.66 Instead, we use a COI unit value with two components: the direct medical costs 

and the opportunity cost (lost earnings) associated with the illness event. 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% discount rate, we estimated a rounded present discounted value in lost 

earnings (in 2000$) over 5 years due to a myocardial infarction of $8,800 for someone between 

the ages of 25 and 44, $13,000 for someone between the ages of 45 and 54, and $75,000 for 

someone between the ages of 55 and 65. The rounded corresponding age-specific estimates of 

lost earnings (in 2000$) using a 7% discount rate are $7,900, $12,000, and $67,000, respectively. 

Cropper and Krupnick (1990) do not provide lost earnings estimates for populations under 25 or 

over 65. As such, we do not include lost earnings in the cost estimates for these age groups.

We found three possible sources in the literature of estimates of the direct medical costs 

of myocardial infarction, which provide significantly different values (see Table 5-13):

 Wittels et al. (1990) estimated expected total medical costs of myocardial infarction over 

5 years to be $51,000 (rounded in 1986$) for people who were admitted to the hospital 

and survived hospitalization. (There does not appear to be any discounting used.) 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 authors note that the average 

length of hospitalization for acute myocardial infarction has decreased over time (from an 

average of 12.9 days in 1980 to an average of 11 days in 1983). Wittels et al. used 10 

days as the average in their study. It is unclear how much further the length of stay for 

myocardial infarction may have decreased from 1983 to the present. The average length 

 

66 We note that this endpoint was only modeled as part of the cobenefits analysis for PM2.5 and is not included in the 

ozone-related benefits analysis. As such, we could have moved this discussion to Appendix 5D (as was done with 

the discussion of PM2.5-related effect estimates). However, since this was the only broader discussion related to 

valuation which is exclusively related to PM2.5 we left it in this section.
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of stay for ICD code 410 (myocardial infarction) in the year-2000 Agency for Healthcare 

Research and Quality (AHRQ) HCUP database is 5.5 days (AHRQ, 2000). However, this 

may include patients who died in the hospital (not included among our nonfatal 

myocardial infarction cases), and whose length of stay was therefore substantially shorter 

than it would be if they had not died.

 Eisenstein et al. (2001) estimated 10-year costs of $45,000 in rounded 1997$ (using a 3% 

discount rate) for myocardial infarction patients, using statistical prediction (regression) 

models to estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) 

were included.

Table 5-13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart 

Attacks a

Study Direct Medical Costs (2011$) Over an x-Year Period, for x =

Wittels et al. (1990) $170,000 b 5

Russell et al. (1998) $34,000 c 5

Average (5-year) costs $100,000 5

Eisenstein et al. (2001) $76,000 c 10

a All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in appendix J of the 

BenMAP user manual (Abt Associates, 2012).

b Wittels et al. (1990) did not appear to discount costs incurred in future years.

c Using a 3% discount rate. Discounted values as reported in the study.

As noted above, the estimates from these three studies are substantially different, and we 

have not adequately resolved the sources of differences in the estimates. Because the wagerelated 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 (i.e., estimates from Wittels 

et al. (1990) and Russell et al. (1998). We used a simple average of the two 5-year estimates, or 

rounded to $85,000, and added it to the 5-year opportunity cost estimate. The resulting estimates 

are given in Table 5-14.

Table 5-14. Estimated Costs Over a 5-Year Period of a Nonfatal Myocardial Infarction 

(in 2011$) a

Age Group Opportunity Cost Medical Cost b Total Cost

0–24 $0 $100,000 $100,000

25–44 $12,000 c $100,000 $110,000

45–54 $18,000 c $100,000 $120,000

55–65 $100,000 c $100,000 $210,000

> 65 $0 $100,000 $100,000

a All estimates rounded to two significant digits, so estimates may not sum across columns. Unrounded estimates in 

2000$ are available in appendix J of the BenMAP user manual (Abt Associates, 2012).

b An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998).

c From Cropper and Krupnick (1990), using a 3% discount rate for illustration.
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5.6.4.4 Valuation of Acute Health Events

Asthma exacerbation. Several respiratory symptoms in asthmatics or characterizations of an 

asthma episode have been associated with exposure to air pollutants. All of these can generally 

be taken as indications of an asthma exacerbation when they occur in an asthmatic. Therefore, 

we apply the same set of unit values for all of the variations of “asthma exacerbation”. 

Specifically, we use a unit value based on the mean WTP estimates for 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.

Minor Restricted Activity Days Valuation. No studies are reported to have estimated WTP to 

avoid a minor restricted activity day. However, Neumann et al. (1994) 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. This estimate of WTP to avoid a minor respiratory restricted activity day is $38 

(1990$), or about $71 (2011$). Although Ostro and Rothschild (1989) statistically linked ozone 

and minor restricted activity days, it is likely that most MRADs associated with ozone and PM2.5

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.

School Loss Days Valuation. 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 
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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 $551 (2000$). Dividing by five gives an estimated median 

daily wage of $103 (2000$). To estimate the expected lost wages on a day when a mother has to 

stay home with a school-age child, we first estimated the probability that the mother is in the 

workforce then multiplied that estimate by the daily wage she would lose by missing a workday: 

72.85 percent times $103, for a total loss of $75 (2000$). This valuation approach is similar to 

that used by Hall et al. (2003).

Work Loss Days Valuation. Work loss days are valued at a day’s wage. BenMAP-CE 

calculates county-specific median daily wages from county-specific annual wages (by dividing 

the annual wage by 52 weeks multiplied by 5 work days per week), on the theory that a worker’s 

vacation days are valued at the same daily rate as work days.

Upper and Lower respiratory symptoms. Lower and upper respiratory symptoms are each 

considered a complex of symptoms. A dollar value was derived for clusters of these symptoms 

that most closely match the studies used to calculate incidence (Schwartz and Neas, 2000; Pope 

et al, 1991) based on mid-range estimates from each cluster (IEc, 1994). 

5.6.4.5 Growth in WTP Reflecting National Income Growth over Time

Our analysis accounts for expected growth in real income over time. This is a distinct 

concept from inflation and currency year. Economic theory argues that WTP for most goods 

(such as environmental protection) will increase if real incomes increase. There is substantial 
5-72

empirical evidence that the income elasticity67 of WTP for health risk reductions is positive, 

although there is uncertainty about its exact value. Thus, as real income increases, the WTP for 

environmental improvements also increases. Although many analyses assume that the income 

elasticity of WTP is unit elastic (i.e., a 10% higher real income level implies a 10% higher WTP 

to reduce risk changes), empirical evidence suggests that income elasticity is substantially less 

than one and thus relatively inelastic. As real income rises, the WTP value also rises but at a 

slower rate than real income.

The effects of real income changes on WTP estimates can influence benefits estimates in 

two different ways: through real (national average) income growth between the year a WTP 

study was conducted and the year for which benefits are estimated, and through differences in 

income between study populations and the affected populations at a particular time. The SABEEAC advised the EPA to adjust WTP for increases in real income over time but not to adjust 

WTP to account for cross-sectional income differences “because of the sensitivity of making 

such distinctions, and because of insufficient evidence available at present” (U.S. EPA-SAB, 

2000). An advisory by another committee associated with the SAB, the Advisory Council on 

Clean Air Compliance Analysis (SAB-Council), has provided conflicting advice. While agreeing 

with “the general principle that the willingness to pay to reduce mortality risks is likely to 

increase with growth in real income” and that “[t]he same increase should be assumed for the 

WTP for serious nonfatal health effects,” they note that “given the limitations and uncertainties 

in the available empirical evidence, the Council does not support the use of the proposed 

adjustments for aggregate income growth as part of the primary analysis” (U.S. EPA-SAB, 

2004b). Until these conflicting advisories can be reconciled, the EPA will continue to adjust 

valuation estimates to reflect income growth using the methods described below, while providing 

sensitivity analyses for alternative income growth adjustment factors.

Based on a review of the available income elasticity literature, we adjusted the valuation 

of human health benefits upward to account for projected growth in real U.S. income. Faced with 

a dearth of estimates of income elasticities derived from time-series studies, we applied estimates 

derived from cross-sectional studies in our analysis. Details of the procedure can be found in 

 

67 Income elasticity is a common economic measure equal to the percentage change in WTP for a 1% change in 

income.
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Kleckner and Neumann (1999). We note that the literature has evolved since the publication of 

this memo and that an array of newer studies identifying potentially suitable income elasticity 

estimates are available (IEc, 2012). The EPA anticipates seeking an SAB review of these studies, 

and its approach to adjusting WTP estimates to account for changes in personal income, in the 

near future. As such, these newer studies have not yet been incorporated into the benefits 

analysis. An abbreviated description of the procedure we used to account for WTP for real 

income growth between 1990 and 2024 is presented below. Income growth projections are only 

currently available in BenMAP through 2024, so both the 2025 and 2038 estimates use income 

growth only through 2024 and are therefore likely underestimates. 

Reported income elasticities suggest that the severity of a health effect is a primary 

determinant of the strength of the relationship between changes in real income and WTP. As 

such, we use different elasticity estimates to adjust the WTP for minor health effects, severe and 

chronic health effects, and premature mortality. Note that because of the variety of empirical 

sources used in deriving the income elasticities, there may appear to be inconsistencies in the 

magnitudes of the income elasticities relative to the severity of the effects (a priori one might 

expect that more severe outcomes would show less income elasticity of WTP). We have not 

imposed any additional restrictions on the empirical estimates of income elasticity. One 

explanation for the seeming inconsistency is the difference in timing of conditions. WTP for 

minor illnesses is often expressed as a short-term payment to avoid a single episode. WTP for 

major illnesses and mortality risk reductions are based on longer-term measures of payment 

(such as wages or annual income). Economic theory suggests that relationships become more 

elastic as the length of time grows, reflecting the ability to adjust spending over a longer time 

period (U.S. EPA, 2010e, p. A-9). Based on this theory, it would be expected that WTP for 

reducing long-term risks would be more elastic than WTP for reducing short-term risks. The 

relative magnitude of the income elasticity of WTP for visibility compared with those for health 

effects suggests that visibility is not as much of a necessity as health, thus, WTP is more elastic 

with respect to income. The elasticity values used to adjust estimates of benefits in 2024 are 

presented in Table 5-15.68

 

68 We expect that the WTP for improved visibility in Class 1 areas would also increase with growth in real income.
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Table 5-15. Elasticity Values Used to Account for Projected Real Income Growth a

Benefit Category Central Elasticity Estimate

Minor Health Effect 0.14

Severe and Chronic Health Effects 0.45

Premature Mortality 0.40

a Derivation of estimates can be found in Kleckner and Neumann (1999). COI estimates are not adjusted for income 

growth.

In addition to elasticity estimates, projections of real gross domestic product (GDP) and 

populations from 1990 to 2024 are needed to adjust benefits to reflect real per capita income 

growth. For consistency with the emissions and benefits modeling, we used national population 

estimates for the years 1990 to 1999 based on U.S. Census Bureau estimates (Hollman, Mulder, 

and Kallan, 2000). These population estimates are based on application of a cohort-component 

model applied to 1990 U.S. Census data projections (U.S. Bureau of Census, 2000). For the 

years between 2000 and 2024, we applied growth rates based on the U.S. Census Bureau 

projections to the U.S. Census estimate of national population in 2000. We used projections of 

real GDP provided in Kleckner and Neumann (1999) for the years 1990 to 2010.69 We used 

projections of real GDP (in chained 1996 dollars) provided by Standard and Poor’s (2000) for 

the years 2010 to 2024.70

Using the method outlined in Kleckner and Neumann (1999) and the population and 

income data described above, we calculated WTP adjustment factors for each of the elasticity 

estimates listed in Table 5-16. Benefits for each of the categories (minor health effects, severe 

and chronic health effects, premature mortality, and visibility) are adjusted by multiplying the 

unadjusted benefits by the appropriate adjustment factor. For premature mortality, we applied the 

income adjustment factor specific to the analysis year, but we do not adjust for income growth 

over the 20-year cessation lag. Our approach could underestimate the benefits for the later years 

of the lag.

 

69 U.S. Bureau of Economic Analysis, Table 2A—Real Gross Domestic Product (1997) and U.S. Bureau of 

Economic Analysis, The Economic and Budget Outlook: An Update, Table 4—Economic Projections for 

Calendar Years 1997 Through 2007 (1997). Note that projections for 2007 to 2010 are based on average GDP 

growth rates between 1999 and 2007.

70 In previous analyses, we used the Standard and Poor’s projections of GDP directly. This led to an apparent 

discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the relative 

growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP based on the 

Bureau of Economic Analysis projections.
5-75

There is some uncertainty regarding the total costs of illness in the future. Specifically, 

the nature of medical treatment is changing, including a shift towards more outpatient treatment. 

Although we adjust the COI estimates for inflation, we do not have data to project COI estimates 

for the cost of treatment in the future or income growth over time, which leads to an inherent 

though unavoidable inconsistency between COI- and WTP-based estimates. This approach may 

under predict benefits in future years because it is likely that increases in real U.S. income would 

also result in increased COI (due, for example, to increases in wages paid to medical workers) 

and increased cost of work loss days and lost worker productivity (reflecting that if worker 

incomes are higher, the losses resulting from reduced worker production would also be higher). 

In addition, cost-of-illness estimates do not include sequelae costs or pain and suffering, the 

value of which would likely increase in the future. To the extent that costs would be expected to 

increase over time, this increase may be partially offset by advancement in medical technology 

that improves the effectiveness of treatment at lower costs. For these reasons, we believe that the 

cost-of-illness estimates in this RIA may underestimate (on net) the total economic value of 

avoided health impacts.

Table 5-16. Adjustment Factors Used to Account for Projected Real Income Growth a

Benefit Category 2024

Minor Health Effect 1.07

Severe and Chronic Health Effects 1.22

Premature Mortality 1.20

a Based on elasticity values reported in Table 5-15, U.S. Census population projections, and projections of real GDP 

per capita.

5.7 Benefits Results

As stated in section 5.1 and described in detail in section 5.4.3, we have estimated 

nationwide benefits for 2025 associated with attainment of alternative ozone standards across the 

U.S. with the exception of California. We have also estimated the nationwide benefits of 

attaining in California for 2038. Because of the temporal disconnect between these two 

scenarios, benefit estimates for each are not totaled and instead, are presented separately (section 

5.7.1 for 2025 and section 5.7.2 for post-2025).

In addition to these core incidence and benefits estimates, we also present a number of 

additional analyses which are intended to inform interpretation of these core benefit estimates 

(see section 5.7.3). In completing these additional analyses, in many cases, we did not have to 
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generate separate assessments for both scenarios since observations from one scenario could be 

readily applied to the other (in these cases, we tended to model the 2025 scenario and then 

discuss application of those observations to the post-2025 scenario).

5.7.1 Benefits of the Proposed and Alternative Annual Primary Ozone Standards for the 2025 

Scenario

This section presents incidence reductions and associated dollar benefit estimates 

associated with the 2025 scenario (i.e., every state apart from California – see section 5.4.3). 

Applying the impact and valuation functions described previously in this chapter to the estimated 

changes in ozone yields estimates of the changes in physical damages (e.g., premature 

mortalities, cases of hospital admissions) and the associated monetary values for those changes. 

Similarly applying the incidence per ton and dollar per ton values to the estimates of NOx 

reductions produces estimates of changes in PM-related health effect incidence and associated 

dollar benefits. Not all known ozone and PM health effects could be quantified or monetized. 

The monetized value of these unquantified effects is represented by adding an unknown “B” to 

the aggregate total. The estimate of total monetized health benefits is thus equal to the subset of 

monetized ozone and PM-related health benefits plus B, the sum of the non-monetized health 

benefits and welfare co-benefits; this B represents both uncertainty and a bias in this analysis, as 

it reflects those benefits categories that we are unable to monetize in this analysis.

We follow our standard rounding conventions in presenting these benefits results. After 

reviewing the presentation of EPA’s benefits results, NRC (2002) concluded, “EPA should strive 

to present the results of the analyses in ways that avoid conveying an unwarranted degree of 

certainty. Such ways include rounding to few significant digits, increasing the use of graphs, and 

placing less emphasis on single numbers and greater emphasis on ranges” (p. 161). Following 

this advice, we round all benefits estimates to two significant digits, and all rounding occurs after 

final summing of unrounded estimates. As such, totals may not sum across columns or rows. In 

addition, all incidence estimates are rounded to whole numbers with a maximum of two 

significant digits. 

Table 5-17 shows the population-weighted air quality change for the alternative standards 

averaged across the continental U.S. Table 5-18 summarizes the tons of VOC and NOx 
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emissions required to simulate attainment of each alternative standard (further differentiated by 

geographic region including east, west and California). Tables 5-19 through 5-23 present the 

benefits results for the proposed and alternative ozone standards. Table 5-24 summarizes total 

benefits by geographic region (including east, west minus California, and California). Note that 

in presenting estimates related to reductions in ozone (Tables 5-19 and 5-20), we include the full 

set of core estimates together with a subset of sensitivity analysis results (specifically, alternative 

estimates for both short-term and long-term mortality). The benefit estimates presented are 

relative to a 2025 analytical baseline reflecting attainment nationwide (excluding California) of 

the current primary ozone standards (i.e., 75 ppb) that includes promulgated national regulations 

and illustrative emissions controls to simulate attainment with 75 ppb. 

Table 5-17. Population-Weighted Air Quality Change for the Proposed and Alternative 

Annual Primary Ozone Standards Relative to Analytical Baseline for 2025a

Standard

Population-Weighted Summer Season 

Ozone Concentration Change (8hr max)b

70 ppb 0.5285

65 ppb 1.6317

60 ppb 3.0222

a Because we used benefit-per-ton estimates for the PM2.5 co-benefits, population-weighted PM2.5 changes are not 

available.

b Population weighting based on all ages (demographic used in modeling short-term exposure-related mortality for 

ozone) for 2025.

Table 5-18. Emission Reductions in Illustrative Emission Reduction Strategies for the 

Proposed and Alternative Annual Primary Ozone Standards, by Pollutant and 

Region Relative to Analytical Baseline – Full Attainment (tons)a

70 ppb 65 ppb 60 ppb

NOx

East 600 1,700 2,800

West 0 110 500

CA 53 110 140

VOC

East 55 99 150

West 0 7 7

CA 0 0 0

a See Chapter 4 for more information on the illustrative emission reduction strategies. The emissions in this table 

reflect both known and unknown controls. Several recent rules such as Tier 3 will have substantially reduced ozone 

concentrations by 2025 in the East, thus few additional controls would be needed to reach 70 ppb. 
5-78

Table 5-19. Estimated Number of Avoided Ozone-Only Health Impacts for the Proposed 

and Alternative Annual Ozone Standards (Incremental to the Analytical 

Baseline) for the 2025 Scenario (nationwide benefits of attaining each alternative 

standard everywhere in the U.S. except California) a, b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. 

b All incidence estimates are based on ozone-only models unless otherwise noted.

c See Appendix 5B, section 5B.1 for additional detail on the threshold-based sensitivity analysis.

70 ppb 65 ppb 60 ppb

Avoided Short-Term Mortality - Core Analysis

200 630 1,100

(97 to 300) (310 to 940) (560 to 1,700)

340 1,000 1,900

(180 to 490) (560 to 1,500) (1,000 to 2,800)

Avoided Long-term Respiratory Mortality - Core Analysis

680 2,100 3,900

(230 to 1,100) (710 to 3,500) (1,300 to 6,400)

Avoided Short-Term Mortality - Sensitivity Analysis

160 500 920

(-44 to 360) (-140 to 1,100) (-250 to 2,100)

250 780 1,400

(77 to 420) (240 to 1,300) (440 to 2,400)

240 740 1,400

(88 to 380) (280 to 1,200) (510 to 2,200)

160 510 930

(54 to 270) (170 to 850) (310 to 1,600)

520 1,600 3,000

(250 to 800) (780 to 2,500) (1,400 to 4,600)

730 2,300 4,200

(440 to 1,000) (1,400 to 3,200) (2,500 to 5,800)

740 2,300 4,200

(510 to 970) (1,600 to 3,000) (2,900 to 5,600)

Avoided Long-term Respiratory Mortality - Sensitivity Analysis

460 1,400 2,600

(130 to 790) (410 to 2,500) (760 to 4,500)

500 1,500 2,800

(170 to 810) (550 to 2,500) (1,000 to 4,600)

60 ppb threshold 520 1,600 2,900

56 ppb threshold 410 1,100 1,700

55 ppb threshold 120 280 510

50 ppb threhsold 6 58 140

45 ppb threshold 3 47 105

40 ppb threshold <1 10 13

Avoided Morbidity - Core Analysis

360 1,100 2,100

(-97 to 820) (-310 to 2,600) (-560 to 4,700)

1,100 3,500 6,600

(100 to 3,400) (330 to 11,000) (610 to 20,000)

300,000 910,000 1,700,000

(-440,000 to 900,000) (-1,300,000 to 2,700,000) (-2,500,000 to 5,000,000)

930,000 2,900,000 5,300,000

(380,000 to 1,500,000) (1,200,000 to 4,500,000) (2,200,000 to 8,300,000)

330,000 1,000,000 1,900,000

(120,000 to 730,000) (360,000 to 2,200,000) (660,000 to 4,500,000)

multi-city 

study

Jerrett et al (2009) - copollutant (PM2.5) model with: c 

Hospital admissions - respiratory 

(age 65+)

Emergency department visits for 

asthma (all ages)

Asthma exacerbation (age 6-18)

Minor restricted-activity days (age 

18-65)

School Loss Days (age 5-17)

metaanalyses

Bell et al. (2005) (all ages)

Ito et al. (2005) (all ages)

Levy et al. (2005) (all ages)

Jerrett et al. (2009) (age 30-99) (86 

cities) (ozone-only)

Jerrett et al. (2009) (age 30-99) (96 

cites) (ozone-only)

multi-city 

study

Jerrett et al. (2009) (30-99yrs) 

copollutants model (PM2.5) 

multi-city 

studies

Smith et al. (2009) (all ages) 

copollutants model (PM1 0)

Schwartz (2005) (all ages)

Huang et al. (2005) 

(cardiopulmonary)

Bell et al. (2004) (all ages)

Health Effectb

Proposed and Alterative Standards

(95th percentile confidence intervals)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)
5-79

Table 5-20. Total Monetized Ozone-Only Benefits for the Proposed and Alternative 

Annual Ozone Standards (Incremental to the Analytical Baseline) for the 2025

Scenario (nationwide benefits of attaining each alternative standard everywhere 

in the U.S. except California) (millions of 2011) a, b

a All benefits estimates are rounded to whole numbers with a maximum of two significant digits. The monetized 

value of the ozone-related morbidity benefits are included in the estimates shown in this table for each mortality 

study (and when combined account for from 4-6% of the total benefits, depending on the total mortality estimate 

compared against. Note that asthma exacerbations accounts for <<1% of the total).

b The sensitivity analysis for long-term exposure-related mortality included an assessment of potential thresholds 

however this assessment was implemented using incidence estimates (see Table 5-19). Observations from that 

analysis (in terms of fractional impacts on incidence can be directly applied to these benefit results) (see Appendix 

5B, section 5B.1)

c A single central-tendency value is provided in each cell, since the zero-lag model used here did not require 

application of a 3% and 7% discount rates (see footnote d below) (note, however that as with all other entries in this 

study, we do include a 95th percentile confidence interval range – these are the values within parentheses).

d The range (outside of the parentheses) within each cell results from application of a 7% and 3% discount rates in 

the context of applying the 20year segmented lag (with the 7% resulting in the lower estimate and the 3% the higher 

estimate). The range presented within the parentheses reflects consideration for the 95th% confidence interval 

generated for each of these estimates and ranges from a low value (2.5th% CI for the 7% discount-based dollar 

benefit) to an upper value (97.5th% of the 3% discount-based dollar benefit).

70 ppb 65 ppb 60 ppb

Avoided Short-Term Mortality - Core Analysis

$2,000 $6,400 $12,000

($180 to $5,800) ($560 to $18,000) ($1,000 to $33,000)

3,400 11,000 20,000

($300 to $9,600) ($950 to $30,000) ($1,700 to $55,000)

Avoided Short-Term Mortality - Sensitivity Analysis

$1,600 $5,100 $9,400

(-$390 to $5,900) (-$1,200 to $19,000) (-$2,200 to $34,000)

$2,500 $7,900 $15,000

($200 to $7,700) ($630 to $24,000) ($1,200 to $44,000)

$2,400 $7,500 $14,000

($200 to $7,000) ($620 to $22,000) ($1,100 to $41,000)

1,700 5,200 9,500

($130 to $4,900) ($420 to $15,000) ($760 to $28,000)

5,300 17,000 31,000

($470 to $15,000) ($1,500 to $48,000) ($2,700 to $88,000)

$7,400 $23,000 $42,000

($680 to $21,000) ($2,100 to $64,000) ($3,900 to $120,000)

$7,500 $24,000 $43,000

($700 to $20,000) ($2,200 to $64,000) ($4,000 to $120,000)

Avoided Long-term Respiratory Mortality - Sensitivity Analysis

$6,900 $22,000 $40,000

($560 to $21,000) ($1,700 to $64,000) ($3,200 to $120,000)

$5,600 to $6300 $18,000 to $20,000 $32,000 to $36,000

($460 to $19,000) ($1,400 to $58,000) ($2,600 to $110,000)

Proposed and Alterative Standards

(95th percentile confidence intervals)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

Smith et al. (2009) (all ages) 

copollutants model (PM1 0)

Schwartz (2005) (all ages)

Bell et al. (2004) (all ages)

Health Effectb

Huang et al. (2005) 

(cardiopulmonary)

multi-city 

studies

metaanalyses

Bell et al. (2005) (all ages)

Ito et al. (2005) (all ages)

Levy et al. (2005) (all ages)

multi-city 

study

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) no lag c

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) 20 yr 

segmented lag d
5-80

Table 5-21. Estimated Number of Avoided PM2.5-Related Health Impacts for the 

Proposed and Alternative Annual Ozone Standards (Incremental to the 

Analytical Baseline) for the 2025 Scenario (nationwide benefits of attaining each 

alternative standard everywhere in the U.S. except California) a

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. Because these 

estimates were generated using benefit-per-ton estimates, confidence intervals are not available. In general, the 95th

percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for 

mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

Table 5-22. Monetized PM2.5-Related Health Co-Benefits for the Proposed and 

Alternative Annual Ozone Standards (Incremental to Analytical Baseline) for the 2025

70ppb 65ppb 60ppb

Avoided PM2.5-related Mortality

Krewski et al. (2009) (adult mortality age 30+) 510 1,400 2,600

Lepeule et al. (2012) (adult mortality age 25+) 1,100 3,300 6,000

Woodruff et al. (1997) (infant mortality) 1 2 5

Avoided PM2.5-related Morbidity

Non-fatal heart attacks

 Peters et al. (2001) (age >18) 600 1,700 3,100

 Pooled estimate of 4 studies (age >18) 64 180 330

Hospital admissions—respiratory (all ages) 150 430 780

Hospital admissions—cardiovascular (age > 18) 180 530 950

Emergency department visits for asthma (all ages) 280 790 1,400

Acute bronchitis (ages 8–12) 790 2,300 4,100

Lower respiratory symptoms (ages 7–14) 10,000 29,000 53,000

Upper respiratory symptoms (asthmatics ages 9–11) 14,000 41,000 75,000

Asthma exacerbation (asthmatics ages 6–18) 17,000 51,000 100,000

Lost work days (ages 18–65) 65,000 180,000 340,000

Minor restricted-activity days (ages 18–65) 380,000 1,100,000 2,000,000

Proposed and Alterative Standards

Health Effectb
5-81

Scenario (nationwide benefits of attaining each alternative standard everywhere in the 

U.S. except California) (Millions of 2011)a,b,c

Monetized Benefits

Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

3% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $4,800 $14,000 $25,000

Lepeule et al. (2012) (adult mortality age 25+) $11,000 $31,000 $56,000

7% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $4,300 $12,000 $22,000

Lepeule et al. (2012) (adult mortality age 25+) $9,700 $28,000 $50,000

a All estimates are rounded to two significant digits. Because these estimates were generated using benefit-per-ton 

estimates, confidence intervals are not available. In general, the 95th percentile confidence interval for monetized 

PM2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et 

al. (2009) and Lepeule et al. (2012). Estimates do not include unquantified health benefits noted in Table 5-2 or 

Section 5.6.3.6 or welfare co-benefits noted in Chapter 6. 

b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.

Table 5-23. Estimate of Monetized Ozone and PM2.5 Benefits for Proposed and 

Alternative Annual Ozone Standards Incremental to the Analytical Baseline for 

the 2025 Scenario (nationwide benefits of attaining each alternative standard 

everywhere in the U.S. except California) – Full Attainment (billions of 2011$) a

Discount 

Rate 70 ppb 65 ppb 60 ppb

Ozone-only Benefits (range reflects 

Smith et al., 2009 and Zanobetti and 

Schwartz, 2008)

b $2.0 to $3.4 +B $6.4 to $11 +B $12 to $20 +B

PM2.5 Co-benefits (range reflects 

Krewski et al., 2009 and Lepeule et 

al., 2012)

3% $4.8 to $11 $14 to $31 $25 to $56

7% $4.3 to $9.7 $12 to $28 $22 to $50

Total Benefits 3% $6.9 to $14 +B $20 to $41 +B $37 to $75 +B

7% $6.4 to $13 +B $19 to $38 +B $34 to $70 +B

a Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% of total 

monetized benefits in this analysis. Mortality risk valuation for PM2.5 assumes discounting over the SABrecommended 20-year segmented lag structure. Not all possible benefits are quantified and monetized in this 

analysis. B is the sum of all unquantified health and welfare co-benefits. Data limitations prevented us from 

quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we 

were able to quantify. These estimates reflect the economic value of avoided morbidities and premature deaths using 

risk coefficients from the studies noted.

b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as 

ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category. 
5-82

Table 5-24. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

2025 Scenario (nationwide benefits of attaining each alternative standard 

everywhere in the U.S. except California) – Full Attainment a

Region

Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

East b 99% 96% 92%

California 0% 0% 0%

Rest of West 1% 4% 7%

a Because we use benefit-per-ton estimates to calculate the PM2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits.

b

Includes Texas and those states to the north and east. Several recent rules such as Tier 3 will have substantially 

reduced ozone concentrations by 2025 in the East, thus few additional controls would be needed to reach 70 ppb. 

5.7.2 Benefits of the Proposed and Alternative Annual Primary Ozone Standards for the post2025 Scenario

This section presents incidence reductions and associated dollar benefit estimates 

associated with the post-2025 scenario (i.e., nationwide benefits estimates reflecting attainment 

of alternative standards in California – see section 5.4.3). The same rounding conventions 

described in section 5.7.1 (for the 2025 estimates) were applied in generating these estimates. As 

with estimates generated for the 2025 scenario, total monetized health benefits are equal to the 

subset of monetized ozone and PM-related health benefits plus B, the sum of the non-monetized 

health benefits and welfare co-benefits. Organization and general content of tables presented 

here for the post-2025 scenario mirrors that of tables presented in section 5.7.1 for the 2025 

scenario and the reader is referred there for further clarification. 

Table 5-25. Population-Weighted Air Quality Change for the Proposed and Alternative 

Annual Primary Ozone Standards Relative to Analytical Baseline for post-2025 a

Standard

Population-Weighted Ozone Season Ozone Concentration Change (8hr 

max)b

70 ppb 0.1526

65 ppb 0.3311

60 ppb 0.5007 

a Because we used benefit-per-ton estimates for the PM2.5 co-benefits, population-weighted PM2.5 changes are not 

available.

b Population weighting based on all ages (demographic used in modeling short-term exposure-related mortality for 

ozone) for 2025.
5-83

Table 5-26. Estimated Number of Avoided Ozone-Only Health Impacts for the Proposed 

and Alternative Annual Ozone Standards (Incremental to the Analytical 

Baseline) for the Post-2025 Scenario (nationwide benefits of attaining each 

alternative standard just in California) a, b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. 

b All incidence estimates are based on ozone-only models unless otherwise noted.

c The sensitivity analysis for long-term exposure-related mortality included an assessment of potential thresholds, 

which was completed for the 2025 scenario (see Table 5-19). Care should be taken in applying the results of that 

sensitivity analysis to the post-2025 scenario, although general patterns of impact across the thresholds may apply.

70 ppb 65 ppb 60 ppb

Avoided Short-Term Mortality - Core Analysis

65 140 210

(31 to 97) (68 to 210) (100 to 320)

110 230 350

(57 to 160) (120 to 340) (190 to 510)

Avoided Long-term Respiratory Mortality - Core Analysis

260 560 840

(88 to 430) (190 to 930) (290 to 1,400)

Avoided Short-Term Mortality - Sensitivity Analysis

52 110 170

(-14 to 120) (-31 to 250) (-46 to 380)

80 170 260

(25 to 140) (54 to 290) (81 to 440)

88 190 290

(33 to 140) (71 to 310) (110 to 470)

52 110 170

(17 to 87) (38 to 190) (57 to 280)

170 360 550

(80 to 250) (170 to 550) (260 to 830)

230 510 770

(140 to 330) (300 to 710) (460 to 1,100)

240 510 770

(160 to 310) (350 to 670) (530 to 1,000)

Avoided Long-term Respiratory Mortality - Sensitivity Analysis c

180 380 570

(51 to 300) (110 to 660) (160 to 980)

190 410 620

(68 to 310) (150 to 680) (220 to 1,000)

Avoided Morbidity - Core Analysis

120 260 390

(-32 to 270) (-69 to 580) (-100 to 880)

320 690 1,000

(29 to 980) (64 to 2,100) (97 to 3,200)

97,000 210,000 310,000

(-140,000 to 290,000) (-310,000 to 620,000) (-460,000 to 930,000)

290,000 630,000 950,000

(120,000 to 460,000) (260,000 to 990,000) (390,000 to 1,500,000)

110,000 230,000 350,000

(38,000 to 240,000) (81,000 to 500,000) (120,000 to 830,000)

Health Effectb

Proposed and Alterative Standards

(95th percentile confidence intervals)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

multi-city 

study

Jerrett et al. (2009) (age 30-99) (86 

cities) (ozone-only)

Jerrett et al. (2009) (age 30-99) (96 

cites) (ozone-only)

Hospital admissions - respiratory 

(age 65+)

multi-city 

study

Jerrett et al. (2009) (30-99yrs) 

copollutants model (PM2.5) 

Huang et al. (2005) 

(cardiopulmonary)

multi-city 

studies

Smith et al. (2009) (all ages) 

copollutants model (PM1 0)

Schwartz (2005) (all ages)

Bell et al. (2004) (all ages)

metaanalyses

Bell et al. (2005) (all ages)

Ito et al. (2005) (all ages)

Levy et al. (2005) (all ages)

Emergency department visits for 

asthma (all ages)

Asthma exacerbation (age 6-18)

Minor restricted-activity days (age 

18-65)

School Loss Days (age 5-17)
5-84

Table 5-27. Total Monetized Ozone-Only Benefits for the Proposed and Alternative 

Annual Ozone Standards (Incremental to the Analytical Baseline) for the post2025 Scenario (nationwide benefits of attaining each alternative standard just in 

California) (millions of 2011) a, b

a All benefits estimates are rounded to whole numbers with a maximum of two significant digits. The monetized 

value of the ozone-related morbidity benefits are included in the estimates shown in this table for each mortality 

study (and when combined account for from 4-6% of the total benefits, depending on the total mortality estimate 

compared against. Note that asthma exacerbations accounts for <<1% of the total).

b The sensitivity analysis for long-term exposure-related mortality included an assessment of potential thresholds 

however this assessment was implemented using incidence estimates (see Table 5-19). Observations from that 

analysis (in terms of fractional impacts on incidence can be directly applied to these benefit results) (see Appendix 

5B, section 5B.1)

c A single central-tendency value is provided in each cell, since the zero-lag model used here did not require 

application of a 3% and 7% discount rates (see footnote d below) (note, however that as with all other entries in this 

study, we do include a 95th percentile confidence interval range – these are the values within parentheses).

d The range (outside of the parentheses) within each cell results from application of a 7% and 3% discount rates in 

the context of applying the 20year segmented lag (with the 7% resulting in the lower estimate and the 3% the higher 

estimate). The range presented within the parentheses reflects consideration for the 95th% confidence interval 

generated for each of these estimates and ranges from a low value (2.5th% CI for the 7% discount-based dollar 

benefit) to an upper value (97.5th% of the 3% discount-based dollar benefit).

70 ppb 65 ppb 60 ppb

Avoided Short-Term Mortality - Core Analysis

$660 $1,400 $2,100

($58 to $1,900) ($130 to $4,100) ($190 to $6,100)

1,100 2,400 3,600

($96 to $3,100) ($210 to $6,700) ($320 to $10,000)

Avoided Short-Term Mortality - Sensitivity Analysis

$530 $1,100 $1,700

(-$120 to $1,900) (-$270 to $4,200) (-$410 to $6,300)

$820 $1,800 $2,700

($65 to $2,500) ($140 to $5,400) ($210 to $8,100)

$900 $1,900 $2,900

($74 to $2,600) ($160 to $5,700) ($240 to $8,600)

530 1,200 1,700

($43 to $1,600) ($93 to $3,500) ($140 to $5,200)

1,700 3,700 5,600

($150 to $4,900) ($320 to $11,000) ($490 to $16,000)

$2,400 $5,200 $7,800

($220 to $6,600) ($470 to $14,000) ($710 to $22,000)

$2,400 $5,200 $7,800

($220 to $6,500) ($480 to $14,000) ($730 to $21,000)

Avoided Long-term Respiratory Mortality - Sensitivity Analysis

$2,700 $5,700 $8,600

($220 to $7,900) ($470 to $17,000) ($700 to $25,000)

$2,200 to $2,400 $4,700 to $5,200 $7,000 to $7,800

($180 to $7,200) ($380 to $15,000) ($570 to $23,000)

Proposed and Alterative Standards

(95th percentile confidence intervals)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

Health Effectb

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) 20 yr 

segmented lag d

multi-city 

study

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) no lag c

Smith et al. (2009) (all ages) 

copollutants model (PM1 0)

Huang et al. (2005) 

(cardiopulmonary)

multi-city 

studies

Schwartz (2005) (all ages)

Bell et al. (2004) (all ages)

metaanalyses

Bell et al. (2005) (all ages)

Ito et al. (2005) (all ages)

Levy et al. (2005) (all ages)
5-85

Table 5-28. Estimated Number of Avoided PM2.5-Related Health Impacts for the 

Proposed and Alternative Annual Ozone Standards (Incremental to the 

Analytical Baseline) for the post-2025 Scenario (nationwide benefits of attaining 

each alternative standard just in California) a

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. Because these

estimates were generated using benefit-per-ton estimates, confidence intervals are not available. In general, the 95th

percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for 

mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

70 ppb 65 ppb 60 ppb

Avoided PM2.5-related Mortality

Krewski et al. (2009) (adult mortality age 30+) 45 89 120

Lepeule et al. (2012) (adult mortality age 25+) 100 200 280

Woodruff et al. (1997) (infant mortality) <1 <1 <1

Avoided PM2.5-related Morbidity

Non-fatal heart attacks

 Peters et al. (2001) (age >18) 54 110 140

 Pooled estimate of 4 studies (age >18) 6 11 16

Hospital admissions—respiratory (all ages) 14 27 37

Hospital admissions—cardiovascular (age > 18) 16 32 45

Emergency department visits for asthma (all ages) 24 46 64

Acute bronchitis (ages 8–12) 67 130 180

Lower respiratory symptoms (ages 7–14) 860 1,700 2,300

Upper respiratory symptoms (asthmatics ages 9–11) 1,200 2,400 3,300

Asthma exacerbation (asthmatics ages 6–18) 1,900 3,800 5,200

Lost work days (ages 18–65) 5,500 11,000 15,000

Minor restricted-activity days (ages 18–65) 32,000 64,000 88,000

Proposed and Alterative Standards

Health Effectb
5-86

Table 5-29. Monetized PM2.5-Related Health Co-Benefits for the Proposed and 

Alternative Annual Ozone Standards (Incremental to Analytical Baseline) for the post2025 Scenario (nationwide benefits of attaining each alternative standard just in 

California) (Millions of 2011) a,b

Monetized Benefits Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

3% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $420 $830 $1,100

Lepeule et al. (2012) (adult mortality age 25+) $950 $1,900 $2,600

7% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $380 $750 $1,000

Lepeule et al. (2012) (adult mortality age 25+) $860 $1,700 $2,300

a All estimates are rounded to two significant digits. Because these estimates were generated using benefit-per-ton 

estimates, confidence intervals are not available. In general, the 95th percentile confidence interval for monetized 

PM2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et 

al. (2009) and Lepeule et al. (2012). Estimates do not include unquantified health benefits noted in Table 5-3 or 

Section 5.6.3.6 or welfare co-benefits noted in Chapter 6. 

b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.

Table 5-30. Estimate of Monetized Ozone and PM2.5 Benefits for Proposed and Alternative 

Annual Ozone Standards Incremental to the Analytical Baseline for the post2025 Scenario (nationwide benefits of attaining each alternative standard just in 

California) – Full Attainment (billions of 2011$) a

Discount 

Rate 70 ppb 65 ppb 60 ppb

Ozone-only Benefits (range reflects 

Smith et al., 2009 and Zanobetti and 

Schwartz, 2008)

b $0.66 to $1.1 $1.4 to $2.4 $2.1 to $3.6

PM2.5 Co-benefits (range reflects 

Krewski et al., 2009 and Lepeule et 

al., 2012)

3% $0.42 to $0.95 $0.83 to $1.9 $1.1 to $2.6

7% $0.38 to $0.86 $0.75 to $1.7 $1.0 to $2.3

Total Benefits 3% $1.1 to $2.0 +B $2.3 to $4.2 +B $3.4 to $6.2 +B

7% $1.1 to $2.0 +B $2.2 to $4.1 +B $3.2 to $5.9 +B

a Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% of total 

monetized benefits in this analysis. Mortality risk valuation for PM2.5 assumes discounting over the SABrecommended 20-year segmented lag structure. Not all possible benefits are quantified and monetized in this 

analysis. B is the sum of all unquantified health and welfare co-benefits. Data limitations prevented us from 

quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we 

were able to quantify. These estimates reflect the economic value of avoided morbidities and premature deaths using 

risk coefficients from the studies noted.

b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as 

ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category. 
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Table 5-31. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

post-2025 Scenario (nationwide benefits of attaining each alternative standard 

just in California) – Full Attainment a

Region

Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

East 0% 0% 0%

California 93% 94% 94%

Rest of West 6% 6% 6%

a Because we use benefit-per-ton estimates to calculate the PM2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits.

5.7.3 Uncertainty in Benefits Results (including Discussion of Sensitivity Analyses and 

Supplemental Analyses)

The dollar value of avoided ozone and PM2.5 related premature deaths account for 94% to 

98% of the total monetized benefits. This is true in part because we are unable to quantify many 

categories of benefits. The next largest benefit is for reducing the incidence of nonfatal heart 

attacks. The remaining categories each account for a small percentage of total benefit; however, 

they represent a large number of avoided incidences affecting many individuals. Comparing an 

incidence table to the monetary benefits table reveals that the number of incidences avoided and 

the dollar value for that endpoint do not always closely correspond. For example, for ozone we 

estimate almost 1,000 times more asthma exacerbations would be avoided than premature 

mortalities, yet asthma exacerbations account for only a very small fraction (<<1%) of total 

monetized benefits (see Tables 5-20). This reflects the fact that many of the less severe health 

effects, while more common, are valued at a lower level than the more severe health effects. 

Also, some effects, such as hospital admissions, are valued using a proxy measure of WTP. As 

such, the true value of these effects may be higher than that reported in the tables above.

Sources of uncertainty associated with both the modeling of ozone-related benefits and 

PM2.5-related cobenefits are discussed qualitatively in Appendix A. Key assumptions and 

uncertainties related to the modeling of ozone are presented below.

 We assume that short-term exposure to ozone is associated with mortality and that this 

relationship holds across the full range of exposure. Furthermore, we assume that longterm exposure to ozone is associated with respiratory mortality and while we favor a nothreshold linear C-R function, we consider the potential impact of thresholds ranging 

from 40 ppb to 60 ppb. 
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 In modeling long-term exposure-related respiratory mortality, we acknowledge 

uncertainty in specifying the nature of the cessation lag and have consequently included 

two alternative lag structures including a 20 year segmented lag and a zero lag (the zero 

lag reflects the potential that estimates of long-term exposure-related mortality could 

actually be capturing the accumulation of short-term exposure-related mortality events).

In addition, we acknowledge the value in exploring the impact of potential thresholds in 

effect on the core incidence and benefits estimates.

PM2.5 mortality co-benefits represent a substantial proportion of total monetized benefits 

(over 98% of the co-benefits), and these estimates have the following key assumptions and 

uncertainties.

 We assume that all fine particles, regardless of their chemical composition, are equally 

potent in causing premature mortality. This is an important assumption, because PM2.5

produced varies considerably in composition across sources, but the scientific evidence is 

not yet sufficient to allow differential effects estimates by particle type. The PM ISA, 

which was twice reviewed by SAB-CASAC, concluded that “many constituents of PM2.5

can be linked with multiple health effects, and the evidence is not yet sufficient to allow 

differentiation of those constituents or sources that are more closely related to specific 

outcomes” (U.S. EPA, 2009b).

 We assume that the health impact function for fine particles is log-linear without a 

threshold in this analysis. Thus, the estimates include health benefits from reducing fine 

particles in areas with varied concentrations of PM2.5, including both areas that do not 

meet the fine particle standard and those areas that are in attainment, down to the lowest 

modeled concentrations.

 We assume that there is a “cessation” lag between the change in PM exposures and the 

total realization of changes in mortality effects. Specifically, we assume that some of the 

incidences of premature mortality related to PM2.5 exposures occur in a distributed 

fashion over the 20 years following exposure based on the advice of the SAB-HES (U.S. 

EPA-SAB, 2004c), which affects the valuation of mortality benefits at different discount 

rates. 

 We recognize uncertainty associated with application of the benefit-per-ton approach 

used in modeling PM2.5 cobenefits. The benefit-per-ton estimates used here reflect 

specific geographic patterns of emissions reductions and specific air quality and benefits 

modeling assumptions associated with the derivation of those estimates (see the TSD 

describing the calculation of the national benefit-per-ton estimates (U.S. EPA, 2013b) 

and Fann et al. (2012c)). Consequently, these estimates may not reflect local variability in 

population density, meteorology, exposure, baseline health incidence rates, or other local 

factors associated with the current ozone NAAQS review. Therefore, use of these benefitper-ton values to estimate co-benefits may lead to higher or lower benefit estimates than 

if co-benefits were calculated based on direct air quality modeling. 
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In order to evaluate the quantitative impact of specific sources of uncertainty on the core 

risk estimates, we completed a number of sensitivity analyses, some of which have already been 

described in presenting the core risk estimates and some of which are presented in Appendix 5B. 

A brief overview of these sensitivity analyses, including key observations resulting from those 

observations are presented below in section 5.7.3.1. In addition to these sensitivity analyses, we 

have also included several supplemental analyses intended to provide additional perspectives on 

the core incidence and benefits analyses. These supplemental analyses are presented in detail in 

Appendix 5C and are also briefly summarized below in section 5.7.3.2.

5.7.3.1 Sensitivity Analyses

A number of sensitivity analyses have been completed as part of this RIA. Given the 

importance of mortality in driving benefits estimates (both for ozone and PM2.5 cobenefits), the 

sensitivity analyses completed have been focused largely on the mortality endpoint. Each 

sensitivity analysis is briefly described below (including an overview of the approach used in 

conducting the analysis and key observations). As identified below, several of the sensitivity 

analyses have been presented earlier parallel to presentation of the core estimates, while others 

are described in Appendix B.71

 Short-term ozone-exposure related mortality (alternative epidemiological studies 

and C-R functions): As described in section 5.6.3.1, in addition to the two core effect 

estimates we estimated benefits using seven additional effect estimates including four 

multi-city studies and three meta-analysis studies. This sensitivity analysis showed that 

the two core incidence and benefits estimates fall within (and towards the lower end of) 

the broader range resulting from application of the seven alternative effect estimates (see 

Tables 5-19 and 5-20). This increased our overall confidence in the two core studies, 

particularly with regard to epidemiological study design and the characterization of the 

relationship between short-term exposure and mortality.

 Long-term ozone-exposure related respiratory mortality dollar benefits: As 

discussed in section 5.1, we could not specify a cessation lag structure specific to longterm ozone exposure-related respiratory mortality. While we included incidence estimates 

as a part of the core analysis, we present the associated dollar benefits as a sensitivity 

analysis (see Tables 5-20 and 5-27). Given uncertainty related to the lag structure, we 

also included two different lags in modeling these benefits as a sensitivity analysis – a 20 

 

71 Generally, we gave greater emphasis to those sensitivity analyses examining sources of potential uncertainty 

directly associated with estimates of ozone-related mortality and have included summaries of those sensitivity 

analyses results parallel to presentation of core incidence and benefits estimates in sections 5.7.1 and 5.7.2. 
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year segment lag (as used for PM2.5) and a zero lag (see section 5.6.4.1 for additional 

detail on the lag structures used). The sensitivity analysis suggests that if included in the 

core benefit estimate, long-term ozone exposure-related mortality could add substantially 

to the overall benefits (see Table 5-20 and 5-27). Additionally, use of a 20 year segment 

lag can reduce benefits by 10-20% (relative to a zero lag) depending on the discount rate 

applied. 

 Long-term ozone-exposure related respiratory mortality and potential thresholds:

As discussed in section 5.6.3.1, while we favor the no-threshold model in estimating 

benefits related to long-term ozone exposure-related respiratory mortality, as a sensitivity 

analysis, we evaluated the impact of thresholds ranging from 40-60ppb.

72 These results 

are included alongside core incidence estimates in Table 5-19 (see Appendix 5B, section 

5B1 for additional details). This sensitivity analysis suggested that a threshold of 50ppb 

or greater could have a substantial impact on estimated benefits, while thresholds below 

this range have a relatively minor impact. 

 Long-term PM2.5 exposure-related mortality and alternative C-R functions (based 

on expert elicitation): As discussed in Appendix 5D (section 5D.1) in 2006 we 

conducted an expert elicitation to help better characterize uncertainty associated with 

long-term PM2.5 exposure-related mortality and specifically the C-R functions used in 

modeling that endpoint, including the shape of the functions and potential for thresholds 

in effect. As part of the sensitivity analysis for the current ozone RIA, we applied the set 

of expert elicitation-based functions to generate an alternative set of PM2.5 incidence and 

benefit estimates (see Appendix 5B, section 5B.2). That sensitivity analysis showed that 

the two core incidence estimate fall within the range of alternative C-R function based 

estimates obtained through expert elicitation (see Table 5B-2). This increases overall 

confidence in the core estimates with regard to the form of the functions and magnitude 

of the effect estimates.

 Income elasticity and the willingness-to-pay (WTP) values used for mortality and 

certain morbidity endpoints: As described in Appendix 5B (section 5B.3) we examined 

the impact of alternative assumptions regarding income elasticity (i.e., the degree to 

which WTP changes as income changes) and the degree of impact on WTP functions 

used for mortality and for morbidity endpoints. That sensitivity analysis, suggests that 

alternative assumptions regarding income elasticity could result in a moderate impact on 

mortality benefits (values ranging from ~90% to ~130% of the core estimate depending 

on the assumption regarding elasticity). Income elasticity was found to have a far more 

modest impact on morbidity endpoints modeled using WTP functions.

 

72 As noted in section 5.6.3.1, our decision to include benefit estimates based on the no-threshold model in the core 

analysis reflects recommendations on the HREA provided by CASAC (Frey, 2014).
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5.7.3.2 Supplemental Analyses

In addition to the sensitivity analyses described in section 5.7.3.1, we also included a 

number of supplemental analyses intended to examine other attributes of the core risk estimates 

(these are briefly described below with additional detail found in Appendix 5C).

 Consideration for age group-differentiated aspects of short-term ozone exposurerelated mortality (including total avoided incidence, life years gained and percent 

reduction in baseline mortality): These analyses expand on the basic mortality 

incidence estimates presented in section 5.7 by considering (a) estimates of the reduction 

in mortality incidence differentiated by age range (i.e., how the avoided deaths map to 

different age ranges) and (b) estimates of life years gained by age range and (c) percent 

reduction in baseline mortality by age range. By comparing the projected age distribution 

of the avoided premature deaths with the age distribution of life years gained, we 

observed that about half of the ozone-attributable deaths occur in populations age 75–99 

(see Appendix 5C, section 5C.1 Table 5C-1), but half of the life years would occur in 

populations younger than 65 (see Appendix 5C, section 5C.1, Table 5C-2). This is 

because the younger populations have the potential to lose more life years per death than 

older populations based on changes in ozone exposure for the 2025 scenario. Results 

presented in Table 5C-3 highlight that when reductions in ozone-attributable mortality (in 

going from baseline to an alternative standard level) are considered as a percentage of 

total all-cause baseline mortality, the estimates are relatively small and are fairly constant 

across age ranges. However, it is important to point out that estimates of total ozoneattributable mortality represent a substantially larger fraction of all-cause baseline 

mortality than the increment attributable to the simulated reduction in ozone associated 

with an alternative standard level.

 Evaluation of mortality impacts relative to the baseline pollutant concentrations 

(used in generating those mortality estimates) for both short-term ozone exposurerelated mortality and long-term PM2.5 exposure-related mortality: In this 

supplemental analysis, we begin by comparing the distribution of short-term ozone 

exposure-related mortality against the ozone season-averaged 8hr max values used in 

deriving those estimates (for the three alternative standards modeled for the 2025 

scenario) (see section 5C.2). In making this comparison, we point out that, rather than 

using a full daily time series of 8hr max values for each grid cell, we used an ozone 

season-average 8hr max value for incidence modeling. While this simplification did not 

impact the overall core incidence estimate for this mortality endpoint, it does impact 

efforts to compare the distribution of mortality estimates against associated daily values 

(by reducing temporal variation associated with each grid cell calculation). Key 

observations resulting from this supplemental analysis are that (a) the vast majority of 

reductions in short-term exposure-related mortality for ozone occur in grid cells with 

mean 8hr max baseline levels (across the ozone season) between 35 and 55ppb and (b) 

virtually all of the mortality reductions are associated with ozone levels above the 

<20ppb range identified within the Ozone ISA as being associated with less confidence in 

specifying the nature of the C-R function for ozone mortality (ozone ISA, section 
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2.5.4.4). The other portion of this supplemental analysis (which compares the distribution 

of PM2.5 mortality estimates relative to associated PM2.5 levels used in modeling) relies

on the concept of the lowest measured level (LML) associated with the epidemiological 

studies providing the effect estimates used in morality modeling. Specifically, we observe 

that there is reduced confidence in specifying the nature of the C-R function at exposure 

levels below the range used in the epidemiological study (i.e., below the LML). In the 

supplemental analysis, we found that, depending on the mortality study, between 67% 

and 93% of the mortality estimate is based on modeling involving PM2.5 levels above the 

LML (i.e., levels at which we have increased confidence in specifying the PM2.5-

mortality relationship) (see Appendix 5C.2).

 Core Incidence and Dollar Benefits Estimates Reflecting Application of Known 

Controls for the 2025 Scenario: In Appendix 5C, section 5C.3, we present a subset of 

core incidence and benefits for the 2025 scenario reflecting only application of known 

controls in modeling reductions in ozone to attain each alternative standard level. These 

known-control based estimates include both ozone-related and PM2.5 co-benefit estimates 

as well as total benefits. As expected, the percent of benefits reflecting application of 

known controls decreases as you consider more stringent alternative standards. Estimates 

presented in Appendix 5C.3 suggest that 77%, 59% 34% of total benefits are associated 

with application of known controls (for the 70, 65 and 60 ppb alternative standards, 

respectively). 

5.8 Discussion

The analysis in this Chapter demonstrates the potential for significant health benefits of 

the illustrative emissions controls applied to simulate attainment with the alternative primary 

ozone standards. We estimate that by 2025, the emissions reductions to reach the alternative 

standards everywhere except California, would have reduced the number of ozone- and PM2.5-

related premature mortalities and produce substantial non-mortality benefits. Furthermore, 

emissions reductions required to meet alternative standards in California post-2025 are also 

likely to produce substantial reductions in these same endpoints. This proposed rule also 

promises to yield significant welfare impacts as well (see Chapter 6). Even considering the 

quantified and unquantified uncertainties identified in this chapter, we believe that implementing 

the alternative standards would have substantial public health benefits that are likely to outweigh 

the costs for the three alternative standards analyzed (see Chapter 7). 

Inherent in any complex RIA such as this one are multiple sources of uncertainty. Some 

of these we characterized through our quantification of statistical error in the concentrationresponse relationships and our use of alternate mortality functions. Others, including the 
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projection of atmospheric conditions and source-level emissions, the projection of baseline 

morbidity rates, incomes and technological development are unquantified. When evaluated 

within the context of these uncertainties, the health impact and monetized benefits estimates in 

this RIA can provide useful information regarding the public health benefits associated with the 

proposed and alternative primary standards.

There are important differences worth noting in the design and analytical objectives of 

NAAQS RIAs compared to RIAs for implementation rules, such as the Tier 3 (U.S. EPA, 

2014c). The NAAQS RIAs illustrate the potential costs and benefits of a revised air quality 

standard nationwide based on an array of emission reduction strategies for different sources, 

incremental to implementation of existing regulations and controls needed to attain the current 

standards. In short, NAAQS RIAs hypothesize, but do not predict, the emission reduction 

strategies that States may choose to enact when implementing a revised NAAQS. The setting of 

a NAAQS does not directly result in costs or benefits, and as such, NAAQS RIAs are merely 

illustrative and are not intended to be added to the costs and benefits of other regulations that 

result in specific costs of control and emission reductions. By contrast, the emission reductions 

from implementation rules are generally for specific, well-characterized sources, such as the 

recent MATS rule (U.S. EPA, 2011e). In general, the EPA is more confident in the magnitude 

and location of the emission reductions for implementation rules. As such, emission reductions 

achieved under promulgated implementation rules such as Tier 3 have been reflected in the 

baseline of this NAAQS analysis. Subsequent implementation rules will be reflected in the 

baseline for the next ozone NAAQS review. For this reason, the benefits estimated provided in 

this RIA and all other NAAQS RIAs should not be added to the benefits estimated for 

implementation rules.

In setting the NAAQS, the EPA considers that ozone concentrations vary over space and 

time. While the standard is designed to limit concentrations at the highest monitor in an area, it is 

understood that emission controls put in place to meet the standard of the highest monitor will 

simultaneously result in lower ozone concentrations throughout the entire area. In fact, the 

Health Risk and Exposure Assessment for Ozone (HREA) (U.S. EPA, 2014b) shows how 

different standard levels would affect the entire distribution of ozone concentrations, and thus 
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people’s exposures and risk, across a selected set of urban areas. For this reason, it is 

inappropriate to use the NAAQS level as a bright line for health effects.

The NAAQS are not set at levels that eliminate the risk of air pollution completely. 

Instead, the Administrator sets the NAAQS at a level requisite to protect public health with an 

adequate margin of safety, taking into consideration effects on susceptible populations based on 

the scientific literature. The risk analysis prepared in support of this ozone NAAQS reported 

risks below these levels, while acknowledging that the confidence in those effect estimates is 

higher at levels closer to the standard (U.S. EPA, 2014b). While benefits occurring below the 

standard may be somewhat more uncertain than those occurring above the standard, the EPA 

considers these to be legitimate components of the total benefits estimate. Though there are 

greater uncertainties at lower ozone and PM2.5 concentrations, there is no evidence of a threshold 

in short-term ozone or PM2.5-related health effects in the epidemiology literature.73 Given that 

the epidemiological literature in most cases has not provided estimates based on threshold 

models, there would be additional uncertainties imposed by assuming thresholds or other nonlinear concentration-response functions for the purposes of benefits analysis.

The estimated benefits for the proposed and alternative standards are in addition to the 

substantial benefits estimated for several recent implementation rules (U.S. EPA, 2009a, 2011d, 

2014c). Rules such as Tier 3 and other emission reductions will have substantially reduced 

ambient ozone concentrations by 2025 in the East, such that few additional controls would be 

needed to reach 70 ppb in the East beyond the analytical baseline. These rules that have already 

been promulgated have tremendous combined benefits that explain why the number of avoided 

premature deaths associated with this NAAQS revision are smaller than were estimated in the 

previous ozone NAAQS RIA (U.S. EPA, 2006) for the year 2020 and even smaller than the 

mortality risks estimated for the current year in the ozone HREA (U.S. EPA, 2014b). 

 

73 As discussed 5.7.3.1, our modeling of long-term ozone exposure-related respiratory mortality did include 

consideration of potential thresholds as a sensitivity analysis. However, key points need to be emphasized in the 

context of interpreting this endpoint: (a) based on recommendations from CASAC the core incidence reduction 

estimates was based on a non-threshold model (Frey, 2014) and (b) while the incidence estimates for this endpoint 

were included as core estimates, associated dollar benefit estimates were only included as sensitivity analyses and 

consequently did not factor in to the core dollar benefit estimates generated.
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5A-1

APPENDIX 5A: COMPREHENSIVE CHARACTERIZATION OF UNCERTAINTY IN 

OZONE BENEFITS ANALYSIS

Overview

As noted in Chapter 5, the benefits analysis relies on an array of data inputs—including air 

quality modeling, health impact functions and valuation estimates among others—which are 

themselves subject to uncertainty and may also in turn contribute to the overall uncertainty in this 

analysis. The RIA employs a variety of analytic approaches designed to reduce the extent of the 

uncertainty and/or characterize the impact that uncertainty has on the final estimate. We strive to 

incorporate as many quantitative assessments of uncertainty as possible (e.g., Monte Carlo 

assessments, sensitivity analyses); however, there are some aspects we are only able to characterize 

qualitatively. 

To more comprehensively and systematically address these uncertainties, including those we 

cannot quantify, we adapt the World Health Organization (WHO) uncertainty framework (WHO, 

2008), which provides a means for systematically linking the characterization of uncertainty to the 

sophistication of the underlying health impact assessment. EPA has applied similar approaches in peerreviewed analyses of PM2.5-related impacts (U.S. EPA, 2010b, 2011, 2012) and ozone-related impacts 

(U.S. EPA, 2014). EPA’s Science Advisory Board (SAB) has supported using a tabular format to 

qualitatively assess the uncertainties inherent in the quantification and monetization of health impacts, 

including identifying potential bias, potential magnitude, confidence in our approach, and the level of 

quantitative assessment of each uncertainty (U.S. EPA-SAB, 1999, 2001, 2004a, 2004b, 2011a, 

2011b). The assessments presented here are largely consistent with those previous peer-reviewed 

assessments. 

This appendix focuses on uncertainties inherent in the ozone benefits estimates. For more 

information regarding the uncertainties inherent in the PM benefits estimates, please see the 2012 PM 

NAAQS RIA (U.S. EPA, 2012).

5A.1 Description of Classifications Applied in the Uncertainty Characterization

Table 5A-1 catalogs the most significant sources of uncertainty in the ozone benefits analysis 

and then characterizes four dimensions of that uncertainty (listed below). The first two dimensions 

focus on the nature of the uncertainty. The third and fourth dimensions focus on the extent to which the 
5A-2

analytic approach chosen in the benefits analysis either minimizes the impact of the uncertainty or 

quantitatively characterizes its impact.

1) The direction of the bias that a given uncertainty may introduce into the benefits 

assessment if not taken into account in the analysis approach; 

2) The magnitude of the impact that uncertainty is likely to have on the benefits estimate if 

not taken into account in the analysis approach; 

3) The extent to which the analytic approach chosen is likely to minimize the impact of 

that uncertainty on the benefits estimate; and

4) The extent to which EPA has been able to quantify the residual uncertainty after the 

preferred analytic approach has been incorporated into the benefits model. 

5A.1.1 Direction of Bias

The “direction of bias” column in Table 5A-1 is an assessment of whether, if left unaddressed, 

an uncertainty would likely lead to an underestimate or overestimate the total monetized benefits. In 

some cases we indicate that there are reasons why the bias might go either direction, depending upon 

the true nature of the underlying relationship. Where available, we base the classification of the 

“direction of bias” on the analysis in the Integrated Science Assessment for Ozone and Related 

Photochemical Oxidants (hereafter, “O3 ISA”) (U.S. EPA, 2013a). Additional sources of information 

include advice from SAB and the National Academies of Science (NAS), as well as studies from the 

peer-reviewed literature. In some cases we indicate that there is not sufficient information to estimate 

whether the uncertainty would likely lead to under or overestimation of benefits; these cases are 

identified as “unable to determine.” 

5A.1.2 Magnitude of Impact

The “magnitude of impact” column in Table 5A-1 is an assessment of how much plausible 

alternative assumptions about the underlying relationship about which we are uncertain could influence 

the overall monetary benefits. EPA has applied similar classifications in previous risk and benefit 

analyses (U.S. EPA, 2010b, 2011, 2014), but we have slightly revised the category names and the cutoffs here.74 The definitions used here are provided below. 

 

74 In The Benefits and Costs of the Clean Air Act from 1990 to 2020 (U.S. EPA, 2011), EPA applied a classification of 

“potentially major” if a plausible alternative assumption or approach could influence the overall monetary benefit 

estimate by five percent or more and “probably minor” if an alternative assumption or approach is likely to change the 

total benefit estimate by less than five percent. In the Quantitative Health Risk Assessment for Particulate Matter (U.S. 
5A-3

 High—if the uncertainty associated with an assumption could influence the total 

monetized benefits by more than 25%.

 Medium—if the uncertainty associated with an assumption could influence the total 

monetized benefits by 5% to 25%.

 Low—if the uncertainty associated with an assumption could influence the total 

monetized benefits by less than 5%.

For each uncertainty, we provide as much quantitative information as is available in the table to 

support the classification. 

Although many of the sources of uncertainty could affect both morbidity and mortality 

endpoints, because mortality benefits comprise over 94% of the monetized benefits that we are able to 

quantify in this analysis, uncertainties that affect the mortality estimate have the potential to have 

larger impacts on the total monetized benefits than uncertainties affecting only morbidity endpoints. 

One morbidity-related uncertainty that could have a significant impact on the benefits estimate is the 

extent to which omitted morbidity endpoints are included in the benefits analysis. Including additional 

morbidity endpoints that are currently not monetized would reduce the fraction of total benefits from 

mortality. Ultimately, the magnitude classification is determined by professional judgment of EPA 

staff based on the results of available information, including other U.S. EPA assessments of 

uncertainty (U.S. EPA, 2010b, 2011) 

Based on this assessment, the uncertainties that we classified as high or medium-high impact 

are: the causal relationship between long-term and short-term ozone exposure and mortality, the shape 

of the concentration-response function for both categories of ozone-related mortality, and the mortality 

valuation, specifically for long-term exposure-related mortality. The classification of these 

uncertainties as “high magnitude” is generally consistent with the results of EPA’s Influence Analysis

(Mansfield et al., 2009), the Quantitative Health Risk Assessment for Particulate Matter (U.S. EPA, 

2010b), and the Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011).

5A.1.3 Confidence in Analytic Approach

The “confidence in analytic approach” column of Table 5A-1 is an assessment of the scientific 

support for the analytic approach chosen (or the inherent assumption made) to account for the 

relationship about which we are uncertain. In other words, based on the available evidence, how 

 

EPA, 2010b), EPA applied classifications of “low” if the impact would not be expected to impact the interpretation of 

risk estimates in the context of the PM NAAQS review, “medium” if the impact had the potential to change the 

interpretation; and “high” if it was likely to influence the interpretation of risk in the context of the PM NAAQS review.
5A-4

certain are we that EPA’s selected approach is the most plausible of the potential alternatives. Similar 

classifications have been included in previous risk and benefits analyses (U.S. EPA, 2010b, 2011).75

The three categories used to characterize the degree of confidence are: 

 High—the current evidence is plentiful and strongly supports the selected approach;

 Medium—some evidence exists to support the selected approach, but data gaps are 

present; and

 Low—limited data exists to support the selected approach.

Ultimately, the degree of confidence in the analytic approach is EPA staff’s professional 

judgment based on the volume and consistency of supporting evidence, much of which has been 

evaluated in the O3 ISA (U.S. EPA, 2013a) and by EPA’s independent SAB. The O3 ISA evaluated the 

entire body of scientific literature on ozone science and was twice peer-reviewed by EPA’s Clean Air 

Scientific Advisory Committee (CASAC). In general, we regard a conclusion in the O3 ISA or specific 

advice from SAB as supporting a high degree of confidence in the selected approach. 

Based on this assessment, we have low or low-medium confidence in the evidence available to 

assess exposure error in epidemiology studies, morbidity valuation, baseline incidence projections for 

morbidity, and omitted morbidity endpoints. However, because these uncertainties have been classified 

as having a low or low-medium impact on the magnitude of the benefits, further investment in 

improving the available evidence would not have a substantial impact on the total monetized benefits. 

5A.1.4 Uncertainty Quantification

The column of Table 5A-1 labeled “uncertainty quantification” is an assessment of the extent to 

which we were able to use quantitative methods to characterize the residual uncertainty in the benefits 

analysis, after addressing it to the extent feasible in the analytic approach for this RIA. We categorize 

the level of quantification using the four tiers used in the WHO uncertainty framework (WHO, 2008). 

The WHO uncertainty framework is a well-established approach to assess uncertainty in risk estimates 

that systematically links the characterization of uncertainty to the sophistication of the health impact 

assessment. The advantage of using this framework is that it clearly highlights the level of uncertainty 

quantification applied in this assessment and the potential sources of uncertainty that require methods 

 

75 We have applied the same classification as The Benefits and Costs of the Clean Air Act from 1990 to 2020 (U.S. EPA, 

2011a) in this analysis. In the Quantitative Health Risk Assessment for Particulate Matter (U.S. EPA, 2010b), EPA 

assessed the degree of uncertainty (low, medium, or high) associated with the knowledge-base (i.e., assessed how well 

we understand each source of uncertainty), but did not provide specific criteria for the classification.
5A-5

development in order to assess quantitatively. Specifically, EPA applied this framework in multiple 

risk and exposure assessments (U.S. EPA, 2010b, 2014), and it has been recommended in EPA 

guidance documents assessing air toxics-related risk and Superfund site risks (U.S. EPA, 2004 and 

2001, respectively). Ultimately, the tier decision is the professional judgment of EPA staff based on the 

availability of information for this assessment. The tiers used in this assessment are defined below.

 Tier 0—screening level, generic qualitative characterization.

 Tier 1—Scenario-specific qualitative characterization.

 Tier 2—Scenario-specific sensitivity analysis.

 Tier 3—Scenario-specific probabilistic assessment of individual and combined 

uncertainty.

Within the limits of the data, we strive to use more sophisticated approaches (e.g., Tier 2 or 3) 

for characterizing uncertainties that have the largest magnitudes and could not be completely addressed 

through the analytic approach. The uncertainties for which we have conducted probabilistic (Tier 3) 

assessments in this analysis are mortality causality, the shape of the concentration-response function, 

and mortality and morbidity valuation. For lower magnitude uncertainties, we include qualitative 

discussions of the potential impact of uncertainty on risk results (WHO Tier 0/1) and/or completed 

sensitivity analyses assessing the potential impact of sources of uncertainty on risk results (WHO Tier 

2). 

5A.2 Organization of the Qualitative Uncertainty Table

Table 5A-1 is organized as follows. The uncertainties are grouped by category (i.e., 

concentration-response function, valuation, population and baseline incidence, omitted benefits 

categories, and exposure changes). Within each category, the uncertainties are sorted by magnitude of 

impact (i.e., high to low) then by confidence in our approach (i.e., low to high). In the table, red (bold) 

text is used to indicate the uncertainties that likely have a high magnitude of impact on the total 

benefits estimate. This organization highlights the uncertainty with the largest potential impact and the 

lowest confidence at the top of each category.
5A-6

Table 5A-1. Summary of Qualitative Uncertainty for Key Modeling Elements in Ozone Benefits

Potential Source of 

Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

Uncertainties Associated with Concentration-Response Functions

Causal relationship 

between short-term 

ozone exposure and 

premature mortality

Overestimate, if short-term ozone

exposure does not have a causal 

relationship with premature 

mortality.

High High Tier 1 (qualitative)

Mortality generally dominates 

monetized benefits, so small 

uncertainties could have large 

impacts on the total monetized 

benefits.

Our approach is consistent with the O3

Integrated Science Assessment (ISA), 

which determined that premature mortality 

has a likely causal relationship with shortterm ozone exposure based on the 

collective body of evidence (p. 6-264). In 

addition, the NAS recommended that EPA 

“should give little or no weight to the 

assumption that there is no causal 

association between estimated reductions 

in premature mortality and reduced ozone 

exposure” (NRC, 2008). In 2010, the 

Health Effects Subcommittee of the 

Advisory Council on Clean Air 

Compliance Analysis, while reviewing 

EPA’s The Benefits and Costs of the Clean 

Air Act 1990 to 2020 (U.S. EPA, 2011), 

also confirmed the NAS recommendation 

to include ozone mortality benefits (U.S. 

EPA-SAB, 2010).

Either Medium-High Medium Tier 2 (sensitivity analysis)
5A-7

Potential Source of 

Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

Shape of the C-R 

functions, particularly 

at low concentrations 

for short-term ozone 

exposure-related 

mortality

The direction of bias that 

assuming linear-no threshold 

model or alternative model 

introduces depends upon the 

“true” functional from of the 

relationship and the specific 

assumptions and data in a 

particular analysis. For example, if 

the true function identifies a 

threshold below which health 

effects do not occur, benefits may 

be overestimated if a substantial 

portion of those benefits were 

estimated to occur below that 

threshold. Alternately, if a 

substantial portion of the benefits 

occurred above that threshold, the 

benefits may be underestimated 

because an assumed linear nothreshold function may not reflect 

the steeper slope above that 

threshold to account for all health 

effects occurring above that 

threshold.

The magnitude of this impact 

depends on the fraction of 

benefits occurring in areas with 

lower concentrations. Mortality 

generally dominates monetized 

benefits, so small uncertainties 

could have large impacts on total 

monetized benefits.

The O3 ISA did not find any evidence that 

supports a threshold in the relationship 

between short-term exposure to ozone and 

mortality within the range of ozone 

concentrations observed in the United 

States, and recent evidence suggests that 

the shape of the ozone-mortality C-R curve 

remains linear across the full range of 

ozone concentrations (p. 6-257). Consistent 

with the O3 ISA, we assume a log-linear 

no-threshold model for the concentrationresponse functions for short-term ozone

mortality. However, the ISA notes that 

there is less certainty in the shape of the CR function below 20 ppb due to the low 

density of data in this range (p. 6-254-255). 

The comparison of short-term 

mortality against the associated 

distribution of (ozone seasonaveraged) 8hr max ozone levels 

(see Appendix 5C, section 5C.2) 

suggests that the vast majority of 

predicted reductions in mortality 

are associated with days having 

8hr max values that fall within the 

range of increased confidence in 

specifying the nature of the 

mortality response (as identified 

in the O3 ISA). 

Causal relationship 

between long-term 

ozone exposure and 

Overestimate, if short-term ozone

exposure does not have a causal 

relationship with premature 

mortality.

High High Tier 1 (qualitative)
5A-8

Potential Source of 

Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

premature respiratory 

mortality

The total dollar benefits of 

reducing ozone levels are 

comprised mostly of the value 

placed on reducing the risk of 

premature death, so small 

uncertainties could have large 

impacts on the total monetized 

benefits.

While the O3 ISA concludes that evidence 

is suggestive of a causal association

between total mortality and long-term 

ozone exposure (section 7.7.1), specifically 

with regard to respiratory health effects 

(including mortality), the ISA concludes 

that there is likely to be a causal 

association (section 7.2.8). Furthermore, in 

their review of the HREA completed for 

the Ozone NAAQS review, the CASAC 

expressed their support for EPA’s plan to 

include a non-threshold-based modeling of 

long-term exposure-related respiratory 

mortality in the core estimate. The CASAC 

also supported EPA’s plan to consider the 

potential for thresholds in the response (in 

the range of 40-60 ppb) as a sensitivity 

analysis in the HREA (see section 5.6.3.1). 

This same approach (with regard to the 

core approach and sensitivity analysis for 

long-term exposure-related morality) has 

been adopted for the RIA (see below). 

Shape of the C-R 

functions, particularly 

at low concentrations 

for long-term ozone 

exposure-related 

respiratory mortality

Either Medium-High Medium Tier 2 (sensitivity analysis)
5A-9

Potential Source of 

Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

The direction of bias that 

assuming linear-no threshold 

model or alternative model 

introduces depends upon the 

“true” functional form of the 

relationship and the specific 

assumptions and data in a 

particular analysis. For example, if 

the true function identifies a 

threshold below which health 

effects do not occur, benefits may 

be overestimated if a substantial 

portion of those benefits were 

estimated to occur below that 

threshold. Alternately, if a 

substantial portion of the benefits 

occurred above that threshold, the 

benefits may be underestimated 

because an assumed linear nothreshold function may not reflect 

the steeper slope above that 

threshold to account for all health 

effects occurring above that 

threshold.

The magnitude of this impact 

depends on the fraction of 

benefits occurring in areas with 

lower concentrations. Note, that 

due to limitations in our ability to 

predict the lag structure 

associated with reductions in 

long-term ozone exposure-related 

mortality, we are not including 

dollar benefits associated with 

this endpoint in the core benefits 

analysis (which significantly 

reduces the role of this source of 

uncertainty in impact core benefit 

estimates).

In their memo (see Sasser 2014) clarifying 

the results of their study (Jerrett et al., 

2009) regarding long-term ozone exposurerelated respiratory mortality, the study 

authors note that in terms of goodness of 

fit, long-term health risk models including 

ozone clearly performed better than models 

without ozone, indicating the improved 

predictions of respiratory mortality when 

ozone is included. In the article proper, the 

authors state that, “There was limited 

evidence that a threshold model 

specification improved model fit as 

compared with a non-threshold linear 

mode...”. Furthermore, in the memo 

referenced above, the authors conclude that 

considerable caution should be exercised in 

using any specific threshold, particularly 

when the more stringent statistical test 

indicates there is no significantly improved 

prediction. The CASAC was supportive of 

the approach EPA used in the HREA of 

using a non-threshold C-R function based 

on this study to generate core estimates and 

consider the impact of potential thresholds 

(ranging from 40-60 ppb) as a sensitivity 

analysis. 

As part of the sensitivity analyses 

completed for the RIA, we did 

examine the potential impact of 

thresholds (from 40 to 60 ppb) in 

the response function for longterm exposure-related morality 

(see Appendix 5B, section 5B.1). 

That analysis suggested that 

thresholds between 55 and 60 ppb 

would have a substantial impact 

on overall modeled benefits, 

while thresholds below 50 ppb 

would have a minor impact on 

predicted benefits (see Appendix 

5B, Table 5B-1). 

Exposure error in 

epidemiology studies

Underestimate (generally) Medium Low-Medium Tier 1 (qualitative)

The O3 ISA states that exposure 

measurement error can also be an 

important contributor to 

uncertainty in effect estimates 

associated with both short-term 

and long-term studies (p. 1xii). 

Together with other factors (e.g., 

low data density), exposure error 

can smooth the C-R functions and 

obscure potential thresholds (p. 

lxix). In addition, the O3 ISA 

states that exposure error can bias 

effect estimates toward or away 

from the null and widen 

confidence intervals (p. lxii). 

Recent analyses reported in 

Krewski et al. (2009) demonstrate 

the potentially significant effect 

that this source of uncertainty can 

have on effect estimates. These 

analyses also illustrate the 

complexity and site-specific 

nature of this source of 

uncertainty.

Although this underestimation is well 

documented, including in the O3 ISA, the 

SAB has not suggested an approach to 

adjust for this bias.

(No quantitative method 

available)

Unknown Medium Medium Tier 1 (qualitative)
5A-10

Potential Source of 

Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

Adjustment of risk 

coefficients to 8-hour 

maximum

Several of the mortality 

epidemiological studies were 

reported for a 24-hour average or 

1-hour maximum ozone level. 

These metrics are not the most 

relevant to characterizing 

population-level exposure. Thus, 

we have converted ozone mortality 

health impact functions that use 

these metric to maximum 8-hour 

average ozone concentration using 

standard conversion functions. 

The conversion ratios are based on 

observed relationships between the 

24-hour average and 8-hour 

maximum in the underlying 

studies.

This conversion also does not 

affect the relative magnitude of 

the health impact function. 

However, we do note that the 

pattern of 8hr max concentrations 

for a particular location over an 

ozone season could differ from 

the pattern of 1hr max or 24 hour 

average metrics for that same 

location. Consequently, estimates 

of incidence reductions (and 

associated dollar benefits) could 

differ for a particular location 

depending on the metric used for 

risk calculations.76

This practice is consistent both with the 

available exposure modeling and with the 

form of the current ozone standard. 

However, in some cases, these conversions 

were not specific to the ozone “warm” 

season which was the period used in the 

benefits analysis, which introduces 

additional uncertainty due to the use of 

effect estimates based on a mixture of 

warm season and all year data in the 

epidemiological studies.

(No quantitative method 

available)

Confounding by 

individual risk factors, 

other than 

socioeconomic status—

e.g., smoking, or 

ecologic factors, which 

represent the 

neighborhood, such as 

unemployment

Either, depending on the factor 

and study Medium Medium Tier 2 (sensitivity analysis)

Individual, social, economic, and 

demographic covariates can bias 

the relationship between 

particulate air pollution and 

mortality, particularly in cohort 

studies that rely on regional air 

pollution levels.

Because mortality dominates 

monetized benefits, even a small 

amount of confounding could 

have medium impacts on total 

monetized benefits.

To minimize confounding effects, we use 

risk coefficients that control for individual 

risk factors to the extent practical.

(Quantitative methods available 

but not assessed in this analysis.)

Either, depending upon the 

pollutant. Medium Medium Tier 1 (qualitative)

 

76 We note that if metric adjustment ratios were derived for individual point locations (reflecting the relationship between metrics for each location) and those metrics were used to 

generate adjusted C-R functions for each location, then some of this potential uncertainty could be reduced, however available data does not support the application of highly locationspecific adjustment ratios (with cross-city or national adjustment ratios being typically used instead).
5A-11

Potential Source of 

Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

Confounding and effect 

modification by copollutants

Disentangling the health responses 

of combustion-related pollutants 

(i.e., PM, SOx, NOx, ozone, and 

CO) is a challenge. The O3 ISA 

defines a confounder as the true 

cause of the association observed 

between the exposure and the 

outcome; in contrast, an effect 

modifier changes the magnitude of 

the association between the 

exposure and the outcome (p. lxii).

Because this uncertainty could 

affect mortality and because 

mortality generally dominates 

monetized benefits, even small 

uncertainties could have medium 

impacts on total monetized 

benefits.

The O3 ISA states that there is high 

confidence that unmeasured confounders 

are not producing the findings when 

multiple studies are conducted in various 

settings using different subjects or 

exposures, such as multi-city studies (p. 

lxi). When modeling effects of pollutants 

jointly (e.g., PM and O3), we apply multipollutant effect estimates when those 

estimates are available to avoid doublecounting and satisfy other selection criteria. 

In addition, we apply multi-city effect 

estimates when available.

(No quantitative method 

available)

Application of C-R 

relationships only to the 

original study 

population

Underestimate Low High Tier 2 (sensitivity analysis)

Estimating health effects for only 

the original study population may 

underestimate the whole 

population benefits of reductions 

in pollutant exposures.

Mortality generally dominates 

monetized benefits, so further age 

range expansions for morbidity 

endpoints would have a small 

impact on total monetized 

benefits.

Following advice from the SAB (U.S. 

EPA-SAB, 2004a, pg. 7) and NAS (NRC, 

2002, pg. 114), we expanded the age range 

for childhood asthma exacerbations beyond 

the original study population to ages 6-18.

(Quantitative methods available 

but not assessed in this analysis.)

Uncertainties Associated with Economic Valuation

Mortality Risk 

Valuation/Value-of-aStatistical-Life (VSL)

Unknown High Medium Tier 3 (probabilistic)

Some studies suggest that EPA’s 

mortality valuation is too high, 

while other studies suggest that it 

is too low. Differences in age, 

income, risk aversion, altruism, 

nature of risk (e.g., cancer), and 

study design could lead to higher 

or lower estimates of mortality 

valuation.

Mortality generally dominates 

monetized benefits, so moderate 

uncertainties could have a large 

effect on total monetized benefits.

The VSL used by EPA is based on 26 labor 

market and stated preference studies 

published between 1974 and 1991. EPA is 

in the process of reviewing this estimate 

and will issue revised guidance based on 

the most up-to-date literature and 

recommendations from the SAB-EEAC in 

the near future (U.S. EPA, 2010a, U.S. 

EPA-SAB, 2011c).

Assessed uncertainty in mortality 

valuation using a Weibull 

distribution.

Underestimate Medium-High Low Tier 2 (sensitivity analysis)
5A-12

Potential Source of 

Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

Cessation lag structure 

for long-term ozone

mortality

Jerrett et al. (2009) notes that, 

“Allowing for a 10-year period of 

exposure to ozone (5 years of 

follow-up and 5 years before the 

follow-up period) did not 

appreciably alter the risk 

estimates...” We acknowledge 

substantial uncertainty associated 

with specifying the lag for longterm respiratory mortality.

Although the cessation lag does 

not affect the number of 

premature deaths attributable to 

long-term ozone exposure, it 

affects the timing of those deaths 

and thus the discounted 

monetized benefits. Mortality 

generally dominates monetized 

benefits, so moderate 

uncertainties could have a large 

effect on total monetized benefits.

As discussed in section 5.6.4.1, in 

presenting dollar benefit estimates as part 

of the sensitivity analysis (presenting dollar 

benefits for long-term ozone-related 

morality), we include both an assumption 

of zero lag and a lag structure matching 

that used for the core PM2.5 estimate (the 

SAB 20 year segmented lag). Inclusion of 

the zero lag reflects consideration for the 

possibility that the long-term respiratory 

mortality estimate captures primarily an 

accumulation of short-term mortality 

effects across the ozone season. The use of 

the 20 year segmented lag reflects 

consideration for advice provided by the 

HES (USEPA-SAB, 2010).

As shown in sensitivity analysis 

results presented in Appendix 5B, 

the use of the 20 year segmented 

lag results in a 10-20% reduction 

in the total dollar benefit (using a 

3% and 7% discount rate, 

respectively) relative to the 

alternative approach of applying 

no lag (i.e., assuming all of the 

mortality reductions occur in the 

same year).

Income growth 

adjustments

Either Medium Medium Tier 2 (sensitivity analysis)

Income growth increases 

willingness-to-pay (WTP) 

valuation estimates, including 

mortality, over time. From 1997 to 

2010, personal income and GDP 

growth have begun to diverge. If 

this trend continues, the 

assumption that per capita GDP 

growth is a reasonable proxy for 

income growth may lead to an 

overstatement of benefits. (IEc, 

2012).

Income growth from 1990 to 

2020 increases mortality 

valuation by 20%. Alternate 

estimates for this adjustment vary 

by 20% (IEc, 2012). Because we 

do not adjust for income growth 

over the 20-year cessation lag, 

this approach could also 

underestimate the benefits for the 

later years of the lag.

Consistent with SAB recommendations 

(U.S. EPA,-SAB, 2000, pg. 16), we adjust 

WTP for income growth. Difficult to 

forecast future income growth. However, in 

the absence of readily available income 

data projections, per capita GDP is the best 

available option.

As shown in Appendix 5B, the 

use of alternate income growth 

adjustments would change the 

monetized benefits by +33% to 

−14%.

Morbidity valuation

Underestimate Low Low Tier 3 (probabilistic), where 

available

Morbidity benefits such as 

hospital admissions are calculated 

using cost-of-illness (COI) 

estimates, which are generally half 

the WTP to avoid the illness 

(Alberini and Krupnick, 2000). In 

addition, the morbidity costs do 

not reflect physiological responses 

or sequelae events, such as 

increased susceptibility for future 

morbidity.

Even if we doubled the 

monetized valuation of morbidity 

endpoints using COI valuation 

that are currently included in the 

RIA, the change would still be 

less than 5% of the monetized 

benefits. It is unknown how much 

including sequelae events could 

increase morbidity valuation.

Although the COI estimates for 

hospitalizations reflect recent data, we have 

not yet updated other COI estimates such 

as for school loss days. The SAB 

concluded that COI estimates could be 

used as placeholders where WTP estimates 

are unavailable, but it is reasonable to 

presume that this strategy typically 

understates WTP values (U.S. EPA-SAB, 

2004b, pg. 3).

Assessed uncertainty in morbidity 

valuation using distributions 

specified in the underlying 

literature, where available (see 

Table 5-10).

Uncertainties Associated with Baseline Incidence and Population Projections

Either Low–Medium Medium Tier 1 (qualitative)
5A-13

Potential Source of 

Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

Population estimates 

and projections

The monetized benefits would 

change in the same direction as the 

over- or underestimate in 

population projections in areas 

where exposure changes.

Monetized benefits are 

substantially affected by 

population density. Comparisons 

using historical census data show 

that population projections are 

±5% nationally, but projection 

accuracy can vary by locality. 

Historical error for Woods & 

Poole’s population projections 

has been ±8.1% for county-level 

projections and ±4.1% for states 

(Woods and Poole, 2012). The 

magnitude of impact on total 

monetized benefits depends on 

the specific location where PM is 

reduced.

We use population projections for 5-year 

increments for 304 

race/ethnicity/gender/age groups (Woods 

and Poole, 2012) at Census blocks. 

Population forecasting is well-established 

but projections of future migration due to 

possible catastrophic events are not 

considered. In addition, projections at the 

small spatial scales used in this analysis are 

inherently more uncertain than projections 

at the county- or state-level.

(No quantitative method 

available)

Uncertainty in 

projecting baseline 

incidence rates for 

mortality

Unknown Low-Medium Medium Tier 1 (qualitative)

Because the mortality rate 

projections for future years reflect 

changes in mortality patterns as 

well as population growth, the 

projections are unlikely to be 

biased.

Because mortality generally 

dominates monetized benefits, 

small uncertainties could have 

medium impacts on total 

monetized benefits.

The county-level baseline mortality rates 

reflect recent databases (i.e., 2004–2006 

data) and are projected for 5-year 

increments for multiple age groups. This 

database is generally considered to have 

relatively low uncertainty (CDC Wonder, 

2008). The projections account for both 

spatial and temporal changes in the 

population.

(No quantitative method 

available)

Uncertainty in 

projecting baseline 

incidence rates and 

prevalence rates for 

morbidity

Either, depending on the health 

endpoint Low Low-Medium Tier 1 (qualitative)

Morbidity baseline incidence is 

available for current year only 

(i.e., no projections available). 

Assuming current year levels can 

bias the benefits for a specific 

endpoint if the data has clear 

trends over time. Specifically, 

asthma prevalence rates have 

increased substantially over the

past few years while hospital 

admissions have decreased 

substantially.

The magnitude varies with the 

health endpoint, but the overall 

impact on the total benefits 

estimate from these morbidity 

endpoints is likely to be low.

We do not have a method to project future 

baseline morbidity rates, thus we assume 

current year levels will continue. While we 

try to update the baseline incidence and 

prevalence rates as frequently as 

practicable, this does not continue trends 

into the future. Some endpoints such as 

hospitalizations and ER visits have more 

recent data (i.e., 2007) stratified by age and 

geographic location. Other endpoints, such 

as respiratory symptoms reflect a national 

average. Asthma prevalence rates reflect 

recent increases in baseline asthma rates 

(i.e., 2008).

(No quantitative method 

available)

Uncertainties Associated with Omitted Benefits Categories

Underestimate Medium-High Low Tier 2 (sensitivity analysis)
5A-14

Potential Source of 

Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

Unquantified ozone

health benefit 

categories, such as 

worker productivity 

and long-term mortality

EPA has not included monetized 

estimates of these benefits 

categories in the core benefits 

estimate.

Although the potential magnitude 

is unknown, including all of the 

additional endpoints associated 

with ozone exposure that are 

currently not monetized could 

increase the total benefits by a 

large amount.

Current data and methods are insufficient 

to value national quantitative estimates of 

these health effects. The O3 ISA 

determined that respiratory effects 

(including mortality) are causally 

associated with long-term ozone exposure 

(p. 2–17). The O3 ISA also determined that 

outdoor workers have an increased risk of 

ozone-related health effects (p. 1-15), and 

that studies on outdoor workers show 

consistent evidence that short-term 

increases in ambient ozone exposure can 

decrease lung function in healthy adults 

(p.6-38). Additional studies link short-term 

ozone exposure to reduced productivity in 

outdoor workers (Graf Zivin and Neidell, 

2013; Crocker and Horst, 1981).

We include sensitivity analyses 

reflecting long-term mortality 

which shows that this endpoint 

could add substantially to the total 

core benefits range (see Tables 5-

20 and 5-27). We are still 

considering options for updating 

the worker productivity analysis 

and including it as a sensitivity 

analyses.

Uncertainties Associated with Estimated Exposure Changes

Spatial matching of air 

quality estimates from 

epidemiology studies to 

air quality estimates 

from air quality 

modeling

Unknown Unknown Low Tier 1 (qualitative)

Epidemiology studies often 

assume one air quality 

concentration is representative of 

an entire urban area when 

calculating hazard ratios, while 

benefits are calculated using air 

quality modeling conducted at 12 

km spatial resolution. This spatial 

mismatch could introduce 

uncertainty.

We have not controlled for this potential 

bias, and the SAB has not suggested an 

approach to adjust for this bias.

(No quantitative method 

available)

Uncertainties Associated with the Dollar-per-ton Approach Used in Modeling PM2.5 Co-benefits

Unknown Unknown Medium Tier 1 (qualitative)
5A-15

Potential Source of 

Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

Derivation of dollarper-ton estimates for 

PM2.5

In the analysis used to generate the 

dollar-per-ton values we assume 

that all fine particles, regardless of 

their chemical composition, are 

equally potent in causing 

premature mortality. However, the 

scientific evidence is not yet 

sufficient to allow differentiation 

of effect estimates by particle type. 

We also assume that the health 

impact function for fine particles 

is linear down to the lowest air 

quality levels modeled in this 

analysis. Thus, the estimates 

include health benefits from 

reducing fine particles in areas 

with varied concentrations of 

PM2.5, including regions that are in 

attainment with the fine particle 

standard.

While there are concerns regarding the 

assumption of a uniform PM2.5 toxicity 

across sectors and a linear C-R function all 

the way down to zero, these sources of 

uncertainty impact benefits modeling for 

PM2.5 in general and have been discussed 

as part of earlier RIAs (see section 5.7.2, of 

the final PM RIA, U.S. EPA. 2012). We do 

recognize that, as discussed below (and on 

pp. 24-25 of US. EPA, 2013), there is 

increased uncertainty when dollar-per-ton 

benefits are applied outside of the specific 

scenario used in their derivation. 

(No quantitative method 

available)

Application of dollarper-ton estimates in the 

current Ozone NAAQS 

review

Unknown Unknown Medium

As discussed in section 5.4.4, we 

used a method to calculate the 

regional benefit-per-ton estimates 

that is a slightly modified version 

of the national benefit-per-ton 

estimates described in the TSD: 

Estimating the Benefit per Ton of 

Reducing PM2.5 Precursors from 

17 Sectors (U.S. EPA, 2013b). 

The national estimates were 

derived using the approach 

published in Fann et al. (2012c), 

but they have since been updated 

to reflect the epidemiology studies 

and Census population data first 

applied in the final PM NAAQS 

RIA (U.S. EPA, 2012). These 

dollar-per-ton estimates were 

applied to sector-specific NOx 

emissions reductions modeled as 

part of the current ozone NAAQS 

review. 

While we acknowledge 

uncertainty associated with 

applying dollar-per-ton estimates 

in the context of this ozone 

NAAQS review (and outside of 

the scenario in which they were 

derived), we are not in a position 

to characterize the magnitude or

direction of any bias that might 

result from that application.

All benefit-per-ton estimates have inherent 

limitations, including that the estimates 

reflect the geographic distribution of the 

modeled sector emissions, which may not 

match the emissions reductions anticipated 

by the proposed standards, and they may 

not reflect local variability in population 

density, meteorology, exposure, baseline 

health incidence rates, or other local factors 

for any specific locations reflected in 

benefits modeling. However, the fact that 

we are modeling regional/national benefits 

rather than attempting a more spatially 

refined application of the dollar-per-ton 

values does reduce uncertainty in the 

benefits estimates that are generated.

(No quantitative method 

available)
5A-16

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U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004c. Advisory Council on 

Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. EPA-COUNCIL-LTR-05-001. 

December. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E0/$File/council_ltr_05_

001.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2010. Review of EPA’s 

DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act. EPA-COUNCIL10-001. June. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/72D4EFA39E48CDB2852

5774500738776/$File/EPA-COUNCIL-10-001-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011a. CASAC Review of 

Quantitative Health Risk Assessment for Particulate Matter—Second External Review Draft (February 2010). 

EPA-CASAC-10-008. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/BC4F6E77B6385155852577070002F09F/$File/EPA-CASAC-10-

008-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011b. Review of the Final 

Integrated Report for the Second Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act 

(August 2010). EPA-COUNCIL-11-001. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/1E6218DE3BFF682E852577FB005D46F1/$File/EPA-COUNCIL11-001-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011c. Review of Valuing 

Mortality Risk Reductions for Environmental Policy: A White Paper (December 10, 2010). EPA-SAB-11-011. 

July. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/$File/EPA-SAB-11-

011-unsigned.pdf>.

Woods & Poole Economics, Inc. 2012. Complete Economic and Demographic Data Source (CEDDS). CD-ROM. 

Woods & Poole Economics, Inc. Washington, D.C.

World Health Organization (WHO). 2008. Part 1: Guidance Document on Characterizing and Communicating 

Uncertainty in Exposure Assessment, Harmonization Project Document No. 6. Published under joint 

sponsorship of the World Health Organization, the International Labour Organization and the United Nations 

Environment Programme. WHO Press: Geneva, Switzerland. Available on the Internet at 

<www.who.int/ipcs/methods/harmonization/areas/uncertainty%20.pdf>.
5B-1

APPENDIX 5B: ADDITIONAL SENSITIVITY ANALYSES RELATED TO THE 

OZONE HEALTH BENEFITS ANALYSIS

Overview

The benefits analysis presented in Chapter 5 of this RIA is based on our current 

interpretation of the scientific and economic literature. That interpretation requires judgments 

regarding the best available data, models, and analytical 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 main estimates of benefits have been reviewed and 

supported by EPA’s independent Science Advisory Board (SAB). Both EPA and the SAB 

recognize that data and modeling limitations as well as simplifying assumptions can introduce 

significant uncertainty into the estimates of benefits and that alternative choices exist for some 

inputs to the analysis, such as the concentration-response functions for mortality.

This appendix assesses the sensitivity of the core benefits to: (a) the potential impact of 

thresholds in long-term ozone exposure-related mortality in incidence and benefits estimates 

(section 5B.2), (b) alternative response functions developed through expert elicitation (EE) for 

long-term PM2.5 exposure-related mortality (section 5B.3), and (c) alternative assumptions 

regarding income elasticity on benefits derived using willingness-to-pay (WTP) functions 

(section 5B.3).

For the core analysis, we estimated incidence and dollar benefits for two scenarios: 2025 

and post-2025 (see Chapter 5, Sections 5.2 and 5.4.3). However, in conducting these sensitivity 

analyses, we used the 2025 scenario as the basis for making our calculations, since sensitivity 

analysis findings for this scenario would generally hold for the post-2025 scenario.

In addition to the three sensitivity analyses covered in this appendix, we also included 

two sensitivity analyses that are covered in detail in Chapter 5. The first of these are estimates of 

dollar benefits associated with mortality resulting from long-term exposure to ozone. As 

discussed in Section 5.2, while we felt that we had sufficient confidence to include incidence 

estimates associated with long-term ozone exposure (based on effect estimates obtained from 

Jerrett et al., 2009) in the core analysis, limitations in our ability to specify an appropriate lag for 
5B-2

reductions in this endpoint meant that we could not include dollar benefit estimates in the core 

analysis. Instead, we have included those values as sensitivity analyses, including consideration 

of alternative lag structures (they are presented in Tables 5-20 and 5-27, respectively for the 

2025 and post-2025 scenarios).77 The second of the sensitivity analyses already covered in the 

chapter addresses alternative models for short-term ozone exposure-related mortality. As 

discussed in Section 5.6.3.1, in addition to the two epidemiological studies (Smith et al., 2009 

and Zanobetti and Schwartz 2008) that provide effect estimates for the core benefits estimates, 

we have also considered seven additional epidemiological studies as sensitivity analyses. These 

additional studies include a mix of multi-city and meta-analysis designs. Results of this 

sensitivity analysis are presented in Tables 5-19 and 5-20 (incidence and benefits, respectively 

for the 2025 scenario) and Tables 5-26 and 5-27 (incidence and benefits, respectively for the 

post-2025 scenario).

5B.1 Threshold Sensitivity Analysis for Premature Mortality Incidence and Benefits from 

Long-term Exposure to Ozone

In estimating long-term ozone mortality, we employed a continuous non-threshold 

concentration-response (C-R) function relating ozone exposure to premature death. However, as 

discussed in Section 5.6.3.1, there is uncertainty regarding the potential existence and location of 

a threshold in the C-R function relating mortality and long-term ozone concentrations. Thus, we 

have included a sensitivity analysis exploring the impact of potential thresholds in the C-R 

relationship on estimates of long-term exposure-related mortality that were evaluated in Jerrett et 

al. (2009), consistent with advice from CASAC (Frey, 2014). 

In their memo clarifying the results of their study (Sasser, 2014), the authors note that in 

terms of goodness of fit, long-term health risk models including ozone clearly performed better 

than models without ozone, indicating the improved predictions of respiratory mortality when 

ozone is included. In exploring different functional forms, the authors report that the model 

including a threshold at 56 ppb had the lowest log-likelihood value of all models evaluated (i.e., 

 

77 The sensitivity analysis-related benefits estimates presented in these tables include (a) benefits estimates reflecting 

application of a zero lag model and (b) estimates reflecting application of the same 20-year segmented lag used in 

modeling benefits for PM2.5, together with application of a 3% and 7% discount rate. See Sections 5.6.3 and 5.6.4 

for additional discussion of lags in relation to long-term ozone-related mortality.
5B-3

linear models and models including thresholds ranging from 40-60 ppb), and thus provided the 

best overall statistical fit to the data. However, they also note that it is not clear whether the 56 

ppb threshold model is a better predictor of respiratory mortality than when using a linear (nothreshold) model for this dataset. Using one statistical test, the model with a threshold at 56 ppb 

was determined to be statistically superior to the linear model. Using another, more stringent test, 

none of the threshold models considered were statistically superior to the linear model. Under the 

less stringent test, although the threshold model produces a statistically superior prediction than 

the linear model, there is uncertainty about the specific location of the threshold, if one exists. 

This is because the confidence intervals on the model predictions indicate that a threshold could 

exist anywhere from 0 to 60 ppb. The authors conclude that considerable caution should be 

exercised in using any specific threshold, particularly when the more stringent statistical test 

indicates there is no significantly improved prediction. Based on this additional information from 

the authors, we have chosen to reflect the uncertainty about the existence and location of a 

potential threshold by estimating mortality attributable to long-term ozone exposures using a 

range of threshold-based effect estimates as sensitivity analyses. Specifically, we estimate longterm ozone mortality benefits using unique risk coefficients that include a range of thresholds 

from 40 ppb to 60 ppb in 5 ppb increments, while also including a model with a threshold equal 

to 56 ppb, which had the lowest log-likelihood value for all models examined.78 Table 5B-1 

provides the results of these sensitivity analyses (based on modeling incidence) for 60 ppb, 65 

ppb and 70 ppb. We note that the same pattern in terms of relative reductions across thresholds 

(relative to the core estimate) would hold for the dollar benefit estimates generated for this 

endpoint.

 

78 There is a separate effect estimate (and associated standard error) for each of the fitted threshold models estimated 

in Jerrett et al. (2009). As a result, the sensitivity of estimated mortality attributable to long-term ozone

concentrations is affected by both the assumed threshold level (below which there is no effect of ozone) and the 

effect estimate applied to ozone concentrations above the threshold.
5B-4

Table 5B-1. Long-term Ozone Mortality Incidence at Various Assumed Thresholds a

Threshold Concentration 70 ppb 65 ppb 60 ppb

No threshold (core 

model)

680 2,100 3,900

40 ppb 520 1,600 2,900

45 ppb 410 1,100 1,700

50 ppb 120 280 510

55 ppb 5.6 58 140

56 ppb 3.3 47 105

60 ppb <1 10 13

a All estimates rounded to two significant digits.

The results of the sensitivity analysis based on the suite of threshold-based risk 

coefficients suggests that threshold models can result in substantially lower estimates of ozoneattributable long-term mortality. For example, estimated incidence and dollar benefits for longterm mortality using a model that includes a 55 ppb threshold are approximately 70% less than 

long-term mortality benefits estimated using the core co-pollutant non-threshold model. 

Generally, estimated long-term mortality benefits are progressively reduced when using models 

with increasing thresholds, with the highest threshold considered (60 ppb) removing virtually all 

of the estimated incidence reduction and associated benefits.

5B.2 Alternative Concentration-Response Functions for PM2.5–Related Mortality

In modeling PM2.5 cobenefits, we estimate that total dollar benefits are driven largely by 

reductions in mortality (see Table 5-22 and 5-29). Therefore, it is particularly important to 

attempt to characterize the uncertainties associated with reductions in premature mortality as 

modeled in the PM2.5 cobenefits analysis. To better understand the concentration-response 

relationship between PM2.5 exposure and premature mortality, the EPA conducted an expert 

elicitation in 2006 (Roman et al., 2008; IEc, 2006).79 In general, the results of the expert 

elicitation support the conclusion that the benefits of PM2.5 control are very likely to be 

substantial. 

Alternative concentration-response functions are useful for assessing uncertainty beyond

random statistical error, including uncertainty in the functional form of the model or alternative 

study design. In this analysis, we present the results derived from the expert elicitation as 

 

79 Expert elicitation is a formal, highly-structured and well-documented process whereby expert judgments, usually 

of multiple experts, are obtained (Ayyub, 2002).
5B-5

indicative of the uncertainty associated with a major component of the health impact functions, 

and we provide the independent estimates derived from each of the twelve experts to better 

characterize the degree of variability in the expert responses.

In previous RIAs, the EPA presented benefits estimates using concentration-response 

functions derived from the PM2.5 Expert Elicitation (Roman et al., 2008) as a range from the 

lowest expert value (Expert K) to the highest expert value (Expert E). However, this approach 

did not indicate the Agency’s judgment on what the best estimate of PM2.5 benefits may be, and 

the EPA’s independent SAB recommended refinements to the way EPA presented the results of 

the elicitation (U.S. EPA-SAB, 2008). Therefore, we began to present the cohort-based studies 

(Krewski et al., 2009; Laden et al., 2006)80 as our core estimates in the proposal RIA for the 

Portland Cement NESHAP (U.S. EPA, 2009a). Using alternate relationships between PM2.5 and 

premature mortality supplied by experts, higher and lower benefits estimates are plausible, but 

most of the expert-based estimates of the mean PM2.5 effect on mortality fall between the two 

epidemiology-based estimates (Roman et al., 2008). In addition to these studies, we have 

included a discussion of other recent multi-state cohort studies conducted in North America, but 

we have not estimated benefits using the effect coefficients from these studies (see Appendix 

5D). Please note that the benefits estimates results presented are not the direct results from the 

studies or expert elicitation; rather, the estimates are based in part on the effect coefficients 

provided in those studies or by experts. In addition, because we are using a dollar-per-ton 

approach in modeling PM2.5 cobenefits in this RIA, we cannot generate confidence intervals 

reflecting the statistical fit characterized in the expert elicitation-based functions.

Even these multiple characterizations based on application of the range of expert 

elicitation-based effect estimates omit the contribution to overall uncertainty from uncertainty in 

air quality changes, baseline incidence rates, and populations exposed. Furthermore, the 

approach presented here does not yet include methods for addressing correlation between input 

 

80 We have since updated the Harvard Six Cities cohort study from Laden et al. (2006) to use the most recent followup publication of this cohort (Lepeule et al, 2012). This study is reflected in the dollar-per-ton values used in this 

RIA.
5B-6

parameters and the identification of reasonable upper and lower bounds for input distributions 

characterizing uncertainty in additional model elements. 

The PM2.5 expert elicitation and the derivation of effect estimates from the expert 

elicitation results (used in generating these alternative dollar-per-ton estimates) are described in 

detail in the 2006 PM2.5 NAAQS RIA (U.S. EPA, 2006), the elicitation summary report (IEc, 

2006) and Roman et al. (2008), and consequently, we do not present those effect estimates (and 

associated functional forms) here.

Table 5B-2 presents the results of this sensitivity analysis as completed for the 2025 

scenario (overall conclusions generated from this analysis are transferable to the post-2025 

scenario). The alternative mortality estimates presented in Table 5B-2 were generated similar to 

the core cobenefits PM2.5 mortality estimates, but applying dollar-per-ton values (for each expert 

elicitation-based effect estimate) to the sector-level estimates of NOx reductions associated with 

the 2025 scenario. We have also included the core cobenefits estimates for each alternative 

standard to facilitate comparison against these alternative sensitivity analysis estimates. Because 

application of these effect estimates (using a dollar-per-ton approach) represents a linear 

calculation, results in terms of the relative ranking of the core estimates compared with expert 

elicitation estimates will remain the same for all alternative standards evaluated. Therefore, we 

only present estimates for the 70 ppb alternative standard, observing that observations drawn 

from this sensitivity analysis would hold for other alternative standards considered (and for the 

post-2025 scenario – for all alternative standards – as well).
5B-7

Table 5B-2. Application of Alternative (Expert Elicitation-Based Effect Estimates) to the 

Modeling of PM2.5 Co-benefit Estimates for PM2.5 (avoided incidence)

C-R Function (and effect estimate)b 70 ppb

Krewski et al., (2012) (core model) 45

Lepeule et al., (2012) (core model) 100

Expert K 10

Expert G 54

Expert L 63

Expert D 65

Expert H 67

Expert J 75

Expert F 88

Expert C 92

Expert I 92

Expert B 95

Expert A 120

Expert E 150

a All estimates rounded to two significant digits.

b Expert elicitation-based values ordered by magnitude of incidence reduction

The values presented in Table 5B-2 suggest that the two core incidence estimate fall 

within the range of alternative C-R function based estimates obtained through expert elicitation. 

This increases overall confidence in the core estimates with regard to the form of the functions 

and magnitude of the effect estimates. We would expect the relationship between the core 

estimates and the expert-derived estimates to remain constant for the remaining scenarios as 

well.

5B.3 Income Elasticity of Willingness-to-Pay

As discussed in Chapter 5, 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. Income growth projections are only 

currently available in BenMAP through 2024, so both the 2025 and post-2025 scenario estimates 

use income growth only through 2024 and are therefore likely underestimates. 
5B-8

Table 5B-3 lists the ranges of elasticity values used to calculate the income adjustment 

factors, while Table 5B-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 5B-5. 

Table 5B-3. Ranges of Elasticity Values Used to Account for Projected Real Income 

Growth a

Benefit Category

Lower Sensitivity 

Bound

Upper Sensitivity 

Bound

Minor Health Effectb 0.04 0.30

Premature Mortality 0.08 1.00

a Derivation of these ranges can be found in Kleckner and Neumann (1999). COI estimates are assigned an 

adjustment factor of 1.0.

b Minor health effects included in this RIA and valued using WTP-based functions include: upper and lower 

respiratory symptoms, asthma exacerbations, minor restricted activity days, and acute bronchitis.

Table 5B-4. Ranges of Adjustment Factors Used to Account for Projected Real Income 

Growth to 2024 a

Benefit Category

Lower Sensitivity 

Bound

Upper Sensitivity 

Bound

Minor Health Effectb

1.021 1.170

Premature Mortality 1.043 1.705

a Based on elasticity values reported in Table C-4, U.S. Census population projections, and projections of real GDP 

per capita.

b Minor health effects included in this RIA and valued using WTP-based functions include: upper and lower 

respiratory symptoms, asthma exacerbations, minor restricted activity days, and acute bronchitis.

Table 5B-5. Sensitivity of Monetized Ozone Benefits to Alternative Income Elasticities in 

2025 (Millions of 2011$) a

Benefit Category No adjustment Lower Sensitivity Bound Upper Sensitivity Bound

70 ppb 65 ppb 70 ppb 65 ppb 70 ppb 65 ppb

Minor Health Effect b $66 $200 $67 $210 $77 $240

Premature Mortality c $2,000 $6,400 $2,100 $6,600 $3,500 $11,000

a All estimates rounded to two significant digits. Only reflects income growth to 2024.

b For purposes of completing this sensitivity analysis, we have included minor restricted activity days (MRADS) 

based resulting from short-term ozone exposure as the minor health effect evaluated here.

c Using short-term mortality effect estimate from Smith et al. (2009) and 3% discount rate. Results using other shortterm mortality studies and a 7% discount rate would show the same proportional range.

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 2025 ranges from 

86% to 133% of the main estimate for mortality based on the lower and upper sensitivity bounds 

on the mortality income adjustment factor. The effect on the value of minor health effects is 
5B-9

much less pronounced, ranging from 96% to 108% of the main estimate for minor effects. These 

observations (in terms of relative impact from alternative elasticities) hold for all three of the 

alternative standard levels evaluated under both the 2025 and post-205 scenarios.

5B.4 References

Frey, C. 2014. CASAC Review of the EPA’s Health Risk and Exposure Assessment for Ozone (Second External 

Review Draft – February, 2014). U.S. Environmental Protection Agency Science Advisory Board. EPACASAC-14-XXX.Gent, J.F.; E.W. Triche; T.R. Holford; K. Belanger; M.B. Bracken; W.S. Beckett, et al. 2003. 

Association of low-level ozone and fine particles with respiratory symptoms in children with asthma. Journal of 

the American Medical Association. 290(14):1859-1867.

Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the ConcentrationResponse Relationship Between PM2.5 Exposure and Mortality. Prepared for: Office of Air Quality Planning and 

Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. September. Available at 

<http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_tsd_expert_interview_summaries.pdf>.

Jerrett M, Burnett RT, Pope CA, III, et al. 2009. “Long-Term Ozone Exposure and Mortality.” New England 

Journal of Medicine 360:1085-95.

Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. Extended follow-up and spatial analysis of 

the American Cancer Society study linking particulate air pollution and mortality. HEI Research Report, 140, 

Health Effects Institute, Boston, MA.

Kleckner, N., and J. Neumann. 1999. “Recommended Approach to Adjusting WTP Estimates to Reflect Changes in 

Real Income.” Memorandum to Jim DeMocker, US EPA/OPAR. June 3, Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/klecknerandneumann1999.pdf>.

Lepeule J, Laden F, Dockery D, Schwartz J. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended 

Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives 120 

(7):965-70.

Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan J. Hubbell, and 

Patrick L. Kinney. 2008. “Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine 

Particulate Matter in the U.S.” Environmental Science & Technology 42(7):2268-2274.

Smith, R.L.; B. Xu and P. Switzer. 2009. Reassessing the relationship between ozone and short- term mortality in 

U.S. urban communities. Inhalation Toxicology. 21:37-61.

U.S. Environmental Protection Agency (U.S. EPA). 2009. Regulatory Impact Analysis: National Emission 

Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. April. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2012. Regulatory Impact Analysis for the Final Revisions to the 

National Ambient Air Quality Standards for Particulate Matter. EPA-452/R-12-003. Office of Air Quality 

Planning and Standards, Health and Environmental Impacts Division. December. Available at: 

<http://www.epa.gov/pm/2012/finalria.pdf>. 

U.S. Environmental Protection Agency—Science Advisory Board (EPA-SAB). 1999a. 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-
5B-10

COUNCIL-ADV-99-012. July. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/F64A21824D19766885257193005E51CA/$File/conadv1

2.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (EPA-SAB). 1999b. 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-SABCOUNCIL-ADV-00-001. October. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/7D54675178C12703852571B90044B4F8/$File/coua0001

.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (EPA-SAB). 2004a. 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. EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400433D5D/$File/council

_adv_04002.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2004b. Advisory Council on 

Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. EPA-COUNCIL-LTR-05-001 

December. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/39F44B098DB49F3C85257170005293E0/$File/council_l

tr_05_001.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2008. Characterizing 

Uncertainty in Particulate Matter Benefits Using Expert Elicitation. EPA-COUNCIL-08-002. July. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/43B91173651AED9E85257487004EA6CB/$File/EPACOUNCIL-08-002-unsigned.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010. Review of EPA’s DRAFT 

Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act. EPA-COUNCIL-10-001. June. 

Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/59e06b6c5ca66597852575e7

006c5d09!OpenDocument&TableRow=2.3#2.>.

Zanobetti, A; J. Schwartz. 2008. Mortality displacement in the association of ozone with mortality: an analysis of 48 

cities in the United States. American Journal of Respiratory and Critical Care Medicine. 177:184-189.
5C-1

APPENDIX 5C: SUPPLEMENTAL ANALYSES RELATED TO THE OZONE HEALTH 

BENEFITS ANALYSIS

Overview

A number of additional analyses have been completed to supplement the core estimates 

generated for the RIA. These analyses give greater insight to the manner in which these impacts 

are distributed among populations of different ages, the ambient levels of ozone at which the 

avoided deaths are estimated to occur, and the benefits attributable to the known emissions

control measures. These supplemental analyses, which are presented in detail here (and 

summarized in Section 5.7.3.2) include: (a) age group-differentiated aspects of short-term ozone 

exposure-related mortality (including total avoided incidence, life years gained and percent 

reduction in baseline mortality - Section 5C.1), (b) evaluation of mortality impacts relative to the 

baseline pollutant concentrations (used in generating those mortality estimates) for both shortterm ozone exposure-related mortality and long-term PM2.5 exposure-related mortality (Section 

5C.2)81 and (c) presentation of core incidence and benefits estimates reflecting application of 

known controls for the 2025 scenario (Section 5C.3). 

5C.1 Age Group-Differentiated Aspects of Short-Term Ozone Exposure-Related 

Mortality

In their 2008 review of the EPA’s approach to estimating ozone-related mortality 

benefits, NRC indicated, “EPA should consider placing greater emphasis on reporting decreases 

in age-specific death rates in the relevant population and develop models for consistent 

calculation of changes in life expectancy and changes in number of deaths at all ages” (NRC, 

2008). In addition, NRC noted in an earlier report that “[f]rom a public-health perspective, lifeyears lost might be more relevant than annual number of mortality cases” (NRC, 2002). This 

advice is consistent with that of the Health Effects Subcommittee of the Advisory Council on 

Clean Air Compliance Analysis (SAB-HES), which agreed that “...the interpretation of mortality 

risk results is enhanced if estimates of lost life-years can be made” (U.S. EPA-SAB, 2004a). To 

address these recommendations, we use simplifying assumptions to estimate the number of life 

 

81 The plot of long-term PM2.5 exposure-related mortality incidence versus PM2.5 levels is taken from previous RIAs 

and reflects the benefits simulation used to generate the dollar-per-ton estimates used in deriving PM2.5-related 

cobenefits for this analysis (i.e., these plots are not derived using new data specific to this RIA) (see Section 5C.3).
5C-2

years that might be gained. We also estimate the reduction in the percentage of deaths attributed 

to ozone resulting from the illustrative emissions reduction strategies to reach the proposed and 

alternative primary standards. The EPA included similar estimates of life years gained in a 

previous assessment of ozone and/or PM2.5 benefits (U.S. EPA, 2006, 2010c, 2011b), the latter of 

which was peer reviewed by the SAB-HES (U.S. EPA-SAB, 2010a).

Changes in life years and changes in life expectancy at birth are frequently conflated, 

thus it is important to distinguish these two very different metrics. Life expectancy varies by age. 

The Centers for Disease Control and Prevention (CDC) defines life expectancy as the “average 

number of years of life remaining for persons who have attained a given age” (CDC, 2011). In 

other words, changes in life expectancy refer to an average change for the entire population, and 

refer to the future. Over the past 50 years, average life expectancy at birth in the U.S. has 

increased by 8.4 years (CDC, 2001). For example, life expectancy at birth was estimated in 2007 

to be 77.9 years for an average person born in the U.S., but for people surviving to age 60, 

estimated life expectancy is 82.5 years (i.e., 4.6 years more than life expectancy at birth) (CDC, 

2011). Life years, on the other hand, measure the amount of time that an individual loses if they 

die before the age of their life expectancy. Life years refer to individuals, and refer to the past, 

e.g., when the individual has already died. If a 60-year old individual dies, we estimate that this 

individual would lose about 22.5 years of life (i.e., the average population life expectancy for an 

individual of this age minus this person’s age at death).

Due to the use of benefit-per-ton estimates for the PM2.5 co-benefits, we are unable to 

estimate the life years gained by reducing exposure to PM2.5 in this analysis. Instead, we refer the 

reader to the 2012 PM NAAQs RIA for more information about the avoided life years lost from 

PM2.5 exposure (U.S. EPA, 2012b). This analysis found that about half of the avoided PM-related 

deaths occur in populations age 75 to 99, but half of the avoided life years lost would occur in 

populations younger than 65 because the younger populations have the potential to lose more life 

years per death than older populations. In addition, this analysis found that the average individual 

who would otherwise have died prematurely from PM exposure would gain 16 additional years 

of life.
5C-3

Estimated Life Years Gained

For estimating the potential life years gained by reducing exposure to ozone in the U.S. 

adult population, we use the same general approach as Hubbell (2006) and Fann et al. (2012a). 

We have not estimated the change in average life expectancy at birth in this RIA. Because life 

expectancy is an average of the entire population (including both those whose deaths would 

likely be attributed to air pollution exposure as well as those whose deaths would not), average 

life expectancy changes associated with air pollution exposure would be expected to always be 

significantly smaller than the average number of life years lost by an individual who is projected 

to die prematurely from air pollution exposure.

To estimate the potential distribution of life years gained for population subgroups 

defined by the age range at which their reduction in air pollution exposure is modeled to occur,

we use standard life tables available from the CDC (2014) and the following formula:

Total Life Years = ∑ LEi × Mi

n

i=1

(5.2)

where LEi is the average remaining life expectancy for age interval i, Mi is the estimated change 

in number of deaths in age interval i, and n is the number of age intervals. 

To get Mi (the estimated number of avoided premature deaths attributed to changes in 

ozone exposure for the 2025 scenario), we use a health impact function that incorporates risk 

coefficients estimated for the adult population in the U.S. and age-specific mortality rates. That 

is, we use risk coefficients that do not vary by age, but use baseline mortality rates that do. 

Because mortality rates for younger populations are much lower than mortality rates for older 

populations, most but not all, of the avoided deaths tend to be in older populations. Table 5C-1 

summarizes the number of avoided deaths (by age range) attributable to ozone for each 

alternative standard for the 2025 scenario. Table 5C-2 summarizes the modeled number of life 

years gained (for each age range) by reducing ozone for each alternative standard evaluated for 

the 2025 scenario. We then calculated the average number of life years gained per avoided 

premature mortality. 
5C-4

Table 5C-1. Potential Reduction in Premature Mortality by Age Range from Attaining 

Alternate Ozone Standards (2025 scenario) a, b

Age Range b Standard Alternative

70 ppb 65 ppb 60 ppb

0-4 0.65 1.9 3.6

5-9 0.14 0.44 0.81

10-14 0.15 0.46 0.85

15-19 0.21 0.65 1.2

20-24 0.3 0.9 1.7

25–29 0.61 1.8 3.4

30–34 0.63 1.9 3.5

35–44 3.4 11 19

45–54 8.6 27 49

55–64 22 67 120

65–74 45 140 260

75–84 60 190 340

85–99 59 190 340

Total ozone-attributable mortality 200 630 1,100

a Estimates rounded to two significant figures.

b Effects calculated using the core Smith et al. (2009) effect estimate for the 2025 scenario

Table 5C-2. Potential Years of Life Gained by Age Range from Attaining Alternate 

Ozone Standards (2025 Scenario) a,b

Age Range b Standard Alternative

70 ppb 65 ppb 60 ppb

0-4 51 150 280 

5-9 10 30 56 

10-14 10 30 55 

15-19 13 39 71 

20-24 16 49 90 

25–29 30 91 170 

30–34 28 86 160 

35–44 120 380 690 

45–54 230 720 1,300 

55–64 410 1,300 2,300 

65–74 550 1,700 3,100 

75–84 390 1,200 2,300 

85–99 130 430 790 

Total life years gained 2,000 6,200 11,000 

Average life years gained per individual 10.0 9.93 9.92 

a Estimates rounded to two significant figures (except for average life years gained – presented to three significant 

figures to allow differences across values to be evident.

b Effects calculated using the core Smith et al. (2009) effect estimate for the 2025 scenario

By comparing the projected age distribution of the avoided premature deaths with the age 

distribution of life years gained, we observed that about half of the deaths occur in populations 

age 75–99 (see Table 5C-1), but half of the life years would occur in populations younger than 
5C-5

65 (see Table 5C-2). This is because the younger populations have the potential to lose more life 

years per death than older populations based on changes in ozone exposure for the 2025 scenario. 

We estimate that the average individual who would otherwise have died prematurely from ozone

exposure would gain 10 additional years of life. However, this approach does not account for 

whether or not people who are older are more likely to be susceptible to the health effects of air 

pollution or whether that susceptibility was in and of itself caused by air pollution exposure (for 

a more complete discussion of this issue, see Kunzli et al., 2001). 

Percent of Ozone-related Mortality Reduced

To estimate the percentage reduction in all-cause mortality attributed to reduced ozone

exposure for the 2025 scenario as a result of the illustrative emissions reduction strategies, we 

use Mi from the equation above, dividing the number of excess deaths estimated for each 

alternative standard by the total number of deaths in each county. Table 5C-3 shows the 

reduction in all-cause mortality attributed to reducing ozone exposure to the proposed primary 

standards for the 2025 scenario.

Table 5C-3. Estimated Percent Reduction in All-Cause Mortality Attributed to the 

Proposed Primary Ozone Standards (2025 Scenario) a

Age Range b

Standard Alternative

70 ppb 65 ppb 60 ppb

0-4 0.0183% 0.0547% 0.101%

5-9 0.0176% 0.0538% 0.100%

10-14 0.0178% 0.0542% 0.100%

15-19 0.0180% 0.0546% 0.101%

20-24 0.0177% 0.0536% 0.099%

25–29 0.0177% 0.0535% 0.099%

30–34 0.0176% 0.0535% 0.098%

35–44 0.0174% 0.0535% 0.099%

45–54 0.0174% 0.0536% 0.099%

55–64 0.0179% 0.0554% 0.101%

65–74 0.0179% 0.0557% 0.102%

75–84 0.0176% 0.0549% 0.101%

85–99 0.0164% 0.0522% 0.096%

a

In order to illustrate the slight variations in percent reductions across age ranges (for a given alternative standard 

level) we have presented results to three significant figures (rather than two as is typically done for other estimates 

in this RIA). 
5C-6

Results presented in Table 5C-3 highlight that when reductions in ozone-attributable 

mortality (in going from baseline to an alternative standard level) are considered as a percentage 

of total all-cause baseline mortality, the estimates are relatively small and are fairly constant 

across age ranges. However, it is important to point out that estimates of total ozone-attributable 

mortality represent a substantially larger fraction of all-cause baseline mortality.

5C.2 Evaluation of Mortality Impacts Relative to the Baseline Pollutant Concentrations 

(used in generating those mortality estimates) for both Short-Term Ozone 

Exposure-Related Mortality and Long-Term PM2.5 Exposure-Related Mortality

Analysis of baseline ozone levels used in modeling short-term ozone exposure-related mortality 

Our review of the current body of scientific literature indicates that a log-linear nothreshold model provides the best estimate of ozone-related short-term mortality (see section 

2.5.4.4, in the O3 ISA, U.S. EPA, 2013a), which was reviewed by the EPA’s Clean Air Scientific 

Advisory Committee. Consistent with this finding, we estimate benefits associated with the full 

range of ozone exposure. Our confidence in the estimated number of premature deaths avoided 

(but not in the existence of a causal relationship between ozone and premature mortality) 

diminishes as we estimate these impacts at successively lower concentrations. However, there 

are uncertainties inherent in identifying any particular point at which our confidence in reported 

associations becomes appreciably less, and the scientific evidence provides no clear dividing 

line. The O3 ISA noted that the studies indicate reduced certainty in specifying the shape of the 

C-R function specifically for short-term ozone-attributable respiratory morbidity and mortality, 

in the range generally below 20 ppb (for these reasons, the ≤ 20 ppb range discussed in the O3

ISA should be viewed as a more generalized range to be considered qualitatively or semiquantitatively, along with many other factors, when interpreting the risk estimates rather than as 

a fixed, bright-line).82

 

82 While clinical studies have suggested the presence of a threshold for respiratory effects, these should not be used 

to support specification of population-level thresholds for use in the epidemiological-based risk assessment 

focusing on short-term exposure-related endpoints. The clinical studies focus on relatively small and clearly 

defined populations of healthy adults, which are not representative of the broader residential populations typically 

associated with epidemiological studies, including older individuals and individuals with existing health 

conditions that place them at greater risk for ozone-related effects. Therefore, the clinical studies are unlikely to 

have the power to capture population thresholds in a broader and more diverse urban residential population, 

should those thresholds exist.
5C-7

Figures 5C-1 and 5C-2 compare the distribution of short-term ozone exposure-related 

mortality to the underlying distribution of 8hr max baseline ozone levels used in generating those 

estimates (these two plots present probability and cumulative probability plots, respectively). 

Both figures are based on the core estimate of short-term mortality generated using effect 

estimates obtained from Smith et al., 2009.83 In addition, each figure includes separate plots for 

the three alternative standard levels being analyzed (with all being based on the 2025 scenario). 

If we look at Figure 5C-1, we see that approximately 45% of the mortality benefit estimated for 

the 65 ppb alternative standard is associated with days having baseline ozone level of between 40 

and 45 ppb.

84

Figure 5C-1. Premature Ozone-related Deaths Avoided for the Alternative Standards 

(2025 scenario) According to the Baseline Ozone Concentrations

 

83 The set of 12km-level mortality estimates (and associated 8hr max baseline values) generated using BenMAP 

forms the basis for the plots. 

84 As noted later in this section, this baseline range is actually for the mean across the ozone season of 8hr max 

values within a given grid cell, so the actual distribution of baseline 8hr max values associated with this segment of 

benefits reductions is likely wider than the 40-45 ppb range.

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

0 to 15

Percentage of 

15 to 2020 to 2525 to 3030 to 3535 to 4040 to 4545 to 5050 to 5555 to 6060 to 6565 to 7070 to 7575 to 80

reduction in short-term ozone 

related mortality 

Ozone season mean daily 8hr max baseline (ppb)

75-70 75-65 75-60
5C-8

Figure 5C-2. Cumulative Probability Plot of Premature Ozone-related Deaths Avoided for 

the Alternative Standards (2025 scenario) According to the Baseline Ozone

Concentrations

When interpreting these results, it is important to understand that the avoided ozonerelated deaths are estimated to occur from ozone reductions in the baseline air quality simulation, 

which assumes that 75 ppb is already met. When simulating attainment with proposed and 

alternative standards, we adjust the design value at each monitor exceeding the standard 

alternative to equal that standard and use an air quality interpolation technique to simulate the 

change in ozone concentrations surrounding that monitor. This technique tends to simulate the 

greatest air quality changes nearest the monitor. We estimate benefits using modeled air quality 

data with 12 km grid cells, which is important because the grid cells are often substantially 

smaller than counties and ozone concentrations vary spatially within a county. Therefore, there 

may be a small number of grid cells with concentrations slightly greater than 75 ppb in the 

gridded baseline even though all monitors could meet an annual standard of 75 ppb. In addition, 

some grid cells in a county can be below the level of a standard even though the highest monitor 

value is above that standard. Thus, emissions reductions can lead to benefits in grid cells that are 

below a standard even within a county with a monitor that exceeds that standard. Furthermore, 

our approach to simulating attainment can lead to benefits in counties that are below the 

0

0.2

0.4

0.6

0.8

1

1.2

0 to 15

15 to 2020 to 2525 to 3030 to 3535 to 4040 to 4545 to 5050 to 5555 to 6060 to 6565 to 7070 to 7575 to 80

Cumulative percentage of reduction in short- term ozone related mortality

Ozone season mean daily 8hr max baseline (ppb)

75-70 75-65 75-60
5C-9

alternative standard being evaluated. Emissions reduction strategies designed to reduce ozone

concentrations at a given monitor will frequently improve air quality in neighboring counties. In 

order to make a direct comparison between the benefits and costs of these emissions reduction 

strategies, it is appropriate to include all the benefits occurring as a result of the emissions

reduction strategies applied, regardless of where they occur. Therefore, it is not appropriate to 

estimate the fraction of benefits that occur only in counties that exceed the alternative standards 

because it would omit benefits attributable to emissions reductions in exceeding counties. 

One final caveat in interpreting the information presented in these figures is that in 

modeling this mortality endpoint, rather than using a true distribution of daily 8hr max ozone 

levels for each grid cell, due to resource limitations, we used a single mean value for the ozone 

season within each grid cell. While this will generate the same total ozone benefit estimate for 

each grid cell compared with application of a full distribution of daily 8hr max values, use of a 

mean daily value means that an assessment such as this one that considers both the spatial and 

temporal association between mortality benefit estimates and ozone levels, will be limited 

somewhat in its treatment of the temporal dimension.

Consideration for the plots presented in Figures 5C-1 and 5C-2 results in a number of 

observations. The vast majority of reductions in short-term exposure-related mortality for ozone 

occur in grid cells with mean 8hr max baseline levels (across the ozone season) between 35 and 

55ppb. Importantly, virtually all of the mortality reductions are associated with ozone levels 

above the <20ppb range identified within the O3 ISA as being associated with less confidence in 

specifying the nature of the C-R function for ozone mortality (O3 ISA, section 2.5.4.4).85 We also 

note that as we compare patterns across the three alternative standard levels, we see that, as 

expected, the upper end of the distribution is being shifted downwards as increasingly lower 

standard levels are analyzed (see Figure 5C-2). 

 

85 As noted earlier, care needs to be taken in interpreting these mortality vs. ozone air level distributions in the 

context of the range of reduced confidence (<20ppb) identified in the O3 ISA. The region of reduced confidence 

identified by the ISA reflects the composite monitor daily time series values (including 8hr max values) used in 

short-term mortality studies, while the ozone levels summarized in Figures 5C-1 and 5C-2 are the mean (across the 

ozone season) of daily 8hr max values within each grid cell. The use of these mean values, while not impacting the 

total mortality reductions estimated, could significantly reduce variability in the spread of values presented in these 

two figures.
5C-10

Concentration Benchmark Analysis for PM2.5 Benefit-per-ton Estimates

In general, we are more confident in the magnitude of the risks we estimate from simulated 

PM2.5 concentrations that coincide with the bulk of the observed PM concentrations in the 

epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in 

the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the observed 

data in these studies. Concentration benchmark analyses (e.g., lowest measured level [LML], one 

standard deviation below the mean of the air quality data in the study, etc.) allow readers to 

determine the portion of population exposed to annual mean PM2.5 levels at or above different 

concentrations, which provides some insight into the level of uncertainty in the estimated PM2.5 

mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML and 

one standard deviation below the mean) that have been incorporated into recent RIAs and EPA’s 

Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are uncertainties inherent in 

identifying any particular point at which our confidence in reported associations becomes 

appreciably less, and the scientific evidence provides no clear dividing line. However, the EPA 

does not view these concentration benchmarks as a concentration threshold below which we 

would not quantify health co-benefits of air quality improvements.86 Rather, the co-benefits 

estimates reported in this RIA are the best estimates because they reflect the full range of air 

quality concentrations associated with the emissions reduction strategies. The PM ISA concluded 

that the scientific evidence collectively is sufficient to conclude that the relationship between 

long-term PM2.5 exposures and mortality is causal and that overall the studies support the use of a 

no-threshold log-linear model to estimate PM-related long-term mortality (U.S. EPA, 2009b). 

For this analysis, policy-specific air quality data is not available, and the compliance 

strategies are illustrative of what states may choose to do. For this RIA, we are unable to 

estimate the percentage of premature mortality associated with the emissions reductions at each 

PM2.5 concentration, as we have done for previous rules with air quality modeling (e.g., U.S. 

EPA, 2011b, 2012a). However, we believe that it is still important to characterize the distribution 

of exposure to baseline concentrations. As a surrogate measure of mortality impacts, we provide 

 

86 For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-mortality 

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-related Mortality (U.S. EPA, 2010b).
5C-11

the percentage of the population exposed at each PM2.5 concentration in the baseline of the 

source apportionment modeling used to calculate the benefit-per-ton estimates for this sector 

using 12 km grid cells across the contiguous U.S.87 It is important to note that baseline exposure 

is only one parameter in the health impact function, along with baseline incidence rates, 

population and change in air quality. In other words, the percentage of the population exposed to 

air pollution below the LML is not the same as the percentage of the population experiencing 

health impacts as a result of a specific emissions reduction policy. The most important aspect, 

which we are unable to quantify without rule-specific air quality modeling, is the shift in 

exposure anticipated by implementing the proposed standards. Therefore, caution is warranted 

when interpreting the LML assessment in this RIA because these results are not consistent with 

results from RIAs that had air quality modeling. 

Table 5C-4 provides the percentage of the population exposed above and below two 

concentration benchmarks (i.e., LML and one standard deviation below the mean) in the 

modeled baseline for the sector modeling. Figure 5C-3 shows a bar chart of the percentage of the 

population exposed to various air quality levels in the baseline, and Figure 5C-4 shows a 

cumulative distribution function of the same data. Both figures identify the LML for each of the 

major cohort studies.

 

87 As noted above, the modeling used to generate the benefit-per-ton estimates does not reflect emissions reductions 

anticipated from MATS rule. Therefore, the baseline PM2.5 concentrations in the LML assessment are higher than 

would be expected if MATS was reflected.
5C-12

Table 5C-4. Population Exposure in the Baseline Sector Modeling (used to generate the 

benefit-per-ton estimates) Above and Below Various Concentrations 

Benchmarks in the Underlying Epidemiology Studies a

Epidemiology Study

Below 1 Standard 

Deviation.

Below AQ Mean

At or Above 1 

Standard Deviation 

Below AQ Mean

Below LML At or Above LML

Krewski et al. (2009) 89% 11% 7% 93%

Lepeule et al. (2012) N/A N/A 23% 67%

a One standard deviation below the mean is equivalent to the middle of the range between the 10th and 25th

percentile. For Krewski, the LML is 5.8 μg/m3and one standard deviation below the mean is 11.0 μg/m3. For 

Lepeule et al., the LML is 8 μg/m3and we do not have the data for one standard deviation below the mean. It is 

important to emphasize that although we have lower levels of confidence in levels below the LML for each study, 

the scientific evidence does not support the existence of a level below which health effects from exposure to PM2.5

do not occur.

Figure 5C-3. Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure in 

the Baseline Sector Modeling (used to generate the benefit-per-ton estimates)*

* This graph shows the population exposure in the modeling baseline used to generate the benefit-per-ton estimates. 

Similar graphs for analyses with air quality modeling show premature mortality impacts at each PM2.5 concentration. 

Therefore, caution is warranted when interpreting this graph because it is not consistent with similar graphs from 

RIAs that had air quality modeling (e.g., MATS).

Among the populations exposed to PM2.5 in the baseline:

93% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study

67% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

0%

5%

10%

15%

20%

25%

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 Percentage of Population Exposed

Baseline Annual Mean PM2.5 Level (μg/m3)

Percentage of Population Exposed by Baseline Air Quality Level

LML of Lepeule et al. 

(2012) study

LML of Krewski et 

al. (2009) study
5C-13

Figure 5C-4. Cumulative Distribution of Adult Population (age 30+) by Annual Mean 

PM2.5 Exposure in the Baseline Sector Modeling (used to generate the benefitper-ton estimates)*

* This graph shows the population exposure in the modeling baseline used to generate the benefit-per-ton estimates. 

Similar graphs for analyses with air quality modeling show premature mortality impacts at each PM2.5 concentration. 

Therefore, caution is warranted when interpreting this graph because it is not consistent with similar graphs from 

RIAs that had air quality modeling (e.g., MATS).

Among the populations exposed to PM2.5 in the baseline:

93% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study

67% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Cumulative percentage of population exposed

Baseline Annual Mean PM2.5 Level (μg/m3)

Cumulative Percentage of Population Exposed by Baseline Air Quality 

Level

LML of Krewski et 

al. (2009) study

LML of Lepeule et 

al. (2012) study
5C-14

5C.3 Core Incidence and Dollar Benefits Estimates Reflecting Application of Known 

Controls for the 2025 Scenario

This section presents a subset of the core incidence and dollar benefits estimates for the 

2025 scenario reflecting only application of known controls in simulating each of the alternative 

standard levels (i.e., partial-2025 scenario estimates). The presentation of these estimates 

parallels results summarized for the 2025 and post-2025 scenario in Section 5.7 and the reader is 

referred to that section for further explanation of the tables and types of estimates included in 

those tables. However, before presenting detailed benefits summary tables for the partial-2025 

scenario, we first (in Table 5C-5) present an overview of the percentage of benefits associated 

with known controls for each of the alternative standards considered. Then, following that 

overview table, we present a set of more detailed tables including: core incidence attributable to 

reductions in ozone (Table 5C-6), core dollar benefits associated with ozone reductions (Table 

5C-7), core incidence estimates associated with reductions in PM2.5 (Table 5C-8) and core dollar 

benefits estimates associated with PM2.5 reductions (Table 5C-9). A summary of overall core 

benefits associated with application of known controls is presented in Table 5C-10.

Table 5C-5. Fraction of Total Core Benefits Associated with Partial Attainment 

(application of known controls) (2025 Scenario)

Category of Benefit

Percentage of Benefits Resulting from Application of Known 

Controls

70 ppb 65 ppb 60 ppb

Ozone Benefits 82% 59% 35%

PM2.5 Co-benefits 76% 59% 33%

Total Benefits 77% 59% 34%
5C-15

Table 5C-6. Estimated Number of Avoided Ozone-Only Health Impacts for the 

Alternative Annual Primary Ozone Standards (Incremental to the Analytical 

Baseline) for the Partial Attainment of the 2025 Scenario (known controls) a,b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. 

b All incidence estimates are based on ozone-only models unless otherwise noted.

c The sensitivity analysis for long-term exposure-related mortality included an assessment of potential thresholds, 

which was completed for the 2025 scenario (see Table 5-19). Care should be taken in applying the results of that 

sensitivity analysis to the partial 2025 scenario, although general patterns of impact across the thresholds may apply.

70 ppb 65 ppb 60 ppb

Avoided Short-Term Mortality - Core Analysis

160 370 400

(79 to 250) (180 to 550) (200 to 610)

270 620 680

(150 to 400) (330 to 900) (360 to 990)

Avoided Long-term Respiratory Mortality - Core Analysis

550 1,200 1,400

(190 to 910) (420 to 2,100) (460 to 2,300)

Avoided Short-Term Mortality - Sensitivity Analysis

130 290 320

(-36 to 300) (-80 to 660) (-88 to 730)

200 460 500

(63 to 340) (140 to 770) (160 to 850)

190 430 480

(72 to 310) (160 to 710) (180 to 780)

130 300 330

(44 to 220) (99 to 490) (110 to 550)

430 960 1,100

(200 to 650) (460 to 1,500) (510 to 1,600)

590 1,300 1,500

(360 to 830) (800 to 1,900) (880 to 2,100)

600 1,400 1,500

(410 to 790) (930 to 1,800) (1,000 to 2,000)

Avoided Long-term Respiratory Mortality - Sensitivity Analysis c

370 840 920

(110 to 640) (240 to 1,400) (260 to 1,600)

400 900 990

(140 to 660) (320 to 1,500) (350 to 1,600)

Avoided Morbidity - Core Analysis

300 680 740

(-79 to 670) (-180 to 1,500) (-200 to 1,700)

930 2,100 2,400

(87 to 2,900) (200 to 6,600) (230 to 7,500)

240,000 550,000 600,000

(-360,000 to 740,000) (-800,000 to 1,600,000) (-880,000 to 1,800,000)

760,000 1,700,000 1,900,000

(310,000 to 1,200,000) (700,000 to 2,700,000) (780,000 to 3,000,000)

270,000 600,000 660,000

(95,000 to 600,000) (210,000 to 1,300,000) (230,000 to 1,500,000)

Proposed and Alterative Standards

(95th percentile confidence intervals)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

metaanalyses

Bell et al. (2005) (all ages)

Ito et al. (2005) (all ages)

multi-city 

study

School Loss Days (age 5-17)

Jerrett et al. (2009) (age 30-99) (96 

cites) (ozone-only)

Levy et al. (2005) (all ages)

Jerrett et al. (2009) (age 30-99) (86 

cities) (ozone-only)

Hospital admissions - respiratory 

(age 65+)

Emergency department visits for 

asthma (all ages)

Asthma exacerbation (age 6-18)

Minor restricted-activity days (age 

18-65)

Smith et al. (2009) (all ages) 

copollutants model (PM10)

Schwartz (2005) (all ages)

Bell et al. (2004) (all ages)

Health Effectb

multi-city 

study

Jerrett et al. (2009) (30-99yrs) 

copollutants model (PM2.5) 

Huang et al. (2005) 

(cardiopulmonary)

multi-city 

studies
5C-16

Table 5C-7. Total Monetized Ozone-Only Benefits for the Alternative Annual Primary

Ozone Standards (Incremental to the Analytical Baseline) for the Partial

Attainment of the 2025 Scenario (using known controls) a,b

a All benefits estimates are rounded to whole numbers with a maximum of two significant digits. The monetized 

value of the ozone-related morbidity benefits are included in the estimates shown in this table for each mortality 

study (and when combined account for from 4-6% of the total benefits, depending on the total mortality estimate 

compared against. Note that asthma exacerbations accounts for <<1% of the total).

b The sensitivity analysis for long-term exposure-related mortality included an assessment of potential thresholds 

however this assessment was implemented using incidence estimates (see Table 5-19). Observations from that 

analysis (in terms of fractional impacts on incidence can be directly applied to these benefit results) (see Appendix 

5B, section 5B.1)

c A single central-tendency value is provided in each cell, since the zero-lag model used here did not require 

application of a 3% and 7% discount rates (see footnote d below) (note, however that as with all other entries in this 

study, we do include a 95th percentile confidence interval range – these are the values within parentheses).

d The range (outside of the parentheses) within each cell results from application of a 7% and 3% discount rates in 

the context of applying the 20year segmented lag (with the 7% resulting in the lower estimate and the 3% the higher 

estimate). The range presented within the parentheses reflects consideration for the 95th% confidence interval 

generated for each of these estimates and ranges from a low value (2.5th% CI for the 7% discount-based dollar 

benefit) to an upper value (97.5th% of the 3% discount-based dollar benefit).

70 ppb 65 ppb 60 ppb

Avoided Short-Term Mortality - Core Analysis

$1,700 $3,700 $4,100

($150 to $4,700) ($330 to $11,000) ($360 to $12,000)

2,800 6,300 6,900

($250 to $7,900) ($560 to $18,000) ($610 to $20,000)

Avoided Short-Term Mortality - Sensitivity Analysis

$1,300 $3,000 $3,300

(-$310 to $4,800) (-$710 to $11,000) (-$780 to $12,000)

$2,100 $4,700 $5,100

($160 to $6,300) ($370 to $14,000) ($410 to $16,000)

$2,000 $4,400 $4,900

($160 to $5,800) ($370 to $13,000) ($400 to $14,000)

1,300 3,000 3,300

($110 to $4,000) ($240 to $9,100) ($270 to $10,000)

4,400 9,800 11,000

($380 to $12,000) ($860 to $28,000) ($950 to $31,000)

$6,000 $14,000 $15,000

($550 to $17,000) ($1,200 to $38,000) ($1,400 to $42,000)

$6,100 $14,000 $15,000

($570 to $17,000) ($1,300 to $38,000) ($1,400 to $41,000)

Avoided Long-term Respiratory Mortality - Sensitivity Analysis

$5,600 $13,000 $14,000

($460 to $17,000) ($1,000 to $38,000) ($1,100 to $41,000)

$4,600 to $5,100 $10,000 to $11,000 $11,000 to $13,000

($370 to $15,000) ($840 to $34,000) ($920 to $37,000)

Proposed and Alterative Standards

Health Effectb

(95th percentile confidence intervals)

Smith et al. (2009) (all ages) 

copollutants model (PM10)

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) 20 yr 

segmented lag d

multi-city 

study

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) no lag c

Schwartz (2005) (all ages)

Bell et al. (2004) (all ages)

Bell et al. (2005) (all ages)

Huang et al. (2005) 

(cardiopulmonary)

multi-city 

studies

metaanalyses

Levy et al. (2005) (all ages)

Ito et al. (2005) (all ages)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)
5C-17

Table 5C-8. Estimated Number of Avoided PM2.5-Related Health Impacts for the 

Alternative Annual Primary Ozone Standards (Incremental to the Analytical 

Baseline) for the Partial Attainment of the 2025 Scenario (using known controls)

a,b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. Because these 

estimates were generated using benefit-per-ton estimates, confidence intervals are not available. In general, the 95th

percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for 

mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

60ppb 65ppb 70ppb

Avoided PM2.5-related Mortality

Krewski et al. (2009) (adult mortality age 30+) 390 860 880

Lepeule et al. (2012) (adult mortality age 25+) 870 1,900 2,000

Woodruff et al. (1997) (infant mortality) 1 1 1

Avoided PM2.5-related Morbidity

Non-fatal heart attacks

 Peters et al. (2001) (age >18) 450 1,000 1,000

 Pooled estimate of 4 studies (age >18) 49 110 110

Hospital admissions—respiratory (all ages) 120 260 260

Hospital admissions—cardiovascular (age > 18) 140 310 320

Emergency department visits for asthma (all ages) 210 470 480

Acute bronchitis (ages 8–12) 600 1,300 1,400

Lower respiratory symptoms (ages 7–14) 7,700 17,000 17,000

Upper respiratory symptoms (asthmatics ages 9–11) 11,000 24,000 25,000

Asthma exacerbation (asthmatics ages 6–18) 11,000 25,000 26,000

Lost work days (ages 18–65) 49,000 110,000 110,000

Minor restricted-activity days (ages 18–65) 290,000 640,000 660,000

Health Effectb

Proposed and Alterative Standards
5C-18

Table 5C-9. Monetized PM2.5-Related Health Co-Benefits for the Alternative Annual 

Primary Ozone Standards (Incremental to Analytical Baseline) for the Partial

Attainment of the 2025 Scenario (using known controls) a,b,c 

Monetized Benefits Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

3% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $3,600 $8,000 $8,300

Lepeule et al. (2012) (adult mortality age 25+) $8,200 $18,000 $19,000

7% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $3,300 $7,200 $7,400

Lepeule et al. (2012) (adult mortality age 25+) $7,400 $16,000 $17,000

a All estimates are rounded to two significant digits. Because these estimates were generated using benefit-per-ton 

estimates, confidence intervals are not available. In general, the 95th percentile confidence interval for monetized 

PM2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et 

al. (2009) and Lepeule et al. (2012). Estimates do not include unquantified health benefits noted in Table 5-2 or 

Section 5.6.5 or welfare co-benefits noted in Chapter 6. 

b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.

Table 5C-10. Combined Estimate of Monetized Ozone and PM2.5 Benefits for the 

Alternative Annual Primary Ozone Standards for the Partial Attainment of the 

2025 Scenario (using known controls) (billions of 2011$) a,b

Discount 

Rate 70 ppb 65 ppb 60 ppb

Total Benefits 3% $5.3 to $11 +B $12 to $24 +B $12 to $26 +B

7% $5.0 to $10 +B $11 to $23 +B $12 to $24 +B

Ozone-only Benefits (range reflects 

Smith et al., 2009 and Zanobetti and 

Schwartz, 2008)

b $1.7 to $2.8 +B $3.7 to $6.3 +B $4.1 to $6.9 +B

PM2.5 Co-benefits (range reflects 

Krewski et al., 2009 and Lepeule et 

al., 2012)

3% $3.6 to $8.2 +B $8.0 to $18 +B $8.3 to $19 +B

7% $3.3 to $7.4 +B $7.2 to $16 +B $7.4 to $17 +B

a Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% of total 

monetized benefits in this analysis. Mortality risk valuation for PM2.5 assumes discounting over the SABrecommended 20-year segmented lag structure. Not all possible benefits are quantified and monetized in this 

analysis. B is the sum of all unquantified health and welfare co-benefits. Data limitations prevented us from 

quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we 

were able to quantify. These estimates reflect the economic value of avoided morbidities and premature deaths using 

risk coefficients from the studies noted.

b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as 

ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category. 
5C-19

5C.4 References

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Admissions in 202 US Counties, 1999–2005.” American Journal of Epidemiology 168(11): 1301-1310.

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5D-1

APPENDIX 5D: DISCUSSION OF EFFECT ESTIMATES REFLECTED IN THE 

DEVELOPMENT OF DOLLAR-PER-TON VALUES USED IN MODELING PM2.5 

COBENEFITS

Overview

This section describes how we selected effect estimates to estimate the benefits of 

reducing PM2.5 (see Section 5.4.4). This section mirrors that covering effect estimate selection 

for ozone presented in Section 5.6.3 in terms of organization (in some cases, we have repeated 

introductory/setup content here to facilitate review by the reader).

The first step in selecting effect coefficients is to identify the health endpoints to be 

quantified. We base our selection of health endpoints on consistency with the EPA’s Integrated 

Science Assessments (which replace previous “Criteria Documents”), with input and advice from 

the Health Effects Subcommittee (HES), a scientific review panel specifically established to 

provide advice on the use of the scientific literature in developing benefits analyses for the 

EPA’s Report to Congress on The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. 

EPA, 2011a). In addition, we have included more recent epidemiology studies from the PM ISA 

(U.S. EPA, 2009b) and the PM Provisional Assessment (U.S. EPA, 2012b).88 In general, we 

follow a weight of evidence approach, based on the biological plausibility of effects, availability 

of concentration-response functions from well conducted peer-reviewed epidemiological studies, 

cohesiveness of results across studies, and a focus on endpoints reflecting public health impacts 

(like hospital admissions) rather than physiological responses (such as changes in clinical 

measures like Forced Expiratory Volume [FEV1]).

There are several types of data that can support the determination of types and magnitude 

of health effects associated with air pollution exposures. These sources of data include 

toxicological studies (including animal and cellular studies), human clinical trials, and 

observational epidemiology studies. All of these data sources provide important contributions to 

the weight of evidence surrounding a particular health impact. However, only epidemiology 

 

88 The peer-reviewed studies in the Provisional Assessment have not yet undergone external review by the Science 

Advisory Board. 
5D-2

studies provide direct concentration-response relationships that can be used to evaluate 

population-level impacts of reductions in ambient pollution levels in a health impact assessment.

For the data-derived estimates, we relied on the published scientific literature to ascertain 

the relationship between PM2.5 and adverse human health effects. We evaluated epidemiological 

studies using the selection criteria summarized in Table 5-6 (see section 5.6.3). These criteria 

include consideration of whether the study was peer-reviewed, the match between the pollutant 

studied and the pollutant of interest, the study design and location, and characteristics of the 

study population, among other considerations. In general, the use of concentration-response 

functions from more than a single study can provide a more representative distribution of the 

effect estimate. However, there are often differences between studies examining the same 

endpoint, making it difficult to pool the results in a consistent manner. For example, studies may 

examine different pollutants or different age groups. For this reason, we consider very carefully 

the set of studies available examining each endpoint and select a consistent subset that provides a 

good balance of population coverage and match with the pollutant of interest. In many cases, 

either because of a lack of multiple studies, consistency problems, or clear superiority in the 

quality or comprehensiveness of one study over others, a single published study is selected as the 

basis of the effect estimate.

When several effect estimates for a pollutant and a given health endpoint have been 

selected, they are quantitatively combined or pooled to derive a more robust estimate of the 

relationship. The BenMAP Manual Technical Appendices for an earlier version of the program 

provide details of the procedures used to combine multiple impact functions (Abt Associates, 

2012). In general, we used fixed or random effects models to pool estimates from different 

single-city studies of the same endpoint. Fixed effect pooling simply weights each study’s 

estimate by the inverse variance, giving more weight to studies with greater statistical power 

(lower variance). Random effects pooling accounts for both within-study variance and betweenstudy variability, due, for example, to differences in population susceptibility. We used the fixed 

effect model as our null hypothesis and then determined whether the data suggest that we should 
5D-3

reject this null hypothesis, in which case we would use the random effects model.89 Pooled 

impact functions are used to estimate hospital admissions and asthma exacerbations. When 

combining evidence across multi-city studies (e.g., cardiovascular hospital admission studies), 

we use equal weights pooling. The effect estimates drawn from each multi-city study are 

themselves pooled across a large number of urban areas. For this reason, we elected to give each 

study an equal weight rather than weighting by the inverse of the variance reported in each study. 

For more details on methods used to pool incidence estimates, see the BenMAP Manual 

Appendices (Abt Associates, 2012).

Effect estimates selected for a given health endpoint were applied consistently across all 

locations nationwide. This applies to both impact functions defined by a single effect estimate 

and those defined by a pooling of multiple effect estimates. Although the effect estimate may, in 

fact, vary from one location to another (e.g., because of differences in population susceptibilities 

or differences in the composition of PM), location-specific effect estimates are generally not 

available.

The specific studies from which effect estimates were quantified for the core analysis for 

PM2.5 (generated using the dollar-per-ton approach – see Section 5.4.4) are presented in Table 5-

8 and repeated below for ease of access in Table 5D-1. We highlight in red those studies that 

have been added since the benefits analysis conducted for the ozone reconsideration (U.S. EPA, 

2010d) or the Ozone NAAQS RIA (U.S. EPA, 2008). In all cases where effect estimates are 

drawn directly from epidemiological studies, standard errors are used as a partial representation 

of the uncertainty in the size of the effect estimate. 

 

89 EPA recently changed the algorithm BenMAP uses to calculate study variance, which is used in the pooling 

process. Prior versions of the model calculated population variance, while the version used here calculated sample 

variance. This change did not affect the selection of random or fixed effects for the pooled incidence estimates 

between the proposal and final PM RIA.
5D-4

Table 5D-1. Health Endpoints and Epidemiological Studies Used to Quantify PM2.5-

related Health Impacts in the Core Analysis a

Endpoint Study

Study 

Population

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE, respectively)

Premature Mortality

Premature mortality—

cohort study, all-cause

Krewski et al. (2009) > 29 years

> 24 years

RR = 1.06 (1.04–1.06) per 10 μg/m3

Lepeule et al. (2012) RR = 1.14 (1.07–1.22) per 10 μg/m3

Premature mortality—

all-cause

Woodruff et al. (1997) Infant (< 1 

year)

OR = 1.04 (1.02–1.07) per 10 μg/m3

Chronic Illness

Nonfatal heart attacks Peters et al. (2001) Adults (> 18 

years)

OR = 1.62 (1.13–2.34) per 20 μg/m3

Pooled estimate:

Pope et al. (2006) β = 0.00481 (0.00199)

Sullivan et al. (2005) β = 0.00198 (0.00224)

Zanobetti et al. (2009) β = 0.00225 (0.000591)

Zanobetti and Schwartz (2006) β = 0.0053 (0.00221)

Hospital Admissions 

Respiratory Zanobetti et al. (2009)—ICD 

460-519 (All respiratory)

> 64 years β=0.00207 (0.00446)

Kloog et al. (2012)—ICD 460-

519 (All Respiratory

β=0.0007 (0.000961)

Moolgavkar (2000)—ICD 490–

496 (Chronic lung disease)

18–64 years 1.02 (1.01–1.03) per 36 μg/m3

Babin et al. (2007)—ICD 493 

(asthma)

< 19 years β=0.002 (0.004337)

Sheppard (2003)—ICD 493 

(asthma) < 18 RR = 1.04 (1.01–1.06) per 11.8 μg/m3

Cardiovascular Pooled estimate:

Zanobetti et al. (2009)—ICD 

390-459 (all cardiovascular)

> 64 years

β=0.00189 (0.000283)

Peng et al. (2009)—ICD 426-

427; 428; 430-438; 410-414; 

429; 440-449 (Cardio-, cerebroand peripheral vascular disease)

β=0.00068

(0.000214)

Peng et al. (2008)—ICD 426-

427; 428; 430-438; 410-414; 

429; 440-449 (Cardio-, cerebroand peripheral vascular disease)

β=0.00071

(0.00013)

Bell et al. (2008)—ICD 426-427; 

428; 430-438; 410-414; 429; 

440-449 (Cardio-, cerebro- and 

peripheral vascular disease)

β=0.0008

(0.000107)

Moolgavkar (2000)—ICD 390–

429 (all cardiovascular)

20–64 years RR=1.04 (t statistic: 4.1) per 10 μg/m3

Asthma-related 

emergency department 

visits

Pooled estimate:

Mar et al. (2010)

All ages RR = 1.04 (1.01–1.07) per 7 μg/m3

Slaughter et al. (2005) RR = 1.03 (0.98–1.09) per 10 μg/m3

Glad et al. (2012) β=0.00392 (0.002843)

Other Health Endpoints

Acute bronchitis Dockery et al. (1996) 8–12 years OR = 1.50 (0.91–2.47) per 14.9 μg/m3
5D-5

Asthma exacerbations Pooled estimate:

Ostro et al. (2001) (cough, 

wheeze and shortness of 

breath) b

6–18 years b

OR = 1.03 (0.98–1.07) 

OR = 1.06 (1.01–1.11) 

OR = 1.08 (1.00–1.17) per 30 μg/m3

Mar et al. (2004) (cough, 

shortness of breath)

RR = 1.21 (1–1.47) per 

RR = 1.13 (0.86–1.48) per 10 μg/m3

Work loss days Ostro (1987) 18–65 years β=0.0046 (0.00036)

Acute respiratory 

symptoms (MRAD)

Ostro and Rothschild (1989) 

(Minor restricted activity days) 18–65 years β=0.00220 (0.000658)

Upper respiratory 

symptoms

Pope et al. (1991) Asthmatics, 

9–11 years 1.003 (1–1.006) per 10 μg/m3

Lower respiratory 

symptoms

Schwartz and Neas (2000) 7–14 years OR = 1.33 (1.11–1.58) per 15 μg/m3

a Studies highlighted in red represent updates incorporated since the Ozone NAAQS RIA (U.S. EPA, 2008). These 

updates were introduced in the PM NAAQS RIA (U.S. EPA, 2012).

b The original study populations were 8 to 13 for the Ostro et al. (2001) study and 7 to 12 for the Mar et al. (2004) 

study. Based on advice from the SAB-HES, we extended the applied population to 6-18, reflecting the common 

biological basis for the effect in children in the broader age group. See: U.S. EPA-SAB (2004) and NRC (2002).

5D.1 PM2.5 Premature Mortality Effect Coefficients

Core Mortality Effect Coefficients for Adults. A substantial body of published scientific 

literature documents the association between elevated PM2.5 concentrations and increased 

premature mortality (U.S. EPA, 2009b). This body of literature reflects thousands of 

epidemiology, toxicology, and clinical studies. The PM ISA completed as part of the most recent 

review of the PM standards, which was twice reviewed by the SAB-CASAC (U.S. EPA-SAB, 

2009, 2009b), concluded that there is a causal relationship between mortality and both long-term 

and short-term exposure to PM2.5 based on the entire body of scientific evidence (U.S. EPA, 

2009b). The size of the mortality effect estimates from epidemiological studies, the serious 

nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality 

risk reduction the most significant health endpoint quantified in this analysis. 

Researchers have found statistically significant associations between PM2.5 and 

premature mortality using different types of study designs. Time-series methods have been used 

to relate short-term (often day-to-day) changes in PM2.5 concentrations and changes in daily 

mortality rates up to several days after a period of elevated PM2.5 concentrations. Cohort 

methods have been used to examine the potential relationship between community-level PM2.5

exposures over multiple years (i.e., long-term exposures) and community-level annual mortality 

rates that have been adjusted for individual level risk factors. When choosing between using 

short-term studies or cohort studies for estimating mortality benefits, cohort analyses are thought 
5D-6

to capture more of the public health impact of exposure to air pollution over time because they 

account for the effects of long-term exposures as well as some fraction of short-term exposures 

(Kunzli et al., 2001; NRC, 2002). The NRC stated that “it is essential to use the cohort studies in 

benefits analysis to capture all important effects from air pollution exposure” (NRC, 2002, p. 

108). The NRC further notes that “the overall effect estimates may be a combination of effects 

from long-term exposure plus some fraction from short-term exposure. The amount of overlap is 

unknown” (NRC, 2002, p. 108-9). To avoid double counting, we focus on applying the risk 

coefficients from the long-term cohort studies in estimating the mortality impacts of reductions 

in PM2.5.

Over the last two decades, several studies using “prospective cohort” designs have been 

published that are consistent with the earlier body of literature. Two prospective cohort studies, 

often referred to as the Harvard “Six Cities Study” (Dockery et al., 1993; Laden et al., 2006; 

Lepeule et al., 2012) and the “American Cancer Society” or “ACS study” (Pope et al., 1995; 

Pope et al., 2002; Pope et al., 2004; Krewski et al., 2009), provide the most extensive analyses of 

ambient PM2.5 concentrations and mortality. These studies have found consistent relationships 

between fine particle indicators and premature mortality across multiple locations in the United 

States. The credibility of these two studies is further enhanced by the fact that the initial 

published studies (Pope et al., 1995; Dockery et al., 1993) were subject to extensive 

reexamination and reanalysis by an independent team of scientific experts commissioned by the 

Health Effects Institute (HEI) and by a Special Panel of the HEI Health Review Committee 

(Krewski et al., 2000). Publication of studies confirming and extending the findings of the 1993 

Six Cities Study and the 1995 ACS study using more recent air quality and a longer follow-up 

period for the ACS cohort provides additional validation of the findings of these original studies 

(Pope et al., 2002, 2004; Laden et al., 2006; Krewski et al., 2009; Lepeule et al., 2012). The 

SAB-HES also supported using these two cohorts for analyses of the benefits of PM reductions, 

and concluded, “the selection of these cohort studies as the underlying basis for PM mortality 

benefit estimates to be a good choice. These are widely cited, well studied and extensively 

reviewed data sets” (U.S. EPA-SAB, 2010a). As both the ACS and Six Cities studies have 

inherent strengths and weaknesses, we present benefits estimates using relative risk estimates 

from the most recent extended reanalysis of these cohorts (Krewski et al., 2009; Lepeule et al., 

2012). Presenting results using both ACS and Six Cities is consistent with other recent RIAs 
5D-7

(e.g., U.S. EPA, 2006, 2010c, 2011b, 2011c). The PM ISA concludes that the ACS and Six 

Cities cohorts provide the strongest evidence of the association between long-term PM2.5

exposure and premature mortality with support from a number of additional cohort studies 

(described below).

The extended analyses of the ACS cohort data (Krewski et al., 2009) provides additional 

refinements to the analysis of PM-related mortality by (a) extending the follow-up period by 

2 years to the year 2000, for a total of 18 years; (b) incorporating almost double the number of 

urban areas (c) addressing confounding by spatial autocorrelation by incorporating ecological, or 

community-level, co-variates; and (d) performing an extensive spatial analysis using land use 

regression modeling in two large urban areas. These enhancements make this analysis wellsuited for the assessment of mortality risk from long-term PM2.5 exposures for the EPA’s 

benefits analyses. 

In 2009, the SAB-HES again reviewed the choice of mortality risk coefficients for 

benefits analysis, concluding that “[t]he Krewski et al. (2009) findings, while informative, have 

not yet undergone the same degree of peer review as have the aforementioned studies. Thus, the 

SAB-HES recommends that EPA not use the Krewski et al. (2009) findings for generating the 

Primary Estimate” (U.S. EPA-SAB, 2010a). Since this time, the Krewski et al. (2009) has 

undergone additional peer review, which we believe strengthens the support for including this 

study in this RIA. For example, the PM ISA (U.S. EPA, 2009b) included this study among the 

key mortality studies. In addition, the risk assessment supporting the PM NAAQS (U.S. EPA, 

2010a) utilized risk coefficients drawn from the Krewski et al. (2009) study, the most recent 

reanalysis of the ACS cohort data. The risk assessment cited a number of advantages that 

informed the selection of the Krewski et al. (2009) study as the source of the core effect 

estimates, including the extended period of observation, the rigorous examination of model 

forms and effect estimates, the coverage for ecological variables, and the large dataset with over 

1.2 million individuals and 156 MSAs (U.S. EPA, 2010a). The CASAC also provided extensive 

peer review of the risk assessment and supported the use of effect estimates from this study (U.S. 

EPA-SAB, 2009, 2010b, c). 
5D-8

Consistent with the Quantitative Health Risk Assessment for Particulate Matter (U.S. 

EPA, 2010a) which was reviewed by the CASAC (U.S. EPA-SAB, 2009), we use the all-cause 

mortality risk estimate based on the random-effects Cox proportional hazard model that 

incorporates 44 individual and 7 ecological covariates (RR=1.06, 95% confidence intervals 1.04–

1.08 per 10μg/m3increase in PM2.5). The relative risk estimate (1.06 per 10μg/m3increase in 

PM2.5) is identical to the risk estimate drawn from the earlier Pope et al. (2002) study, though the 

confidence interval around the Krewski et al. (2009) risk estimate is tighter.

In the most recent Six Cities study, which was published after the last SAB-HES review, 

Lepeule et al. (2012) evaluated the sensitivity of previous Six-Cities results to model 

specifications, lower exposures, and averaging time using eleven additional years of cohort 

follow-up that incorporated recent lower exposures. The authors found significant associations 

between PM2.5 exposure and increased risk of all-cause, cardiovascular and lung cancer 

mortality. The authors also concluded that the concentration-response relationship was linear 

down to PM2.5 concentrations of 8 μg/m3, and that mortality rate ratios for PM2.5 fluctuated over 

time, but without clear trends, despite a substantial drop in the sulfate fraction. We use the allcause mortality risk estimate based on a Cox proportional hazard model that incorporates 3 

individual covariates. (RR=1.14, 95% confidence intervals 1.07–1.22 per 10 μg/m3increase in 

PM2.5). The relative risk estimate is slightly smaller than the risk estimate drawn from Laden et 

al. (2006), with relatively smaller confidence intervals.

Implicit in the calculation of PM2.5-related premature mortality impacts are several key 

assumptions, which are described in further detail later in this Appendix. First, we assume that 

there is a “cessation” lag in time between the reduction in PM exposure and the full reduction in 

mortality risk that affects the timing (and thus discounted monetary valuation) of the resulting 

premature deaths (see Section 5.6.4.1). Second, following conclusions of the PM ISA, we 

assume that all fine particles are equally potent in causing premature mortality (see Section 

5.7.3). Third, following conclusions of the PM ISA, we assume that the health impact function 

for fine particles is linear within the range of ambient concentrations affected by these standards 

(see Section 5.7.3).
5D-9

Alternate Mortality Effect Coefficients for Adults. In addition to the ACS and Six Cities 

cohorts, several recent cohort studies conducted in North America provide evidence for the 

relationship between long-term exposure to PM2.5 and the risk of premature death. Many of these 

additional cohort studies are described in the PM ISA (U.S. EPA, 2009b) and the Provisional 

Assessment (U.S. EPA, 2012b) (and thus not summarized here).90,91 Table 5D-2 provides the 

effect estimates from each of these cohort studies for all-cause, cardiovascular, cardiopulmonary, 

and ischemic heart disease (IHD) mortality, as well as the lowest measured air quality level 

(LML) and mean concentration in the study. 

We also draw upon the results of the 2006 expert elicitation sponsored by the EPA 

(Roman et al., 2008; IEc, 2006) to demonstrate the sensitivity of the benefits estimates to 12 

expert-defined concentration-response functions. The PM2.5 expert elicitation and the derivation 

of effect estimates from the expert elicitation results are described in detail in the 2006 PM2.5

NAAQS RIA (U.S. EPA, 2006), the elicitation summary report (IEc, 2006) and Roman et al. 

(2008), and so we summarize the key attributes of this study relative to the interpretation of the 

estimates of PM-related mortality reported here. We describe also how the epidemiological 

literature has evolved since the expert elicitation was conducted in 2005 and 2006. 

 

90 It is important to note that the newer studies in the Provisional Assessment are published in peer-reviewed 

journals and meet our study selection criteria, but they have not been assessed in the context of an Integrated 

Science Assessment nor gone through review by the SAB. In addition, only the ACS and Harvard Six Cities’

cohort studies have been recommended by the SAB as appropriate for benefits analysis of national rulemakings.

91 In this Appendix, we only describe multi-state cohort studies. There are additional cohort studies that we have not 

included in this list, including cohort studies that focus on single cities (e.g., Gan et al., 2012) and cohort studies 

focusing on methods development. In Appendix 5A, we provide additional information regarding cohort studies in 

California, which is the only state for which we identified single state cohorts. 
5D-10

Table 5D-2. Summary of Effect Estimates from Associated with Change in Long-Term 

Exposure to PM2.5 in Recent Cohort Studies in North America

Study Cohort (age)

LML 

(μg/m3)

Mean

(μg/m3)

Hazard Ratios per 10 μg/m3 Change in PM2.5

(95th percentile confidence intervals)

All Causes Cardiovascular Cardiopulmonary IHD

Pope et al. 

(2002)

ACS 

(age >30)

7.5 18.2 1.06

(1.02–1.11)

1.12

(1.08–1.15)

1.09

(1.03–1.16)

N/A

Laden et al. 

(2006)

Six Cities

(age > 25)

10 16.4 1.16

(1.07–1.26)

1.28

(1.13–1.44)

N/A N/A

Lipfert et al. 

(2006)a

Veterans

(age 39–63)

<14.1 14.3 1.15

(1.05–1.25)

N/A N/A N/A

Miller et al. 

(2007)b

WHI

(age 50–79)

3.4 13.5 N/A 1.76

(1.25–2.47)

N/A 2.21

(1.17–4.16)

Eftim et al. 

(2008)

Medicare (age > 

65)

6 13.6 1.21

(1.15–1.27)

N/A N/A N/A

Zeger et al. 

(2008)c

Medicare (age > 

65)

<9.8 13.2 1.068

(1.049–1.087)

N/A N/A N/A

Krewski et 

al. (2009)d

ACS

(age >30)

5.8 14 1.06

(1.04–1.08)

N/A 1.13

(1.10–1.16)

1.24

(1.19–1.29)

Puett et al. 

(2009)b

NHS 

(age 30–55)

5.8 13.9 1.26

(1.02–1.54)

N/A N/A 2.02

(1.07–3.78)

Crouse et al. 

(2011)d,e

Canadian 

census

1.9 8.7 1.06

(1.01–1.10)

N/A N/A N/A

Puett et al. 

(2011)f

Health 

Professionals 

(age 40–75)

<14.4 17.8 0.86

(0.70–1.00)

1.02

(0.84–1.23)

N/A N/A

Lepeule et 

al. (2012)d

Six Cities

(age > 25)

8 15.9 1.14

(1.07–1.22)

1.26

(1.14–1.40)

N/A N/A

a Low socio-economic status (SES) men only. Used traffic proximity as a surrogate of exposure. 

b Women only.

c Reflects risks in the Eastern U.S. Risks in the Central U.S. were higher, but the authors found no association in the 

Western U.S.

d Random effects Cox model with individual and ecologic covariates.

e Canadian population.

f Men with high socioeconomic status only.

The primary goal of the 2006 study was to elicit from a sample of health experts 

probabilistic distributions describing uncertainty in estimates of the reduction in mortality among 

the adult U.S. population resulting from reductions in ambient annual average PM2.5 levels. 

These distributions were obtained through a formal interview protocol using methods designed to 

elicit subjective expert judgments. These experts were selected through a peer-nomination 

process and included experts in epidemiology, toxicology, and medicine. The elicitation 

interview consisted of a protocol of carefully structured questions, both qualitative and 

quantitative, about the nature of the PM2.5-mortality relationship designed to build twelve 

individual distributions for the coefficient (or slope) of the C-R function relating changes in 

annual average PM2.5 exposures to annual, adult all-cause mortality. The elicitation also provided 

useful information regarding uncertainty characterization in the PM2.5-mortality relationship. 
5D-11

Specifically, during their interviews, the experts highlighted several uncertainties inherent within 

the epidemiology literature, such as causality, concentration thresholds, effect modification, the 

role of short- and long-term exposures, potential confounding, and exposure misclassification. In 

Appendix 5C, we evaluate each of these uncertainties in the context of this health impact 

assessment. For several of these uncertainties, such as causality, we are able to use the expertderived functions to quantify the impacts of applying different assumptions. The elicitation 

received favorable peer review in 2006 (Mansfield and Patil, 2006).

Prior to providing a quantitative estimate of the risk of premature death associated with 

long-term PM2.5 exposure, the experts answered a series of “conditioning questions.” One such 

question asked the experts to identify which epidemiological studies they found most 

informative. The “ideal study attributes”92 according to the experts included:

 Geographic representation of the entire U.S. (e.g., monitoring sites across the country)

 Collection of information on individual risk factors and residential information both at 

the beginning and throughout the follow-up period

 Large sample size that is representative of the general U.S. population

 Collection of genetic information from cohort members to identify and assess potential 

effect modifiers

 Monitoring of individual exposures (e.g., with a personal monitor)

 Collection of data on levels of several co-pollutants (not only those that are monitored for 

compliance purposes)

 Accurate characterization of outcome (i.e., cause of death)

 Follow-up for a long period of time, up to a lifetime

 Prospective study design

Although no single epidemiological study completely satisfies each of these criteria, the 

experts determined that the ACS and Six Cities’ cohort studies best satisfy a majority of these 

ideal attributes. To varying degrees the studies examining these two cohorts are geographically 

representative; have collected information on individual risk factors; include a large sample size; 

have collected data on co-pollutants in the case of the ACS study; have accurately characterized 

the health outcome; include a long (and growing) follow-up period; and, are prospective in 

 

92 These criteria are substantively similar to EPA’s study selection criteria identified in Table 5-5 of Chapter 5.
5D-12

nature. The experts also noted a series of limitations in these two cohort studies. In the case of 

the Six Cities study (Laden et al., 2006), the experts identified the “small sample size, limited 

number of cities, and concerns about representativeness of the six cities for the U.S. as a whole” 

as weaknesses. When considering the ACS study (Pope et al., 2002), the experts indicated that 

the “method of recruitment for the study, which resulted in a group with higher income, more 

education, and a greater proportion of whites than is representative of the general U.S. 

population” represented a shortcoming. Several experts also argued that because the ACS study 

relied upon “...whatever monitors were available to the study...a single monitor represent[ed] 

exposure for an entire metropolitan area...whereas [the Six Cities study] often had exposures 

assigned at the county level.” Despite these limitations, the experts considered the Pope et al. 

(2002) extended analysis of the ACS cohort and the Laden et al. (2006) extended analysis of the 

Six Cities cohort to be particularly influential in their opinions (see Exhibit 3-3 of the elicitation 

summary report [IEc, 2006]). 

Please note that the benefits estimates results presented are not the direct results from the 

studies or expert elicitation; rather, the estimates are based in part on the effect coefficients 

provided in those studies or by experts. In addition, the experts provided distributions around 

their mean PM2.5 effect estimates, which provides more information regarding the overall range 

of uncertainty, and this overall range is larger than the range of the mean effect estimates from 

each of the experts.

Since the completion of the EPA’s expert elicitation in 2006, additional epidemiology 

literature has become available, including 9 new multi-state cohort studies shown in Table 5-12.

This newer literature addresses some of the weaknesses identified in the prior literature. For 

example, in an attempt to improve its characterization of population exposure the most recent 

extended analysis of the ACS cohort Krewski et al. (2009) incorporates two case studies that 

employ more spatially resolved estimates of population exposure. 

In light of the availability of this newer literature, we have updated the presentation of 

results in the RIA. Specifically, we focus the core analysis on results derived from the two most 

recent studies of the ACS and Six Cities cohorts (Krewski et al., 2009; Lepeule et al., 2012). 

Because the other multi-state cohorts generally have limited geography and age/gender 
5D-13

representativeness, these limitations preclude us from using these studies in our core benefits 

results, and we instead present the risk coefficients from these other multi-state cohorts in Table 

5D-2. In addition, we now include as a sensitivity analysis, mortality estimates based on 

application of the full set of expert-derived effect estimates (see Appendix 5B, Section 5B.2). 

We do not combine the expert results in order to preserve the breadth and diversity of opinion on 

the expert panel (Roman et. al., 2008). This presentation of the expert-derived results is generally 

consistent with SAB advice (U.S. EPA-SAB, 2008), which recommended that the EPA 

emphasize that “scientific differences existed only with respect to the magnitude of the effect of 

PM2.5 on mortality, not whether such an effect existed” and that the expert elicitation “supports 

the conclusion that the benefits of PM2.5 control are very likely to be substantial”. Although it is 

possible that the newer literature could revise the experts’ quantitative responses if elicited again, 

we believe that these general conclusions are unlikely to change.

Mortality Effect Coefficients for Infants. In addition to the adult mortality studies described 

above, several studies show an association between PM exposure and premature mortality in 

children under 5 years of age.93 The PM ISA states that less evidence is available regarding the 

potential impact of PM2.5 exposure on infant mortality than on adult mortality and the results of 

studies in several countries include a range of findings with some finding significant 

associations. Specifically, the PM ISA concluded that evidence exists for a stronger effect at the 

post-neonatal period and for respiratory-related mortality, although this trend is not consistent 

across all studies. In addition, compared to avoided premature deaths estimated for adult 

mortality, avoided premature deaths for infants are significantly smaller because the number of 

infants in the population is much smaller than the number of adults and the epidemiology studies 

on infant mortality provide smaller risk coefficients associated with exposure to PM2.5.

In 2004, the SAB-HES noted the release of the WHO Global Burden of Disease Study 

focusing on ambient air, which cites several recently published time-series studies relating daily 

PM exposure to mortality in children (U.S. EPA-SAB, 2004). The SAB-HES also cites the study 

by Belanger et al. (2003) as corroborating findings linking PM exposure to increased respiratory 

inflammation and infections in children. A study by Chay and Greenstone (2003) found that 

 

93 For the purposes of this analysis, we only calculate benefits for infants age 0–1, not all children under 5 years old.
5D-14

reductions in TSP caused by the recession of 1981–1982 were statistically associated with 

reductions in infant mortality at the county level. With regard to the cohort study conducted by 

Woodruff et al. (1997), the SAB-HES notes several strengths of the study, including the use of a 

larger cohort drawn from a large number of metropolitan areas and efforts to control for a variety 

of individual risk factors in infants (e.g., maternal educational level, maternal ethnicity, parental 

marital status, and maternal smoking status). Based on these findings, the SAB-HES 

recommended that the EPA incorporate infant mortality into the primary benefits estimate and 

that infant mortality be evaluated using an impact function developed from the Woodruff et al. 

(1997) study (U.S. EPA-SAB, 2004).

In 2010, the SAB-HES again noted the increasing body of literature relating infant 

mortality and PM exposure and supported the inclusion of infant mortality in the monetized 

benefits (U.S. EPA-SAB, 2010a). The SAB-HES generally supported the approach of estimating 

infant mortality based on Woodruff et al. (1997) and noted that a more recent study by Woodruff 

et al. (2006) continued to find associations between PM2.5 and infant mortality in California. The 

SAB-HES also noted, “when PM10 results are scaled to estimate PM2.5 impacts, the results yield 

similar risk estimates.” Consistent with the Costs and Benefits of the Clean Air Act (U.S. EPA, 

2011a), we continue to rely on the earlier 1997 study in part due to the national–scale of the 

earlier study.

5D.2 Hospital Admissions and Emergency Department Visits

Because of the availability of detailed hospital admission and discharge records, there is 

an extensive body of literature examining the relationship between hospital admissions and air 

pollution. For this reason, we pool together the incidence estimates using several different 

studies for many of the hospital admission endpoints. In addition, some studies have examined 

the relationship between air pollution and emergency department visits. Since most emergency 

department visits do not result in an admission to the hospital (i.e., most people going to the 

emergency department are treated and return home), we treat hospital admissions and emergency 

department visits separately, taking account of the fraction of emergency department visits that 

are admitted to the hospital. Specifically, within the baseline incidence rates, we parse out the 

scheduled hospital visits from unscheduled ones as well as the hospital visits that originated in 

the emergency department.
5D-15

The two main groups of hospital admissions are estimated in this analysis for PM2.5: 

respiratory admissions and cardiovascular admissions. There is not sufficient evidence linking 

PM2.5 with other types of hospital admissions. Both asthma- and cardiovascular-related visits 

have been linked to PM2.5 in the United States, though as we note below, we are able to assign an 

economic value to asthma-related events only. To estimate the effects of PM2.5 air pollution 

reductions on asthma-related ER visits, we use the effect estimate from a study of children 18 

and under by Mar et al. (2010), Slaughter et al. (2005), and Glad et al. (2012). The first two 

studies examined populations 0 to 99 in Washington State, while Glad et al. examined 

populations 0-99 in Pittsburgh, PA. Mar and colleagues perform their study in Tacoma, while 

Slaughter and colleagues base their study in Spokane. We apply random/fixed effects pooling to 

combine evidence across these two studies.

To estimate avoided incidences of cardiovascular hospital admissions associated with 

PM2.5, we used studies by Moolgavkar (2000), Zanobetti et al. (2009), Peng et al. (2008, 2009) 

and Bell et al., (2008). Only Moolgavkar (2000) provided a separate effect estimate for adults 20 

to 64, while the remainder estimate risk among adults over 64.94 Total cardiovascular hospital 

admissions are thus the sum of the pooled estimate for adults over 65 and the single study 

estimate for adults 20 to 64. Cardiovascular hospital admissions include admissions for 

myocardial infarctions. To avoid double-counting benefits from reductions in myocardial 

infarctions when applying the impact function for cardiovascular hospital admissions, we first 

adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial 

infarctions. We applied equal weights pooling to the multi-city studies assessing risk among 

adults over 64 because these studies already incorporated pooling across the city-level estimates. 

One potential limitation of our approach is that while the Zanobetti et al. (2009) study assesses 

all cardiovascular risk, Bell et al. (2008), and Peng et al., (2008, 2009) studies estimate a subset 

of cardiovascular hospitalizations as well as certain cerebro- and peripheral-vascular diseases. To 

address the potential for the pooling of these four studies to produce a biased estimate, we match 

 

94 Note that the Moolgavkar (2000) study has not been updated to reflect the more stringent GAM convergence 

criteria. However, given that no other estimates are available for this age group, we chose to use the existing 

study. Given the very small (<5%) difference in the effect estimates for people 65 and older with cardiovascular 

hospital admissions between the original and reanalyzed results, we do not expect this choice to introduce much 

bias. For a discussion of the GAM convergence criteria, and how it affected the size of effect coefficients reported 

by time series epidemiological studies using NMMAPS data, see: http://www.healtheffects.org/Pubs/sttimeseries.htm.
5D-16

the pooled risk estimate with a baseline incidence rate that excludes cerebro- and peripheralvascular disease. An alternative approach would be to use the Zanobetti et al. (2009) study alone, 

though this would prevent us from drawing upon the strengths of the three multi-city studies.

To estimate avoided incidences of respiratory hospital admissions associated with PM2.5, 

we used a number of studies examining total respiratory hospital admissions as well as asthma 

and chronic lung disease. We estimated impacts among three age groups: adults over 65, adults 

18 to 64 and children 0 to 17. For adults over 65, the multi-city studies by Zanobetti et al. (2009) 

and Kloog et al. (2012) provide effect coefficients for total respiratory hospital admissions 

(defined as ICD codes 460–519). We pool these two studies using equal weights. Moolgavkar et 

al. (2003) examines PM2.5 and chronic lung disease hospital admissions (less asthma) in Los 

Angeles, CA among adults 18 to 64. For children 0 to 18, we pool two studies using 

random/fixed effects. The first is Babin et al. (2007) which assessed PM2.5 and asthma hospital 

admissions in Washington, DC among children 1 to 18; we adjusted the age range for this study 

to apply to children 0 to 18. The second is Sheppard et al. (2003) which assessed PM2.5 and 

asthma hospitalizations in Seattle, Washington, among children 0 to 18.

5D.3 Acute Health Events and School/Work Loss Days

In addition to mortality, chronic illness, and hospital admissions, a number of acute 

health effects not requiring hospitalization are associated with exposure to PM2.5. The sources for 

the effect estimates used to quantify these effects are described below.

Asthma exacerbations. For this RIA, we have followed the SAB-HES recommendations 

regarding asthma exacerbations in developing the core estimate (U.S. EPA-SAB, 2004). 

Although certain studies of acute respiratory events characterize these impacts among only 

asthmatic populations, others consider the full population, including both asthmatics and nonasthmatics. For this reason, incidence estimates derived from studies focused only on asthmatics 

cannot be added to estimates from studies that consider the full population—to do so would 

double-count impacts. To prevent such double-counting, we estimated the exacerbation of 

asthma among children and excluded adults from the calculation. Asthma exacerbations 

occurring in adults are assumed to be captured in the general population endpoints such as work 

loss days and MRADs. Finally, we note the important distinction between the exacerbation of 
5D-17

asthma among asthmatic populations, and the onset of asthma among populations not previously 

suffering from asthma; in this RIA, we quantify the exacerbation of asthma among asthmatic 

populations and not the onset of new cases of asthma.

Based on advice from the SAB-HES (2004), regardless of the age ranges included in the 

source epidemiology studies, we extend the applied population to ages 6 to 18, reflecting the 

common biological basis for the effect in children in the broader age group. This age range 

expansion is also supported by NRC (2002, pp. 8, 116).

To characterize asthma exacerbations in children from exposure to PM2.5, we selected 

two studies (Ostro et al., 2001; Mar et al., 2004) that followed panels of asthmatic children. 

Ostro et al. (2001) followed a group of 138 African-American children in Los Angeles for 13 

weeks, recording daily occurrences of respiratory symptoms associated with asthma 

exacerbations (e.g., shortness of breath, wheeze, and cough). This study found a statistically 

significant association between PM2.5, measured as a 12-hour average, and the daily prevalence 

of shortness of breath and wheeze endpoints. Although the association was not statistically 

significant for cough, the results were still positive and close to significance; consequently, we 

decided to include this endpoint, along with shortness of breath and wheeze, in generating 

incidence estimates (see below).

Mar et al. (2004) studied the effects of various size fractions of particulate matter on 

respiratory symptoms of adults and children with asthma, monitored over many months. The 

study was conducted in Spokane, Washington, a semi-arid city with diverse sources of 

particulate matter. Data on respiratory symptoms and medication use were recorded daily by the 

study’s subjects, while air pollution data was collected by the local air agency and Washington 

State University. Subjects in the study consisted of 16 adults—the majority of whom participated 

for over a year—and nine children, all of whom were studied for over eight months. Among the 

children, the authors found a strong association between cough symptoms and several metrics of 

particulate matter, including PM2.5. However, the authors found no association between 

respiratory symptoms and PM of any metric in adults. Mar et al. therefore concluded that the 

discrepancy in results between children and adults was due either to the way in which air quality 
5D-18

was monitored, or a greater sensitivity of children than adults to increased levels of PM air 

pollution.

We employed the following pooling approach in combining estimates generated using 

effect estimates from the two studies to produce a single estimate for PM-related asthma 

exacerbation incidence. First, we used random/fixed effects pooling to combine the Ostro and 

Mar estimates for shortness of breath and cough. Next, we pooled the Ostro estimate of wheeze 

with the pooled cough and shortness of breath estimates to derive an overall estimate of asthma 

exacerbation in children.

Acute Respiratory Symptoms. We estimate three types of acute respiratory symptoms related 

to PM2.5 exposure: lower respiratory symptoms, upper respiratory symptoms, and minor 

restricted activity days (MRAD). 

Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children aged 

7 to 14 were estimated for PM2.5 using an effect estimate from Schwartz and Neas (2000). 

Incidences of upper respiratory symptoms in asthmatic children aged 9 to 11 are estimated for 

PM2.5 using an effect estimate developed from Pope et al. (1991). Because asthmatics have 

greater sensitivity to stimuli (including air pollution), children with asthma can be more 

susceptible to a variety of upper respiratory symptoms (e.g., runny or stuffy nose; wet cough; and 

burning, aching, or red eyes). Research on the effects of air pollution on upper respiratory 

symptoms has thus focused on effects in asthmatics. 

MRADs result when individuals reduce most usual daily activities and replace them with 

less strenuous activities or rest, yet not to the point of missing work or school. For example, a 

mechanic who would usually be doing physical work most of the day will instead spend the day 

at a desk doing paper work and phone work because of difficulty breathing or chest pain. The 

effect of PM2.5 on MRAD was estimated using an effect estimate derived from Ostro and 

Rothschild (1989).

More recently published literature examining the relationship between short-term PM2.5

exposure and acute respiratory symptoms was available in the PM ISA (U.S. EPA, 2009b), but 

proved to be unsuitable for use in this benefits analysis. In particular, the best available study 
5D-19

(Patel et al., 2010) specified a population aged 13–20, which overlaps with the population in 

which we asses asthma exacerbation. As we describe in detail below, to avoid the chance of 

double-counting impacts, we do not estimate changes in acute respiratory symptoms and asthma 

exacerbation among populations of the same age.

Acute Bronchitis. Approximately 4% of U.S. children between the ages of 5 and 17 experience 

episodes of acute bronchitis annually (ALA, 2002). Acute bronchitis is characterized by 

coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a number of days. 

According to the MedlinePlus medical encyclopedia,95 with the exception of cough, most acute 

bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of acute bronchitis in 

children between the ages of 5 and 17 were estimated using an effect estimate developed from 

Dockery et al. (1996). 

Work Loss Days. Health effects from air pollution can also result in missed days of work 

(either from personal symptoms or from caring for a sick family member). Days of work lost due 

to PM2.5 were estimated using an effect estimate developed from Ostro (1987). Children may 

also be absent from school because of respiratory or other diseases caused by exposure to air 

pollution, but we have not quantified these effects for this rule.

Work loss days. Health effects from air pollution can also result in missed days of work (either 

from personal symptoms or from caring for a sick family member). Days of work lost due to 

PM2.5 were estimated using an effect estimate developed from Ostro (1987). Ostro (1987) 

estimated the impact of PM2.5 on the incidence of work loss days in a national sample of the adult 

working population, ages 18 to 65 living in metropolitan areas. Ostro reported that two-week 

average PM2.5 levels were significantly linked to work loss days, but there was some year-to-year 

variability in the results.

5D.4 Nonfatal Acute Myocardial Infarctions (AMI) (Heart Attacks)

Nonfatal heart attacks have been linked with short-term exposures to PM2.5 in the United 

States (Mustafić et al., 2012; Peters et al., 2001; Sullivan et al., 2005; Pope et al., 2006; 

Zanobetti and Schwartz, 2006; Zanobetti et al., 2009) and other countries (Poloniecki et al., 

 

95 See http://www.nlm.nih.gov/medlineplus/ency/article/001087.htm, accessed April 2012. 
5D-20

1997; Barnett et al., 2006; Peters et al., 2005). In previous health impact assessments, we have 

relied upon a study by Peters et al. (2001) as the basis for the impact function estimating the 

relationship between PM2.5 and nonfatal heart attacks. The Peters et al. (2001) study exhibits a 

number of strengths. In particular, it includes a robust characterization of populations 

experiencing acute myocardial infarctions (AMIs). The researchers interviewed patients within 4 

days of their AMI events and, for inclusion in the study, patients were required to meet a series 

of criteria including minimum kinase levels, an identifiable onset of pain or other symptoms and 

the ability to indicate the time, place and other characteristics of their AMI pain in an interview.

Since the publication of Peters et al. (2001), a number of other single and multi-city 

studies have appeared in the literature. These studies include Sullivan et al. (2005), which 

considered the risk of PM2.5-related hospitalization for AMIs in King County, Washington; Pope 

et al. (2006), based in Wasatch Range, Utah; Zanobetti and Schwartz (2006), based in Boston, 

Massachusetts; and, Zanobetti et al. (2009), a multi-city study of 26 U.S. communities. Each of 

these single and multi-city studies, with the exception of Pope et al. (2006), measure AMIs using 

hospital discharge rates. Conversely, the Pope et al. (2006) study is based on a large registry with 

angiographically characterized patients—arguably a more precise indicator of AMI. Because the 

Pope et al. (2006) study reflected both myocardial infarctions and unstable angina, this produces 

a more comprehensive estimate of acute ischemic heart disease events than the other studies. 

However, unlike the Peters study (Peters et al., 2006), Pope and colleagues did not measure the 

time of symptom onset, and PM2.5 data were not measured on an hourly basis.

As a means of recognizing the strengths of the Peters study while also incorporating the 

newer evidence found in the four single and multi-city studies, we present a range of AMI 

estimates. The upper end of the range is calculated using the Peters study, while the lower end of 

the range is the result of an equal-weights pooling of these four newer studies. It is important to 

note that when calculating the incidence of nonfatal AMI, the fraction of fatal heart attacks is 

subtracted to ensure that there is no double-counting with premature mortality estimates. 

Specifically, we apply an adjustment factor in the concentration-response function to reflect the 

probability of surviving a heart attack. Based on recent data from the Agency for Healthcare 

Research and Quality’s Healthcare Utilization Project National Inpatient Sample database 

(AHRQ, 2009), we identified death rates for adults hospitalized with acute myocardial infarction 
5D-21

stratified by age (e.g., 1.852% for ages 18–44, 2.8188% for ages 45–64, and 7.4339% for ages 

65+). These rates show a clear downward trend over time between 1994 and 2009 for the 

average adult and thus replace the 7% survival rate previously applied across all age groups from 

Rosamond et al. (1999).

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5E-1

APPENDIX 5E: INPUTS TO PM2.5 COBENEFIT MODELING 

Overview

This section presents inputs used in generating PM2.5 cobenefits estimates including (a) 

benefit-per-ton estimates for each sector (dimensioned by mortality study and simulation year)96

(Table 5E-1), and (b) NOx emissions reductions by sector for both the 2025 and post-2025 

scenarios (Table 5E-2).97 For additional detail on the approach used to generate PM2.5 cobenefits 

estimates and the role played by these two types of inputs, see Chapter 5, Section 5.4.4. 

 

96 Benefit-per-ton estimates were generated for each of the long-term exposure-related mortality studies used in 

generating core benefits estimates for this RIA including Krewski et al., 2009 and Lepeule et al., 2012 (see 

Appendix 5D, section 5D.1). Estimates were available for 2025 and 2030, with those being used to model 

cobenefits for the 2025 scenario and post-2025 scenario, respectively.

97 Sector-level NOx reductions (for each alternative standard level) were generated using methods described in

Chapter 4, section 4.2 and 4.3. As noted in section 5.4.4, NOx emissions reductions associated with alternative 

standard levels considered for this NAAQS review involved seven of the 17 sectors for which we had benefit-perton values and consequently, the cobenefits PM2.5 estimates are based on simulated benefits for those seven sectors.
5E-2

Table 5E-1. Summary of Effect Estimates from Associated Sectors with Change in Long-Term Exposure to PM2.5 in Recent 

Cohort Studies in North Americaa

a Benefit-per-ton estimates reflect application of the 20-year segmented lag used in the core analysis (see section 5.6.4.1) together with either a 3% or 7% social 

discount rate as noted in the table. In addition, separate sets of benefit-per-ton estimates were generate for 2025 and 2030, reflecting application of appropriate 

projected demographic and baseline incidence data (see section 5.4.4 for additional detail).

b Benefit-per-ton estimates for the non-specified source category were generated as a weighted average of values for the 17 source categories, with weighting 

based on sector-specific NOx emissions for 2005 obtained from Fann et al., 2012. 

air, 

locamotive 

and 

marine

cement 

kilns

coke 

ovens

EGU 

point

electric 

arc 

furnaces

farro 

alloys

integrated 

iron and 

steel

iron and 

steel

non-EGU 

point 

other

nonpoint 

other nonroad onroad

pulp and 

paper refineries

residenti

al wood

taconite 

mining

ocean 

going 

vessels

Nonspecified 

source b 

2025 at 7% social discount

Krewski et al., 2009 $7,221 $5,692 $10,438 $5,245 $9,610 $4,358 $13,424 $16,944 $6,341 $7,859 $6,979 $7,620 $3,746 $6,924 $13,598 $5,998 $2,033 $6,433

Lepeule et al., 2012 $16,295 $12,856 $23,578 $11,841 $21,708 $9,842 $30,325 $38,262 $14,316 $17,737 $15,749 $17,177 $8,460 $15,636 $30,706 $13,538 $4,586 $14,514

2025 at 3% social discount

Krewski et al., 2009 $8,005 6,311 $11,572 $5,815 $10,654 $4,832 $14,883 $18,783 $7,029 $8,713 $7,736 $8,447 $4,154 $7,676 $15,075 $6,649 $2,254 $7,132

Lepeule et al., 2012 $18,071 14,257 $26,149 $13,132 $24,074 $10,916 $33,630 $42,433 $15,876 $19,671 $17,466 $19,049 $9,383 $17,340 $34,054 $15,014 $5,086 $16,096

2030 at 7% social discount

Krewski et al., 2009 $7,829 $6,125 $11,056 $5,591 $10,219 $4,637 $14,264 $18,373 $6,814 $8,469 $7,587 $8,214 $4,017 $7,531 $14,695 $6,403 $2,258 $6,941

Lepeule et al., 2012 $17,662 $13,830 $24,969 $12,621 $23,080 $10,473 $32,217 $41,475 $15,380 $19,109 $17,116 $18,512 $9,070 $17,000 $33,176 $14,451 $5,091 $15,655

2030 at 3% social discount

Krewski et al., 2009 $8,680 $6,791 $12,258 $6,199 $11,330 $5,142 $15,815 $20,369 $7,554 $9,389 $8,412 $9,106 $4,454 $8,349 $16,293 $7,099 $2,504 $7,695

Lepeule et al., 2012 $19,587 $15,338 $27,691 $13,996 $25,596 $11,615 $35,729 $45,997 $17,057 $21,192 $18,983 $20,530 $10,059 $18,854 $36,794 $16,026 $5,646 $17,362

Emissions sector

Long-term mortality 

study
5E-3

Table 5E-2. Sector-Specific NOx Emissions Reductions for Each Alternative Standard Levela

a All values are tons of NOx reductions (75ppb vs alternative standard). Results are presented both for “CA NOx” (emissions in CA only – used in post-2025 

scenario PM2.5 cobenefits modeling) and “nonCA NOx” (emissions reductions outside of CA – used in 2025 scenario PM2.5 cobenefits modeling)

 CA NOx nonCA NOx CA NOx nonCA NOx CA NOx nonCA NOx 

Aircraft, locomotives and marine vessels - - - - - -

Area sources - 45,708 - 94,340 - 95,332

Cement kilns - 20,135 - 43,867 - 43,867

Electricity Generating Units - 32,315 - 217,881 - 244,079

Industrial point sources - 382,743 - 741,588 - 750,620

Non-road mobile sources - 4,984 - 12,863 - 12,863

On-road mobile sources - - - - - -

Pulp and paper facilities - 357 - 617 - 617

Refineries - 8,243 - 12,384 - 12,411

Residential wood combustion - - - - - -

Unknown sector 53,289 154,343 104,708 752,162 143,916 2,234,709

TOTAL 53,289 648,828 104,708 1,875,702 143,916 3,394,497

 60ppb 

Emissions Sector

Alternative Standard Level

 70ppb 65ppb 
6-1

CHAPTER 6: IMPACTS ON PUBLIC WELFARE OF ATTAINMENT STRATEGIES 

TO MEET PRIMARY AND SECONDARY OZONE NAAQS 

Overview

This chapter provides a discussion of the welfare-related benefits of meeting alternative 

primary and secondary ozone standards. Welfare benefits of reductions in ambient ozone 

include increased growth and/or biomass production in sensitive plant species, including forest 

trees, increased crop yields, reductions in visible foliar injury, increased plant vigor (e.g. 

decreased susceptibility to harsh weather, disease, insect pest infestation, and competition), and 

changes in ecosystems and associated ecosystem services. We provide a limited quantitative 

analysis for effects associated with changes in yields of commercial forests and agriculture, and 

associated changes in carbon sequestration and storage. 

The EPA is proposing to revise the level of the secondary standard to within the range 

proposed for the primary standard of 65 parts per billion (ppb) to 70 ppb to provide increased 

protection against vegetation-related effects on public welfare. As an initial matter, the EPA is 

proposing that ambient ozone concentrations in terms of a three-year average W126 index value 

within the range from 13 parts per million-hours (ppm-hours) to 17 ppm-hours, would provide 

the requisite protection against known or anticipated adverse effects to the public welfare, which 

data analyses indicate would provide air quality in terms of three-year average W126 index 

values of a range at or below 13 ppm-hours to 17 ppm-hours. Data analyses also indicate that 

actions taken to attain a standard in the range of 65 ppb to 70 ppb would also improve air quality 

as measured by the W126 metric. The quantitative analysis in this chapter assesses the welfare 

benefits of strategies to attain ozone standard levels of 65 to 70 ppb. 

In addition to the direct welfare benefits of decreased levels of ambient ozone, the 

emissions reduction strategies used to demonstrate attainment with alternative ozone standards 

may result in additional benefits associated with reductions in nitrogen deposition and reductions 

in ambient concentrations of PM2.5 and its components. These additional benefits include 

reductions in nutrient enrichment and acidification impacts on sensitive aquatic and terrestrial 

ecosystems and improvements in visibility in state and national parks, wilderness areas, and in 

the areas where people live and work. We are not able to quantify or monetize these benefits in 

this RIA.
6-2

6.1 Welfare Benefits of Strategies to Attain Primary and Secondary Ozone Standards

The Clean Air Act defines welfare effects to include any non-health effects, including

direct economic damages in the form of lost productivity of crops and trees, indirect damages

through alteration of ecosystem functions, indirect economic damages through the loss in value

of recreational experiences or the existence value of important resources, and direct damages to

property, either through impacts on material structures or by soiling of surfaces (Section 302(h) 

(42 U.S.C. § 7602(h)). For welfare effects associated with changes to ecosystem functions, we 

use the concept of ecosystem services as a useful framework for analyzing the impact of 

ecosystem changes on public welfare. Ecosystem services can be generally defined as the 

benefits that individuals and organizations obtain from ecosystems. The EPA has defined 

ecological goods and services as the “outputs of ecological functions or processes that directly or 

indirectly contribute to social welfare or have the potential to do so in the future. Some outputs 

may be bought and sold, but most are not marketed” (U.S. EPA, 2006). Changes in these 

services can affect human well-being by affecting security, health, social relationships, and 

access to basic material goods (MEA, 2005).

This RIA employs reductions in nitrogen oxides (NOx) and volatile organic compound 

(VOC) emissions to demonstrate attainment with alternative levels of the NAAQS. Reductions 

in these emissions will result in changes in ambient concentrations of ozone, as well as changes 

in ambient concentrations of NOx, PM2.5 and its components, and deposition of nitrogen. It is

appropriate and reasonable to include all the benefits associated with these emissions reductions

to provide a comprehensive understanding of the likely public welfare impacts of attaining

alternative standards. Table 6-1 shows the welfare effects associated with emissions of NOx and 

VOC. The following subsections discuss the direct benefits of reducing ambient ozone 

concentrations and the additional welfare benefits associated with reduced emissions of NOx and 

VOC.
6-3

Table 6-1. Welfare Effects of NOx and VOC Emissions

Pollutant

Atmospheric Effects Atmospheric and 

Deposition Effects Deposition Effects

Vegetation 

Injury (Ozone)

Visibility 

Impairment

Materials 

Damage Climate

Ecosystem 

Effects—

(Organics)

Acidification 

(freshwater)

Nitrogen 

Enrichment

NOx      

VOCs    

6.2 Welfare Benefits of Reducing Ozone

Ozone can affect ecological systems, leading to changes in the ecological community and 

influencing the diversity, health, and vigor of individual species (U.S. EPA, 2013). Ozone causes 

discernible injury to a wide array of vegetation (U.S. EPA, 2013). In terms of forest productivity 

and ecosystem diversity, ozone may be the pollutant with the greatest potential for region-scale 

forest impacts (U.S. EPA, 2013). Studies have demonstrated repeatedly that ozone 

concentrations observed in polluted areas can have substantial impacts on plant function (De 

Steiguer et al,. 1990; Pye, 1988).

When ozone is present in ambient air, it can enter the leaves of plants, where it can cause 

significant cellular damage. Like carbon dioxide and other gaseous substances, ozone enters 

plant tissues primarily through the stomata in leaves in a process called “uptake” (Winner and 

Atkinson, 1986). Once sufficient levels of ozone (a highly reactive substance), or its reaction 

products, reaches the interior of plant cells, it can inhibit or damage essential cellular 

components and functions, including enzyme activities, lipids, and cellular membranes, 

disrupting the plant’s osmotic (i.e., water) balance and energy utilization patterns (U.S. EPA, 

2013; Tingey and Taylor, 1982). With fewer resources available, the plant reallocates existing 

resources away from root growth and storage, above ground growth or yield, and reproductive 

processes, and toward leaf repair and maintenance, leading to reduced growth and/or 

reproduction. Studies have shown that plants stressed in these ways may exhibit a general loss 

of vigor, which can lead to secondary impacts that modify plants' responses to other 

environmental factors. Specifically, plants may become more sensitive to other air pollutants, or 
6-4

more susceptible to disease, pest infestation, harsh weather (e.g., drought, frost) and other 

environmental stresses, which can all produce a loss in plant vigor in ozone-sensitive species that 

over time may lead to premature plant death. Furthermore, there is evidence that ozone can 

interfere with the formation of mycorrhizae, essential symbiotic fungi associated with the roots 

of most terrestrial plants, by reducing the amount of carbon available for transfer from the host to 

the symbiont (U.S. EPA, 2013).

This ozone damage may or may not be accompanied by visible injury on leaves, and 

likewise, visible foliar injury may or may not be a symptom of the other types of plant damage 

described above. Foliar injury is usually the first visible sign of injury to plants from ozone 

exposure and indicates impaired physiological processes in the leaves (Grulke, 2003). When 

visible injury is present, it is commonly manifested as chlorotic or necrotic spots, and/or 

increased leaf senescence (accelerated leaf aging). Visible foliar injury reduces the aesthetic 

value of ornamental vegetation and trees in urban landscapes and negatively affects scenic vistas 

in protected natural areas. 

Ozone can produce both acute and chronic injury in sensitive species depending on the 

concentration level and the duration of the exposure. Ozone effects also tend to accumulate over 

the growing season of the plant, so that even lower concentrations experienced for a longer 

duration have the potential to create chronic stress on sensitive vegetation. Not all plants, 

however, are equally sensitive to ozone. Much of the variation in sensitivity between individual 

plants or whole species is related to the plant’s ability to regulate the extent of gas exchange via 

leaf stomata (e.g., avoidance of ozone uptake through closure of stomata) and the relative ability 

of species to detoxify ozone-generated reactive oxygen free radicals (U.S. EPA, 2013; Winner, 

1994). After injuries have occurred, plants may be capable of repairing the damage to a limited 

extent (U.S. EPA, 2013). Because of the differing sensitivities among plants to ozone, ozone 

pollution can also exert a selective pressure that leads to changes in plant community 

composition. Given the range of plant sensitivities and the fact that numerous other 

environmental factors modify plant uptake and response to ozone, it is not possible to identify 

threshold values above which ozone is consistently toxic for all plants. 
6-5

Because plants are at the base of the food web in many ecosystems, changes to the plant 

community can affect associated organisms and ecosystems (including the suitability of habitats 

that support threatened or endangered species and below ground organisms living in the root 

zone). Ozone impacts at the community and ecosystem level vary widely depending upon 

numerous factors, including concentration and temporal variation of tropospheric ozone, species 

composition, soil properties and climatic factors (U.S. EPA, 2013). In most instances, responses 

to chronic or recurrent exposure in forested ecosystems are subtle and not observable for many 

years. These injuries can cause stand-level forest decline in sensitive ecosystems (U.S. EPA, 

2013, McBride et al., 1985; Miller et al., 1982). It is not yet possible to predict ecosystem 

responses to ozone with certainty; however, considerable knowledge of potential ecosystem 

responses is available through long-term observations in highly damaged forests in the U.S. (U.S 

EPA, 2013). Biomass loss due to ozone exposure affects climate regulation by ecosystems by 

reducing carbon sequestration. More carbon stays in the atmosphere because carbon uptake by 

forests is reduced. The studies cited in the Ozone ISA demonstrate a consistent pattern of 

reduced carbon uptake because of ozone damage, with some of the largest reductions projected 

over North America (U.S. EPA, 2013). 

Ozone also directly contributes to climate change because tropospheric ozone traps heat, 

leading to increased surface temperatures. Projections of radiative forcing due to changing 

ozone concentrations over the 21st century show wide variation, due in large part to the 

uncertainty of future emissions of source gases (U.S. EPA 2014). However, reduction of 

tropospheric ozone concentrations could provide an important means to slow climate change in 

addition to the added benefit of improving surface air quality (U.S. EPA, 2014).

While it is clear that increases in tropospheric ozone lead to warming, the precursors of 

ozone also have competing effects on methane, complicating emissions reduction strategies. A 

decrease in carbon monoxide or VOC emissions would shorten the lifetime of methane, leading 

to an overall cooling effect. A decrease in NOX emissions could lengthen the methane lifetime in 

certain regions, leading to warming (U.S. EPA, 2014). Additionally, some strategies to reduce 

ozone precursor emissions could also lead to the reduced formation of aerosols (e.g., nitrates and 

sulfates) that currently have a cooling effect.
6-6

In this RIA, we are able to quantify only a small portion of the welfare impacts associated 

with reductions in ozone concentrations to meet alternative ozone standards. Using a model of 

commercial agriculture and forest markets, we are able to analyze the effects on consumers and 

producers of forest and agricultural products of changes in the W126 index resulting from 

meeting alternative standards within the proposed range of 70 to 65 ppb, as well as a lower 

standard level of 60 ppb. We also assess the effects of those changes in commercial agricultural 

and forest yields on carbon sequestration and storage. This analysis provides limited quantitative 

information on the welfare benefits of meeting these alternative standards, focused only on one 

subset of ecosystem services. Commercial and non-commercial forests provide a number of 

additional services, including medicinal uses, non-commercial food and fiber production, arts 

and crafts uses, habitat, recreational uses, and cultural uses for Native American tribes. A more 

complete discussion of these additional ecosystem services is provided in the final Welfare Risk 

and Exposure Assessment for Ozone (WREA) (U.S. EPA, 2014).

6.3 Additional Welfare Benefits of Strategies to Meet the Ozone NAAQS

Reductions in emissions of NOx and VOC are associated with additional welfare benefits, 

including reductions in nutrient enrichment and acidification impacts on sensitive aquatic and 

terrestrial ecosystems and improvements in visibility in state and national parks, wilderness 

areas, and in the areas where people live and work. 

Excess nitrogen deposition can lead to eutrophication of estuarine waters, which is 

associated with a range of adverse ecological effects. These include low dissolved oxygen 

(DO), harmful algal blooms (HABs), loss of submerged aquatic vegetation (SAV), and low water 

clarity. Low DO disrupts aquatic habitats, causing stress to fish and shellfish, which, in the 

short-term, can lead to episodic fish kills and, in the long-term, can damage overall growth in 

fish and shellfish populations. HAB are often toxic to fish and shellfish, lead to fish kills and 

aesthetic impairments of estuaries, and can in some instances be harmful to human health. SAV 

provides critical habitat for many aquatic species in estuaries and, in some instances, can also 

protect shorelines by reducing wave strength. Low water clarity is in part the result of 

accumulations of both algae and sediments in estuarine waters. In addition to contributing to 
6-7

declines in SAV, high levels of turbidity also degrade the aesthetic qualities of the estuarine 

environment.

Nutrient enrichment from nitrogen deposition to terrestrial ecosystems is causally linked 

to alteration of species richness, species composition, and biodiversity (U.S. EPA, 2008b). 

Nitrogen enrichment occurs over a long time period; as a result, it may take as much as 50 years or 

more to see changes in ecosystem conditions, indicators, and services. 

Terrestrial acidification resulting from deposition of nitrogen can result in declines in

sensitive tree species, such as red spruce (Picea rubens) and sugar maple (Acer saccharum), and 

can also impact other plant communities including shrubs and lichen (U.S. EPA, 2008b).

Biological effects of acidification in terrestrial ecosystems are generally linked to aluminum

toxicity and decreased ability of plant roots to take up base cations (U.S. EPA, 2008b).

Terrestrial acidification affects several important ecosystem services, including declines in 

habitat for threatened and endangered species, declines in forest aesthetics, declines in forest

productivity, and increases in forest soil erosion and reductions in water retention. 

Aquatic acidification resulting from deposition of nitrogen can result in effects on health,

vigor, and reproductive success for aquatic species; and effects on biodiversity. Deposition of 

nitrogen results in decreases in the acid neutralizing capacity and increases in inorganic aluminum

concentration, which contribute to declines in zooplankton, macro invertebrates, and fish species

richness in aquatic ecosystems (U.S. EPA, 2008b).

Reductions in NOx emissions will improve visibility in parks and wilderness areas and in 

places where people live and work because of their impact on light extinction (U.S. EPA, 2009).

Good visibility increases quality of life where individuals live and work, and where they travel

for recreational activities, including sites of unique public value, such as the Great Smoky

Mountains National Park (U. S. EPA, 2009). Particulate nitrate is an important contributor to

light extinction in California and the upper Midwestern U.S., particularly during winter (U.S.

EPA, 2009). While EPA typically estimates the visibility benefits associated with reductions in NOx 

(U.S. EPA, 2008a), we have not done so here because we do not have estimates of the changes in 

particulate nitrate needed to calculate changes in light extinction and the resulting changes in 

economic benefits.
6-8

Strategies implemented by state and local governments to reduce emissions of ozone 

precursors may also impact emissions of CO2 or other long-lived climate gases. Our ability to 

quantify the climate effects of the proposed standard levels is limited due to lack of available 

information on the energy and associated climate gas implications of control technologies 

assumed in the illustrative control strategy alternatives, remaining uncertainties regarding the 

impact of ozone precursors on climate change, and lack of available information on the cocontrolled greenhouse gas (GHG) emission reductions. As a result, we do not attempt to 

quantify the impacts of the illustrative attainment scenarios on GHG emissions and impacts.

6.4 Analysis of Commercial Agricultural and Forestry Related Benefits Using the 

Forest and Agricultural Sector Optimization Model – Greenhouse Gas Version 

(FASOMGHG)

To estimate the commercial timber effects of ozone induced biomass loss we used the 

FASOMGHG (Adams et al., 2005) model for the forest and agricultural sectors to calculate the 

market-based welfare benefits associated with the illustrative attainment strategies for the three 

alternative ozone standard levels of 70, 65, and 60 ppb incremental to attainment of the current 

standard level of 75 ppb. The air quality surfaces used are discussed in detail in Chapter 3; the 

alternative primary standards modeled were recalculated to a W126 index appropriate for use 

with the exposure-response functions available for trees and crops. This section provides a brief 

summary of the analytical approach and results of the analysis. More details of the 

FASOMGHG modeling conducted for this RIA are provided in Appendix 6A, while additional 

details on the overall FASOMGHG methodology are provided in Appendix 6B of the WREA 

(U.S. EPA, 2014).

6.4.1 Summary of the Analytical Approach

We used the ozone exposure-response functions evaluated in the ISA (U.S. EPA, 2013) for 

tree seedlings to calculate relative yield loss (RYL), which is equivalent to relative biomass loss, 

for trees over their entire life span. The RYL for species were aggregated into average RYL for 

FASOMGHG forest types, based on mapping tree species to forest types using the Atlas of 

United States Trees (Little, 1971, 1976, 1977, 1978). 
6-9

We used the NCLAN ozone exposure-response functions evaluated in the ISA to generate 

RYL for commercial agricultural crops. For those crops that do not have E-R functions, we 

assign them RYLs for each scenario based on the crop proxy mapping shown in Table 6A-2 of 

Appendix 6A.98 The RYL for each crop are aggregated to the regional level by computing a 

weighted average RYL with weights determined by a county’s share of production for the crop. 

Additional details of the calculations of RYL are provided in Appendix 6A.

Yield gains for both forest species and crops are calculated as the difference in RYL 

between the 75 ppb standard baseline and the attainment scenarios for 70, 65, and 60 ppb 

alternative standard levels. The FASOMGHG model requires estimates of yields over a time 

horizon from 2010 to 2040. As such, we needed to specify yield gains over this range of years. 

Yield gains are calculated for three periods, 2010 to 2025, 2025-2038, and post-2038. The pre2025 period has no changes in yields. The 2025-2038 period represents the effects on the W126 

index of attainment of the alternative standards across the U.S. with the exception of California. 

The post-2038 period includes the effects on the W126 index of attaining everywhere across the 

U.S. including California. There is clearly uncertainty in the path of the yield changes 

introduced by uncertainties about the specific time pattern of emissions reductions that will be 

applied in California to attain alterative standards.

Changes in yield are associated with changes in consumer and producer/farmer surplus. 

Consumer surplus is the difference between what a consumer would be willing to pay for a 

product and the price they have to pay for the product. Producer surplus refers to the benefit, or 

profit, a producer receives from providing a good or service at a market price when they would 

have been willing to sell that good or service at a lower price. In general, increases in yields will 

cause crop and timber prices to fall. These reductions in prices will have different impacts on 

consumer and producer surplus. Overall effects on producer and consumer surplus depend on 

the (1) ability of producers/farmers to substitute other crops that are less ozone sensitive, and (2) 

responsiveness of demand and supply. The FASOMGHG model estimates changes in consumer 

and producer surplus and net welfare (sum of consumer and producer surplus). The 

 

98 For oranges, rice, and tomatoes, which have ozone E-R functions that are not W126-based (they are defined based 

on alternative measures of ozone concentrations), we directly used the median RYG values under the “13 ppm-hrs” 

ozone concentration reported in Table G-7 of Lehrer et al. (2007). 
6-10

FASOMGHG model also provides estimates of the changes in carbon sequestration for the 

commercial forestry and agricultural sectors.

The model calculates market equilibria under each of the alternative standard scenarios 

reflecting different forest and agricultural yields resulting from different ozone exposures. By 

comparing the market equilibria under different scenarios, we can calculate the welfare and 

carbon sequestration impacts of alternative ozone standards for the U.S. agricultural and forest 

sector. 

6.4.2 Summary of FASOMGHG Results

Tables 6-2 and 6-4 show the estimated changes in consumer and producer surplus 

associated with attainment of the alternative ozone standards compared to attaining the current 

standard for the forestry and agricultural sectors, respectively. Tables 6-3 and 6-5 show the 

percent change in consumer and producer surplus associated with attainment of the alternative 

ozone standards compared to attaining the current standard for the forestry and agricultural 

sectors, respectively. Consumer and producer welfare are affected more in the forestry sector 

than the agricultural sector. In general, consumer welfare increases in both the forestry and 

agricultural sectors because higher yields lead to lower prices. Because the quantity demanded 

for most forestry and agricultural commodities is not highly responsive to changes in price, 

producer surplus often declines when lower prices reduce producer profits more than can be 

offset by higher yields. In other words, consumers do not increase their demand in response to 

the falling prices enough to offset the producer’s loss of revenue. The increase in consumer 

welfare is not as large as the loss of producer welfare resulting in net welfare losses in the 

forestry sector nationally.
6-11

Table 6-2. Change in Consumer and Producer Surplus in the Forestry Sector from 

Attaining Alternative Ozone Standard Levels Compared to Attaining the 

Current Ozone Standard (Million 2011$)

Alternative Standard Level 2010 2015 2020 2025 2030 2035 2040

Consumer 

Surplus

Change with Respect to Existing Standard

70 ppb 3 24,592 -8 111 136 225 61

65 ppb 11 24,596 8 305 337 552 162

60 ppb 30 24,742 136 719 533 1,094 311

Producer 

Surplus

Change with Respect to Existing Standard

70 ppb 1,203 -22,160 1,768 703 -1,167 523 -52,496

65 ppb 211 -24,091 -480 1,464 -3,044 -39,691 -10,246

60 ppb -233 -23,711 -1,731 1,455 -1,963 -39,291 -8,082

Table 6-3. Percent Change in Consumer and Producer Surplus in the Forestry Sector from 

Attaining Alternative Ozone Standard Levels Compared to Attaining the 

Current Ozone Standard

Alternative Standard Level 2010 2015 2020 2025 2030 2035 2040

Consumer 

Surplus

Percent Change with Respect to Existing Standard

70 ppb 0.00 3.10 0.00 0.01 0.02 0.03 0.01

65 ppb 0.00 3.10 0.00 0.04 0.04 0.06 0.02

60 ppb 0.00 3.12 0.02 0.09 0.06 0.12 0.03

Producer 

Surplus

Percent Change with Respect to Existing Standard

70 ppb 0.15% -2.28 0.18 0.07 -0.12 0.05 -5.04

65 ppb 0.03% -2.48 -0.05 0.14 -0.32 -3.88 -0.98

60 ppb -0.03% -2.44 -0.18 0.14 -0.20 -3.84 -0.78

Table 6-4. Change in Consumer and Producer Surplus in the Agricultural Sector from 

Attaining Alternative Ozone Standard Levels Compared to Attaining the 

Current Ozone Standard (Million 2011$)

Product

Alternative 

Standard 

Level 2010 2015 2020 2025 2030 2035 2040

Consumer 

Surplus

Change with Respect to Existing Standard

70 ppb 0 0 0 113 66 58 -10

65 ppb -1 1 4 262 148 289 24

60 ppb 0 47 9 462 100 408 66

Producer 

Surplus

Change with Respect to Existing Standard

70 ppb 1,202 2,454 1,796 609 -1,144 675 -52,504

65 ppb 216 530 -407 1,174 -2,933 -39,605 -10,211

60 ppb -176 1,015 -1,470 1,320 -1,492 -38,788 -8,119
6-12

Table 6-5. Percent Change in Consumer and Producer Surplus in the Agricultural Sector 

from Attaining Alternative Ozone Standard Levels Compared to Attaining the 

Current Ozone Standard

Product

Alternative 

Standard 

Level 2010 2015 2020 2025 2030 2035 2040

Consumer 

Surplus

Percent Change with Respect to Existing Standard

70 ppb 0.00 0.00 0.00 0.00 0.00 0.00 0.00

65 ppb 0.00 0.00 0.00 0.00 0.00 0.00 0.00

60 ppb 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Producer 

Surplus

Percent Change with Respect to Existing Standard

70 ppb 0.17 0.30 0.22 0.07 -0.14 0.08 -5.78

65 ppb 0.03 0.06 -0.05 0.14 -0.36 -4.53 -1.12

60 ppb -0.02 0.12 -0.18 0.15 -0.18 -4.44 -0.89

Since the forestry and agriculture sectors are interlinked and factors affecting one sector 

can lead to changes in the other, it is important to consider the overall effect of ozone changes in 

the context of producer and consumer welfare across both sectors. The impacts on consumer 

surplus are positive for both sectors, with benefits increasing with lower alternative standards. 

For producer surplus, however, impacts are negative for all alternative standards. Table 6-6 and 

6-8 present the annualized surplus (over the period 2010 to 2040) for both sectors using 3 and 7 

percent discount rates, while Table 6-7 and 6-9 present the percent change in surplus for both 

sectors using 3 and 7 percent discount rates.
6-13

Table 6-6. Annualized Changes in Consumer and Producer Surplus in Agriculture and 

Forestry from Attaining Alternative Ozone Standard Levels Compared to 

Attaining the Current Ozone Standard, 2010-2040, Million 2011$ (3% Discount 

Rate)

Alternative 

Standard Level Agriculture Forestry Total

Consumer surplus

Change with Respect to Existing Standard

70 ppb 28 4,552 4,580

65 ppb 86 4,592 4,678

60 ppb 132 4,744 4,877

Producer surplus

Change with Respect to Existing Standard

70 ppb -3,601 -4,534 -8,135

65 ppb -5,035 -4,524 -9,559

60 ppb -4,741 -4,684 -9,425

Total surplus

Change with Respect to Existing Standard

70 ppb -3,573 18 -3,555

65 ppb -4,949 68 -4,882

60 ppb -4,608 60 -4,548

Table 6-7. Annualized Percent Changes in Consumer and Producer Surplus in Agriculture 

and Forestry from Attaining Alternative Ozone Standard Levels Compared to 

Attaining the Current Ozone Standard, 2010-2040, (3% Discount Rate)

Alternative 

Standard Level Agriculture Forestry Total

Consumer surplus

Percent Change with Respect to Existing Standard

70 ppb 0.00 0.57 0.17

65 ppb 0.00 0.57 0.17

60 ppb 0.01 0.59 0.18

Producer surplus

Percent Change with Respect to Existing Standard

70 ppb -0.44 -3.35 -0.85

65 ppb -0.62 -3.34 -1.00

60 ppb -0.58 -3.46 -0.99

Total surplus

Percent Change with Respect to Existing Standard

70 ppb -0.13 0.00 -1.10

65 ppb -0.18 0.01 -0.13

60 ppb -0.17 0.01 -0.12

Table 6-8. Annualized Changes in Consumer and Producer Surplus in Agriculture and 

Forestry from Attaining Alternative Ozone Standard Levels Compared to 
6-14

Attaining the Current Ozone Standard, 2010-2040, Million 2011$ (7% Discount 

Rate)

Alternative 

Standard Level Agriculture Forestry Total

Change with Respect to Existing Standard

Consumer surplus 70 ppb 21 5,570 5,592

65 ppb 61 5,599 5,660

60 ppb 100 5,721 5,821

Producer surplus

Change with Respect to Existing Standard

70 ppb -945 -5,561 -6,506

65 ppb -2,719 -5,555 -8,273

60 ppb -2,635 -5,694 -8,328

Total surplus

Change with Respect to Existing Standard

70 ppb -923 9 -914

65 ppb -2,658 45 -2,613

60 ppb -2,535 27 -2,508

Table 6-9. Annualized Percent Changes in Consumer and Producer Surplus in Agriculture 

and Forestry from Attaining Alternative Ozone Standard Levels Compared to 

Attaining the Current Ozone Standard, 2010-2040, (7% Discount Rate)

Alternative 

Standard Level Agriculture Forestry Total

Percent Change with Respect to Existing Standard

Consumer surplus 70 ppb 0.00 0.71 0.20

65 ppb 0.00 0.72 0.21

60 ppb 0.01 0.73 0.21

Producer surplus

Percent Change with Respect to Existing Standard

70 ppb -0.12 -4.26 -0.70

65 ppb -0.34 -4.25 -0.89

60 ppb -0.33 -4.36 -0.89

Total surplus

Percent Change with Respect to Existing Standard

70 ppb -0.03 0.00 -0.02

65 ppb -0.10 0.00 -0.07

60 ppb -0.09 0.00 -0.07

The impacts of the simulations of meeting the existing and alternative ozone standards on 

carbon sequestration potential in U.S. forest and agricultural sectors are presented in Table 6-10, 

in millions of metric tons of carbon dioxide equivalence, and the percent change in carbon 

sequestration potential is presented in Table 6-11. As shown in the table, much greater 
6-15

sequestration changes are projected in the forest sector than in the agricultural sector. The 

baseline stock of carbon storage decreases over time for agriculture because the agriculture 

sector GHG emissions sources are released every year and soil carbon sequestration stabilizes 

over the 30-year period. There are only small increases in net carbon sequestration compared to 

the existing standard for each of the alternative scenarios modeled.

While we did not quantify the effects in this RIA, increases in growth for trees in urban 

settings that results from reduced ozone concentrations can have additional benefits from 

removal of air pollution. The WREA (U.S. EPA, 2014) provides a case study of the potential 

impacts of ozone reductions on pollution removal in several eastern U.S. urban areas using the iTree model. See appendix 6D of the WREA (U.S. EPA, 2014) for details and references for the 

i-Tree model.

Table 6-10. Increase in Carbon Sequestration from Attaining Alternative Ozone 

Standard Levels Compared to Attaining the Current Ozone Standard, 

MMtCO2e

Alternative Standard Level 2010 2020 2030 2040 2010-2040*

Agriculture

Change with Respect to Existing Standard

70 ppb 0 1 3 1 24

65 ppb 0 2 6 4 62

60 ppb 1 4 11 11 132

Forestry

Change with Respect to Existing Standard

70 ppb -1 -51 100 259 1,537

65 ppb -2 -53 323 774 5,207

60 ppb -12 -94 465 1,189 7,739
6-16

Table 6-11. Percent Change in Carbon Sequestration from Attaining Alternative Ozone 

Standard Levels Compared to Attaining the Current Ozone Standard

Alternative Standard Level 2010 2020 2030 2040 2010-2040*

Agriculture

Percent Change with Respect to Existing Standard

70 ppb 0.00 0.00 0.03 0.01 0.01

65 ppb 0.00 0.01 0.05 0.06 0.02

60 ppb 0.00 0.03 0.10 0.13 0.05

Forestry

Percent Change with Respect to Existing Standard

70 ppb 0.00 -0.07 0.13 0.32 0.10

65 ppb 0.00 -0.07 0.41 0.95 0.34

60 ppb -0.02 -0.13 0.60 1.47 0.50

6.5 References

Adams, D.; Alig, R.; McCarl, B.A.; Murray, B.C. (2005). FASOMGHG Conceptual Structure, and Specification: 

Documentation. Available at http://agecon2.tamu.edu/people/faculty/mccarl-bruce/FASOM.html 

De Steiguer, J., Pye, J., Love, C. 1990. Air Pollution Damage to U.S. Forests. Journal of Forestry, 88(8), 17-22.

Grulke, N.E. 2003. The physiological basis of ozone injury assessment attributes in Sierran conifers. In A. 

Bytnerowicz, M.J. Arbaugh, & R. Alonso (Eds.), Ozone air pollution in the Sierra Nevada: Distribution and 

effects on forests. (pp. 55-81). New York, NY: Elsevier Science, Ltd.

McBride, J.R., Miller, P.R., Laven, R.D. 1985. Effects of oxidant air pollutants on forest succession in the mixed 

conifer forest type of southern California. In: Air Pollutants Effects on Forest Ecosystems, Symposium 

Proceedings, St. P, 1985, p. 157-167.

Millennium Ecosystem Assessment Board (MEA). 2005. Ecosystems and Human Well-being: Synthesis. 

Washington, DC: World Resources Institute. Available on the Internet at 

<http://www.who.int/globalchange/ecosystems/ecosystems05/en/index.html>.

Miller, P.R., O.C. Taylor, R.G. Wilhour. 1982. Oxidant air pollution effects on a western coniferous forest 

ecosystem. Corvallis, OR: U.S. Environmental Protection Agency, Environmental Research Laboratory 

(EPA600-D-82-276).

Pye, J.M. 1988. Impact of ozone on the growth and yield of trees: A review. Journal of Environmental Quality, 17, 

347-360.

Tingey, D.T., and Taylor, G.E. 1982. Variation in plant response to ozone: a conceptual model of physiological 

events. In M.H. Unsworth & D.P. Omrod (Eds.), Effects of Gaseous Air Pollution in Agriculture and 

Horticulture. (pp.113-138). London, UK: Butterworth Scientific.

U.S. Environmental Protection Agency (U.S. EPA). 2006. Ecological Benefits Assessment Strategic Plan. EPA-240-

R-06-001. Office of the Administrator. Washington, DC. October. Available on the Internet at 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/EcologBenefitsPlan.html>.
6-17

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis, 2008 National Ambient Air 

Quality Standards for Ground-level Ozone, Chapter 6. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC. March. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/6-

ozoneriachapter6.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2008b. Integrated Science Assessment for Oxides of Nitrogen 

and Sulfur—Ecological Criteria National (Final Report). National Center for Environmental Assessment, 

Research Triangle Park, NC. EPA/600/R-08/139. December. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>.

U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter 

(Final Report). EPA-600-R-08-139F. National Center for Environmental Assessment—RTP Division. 

December. Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

U.S. Environmental Protection Agency (U.S. EPA). 2013. Integrated Science Assessment of Ozone and Related 

Photochemical Oxidants (Final Report). EPA-600/R-10/076F. February. Available on the Internet at 

http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492. 

U.S. EPA. (U.S. Environmental Protection Agency). 2014 Welfare Risk and Exposure Assessment (Final). August. 

Available on the internet at: http://www.epa.gov/ttn/naaqs/standards/ozone/data/20141021welfarerea.pdf.

Winner, W.E. 1994. Mechanistic analysis of plant responses to air pollution. Ecological Applications, 4(4), 651-

661.

Winner, W.E., and C.J. Atkinson. 1986. Absorption of air pollution by plants, and consequences for growth. 

Trends in Ecology and Evolution 1:15-18.
6A-1

APPENDIX 6A: METHODS AND DATA USED TO DEVELOP ESTIMATES OF 

OZONE EFFECTS ON CROP AND FOREST PRODUCTIVITY

Incorporating the impacts of different ambient ozone concentration levels into 

FASOMGHG requires determining crop yield and forest productivity impacts associated with 

changes in concentrations. Productivity impacts are required for each crop/region and forest 

type/region combination included within the model. In this section, we describe our methods for 

calculating relative yield losses (RYLs) and relative yield gains (RYGs) of crops and tree species 

under alternative ambient ozone concentration levels.

These data are essential for our market analysis because crop and forest yields play an 

important role in determining the economic returns to agricultural and forest production 

activities. Thus, they affect landowner decisions regarding land use, crop mix, forest rotation 

lengths, production practices, and others. Alterations in ambient ozone concentration levels will 

therefore change the supply curves of U.S. agricultural and forest commodities, resulting in new 

market equilibriums. Because both the changes in ozone concentrations and the distribution of 

ozone-sensitive crops and tree species vary spatially, there may be substantial differences in the 

net impacts across regions. There may also be distributional impacts as commodity production 

shifts between regions in response to changes in relative productivity. 

6A.1 Methodology

There are several alternative metrics used for assessing ozone concentrations (see Lehrer et 

al. [2007] for more information). For this assessment, we are using the W126 metric, which is a 

weighted sum of all ozone concentrations observed from 8 a.m. to 8 p.m available in 2025 and 

2038. More specifically, we are using W126 ozone concentration surfaces generated using 

enhanced Voronoi Neighbor Averaging (eVNA). W126 concentration surfaces based on meeting 

the current ozone standard99 were provided by EPA in the previous iteration of this project 

(2013) and served again as the baseline for this analysis. According to information provided by 

EPA, the eVNA W126 ozone surface is built from monitor data fused with Community 

 

99The current primary and secondary ozone standards are 75 parts per billion (ppb) based on the annual fourthhighest daily maximum 8-hr concentration, averaged over 3 years. For the purposes of calculating impacts on crop 

yields and forest growth rates, we used the W126 equivalent of the current standard. 
6A-2

Multiscale Air Quality (CMAQ) model-based gradient interpolations. The spatial resolution of 

the ozone surface in ArcGIS Shapefile format is 12 km. 

County-level values were extracted from the eVNA W126 ozone surface using ArcGIS. 

Only the ozone concentrations for the cropland and forestland portions of the W126 ozone 

surface are used to derive the county-level average crop and forest W126 ozone levels, 

respectively. These weighting adjustments were made to better reflect the ozone concentration 

that would affect the specific portions of each county containing forested land or cropland, rather 

than basing county-level exposure on the ozone concentration across the whole county. Data 

from the 2011 USGS National Land Cover Database (NLCD), updated from the previously used 

2006 NLCD data are used to extract the cropland and forestland portions from the ozone surface 

(Jin et al., 2013). The maps below demonstrate the change in forest and cropland area resulting 

from updating the data from 2006 to 2011. 

Table 6A-1. Comparison of Total Cropland and Forestland NLCD Area (sq m)

2006 NLCD 2011 NLCD % Increase

Cropland Area 1,730,787,687,000 1,786,333,937,400 3.21

Forestland Area 1,938,825,202,500 1,963,044,107,100 1.25

Figure 6A-1. Cropland Area (sq m) by County according to NLCD 2006 Data
6A-3

Figure 6A-2. Cropland Area (sq m) by County according to NLCD 2011 Data

Figure 6A-3. Forest Area (sq m) by County according to NLCD 2006 Data
6A-4

Figure 6A-4. Forest Area (sq m) by County according to NLCD 2011 Data

6A.1.1 Calculation of Relative Yield Loss

The median W126 ozone concentration response (CR) functions for crops and tree 

seedlings in the 2007 EPA technical report (Lehrer et al., 2007) are used to calculate the RYLs 

for crops and tree species under each ambient ozone concentration scenario used in this analysis. 

Table 6A-2 presents the α and β parameters being used in the W126 ozone CR function for 

different crops and tree species. The W126 ozone CR function is as follows: RYL = 1 −

e

−(W126⁄α)

β

. 
6A-5

Table 6A-2. Parameter Values Used for Crops and Tree Species

α β

Crops

Corn 98.3 2.973

Sorghum 205.9 1.963

Soybean 110.0 1.367

Winter wheat 53.7 2.391

Potato 99.5 1.242

Cotton 94.4 1.572

Tree Species

Ponderosa 159.63 1.1900

Red alder 179.06 1.2377

Black cherry 38.92 0.9921

Tulip poplar 51.38 2.0889

Sugar maple 36.35 5.7785

Eastern white 63.23 1.6582

Red maple 318.12 1.3756

Douglas fir 106.83 5.9631

Quaking aspen 109.81 1.2198

Virginia pine 1,714.64 1.0000

6A.1.1.1 Relative Yield Loss for Crops

Specifically, for crops, we first calculate the FASOMGHG subregion RYLs for crops that 

have W126 ozone CR functions using the subregion-level, cropland-based ozone concentration 

values under each scenario. The FASOMGHG subregion-level ozone concentration values are 

initially calculated for all crops as the simple averages of the county-level ozone concentration 

values. For crops that do not have W126 ozone CR functions, we assign them W126 ozone CR 

functions based on the crop proxy mapping shown in Table 6A-3. This crop mapping was based 

on the authors’ judgment and previous experience.100 In addition, for oranges, rice, and tomatoes, 

which have ozone CR functions that are not W126-based (they are defined based on alternative 

measures of ozone levels), we directly used the median RYG values under the 13 ppm-hr ozone 

level reported in Table G-7 of Lehrer et al. (2007). More details on RYG are presented in further 

subsections. 

 

100 Also, note that FASOMGHG defines short-rotation woody trees such as hybrid poplar and willow as crops. 

Ozone impacts on short-rotation woody trees were based on ozone RYLs for aspen. 
6A-6

Table 6A-3. Mapping of Ozone Impacts on Crops to FASOMGHG Crops

Crops Used for 

Estimating Ozone 

Impacts FASOMGHG Crops

W126 Crops

Corn Corn

Cotton Cotton

Potatoes Potatoes

Winter wheat Soft white wheat, hard red winter wheat, soft red winter wheat, durum wheat, 

hard red spring wheat, oats, barley, rye, sugar beet, grazing wheat, and 

improved pasture

Sorghum Sorghum, silage, hay, sugarcane, switchgrass, miscanthus, energy sorghum, and 

sweet sorghum

Soybeans Soybeans and canola

Aspen (tree) Hybrid poplar, willow (FASOMGHG places short-rotation woody biomass 

production in the crop sector rather than in the forest sector)

Non-W126 Crops

Oranges Orange fresh/processed, grapefruit fresh/processed

Rice Rice

Tomatoes Tomato fresh/processed

Moreover, for crops that have county-level production data and W126 ozone CR functions 

(including functions based on proxy crops), we updated the RYLs with production-weighted 

W126 values. The 2012 USDA Census of Agriculture (Ag Census) county-level production data 

are used to derive the weighted FASOMGHG subregion RYLs, following Formula (6A.1). 

wRYLik = Ozone CR Functionk(

∑jProdijk∗W126ij

∑jProdijk

),(6A.1)

where i denotes FASOMGHG subregion, j indicates county, and k represents crop. Ozone 

CR Functionk refers to the ozone concentration response function for crop k. Prodijk represents 

the county-level production level of crop k, and W126ij represents the cropland-based ozone 

value for county j in subregion i. Finally, wRYLik stands for the weighted FASOMGHG 

subregion RYL for crop k. RYLs are calculated for each ozone concentration level being 

considered. 
6A-7

6A.1.1.2 Relative Yield Loss for Trees

The ozone CR functions for tree seedlings were used to calculate RYLs for FASOMGHG 

trees over their whole life span. To derive the FASOMGHG region-level RYLs for trees under 

each ozone concentration scenario, we used FASOMGHG region ozone values and the mapping 

in Table 6A-4.

Table 6A-4. Mapping of Ozone Impacts on Forests to FASOMGHG Forest Types

Tree Species Used for Estimating 

Ozone Impacts FASOMGHG Forest Type FASOMGHG Region(s)

Black cherry, tulip poplar Upland hardwood SC, SE

Douglas fir Douglas fir PNWW

Eastern white pine Softwood CB, LS

Ponderosa pine Softwood PNWE, PNWW, PSW, 

RM

Quaking aspen Hardwood RM

Quaking aspen, black cherry, red maple, 

sugar maple, tulip poplar

Hardwood CB, LS, NE

Red alder Hardwood PNWE, PNWW, PSW

Red maple Bottomland hardwood SC, SE

Virginia pine Natural pine, oak-pine, planted pine SC

Virginia pine, eastern white pine Natural pine, oak-pine, planted pine SE

Virginia pine, eastern white pine Softwood NE

Note: CB = Corn Belt; LS = Lake States; NE = Northeast; PNWE = Pacific Northwest—East side; PNWW = Pacific 

Northwest—West side; PSW = Pacific Southwest; RM = Rocky Mountains; SC = South Central; SE = Southeast.

Specifically, the FASOMGHG region-level RYLs are first calculated for each tree species 

listed in first column of Table 6A-4. Then, a simple average of RYLs for each tree species 

mapped to a FASOMGHG forest type in a given region is calculated. The mapping of tree 

species to FASOMGHG forest types is based on Elbert L. Little, Jr.’s Atlas of United States 

Trees (1971, 1976, 1977, 1978). Note that crop RYLs are generated at the FASOMGHG 

subregion level, whereas forest RYLs are calculated at the FASOMGHG region level, consistent 

with the greatest level of regional disaggregation available for these sectors within 

FASOMGHG. 

6A.1.1.3 Calculation of Relative Yield Gain

As described by Lehrer et al. (2007), the RYL is the relative yield loss compared with the 

baseline yield under a “clean air” environment. For implementation within FASOMGHG, we 

calculate the RYG for crops and trees from moving between ambient ozone concentrations (i.e., 

RYG is calculated as a change in RYL when moving between scenarios). 
6A-8

Thus, to obtain the RYG for crops and trees under alternative ozone concentrations, we 

need the RYLs under each scenario. For example, to derive RYG under the current standard 

75 ppb scenario relative to current conditions “currcond,” we use Formula (6A.2):

RYG75ppb =

1−RYL75ppb

1−RYLcurrcond

− 1 =

RYLcurrcond−RYL75ppb

1−RYLcurrcond

(6A.2)

The FASOMGHG subregion-level crop RYGs and the FASOMGHG region-level tree 

RYGs for changes associated with moving from one scenario to another were calculated for 

additional comparisons in the same way. 

6A.1.2 Conducting Model Scenarios in FASOMGHG

The current crop/forest budgets included in FASOMGHG are assumed to reflect 

input/output relationships under current ambient ozone concentrations as these budgets are based 

on historical data. To model the effects of changing ozone concentrations on the agricultural and 

forest sectors, the following five scenarios were constructed and run through the model:

1. “Current Conditions” scenario, where no RYGs of crops and trees are considered 

(assumed to be consistent with current ambient ozone concentration levels);

2. 75 ppb scenario, where crop and forest yields are assumed to increase by the 

percentages calculated in RYG75ppb, calculated relative to the current scenario;

3. 70 ppb scenario, using RYG70ppb, calculated relative to both current and 75 ppb 

scenarios

4. 65 ppb scenario, using RYG65ppb, calculated relative to both current and 75 ppb 

scenarios

5. 60 ppb scenario, using RYG60ppb, calculated relative to both current and 75 ppb 

scenarios

The time scope of the FASOMGHG model scenarios used for these analyses is 2000–

2050, solved in 5-year time steps.101 The crop and tree RYGs are introduced into the model 

starting in 2025. During the time-steps of 2025 and 2030, crop and tree RYGs are assumed to be 

same as the RYGs in 2025. After 2040, RYGs remain constant at the RYGs obtained in 2038. 

The special case is in 2035, for which the weighted average of RYGs in 2025 and 2038 (60% for 

RYGs in 2025 and 40% for RYGs in 2038) are assigned to RYGs in 2035. Figure 6A-5 presents 

 

101 Because of terminal period effects, the model is run out to the 2050 time period, but only results through the 2040 

model time period (representative of 2040–2044) are used in the analyses.
6A-9

the modeling process of simulating ozone scenarios using FASOMGHG. The changes in crop 

and tree yield growth potentially lead to new market equilibriums for agricultural and forestry 

commodities, as well as land use changes between agricultural and forestry uses and 

consequently GHG emissions and sequestration changes. 

By comparing the market equilibriums under different scenarios, we can calculate the 

welfare, land use, and GHG impacts of alternative ozone standards on the U.S. agricultural and 

forest sector, including changes in consumer and producer welfare, land use allocation, and GHG 

mitigation potential over time. 

Figure 6A-5. FASOMGHG Modeling Flowchart

6A.1.3 Data Inputs

In this section, we summarize the input data used in the FASOMGHG scenarios specified 

for this assessment. Following the methods described above, we calculated W126 ozone 

concentration levels by region and crop. Effects on crop yields and forest productivity were 

calculated for each FASOMGHG region. We present the values used as model inputs in tabular 
6A-10

and map format, with a primary focus here on comparison of the more stringent scenarios to the 

current standard.

6A.1.3.1 Ambient Ozone Concentration Data

The county-level forested and cropland W126 ozone values were aggregated at regional 

and sub-regional levels, respectively. 

Table 6A-5. Forestland W126 Ozone Values under Alternative Scenarios

2025 2038

FASOMGHG Region C.C.a

75 ppbb70 ppb 65 ppb 60 ppb 75 

ppbb

70 

ppb 

65 

ppb 

60 

ppb 

CB 11.84 5.24 4.58 3.11 1.99 5.24 4.58 3.10 1.99

GP c 8.95 5.15 4.47 3.73 2.87 5.06 4.37 3.63 2.79

LS 6.33 2.83 2.71 2.23 1.83 2.83 2.71 2.23 1.83

NE 8.48 3.65 3.19 2.29 1.64 3.65 3.19 2.29 1.64

PNWE 5.55 2.24 2.24 2.22 2.19 2.16 2.14 2.10 2.06

PNWW 3.79 1.49 1.49 1.49 1.49 1.49 1.48 1.48 1.48

PSW 17.28 6.65 6.65 6.64 6.54 4.52 3.61 2.91 2.45

RM 13.36 7.32 7.29 6.83 5.75 6.69 6.57 6.08 5.07

SC 11.84 4.12 3.46 2.48 1.75 4.11 3.46 2.48 1.75

SE 13.10 3.03 2.81 2.26 1.82 3.03 2.81 2.26 1.82

SWc 10.03 6.08 4.58 3.43 2.47 6.04 4.55 3.40 2.44

a Current Conditions

b Current Standard

c GP and SW are modeled as agriculture-only regions in FASOMGHG 

Note: CB = Corn Belt; GP = Great Plains; LS = Lake States; NE = Northeast; PNWE = Pacific Northwest—East side; 

PNWW = Pacific Northwest—West side; PSW = Pacific Southwest; RM = Rocky Mountains; SC = South Central; SE 

= Southeast; SW = Southwest.

Table 6A-5 displays the agricultural W126 ozone values at the sub-region level. Similar to 

the forest ozone values, the Pacific Southwest, South Central, Southeast, and Rocky Mountains 

regions experience the greatest agricultural ozone reductions under the 75 ppb scenario 

compared with current conditions. The Corn Belt, Southwest, and Northwest regions also see 

noteworthy agricultural ozone reductions.
6A-11

Table 6A-6. Cropland W126 Ozone Values under Modeled Scenarios

2025 2038

FASOMGHG Region Sub-region C.C.a75 ppbb70 ppb 65 ppb 60 ppb 75 ppbb70 ppb 65 ppb 60 ppb 

CB Illinois, Northern 7.84 3.90 3.51 2.47 1.65 3.90 3.51 2.46 1.65

Illinois, Southern 11.46 6.54 5.79 3.86 2.38 6.53 5.79 3.86 2.38

Indiana, Northern 10.38 4.65 4.43 3.03 1.99 4.64 4.43 3.02 1.99

Indiana, Southern 13.13 6.66 6.40 4.22 2.54 6.65 6.40 4.22 2.54

Iowa, Central 5.71 2.79 2.46 2.06 1.65 2.79 2.46 2.06 1.65

Iowa, Northeast 6.23 2.69 2.42 2.02 1.67 2.69 2.42 2.02 1.67

Iowa, Southern 6.65 3.31 2.68 2.07 1.53 3.31 2.68 2.06 1.53

Iowa, Western 5.74 3.33 2.90 2.51 2.07 3.31 2.90 2.49 2.07

Missouri 11.50 5.17 4.01 2.76 1.81 5.17 4.01 2.76 1.81

Ohio, Northeast 12.71 5.34 5.08 3.51 2.35 5.34 5.08 3.51 2.35

Ohio, Northwest 12.07 5.89 5.65 3.88 2.54 5.88 5.65 3.87 2.54

Ohio, Southern 13.49 5.93 5.73 3.64 2.12 5.93 5.73 3.64 2.12

GP c Kansas 10.93 8.17 6.88 5.54 4.12 8.02 6.74 5.39 4.03

Nebraska 8.87 5.81 5.27 4.41 3.24 5.65 5.12 4.26 3.13

North Dakota 4.46 3.03 3.00 2.86 2.72 3.03 2.96 2.86 2.69

South Dakota 5.66 3.73 3.50 3.12 2.68 3.67 3.43 3.09 2.62

LS Michigan 9.50 5.20 4.96 3.57 2.50 5.20 4.96 3.57 2.50

Minnesota 4.97 2.63 2.49 2.31 2.12 2.63 2.49 2.31 2.11

Wisconsin 7.02 3.04 2.85 2.25 1.78 3.04 2.85 2.25 1.78

(continued)
6A-12

2025 2038

FASOMGHG Region Sub-region C.C.a 75 ppb 70 ppb 65 ppb 60 ppb 75 ppb 70 ppb 65 ppb 60 ppb 

NE Connecticut 11.90 4.53 3.69 2.71 1.85 4.53 3.69 2.71 1.85

Delaware 17.45 7.88 6.29 4.15 2.58 7.88 6.29 4.15 2.58

Maine 3.70 1.62 1.47 1.30 1.20 1.62 1.47 1.30 1.20

Maryland 17.20 7.37 5.88 3.77 2.29 7.37 5.87 3.77 2.29

Massachusetts 10.23 2.98 2.39 1.69 1.18 2.98 2.39 1.69 1.18

New Hampshire 5.84 1.99 1.73 1.32 1.06 1.99 1.73 1.32 1.06

New Jersey 16.70 6.95 5.42 3.49 2.19 6.93 5.42 3.49 2.19

New York 8.36 3.93 3.53 2.78 2.18 3.93 3.53 2.78 2.18

Pennsylvania 12.14 5.37 4.42 2.98 1.97 5.37 4.42 2.98 1.97

Rhode Island 11.72 4.47 3.71 2.69 1.85 4.47 3.71 2.69 1.85

Vermont 5.51 2.01 1.84 1.55 1.35 2.01 1.84 1.55 1.34

West Virginia 10.74 4.63 4.24 2.73 1.68 4.63 4.24 2.73 1.68

PNWE Oregon 6.66 2.64 2.64 2.62 2.55 2.53 2.48 2.43 2.35

Washington 4.96 1.79 1.79 1.79 1.78 1.78 1.78 1.77 1.77

PNWW 3.59 1.35 1.35 1.36 1.36 1.34 1.34 1.35 1.35

PSW California, Northern 21.86 9.61 9.61 9.58 9.49 6.20 4.54 3.21 2.35

California, Southern 21.46 12.49 12.48 12.43 12.31 6.20 5.52 4.82 4.23

(continued)
6A-13

2025 2038

FASOMGHG Region Sub-region C.C.a 75 ppb 70 ppb 65 ppb 60 ppb 75 ppb 70 ppb 65 ppb 60 ppb 

RM Arizona 13.53 9.63 9.63 9.17 7.91 8.24 8.11 7.51 6.24

Colorado 16.33 10.71 10.41 8.98 6.39 10.23 9.88 8.46 6.06

Idaho 12.75 5.05 5.04 4.79 4.15 4.62 4.57 4.30 3.75

Montana 6.61 3.25 3.25 3.15 3.00 3.16 3.16 3.09 2.89

Nevada 15.48 6.57 6.56 6.29 5.58 5.58 5.25 4.75 4.03

New Mexico 12.58 8.54 8.15 7.40 6.20 8.31 7.89 7.11 5.91

Utah 18.06 8.62 8.61 7.81 5.85 7.53 7.40 6.58 4.95

Wyoming 14.28 7.56 7.50 6.79 5.20 7.02 6.91 6.22 4.76

SC Alabama 13.16 2.89 2.70 2.29 1.91 2.89 2.70 2.29 1.91

Arkansas 12.30 4.81 3.76 2.57 1.71 4.81 3.76 2.57 1.70

Kentucky 13.79 5.59 5.35 3.52 2.16 5.59 5.34 3.52 2.16

Louisiana 9.59 4.62 3.58 2.66 1.95 4.62 3.58 2.66 1.95

Mississippi 11.21 3.32 2.75 2.11 1.55 3.32 2.75 2.11 1.54

Tennessee 15.55 4.57 4.22 2.85 1.83 4.57 4.22 2.85 1.83

Texas, East 9.92 5.67 3.88 2.79 1.99 5.64 3.87 2.77 1.98

SE Florida 9.16 2.95 2.86 2.76 2.60 2.95 2.86 2.76 2.60

Georgia 12.79 2.56 2.45 2.21 1.95 2.56 2.45 2.21 1.95

North Carolina 14.31 3.35 3.08 2.49 2.00 3.35 3.08 2.49 2.00

South Carolina 12.82 2.18 2.07 1.77 1.52 2.18 2.07 1.77 1.51

Virginia 12.62 3.95 3.47 2.35 1.59 3.95 3.47 2.35 1.59

(continued)
6A-14

2025 2038

FASOMGHG Region Subregion C.C.a75 ppb 70 ppb 65 ppb 60 ppb 75 ppb 70 ppb 65 ppb 60 ppb 

SWc Oklahoma 11.52 7.44 5.72 4.31 3.07 7.38 5.67 4.25 3.04

Texas, Central 

Blacklands 9.17 5.45 4.06 3.05 2.18 5.43 4.05 3.03 2.16

Texas, Coastal 

Bend 7.24 5.23 4.13 3.24 2.44 5.23 4.12 3.23 2.42

Texas, Edwards 

Plateau 9.02 6.38 5.24 4.28 3.38 6.32 5.17 4.20 3.31

Texas, High Plains 11.74 8.49 7.50 6.44 5.18 8.36 7.38 6.27 5.03

Texas, Rolling 

Plains 10.64 7.13 5.71 4.56 3.45 7.07 5.66 4.48 3.38

Texas, South 4.33 3.45 2.91 2.48 2.09 3.44 2.90 2.47 2.05

Texas, Trans Pecos 11.83 7.49 7.09 6.61 5.86 7.38 6.96 6.44 5.68

a Current Conditions

b Current Standard

c GP and SW are modeled as agriculture-only regions in FASOMGHG
6-15

Figure 6A-6 presents the incremental ozone reductions under alternative 2038 ozone 

standards with respect to the current 75 ppb standard. As the standard is tightened from the 70 

ppb scenario to the 60 ppb scenario, the greatest ozone reductions are observed in the Pacific 

Southwest region. Central parts of the Rocky Mountains region extending into northern areas of 

the Southwest and South central regions also see substantial ozone reductions. These ozone 

reductions would affect the production of crops and timber that are susceptible to ground-level 

ozone in these regions. 

Figure 6A-6. Ozone Reductions with Respect to 75 ppb under Alternative Scenarios
6-16

6A.1.3.2 Changes in Crop and Forest Yields with Respect to 75 ppb Scenario

Figures 6A-7 through 6A-12 display major crops’ RYGs under alternative ozone 

standards scenarios at the FASOMGHG subregion level, with respect to the current 75 ppb 

standard. These are the values that were directly incorporated into FASOMGHG to define the 

scenarios modeled. Figures 6A-13 and 6A-14 display changes in forest RYGs. As discussed 

previously, the Rocky Mountains, Corn Belt, and parts of the southern regions of the United 

States (e.g., within the Pacific Southwest, South Central, and Southeast regions) are shown to 

experience the most significant further ozone reductions under the alternative policy scenarios. 

Hence, one would expect to see the most sizable increases in RYGs for crops and tree species 

grown in those regions. This finding is consistent with our calculations. 
6-17

Figure 6A-7. Percentage Changes in Corn RYGs with Respect to the 75 ppb Scenario
6-18

Figure 6A-8. Percentage Changes in Cotton RYGs with Respect to the 75 ppb Scenario
6-19

Figure 6A-9. Percentage Changes in Potato RYGs with Respect to the 75 ppb Scenario
6-20

Figure 6A-10.Percentage Changes in Sorghum RYGs with Respect to the 75 ppb Scenario
6-21

Figure 6A-11.Percentage Changes in Soybean RYGs with Respect to the 75 ppb Scenario
6-22

Figure 6A-12.Percentage Changes in Winter Wheat RYGs with Respect to the 75 ppb 

Scenario
6-23

Figure 6A-13.Percentage Changes in Softwood RYGs with Respect to the 75 ppb Scenario
6-24

Figure 6A-14.Percentage Changes in Hardwood RYGs with Respect to the 75 ppb Scenario
6-25

6A.2 Model Results

FASOMGHG was used to estimate the projected effects of alternative ozone 

concentration standards on the U.S. agricultural and forestry sectors. As introduced earlier, the 

comparisons considered for this report focus on the differences between a scenario assuming 

compliance with the existing 2008 standards (75 ppb) and scenarios in which three more 

stringent ozone standards are met. Those three scenarios are 70 ppb, 65 ppb, and 60 ppb W126 

values. Our analysis included changes to production, prices, forest inventory, land use, welfare, 

and GHG mitigation potential associated with achieving each of the more stringent standards. 

6A.2.1 Agricultural Sector

Ozone negatively affects growth in many plants, leading to lower crop yields. In addition, 

some crops are more sensitive to ozone than others, so the percentage changes in yield will vary 

by crop and region. However, reducing ambient ozone concentrations would generally increase 

agricultural yields and total production, though the reductions in ozone concentrations that would 

be achieved under a given standard vary across regions. Our analysis began by determining the 

extent to which current yield losses caused by ozone could be reversed by reducing ozone levels. 

Increased crop yields lead to a greater available supply of most agricultural crops, which in turn 

tends to reduce market prices. There is also an overall tendency toward acreage shifting away 

from ozone-sensitive crops. In general, impacts in the agricultural sector are relatively limited, 

especially when compared with the forestry sector. By and large, more stringent standards led to 

increased incremental impacts, but the additional impact in moving to increasingly stringent 

ozone standards was relatively small. 

6A.2.1.1 Production and Prices

Changes in U.S. agricultural production and prices were measured using Fisher indices

(sees Tables 6A-7 and 6A-7a).102 Both primary and secondary commodity production levels are 

projected to increase by 2040 as a result of heightened productivity. Agricultural production 

changes were generally relatively small across products, rarely exceeding an increase of 0.50% 

with respect to the current standard and often changing by 0.02% or less.

 

102 The Fisher price index is known as the “ideal” price index. It is calculated as the geometric mean of an index of 

current prices and an index of past prices.
6-26

Table 6A-7. Agricultural Production Fisher Indices (Current conditions =100)

Sector Policy 2010 2020 2030 2040

Primary 

Commodities

Crops 75 ppb 100.0 99.8 100.7 101.3

70 ppb 100.0 99.8 100.7 101.3

65 ppb 100.0 99.8 100.8 101.4

60 ppb 100.0 99.8 100.9 101.4

Livestock 75 ppb 100.0 99.9 100.2 100.3

70 ppb 100.0 99.9 100.2 100.3

65 ppb 100.0 99.9 100.2 100.4

60 ppb 100.0 99.9 100.2 100.5

Farm productsa 75 ppb 100.0 99.8 100.1 100.3

70 ppb 100.0 99.8 100.4 100.3

65 ppb 100.0 99.8 100.5 100.4

60 ppb 100.0 99.9 100.5 100.5

Secondary

Commodities

Processed 75 ppb 100.0 100.0 100.1 100.1

70 ppb 100.0 99.9 100.1 100.1

65 ppb 100.0 99.9 100.1 100.1

60 ppb 100.0 100.0 100.1 100.1

Meats 75 ppb 100.0 100.0 100.1 100.3

70 ppb 100.0 100.0 100.1 100.2

65 ppb 100.0 100.0 100.1 100.3

60 ppb 100.0 100.0 100.2 100.3

Mixed feeds 75 ppb 100.0 99.9 100.1 100.5

70 ppb 100.0 99.9 100.1 100.5

65 ppb 100.0 99.9 100.1 100.5

60 ppb 100.0 99.9 100.2 100.5

a

Farm Products is the composite of Crops and Livestock.

Table 6A-7a. Agricultural Price Fisher Indices (Current Conditions = 100)

Sector Policy 2010 2020 2030 2040

Primary Commodities

Crops 75 ppb 100.0 100.2 98.5 97.2

70 ppb 100.0 100.2 98.4 97.0
6-27

Sector Policy 2010 2020 2030 2040

65 ppb 100.0 100.2 98.3 97.0

60 ppb 100.0 100.2 98.3 97.0

Livestock 75 ppb 100.1 101.4 99.2 99.9

70 ppb 100.6 102.2 100.3 97.0

65 ppb 100.1 102.2 100.6 97.0

60 ppb 100.1 101.8 100.4 97.0

Farm productsa 75 ppb 100.0 100.2 100.1 99.9

70 ppb 100.0 100.2 98.4 97.0

65 ppb 100.0 100.2 98.3 97.0

60 ppb 100.0 100.2 98.3 97.0

Secondary Commodities

Processed 75 ppb 100.1 100.3 98.8 98.3

70 ppb 100.1 100.3 98.6 98.2

65 ppb 100.1 100.3 98.2 98.0

60 ppb 100.1 100.3 98.2 97.9

Meats 75 ppb 100.1 99.8 100.1 99.9

70 ppb 100.1 99.8 100.1 99.9

65 ppb 100.1 99.8 100.1 100.0

60 ppb 100.1 99.8 100.1 100.0

Mixed feeds 75 ppb 100.1 99.7 98.2 99.3

70 ppb 100.1 99.7 98.1 99.5

65 ppb 100.1 99.7 97.4 99.3

60 ppb 100.1 99.7 97.5 99.5

Increased production led to a general decline in market prices because the equilibrium 

price adjusts to higher levels of supply. This result is consistent with expectations because higher 

productivity leads to greater supply, which tends to decrease market prices. Changes in price 

were generally more pronounced than changes in production, with the largest decreases in the 

Farm Products, Livestock, and Processed categories. Agricultural prices tend to decline by a 

greater percentage than production increases because the demand for most agricultural 
6-28

commodities is inelastic.103 However, almost all declines in price were less than 2.0% of prices at 

the current standards, and most were less than 0.5%. 

6A.2.1.2 Crop Acreage

Crop acreage was projected to decline with the introduction of the ozone standards because

additional productivity per acre reduces the demand for crop acreage. In aggregate, farmers will 

be able to meet the demand for agricultural commodities using less land under scenarios with 

lower ozone concentrations. Consistent with these expectations, the total cropped area is slightly 

smaller for each model year in the alternative standard cases. However, land allocation also 

depends on relative returns across various uses and is influenced by forest harvest timing. 

Changes in land allocation between the agricultural and forestry sectors are discussed later in this 

section.

Table 6A-8 provides projections of acreage in each of the major U.S. crops, as well as 

composites of all remaining crops and total cropland. The absolute change relative to the current 

standard is presented for each alternative standard. Larger changes occurred in sorghum acreage, 

whereas only minor changes occurred in all other crops, leading to almost no net change in crop 

acreage across all crops. This shift occurred largely because of differential crop sensitivity to 

ozone concentrations. Note that the sum of the crop-specific changes will not necessarily equal 

the total changes shown in Table 6A-8 because some double-cropping is reflected in the model 

(e.g., soybeans and winter barley). 

 

103 Demand elasticities are measures of the responsiveness of the quantity demanded to a change in price. 

Commodities with inelastic demands are those where consumers change the quantity of a good they purchase by a 

smaller percentage than the change in market price. Many food products fall into this category because they are 

relatively low-priced necessities. 
6-29

Table 6A-8. Major Crop Acreage, Million Acres

Crop Policy 2010 2020 2030 2040

Corn

75 ppb 91.2 85.8 77.4 70.9

Change with Respect to Current Standard

70 ppb 0.00 0.02 0.00 -0.05

65 ppb 0.00 0.01 0.06 -0.07

60 ppb 0.00 0.01 0.04 -0.08

Soybeans

75 ppb 4.9 5.2 5.8 6.9

Change with Respect to Current Standard

70 ppb 0.00 0.00 0.00 0.00

65 ppb 0.00 0.00 0.00 -0.03

60 ppb 0.00 0.00 0.00 -0.03

Hay

75 ppb 43.7 41.1 41.7 42.0

Change with Respect to Current Standard

70 ppb 0.00 -0.02 0.03 0.00

65 ppb 0.00 -0.01 0.05 -0.01

60 ppb 0.00 0.01 0.06 -0.07

Hard Red 

Winter Wheat

75 ppb 25.5 23.7 23.2 22.4

Change with Respect to Current Standard

70 ppb 0.00 0.00 0.00 -0.06

65 ppb 0.00 0.00 -0.06 -0.11

60 ppb 0.00 0.00 -0.03 -0.10

Cotton

75 ppb 13.6 13.6 14.2 14.0

Change with Respect to Current Standard

70 ppb 0.00 0.00 -0.04 -0.01

65 ppb 0.00 0.00 -0.05 -0.01

60 ppb 0.00 0.00 -0.04 0.00

Hard Red 

Spring Wheat

75 ppb 13.5 12.9 13.4 13.0

Change with Respect to Current Standard

70 ppb 0.00 0.00 0.01 0.01

65 ppb 0.00 0.01 -0.03 0.00

60 ppb 0.00 0.01 -0.08 -0.01

Sorghum

75 ppb 73.1 71.9 72.0 72.2

Change with Respect to Current Standard

70 ppb 0.00 0.20 0.02 -4.88

65 ppb 0.00 0.24 0.02 -4.90

60 ppb 0.00 0.26 0.09 -4.89

Switch Grass

75 ppb 0.1 0.1 0.1 0.1

Change with Respect to Current Standard
6-30

Crop Policy 2010 2020 2030 2040

70 ppb 0.00 0.00 0.00 0.00

65 ppb 0.00 0.00 0.00 0.00

60 ppb 0.00 0.00 0.00 0.00

All Othersa

75 ppb 352.2 366.7 355.8 340.0

Change with Respect to Current Standard

70 ppb 0.00 0.10 0.01 -0.17

65 ppb 0.00 0.13 -0.12 -0.32

60 ppb 0.00 0.19 -0.25 -0.45

Total

75 ppb 5.6 5.4 4.6 4.0

Change with Respect to Current Standard

70 ppb 0.00 0.00 0.01 -0.01

65 ppb 0.00 -0.01 0.02 0.00

60 ppb 0.00 -0.01 0.01 0.00

a Canola, durum wheat, fresh grapefruit, fresh orange, fresh tomato, grazing wheat, hybrid poplar, oats, potato, 

processed grapefruit, processed orange, processed tomato, rice, rye, silage, soft red winter wheat, soft white 

wheat, spring barley, sugar beet, sugarcane, sweet sorghum, winter barley. 

6A.2.2 Forestry Sector

As with agricultural crops, ozone diminishes growth in most tree species, and our analysis 

began by estimating how much of this diminished growth would be reversed under the more 

stringent ozone standards. Impacts are significantly higher in the forestry sector, especially in 

hardwood species and species more prevalent in the southern regions. Impacts are more 

significant for southern regions because of the higher baseline ozone concentrations in the South 

Central and Southeast regions. Higher initial concentrations resulted in higher reductions to meet 

the alternative standards and, thus, higher impacts to tree growth. This relationship also 

contributes to the larger changes in the forestry sector as a whole. 

6A.2.2.1 Production and Prices

Reducing ozone concentrations led to increased forest growth, which was reflected in 

increased production in FASOMGHG. Some of the most substantial ozone standard impacts 

occurred in saw log and pulp log harvest quantities and prices. Compared with the current 

standard, alternative standard cases had consistently higher production except for hardwood pulp 

logs in 2030 and softwood pulp logs in 2040, where production increased only marginally and at 

times fell below the baseline estimates, especially in 2040. The most significant impacts 
6-31

occurred in hardwood pulp logs, where harvests were projected to be more than 1% higher than 

under the current standard level by 2040 for the 60 ppb concentration. There are some cases 

where production of pulp logs and saw logs moved in opposite directions. There are two primary 

explanations for these trends. The first is that as softwood saw log production expands, the price 

for softwood saw logs drops, allowing processers to substitute saw logs for cases of production 

in which pulp logs are traditionally used. The second is that even when the primary log size 

being harvested is pulp logs, saw logs will generally also be present because of natural variation 

in tree growth rates (and vice versa for harvest of saw logs). With higher growth rates, there 

would tend to be more saw logs in stands harvested primarily for pulp logs over time. 

The largest changes in production occurred at the 60 ppb level. Changes from the current 

standard to 70 ppb were fairly small, but increased in 65 ppb, and changes were even larger in 60 

ppb. Table 6A-9 presents these changes by major product.

The impact of policy intervention on timber market prices was more substantial than the 

change in production in terms of percentage changes compared with the current standard. 

Although increases in production of forest products did not exceed 1.5% compared with the 

current standard, changes in price were as large as 12.8%. As with agricultural products, many 

forest products have relatively inelastic demand so prices tend to change by a larger percentage 

than quantities. Table 6A-10 lists absolute changes with respect to the current standard, whereas 

Table 6A-11 lists the percentage change in forest product prices for each year, alternative 

standard, and forest product. 

Table 6A-9. Forest Products Production, Million Cubic Feet

Product Policy 2010 2020 2030 2040

Hardwood Saw logs 75 ppb 3,588 3,394 3,708 4,246

Change with Respect to Existing Standard

70 ppb 1 1 38 3

65 ppb 1 0 33 -6

60 ppb 3 0 54 -13

Hardwood Pulp logs 75 ppb 2,448 2,162 2,510 2,220

Change with Respect to Existing Standard

70 ppb 2 1 -29 3

65 ppb 3 -2 -29 14
6-32

60 ppb 10 -1 -40 25

Softwood saw logs 75 ppb 4,568 5,114 5,449 6,542

Change with Respect to Existing Standard

70 ppb -1 10 -2 7

65 ppb 0 22 1 53

60 ppb 5 35 21 61

Softwood pulp logs 75 ppb 3,437 3,878 4,350 4,298

Change with Respect to Existing Standard

70 ppb 1 -2 3 -17

65 ppb 0 1 4 -23

60 ppb 2 1 8 -8

Table 6A-10. Forest Product Prices, U.S. Dollars per Cubic Foot

Product Policy 2010 2020 2030 2040

Hardwood saw logs 75 ppb 0.80 0.89 0.58 0.33

Change with Respect to Existing Standard

70 ppb 0.00 -0.01 -0.01 -0.02

65 ppb 0.00 -0.01 -0.02 -0.03

60 ppb 0.00 -0.01 -0.03 -0.04

Hardwood pulp logs 75 ppb 0.30 0.64 0.45 0.24

Change with Respect to Current Standard

70 ppb 0.00 -0.01 -0.01 -0.01

65 ppb 0.00 -0.01 -0.03 -0.02

60 ppb 0.00 -0.02 -0.04 -0.03

Softwood saw logs 75 ppb 2.46 2.08 1.78 1.50

Change with Respect to Current Standard

70 ppb 0.00 0.00 0.00 0.00

65 ppb 0.00 -0.01 -0.01 -0.02

60 ppb -0.01 -0.02 -0.02 -0.04

Softwood pulp logs 75 ppb 1.49 1.33 1.47 1.14

Change with Respect to Current Standard

70 ppb 0.00 0.00 0.00 0.00

65 ppb 0.00 0.00 -0.01 -0.02

60 ppb -0.01 0.00 -0.03 -0.05
6-33

Table 6A-11. Forest Product Prices and Percentage Change, U.S. Dollars per Cubic Foot

Product Policy 2010 2020 2030 2040

Hardwood saw logs 75 ppb 0.80 0.89 0.58 0.33

% Change with Respect to Existing Standard

70 ppb -0.41% -0.75% -2.16% -5.07%

65 ppb -0.24% -1.00% -3.89% -9.11%

60 ppb -0.25% -1.22% -5.60% -11.36%

Hardwood pulp logs 75 ppb 0.30 0.64 0.45 0.24

% Change with Respect to Existing Standard

70 ppb 0.07% -0.96% -3.19% -5.15%

65 ppb 0.12% -1.92% -5.57% -10.53%

60 ppb -0.06% -2.87% -8.98% -12.75%

Softwood saw logs 75 ppb 2.46 2.08 1.78 1.50

% Change with Respect to Existing Standard

70 ppb 0.01% -0.17% -0.16% -0.13%

65 ppb 0.03% -0.27% -0.36% -1.08%

60 ppb -0.28% -0.97% -1.20% -2.65%

Softwood pulp logs 75 ppb 1.49 1.33 1.47 1.14

% Change with Respect to Existing Standard

70 ppb -0.05% 0.28% -0.28% -0.13%

65 ppb -0.18% 0.10% -0.76% -1.90%

60 ppb -0.57% -0.27% -1.87% -4.11%

6A.2.2.2 Forest Acres Harvested

Harvested acres are projected to decline in hardwoods as a result of higher productivity in 

the policy cases. Conversely softwood acres harvested increases over time. The difference 

between the hardwood harvested acres in the current standard case and in the alternative 

standards widens from 2010 to 2040, increasing to a difference of more than 4% under the 60 

ppb case. The impact to total acres of softwood harvested shows a less uniform pattern, with the 

largest impacts under the 65 ppb scenario, and an increase of up to 6% of harvested acres by 

2040 compared to 2010. Table 6A-12 presents the model results for forest acres harvested.
6-34

Table 6A-12. Forest Acres Harvested, Thousand Acres

Product Policy 2010 2020 2030 2040

Total hardwood 75 ppb 14,923 11,701 12,277 13,138 

Change with Respect to Current Standard

70 ppb 4 50 -139 -151

65 ppb 8 37 -265 -352

60 ppb 37 50 -377 -513

Total softwood 75 ppb 17,539 16,158 14,911 19,031

Change with Respect to Current Standard

70 ppb -11 35 47 106 

65 ppb -13 107 223 361 

60 ppb -2 142 328 314 

6A.2.2.3 Forest Inventory

Under FASOMGHG definitions, existing inventory includes only trees that have been 

standing since the initial model year of 2000. All trees planted since then, including both 

reforestation and afforestation, are included in new inventory. The model projected significant 

increases in existing inventory for hardwood species under the current standard, and consistent 

with the increase in the acres harvested of softwood, the existing inventory of softwoods declines 

through 2040 under the current standard. The difference in responses is partially explained by 

differential sensitivity to ozone between species. Hardwood species show a much higher 

sensitivity to ozone levels and are thus modeled to respond more dramatically to reductions in 

ozone concentration. 

Some relatively large differences between ozone standards occurred in the forest inventory 

projections. For example, existing hardwood inventory was projected to be 4.0% small under the 

60 ppb case than the 70 ppb case by 2040. New hardwood inventory is similarly sensitive, with 

the model projecting a 2% decrease for this same comparison. For new and existing inventory of 

both hardwoods and softwoods, the largest impacts occurred at the 70 ppb standard. This type of 

nonlinear response can occur because of differences in the relative impacts on alternative forest 

and agricultural products that lead to land reallocation. Table 6A-13 presents the model results 

for forest inventory.
6-35

Table 6A-13. Existing and New Forest Inventory, Million Cubic Feet

Product Policy 2010 2020 2030 2040

Existing Hardwood 75 ppb 281,924 273,055 292,685 306,296

Change with Respect to Existing Standard 

70 ppb 0 -64 1,983 4,998

65 ppb 0 -138 5,974 14,937

60 ppb 0 -278 8,896 22,548

Existing Softwood 75 ppb 184,828 153,771 135,137 133,794

Change with Respect to Existing Standard 

70 ppb 0 -19 185 494

65 ppb 0 -38 683 1,583

60 ppb -6 -77 1,059 2,553

New Hardwood 75 ppb 1,932 9,437 18,872 29,732

Change with Respect to Existing Standard 

70 ppb 0 -152 -107 77

65 ppb 0 -150 -63 577

60 ppb 1 -156 0 931

New Softwood 75 ppb 8,837 64,254 114,454 128,581

Change with Respect to Existing Standard 

70 ppb 0 -42 -17 -497

65 ppb 4 6 -128 -1,164

60 ppb 7 -18 -312 -1,798

6A.2.3 Cross-Sectoral Policy Impacts

One of the advantages of a model such as FASOMGHG for analysis of impacts on major 

land-using activities is the ability to account for shifts in land use. Differentiated impacts on 

productivity across products will lead to changes in market prices and in the relative profitability 

of alternative land uses. In response, landowners will change their allocation of land across 

different productive activities, which will contribute to market impacts. In addition, these 

changes in land use have implications for GHG emissions and other environmental impacts. In 

this section, we discuss changes in land use, net GHG emissions, and producer and consumer 

welfare across the agricultural and forest sectors. 
6-36

6A.2.3.1 Land Use

FASOMGHG projected changes in eight land use categories: existing forest, 

reforestation, afforestation, cropland, pasture, cropland pasture,

104 and lands enrolled in the 

Conservation Research Program (CRP).

105 The largest impacts under the current standard were 

projected in afforestation. The general projected pattern under the alternative standards within 

these categories was a decline in reforested area, afforested area, and cropland in 2030 and 2040, 

coupled with increases in the pasture and cropland pastured areas. There was no change in 

acreage retained in CRP or rangeland.

The incremental impact of more stringent ozone standards appears to be non-linear in 

some cases, especially in existing and reforested areas. Existing forest exhibits a general decline 

in the area under the 70 ppb scenario; in the 60 ppb scenario, however, there are small declines 

through 2020, followed by large increases in 2030 and 2040. This is due to the different trends in 

the hardwood and softwood species. Table 6A-14 presents the model results by major land use 

type.

Table 6A-14. Land Use by Major Category, Thousand Acres

Product Policy 2010 2020 2030 2040

Existing forest 75 ppb 257,565 201,587 161,426 131,210

Change with Respect to Current Standard

70 ppb -7 82 -113 -269

65 ppb 0 0 0 0

60 ppb -2 -84 159 691

Reforested 75 ppb 72,201 117,974 148,837 172,267

Change with Respect to Current Standard

70 ppb -10 -7 -56 -189

65 ppb -16 52 -212 -878

60 ppb 28 107 -267 -1,346

Afforested 75 ppb 14,086 10,886 6,404 11,474

Change with Respect to Current Standard

 

104 Cropland pasture is managed land suitable for crop production (i.e., relatively high productivity) that is being 

used as pasture.

105 Rangeland estimates are also included, but rangeland is held fixed in FASOMGHG by assumption because it 

cannot be allocated to any other use.
6-37

Product Policy 2010 2020 2030 2040

70 ppb 0 0 -134 -134

65 ppb 0 0 -309 -309

60 ppb 0 0 -427 -427

Cropland 75 ppb 311,713 313,325 304,227 292,678

Change with Respect to Current Standard

70 ppb 0 96 -38 -60

65 ppb 0 124 -102 -157

60 ppb 0 180 -157 -200

Pasture 75 ppb 84,280 85,049 86,133 82,571

Change with Respect to Current Standard

70 ppb 3 -7 86 64

65 ppb 10 -11 189 168

60 ppb 10 24 257 263

Cropland pasture 75 ppb 45,381 44,634 55,061 61,042

Change with Respect to Current Standard

70 ppb 0 1 29 51

65 ppb 0 1 161 216

60 ppb 0 6 235 277

Rangeland 75 ppb 302,210 301,104 300,049 299,039

Change with Respect to Current Standard

70 ppb 0 0 0 0

65 ppb 0 0 0 0

60 ppb 0 0 0 0

CRP 75 ppb 36,879 36,659 36,659 36,659

Change with Respect to Current Standard

70 ppb 0 0 0 0

65 ppb 0 0 0 0

60 ppb 0 0 0 0

6A.2.3.2 Welfare

Welfare impacts resulting from the implementation of alternative standard levels

followed the same pattern between the agriculture and forestry sectors, although it was more 

pronounced in forestry. Consumer surplus typically increased in both cases as higher 
6-38

productivity under reduced ozone conditions tended to increase total production and reduce 

market prices. Because demand for most forestry and agricultural commodities is inelastic, there 

are more instances in which producer surplus declines. In some year/ozone concentration 

combinations, the effect of falling prices on producer profits more than outweighs the effects of 

higher production levels. 

Percentage changes in agricultural sector consumer and producer surplus between the

current standard and the alternative standards were relatively small in many cases, with the 

largest percentage change being a 5.8% decline in producer surplus in the 2040 model period. 

However, the agricultural sector is a very large market, and even small percentage changes in 

welfare can result in annualized values of tens or even hundreds of millions of dollars. Table 6A15 provides consumer and producer surplus for the agricultural sectors under the current 

standard, along with the change in surplus for each alternative standard. There is considerable 

variability in the magnitude of consumer and producer impacts from year to year, which is not 

surprising given the dynamic nature of the model and numerous adjustments taking place over 

time in response to changes in net returns associated with alternative land uses. 

Table 6A-15. Consumer and Producer Surplus in Agriculture, Million 2010 U.S. Dollars

Policy 2010 2015 2020 2025 2030 2035 2040

Consumer 

Surplus

75 ppb 1,907,219 1,928,147 1,955,266 1,983,375 2,013,518 2,042,566 2,071,338

Change with Respect to Existing Standard

70 ppb 0 0 0 113 66 58 -10

65 ppb -1 1 4 262 148 289 24

60 ppb 0 47 9 462 100 408 66

Producer 

Surplus

75 ppb 718,105 824,802 815,996 865,123 817,707 874,549 908,352

Change with Respect to Existing Standard

70 ppb 1,202 2,454 1,796 609 -1,144 675 -52,504

65 ppb 216 530 -407 1,174 -2,933 -39,605 -10,211

60 ppb -176 1,015 -1,470 1,320 -1,492 -38,788 -8,119

The impacts of the scenarios with more stringent ozone standards were larger in the 

forestry sector, with bigger increases in consumer surplus and greater declines in producer 

surplus. Table 6A-16 presents the model results of the welfare analysis in the forestry sector.
6-39

Table 6A-16. Consumer and Producer Surplus in Forestry, Million 2010 U.S. Dollars

Policy 2010 2015 2020 2025 2030 2035 2040

Consumer 

surplus

75 ppb 715,634 760,957 801,653 819,407 867,332 885,687 926,837

Change with Respect to Current Standard

70 ppb 3 24,592 -9 -2 70 167 72

65 ppb 12 24,595 4 43 189 263 138

60 ppb 30 24,695 127 257 433 686 245

Producer 

surplus

75 ppb 93,795 147,719 154,137 144,888 147,039 147,797 132,850

Change with Respect to Current Standard

70 ppb 1 -24,614 -28 94 -24 -152 8

65 ppb -5 -24,621 -73 291 -111 -86 -35

60 ppb -57 -24,727 -261 135 -471 -503 37

Because of the complex dynamics of the agriculture and forestry sectors and variability in 

welfare impacts over time, it is often helpful to summarize the impacts in terms of annualized 

values. Table 6A-17 and Table 6A-18 summarize the annualized impacts of alternative ozone 

standards on consumer and producer surplus in the agricultural and forestry sectors for 2010–

2044 at a discount rate of 3% and 7% respectively.106 The impacts of alternative standards on 

consumer surplus are positive for each of the tighter standards for both agricultural and forestry 

sectors, with the benefits increasing with more stringent requirements. For producer surplus, on 

the other hand, annualized impacts are negative for all of the tighter standards for both 

agriculture and forestry sector, becoming more negative as stringency is increased. Overall, total 

surplus declines for the agriculture sector while rises slightly for the forest sector across 

alternative standard levels, leading to a net decrease for both sectors.

Table 6A-17. Annualized Changes in Consumer and Producer Surplus in Agriculture and 

Forestry, 2010–2044, Million 2010 U.S. Dollars (3% Discount Rate)

Policy Agriculture Forestry Total

Consumer surplus 75 ppb 1,969,838 805,563 2,775,401

Change with Respect to Current Standard

 

106 Each model period in FASOMGHG is representative of the 5-year period starting with that year, so results 

reported for 2040 are representative of 2040–2044. Thus, we use values through 2044 in the annualization 

calculations. 
6-40

70 ppb 28 4,552 4,580

65 ppb 86 4,592 4,678

60 ppb 132 4,744 4,877

Producer surplus 75 ppb 817,744 135,478 953,222

Change with Respect to Current Standard

70 ppb -3,601 -4,534 -8,135

65 ppb -5,035 -4,524 -9,559

60 ppb -4,741 -4,684 -9,425

Total surplus 75 ppb 2,787,582 941,041 3,728,623

70 ppb -3,573 18 -3,555

65 ppb -4,949 68 -4,882

60 ppb -4,608 60 -4,548

Table 6A-18. Annualized Changes in Consumer and Producer Surplus in Agriculture and 

Forestry, 2010–2044, Million 2010 U.S. Dollars (7% Discount Rate)

Product Policy Agriculture Forestry Total

Consumer surplus

75 ppb 1,951,843 782,748 2,734,591

Change with Respect to Existing Standard

70 ppb 21 5,570 5,592

65 ppb 61 5,599 5,660

60 ppb 100 5,721 5,821

Producer surplus

75 ppb 800,022 130,681 930,703

Change with Respect to Existing Standard

70 ppb -945 -5,561 -6,506

65 ppb -2,719 -5,555 -8,273

60 ppb -2,635 -5,694 -8,328

Total surplus

75 ppb 2,751,865 913,429 3,665,294

Change with Respect to Existing Standard

70 ppb -923 9 -914

65 ppb -2,658 45 -2,613

60 ppb -2,535 27 -2,508

6A.2.3.3 Greenhouse Mitigation Potential

The capacity for both the agricultural and forest sectors to sequester carbon is enhanced

in each of the alternative standard cases, with increasing magnitude as policy stringency is 
6-41

increased. Although FASOMGHG projects fewer acres of forestland and total cropland, the 

accelerated storage of carbon in trees and forestland and cropland soils outweighs any decline 

from reductions in covered area. Carbon storage in both sectors is consistently higher in the 

alternative standard cases, with the gap widening over time (see Figure 6A-15 for change in 

forest carbon stock). By 2040, the agricultural sector sequestered 0.01%~0.05%% more carbon 

under the alternative standard cases and the forestry sector up to 0.5% more, resulting in gains of 

more than 1,500 million metric tons CO2 equivalent (MMtCO2e). Table 6A-19 presents carbon 

sequestration projections under the current standard and changes under each alternative 

standard.

107 Note that negative values in the row for the current standard indicate sequestration or 

carbon storage. Negative values in the change rows indicate that the alternative standard stores 

more carbon than the current standard (and vice versa for positive changes). 

Notice that for the agricultural sector, the overall stock of net GHG would decrease over 

time in the baseline because cropping activities involve fertilizer and chemical usage, fossil 

fuels, running machinery, livestock emissions from enteric fermentation and manure 

management, and so forth—all these GHG emissions are being released each year, while soil 

carbon sequestration moves toward equilibrium within 25 years of a change in tillage. As soil 

carbon reaches equilibrium, little additional sequestration is taking place each year but annual 

emissions from other sources continue. Thus, over time, the annual emissions tend to outweigh 

the increase in carbon stocked in agricultural soils, and net stock of GHG tends to become less 

negative and eventually positive relative to the starting point.108

 

107 These are total stocks of net GHG emissions over time, not annual emissions. If the total stock of GHG is 

becoming more negative over time, more net sequestration is taking place than emissions. If the total stock of GHG 

is becoming less negative or positive over time, emissions are greater than the increase in sequestration. 

108 This change is consistent with the fact that U.S. agriculture is a net source of emissions on an annual basis. The 

value of the total GHG stock associated with agriculture is starting at a negative value because of the FASOMGHG 

convention of accounting for total carbon sequestration present in agricultural soils in the first year of the model run. 

A large stock of carbon is sequestered, but it does not increase by much over time. 
6-42

Figure 6A-15.Carbon Storage in Forestry Sector, MMtCO2e

Table 6A-19. Carbon Storage, MMtCO2e

Product Policy 2010 2020 2030 2040 2010-2044

Agriculture

75 ppb -18,621 -15,240 -11,772 -8,009 -268,210

Change with Respect to Existing Standard

70 ppb 0 -1 -3 -1 -24

65 ppb 0 -2 -6 -4 -62

60 ppb -1 -4 -11 -11 -132

Forestry

75 ppb -73,321 -74,338 -77,963 -81,063 -1,533,424

Change with Respect to Existing Standard

70 ppb 1 51 -100 -259 -1,537

65 ppb 2 53 -323 -774 -5,207

60 ppb 12 94 -465 -1,189 -7,739

Changes in forestry sector carbon sequestration are largely driven by changes in forest 

management, which include the increases in tree yield in the lower ozone environments. The 

increased sequestration in this category outweighs losses in sequestration in the other major 

 (84,000)

 (82,000)

 (80,000)

 (78,000)

 (76,000)

 (74,000)

 (72,000)

 (70,000)

2010 2015 2020 2025 2030 2035 2040

Base 75 ppb 70 ppb 65 ppb 60 ppb
6-43

forestry categories: afforestation and forest soil. Table 6A-20 presents the detailed changes in 

forestry carbon sequestration.

Table 6A-20. Forestry Carbon Sequestration, MMtCO2e

Product Policy 2010 2020 2030 2040

Afforestation, 

Trees

75 ppb -730 -1,594 -963 -1,502

Change with Respect to Existing Standard

70 ppb 0 0 22 29

65 ppb 0 0 48 64

60 ppb 0 0 66 88

Afforestion, Soils

75 ppb -767 -598 -550 -1,018

Change with Respect to Existing Standard

70 ppb 0 0 11 11

65 ppb 0 0 27 27

60 ppb 0 0 37 37

Forest 

Management

75 ppb -39,827 -37,995 -40,023 -39,556

Change with Respect to Existing Standard

70 ppb 1 43 -125 -291

65 ppb 2 44 -387 -852

60 ppb 11 77 -553 -1,298

Forest Soils

75 ppb -28,320 -27,698 -27,243 -27,474

Change with Respect to Existing Standard

70 ppb 0 10 5 4

65 ppb 1 13 20 19

60 ppb 1 21 27 28

6A.3 Summary

Impacts to both sectors generally mirror one another, although they are more prominent in 

the forestry sector. Not only are tree species more responsive to changes in ozone, but the largest 

reductions to meet the alternative standards will occur in regions with large forestry sectors: 

South Central, Southeast, and Rocky Mountains. Reductions in agricultural regions are 

comparatively moderate. Productivity of both crops and forests is projected to increase at each of 

the alternative standard levels. This increase in supply resulted in decreased prices for forest 

products and agricultural commodities, which benefits consumer welfare while reducing 

producer welfare. Unless there are significant changes to wood products markets in particular, 

producers will be forced to sell at reduced prices to absorb the increased supply. Nonetheless, 
6-44

gains to consumers become increasingly large with more stringent ozone standards. Gains to 

agricultural producers in the most stringent case are associated with a decline in forestry returns 

that results in a net shift in land use toward agriculture. 

Increased productivity is also projected to affect land use both within and between the 

agricultural and forest sectors. Within sectors, acreage is projected to shift from crops and tree 

species that are more sensitive to ozone to those that are less sensitive because productivity in the 

former will be more substantially affected by reductions in ozone concentrations. For ozonesensitive crops and species, producers are projected to require less land to produce at the same or 

higher levels. Forest acreage in particular is projected to decline sharply, driven by declines in 

both reforestation and afforestation.

Despite reductions in crop and forest area, carbon sequestration is expected to increase 

over time, led almost entirely by increased forest sequestration. Although there is less 

reforestation and afforestation and lower sequestration in new inventory, the change is a result of 

existing inventories becoming so much larger as trees grow faster. Lower sequestration in new 

inventory is outweighed by increased inventory in standing forests, represented in the model as a 

change in forest management. 

Increased stringency in the ozone standard generally produces larger impacts on all of the 

model outputs. However, the additional impact of moving from the current standard to 65 ppb, or 

to 60 ppb was sometimes marginal compared with changes occurring between the current 

standard to 70 ppb. In particular, the impacts to the forestry sector, most notably in forest 

inventories and the forest sector welfare analysis, tended to increase at a decreasing rate after 

meeting the 70 ppb standard. 

The model results are subject to several limitations: First, the ozone concentration response 

functions applied to crops and trees were using “median” parameters in Lehrer et al. (2007)—the 

RYLs and RYGs calculated are thus “median” ones; second, the use of crop proxy mapping and 

the forest-type mapping due to incomplete data specified in Section 6.1 adds to the uncertainty of 

these model results; third, the potential changes in tree species mixes within forest types due to 

ground ozone-level changes were not considered; and last, the international trade component in 

FASOMGHG that assumes USDA-based future projections under current conditions may present 
6-45

another uncertainty for the model results, especially when soybeans and wheat are among the 

major crop commodities for U.S. exports and have relatively large responses to changed ozone 

environments. 

6A.4 References

Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., and Xian, G. 2013. A comprehensive change detection method 

for updating the National Land Cover Database to circa 2011. Remote Sensing of Environment, 132: 159 –

175.

Lehrer, J.A., M. Bacou, B. Blankespoor, D. McCubbin, J. Sacks, C.R. Taylor, and D.A. Weinstein. 2007. Technical 

Report on Ozone Exposure, Risk, and Impact Assessments for Vegetation. EPA 452/R-07-002. 

Little, E.L., Jr. 1971. Atlas of United States Trees, Volume 1, Conifers and Important Hardwoods. U.S. Department 

of Agriculture Miscellaneous Publication 1146, 9 p., 200 maps. 

Little, E.L., Jr. 1976. Atlas of United States Trees, Volume 3, Minor Western Hardwoods. U.S. Department of 

Agriculture Miscellaneous Publication 1314, 13 p., 290 maps. 

Little, E.L., Jr. 1977. Atlas of United States Trees, Volume 4, Minor Eastern Hardwoods. U.S. Department of 

Agriculture Miscellaneous Publication 1342, 17 p., 230 maps. 

Little, E.L., Jr. 1978. Atlas of United States Trees, Volume 5, Florida. U.S. Department of Agriculture 

Miscellaneous Publication 1361, 262 maps.

U.S. Department of Agriculture, National Agricultural Statistics Service (USDA NASS). Various years. USDA 

Agricultural Statistics (1990–2012). Available at: http://www.nass.usda.gov/Publications/Ag_Statistics/.

U.S. Department of Agriculture, Natural Resource Conservation Service (USDA NRCS). 2003. Annual NRI—Land 

Use. Available at: http://www.nrcs.usda.gov/technical/NRI. 
7-1

CHAPTER 7: ENGINEERING COST ANALYSIS AND ECONOMIC IMPACTS

Overview

This chapter summarizes the data sources and methodologies used to estimate 

engineering costs of attaining the alternative, more stringent levels for the ozone primary 

standards analyzed in this regulatory impact analysis (RIA). The chapter also provides estimates 

of the engineering costs of control strategies presented in Chapter 4 for the alternative standards 

of 70, 65, and 60 ppb. The discussion is presented as follows: Section 7.1 presents the costs 

associated with the application of known controls and is followed by discussions about the 

challenges of estimating costs for unknown controls (Section 7.2); costs associated with 

emissions reductions from unknown controls that are needed to demonstrate full attainment of 

the alternative standards analyzed (Section 7.3); total compliance cost estimates (Section 7.4); 

updated methodology (Section 7.5); economic impacts (Section 7.6); and the uncertainties and 

limitations associated with these components of the RIA (Section 7.7). 

The engineering costs described in this chapter generally include the costs of purchasing, 

installing, operating, and maintaining the referenced technologies. The costs associated with 

monitoring, testing, reporting, and record keeping for affected sources are not included in the 

annualized cost estimates. For a variety of reasons, actual control costs may vary from the 

estimates the EPA presents. As discussed throughout this document, 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 the EPA 

anticipates that state and local governments will consider programs that are best suited for local 

conditions. Also, the EPA recognizes the unknown emissions control portion of the engineering 

cost estimates (Section 7.3) reflects substantial uncertainty about the sectors and technologies

that might become available for cost-effective application in the future.

The engineering cost estimates are limited in their scope. This analysis focuses on the 

emissions reductions needed for attainment of a range of alternative revised standards. The EPA 

understands that some states will incur costs both designing State Implementation Plans (SIPs) 

for and implementing new control strategies to meet final revised standards. However, the EPA 
7-2

does not know what specific actions states will take to design their SIPs to meet final revised 

standards. Therefore, we do not present estimated costs that government agencies may incur for 

managing the requirement, implementing these (or other) control strategies, or for offering 

incentives that may be necessary to encourage the implementation of specific 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.

7.1 Estimating Engineering Compliance Costs 

7.1.1 Methods and Data

After designing the hypothetical control strategy using the methodology discussed in 

Chapter 4, the EPA used the Control Strategy Tool (CoST) (U.S. EPA, 2014a) to estimate 

engineering control costs for non-electric generating unit (non-EGU point) point, nonpoint and 

mobile nonroad sources. CoST calculates engineering costs using one of two different methods: 

(1) an equation that incorporates key operating unit information, such as unit design capacity or 

stack flow rate, or (2) an average annualized cost-per-ton factor multiplied by the total tons of 

reduction of a pollutant. Most control cost information within CoST was developed based on the 

cost-per-ton approach because estimating engineering costs using an equation requires more 

detailed data, and parameters used in these equations may not be readily available or broadly

representative across sources within the emissions inventory. The cost equations used in CoST

estimate annual, capital and/or operating and maintenance (O&M) costs and are used primarily 

for some larger sources such as industrial/commercial/institutional (ICI) boilers and petroleum 

refinery process heaters. Information on CoST control measures information, including cost-perton factors and cost equations can be found at www.epa.gov/ttnecas1/cost.htm. Costs for 

selective reduction catalysts (SCR) applied as part of the analysis for reducing NOx at coal-fired 

electric generating units (EGUs) were estimated using documentation for the Integrated Planning 

Model (IPM) (Sargent & Lundy, 2013).
7-3

Capital costs are converted to annual costs using the capital recovery factor (CRF).109

Where possible, calculations are used to calculate total annual control cost (TACC), which is a 

function of capital costs (CC) and O&M costs. The CRF incorporates the interest rate and 

equipment life (in years) of the control equipment. Operating costs are calculated as a function of 

annual O&M and other variable costs. The resulting TACC equation is TACC = (CRF * CC) + 

O&M. For more information on this cost methodology, refer to the EPA Air Pollution Control 

Cost Manual (U.S. EPA, 2003) and EPA’s Guidelines for Preparing Economic Analyses, 

Chapter 6 (US. EPA, 2014b).

Engineering costs will differ depending on the quantity of emissions reduced, emissions 

unit capacity, or stack flow, which can vary over time. Engineering costs will also differ in 

nominal terms by the year for which the costs are calculated (e.g., 2011$ versus 2008$).110 For 

capital investment, in order to attain standards in 2025 we assume capital investment occurs at 

the beginning of 2025. We make this simplifying assumption because we do not know what all 

firms making capital investments for control measures will do and when they will do it. Our 

estimates of annualized costs include annualized capital and annual O&M costs for those 

controls included in our known control strategy analysis. Our engineering cost analysis uses the 

equivalent uniform annual costs (EUAC) method, in which annualized costs are calculated based 

on the equipment life for the control measure and the interest rate incorporated into the CRF. 

Annualized costs represent an equal stream of yearly costs over the period the control technology 

is expected to operate. We make no presumption of additional capital investment in years beyond 

2025. The EUAC method is discussed in detail in the EPA Air Pollution Control Cost Manual 

(U.S. EPA, 2003). The controls applied and their respective engineering costs are described in 

the Chapter 7 Appendix.

 

109 The capital recovery factor formula is expressed as r*(1+r)^n/[(1+r)^n -1]. Where r is the real rate of interest and 

n is the number of time periods. 

110 The engineering costs will not be any different in real (inflation-adjusted) terms if calculated in 2011 versus other 

year dollars, if the other-year dollars are properly adjusted. For this analysis, all costs are reported in real 2011 

dollars.
7-4

7.1.2 Compliance Cost Estimates for Known Controls

In this section, we provide engineering cost estimates for the known controls identified in 

Chapter 4 that include control technologies for EGUs, non-EGU point, nonpoint and mobile 

nonroad sources. Onroad mobile source controls were not applied because they are largely 

addressed in existing rules such as the recent Tier 3 rule. Engineering costs generally refer to the 

equipment installation expense, the site preparation costs for the application, and annual 

operating and maintenance costs. Note that in many cases the application of these control 

strategies does not result in areas reaching attainment for the alternative ozone standards of 70, 

65, and 60 ppb and additional emission reductions beyond known controls are needed.

The EPA evaluated the costs of all known NOx controls contained in the CoST control 

measures database for this RIA and found that all nonpoint and nonroad sector controls were 

below $14,000 per ton of NOx emission reduction, and that the bulk of the non-EGU point source 

controls were below this cost. Overall, for all NOx controls prior to application of any cost cap, 

controls costing less than $14,000 per ton account for 96 percent of known emission reductions. 

Figure 7-1 represents the marginal cost curve for all the NOx control measures contained in the 

CoST database. This is an incomplete representation of the marginal abatement cost curve for all 

NOx abatement, because we do not have information on the control measures and costs for the 

remaining uncontrolled NOx emissions (see discussion in section 7.2). The controls above 

$14,000 were investigated and we determined that the higher cost controls were primarily due to 

errors in the cost equations for a few types of sources (mainly ICI Boilers and Process Heaters) 

for certain source sizes. We are taking steps to correct these equations for the final ozone RIA. 

As a result of the error mentioned above, a cost cap of $14,000 per ton of NOx emissions 

reduction for the non-EGU point source controls was applied, to remove the incorrectly applied 

controls from the analysis. A small number of NOx controls were applied for non-EGU point 

sources above $14,000 per ton but these had little impact on the overall emission reductions or 

control cost. A significant portion of the EGU SCR controls were above this level; no cost cap 

was applied to the EGU SCR controls. Note that control costs for California were lower than for 

other areas because there were no EGU SCR controls applied in California because there were 

no coal-fired utility boilers without SCR already in place. 
7-5

We intended to apply a similar cost cap but inadvertently applied a slightly higher cap of 

$15,000 per ton of VOC emission reduction. At the beginning of the analysis we were 

anticipating VOC reductions would play a relatively minor role in the analysis so we did not do a 

separate marginal cost analysis for VOC. However, for the final ozone RIA we will conduct a 

separate analysis for VOC and will make costing decisions accordingly.

Figure 7-1. Marginal Costs for Known NOx Controls for All Source Sectors (EGU, nonEGU Point, Nonpoint, and Nonroad)

See Tables 7-1 through 7-3 for summaries of control costs from the application of known 

controls for alternative standards of 70, 65, and 60 ppb. Costs are listed by sector for both 

eastern and western U.S., except California and presented at 3 and 7 percent discount rates. Note 

that any incremental costs for known controls for California (post-2025) for alternative standards 
7-6

of 70, 65, and 60 ppb are zero because all known controls for California were applied in the 

demonstration of attainment for the baseline standard of 75 ppb.

These numbers reflect the engineering costs annualized at discount rates of 3 percent and 

7 percent, which is to the extent possible consistent with the guidance provided in the Office of 

Management and Budget’s (OMB) (2003) Circular A-4. Discount rates refer to the rate at which 

capital costs are annualized.111 A higher discount, or interest, rate results in a larger annualized 

cost of capital estimate. It is important to note that it is not possible to estimate both 3 percent 

and 7 percent discount rates for a number of the controls included in this analysis. Because we 

obtain control cost data from many sources, we are not always able to obtain consistent data 

across original data sources.112 If disaggregated control cost data is unavailable (i.e., where 

capital, equipment life value, and O&M costs are not separated out), EPA typically assumes that 

the estimated control costs are annualized using a 7 percent discount rate. When disaggregated 

control cost data is available (i.e., where capital, equipment life value, and O&M costs are 

explicit) we can recalculate costs using a 3 percent discount rate.

In addition, the EGU control costs were not estimated for either 3 or 7 percent. The 

interest rate used for this analysis reflects an internal rate of return of 11.51 percent for retrofit 

controls as described in the IPM v5.13 documentation (U.S. EPA, 2013).

For non-EGU point source controls, some disaggregated data is available, and we were 

able to calculate those costs at both 3 and 7 percent discount rates for those controls. For the 

alternative standards analyzed in this RIA, approximately 23 percent of known control costs are 

disaggregated at a level that could be discounted at 3 percent. Because we do not have 

disaggregated control cost data for any EGU, nonpoint, or nonroad source controls, total 

annualized costs for these sectors are assumed to be calculated using a 7 percent discount rate. 

Because we do not have a full set of costs at the 3 percent discount rate, the 3 percent columns in 

 

111 In this analysis, the discount rate refers to the interest rate used in the discounted cash flow analysis to determine 

the present value of future cash flows. A social discount rate is a discount rate used in computing the value of 

monies spent on social projects or investments, such as environmental protection. The social discount rate is directly 

analogous to the discount rate we use in the engineering cost analysis, as well as certain rates used in corporate 

finance (e.g., hurdle rate or a project appropriate discount rate), so the mathematics are identical.

112 Data sources can include states and technical studies, which do not always include the original data source.
7-7

Tables 7-1 to 7-3 reflect the sum of some non-EGU point source controls at a 3 percent discount 

rate, some non-EGU point source controls at a 7 percent discount rate, and the other sectors at a 

7 percent discount rate. With the exception of the 3 percent Total Annualized Cost estimates in 

Tables 7-1 to 7-3, engineering cost estimates presented throughout this chapter and elsewhere in 

this document are based on a 7 percent discount rate.

The total annualized engineering costs associated with the application of known controls, 

incremental to the baseline and using a 7 percent discount rate, are approximately $1.6 billion for 

an alternative annual standard of 70 ppb, $4.2 billion for a 65 ppb alternative standard, and $4.4 

billion for a 60 ppb alternative standard. Costs of NOx controls in terms of dollars per ton of 

NOx reduction for the alternative standards analyses were approximately $12,000/ton on average

for the EGU sector with a range of $2,000/ton to $38,000/ton; $3,000/ton for the non-EGU point 

sector on average, with a range of $17 to $90,000/ton; $1,100/ton for the nonpoint sector on 

average ,with a range of $520 to $2,200/ton; and $4,600/ton for the nonroad sector on average 

with a range of $3,300/ton to $5,300/ton. The overall average cost range was $2,300 to

$3,000/ton for all sectors combined. 

The cost trend in terms of dollars per ton of emissions reduction is increasing for some 

sectors and decreasing for others. The variation is small for most sectors and depends on the 

sources that happen to be in the geographic areas of control. In general, the same set of controls 

is being applied at each level of the alternative standards analyzed. The primary difference in the 

control strategies for the alternative standards is the greater size of the geographic area of control 

as the stringency of the alternative standards increases. Overall for all sectors as a whole, the 

average cost per ton is actually increasing slightly with increasing stringency of the alternative 

standard analyzed. The reason for slight increase in cost per ton relative to the increase in 

stringency is that newly affected areas are estimated to obtain emissions reductions using slightly 

more expensive known technologies as the alternative standards become more stringent.
7-8

Table 7-1. Summary of Known Annualized Control Costs by Sector for 70 ppb for 2025

- U.S., except California (millions of 2011$)a

Geographic Area Emissions Sector Known Control Costs

7 Percent 

Discount Rate

3 Percent

Discount Rate

East

EGU 310b 310b

Non-EGU Point 640 620c

Nonpoint 610 610d

Nonroad 22 22d

Total 1,600 1,600e

West

EGU - -

Non-EGU Point - -

Nonpoint - -

Nonroad - -

Total - -

Total Known Control Costs 1,600 1,600e

a All values are rounded to two significant figures.

b EGU control cost data is calculated using an 11.51 percent for retrofit controls.

c Non-EGU control cost data is calculated at a 3% interest where control cost equations are utilized.

d Nonpoint and nonroad control costs are calculated using a 7% interest rate because no 3% data exists.

e Because we obtain control cost data from many sources, we are not always able to obtain consistent data across 

original data sources. Where disaggregated control cost data is available (i.e., where capital, equipment life value, 

and O&M costs are explicit) we can calculate costs using a 3 percent discount rate. Therefore the cost estimate 

provided here is a summation of costs at 3 percent and 7 percent discount rates.
7-9

Table 7-2. Summary of Known Annualized Control Costs by Sector for 65 ppb for 2025

- U.S., except California (millions of 2011$)a

Geographic Area Emissions Sector Known Control Costs

7 Percent 

Discount Rate

3 Percent 

Discount Rate

East

EGU 1,500b 1,500b

Non-EGU Point 1,100 1,100c

Nonpoint 1,100 1,100d

Nonroad 53 53d

Total 3,800 3,800e

West

EGU 230b 230b

Non-EGU Point 86 86c

Nonpoint 87 87d

Nonroad 5.7 5.7d

Total 410 410e

Total Known Control Costs 4,200 4,200e

a All values are rounded to two significant figures.

b EGU control cost data is calculated using an 11.51 percent for retrofit controls.

c Non-EGU control cost data is calculated at a 3% interest where control cost equations are utilized.

d Nonpoint and nonroad control costs are calculated using a 7% interest rate because no 3% data exists.

e Because we obtain control cost data from many sources, we are not always able to obtain consistent data across 

original data sources. Where disaggregated control cost data is available (i.e., where capital, equipment life value, 

and O&M costs are explicit) we can calculate costs using a 3 percent discount rate. Therefore the cost estimate 

provided here is a summation of costs at 3 percent and 7 percent discount rates.

Table 7-3. Summary of Known Annualized Control Costs by Sector for 60 ppb for 2025

- U.S., except California (millions of 2011$)a

Geographic Area Emissions Sector Known Control Costs

7 Percent Discount Rate 3 Percent Discount 

Rate

East

EGU 1,500b 1,500b

Non-EGU Point 1,200 1,100c

Nonpoint 1,100 1,100d

Nonroad 53 53d

Total 3,800 3,700e

West 

EGU 400 400b

Non-EGU Point 98 97c

Nonpoint 88 88d

Nonroad 5.7 5.7d

Total 590 590e

Total Known Control Costs 4,400 4,400e

a All values are rounded to two significant figures.

b EGU control cost data is calculated using an 11.51 percent for retrofit controls.

c Non-EGU control cost data is calculated at a 3% interest where control cost equations are utilized.

d Nonpoint and nonroad control costs are calculated using a 7% interest rate because no 3% data exists.

e Because we obtain control cost data from many sources, we are not always able to obtain consistent data across 

original data sources. Where disaggregated control cost data is available (i.e., where capital, equipment life value, 

and O&M costs are explicit) we can calculate costs using a 3 percent discount rate. Therefore the cost estimate 

provided here is a summation of costs at 3 percent and 7 percent discount rates.
7-10

7.2 The Challenge of Estimating Costs for Unknown Controls

As described in Chapter 4, the known control measures were applied to EGU, non-EGU 

point, nonpoint (area), and nonroad mobile sources for demonstration of attainment with the 

current and alternative standards. Table 4-7 lists the specific control technologies applied in the 

known control analysis. There were several areas where known controls did not achieve enough 

emissions reductions to attain the alternative standards of 70, 65, and 60 ppb. To complete the 

analysis, the EPA then estimated the additional emissions reductions beyond known controls 

needed to reach attainment, also referred to as unknown controls. For information on the 

methodology used to develop the emissions reductions estimates, see Chapter 3. 

The estimation of engineering costs for unspecified emission reductions needed to reach 

attainment many years in the future is inherently a difficult task. This is because it is likely that 

the abatement supply function will shift out or change shape over time due to a variety of 

economic, technical, and regulatory influences. Our experience with Clean Air Act 

implementation shows that numerous factors, such as technical change and development of 

innovative strategies, can lead to emissions reductions that may not seem possible today, while 

potentially reducing costs over time. For example, facility-level data collected through the U.S. 

Census Bureau’s Pollution Abatement Costs and Expenditures (PACE) survey suggests that this 

may have happened in the manufacturing sector in recent decades. Based on surveys of 

approximately 20,000 plants classified in manufacturing industries, the PACE data show during 

the 1994-2005 time period, a period of increasing regulatory stringency, spending on air 

pollution abatement as a percentage of revenues decreased for the manufacturing 

sector.113 Although exogenous factors such as changes in economic conditions may have 

 

113 The Pollution Abatement Costs and Expenditures (PACE) survey collects facility-level data on pollution 

abatement capital expenditures and operating costs for compliance with local, state, and federal regulations and 

voluntary or market-driven pollution abatement activities. In 2005, the most recent year PACE data were collected, 

the U.S. manufacturing sector spent $3.9 billion dollars on air capital expenditures and incurred $8.6 billion dollars 

in operating costs for air pollution prevention and treatment. These figures represent less than 3% of total new 

capital expenditures and less than 0.18% of total revenue for the manufacturing sector, respectively. These 

percentages have declined since 1994, when air capital expenditures were less than 4% of total new capital 

expenditures and air pollution abatement operating costs were less than 0.2% of total revenue. Levinson (2009) finds 

that most of the pollution reductions in the U.S. come from changes in technology as opposed to changes in imports 

or changes in the types of domestically produced goods. He finds that even though manufacturing output increased 

by 24% from 1987 to 2001, emissions of four common air pollutants from the sector declined 25% over that time 

period and the most important factor contributing to the decrease in pollution is technical change or innovation.
7-11

contributed to the relative share in costs of pollution abatement, it is also possible that

technological change and innovation may have contributed to this relative decline. 

In addition to considering the potential for technological innovation, it is also important 

to understand that EPA’s control strategy tools largely focus on a limited set of emissions 

inventory sectors, whereas abatement opportunities exist in other sectors. EPA’s control strategy 

tools undergo continuous improvement, and as the need for additional abatement opportunities 

grows, more evaluation of uncontrolled emissions takes place. During these evaluations, 

additional abatement opportunities from applying identified controls typically are found.

This section discusses various factors that must be considered in developing a 

methodology for estimating the costs of unknown controls. First, we explain why the abatement 

supply curve from known controls presented in the previous section provides an incomplete 

picture of all currently available abatement opportunities. Second, we show how, as time passes 

and the EPA reviews NAAQS standards, relevant information is revealed in the current RIA 

development process that was not available to analysts developing RIAs for previous reviews, 

such as unforeseen regulatory programs or other exogenous factors that account for significant 

emissions reductions. Third, we discuss a related issue, that technical change may affect the 

marginal abatement cost curve. Additionally, we present evidence from the literature that 

regulatory action can act as a forcing function for technical change. Fourth, we discuss how 

regulatory costs can decrease over time as regulated entities gain experience reducing emissions 

and reduce per unit costs, commonly referred to as “learning by doing”. Fifth, we discuss how 

NOx offset prices could serve as reasonable proxies for the costs associated with emissions 

reductions from unknown controls. Finally, we describe how we use this information to help 

inform the unknown control cost methodology applied in section 7.3.

7.2.1 Incomplete Characterization of NOx Marginal Abatement Cost Curves

Underlying the selection of controls as described in Appendix 4A is the concept of the 

marginal abatement cost curve (MACC). The marginal abatement cost curve (MACC) is a 

representation of how the marginal cost of additional emissions abatement changes with 

increasing levels of abatement. Adding new technologies, or changing either the abatement 

amount or cost of the technology, will change the shape of the overall MACC. 
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In developing engineering cost estimates in section 7.1, the focus was largely on end-ofpipe controls and only includes limited process-oriented control measures, such as switching to 

lower-emitting fuel or energy sources and the installation of energy efficiency measures. These 

measures can result in significant emissions abatement, but are not reflected in the marginal 

abatement cost curve based on traditional control measures. As a result, the MACC derived in 

the previous section from known controls represents an incomplete supply curve that partially 

captures the “true” abatement supply. An illustrative, hypothetical depiction of an “observed but 

incomplete” MACC and the “true” underlying MACC is presented in Figure 7-2. 

Figure 7-2. Observed but incomplete MACC (solid line) based on known controls 

identified by current tools and complete MACC (dashed line) where gaps 

indicate abatement not identified by current tools

In the figure, the solid line traces out a hypothetical observed MACC, while the dashed line 

characterizes the combination of observed and unobserved abatement possibilities. The 

inclusion of the unobserved abatement pushes the abatement supply out.

Due to the incomplete characterization of the full range of the MACC, it is important to 

understand the composition of the cost information that is available to construct the partial 
7-13

MACC. The nature of available information on the cost of NOx abatement measures is 

somewhat complex. The highest cost per ton estimates are often associated with controls that 

achieve very small total reductions in NOx or are special cases. For example, in some cases, 

controls have been developed primarily to address other pollutant emissions, such as SO2, but 

achieve NOx reductions as a co-benefit. These controls are well characterized in the CoST 

database because they have been applied for SO2 control, but the degree to which sources would 

adopt those controls for NOx is uncertain, and it is unlikely that those very high per ton cost 

measures would be the efficient marginal abatement cost (MAC) level for the targeted level of 

NOx emissions reductions needed for attainment. In addition, there are some controls that have 

been identified as applicable for some sources even though they have very high marginal costs 

per ton of NOx, because those controls are required to meet other provisions of the CAA. For 

example, LNB+SCR for process heaters is relatively common at refineries, mainly due to a New 

Source Review enforcement initiative that has been underway since 2001. These plants are 

being required to install the controls regardless of cost as part of the enforcement action, and thus 

the costs may not represent the actual marginal cost at the level of abatement needed to reach the 

NAAQS attainment targets.

Lack of information about the MAC for emissions reductions not characterized in CoST 

is not an indication that controlling those tons is necessarily more difficult than controlling NOx 

from other sources that are in the database, or that the MAC for those tons is necessarily higher 

than all of the costs of controls already in the database. Some sectors are controlled at a higher 

rate than others, and in those cases, getting additional NOx reductions may indeed require higher 

cost controls. For example, EGU NOx has been heavily controlled, and the additional SCR units 

applied in the analysis are relatively more expensive per ton than the typical SCRs that have been 

applied in past analyses. However, other sectors may not be as well-controlled, and lower cost 

controls may be available. 

7.2.2 Comparison of Baseline Emissions and Controls across Ozone NAAQS RIAs from 1997 

to 2014

While each ozone NAAQS analysis since 1997 has required at least some emissions 

reductions from controls that were unknown at the time of the analysis, evidence suggests that 

over time new information on exogenous factors affecting baseline emissions and emissions 
7-14

controls becomes available that can shift emissions reductions from the unknown to the known 

category. Some exogenous factors that might affect baseline emissions include changes in 

economic conditions that may affect production levels, as well as plant closures and openings. 

Baseline emissions may also be affected by EPA or state regulations that require specific 

controls that may not be fully characterized in the set of known controls applied in an earlier 

RIA, or by EPA or state regulations targeting other pollutants, e.g., air toxics, that may result in 

reductions in NOx or VOC as a co-benefit. For example, in the 1997 ozone NAAQS RIA, the 

NOx emissions reductions from the mobile source Tier 2 standards were not included as known 

controls, even though the RIA acknowledged the potential for these standards to provide 

substantial cost effective controls. Likewise, the 2008 ozone NAAQS RIA did not include 

controls on EGUs reflecting the Mercury and Air Toxics Standards or the Clean Power Plan. As 

a result, emissions reductions from unknown controls were much higher in some regions of the 

U.S. than they are in this RIA. 

Furthermore, many of these emission reductions may be achieved at a cost less than was 

originally applied to emissions reductions from unknown tons. Several of the large NOxreducing regulations issued between the 1997 and 2008 RIAs had ex ante estimates of costs per 

ton of NOx reduced well below the $10,000 per ton value applied to emissions reductions from 

unknown controls in the 1997 Ozone NAAQS RIA. Table 7-4 provides information on the cost 

per ton for NOx reductions from five major NOx-reducing regulations. To the extent possible, 

we determined from the RIA for each regulation the projected emissions reductions in 2010 (or 

the closest year) and the estimated costs in 2010. Costs were adjusted from the year reported in 

the RIAs to constant 2010 dollars. In some cases, only an annualized cost was reported. In those 

cases we divided the year specific emissions projection by the annualized cost, recognizing that 

this may under or overstate the actual year specific cost per ton. Where possible, we report the 

separate costs for NOx emissions reductions. However, for most programs, total costs which 

include reductions in multiple pollutants are reported. As such, the cost per ton of NOx alone is 

likely overstated. 

Table 7-4. Emissions and Cost Information for Major NOx Rules Issued Between 1997 and 

2008

Regulation

Projected NOx 

Emissions Reductions in 

2010 (thousands)

Total Annual Cost

in 2010

(Million 2010$)

Average Cost/Tons 

NOx Reduced

(2010$)
7-15

(A) (B) (C = B/A)

NOx SIP Calla

1,141 2,515 $2,204

Clean Air Interstate Ruleb

1,200 $3,034 $2,528

Tier 2 Standardsc

1,236 $5,256 $4,253

Heavy Duty Diesel Enginesd

403 $4,570 $11,340

Nonroad Diesele

203 (in 2015) $660 $3,251

a Costs and emissions reductions obtained from Table ES-2 in the Regulatory Impact Analysis for the NOx SIP Call, 

FIP, and Section 126 Petitions, Volume 2: Health and Welfare Benefits (U.S. EPA, 1998).

b Emissions reductions obtained from Table 7-2 and Costs obtained from Table 7-3 in the Regulatory Impact 

Analysis for the Final Clean Air Interstate Rule (U.S. EPA, 2005).

c Costs and emissions reductions obtained from Appendix VI-C in the Regulatory Impact Analysis - Control of Air 

Pollution from New Motor Vehicles: Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control 

Requirements (U.S. EPA, 1999).

d Emission reductions obtained from Table II.B-4, and costs obtained from Table V.D-1 in the Regulatory Impact 

Analysis: Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (U.S. 

EPA, 2000)

e

.Emissions reductions obtained from Table 8.6-1 (NOx+NMHC), and costs obtained from Table 8.5-2 

(NOx+NMHC) in the Final Regulatory Impact Analysis: Control of Emissions from Nonroad Diesel Engines (U.S. 

EPA, 2004). In this RIA, no information was provided for NOx emissions alone, in all cases, NOx+non-methane 

hydrocarbons (NMHC) was provided. Calculated cost per ton is thus the average for NOx and NMHC emissions 

reductions, rather than just for NOx emissions reductions.

The NOx State Implementation Plan (the “NOx SIP call”) was partially represented in the 

1997 RIA. The other rules in Table 7-4 were not included in 1997 RIA, although the RIA notes 

that the Tier 2 standards may be a way to get additional reductions to meet the ozone NAAQS. 

Table 7-4 shows that for these five major regulations, which account for 4.1 million tons of NOx 

reductions, the expected average cost per ton ranged from about $2,200 to $11,300 per ton of 

NOx reduced. Only in the single case of the heavy duty diesel engine rule was the cost per ton 

NOx reduced expected to be greater than the $10,000/ton value used for unknown controls in the 

1997 RIA. This suggests that unknown controls may be implemented at lower cost than the 

highest point of the MACC for known controls anticipated in the RIA.

Comparing the MACC over time and across analyses is complicated because of (1) 

differences in the regions or areas affected by the proposed changes to the ozone standards, (2) 

the information available at the time of the analyses, and (3) analytical assumptions made about 

the universe of sources that can be controlled. Table 7-5 provides information about 

assumptions used in generating the costs of control measures for the 1997, 2008, and 2014 RIAs. 
7-16

Table 7-5. Comparison of Key Assumptions Used in Developing Estimates of NOx Emissions Controls and Costs across Past 

and Current Ozone NAAQS Regulatory Impact Analyses a

1997 2008 2014

Sectors Excluded

No additional utility NOx controls beyond 

Title IV and OTAG recommendations. 

Some mobile source control measures 

excluded due to mismatch between 

attainment dates and implementation 

timelines for the control measures.

Utility NOx controls beyond 

baseline only applied in the East 

(including East TX)

On-road mobile sources. 

Geographic 

Definition of 

Control Areas

Exceeding county plus CMSA for county 

exceeding the standard. If no CMSA, just 

use the exceeding county

(12 areas in East, 7 areas in West)

Non-EGU point and area controls 

applied to counties exceeding the 

standard plus surrounding counties 

out to 200km. Some additional 

controls were placed on large point 

sources in counties touching the 

buffer.

For mobile controls, both local 

(within 200 km buffer) and 

statewide controls were applied. 

Counties outside the state in which 

the exceeding county resides are 

excluded. Controls were applied 

to all states in the OTC excepting 

VT.

NOx controls for 70 ppb were applied to counties 

exceeding the standard, then surrounding counties 

within 200 km that were also within state 

boundaries (Texas, California) and within OTC 

boundaries including counties closest to exceeding 

counties first (Northeast). Where more controls 

were needed they were applied to counties in other 

states within the region, nearest the county 

exceeding the standard (Oklahoma, Arkansas, 

Louisiana and Kansas for Houston). VOC controls 

for 70 ppb were applied to special areas and 

counties within 100 km from them (California 

North and South, Dallas, Houston, Baltimore, and 

New York City). 

NOX controls for 65 and 60 ppb were applied in 

regions (California, Southwest, Central, Midwest, 

Northeast). VOC controls were applied in special 

areas plus counties within 100 km from these 

special areas (Denver, Dallas, Houston, Chicago, 

New York City)

Cost Cap

$10,000/ton (1990 dollars) – justification is 

that states generally have not chosen to 

require existing sources to apply control 

measures with incremental costs above the 

threshold, even in severe areas like the 

South Coast of CA. Sensitivity on 

$7,000/ton to $20,000/ton.

$23,000/ton (2006$) for non-EGU 

point and area sources – 98% of 

possible reductions are achieved at 

82% of total costs. Based on 

evaluation of marginal cost curves 

for all counties in control areas 

(1,300 counties).

For NOx: $14,000/ton for point non-EGU sources, 

although an error resulted in a small number of 

point non-EGU controls above $14,000/ton. No 

nonpoint (area) source NOx controls were above 

$14,000/ton so cost caps had no effect on nonpoint 

source controls. For ICI boilers, costs were 

calculated through equations without cost 

constraints.
7-17

1997 2008 2014

For VOC: $15,000/ton for point non-EGU and 

nonpoint (area) sources

Minimum 

Emission 

Reduction

Not clearly defined For NOx: 5 tons for point nonEGU and non-point sources

For VOC: 1 ton for point nonEGU and non-point

For NOx: 5 tons for point non-EGU and non-point

For VOC: 1 ton for point non-EGU and non-point

Minimum 

Emissions

Not clearly defined 50 tons for NOx 25 tons for NOx

Assumed 

Control 

Effectiveness (% 

of intended 

effect)

95 to 100 percent Variable Variable

Rule 

Penetrationb

Obtained from published reports from state 

and local agencies

75% for onroad and nonroad SCR 

and diesel particulate filters

75% penetration in CA and 25% in TX and 

Northeast for nonroad

a These analyses also included controls for VOC emissions, however, those controls were generally very local in nature. The current analysis is focused 

primarily on controlling NOx to meet the alternative ozone standards, with relatively modest VOC controls where they are expected to help reach attainment. 

Also, for the 1997 and 2008 RIAs, attainment of the PM standards in place at the time of the analysis was assumed, and as a result, some additional NOx 

measures were already in place.

b Percent of county-level mobile or area source inventory affected by control measure.
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7.2.3 Impact of Technological Innovation and Diffusion

In general, the MACC at any particular point in time for a defined set of emitting sectors 

will be an increasing function of the level of abatement. That is, marginal costs are increasing as 

the amount of emissions are reduced. However, in regulatory analyses of NAAQS, we are 

typically assessing costs of abatement in a future year or years selected to represent 

implementation of the standards. As such, a MACC constructed based on currently available 

information on abatement opportunities will not be the best representation of a future MACC. 

MACC in the future can differ from MACC in the present due to technological innovation and 

diffusion, such as the introduction of new technologies or improvements in effectiveness or 

applicability of existing technologies. Additionally, environmental policy can create incentives 

and constraints that influence the rate and direction of technical change (Jaffe et al. 2002) as well 

as the rate of diffusion and adoption of the innovations (Sterner and Turnheim 2009) . 

In the context of emissions controls examined in this RIA, technological innovation and 

diffusion can affect the MACC in several ways. The following bullets present some examples of 

the potential effects of technical change: 

Case 1: New control technologies can be developed that cost less than existing technologies. 

Case 2: A new control technology is developed to address an uncontrolled emissions source, at 

a higher marginal cost than existing technologies, but still lower than the cost threshold value.

Case 3: The efficiency of an existing control measure increases. In some cases, the control 

efficiency of a measure can be improved through technological advances. 

Case 4: The cost of an existing control measure decreases. 

Case 5: The applicability of an existing control measure to other emissions sources increases. 

Overall, these five cases describe ways that technological change can reduce both the 

amount of unidentified abatement needed, decrease the MAC, decrease average costs, and 

decrease total costs relative to the case where it is assumed that technological change does not 

occur in response to increased demand for abatement. It is also possible in cases where there is a 

strictly binding emissions reduction target that new technologies can be introduced and adopted 

with much higher marginal costs. However, if there are cost off-ramps, such as those provided by 

Section 185 of the CAA, those higher cost technologies will not be adopted. 
7-19

Regulatory policies can also help induce technological change when a standard cannot be 

met either (1) with existing technology or (2) with existing technology at an acceptable cost, but 

over time market demand will provide incentives for industry to invest in research and 

development of appropriate technologies. These incentives are discussed in Gerard and Lave 

(2005), who demonstrate that the 1970 Clean Air Act induced significant technical change that 

reduced emissions for 1975 and 1976 automobiles. Those mandated improvements went beyond 

the capabilities of existing technologies by using regulatory pressure to incentivize the 

development of catalytic converting technology in 1975. Induced technological change can 

correspond to Cases 1 through 3 above.

There are many other examples of low-emission technologies developed and/or 

commercialized over the past 15 or 20 years, such as:

 Selective catalytic reduction (SCR) and ultra-low NOx burners for NOx emissions

 Scrubbers that achieve 95 percent or greater SO2 control on boilers

 Sophisticated new valve seals and leak detection equipment for refineries and chemical 

plants

 Low or zero VOC paints, consumer products and cleaning processes

 Chlorofluorocarbon (CFC) free air conditioners, refrigerators, and solvents

 Water and powder-based coatings to replace petroleum-based formulations

 Vehicles with lower emissions than believed possible in the late 1980s due to 

improvements in evaporative controls, catalyst design and fuel control systems for lightduty vehicles; and treatment devices and retrofit technologies for heavy-duty engines

 Idle-reduction technologies for engines, including truck stop electrification efforts

 Increasing market penetration of gas-electric hybrid vehicles and cleaner fuels

These technologies were not commercially available two decades ago, and some were not 

even in existence. Yet today, all of these technologies are on the market, and many are widely 

employed. Several are key components of major pollution regulatory programs. 

As Brunnermeier and Cohen (2003) demonstrate, there is a positive correlation, other 

things held constant, between environmental innovations (measured as the number of relevant 

environmental patent applications) and specific regulations imposed on an industry (measured in 

terms of the frequency of government compliance inspections). Lanjouw and Mody (1996) 
7-20

show empirically a positive relationship between responses to environmental regulations (i.e., 

increases in pollution abatement expenditure) and new technology (i.e., relevant patent 

applications) in the United States, Japan, and Germany. They show that in each of these 

countries, even though on different timelines, the share of environmental patents increased 

considerably in response to stricter environmental regulations. Similarly, Popp (2004) studied 

the relationship between environmental regulation and new technology focusing on SO2 and 

NOx. The study was performed using patent data from the United States, Japan, and Germany. 

Popp found that more stringent regulation enhanced domestic patenting by domestic inventors.

While regulation may influence the direction and intensity of emissions-related research 

and development activities, “crowding out” of investment resources may occur as resources are 

directed away from other opportunities, potentially leading to opportunity costs that offset 

savings resulting from research and development successes (Popp and Newell 2012). In a study 

that links energy-related patent activity and firm financial data, Popp and Newell (2012) find that 

while increases in alternative energy patents result in fewer patents for other energy 

technologies, this result is due to firm-level profit-maximizing behavior rather than constraints 

on the magnitude of research and development resources. Alternatively, Kneller and Manderson 

(2012) find evidence in the United Kingdom that environment-related research and development 

resulting from more stringent regulation may crowd out other research and development 

activities but that environment-related capital does not crowd out non-environmental capital. 

Another factor to consider is the degree to which a particular sector is likely to be close to fully 

controlled, e.g., in comparing existing emissions with uncontrolled emissions levels, is the 

percent of control close to 100 percent? In those cases, achieving additional reductions through 

technological change is likely to be more difficult and costly, because the benefits of investment 

in those technologies is smaller, due to smaller remaining potential for abatement. 

7.2.4 Learning by Doing

What is known as “learning by doing” or “learning curve impacts” has also made it 

possible to achieve greater emissions reductions than had been feasible earlier, or reduce the 

costs of emissions control relative to original estimates. Learning curve impacts can be defined 

generally as the extent to which variable costs (of production and/or pollution control) decline as 

firms gain experience with a specific technology. This type of change corresponds to case 4 in 
7-21

the discussion of the ways technological change can affect the MACC that appeared earlier. Such 

impacts have been identified to occur in a number of studies conducted for various production 

processes. These impacts would manifest themselves as a lowering of expected costs for 

operation of technologies in the future below what they may otherwise have been. For example, 

Rubin et al. 2004 show that capital costs of Flue gas desulphurization (FGD) and selective 

catalytic reduction (SCR) systems have decreased over time as a result of research and 

development activities and learning by doing, among other factors, and that failing to account for 

these technological dynamics can lead to incorrect estimates of future regulatory costs.

Rubin et al. (2012) also note that when technologies succeed, costs tend to fall over time. 

They offer the example of post-combustion SO2 and NOx combustion systems. After an increase 

in costs during an initial commercialization period, costs decreased by at least 50 percent over 

the course of two decades. Table 9.5 in the 1997 Ozone NAAQS RIA summarizes historical and 

projected “progress ratios” for existing technologies. These ratios show declining costs over 

time, due to learning by doing, economies of scale, reductions in O&M costs, and technological 

improvements in manufacturing processes. There are other discrete examples, for example prices 

of the catalyst used in operating SCR have dropped dramatically over time. From 1980 to 2005, 

catalyst prices dropped by roughly 85 percent (Cichanowicz, 2010). This follows the “learning 

curve,” which finds that production and implementation costs decrease as learning and repetitive 

use occurs. In addition, Table 3a.7 in Appendix 3a of the 2008 Ozone NAAQS RIA lists controls 

applied to new source types (Case 3). For example, SCR is now applicable to the cement 

manufacturing sector, and SNCR is now applicable to a large number of additional boiler source 

categories. In some cases, more effective controls were determined to be applicable where in 

past cases, less effective controls were applied. For example, for industrial and manufacturing 

incinerators, where previously SNCR was the NOx control technology, SCR was applied in 

2008, increasing the control efficiency from 45 percent to 90 percent.

The magnitude of learning curve impacts on pollution control costs has been estimated for 

a variety of sectors as part of the cost analyses done for the Direct Cost Estimates Report for the 
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Second EPA Section 812 Prospective Analysis of the Clean Air Act Amendments of 1990.114 In 

the Report, learning curve adjustments were included for those sectors and technologies for 

which learning curve data was available. For all technologies and industries, a default learning 

rate of 10 percent was adopted based on SAB advice. No adjustments were used for on-road and 

non-road controls. The 10 percent adjustment is a 10 percent cost reduction per doubling of 

emission reductions. The literature supports a rate of up to 20 percent for many technologies 

(Dutton and Thomas, 1984). The impact of this on costs in the Report was to reduce costs of 

local controls in nonattainment areas by 9.9 percent in 2020. 

A typical learning curve adjustment is to reduce either capital or operation and 

maintenance costs by a certain percentage given a doubling of output from that sector or for that 

technology. In other words, capital or operation and maintenance costs will be reduced by some 

percentage for every doubling of output for the given sector or technology. In addition, learning 

by doing may also lead to instances where existing control technologies are found to be 

applicable to additional sources. This corresponds to Case 5 in the discussion in section 7.2.1. 

For example, scrubber technologies applied to electric utilities have been adapted to apply to 

industrial boilers. As a result of this increased applicability due to learning, potential abatement 

has increased at a cost less than the cost threshold. For this RIA, however, we do not have the 

necessary data to properly generate control costs that reflect learning curve impacts.

7.2.5 Using Regional NOx Offset Prices to Estimate Costs of Unknown Emissions Controls

In ozone nonattainment areas, new sources interested in locating in that area and existing 

sources interested in expanding are required to offset any emissions increases. If those emissions 

increases are NOx emissions, the source typically purchases NOx emission reduction credits 

(ERCs), or offsets, from within that particular nonattainment area. Within nonattainment areas, 

offset prices fluctuate because of changes in the available supply of offsets and changes in 

demand for offsets. Offset supply increases when facilities shut down or when they make 

 

114 Industrial Economics, Incorporated and E.H. Pechan and Associates, Direct Cost Estimates for the Clean Air Act 

Second Section 812 Prospective Analysis: Final Report, prepared for U.S. EPA, Office of Air and Radiation, 

February 2011. Available at http://www.epa.gov/cleanairactbenefits/feb11/costfullreport.pdf.
7-23

process or other changes that reduce emissions permanently. Offset demand depends on the 

industrial base in a given area and fluctuates with changes in economic growth. For example, in 

the San Joaquin Valley, in recent years offset prices have increased because of increased oil and 

gas industry development. 

We identified historical NOx offset prices in several nonattainment areas, including the 

San Joaquin Valley and the South Coast in California, Houston, TX, and New York region. For 

the San Joaquin Valley Air Pollution Control District, we collected information on NOx offset

prices using the California Air Resources Board’s Emission Reduction Offset Transaction Cost 

Summary Reports for 2002 through 2013115. For the South Coast Air Quality Management 

District, we collected information on prices for perpetual NOx RECLAIM Trading Credit (RTC) 

for 2003 through 2012 from the Listing of Trade Registrations.

116

 Lastly, we collected 

information on NOx offset prices in the Houston-Galveston nonattainment area for 2010 through 

2013 from the Trade Report117 and the New York-New Jersey-Connecticut nonattainment area 

from 2000 through 2013 from industry representatives. 

Table 7-6 presents the price data we were able to collect for these four regions, adjusted 

to 2011 dollars using the Gross Domestic Product Implicit Price Deflator. The offset prices in 

this table are denominated in units of perpetual tons, or tons per year. The prices constitute 

average of the trades in the regions for the year given.

 

115 http://www.arb.ca.gov/nsr/erco/erco.htm

116 http://www.aqmd.gov/home/programs/business/about-reclaim/reclaim-trading-credits

117 http://www.tceq.state.tx.us/airquality/banking/mass_ect_prog.html
7-24

Table 7-6. Average NOx Offset Prices for Four Areas (2011$) a

Annualized NOx Offset Prices ($/ton)

San Joaquin 

Valley 

California South 

Coast Houston TX New York Region

2000 N/A N/A N/A 25,000

2001 N/A N/A N/A 12,000

2002 36,000 N/A N/A 12,000

2003 28,000 N/A N/A 12,000

2004 25,000 12,000 N/A 12,000

2005 25,000 31,000 N/A 11,000

2006 21,000 163,000 N/A 11,000

2007 21,000 206,000 N/A N/A

2008 48,000 210,000 N/A N/A

2009 58,000 128,000 N/A N/A

2010 62,000 98,000 36,000 N/A

2011 64,000 56,000 N/A N/A

2012 47,000 47,000 N/A N/A

2013 42,000 N/A 97,000 4,000

Average 40,000 106,000 66,000 12,000

Maximum 64,000 210,000 97,000 25,000

a All values are rounded to two significant figures.

The data series for the California regions are more complete than those for Houston and the New 

York region. We are working to obtain more complete data series for future analysis.

To more directly compare offset prices to potential annual costs for unidentified 

emissions controls, we annualized the tons per year prices using the same engineering cost 

equations as used in the main analysis to estimate annualized control cost. We converted the 

offset cost to an annual costs by using the capital recovery factor (CRF) discussed in Section 

7.1.1. In a capital cost context, the CRF incorporates the interest rate and lifetime of the 

purchased capital. In this instance, although the offsets are perpetual in nature, we assumed a 

lifetime of 20 years in order to make the cost basis more comparable to the control cost 

estimates. Also, we used 7 percent for the interest rate. Table 7-7 presents the average and 

maximum annualized NOx offset prices in 2011 dollars.
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Table 7-7. Annualized NOx Offset Prices for Four Areas (2011$)a

Annualized NOx Offset Prices ($/ton)

San Joaquin 

Valley 

California South 

Coast Houston TX New York Region

Average $ 4,000 $ 10,000 $ 6,000 $ 1,000 

Maximum $ 6,000 $ 20,000 $ 9,000 $ 2,000 

a All values are rounded to two significant figures.

From an economic perspective, these offset prices may represent the shadow value of a 

ton of emissions. It is possible that these offset prices could serve as reasonable proxies for the 

costs associated with emissions reductions from unknown controls. The cost information 

informing the known control strategy traces out an incomplete marginal abatement cost curve in 

that, as discussed in Chapter 4, the controls used in the known control analysis are primarily endof-pipe technologies. The known control estimates for NOx do not account for other forms of 

abatement, switching to lower emitting fuels or increasing energy efficiency, for example. The 

estimates also do not account for institutional or market arrangements that allow firms to buy or 

sell emissions offsets in nonattainment regions with emissions constraints. These voluntary 

exchanges may enable abatement at lower costs than may otherwise be available. The benefit of 

these market transaction data is that the prices are revealed by the interaction of offset supply and 

demand in regions with differentiated characteristics and air quality profiles.

7.2.6 Conclusion

The preceding sections have discussed the ways in which various factors might affect the 

observed marginal abatement costs and the resulting total abatement costs estimated in this RIA. 

Based on past experience with Clean Air Act implementation, the EPA believes that it is 

reasonable to anticipate that the marginal cost of emissions reductions will decline over time due 

to technological improvements and more widespread adoption of previously considered niche 

control technologies as well as the development of innovative strategies.

118

 As the EPA 

continuously improves its data and tools, we expect to better characterize the currently 

unobserved pieces of the MACC. As a result of our consideration of these complexities, we are 

 

118 See Chapter 4, Section 4.5 for additional discussion of uncertainties associated with predicting technological 

advancements that may occur between now and 2025.
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currently unable to quantitatively predict future shifts in the abatement supply curve because 

many factors are intertwined and data are incomplete or highly uncertain. 

7.3 Compliance Cost Estimates for Unknown Emissions Controls

This section presents the methodology and results for the costs of emissions reductions 

from unidentified controls needed for attainment of the alternative ozone standards. As 

discussed in Chapter 4, the application of the modeled control strategy was not successful in 

reaching full nationwide attainment of the alternate ozone standards. Many areas remained in 

nonattainment under all four alternate standard scenarios. Therefore, the engineering costs 

detailed in Section 7.1 represent only the costs of partial attainment. 

7.3.1 Methods

Prior to presenting the methodology for estimating costs for unspecified emission 

reductions in this RIA, it is important to provide information from EPA’s Science Advisory 

Board Council Advisory,119 dated June 8, 2007, on the issue of estimating costs of unidentified 

control measures:

812 Council Advisory, Direct Cost Report, Unidentified Measures (charge question 2.a)

“The Project Team has been unable to identify measures that yield sufficient emission 

reductions to comply with the National Ambient Air Quality Standards (NAAQS) and 

relies on unidentified pollution control measures to make up the difference. Emission 

reductions attributed to unidentified measures appear to account for a large share of 

emission reductions required for a few large metropolitan areas but a relatively small 

share of emission reductions in other locations and nationwide. 

“The Council agrees with the Project Team that there is little credibility and hence 

limited value to assigning costs to these unidentified measures. It suggests taking great 

care in reporting cost estimates in cases where unidentified measures account for a 

significant share of emission reductions. At a minimum, the components of the total cost 

associated with identified and unidentified measures should be clearly distinguished. In 

some cases, it may be preferable to not quantify the costs of unidentified measures and to 

 

119

 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.
7-27

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.”

While we have considered alternative methodologies to predict future abatement supply 

curves, we are currently unable to quantitatively predict future shifts in the supply curve with 

sufficient confidence to use in this RIA. For most NAAQS RIAs prepared during the past five 

years, EPA estimated the costs for unidentified controls using a pair of methodologies: what we 

termed a “fixed cost” approach, following the SAB advice, and a “hybrid” approach that has not 

yet been reviewed by the SAB. We now refer to the fixed-cost approach as the “average cost” 

approach because it more accurately characterizes the concepts underlying the approach. The 

average cost methodology uses an assumed national average cost per ton for unidentified 

controls needed for attainment, as well as two alternative assumed values employed for 

sensitivity analysis. The range of estimates reflects different assumptions about the cost of 

additional emissions reductions beyond those in the modeled control strategy. While we use a

constant cost per ton of emissions reduction to estimate the costs of the emissions reductions 

beyond known controls, this does not imply that marginal costs are not increasing in needed 

emissions abatement. Rather, the average cost per ton is intended to capture what might be the 

total costs associated with the abatement of the emissions reductions from unknown controls.

The alternative estimates implicitly reflect different assumptions about the amount of 

technological progress and innovation in emission reduction strategies. The average cost 

methodology reflects a view that because no cost data exists for unspecified future strategies, it is 

unclear whether approaches using hypothetical cost curves will be more accurate or less accurate 

in forecasting total national costs of unspecified controls than an average-cost approach that uses 

a range of national cost per ton values.

The hybrid cost methodology assumed increasing marginal costs of control along an 

upward-sloping marginal cost curve. The hybrid cost methodology assumed the rate of increase 
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in the marginal costs of abatement is proportional to the weighted ratio of the amount of 

abatement using identified controls to the remaining needed abatement using unidentified 

controls.120 Under this approach, the relative costs of unspecified controls in different 

geographic areas reflected the expectation that average per-ton control costs are likely to be 

higher in areas needing a higher ratio of emissions reductions from unspecified and known 

controls. However, the weight, which reflected the anticipated degree of difficulty of achieving 

needed emissions reductions, and the ratios that informed the slope of the marginal abatement 

cost curve in previous NAAQS analyses were strong assumptions that have not been empirically 

tested.

When used to estimate costs for end-of-pipe technologies, the hybrid methodology 

assumed all emissions reductions come from the highest cost margin of the abatement supply 

curve which, as explained in the previous section, is unlikely for much of the unobserved 

abatement capacity in the present and future. For example, EPA’s control strategy tools largely 

focus on a limited set of emissions inventory sectors, whereas abatement opportunities exist in 

other sectors. When new abatement opportunities are identified in other sectors, they typically 

are not at the higher end of the cost curve. 

For areas needing significant additional emission reductions, much pollution abatement is 

likely needed from sources within regulated sectors that historically have not been intensively 

regulated. However, if national standards become more stringent, new regions or firms will be 

added to the regulated domain. These new entrants, with their relatively untapped abatement 

supply, will contribute to an outward shift in abatement supply. The newly regulated regions and 

firms will also face new incentives for technical change and innovation that may lower costs 

over the long run by developing new, more efficient compliance strategies. Because the point of 

departure for the hybrid approach cost curve is based on our current database, which includes 

only existing controls, it will systematically overstate future costs if any cost-reducing 

technological change occurs.

 

120 See, for example, Section 7.2 and Appendix &.A.2 is the December 2012 RIA for the final PM NAAQS, 

available at http://www.epa.gov/ttn/ecas/regdata/RIAs/finalria.pdf.
7-29

As noted in previous NAAQS analyses, the EPA continues to explore other sources of 

information to inform the estimates of extrapolated costs. For this RIA we examined the full set 

of known controls, examined evidence that suggests that over time new information and data 

emerges that shifts emissions reductions from the unknown to the known category, as well as 

explored whether NOx offset prices can serve as reasonable proxies for the costs of emissions 

reductions not identified by current tools. 

Based upon deliberations informing this discussion, the EPA Council’s advice, and the 

requirements of E.O. 12866 and OMB circular A-4, which provides guidance on the estimation 

of benefits and costs of regulations, in this RIA, we follow the Council recommendations by 

using an average cost per ton as a central estimate and conduct sensitivity analysis using 

alternative average costs to explore how sensitive total costs are to these assumptions. While the 

average cost methodology has limitations, we agree with the Council that the approach is both 

transparent and strikes a balance between the likelihood that some unidentified abatement would 

arise at lower segments of the identified cost curve while other sources of abatement may come 

at the higher cost margin.

While the known control analysis limited the application of controls with costs above 

$14,000 per ton, we examined the full set of controls available for application in regions needing 

emissions reductions. The MAC curves from this analysis are presented above in Section 7.1. 

For NOx controls, a total of 1.22 million tons of reductions are available, and about 1.18 million 

of these tons are available for less than $15,000 per ton. The known reductions available for less 

than $15,000 per ton represent about 96 percent of the total known reductions in areas needing 

emissions reductions to meet an alternative standard level. In addition, the average cost per ton 

across all of these abatement opportunities is about $3,400 per ton. As a result, we decided to 

use $15,000 per ton NOx as the main estimate for the extrapolated cost analysis. This assumed 

cost is representative of higher cost controls available in the analysis. If, for example, the true 

costs of the unidentified controls are distributed at the upper end of the identified control costs 

depicted in Figure 7-1, $15,000 per ton may under-estimate the average value of the unidentified 

abatement. Alternatively, the assumed value might overestimate the ‘”unobserved” abatement 

discussed in Section 7.2. The results shown in Table 7-4 (per ton cost of NOx reductions for five 

major NOx-reducing rules) and Table 7-7 (annualized per ton cost of NOs offsets in four 
7-30

regions) may suggest that the $15,000 per ton assumed average cost may be over-estimating the 

average value of the unidentified abatement. 

Because of this uncertainty, we use alternative assumptions of the average cost in the 

Appendix, a first sensitivity analysis using an assumed cost of $10,000 per ton and a second 

sensitivity analysis using an assumed $20,000 per ton.121 This range is inclusive of the 

annualized NOx offset prices observed in recent years in the areas likely to need unknown 

controls to achieve the proposed standard, and if anything, suggests the central estimate of 

$15,000/ton is conservative. EPA requests comments on the methods presented to estimate 

emission reductions needed beyond known controls including the parameter estimate of 

$15,000/ton.

Because cost changes due to technological change will be available on a national-level, it 

makes sense to use national-level average cost per ton in the primary analysis. However, as 

indicated by the variation in NOx offset prices across regions shown in Table 7-6, regional 

factors may play a significant role in the estimation of control costs. As a result, the EPA will 

continue to explore alternative methodologies and sources of regional information that may make 

the average cost methodology more regionally specific for the RIA for the final rule.

7.3.2 Unknown Compliance Cost Estimates

Table 7-8 presents the extrapolated control cost estimates for the East and West in 2025, 

except for California for the alternative standards using an assumed average cost of $15,000/ton. 

Values of $10,000/ton and $20,000/ton are used for the sensitivity analyses found in Appendix 

7.2.

 

121 As shown in Section 7.1, we also performed a similar analysis for VOC controls, which indicated that about 52 

percent of VOC controls available in the analysis for less than $15,000 per ton, with an average of about $12,000 

per ton. While a limited amount of extrapolated VOC emissions reductions were needed for the 60 ppb alternative 

level for the East (41,000 tons), we decided to use the same $15,000 per ton (with $10,000 and $20,000 per ton for 

the sensitivity analysis) for VOC controls for simplicity.
7-31

Table 7-8. Extrapolated Control Costs in 2025 by Alternative Standard for 2025 -- U.S., 

except California (millions of 2011$)

Alternative Level Geographic Area Extrapolated Cost

70 ppb

East 2,300

West -

Total 2,300

65 ppb

East 11,000

West -

Total 11,000

60 ppb

East 28,000

West 5,200

Total 34,000

a All values are rounded to two significant figures. Extrapolated costs are based on the average-cost methodology 

using a $15,000/ton assumed average cost.

Table 7-9 presents the extrapolated control cost estimates for post-2025 for California 

across the alternative standards using an assumed average cost of $15,000/ton. 

Table 7-9. Extrapolated Control Costs in 2025 by Alternative Standard for Post-2025 --

California (millions of 2011$)

Alternative Level Geographic Area Extrapolated Costs

70 ppb California 800

65 ppb California 1,600

60 ppb California 2,200

a All values are rounded to two significant figures. Extrapolated costs are based on the average-cost methodology 

using a $15,000/ton assumed average cost.

7.4 Total Compliance Cost Estimates

As discussed throughout this RIA, we present the primary costs and benefits estimates for 

2025. We assume that potential nonattainment areas everywhere in the U.S., excluding 

California, will be designated such that they are required to reach attainment by 2025, and we 

developed our projected baselines for emissions, air quality, and populations for 2025. 

In estimating the incremental costs and benefits of potential alternative standards, we 

recognize that there are several areas that are not required to meet the existing ozone standard by 

2025. The Clean Air Act allows areas with more significant air quality problems to take 

additional time to reach the existing standard. Several areas in California are not required to 
7-32

meet the existing standard by 2025 and may not be required to meet a revised standard until

sometime between 2032 and December 31, 2037.

122

 We were not able to project emissions and 

air quality beyond 2025 for California, however, we adjusted baseline air quality to reflect 

mobile source emissions reductions for California that would occur between 2025 and 2030; 

these emissions reductions were the result of mobile source regulations expected to be fully 

implemented by 2030. While there is uncertainty about the precise timing of emissions 

reductions and related costs for California, we assume costs occur through the end of 2037 and 

beginning of 2038. In addition, we model benefits for California using projected population 

demographics for 2038. 

 Because of the different timing for incurring costs and accruing benefits and for ease of 

discussion throughout the analyses, we refer to the different time periods for potential attainment 

as 2025 and post-2025 to reflect that (1) we did not project emissions and air quality for any year 

other than 2025; (2) for California, emissions controls and associated costs are assumed to occur 

through the end of 2037 and beginning of 2038; and (3) for California benefits are modeled using 

population demographics in 2038. It is not straightforward to discount the post-2025 results for 

California to compare with or add to the 2025 results for the rest of the U.S. While we estimate 

benefits using 2038 information, we do not have good information on precisely when the costs of 

controls will be incurred. Because of these differences in timing related to California attaining a 

revised standard, the separate costs and benefits estimates for post-2025 should not be added to 

the primary estimates for 2025. 

Tables 7-9 and 7-10 present summaries of the total national annual costs (known and 

extrapolated) of attaining the alternative standards of 70, 65, and 60 ppb. To calculate total cost 

estimates at a 3 percent discount rate and to include the extrapolated costs in those totals, we 

added the known control estimates at a 3 percent discount rate, where available, to the known 

control estimates at a 7 percent discount rate where the costs could not be determined for the 3 

percent rate; we added these to the extrapolated costs at a 7 percent discount rate. Table 7-10 

 

122 The EPA will likely finalize designations for a revised ozone NAAQS in late 2017. Depending on the precise 

timing of the effective date of those designations, nonattainment areas classified as Severe 15 will likely have to 

attain sometime between late 2032 and early 2033 and nonattainment areas classified as Extreme will likely have 

to attain by December 31, 203. 
7-33

presents the total national annual costs by alternative standard for 2025 for all of the U.S., except 

California. Table 7-11 presents the total national annual costs by alternative standard for post2025 for California. 

Table 7-10. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for 2025 - U.S., except California (millions of 2011$, 7% Discount Rate)a

Alternative Level Geographic Area Total Control Costs (Known and 

Extrapolated)

70 ppb

East 3,900

West -

Total $3,900

65 ppb

East 15,000

West 400

Total $15,000

60 ppb

East 33,000

West 5,800

Total $39,000

a All values are rounded to two significant figures. Extrapolated costs are based on the fixed-cost methodology.

Table 7-11. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for post-2025 - California (millions of 2011$, 7% Discount Rate)a

Alternative Level Geographic Area

Total Control Costs 

(Known and 

Extrapolated)

70 ppb California 800

65 ppb California 1,600

60 ppb California 2,200

a All values are rounded to two significant figures. Extrapolated costs are based on the fixed-cost methodology.

7.5 Updated Methodology Presented in this RIA

The cost analysis presented in this chapter incorporates an array of methodological and 

technical updates that the EPA has adopted since the previous review of the ozone standards in 

2008 and proposed reconsideration in 2010. The updates to models, methods, and data are too 

numerous to be able to quantitatively estimate the impact of any of the updates individually. 

Therefore, we present the major updates below qualitatively. Many of these changes reflect 

updates to inputs to the cost analysis, but are discussed here for completeness. Below we note the 

aspects of this analysis that differ from the 2008 RIA as well as the 2010 reconsideration RIA 
7-34

(U.S. EPA, 2010). A few overarching changes that are worth mentioning: the incremental costs 

and benefits for this analysis are measured from a baseline of the current 75 ppb ozone standard; 

in the previous analysis the baseline was the 84 ppb ozone standard. Also, the currency year was 

updated from 2006$ to 2011$. 

Emissions and Air Quality Updates

The base year emissions for this analysis are 2011, and the future analysis year is 2025 

(previously the base year was 2002 and the future analysis year was 2020). Key changes in the 

emission estimates include: increased accuracy of stationary source emissions estimates, updates 

in models and Annual Energy Outlook (AEO) projections for mobile sources and EGUs, 

inclusion of oil and gas sector emissions and numerous updates to the nonpoint emissions. In 

addition, from 2002 to 2011 there have been a number of changes in the energy system, cleaner 

mobile sources, and economic changes that have resulted in reduced emissions since the last 

ozone analysis. There are additional federal control programs included in the emissions

projections to 2025, including Tier 3, MATS, and the Clean Power Plan. 

Air quality monitor design values were updated to reflect 2009 through 2013 air quality

in contrast the 2008 and 2010 analyses which used 2000 through 2004 air quality. As shown in 

Chapter 2 Figure 2-2, ozone design values are generally decreasing over time. For example, the 

figure shows that for the period of years from 2000 through 2004 the 90th percentile 

concentrations ranged from 85.9 to 102.8 ppb; the 75th percentile values for the same period 

ranged from 78.8 to 87.3 ppb. In contrast, the design values for the period of 2009 through 2013 

the 90th percentile concentrations ranged from 77 to 86.2 ppb and the 75th percentile 

concentrations ranged from 70.8 to 81 ppb. These design values are the basis for future year air 

quality projections. A quick comparison across analyses reveals that in the 2008 analysis 89 

counties were projected to exceed the higher end of the proposed range of 70ppb in the future 

year (2020), while only 9 counties are projected to exceed 70 ppb in this analysis for 2025. The 

same is true for the lower end of the proposed range, in 2008 231 counties were projected to 

exceed 65 ppb and that number has decreased to 68 counties in this analysis.

As emissions decrease in the base year and air quality design values decrease, the result is 

a smaller incremental change in air quality needed to meet the revised standards as compared to 
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the 2008 and 2010 analyses. The smaller increment needed to achieve attainment also means 

that fewer controls are needed across a reduced number of geographic areas

Control Strategy Updates

Many improvements were made in the non-EGU point control measures used in this 

analysis that make the data more accurate and defensible. These changes include: removal of 

incorrect links between control measures and SCCs; updates to cost equations where more 

recent data was available to improve their accuracy; inclusion of information that has recently 

become available concerning known and emerging technologies for reducing NOx emissions; 

and revising costs and control efficiencies from control measures in the dataset based on recently 

obtained information from industry and multi-jurisdictional organizations (e.g., Ozone Transport 

Commission and Lake Michigan Air Directors Consortium). In addition, a different mix of 

known control measures across EGU, mobile, nonpoint and non-EGU point were applied due to 

the recently promulgated rules mentioned above.

Costing methodology updates have occurred since previous ozone analyses, the ‘hybrid’ 

approach was not utilized to estimate costs of emission reductions needed beyond known 

controls. Holding cost methodology constant (average/fixed cost approach), fewer emissions 

reductions were needed beyond known controls in this analysis due to the increased application 

of known control measures mentioned above. 

The above mentioned technical and methodological changes resulted in the lower cost 

estimates in this analysis. The most influential factors on the cost analysis were the lower 

number of exceeding counties and increased data accuracy of the known control measures. The 

effect of fewer exceeding counties is that fewer emissions reductions are needed, and therefore 

the costs are lower. The effect of additional and lower-cost known control measures being 

applied is a lowering of the emissions reductions needed beyond known controls, which are

typically the higher cost emissions reductions.
7-36

 7.6 Economic Impacts

7.6.1 Introduction

This section addresses the potential economic impacts of the illustrative control strategies 

for the potential alternative ozone standards. The control costs are uncertain for several reasons. 

The controls that the states ultimately choose to implement will likely differ from the illustrative 

control strategies for which costs are estimated in earlier sections of this chapter. The flexibility 

afforded to states by the Clean Air Act also allows them to adopt programs that include design 

elements that may mitigate or promote particular economic impacts based on their individual 

priorities. The cost estimates become more uncertain because of the length of time before they 

will be implemented. By the 2025 and post-2025 time frames, changes in technology, changes in 

implemented regulations, and changes in relative prices will all add to the uncertainty in the cost 

analysis. Finally, the portion of costs that is extrapolated is not allocated to particular sectors.

Economic impacts focus on the behavioral response to the costs imposed by a policy being 

analyzed. The responses typically analyzed are market changes in prices, quantities produced 

and purchased, changes in international trade, changes in profitability, facility closures, and 

employment. Often, these behavioral changes are used to estimate social costs if there is 

indication that the social costs differ from the estimate of control costs because behavioral 

change results in other ways of meeting the requirements (e.g., facilities choosing to reduce 

emissions by producing less rather than adding pollution control devices). 

The potential alternative ozone standards are anticipated to impact multiple markets in 

many times and places. Computable General Equilibrium (CGE) models are designed to address 

such problems. To support the Final Ozone NAAQS of March 2008 (Final Ozone NAAQS 

Regulatory Impact Analysis), among other rulemakings, the EPA used the Economic Model for 

Policy Analysis (EMPAX) to estimate the market impacts of the portion of the cost that was 

associated with the application of known controls (excluding the extrapolated costs). EMPAX is 

a dynamic computable general equilibrium (CGE) model that forecasts a new equilibrium for the 

entire economy after a policy intervention. While the external Council on Clean Air Compliance 

Analysis (Council) peer review of The Benefits and Costs of the Clean Air Act from 1990 to 

2020(Hammitt 2010) stated that inclusion of benefits in an economy-wide model, specifically 
7-37

adapted for use in that study, “represent[ed] a significant step forward in benefit-cost analysis,” 

EPA recognizes that serious technical challenges remain when attempting to evaluate the 

benefits and costs of potential regulatory actions using economy-wide models. Consistent with 

the Council’s advice regarding the importance of including benefit-side effects demonstrated by 

the Benefits and Costs of the Clean Air Act from 1990 to 2020, and the lack of available multiyear air quality projections needed to include these benefit-side effects, EPA has not conducted 

CGE modeling for this analysis. 

However, the EPA recognizes that serious technical challenges remain when attempting to 

evaluate the impacts of potential regulatory actions using economy-wide models. The EPA is 

therefore establishing a new Science Advisory Board (SAB) panel on economy-wide modeling 

to consider the technical merits and challenges of using this analytical tool to evaluate costs, 

benefits, and economic impacts in regulatory development. The EPA will use the 

recommendations and advice of this SAB panel as an input into its process for improving 

benefit-cost and economic impact analyses that are used to inform decision-making at the 

Agency. The panel will also be asked to identify potential paths forward for improvements that 

could address the challenges posed when economy-wide models are used to evaluate the effects 

of regulations.

The advice from the SAB panel formed specifically to address the subject of economywide modeling will not be available in time for this analysis. Given the ongoing SAB panel on 

economy-wide modeling, and the uncertain nature of costs, this section proceeds with a 

qualitative discussion of market impacts. 

7.6.2 Summary of Market Impacts

Consider an added cost to produce a good associated with the pollution control required to 

reach the alternative ozone standards. Such a good is either one developed for the consumer 

(called a consumption good), or one used in the production of other goods for consumption 

(called an intermediate good). Some goods are both consumption and intermediate goods. First, 

consider the direct impact on the market facing the increased cost. In this case for the market 

facing the increased cost, the price will go up and the amount sold will go down. The magnitude 

of these shifts depends on a number of factors. The greater the unit cost increase relative to the 
7-38

price of the good the greater will be the changes. The more responsive a consumer is to a change 

in the price of a consumption good or the more responsive a purchase of an intermediate good is 

the greater will be the changes. For the alternative ozone standards, many goods will have direct 

changes in costs of production. This makes the assumption of isolated markets too simple. With 

multiple intermediate goods affected, then the intermediate goods and consumption goods they 

are used to produce are affected. As fewer intermediate goods and consumption goods are 

purchased at a higher price, other intermediate goods and consumption goods that serve as 

substitutes become more attractive and more are sold at a higher price. All of these market 

changes lead to changes in income, which can lead to changes in purchases of consumption 

goods. Quantities of intermediate goods used to reduce emissions would also change. 

Considering all of these changes, it is not possible to qualitatively conclude the direction of price 

and quantity changes for any single market. Any conclusions about changes in international 

trade, profits, closures, or social cost is impossible in a qualitative analysis.

7.7 Uncertainties and Limitations

The EPA acknowledges several important limitations of this analysis, which include the 

following:

Boundary of the cost analysis: In this engineering cost analysis we include only the impacts to 

the regulated industry, such as the costs for purchase, installation, operation, and maintenance of 

control equipment over the lifetime of the equipment. As mentioned above, recordkeeping, 

reporting, testing and monitoring costs are not included. In some cases, costs are estimated for 

changes to a process such as switching from one fuel to another less polluting fuel. Additional 

profit or income may be generated by industries supplying the regulated industry, especially for 

control equipment manufacturers, distributors, or service providers. These types of secondary 

impacts are not included in this engineering cost analysis.

Cost and effectiveness of control measures: Our application of control measures reflect 

average retrofit factors and equipment lives that are applied on a national scale. We do not 

account for regional or local variation in capital and annual cost items such as energy, labor, 

materials, and others. Our estimates of control measure costs may over- or under-estimate the 

costs depending on how the difficulty of actual retrofitting and equipment life compares with our 
7-39

control assumptions. In addition, our estimates of control efficiencies for the known controls 

assume that the control devices are properly installed and maintained. There is also variability in 

scale of application that is difficult to reflect for small area sources of emissions.

Discount rate: Because we obtain control cost data from many sources, we are not always able 

to obtain consistent data across original data sources. If disaggregated control cost data are 

unavailable (i.e., where capital, equipment life value, and operation and maintenance [O&M] 

costs are not separated out), the EPA typically assumes that the estimated control costs are 

annualized using a 7 percent discount rate. When disaggregated control cost data are available 

(i.e., where capital, equipment life value, and O&M costs are explicit), we can recalculate costs 

using a 3 percent discount rate. In general, we have some disaggregated data available for nonEGU point source controls, and we do not have any disaggregated control cost data for area 

source controls. In addition, these discount rates are consistent with OMB guidance, but the 

actual real discount rates may vary regionally or locally. 

Known control costs: We estimate that there is an accuracy range of +/- 30 percent for nonEGU point source control costs. This level of accuracy is described in the EPA Air Pollution 

Control Cost Manual, which is a basis for the estimation of non-EGU control cost estimates 

included in this RIA. This level of accuracy is consistent with either the budget or bid/tenderlevel of cost estimation as defined by the AACE International.123

 The accuracy for nonpoint 

control costs estimates has not been determined, but it is likely no more accurate than those for 

non-EGU point source control costs. 

Differences between ex ante and ex post compliance cost estimates: In comparing regulatory 

cost estimates before and after regulation, ex ante cost estimate predictions often overestimate or 

underestimate costs. Harrington et al. (2000) surveyed the predicted and actual costs of 28 

federal and state rules, including 21 issued by the U.S. Environmental Protection Agency and the 

Occupational Safety and Health Administration (OSHA). In 14 of the 28 rules, predicted total 

costs were overestimated, while analysts underestimated costs in three of the remaining rules. In 

 

123 AACE International. Recommended Practice No. 18R-97. Cost Estimate Classification System – As Applied in 

Engineering, Procurement, and Construction For the Process Industries. Revised on November 29, 2011. 

Available at http://www.aacei.org/non/rps/18R-97.pdf. 
7-40

EPA rules where per-unit costs were specifically evaluated, costs of regulations were 

overestimated in five cases, underestimated in four cases, and accurately estimated in four cases 

(Harrington et al. 2000). The collection of literature regarding the accuracy of cost estimates 

seems to reflect these splits. The “Retrospective Study of the Costs of EPA Regulations” found 

that several of the case studies124 suggested that cost estimates were over-estimated. However, 

the EPA stated in the report that the small number of regulatory actions covered and data and 

analytical challenges associated with the case studies limited the certainty of this conclusion.

Costs of unknown controls (extrapolated costs): In addition to the application of known 

controls, the EPA assumes the application of unidentified future controls that make possible the 

additional emissions reductions needed beyond known controls for attainment in the projection 

year for this analysis. 

7.8 References 

Brunnermeier, S.B., Cohen, M.A., 2003. Determinants of environmental innovation in US manufacturing industries. 

Journal of Environmental Economics and Management 45, 278-293.

Dutton, J. M., Thomas, A., 1984. Treating Progress Functions as a Managerial Opportunity. Academy of 

Management Review 9(2), 235-247.

Gerard, D., Lave, L.B., 2005. Implementing technology-forcing policies: The 1970 Clean Air Act Amendments and 

the introduction of advanced automotive emissions controls in the United States. Technological Forecasting and 

Social Change 72, 761-778.

Hammitt, J.K.,2010. Review of the final integrated report for the second section 812 prospective study of the 

benefits and costs of the clean air act. Available at 

http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/1E6218DE3BFF682E852577F

B005D46F1/$File/EPA-COUNCIL-11-001-unsigned.pdf.

Harrington, W., Morgenstern, R.D., Nelson, P., 2000. On the accuracy of regulatory cost estimates. Journal of 

Policy Analysis and Management 19, 297-322.

Jaffe, A., Newell, R., Stavins, R., 2002. Environmental Policy and Technological Change. Environmental and Resource 

Economics 22, 41-70.

Kneller, R., Manderson, E., 2012. Environmental regulations and innovation activity in UK manufacturing 

industries. Resource and Energy Economics 34, 211-235.

 

124 The four case studies in the 2014 Retrospective Study of the Costs of EPA Regulations examine five EPA 

regulations: the 2001/2004 National Emission Standards for Hazardous Air Pollutants and Effluent Limitations 

Guidelines, Pretreatment Standards, and New Source Performance Standards on the Pulp and Paper Industry; 

Critical Use Exemptions for Use of Methyl Bromide for Growing Open Field Fresh Strawberries in California for 

the 2004-2008 Seasons; the 2001 National Primary Drinking Water Regulations for Arsenic; and the 1998 

Locomotive Emission Standards.
7-41

Levinson, A. Technology, 2009. International Trade, and Pollution from US Manufacturing. American Economic 

Review 99, 2177-2192.

Popp, D., 2006. International innovation and diffusion of air pollution control technologies: the effects of NOx and 

SO2 regulation in the US, Japan, and Germany. Journal of Environmental Economics and Management 51, 46-71.

Popp, D., Newell, R., 2012. Where does energy R&D come from? Examining crowding out from energy 

R&D. Energy Economics 34, 980-991.

Rubin, E.S., Yeh, S., Hounshell, D.A., Taylor, M.R., 2004. Experience curves for power plant emission control 

technologies. International Journal of Energy Technology and Policy 2, 52-69.

Sargent & Lundy, L.L.C. 2013. IPM Model – Updates to Cost and Performance for APC Technologies, SCR Cost 

Development Methodology. Chicago, IL. Available at http://www.epa.gov/airmarkets/progsregs/epaipm/docs/v513/attachment5_3.pdf.U.S. Environmental Protection Agency (U.S. EPA). 1997. Regulatory Impact 

Analyses for the Particulate Matter and Ozone National Ambient Air Quality Standards and Proposed Regional 

Haze Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at 

http://www.epa.gov/ttn/oarpg/naaqsfin/ria.html. 

Sterner, T., Turnheim, B., 2009. Innovation and diffusion of environmental technology: Industrial NOx abatement in 

Sweden under refunded emission payments. Ecological Economics 68, 2996-3006.

U.S. Environmental Protection Agency (U.S. EPA). 2003. EPA Air Pollution Control Cost Manual. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. Available at 

http://epa.gov/ttn/catc/products.html#cccinfo.

U.S. Environmental Protection Agency (U.S. EPA). 2008. Final Ozone NAAQS Regulatory Impact Analysis. Office 

of Air Quality Planning and Standards, Research Triangle Park, NC. Available at 

http://www.epa.gov/ttn/ecas/regdata/RIAs/452_R_08_003.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis (RIA) for the Proposed 

Reconsideration of the ozone National Ambient Air Quality Standards (NAAQS). Office of Air Quality Planning 

and Standards, Research Triangle Park, NC. January. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/s1-

supplemental_analysis_full.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2013. Documentation for EPA Base Case v.5.13 Using the 

Integrated Planning Model. Office of Air Atmospheric Programs, Washington, DC. Available at 

http://www.epa.gov/powersectormodeling/BaseCasev513.html.

U.S. Environmental Protection Agency (U.S. EPA). 2014a. Control Strategy Tool (CoST) Documentation Report. 

Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at 

http://www.epa.gov/ttnecas1/cost.htm.

U.S. Environmental Protection Agency (U.S. EPA). 2014b. Guidelines for Preparing Economic Analyses, Chapter 6. 

Available at http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html#download.

U.S. Environmental Protection Agency (U.S. EPA). 2014c. Retrospective Study of the Costs of EPA Regulations: A 

Report of Four Case Studies. Available at http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0575.pdf/$file/EE0575.pdf.
7A-1

APPENDIX 7A: ENGINEERING COST ANALYSIS

Overview

Chapter 7 describes the engineering cost analysis approach that EPA used in applying to 

demonstrate attainment of alternative ozone standard levels of 70, 65, and 60 ppb. This 

Appendix contains more detailed information about the control costs of the known control 

strategy analyses by control measure as well as sensitivity analyses for the fixed cost approach 

used to estimate costs for the unknown emissions controls.

7A.1 Cost of Known Controls in Alternative Standards Analyses

This section presents costs of known controls for the alternative standards analyses. Costs 

are in terms of 2011 dollars and include values for all portions of the U.S. that were part of the 

analyses. However, because all available known controls for California were applied as part of 

the baseline analysis, no known controls were available for the alternative standards analyses in 

California so these costs do not include any known control costs for California. Costs for the 

alternative standard analyses are incremental to the baseline attainment demonstration. Tables 

7A-1 and 7A-2 present the Costs for known controls by measure for the 70ppb alternative 

standard analysis for NOx and VOC respectively. Tables 7A-3 and 7A-4 present the Costs for 

known controls by measure for the 65ppb alternative standard analysis, and Tables 7A-5 and 7A6 present the costs for known controls by measure for the 60 ppb alternative standard analysis.

Table 7A-1. Costs for Known NOx Controls in the 70 ppb Analysis (millions of 2011$)a

NOx Control Measure Cost

Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 0.35

Biosolid Injection Technology - Cement Kilns 0.42

Ignition Retard - IC Engines 0.037

Low Emission Combustion - Gas Fired Lean Burn IC Engines 1

Low NOx Burner - Commercial/Institutional Boilers & IC Engines 6.6

Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 1.4

Low NOx Burner - Industrial Combustion 0.37

Low NOx Burner - Lime Kilns 0.28

Low NOx Burner - Natural Gas-Fired Turbines 8.4

Low NOx Burner - Residential Furnaces 5.2

Low NOx Burner - Residential Water Heaters & Space Heaters 13

Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 0.13

Low NOx Burner and Flue Gas Recirculation - Iron & Steel Mills - Reheating 0.026

Low NOx Burner and SCR - Coal-Fired ICI Boilers 4.1

Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 27
7A-2

NOx Control Measure Cost

Natural Gas Reburn - Natural Gas-Fired EGU Boilers 0

Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 29

Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 10

OXY-Firing - Glass Manufacturing 20

Selective Catalytic Reduction (SCR) - Cement Kilns 16

Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 4

Selective Catalytic Reduction (SCR) - IC Engines, Diesel 5.5

Selective Catalytic Reduction (SCR) - ICI Boilers 8.5

Selective Catalytic Reduction (SCR) - Industrial Incinerators 4

Selective Catalytic Reduction (SCR) - Petroleum Refinery Gas-Fired Process Heaters 3.1

Selective Catalytic Reduction (SCR) - Sludge Incineration 1.4

Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 0.029

Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 0.24

Selective Non-Catalytic Reduction (SNCR) - Utility Boilers 0.43

a All values are rounded to two significant figures.

Table 7A-2. Costs for Known VOC Controls in the 70 ppb Analysis (millions of 2011$) a

VOC Control Measure Cost

Control of Fugitive Releases - Oil & Natural Gas Production 0.044

Control Technology Guidelines - Wood Furniture Surface Coating 1.9

Flare - Petroleum Flare 0.36

Gas Recovery - Municipal Solid Waste Landfill 0.27

Improved Work Practices, Material Substitution, Add-On Controls - Printing 0.4

Incineration - Other 0.84

Incineration - Surface Coating 200

Low-VOC Coatings and Add-On Controls - Surface Coating 0.04

LPV Relief Valve - Underground Tanks 7.1

Permanent Total Enclosure (PTE) - Surface Coating 3.5

RACT - Graphic Arts 20

Reduced Solvent Utilization - Surface Coating 2.7

Reformulation - Architectural Coatings 140

Reformulation - Industrial Adhesives 6.4

Reformulation-Process Modification - Automobile Refinishing 38

Reformulation-Process Modification - Cold Cleaning 3.3

Reformulation-Process Modification - Cutback Asphalt 0.02

Reformulation-Process Modification - Surface Coating 13

Solvent Recovery System - Printing/Publishing 0.038

Wastewater Treatment Controls- POTWs 0.73

a All values are rounded to two significant figures.
7A-3

Table 7A-3. Costs for Known NOx Controls in the 65 ppb Analysis (millions of 2011$) a

NOx Control Measure Cost

Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 12

Biosolid Injection Technology - Cement Kilns 2.7

Ignition Retard - IC Engines 0.96

Low Emission Combustion - Gas Fired Lean Burn IC Engines 110

Low NOx Burner - Coal Cleaning 0.77

Low NOx Burner - Commercial/Institutional Boilers & IC Engines 40

Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 41

Low NOx Burner - Industrial Combustion 3.3

Low NOx Burner - Lime Kilns 4.8

Low NOx Burner - Miscellaneous Sources 0.058

Low NOx Burner - Natural Gas-Fired Turbines 44

Low NOx Burner - Residential Furnaces 16

Low NOx Burner - Residential Water Heaters & Space Heaters 110

Low NOx Burner - Steel Foundry Furnaces 0.27

Low NOx Burner - Surface Coating Ovens 0.092

Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 2.2

Low NOx Burner and Flue Gas Recirculation - Iron & Steel Mills - Reheating 0.55

Low NOx Burner and Over Fire Air - Utility Boilers 1.6

Low NOx Burner and SCR - Coal-Fired ICI Boilers 87

Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 210

Low NOx Burner and SNCR - Industr/Commercial/Institutional Boilers 3.6

Natural Gas Reburn - Natural Gas-Fired EGU Boilers 1.2

Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 58

Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 170

Non-Selective Catalytic Reduction (NSCR) - Nitric Acid Mfg 1.4

OXY-Firing - Glass Manufacturing 140

SCR and Flue Gas Recirculation - Fluid Catalytic Cracking Units 0.91

SCR and Flue Gas Recirculation - ICI Boilers 4.7

SCR and Flue Gas Recirculation - Process Heaters 2.9

Selective Catalytic Reduction (SCR) - Ammonia Mfg 15

Selective Catalytic Reduction (SCR) - Cement Kilns 230

Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 19

Selective Catalytic Reduction (SCR) - IC Engines, Diesel 24

Selective Catalytic Reduction (SCR) - ICI Boilers 76

Selective Catalytic Reduction (SCR) - Industrial Combustion 24

Selective Catalytic Reduction (SCR) - Industrial Incinerators 5.6

Selective Catalytic Reduction (SCR) - Iron Ore Processing 1.3

Selective Catalytic Reduction (SCR) - Petroleum Refinery Gas-Fired Process Heaters 61

Selective Catalytic Reduction (SCR) - Process Heaters 0.26

Selective Catalytic Reduction (SCR) - Sludge Incineration 39

Selective Catalytic Reduction (SCR) - Space Heaters 1.3

Selective Catalytic Reduction (SCR) - Utility Boilers 1,700

Selective Non-Catalytic Reduction (SNCR) - Coke Mfg 6.4

Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 2.9

Selective Non-Catalytic Reduction (SNCR) - ICI Boilers 0.75

Selective Non-Catalytic Reduction (SNCR) - Industrial Combustion 0.065

Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 2.8
7A-4

NOx Control Measure Cost

Selective Non-Catalytic Reduction (SNCR) - Miscellaneous 0.25

Selective Non-Catalytic Reduction (SNCR) - Municipal Waste Combustors 2.5

Selective Non-Catalytic Reduction (SNCR) - Utility Boilers 0.46

Ultra-Low NOx Burner - Process Heaters 1.8

a All values are rounded to two significant figures.

Table 7A-4. Costs for Known VOC Controls in the 65 ppb Analysis (millions of 2011$) a

VOC Control Measure Cost

Control of Fugitive Releases - Oil & Natural Gas Production 0.083

Control Technology Guidelines - Wood Furniture Surface Coating 3

Flare - Petroleum Flare 0.36

Gas Recovery - Municipal Solid Waste Landfill 0.37

Improved Work Practices, Material Substitution, Add-On Controls - Printing 1.4

Incineration - Other 0.91

Incineration - Surface Coating 370

Low VOC Adhesives and Improved Application Methods - Industrial Adhesives 0.06

Low-VOC Coatings and Add-On Controls - Surface Coating 0.57

LPV Relief Valve - Underground Tanks 13

Permanent Total Enclosure (PTE) - Surface Coating 17

Petroleum and Solvent Evaporation - Surface Coating Operations 0.037

RACT - Graphic Arts 38

Reduced Solvent Utilization - Surface Coating 3.1

Reformulation - Architectural Coatings 300

Reformulation - Industrial Adhesives 6.1

Reformulation-Process Modification - Automobile Refinishing 62

Reformulation-Process Modification - Cutback Asphalt 0.075

Reformulation-Process Modification - Surface Coating 24

Solvent Recovery System - Printing/Publishing 1.3

Solvent Substitution and Improved Application Methods - Fiberglass Boat Mfg 0.059

Wastewater Treatment Controls- POTWs 0.82

a All values are rounded to two significant figures.
7A-5

Table 7A-5. Costs for Known NOx Controls in the 60 ppb Analysis (millions of 2011$) a

NOx Control Measure Cost

Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 12

Biosolid Injection Technology - Cement Kilns 2.7

Ignition Retard - IC Engines 1

Low Emission Combustion - Gas Fired Lean Burn IC Engines 120

Low NOx Burner - Coal Cleaning 0.77

Low NOx Burner - Commercial/Institutional Boilers & IC Engines 40

Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 42

Low NOx Burner - Industrial Combustion 3.3

Low NOx Burner - Lime Kilns 4.8

Low NOx Burner - Miscellaneous Sources 0.077

Low NOx Burner - Natural Gas-Fired Turbines 47

Low NOx Burner - Residential Furnaces 17

Low NOx Burner - Residential Water Heaters & Space Heaters 120

Low NOx Burner - Steel Foundry Furnaces 0.27

Low NOx Burner - Surface Coating Ovens 0.092

Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 2.2

Low NOx Burner and Flue Gas Recirculation - Iron & Steel Mills - Reheating 0.55

Low NOx Burner and Over Fire Air - Utility Boilers 1.6

Low NOx Burner and SCR - Coal-Fired ICI Boilers 87

Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 210

Low NOx Burner and SNCR - Industr/Commercial/Institutional Boilers 3.6

Natural Gas Reburn - Natural Gas-Fired EGU Boilers 1.3

Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 58

Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 170

Non-Selective Catalytic Reduction (NSCR) - Nitric Acid Mfg 1.4

OXY-Firing - Glass Manufacturing 140

SCR and Flue Gas Recirculation - Fluid Catalytic Cracking Units 0.91

SCR and Flue Gas Recirculation - ICI Boilers 4.7

SCR and Flue Gas Recirculation - Process Heaters 2.9

Selective Catalytic Reduction (SCR) - Ammonia Mfg 15

Selective Catalytic Reduction (SCR) - Cement Kilns 230

Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 19

Selective Catalytic Reduction (SCR) - IC Engines, Diesel 26

Selective Catalytic Reduction (SCR) - ICI Boilers 76

Selective Catalytic Reduction (SCR) - Industrial Combustion 24

Selective Catalytic Reduction (SCR) - Industrial Incinerators 5.6

Selective Catalytic Reduction (SCR) - Iron Ore Processing 1.3

Selective Catalytic Reduction (SCR) - Petroleum Refinery Gas-Fired Process Heaters 61

Selective Catalytic Reduction (SCR) - Process Heaters 0.26

Selective Catalytic Reduction (SCR) - Sludge Incineration 39

Selective Catalytic Reduction (SCR) - Space Heaters 1.5

Selective Catalytic Reduction (SCR) - Utility Boilers 1,900

Selective Non-Catalytic Reduction (SNCR) - Coke Mfg 6.4

Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 2.9

Selective Non-Catalytic Reduction (SNCR) - ICI Boilers 0.75

Selective Non-Catalytic Reduction (SNCR) - Industrial Combustion 0.087

Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 2.8
7A-6

NOx Control Measure Cost

Selective Non-Catalytic Reduction (SNCR) - Miscellaneous 0.25

Selective Non-Catalytic Reduction (SNCR) - Municipal Waste Combustors 2.5

Selective Non-Catalytic Reduction (SNCR) - Utility Boilers 0.46

Ultra-Low NOx Burner - Process Heaters 1.8

a All values are rounded to two significant figures.

Table 7A-6. Costs for Known VOC Controls in the 60 ppb Analysis (millions of 2011$) a

VOC Control Measure Cost

Control of Fugitive Releases - Oil & Natural Gas Production 0.089

Control Technology Guidelines - Wood Furniture Surface Coating 3.3

Flare - Petroleum Flare 0.36

Gas Recovery - Municipal Solid Waste Landfill 0.41

Improved Work Practices, Material Substitution, Add-On Controls - Printing 1.4

Incineration - Other 0.91

Incineration - Surface Coating 370

Low VOC Adhesives and Improved Application Methods - Industrial Adhesives 0.064

Low-VOC Coatings and Add-On Controls - Surface Coating 0.97

LPV Relief Valve - Underground Tanks 13

Permanent Total Enclosure (PTE) - Surface Coating 28

Petroleum and Solvent Evaporation - Surface Coating Operations 0.071

RACT - Graphic Arts 40

Reduced Solvent Utilization - Surface Coating 3.2

Reformulation - Architectural Coatings 310

Reformulation - Industrial Adhesives 6.1

Reformulation-Process Modification - Automobile Refinishing 66

Reformulation-Process Modification - Cutback Asphalt 0.087

Reformulation-Process Modification - Surface Coating 25

Solvent Recovery System - Printing/Publishing 1.3

Solvent Substitution and Improved Application Methods - Fiberglass Boat Mfg 0.059

Wastewater Treatment Controls- POTWs 0.82

a All values are rounded to two significant figures.

7A.2 Alternative Estimates of Costs Associated with Emissions Reductions from 

Unknown Controls

This section presents alternative estimates of the extrapolated control costs using 

alternative average cost per ton of emission reductions from unknown controls. The alternative 

values used are $10,000/ton and $20,000, as well as the $15,000/ton value used as the primary 

estimate in the RIA. Table 7A-7 presents the alternative estimates for the East and West Regions 

in 2025, without California, while Table 7A-8 presents the estimates for post-2025 California. 
7A-7

Table 7A-7. Extrapolated Control Costs in 2025 by Alternative Standard for 2025 U.S., 

except California, using Alternative Average Cost Assumptions (millions of 

2011$)

Alternative Level Geographic Area Extrapolated Cost

$10,000/ton $15,000/ton $20,000/ton

70 ppb

East 1,500 2,300 3,100

West - - -

65 ppb East 7,500 11,000 15,000

West -

60 ppb East 19,000 28,000 38,000

West 3,500 5,200 7,000

a All values are rounded to two significant figures.

Table 7A-8. Extrapolated Control Costs in 2025 by Alternative Standard for Post-2025 

California, using Alternative Average Cost Assumptions (millions of 2011$)

Alternative Level Geographic Area Extrapolated Cost

$10,000/ton $15,000/ton $20,000/ton

70 ppb California 530 800 1,100

65 ppb California 1,000 1,600 2,100

60 ppb California 1,400 2,200 2,900

a All values are rounded to two significant figures.

Note, by definition, the lower per ton estimate is 50% less than the upper end estimate, and the 

range between the extrapolated cost bounds becomes larger as the alternative levels become 

more stringent and rely more heavily on unknown controls. 

Tables 7A-9 presents the estimates of the total control costs for 2025 East and West, 

without California, when using the alternative per ton cost assumptions for emissions reductions 

from unknown controls. Tables 7A-10 presents the estimates of the total control costs for post2025 California when using the alternative per ton cost assumptions for emissions reductions 

from unknown controls.

Table 7A-9. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for 2025 - U.S. using Alternative Cost Assumption for Extrapolated Costs, 

except California (millions of 2011$)a

Alternative Level Geographic Area

Total Control Costs (Known and Extrapolated)

Extrapolated 

Cost = 

$10,000/ton

Extrapolated 

Cost = 

$15,000/ton

Extrapolated 

Cost = 

$20,000/ton

70 ppb

East 3,100 3,900 4,700

West - - -

65 ppb East 11,000 15,000 19,000
7A-8

Alternative Level Geographic Area

Total Control Costs (Known and Extrapolated)

Extrapolated 

Cost = 

$10,000/ton

Extrapolated 

Cost = 

$15,000/ton

Extrapolated 

Cost = 

$20,000/ton

West 400 400 400

60 ppb

East 23,000 33,000 42,000

West 4,100 5,800 7,600

a All values are rounded to two significant figures.

Table 7A-10. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for Post-2025 California - U.S. using Alternative Cost Assumption for 

Extrapolated Costs (millions of 2011$)a

Alternative Level Geographic Area

Total Control Cost

Extrapolated 

Cost = 

$10,000/ton

Extrapolated 

Cost = 

$15,000/ton

Extrapolated 

Cost = 

$20,000/ton

70 ppb California 530 800 1,100

65 ppb California 1,000 1,600 2,100

60 ppb California 1,400 2,200 2,900

a All values are rounded to two significant figures.
8-1

CHAPTER 8: COMPARISON OF COSTS AND BENEFITS

Overview

The EPA has performed an illustrative analysis to estimate the costs and human health 

benefits of nationally attaining alternative ozone standards. The EPA Administrator is proposing 

to revise the level of the primary ozone standard to within a range of 65 to 70 ppb and is 

soliciting comment on alternative standard levels below 65 ppb, as low as 60 ppb. Per Executive

Order 12866 and the guidelines of OMB Circular A-4, this Regulatory Impact Analysis (RIA) 

presents the analyses of the following alternative standard levels -- 60 ppb, 65 ppb, and 70 ppb. 

This chapter summarizes these results and discusses the implications of the analysis. The cost 

and benefit estimates below are calculated incremental to a 2025 baseline assuming attainment of 

the existing ozone standard of 75 ppb and incorporating air quality improvements achieved 

through the projected implementation of existing regulations. 

8.1 Results

In this RIA we present the primary costs and benefits estimates for full attainment in 

2025. For analytical purposes, we assume that almost all areas of the country will meet each 

alternative standard level in 2025 through the adoption of technologies at least as effective as the 

control strategies used in this illustration. It is expected that some costs and benefits will begin 

occurring earlier, as states begin implementing control measures to attain earlier or to show 

progress towards attainment. For California, we provide estimates of the costs and benefits of 

attaining the standard in a post-2025 time frame. 

In estimating the incremental costs and benefits of potential alternative standard levels, 

we recognize that there are several areas that are not required to meet the existing ozone standard 

by 2025. The Clean Air Act allows areas with more significant air quality problems to take 

additional time to reach the existing standard. Several areas in California are not required to 

meet the existing standard by 2025, and depending on how areas are ultimately designated for a 

revised standard, many areas may not be required to meet a revised standard until sometime 

between 2032 and December 31, 2037. We were not able to project emissions and air quality 

beyond 2025 for California; however, we adjusted baseline air quality to reflect mobile source 

emissions reductions for California that would occur between 2025 and 2030; these emissions 
8-2

reductions were the result of mobile source regulations expected to be fully implemented by 

2030. While there is uncertainty about the precise timing of emissions reductions and related 

costs for California, we assume costs occur through the end of 2037 and beginning of 2038. In 

addition, we model benefits for California using projected population demographics for 2038. 

Because of the different timing for incurring costs and accruing benefits and for ease of 

discussion throughout the analyses, we refer to the different time periods for potential attainment 

as 2025 and post-2025 to reflect that (1) we did not project emissions and air quality for any year 

other than 2025; (2) for California, emissions controls and associated costs are assumed to occur 

through the end of 2037 and beginning of 2038; and (3) for California benefits are modeled using 

population demographics in 2038. It is not straightforward to discount the post-2025 results for 

California to compare with or add to the 2025 results for the rest of the U.S. While we estimate 

benefits using 2038 information, we do not have good information on precisely when the costs of 

controls will be incurred. Because of these differences in timing related to California attaining a 

revised standard, the separate costs and benefits estimates for post-2025 should not be added to 

the primary estimates for 2025.

By the 2030s, various mobile source rules, such as the onroad and nonroad diesel rules 

are expected to be fully implemented. Because California will likely not have all of its areas in 

attainment with a revised standard until sometime after its attainment date for the existing 

standard, it is important to reflect the impact these mobile source rules might have on the 

emissions that affect ozone nonattainment. To reflect the emissions reductions that are expected 

from these rules, we subtract those from the estimates of the emissions reductions that might be 

needed for California to fully attain in 2025, making our analysis more consistent with full 

attainment later than 2025. The EPA did the analysis this way to be consistent with the 

requirements in the Clean Air Act and because forcing full attainment in California in an earlier 

year would likely lead to overstating costs due to (1) benefits those areas might enjoy from 

existing federal or state programs implemented between 2025 and the future potential attainment 

year, (2) the likelihood that energy efficiency and cleaner technologies will be further 

implemented, and/or (3) the potential decline in costs of existing technologies due to economies 

of scale or improvements in the efficiency of installing and operating controls (‘learning by 

doing’).
8-3

Tables 8-1 and 8-2 summarize the costs and benefits of the three potential alternative 

standard levels analyzed and shows the net benefits for each of the levels across a range of 

modeling assumptions related to the calculation of costs and benefits. Tables 8-3 and 8-4 

provide information on the costs by geographic region for the U.S., except California in 2025 

and on the costs for California for post-2025. Tables 8-5 and 8-6 provide a regional breakdown 

of benefits for 2025 and a regional breakdown of benefits for post-2025.

The estimates for benefits reflect the variability in the functions available for estimating 

the largest source of benefits – avoided premature mortality associated with simulated reductions 

in ozone and PM2.5 (as a co-benefit). The low end of the range of net benefits is constructed by 

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

subtracting the cost from the highest benefit. Following these tables is a discussion of the 

implications of these estimates, as well as the uncertainties and limitations that should be 

considered in interpreting the estimates. 

In the RIA we provide estimates of costs of emissions reductions to attain the proposed 

standards in three regions -- California, the rest of the western U.S., and the eastern U.S. In 

addition, we provide estimates of the benefits that accrue to each of these three regions resulting 

from (i) control strategies applied within the region, (ii) reductions in transport of ozone 

associated with emissions reductions in other regions, and (iii) the control strategies for which 

the regional cost estimates are generated. These benefits are not directly comparable to the costs 

of control strategies in a region because the benefits include benefits not associated with those 

control strategies.

The net benefits of emissions reductions strategies in a specific region would be the 

benefits of the emissions reductions occurring both within and outside of the region minus the 

costs of the emissions reductions. Because the air quality modeling is done the national level, we 

do not estimate separately the nationwide benefits associated with the emissions reductions 

occurring in any specific region.125 As a result, we are only able to provide net benefits 

estimates at the national level. The difference between the costs for a specific region and the 

 

125 For California, we provide separate estimates of the costs and nationwide estimates of benefits, so it is 

appropriate to calculate net benefits. As such, we provide net benefits for the post-2025 California analysis.
8-4

benefits accruing to that region is not an estimate of net benefits of the emissions reductions in 

that region.

Table 8-1. Total Costs, Total Monetized Benefits, and Net Benefits in 2025 for U.S., except 

California (billions of 2011$)a

Total Costs Monetized Benefits Net Benefits

7% Discount

Rate

7% Discount

Rate

7% Discount

Rate

Proposed Alternative Standard Levels

70 $3.9 $6.4 to $13 $2.5 to $9.1

65 $15 $19 to $38 $4 to $23

Alternative Standard Level

60 $39 $34 to $70 ($5) to $31

a EPA believes that providing comparisons of social costs and social benefits at 3 and 7 percent is 

appropriate. Estimating multiple years of costs and benefits is not possible for this RIA due to data and resource 

limitations. As a result, we provide a snapshot of costs and benefits in 2025, using the best available information to 

approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.

Table 8-2. Total Costs, Total Monetized Benefits, and Net Benefits of Control Strategies 

Applied in California, Post-2025 (billions of 2011$)a

Total Costs Monetized Benefits Net Benefits

7% Discount

Rate

7% Discount

Rate

7% Discount

Rate

Proposed Alternative Standard Levels

70 $0.80 $1.1 to $2 $0.3 to $1.2

65 $1.6 $2.2 to $4.1 $0.6 to $2.5

Alternative Standard Level

60 $2.2 $3.2 to $5.9 $1 to $3.7

a EPA believes that providing comparisons of social costs and social benefits at 3 and 7 percent is appropriate. 

Estimating multiple years of costs and benefits is not possible for this RIA due to data and resource limitations. As 

a result, we provide a snapshot of costs and benefits in 2025, using the best available information to approximate 

social costs and social benefits recognizing uncertainties and limitations in those estimates.

EPA believes that providing comparisons of social costs and social benefits at 3 and 7 

percent is appropriate. Ideally, streams of social costs and social benefits over time would be 

estimated and the net present values of each would be compared to determine net benefits of the 

illustrative attainment strategies. The three different uses of discounting in the RIA – (i) 

construction of annualized engineering costs, (ii) adjusting the value of mortality risk for lags in 

mortality risk decreases, and (iii) adjusting the cost of illness for non-fatal heart attacks to adjust 

for lags in follow up costs -- are all appropriate. Our estimates of net benefits are the 

approximations of the net value (in 2025) of benefits attributable to emissions reductions needed 

to attain just for the year 2025.
8-5

Table 8-3. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for 2025 - U.S., except California (billions of 2011$, 7% Discount Rate)a

Alternative Level Geographic Area Total Control Costs (Known and 

Extrapolated)

70 ppb

East 3.9

West -

Total $3.9

65 ppb

East 15

West 0.4

Total $15

60 ppb

East 33

West 5.8

Total $39

a All values are rounded to two significant figures. Extrapolated costs are based on the average-cost methodology.

Table 8-4. Summary of Total Control Costs (Known and Extrapolated) by Alternative 

Level for post-2025 - California (billions of 2011$, 7% Discount Rate)a

Alternative Level Geographic Area

Total Control Costs 

(Known and 

Extrapolated)

70 ppb California $0.8

65 ppb California $1.6

60 ppb California $2.2

a All values are rounded to two significant figures. Extrapolated costs are based on the average-cost methodology.

Table 8-5. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 2025 

Scenario (nationwide benefits of attaining each alternative standard everywhere 

in the U.S. except California) – Full Attainment a

Region Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

East b 99% 96% 92%

California 0% 0% 0%

Rest of West 1% 4% 7%

a Because we use benefit-per-ton estimates to calculate the PM2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits.

b

Includes Texas and those states to the north and east. Several recent rules such as Tier 3 will have substantially 

reduced ozone concentrations by 2025 in the East, thus few additional controls would be needed to reach 70 ppb. 
8-6

Table 8-6. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the post2025 Scenario (nationwide benefits of attaining each alternative standard just in 

California) – Full Attainment a

Region Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

East 0% 0% 0%

California 93% 94% 94%

Rest of West 6% 6% 6%

a Because we use benefit-per-ton estimates to calculate the PM2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits.

In this RIA, we quantify an array of adverse health impacts attributable to ozone and 

PM2.5. The Integrated Science Assessment for Ozone and Related Photochemical Oxidants 

(“Ozone ISA”) (U.S. EPA, 2013a) identifies the human health effects associated with ozone 

exposure, which include premature death and a variety of illnesses associated with acute (dayslong) and chronic (months to years-long) exposures. Similarly, the Integrated Science 

Assessment for Particulate Matter (“PM ISA”) (U.S. EPA, 2009) identifies the human health 

effects associated with ambient particles, which include premature death and a variety of 

illnesses associated with acute and chronic exposures. Air pollution can affect human health in a 

variety of ways, and in Table 8-7 we summarize the “categories” of effects and describe those 

that we could quantify in our “core” benefits estimates and those we were unable to quantify 

because we lacked the data, time or techniques. 

Table 8-7. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain 

the Primary Ozone Standards

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Improved Human Health

Reduced incidence 

of premature 

mortality from 

exposure to ozone

Premature mortality based on short-term 

exposure (all ages)

  Section 5.6

Premature respiratory mortality based on 

long-term exposure (age 30–99)

 a Section 5.6

Reduced incidence 

of morbidity from 

exposure to ozone

Hospital admissions—respiratory causes 

(age > 65)

  Section 5.6

Emergency department visits for asthma 

(all ages)

  Section 5.6

Asthma exacerbation (age 6-18)  

Minor restricted-activity days (age 18–65)   Section 5.6

School absence days (age 5–17)   Section 5.6

Decreased outdoor worker productivity 

(age 18–65)

a a Section 5.6
8-7

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Other respiratory effects (e.g., mediation 

use, pulmonary inflammation, decrements 

in lung functioning)

— — ozone ISA c

Cardiovascular (e.g., hospital admissions, 

emergency department visits)

— — ozone ISA c

Reproductive and developmental effects 

(e.g., reduced birthweight, restricted fetal 

growth)

— — ozone ISA c

Reduced incidence 

of premature 

mortality from 

exposure to PM2.5

Adult premature mortality based on 

cohort study estimates and expert 

elicitation estimates (age >25 or age >30)

  Section 5.6 of 

PM RIA

Infant mortality (age <1)   Section 5.6 of 

PM RIA

Reduced incidence 

of morbidity from 

exposure to PM2.5

Non-fatal heart attacks (age > 18)   Section 5.6 of 

PM RIA

Hospital admissions—respiratory (all 

ages)

  Section 5.6 of 

PM RIA

Hospital admissions—cardiovascular (age 

>20)

  Section 5.6 of 

PM RIA

Emergency department visits for asthma 

(all ages)

  Section 5.6 of 

PM RIA

Acute bronchitis (age 8–12)   Section 5.6 of 

PM RIA

Lower respiratory symptoms (age 7–14)   Section 5.6 of 

PM RIA

Upper respiratory symptoms (asthmatics 

age 9–11)

  Section 5.6 of 

PM RIA

Asthma exacerbation (asthmatics age 6–

18)

  Section 5.6 of 

PM RIA

Lost work days (age 18–65)   Section 5.6 of 

PM RIA

Minor restricted-activity days (age 18–65)   Section 5.6 of 

PM RIA

Chronic Bronchitis (age >26) — — Section 5.6 of 

PM RIA

Emergency department visits for 

cardiovascular effects (all ages)

— — Section 5.6 of 

PM RIA

Strokes and cerebrovascular disease (age 

50–79)

— — Section 5.6 of 

PM RIA

Other cardiovascular effects (e.g., other 

ages)

— — PM ISA b

Other respiratory effects (e.g., pulmonary 

function, non-asthma ER visits, nonbronchitis chronic diseases, other ages 

and populations)

— — PM ISA b

Reproductive and developmental effects 

(e.g., low birth weight, pre-term births, 

etc.)

— — PM ISA b,c

Cancer, mutagenicity, and genotoxicity 

effects

— — PM ISA b,c

Asthma hospital admissions (all ages) — — NO2 ISA d
8-8

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Reduced incidence 

of morbidity from 

exposure to NO2

Chronic lung disease hospital admissions 

(age > 65)

— — NO2 ISA d

Respiratory emergency department visits 

(all ages)

— — NO2 ISA d

Asthma exacerbation (asthmatics age 4–

18)

— — NO2 ISA d

Acute respiratory symptoms (age 7–14) — — NO2 ISA d

Premature mortality — — NO2 ISA b,c

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, 

lung function, other ages and populations)

— — NO2 ISA b,c

a We are in the process of considering an update to the worker productivity analysis for ozone based on more recent 

literature.

b We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.

c We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other 

significant concerns over the strength of the association.

d We assess these benefits qualitatively due to time and resource limitations for this analysis.

8.2 Discussion of Results 

The costs and benefits presented in this RIA incorporate an array of methodological and 

technical changes that the EPA has adopted since the previous review of the ozone standards in 

2008 (U.S. EPA, 2008) and proposed reconsideration in 2010 (U.S. EPA 2010). 

Several factors contributed to lower cost estimates, including shifting the baseline year 

from 2020 to 2025, allowing for more time to attain and for Federal measures to work. The 

baseline starting point has changed from 84 ppb to 75 ppb, substantially reducing the amount of 

emissions reductions needed and the associated costs of attainment. Also, we have identified 

additional known controls, which are less expensive per ton than unknown controls. Lastly, 

there are fewer counties exceeding the alternative standards analyzed, therefore fewer emissions 

reductions are needed for attainment, resulting in lower cost estimates.

While the costs presented in this analysis decreased compared to the prior analyses, the 

benefits estimates remained about the same despite the baseline differences. The main factors 

that affected the benefits estimates included the updated analysis year of 2025, which affects 

population projections, baseline mortality rates, and income growth adjustment. The Value of a 

Statistical Life was revised, and we removed thresholds and the assumption of no causality for 

ozone mortality (which as assumed in 2008). The differences resulted in a tighter benefits range, 

but the total benefits are about the same as the previous ozone RIA analyses.
8-9

8.2.1 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 monetized benefits, depending on the specific PM mortality impact function 

used, and on the relative magnitude of ozone benefits, which is dependent on the specific ozone 

mortality function assumed. PM co-benefits based on daily average concentrations are 

calculated over the entire year, while ozone related benefits are calculated only during the 

summer ozone season. Because the control strategies evaluated in this RIA are assumed to 

operate year round rather than only during the ozone season, this means that PM benefits will 

accumulate during both the ozone season and the rest of the year.

For primary benefits estimates in 2025, PM2.5 co-benefits account for between 70 and 75

percent of co-benefits, depending on the standard analyzed and on the choice of ozone and PM 

mortality functions used.126 The estimate with the lowest fraction from PM co-benefits occurs 

when we model benefits for the lowest alternative standard level analyzed (60ppb) and add the 

lower bound core estimates for both ozone-related and PM2.5 (co-benefit) related mortality. The 

estimate with the highest fraction from PM co-benefits results from modeling the highest 

alternative standard level analyzed (70ppb) based on combining the high-bound core ozone and 

PM2.5 (co-benefit) related mortality estimates. 

8.2.2 Developing Future Control Strategies with Limited Data

Because of relatively higher ozone levels in several large urban areas (Southern 

California, Houston, and the Northeastern urban corridor, including New York and Philadelphia) 

and because of limitations associated with the data on currently known emissions control 

technologies, the EPA recognized that known and reasonably anticipated emissions controls 

would likely not be sufficient to bring some areas into attainment with either the existing or 

alternative, more stringent ozone standard levels. 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 

 

126 For the separate results for post-2025, PM2.5 co-benefits account for between 30 and 45 percent of total benefits, 

again depending on the standard level analyzed and the choice of ozone and PM mortality functions.
8-10

benefits associated with those controls. The second stage took the emissions reductions beyond 

known controls and used an extrapolation method to estimate the costs and benefits of these 

additional emissions reductions needed to bring all areas into full attainment with the alternative 

standard levels analyzed. 

The structure of the RIA reflects this two-stage analytical approach. Separate chapters are 

provided for the emissions, air quality, and cost impacts of modeled controls. We 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, 

because of the uncertainty associated with the extrapolation of costs, we judged it appropriate to 

provide separate estimates of the costs and benefits for partial attainment (based on known 

controls) and full attainment (based on known controls and extrapolation), 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 partial attainment and full 

attainment 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 

potentially revised standards. Ultimately, states and local 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 the 

EPA. Our estimates are intended to provide information on the general magnitude of the costs 

and benefits of alternative standard levels rather than on precise predictions of control measures, 

costs, or benefits. With these caveats, we expect that this analysis can provide a reasonable 

picture of the types of emissions controls that are currently available, the direct costs of those 

controls, the levels of emissions reductions that may be achieved with these controls, the air 

quality impact that can be expected to result from reducing emissions, and the public health 

benefits of reductions in ambient ozone levels. This analysis identifies those areas of the U.S. 

where our existing knowledge of control strategies is not sufficient to allow us to model 

attainment, and where additional data or research may be needed to develop strategies for 

attainment.
8-11

In many ways, RIAs for proposed actions are learning processes 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 

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 costeffective known and available controls in our database. With the more stringent NAAQS, more 

areas will need to find new ways of reducing emissions. While we can speculate on what some 

of these technologies might look like based on new developments in energy efficiency and clean 

technology, the specific technological path in different nonattainment areas is not clear.

Because of the uncertainty regarding the development of future emissions reduction 

strategies, a significant portion of the analysis is based on extrapolating from available data on 

known control technologies to generate the emissions reductions necessary to reach full 

attainment of an alternative ozone NAAQS and the resulting costs and benefits. Studies indicate 

that it is not uncommon for pre-regulatory cost estimates to be higher than later estimates, in part 

because of difficulty in predicting technological changes. 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 change is greater (See Chapter 7, Section 7.2 for additional discussion). 

Also, because of 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 nonattainment and attainment monitors). On the other hand, the possibility also exists 

that benefits are overestimated, because it is possible that new technical changes might not meet 

the specifications, development time lines, or cost estimates provided in this analysis.

8.3 Framing Uncertainty 

This section includes a qualitative presentation of key factors that (1) could impact how 

air quality changes over time; (2) could impact the timing for meeting an alternative standard; (3) 
8-12

are difficult to predict and quantify; and (4) introduce additional uncertainty into this analysis. 127 

These factors, summarized in Table 8.8 below, include energy development, distribution, and use 

trends; land use development patterns; economic factors; energy and research and development 

policies; climate signal changes; and the influence of technological change. Additional factors 

that could have an impact on how air quality changes over time include environmental indicators 

other than climate change and societal preferences and attitudes toward the environment and 

conservation; the potential direction and magnitude of these additional factors is less clear.

These key factors can impact the estimated baseline air quality used in the analysis, and 

as a result the types of control measures and associated costs needed to meet an alternative 

standard. In addition, some combinations of the key factors could have significant effects 

beyond the effects of any individual factor. We cannot estimate the probability that any one 

factor or combination of factors will occur, but we do believe that they introduce additional, 

broader uncertainties about future trends that provide important context for the costs and benefits 

presented in this analysis.

Table 8-8. Relevant Factors and Their Potential Implications for Attainment

Individual Factors Potential Implications for NAAQS 

Attainment

Information on Trends

Energy --

Extraction, 

conversion, 

distribution and 

storage, efficiency, 

international energy 

trends 

Geopolitics, reserves, international 

and domestic demand, and 

technological breakthroughs in 

energy technologies can drive fuel 

prices up or down. 

If more renewable sources of energy 

are employed and use of natural gas 

increases, then emissions may be 

lower, potentially lowering 

attainment costs. 

Recently there has been an increase in domestic 

production of oil and a relative decrease in 

imported oil, in addition to policies and 

investments geared toward the development of 

alternative fuels and energy efficiency.128 This 

is likely a result of all of these activities, which 

have led to a reduction of U.S. dependence on 

imports of foreign oil.

Several trends have emerged within the last ten 

years and are expected to continue, including 

increased natural gas production and 

consumption, renewable energy installations, 

 

127 OMB Circular A-4 indicates that qualitative discussions should be included in analyses whenever there is 

insufficient data to quantify uncertainty.

128 http://energy.gov/articles/us-domestic-oil-production-exceeds-imports-first-time-18-years
8-13

Individual Factors Potential Implications for NAAQS 

Attainment

Information on Trends

and energy efficiency technology 

installations.129

Land Use 

Development 

Patterns –

Design of urban areas, 

vehicle-miles travelled 

A move toward denser urban 

settlements, slowing of growth in 

vehicle miles travelled (VMT) and 

increased use of public transit could 

decrease emissions, potentially 

lowering attainment costs.130 

Recent trends in VMT illustrate some of the 

uncertainty around future emissions from 

mobile sources131. In 2006, projections of 

VMT showed a sustained increase,132 yet VMT 

growth slowed in recent years and actually 

declined in 2008 and 2009.133 Between 2000 

and 2010 average growth in VMT was 0.8%, as 

compared to 2.9% from the previous decade. 

The Economy

An increase in economic growth, 

investment in technologies that have 

high energy use, and a return of U.S. 

manufacturing could lead to higher 

emissions making attainment

potentially more costly. A slowing 

of the economy, investments in 

energy efficient technologies, and a 

continuation of a service-based 

economy could lead to lower 

emissions making attainment 

potentially less costly.134

Affluence leads to increased consumption and 

energy use. However, this increase may not be 

proportional. Energy and materials is not 

directly proportional to economic growth, 

decreasing or stabilizing over time in spite of 

continued economic growth.135

Policiesa –

Energy efficiency, 

energy security, 

direction of research 

and development, 

renewable energy

A move toward energy security and 

independence would mean an 

increased use of domestic energy 

sources. If this results in a fuel mix 

where emissions decrease, then 

attainment could likely be less costly. 

State and local policies related to energy 

efficiency, cleaner energy, energy security136, 

as well as the direction of research and 

development of technology can have a direct or 

indirect effect on emissions. Policies that result 

in energy efficiency, renewable energy, the use 

 

129 http://www.eia.gov/forecasts/aeo/er/pdf/0383er%282014%29.pdf; 

http://energy.gov/sites/prod/files/2014/08/f18/2013%20Wind%20Technologies%20Market%20Report%20Present

ation.pdf; http://www.eia.gov/electricity/monthly/update/archive/april2014/;

http://www.ercot.com/content/news/presentations/2014/GCPA%20%2002%20Oct%202013%20FINAL.pdf; 

http://energy.gov/eere/sunshot/photovoltaics.

130 For example, see Cervero (1998), the Center for Clean Air Policy’s Transportation Emissions Guidebook 

(http://www.trb.org/Main/Blurbs/156164.aspx). For ongoing research see 

http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=3092.

131 For example, see the Transportation Research Board’s National Cooperative Highway Research Program 

(NCHRP) 2014.

132 https://www.fhwa.dot.gov/policy/2006cpr/chap9.htm#body

133 https://www.fhwa.dot.gov/policyinformation/travel_monitoring/13jantvt/page2.cfm

134 For example, Bo (2011), and http://www.epa.gov/region1/airquality/nox.html for manufactures contributions to 

NOX emissions.

135 UNEP 2011, http://www.unep.org/resourcepanel/decoupling/files/pdf/decoupling_report_english.pdf

136 For example, http://www2.epa.gov/laws-regulations/summary-energy-independence-and-security-act.
8-14

Individual Factors Potential Implications for NAAQS 

Attainment

Information on Trends

If not, attainment could likely be 

more costly. A move toward 

investments in fuel efficiency and 

low emissions fuels could decrease 

emissions and likely lower 

attainment costs.

of cleaner fuels and conservation measures

would likely result in decreased emissions and 

likely decrease attainment costs.137 Growth in 

energy demand has stayed well below growth 

in gross domestic product, likely as a result of 

technological advances, federal, state and local 

energy efficiency standards and policies, and 

other macroeconomic factors.138 U.S. 

productivity per energy expended relative to 

other countries suggests that additional 

efficiency gains are possible.139

Intensity, Location 

and Outcome of the 

Climate Change 

Signal

Strong climate signals that bring high 

temperatures could increase ozone, 

likely making attainment more 

costly. 

Uncertainty exists regarding how the climate 

signal will interact with air quality, as well as 

with other factors. However, research 

demonstrates that in areas where there are both 

high levels of emissions and high temperatures, 

attaining an ozone standard will likely be much 

harder. The magnitudes of these impacts will 

depend on atmospheric chemical and physical 

processes, as well as anthropogenic activities 

that increase or decrease NOx and/or VOC 

emissions.140 

Technological 

Change -- Including 

emissions reductions 

technologies and other 

technological 

developments

Innovation in production and 

emissions control technologies, 

learning that lower costs, and

breakthroughs in battery/energy 

storage technologies for use with 

renewable energy could improve air 

quality, reducing emissions and 

likely lowering attainment costs. 

Examples of emerging technologies include 

carbon capture and sequestration (CCS), battery 

technologies, emerging advanced biofuels, 

which could all have breakthroughs that could 

impact fuel use. Similarly, shifts in industrial 

production processes, such as a move from 

using primary metals to more recycling could 

impact energy use141.

a Policies refer to any policies or regulations that are not environmental regulations set by U.S. EPA, states, tribes, or 

local authorities. 

8.4 Key Observations from the Analysis

The following are key observations about the RIA results. 

 Tightening the ozone standards can incur significant, but uncertain, costs. 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 

 

137 For example, see http://www.dsireusa.org/solar/solarpolicyguide/.

138 http://bipartisanpolicy.org/sites/default/files/BPC%20SEPI%20Energy%20Report%202013_0.pdf, p. 5.

139 http://bipartisanpolicy.org/sites/default/files/BPC%20SEPI%20Energy%20Report%202013_0.pdf, p. 69.

140 See Jacobs (2009).

141 http://www.eia.gov/todayinenergy/detail.cfm?id=16211
8-15

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 was based on a generalized relationship between emissions and ozone levels.

This introduces significant uncertainty into the calculation of the emissions reductions that 

might be needed to reach full attainment.

 Tightening the ozone standards can also result in significant benefits. Estimates of 

benefits are driven largely by projected reductions in ozone-related short-term mortality 

and co-benefits associated with reductions in PM2.5-related long-term mortality. Although 

using a benefit-per-ton approach in modeling PM2.5-related cobenefits (rather than direct 

modeling) has increased uncertainty, this approach is peer-reviewed and robust. We also 

modeled reductions in ozone-related long-term respiratory mortality, however due to 

concerns over potential double counting of benefits and limitations in our ability to project 

the lag-structure of reductions in this mortality endpoint, we did not include these 

estimates as part of the core benefit estimate. In addition to these mortality endpoints, we 

did quantify a wide-range of morbidity endpoints for both ozone and PM2.5, although these 

contribute only minimally to total monetized benefits.

 Air quality modeling approach can introduce uncertainty. Based on air quality 

modeling sensitivity analyses, there is significant spatial variability in the relationship 

between local and regional NOx emission reductions and ozone levels across urban areas. 

We performed a national scale air quality modeling analysis to estimate ozone 

concentrations for the future base case year of 2025. To accomplish this, we modeled 

multiple emissions cases for 2025, including the 2025 base case and twelve (12) 2025 

emissions sensitivity simulations. The 12 emissions sensitivity simulations were used to 

develop ozone sensitivity factors (ppb/ton) from the modeled response of ozone to changes 

in NOx and VOC emissions from various sources and locations. These ozone sensitivity 

factors were then used to determine the amount of emissions reductions needed to reach 

the 2025 baseline and evaluate potential alternative standard levels of 70, 65, and 60 ppb 

incremental to the baseline. We used the estimated emissions reductions needed to reach 

each of these standard levels to analyze the costs and benefits of alternative standard 

levels. 

 Available technologies that might achieve NOx and VOC reductions to attain 

alternative ozone NAAQS are not sufficient. In some areas of the U.S., the information 

we have about existing controls does not result in sufficient emissions reductions needed to 

meet the existing standard. After applying existing rules and the illustrative known 

controls across the nation (excluding California), in order to reach 70 ppb we were able to 

identify controls that reduce overall NOx emissions by 490,000 tons and VOC emissions 

by 55,000 tons. In order to reach 65 ppb we were able to identify controls that reduce 

overall NOx emissions by 1,100,000 tons and VOC emissions by 110,000 tons. After these 

reductions, in order to reach 70 ppb over 150,000 tons of NOx emissions remained, and in 

order to reach 65 ppb over 750,000 tons of NOx emissions remained.

 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 
8-16

exhausted in attempting to meet the current 75 ppb standard, all of the benefits and costs of 

lower standards in California are based on the application of unknown controls. Both the 

benefits and the costs associated with the assumed NOx and VOC reductions in California 

are particularly uncertain. 

 Some EPA existing mobile source programs will help areas reach attainment. These 

programs promise to continue to help areas reduce ozone concentrations beyond 2025. 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 14,000 tons and VOC emissions by an additional 6,300 

tons between 2025 and 2030. These additional reductions will likely reduce the overall 

emissions reductions needed for attainment relative to what California might have needed 

to reduce from other sectors if attainment were to be required in 2025. 

 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. 

 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 2025, meaning that the amount of emissions 

reductions that will be required in 2025 will be less, and costs and benefits in 2025 will be 

lower.

8.5 References

Cervero, R. (1998) The Transit Metropolis, A Global Inquiry. Island Press, Washington, D.C.

Jacob, D. J., & Winner, D. (2009). Effect of climate change on air quality. Atmospheric Environment, 43, 51-63.

NCHRP (2014). The Effects of Socio-Demographics on Future Travel Demand, Transportation Research Board of 

the National Academies, Washington, D.C. Strategic Issues Facing Transportation, Report 750, Volume 6.

U.S. Environmental Protection Agency (U.S. EPA). 2008. Final Ozone NAAQS Regulatory Impact Analysis. Office 

of Air Quality Planning and Standards, Research Triangle Park, NC. Available at 

http://www.epa.gov/ttn/ecas/regdata/RIAs/452_R_08_003.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis (RIA) for the Proposed 

Reconsideration of the ozone National Ambient Air Quality Standards (NAAQS). Office of Air Quality Planning 

and Standards, Research Triangle Park, NC. January. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/s1-

supplemental_analysis_full.pdf>. 

U.S. EPA. 2009. Integrated Science Assessment for Particulate Matter: Final. Research Triangle Park, NC: U.S. 

Environmental Protection Agency. (EPA document number EPA/600/R-08/139F).

U.S. EPA. 2013a. Integrated Science Assessment for Ozone and Related Photochemical Oxidants: Final. Research 

Triangle Park, NC: U.S. Environmental Protection Agency. (EPA document number EPA/600/R-10/076F).
8-1

CHAPTER 9: STATUTORY AND EXECUTIVE ORDER IMPACT ANALYSIS

Overview

This section explains the statutory and executive orders applicable to EPA rules, and 

discusses EPA’s actions taken pursuant to these orders.

9.1 National Technology Transfer and Advancement Act

Section 12(d) of the National Technology Transfer and Advancement Act of 1995 

(NTTAA), Public Law No. 104-113, §12(d) (15 U.S.C. 272 note) directs the 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 the EPA to provide Congress, through OMB, explanations when the Agency decides not 

to use available and applicable voluntary consensus standards.

Today’s proposed rulemaking does not involve technical standards. Therefore, the EPA is 

not considering the use of any voluntary consensus standards.

9.2 Paperwork Reduction Act

This action 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 the establishment of a NAAQS under section 109 of the 

CAA.

Burden means 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 
A.9-2

of information; and transmit or otherwise disclose the information. Burden is defined at 5 CFR 

1320.3(b).

An agency may not conduct or sponsor, 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 the EPA's regulations in 40 CFR are listed in 40 CFR part 9. Per the 

Implementation of the 2008 National Ambient Air Quality Standards for Ozone: State 

Implementation Plan Requirements, the annual burden for this information collection averaged 

over the first 3 years is estimated to be a total of 40,000 labor hours per year at an annual labor 

cost of $2.4 million (present value) over the 3-year period or approximately $91,000 per state for 

the 26 state respondents, including the District of Columbia. The average annual reporting 

burden is 690 hours per response, with approximately 2 responses per state for 58 state 

respondents. There are no capital or operating and maintenance costs associated with the 

proposed rule requirements. 

In addition, per the draft Supporting Statement for Revisions to Ambient Air Monitoring 

Regulations for Ozone (Proposed Rule) (EPA ICR Number 0940), as part of the ozone NAAQS 

proposed rulemaking EPA is proposing revisions to the length of the required ozone monitoring 

season (see Section VI of the preamble). The draft Information Collection Request (ICR) is 

estimated to involve 158 respondents for a total average cost of approximately $24 million (total 

capital, and labor and operation and maintenance costs) plus a total burden of 339,930 hours for 

the support of all operational aspects of the entire ozone monitoring network. The labor costs 

associated with these hours are approximately $19.8 million. Also included in the total costs are 

other costs of operation and maintenance of $2.2 million and equipment and contract costs of 

$2.1 million. EPA typically funds 60 percent of the approximately $24 million through grants to 

the State/local agencies. In addition to the costs at the State, local, and Tribal air quality 

management agencies, there is a burden to EPA of 41,418 hours and $2.6 million. 

9.3 Regulatory Flexibility Act

The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a 

regulatory flexibility analysis of any rule subject to notice and comment rulemaking 

requirements under the Administrative Procedure Act or any other statute unless the agency 
A.9-3

certifies that the rule will not have a significant economic impact on a substantial number of 

small entities. Small entities include small businesses, small organizations, and small 

governmental jurisdictions.

For purposes of assessing the impacts of today’s proposed rule on small entities, small 

entity is defined as: (1) a small business that is a small industrial entity as defined by the Small 

Business Administration’s (SBA) regulations at 13 CFR 121.201; (2) a small governmental 

jurisdiction that is a government of a city, county, town, school district or special district with a 

population of less than 50,000; and (3) a small organization that is any not-for-profit enterprise 

which is independently owned and operated and is not dominant in its field.

After considering the economic impacts of today's proposed rule on small entities, I 

certify that this action will not have a significant economic impact on a substantial number of 

small entities. This proposed rule will not impose any requirements on small entities. Rather, this 

rule establishes national standards for allowable concentrations of ozone in ambient air as 

required by section 109 of the CAA. See also American Trucking Associations v. EPA. 175 F. 

3d at 1044-45 (NAAQS do not have significant impacts upon small entities because NAAQS 

themselves impose no regulations upon small entities). We continue to be interested in the 

potential impacts of the proposed rule on small entities and welcome comments on issues related 

to such impacts.

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, the 

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 the EPA to identify and consider a reasonable number of regulatory 

alternatives and to 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 
A.9-4

inconsistent with applicable law. Moreover, section 205 allows the 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 for why that alternative was not 

adopted. Before the 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 the EPA regulatory proposals with significant 

federal intergovernmental mandates, and informing, educating, and advising small governments 

on compliance with the regulatory requirements. 

Today’s proposed rule 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 proposed 

rule imposes no new expenditure or enforceable duty on any state, local or tribal governments or 

the private sector, and the EPA has determined that this proposed rule contains no regulatory 

requirements that might significantly or uniquely affect small governments. Furthermore, as 

indicated previously, in setting a NAAQS the 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 

American Trucking Associations v. EPA, 175 F. 3d at 1043 (noting that because the EPA is 

precluded from considering costs of implementation in establishing NAAQS, preparation of a 

Regulatory Impact Analysis (RIA) pursuant to the Unfunded Mandates Reform Act would not 

furnish any information that the court could consider in reviewing the NAAQS). Accordingly, 

the EPA has determined that the provisions of sections 202, 203, and 205 of the UMRA do not 

apply to this proposed 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 will address, 

as appropriate, unfunded mandates if and when it proposes any revisions to 40 CFR parts 51 or 

58.
A.9-5

9.5 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, the EPA 

submitted this action to the Office of Management and Budget (OMB) for review under EO 

12866 and any changes made in response to OMB recommendations have been documented in 

the docket for this action. In addition, the EPA prepared this RIA of the potential costs and 

benefits associated with this action. A copy of the analysis is available in the RIA docket (EPAHQ-OAR-2013-0169) and the analysis is briefly summarized here. The RIA estimates the costs 

and monetized human health and welfare benefits of attaining four alternative ozone NAAQS

nationwide. Specifically, the RIA examines the alternatives of 75 ppb, 70 ppb, 65 ppb, and 60 

ppb. 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 this RIA has been 

prepared, the results of the RIA have not been considered in issuing today’s proposed rule.

9.6 Executive Order 12898: Federal Actions to Address Environmental Justice in 

Minority Populations and Low-Income Populations 

Executive Order 12898 (59 FR 7629 (Feb. 16, 1994)) establishes federal executive policy 

on environmental justice. Its main provision directs federal agencies, to the greatest extent 

practicable and permitted by law, to make environmental justice part of their mission by 

identifying and addressing, as appropriate, disproportionately high and adverse human health or 

environmental effects of their programs, policies, and activities on minority populations and lowincome populations in the United States. 

To gain a better understanding of the populations within the areas potentially impacted by 

a revised ozone NAAQS, the EPA conducted a proximity analysis that examined sociodemographic attributes of populations in these areas. The areas of interest for these analyses 

were defined as all counties contained within any Core-Based Statistical Area (CBSA) with at 
A.9-6

least one monitor with a current (2011-2013) design value above the proposed range of standard 

levels (65 to 70 ppb), as well as counties not in a CBSA with a current (2011-2013) design value 

above the proposed range of standard levels. The tabulation of results from this analysis are 

presented below. Table 9-1 shows the percent of minority populations within CBSAs with 

monitors above the levels of the proposed ozone standard levels, as well as the national 

percentages for comparison. Table 9-2 shows the percent of populations of different ages, 

education, and income level for those same areas. Additional details can be found in Appendix 

9A.

Table 9-1. Summary of Population Totals and Demographic Categories for Areas of 

Interest and National Perspective

Demographic 

Summary Population White

African 

American

Native 

American

Other or 

Multiracial1

Minority/NonWhite 

Hispanica

Area of Interest Total

 65 ppb 221,431,286 153,706,027 30,429,108 1,726,110 35,570,041 67,725,259

 70 ppb 193,316,836 132,112,738 27,193,155 1,488,364 32,522,579 61,204,098

% of Area of Interest Total

 65 ppb 69% 14% 1% 16% 31%

 70 ppb 68% 14% 1% 17% 32%

National 

Total 312,861,256 226,405,205 39,475,216 2,952,087 44,028,748 86,456,051

% of 

National 

Total 72% 13% 1% 14% 28%

a The race Minority/Non-White Hispanic field is computed by subtracting the white population from the total 

population.

Table 9-2. Summary of Population Totals and Demographic Categories for Areas of 

Interest and National Perspective

Demographic 

Summary

Linguistically 

Isolated Age 0 – 4 Age 0 - 17 Age 65+

Without a 

HS Diploma

Low 

Income2

Area of Interest Total

 65 ppb 13,072,109 14,695,948 54,008,810 27,163,990 20,914,891 67,027,700

 70 ppb 12,179,896 12,849,637 47,296,147 23,518,071 18,495,474 58,296,224

% of Area of Interest Total

 65 ppb 6% 7% 24% 12% 14% 30%

 

1 Appendix 9A clarifies that “other or multiracial” is derived from individual reporting on Census forms and 

includes citations to the specific 2010 Census data used in this analysis.

2 Appendix 9A clarifies that “low income” in this analysis is defined as income two times the poverty line or less. 
A.9-7

 70 ppb 6% 7% 24% 12% 15% 30%

National Total 19,196,507 20,465,065 75,217,176 40,830,262 30,952,789 101,429,436

% of National 

Total 6% 7% 24% 13% 15% 32%

The proposed rule will establish uniform national standards for ozone air pollution. This 

analysis identifies, on a limited basis, the subpopulations that may be exposed to elevated ozone 

concentrations, who thus are expected to benefit most from this regulation. This analysis does 

not identify the demographic characteristics of the most highly affected individuals or 

communities; nor does it quantify the level of risk faced by those individuals or communities. To 

the extent that any minority, low-income or indigenous subpopulation is disproportionately 

impacted by ozone levels because it resides in an area of interest, that subpopulation also stands 

to see increased environmental and health benefits from the emission reductions called for by 

this proposed rule. Available data suggest that the counties most likely to experience risk 

reductions from the proposed rule are approximately 14% African American, 1% Native 

American, 16-17% Other and multi-racial, and 31-32% Minority/Hispanic, which are 

approximately equal to the proportions of these populations represented in the U.S. 

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 the 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 planned rule on children, and explain 

why the planned regulation is preferable to other potentially effective and reasonably feasible 

alternatives considered by the Agency.

Today’s proposed 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 proposed rule will establish uniform national ambient air quality standards for 
A.9-8

ozone; 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, especially children with asthma, along with 

other sensitive population subgroups such as all people with lung disease and people active 

outdoors, 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 among children. Discussions of the 

results of the evaluation of the scientific evidence, policy considerations, and the exposure and 

risk assessments pertaining to children occurs throughout the preamble. Table 9-2 above 

includes a summary of available data that indicates that the counties most likely to experience 

risk reductions from the proposed rule are comprised of approximately 24% of the population 

between the ages of zero and 17, which is approximately equal to the proportions of this segment 

of the population represented in the U.S.

9.8 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” are 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.” 

Today’s proposed rule does not have federalism implications. It will 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. The rule does not alter the relationship between the federal 

government and the states regarding the establishment and implementation of air quality 

improvement programs as codified in the CAA. Under section 109 of the CAA, the EPA is

mandated to establish NAAQS; however, CAA section 116 preserves the rights of states to 

establish more stringent requirements if deemed necessary by a state. Furthermore, this proposed 

rule does not impact CAA section 107 which establishes that the states have primary 
A.9-9

responsibility for implementation of the NAAQS. Finally, as noted in section E (above) on 

UMRA, this rule does not impose significant costs on state, local, or tribal governments or the 

private sector. Thus, Executive Order 13132 does not apply to this rule.

However, as also noted in section D (above) on UMRA, EPA recognizes that states will 

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 the EPA 

policy to promote communications between the EPA and state and local governments, the EPA 

has specifically solicited comment on today’s proposed rule from state and local officials.

9.9 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 the EPA to develop an accountable 

process to ensure “meaningful and timely input by tribal officials in the development of 

regulatory policies that have tribal implications.” This rule concerns the establishment of ozone

NAAQS. The Tribal Authority Rule gives tribes the opportunity to develop and implement CAA 

programs such as the ozone NAAQS, but it leaves to the discretion of the tribe whether to 

develop these programs and which programs, or appropriate elements of a program, they will 

adopt. 

Today’s 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. In addition, tribes are not obligated to conduct 

ambient monitoring for ozone or to adopt the ambient monitoring requirements of 40 CFR part 

58. Thus, Executive Order 13175 does not apply to this rule.

The EPA specifically solicits comment on this rule from tribal officials. Prior to 

finalization of this proposal, the EPA intends to conduct outreach consistent with the EPA Policy 

on Consultation and Coordination with Indian Tribes. Outreach to tribal environmental 

professionals will be conducted through participation in Tribal Air call, which is sponsored by 

the National Tribal Air Association. In addition, the EPA intends to offer formal consultation to 
A.9-10

the tribes during the public comment period. If consultation is requested, a summary of the result 

of that consultation will be presented in the notice of final rulemaking and will be available in 

the docket.

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

Distribution, or Use

Today’s 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 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 the EPA cannot predict whether the control options selected 

by states will include regulations on energy suppliers, distributors, or users. Thus, the EPA 

concludes that today’s proposed rule is not likely to have any adverse energy effects and does not 

constitute a significant energy action as defined in Executive Order 13211.

Application of the modeled illustrative control strategy containing known controls for 

power plants, shown in Chapter 7, means that 3 percent of the total projected coal-fired EGU 

capacity nationwide in 2025 could be affected by controls for the alternative standard level of 70 

ppb. Similarly, 21 percent of total projected coal-fired EGU capacity in 2025 could be affected 

for the alternative standard level of 65 ppb, and 22 percent for the alternative standard level of 60 

ppb. In addition, some fuel switching might occur that could alter these percentages, though we 

are unable to estimate the effect on energy impacts from fuel switching. Controls on EGUs 

powered by fuels other than coal were not part of this illustrative analysis, and thus, would not be 

affected. In addition, we are unable to estimate energy impacts resulting from application of 

controls to non-EGUs or mobile sources. It is important to note that the estimates presented 

above are just one illustrative strategy and states may choose to apply control on sources other 

than EGUs for the purposes of attaining a more stringent ozone standard.
A.9-1

APPENDIX 9A: SOCIO-DEMOGRAPHIC CHARACTERISTICS OF POPULATIONS 

IN CORE BASED STATISTICAL AREAS WITH OZONE MONITORS EXCEEDING 

PROPOSED OZONE STANDARDS

Overview

This appendix describes a limited screening-level analysis of the socio-demographic 

characteristics of populations living in areas with an ozone monitor with a current (2011-2013) 

design value exceeding the proposed range of ozone standard levels, 65 to 70 parts per billion 

(ppb). This analysis does not include a quantitative assessment of exposure and/or risk for 

specific populations of potential interest from an environmental justice (EJ) perspective, and 

therefore it cannot be used to draw any conclusions regarding potential disparities in exposure or 

risk across populations of interest from an EJ perspective. This appendix describes the technical 

approach used in the analysis, discusses uncertainties and limitations associated with the 

analysis, and presents results.

Executive Order 12898, Federal Actions to Address Environmental Justice in Minority 

Populations and Low-Income Populations (59 FR 7629; Feb. 16, 1994), directs federal agencies, 

to the greatest extent practicable and permitted by law, to make environmental justice part of 

their mission by identifying and addressing, as appropriate, disproportionately high and adverse 

human health or environmental effects of their programs, policies, and activities on minority 

populations and low-income populations in the United States. . In addition, 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 the EPA has reason to believe may have a disproportionate effect on children. Accordingly, 

the Environmental Protection Agency’s (EPA) Office of Air Quality Planning and Standards 

(OAQPS) has conducted a limited analysis of population demographics in some areas that may 

be affected by the proposed revisions to the National Ambient Air Quality Standards (NAAQS) 

for ozone.

The EPA Administrator is proposing to revise the NAAQS for ozone from the current 

level of 75 ppb to within the range of 65 to 70 ppb, while also soliciting comment on retaining 
9A-2

the current standard and levels down to 60 ppb. This proposed rule will establish uniform 

national standards for ozone in ambient air. The proposed revisions would improve public health 

protection for at-risk groups, especially children. The Agency has elected to conduct a limited 

analysis of key socio-demographic characteristics of populations living in areas of interest, 

defined for this analysis as any Core Based Statistical Area (CBSA) with at least one county 

having an ozone monitor with a current (2011-2013) design value exceeding the proposed range 

of ozone standard levels (65 to 70 ppb), and also including individual counties not included in a 

CBSA with a design value exceeding the proposed range of standards.

9A.1 Design of Analysis

To gain a better understanding of the populations within the areas of interest, the EPA 

conducted an analysis at the county level for this ozone NAAQS review. The areas of interest for 

these analyses were defined as all counties contained within any CBSA with at least one monitor 

with a current (2011-2013) design value above the proposed range of standard levels (65 to 70

ppb) as well as counties not in a CBSA with a current (2011-2013) design value above the 

proposed range of standard levels (65 ppb to 70 ppb). The areas of interest were designed to try 

to capture population and communities most likely to benefit from improved air quality resulting 

from implementation of the proposed ozone NAAQS revisions.

At the lower end of the range of proposed standard levels of 65 ppb, this definition of the 

areas of interest resulted in 953 counties being analyzed, 888 in 265 CBSAs and 65 outside 

CBSAs. At the upper end of the range of proposed standard levels of 70 ppb, this resulted in 707 

counties being analyzed, 680 in 182 CBSAs and 27 outside CBSAs. The demographic variables 

used in this analysis include race, ethnicity, age, economic and education data. Details on these 

demographic groups are provided in the following section (9A.1.1). 

To compare the demographic data in the areas of interest to the national data, the data 

from the identified counties were aggregated to represent the areas of interest identified by the 

lower and upper end of the proposed range of standard levels. This analysis identifies, on a 

limited basis, the subpopulations that are most likely to experience reductions in ozone 

concentrations as a result of actions taken to meet the proposed range of standard levels and thus 

are expected to benefit most from this regulation. This analysis does not identify the 
9A-3

demographic characteristics of the most highly affected individuals or communities nor does it 

quantify the level of risk faced by those individuals or communities. To the extent that any 

minority, low-income or indigenous subpopulation is disproportionately impacted by ozone 

levels because they reside in an area of interest, that subpopulation also stands to see increased 

environmental and health benefit from meeting the more protective proposed standard levels. 

The aggregated demographic sub-population values across the areas of interest are 

compared to the national data and are listed in Table 9A-2 in Section 9A-3. 

9A.1.1 Demographic Variables Included in Analysis

This analysis includes race, ethnicity, and age data derived from the 2010 Census SF1 

datasetiand economic and education data from the Census Bureau’s 2006-2010 American 

Community Survey (ACS) 5-Year Estimates.ii This data is summarized in Table 9A-1.

Table 9A-1. Census Derived Demographic Data

Race, Ethnicity, and Age Data (Census 2010 block-level SF1 data)*

Parameter Definition

Population Total population 

White Number of whites (may include Hispanics)

African Americans Number of African Americans (may include Hispanics)

Native Americans Number of Native Americas (may include Hispanics)

Other and multiracial Number of other race and multiracial (may include Hispanics)

Minority/Non-white Hispanic Total Population less White Population

Age 0 to 4 Number of people age 0 to 4

Age 0 to 17 Number of people age 0 to 17

Age 65 and up Number of people age 65 and up

Economic and Education Date (2006-2010 ACS)*

Parameter Definition

Poverty Number of people living in households with income below the poverty line

2 x Poverty Number of people living in households with income below twice the 

poverty line

Linguistic isolation Number of people linguistically isolated

Education level Adults without a high school diploma

*Census 2010 does not currently report this data for the Virgin Islands, Guam, American Samoa, and the Northern 

Marianas; Census 2000 data are used for these areas.

As noted above, the EPA uses population data collected by the 2010 Census. All data is 

stored at the block level. For those indicators available from the Census at the block group, but 

not block level, the EPA assigns a block the same percentage as the block group of which it is a 

part. For example, a block is assigned the same percentage of people living below the national 
9A-4

poverty line as the block group in which it is contained. Nationally, a census block contains 

about 50 people on average; and a block group contains about 26 blocks on average, or about 

1,350 people. (For comparison, a census tract is larger than a block group, with each tract 

containing an average of 3 block groups, or about 4,300 people). For this analysis the data was 

aggregated to the county level.

Data on race, ethnicity and age for all census blocks in the country except for the Virgin 

Islands, Guam, American Samoa, and the Northern Marianas were obtained from the 2010 

Census SF1 dataset. This dataset gives a breakdown of the population for each census block 

among different racial and ethnic classifications, including: White, African American or Black, 

Hispanic or Latino, American Indian or Native Alaskan, Asian, Native Hawaiian or other South 

Pacific Islander, other race, and two or more races. Data on age distributions in the U.S. and 

Puerto Rico were obtained at the census block level from the 2010 Census of Population and 

Housing Summary File 1 (SF1) short form, Table P12. SF1 contains the information compiled 

from the questions asked of all people about every housing unit. Data on poverty status, 

education level, and linguistic isolation in the U.S. and Puerto Rico were obtained at the block 

group level from the Census Bureau’s 2006-2010 ACS.iii Data for the Virgin Islands and other 

island territories (Guam, American Samoa, and the Northern Marianas) were retrieved from 

similar tables, which are available through the Census’ American Fact Finder internet portal, 

www.factfinder.census.gov. “Minority” means a person, as defined by the U.S. Bureau of 

Census, who is a: (1) Black American (a person having origins in any of the black racial groups 

of Africa); (2) Hispanic person (a person of Mexican, Puerto Rican, Cuban, Central or South

American, or other Spanish culture or origin, regardless of race); (3) Asian American or Pacific 

Islander (a person having origins in any of the original peoples of the Far East, Southeast Asia, 

the Indian subcontinent, or the Pacific Islands); or (4) American Indian or Alaskan Native (a 

person having origins in any of the original people of North America and maintain cultural 

identification through tribal affiliation or community recognition). The Minority/Non-White 

Hispanic field is computed by subtracting the white population from the total population. 

Compared to white populations in the U.S., numerous studies have found that minority 

populations live in closer proximity to pollution sources, experience worse health, and have less 

ability to participate in environmental decision making. iv 
9A-5

The percentage of people living below the national poverty line is defined by the Census 

as the percentage of residents whose household income is at or below the poverty guidelines 

updated periodically in the Federal Register by the U.S. Department of Health and Human 

Services under the authority of 42 U.S.C. 9902(2).v Low income has been linked in many 

studies to poor health, lack of access to health care, closer proximity to pollution sources, and 

greater susceptibility to illnesses caused by exposure to air pollution. vi Low income in this 

appendix is defined as 2 times the national poverty line.

The percentage of residents whose age is less than 5 years includes infants and children –

all of whom are considered a sensitive subpopulation for many forms of environmental 

contaminants, including air pollution.vii The reasons for children’s increased sensitivity to air 

pollution are manifold and include: still-developing respiratory and other bodily systems; smaller 

body size in proportion to inhaled contaminants; and varying behavior patterns including longer 

durations spent outdoors at high breathing rates compared to adults.viii These factors lead to 

increased morbidity and mortality risks for children from exposure to air pollutants.ix 

Furthermore, minority and low-income children are at even greater risk, as the increased 

susceptibility for each of these demographic groups compounds such a child’s overall 

vulnerability.x 

The percentage of residents of age 65 years and over is considered a sensitive 

subpopulation for many forms of environmental contamination, including air pollution.xi The 

increased susceptibility of this subpopulation stems not only from their age, but also from their 

poorer health and lower fitness levels.xii Those age 65 years and up have a higher mortality 

risk—both long-term and short-term—than other populations from air pollution, as well as 

increased morbidity risks including cardiovascular illness and respiratory disease.xiii 

The percentage of residents lacking a high school diploma is considered a relevant 

because lack of education may indirectly increase the effects of environmental contamination, 

including air pollution. Low education may decrease access to health care and information about

environmental risks and how to respond appropriately to such risks.xiv Studies have revealed that 

low education appears to be a factor in health disparities, multi-morbidity, and mortality in 

general; and lower education may increase the relative risk of air pollution and premature 
9A-6

mortality.xv In particular, this association was established after examining the relationship 

between an increase in particulate matter and mortality among persons with lower education.xvi 

The percentage of residents experiencing linguistic isolation is considered a relevant 

because linguistic isolation may render households less able to identify and mitigate 

environmental harms by limiting both access to health care and access to information about 

environmental risks and how to respond appropriately to those risks.xvii Studies have revealed 

that counties with higher concentrations of immigrants and linguistically isolated households 

have more hazardous waste generators and more proposed Superfund sites, compared to other 

counties.xviii For example, a significant cancer risk was found between linguistically isolated 

households and exposure from Toxics Release Inventory (TRI) releases in the San Francisco Bay 

area.xix Finally, immigrants may encounter discrimination and prejudice which impact 

vulnerability.

9A.2 Considerations in Evaluating and Interpreting Results

This analysis characterizes the socio-demographic attributes of populations located in 

areas defined by a county or a CBSA containing a county with a monitor design value greater 

than 65 ppb and 70 ppb. Therefore, the results of this analysis can only be used to inform 

whether there are differences in the composition of populations residing within these areas 

relative to the nation as a whole. As noted earlier, the purpose of the analysis is to determine 

whether populations of interest from an EJ perspective have a higher representation in areas that 

exceed the proposed range of ozone standard levels, and thus may be more affected by strategies 

to attain alternative standards. This analysis does not include a quantitative assessment of 

exposure and/or risk for specific populations of potential interest from an EJ-perspective, and 

therefore it cannot be used to draw any conclusions regarding potential disparities in exposure or 

risk across populations of interest from an EJ perspective. Nor can it be used to draw 

conclusions about any disparities in the health and environmental benefits that result from 

strategies to attain alternative ozone standards.

In order to clearly identify disparities in risk between populations of interest, we would 

need to conduct rigorous site-specific population-level exposure and risk assessments that take 
9A-7

into account short-term mobility (daily patterns of travel linked for example to school or work) 

or long-term mobility (families moving into or out of specific block groups).

9A.3 Presentation of Results 

This section presents a summary of the results for the assessment of demographic 

characteristics of populations in areas with ozone monitors with measured values greater than the 

levels of the proposed range of ozone standards. The results are also provided in tabular form. 

As a whole, the demographic distributions within the areas of interest estimated for the

proposed range of standard levels (i.e., 65 ppb to 70 ppb) correspond well to the national 

averages. Table 9A-2 presents these results and the raw data used in this assessment. The 

population totals and subtotals by demographic group as well as the percentages of the 

demographic groups for the nation and for the lower and upper end of the proposed range of 

standard levels (65 ppb and 70 ppb) are shown. Most of the sub-populations are within a few 

percentage points of the national average. The largest difference is between the national and 

study area percentages for the Minority/Non-White Hispanic demographic group and that is a 

difference of only 4%. Overall, these qualitative results support the determination that the 

proposed rule will tend to benefit geographic areas that have a higher proportion of minority and 

low income residents than the national average. Perhaps more importantly, these results provide 

EPA much needed insight into how and with whom to best target efforts to inform communities 

about the proposed rule and otherwise ensure their meaningful involvement in the rulemaking 

process. 
9A-8

Table 9A-2 Summary of Population Totals and Demographic Categories for Areas of 

Interest and National Perspective

Demographic 

Summary Population White

African 

American

Native 

American

Other or 

Multiracial

Minority/

Non-White 

Hispanica

Area of Interest Total

 65 ppb 221,431,286 153,706,027 30,429,108 1,726,110 35,570,041 67,725,259

 70 ppb 193,316,836 132,112,738 27,193,155 1,488,364 32,522,579 61,204,098

% of Area of Interest Total

 65 ppb 69% 14% 1% 16% 31%

 70 ppb 68% 14% 1% 17% 32%

National Total 312,861,256 226,405,205 39,475,216 2,952,087 44,028,748 86,456,051

% of 

National Total 72% 13% 1% 14% 28%

a The race Minority/Non-White Hispanic field is computed by subtracting the white population from the total 

population.

Demographic 

Summary

Linguistically 

Isolated Age 0 - 4 Age 0 - 17 Age 65+

Without a 

HS Diploma Low Income

Area of Interest Total

 65 ppb 13,072,109 14,695,948 54,008,810 27,163,990 20,914,891 67,027,700

 70 ppb 12,179,896 12,849,637 47,296,147 23,518,071 18,495,474 58,296,224

% of Area of Interest Total

 65 ppb 6% 7% 24% 12% 14% 30%

 70 ppb 6% 7% 24% 12% 15% 30%

National Total 19,196,507 20,465,065 75,217,176 40,830,262 30,952,789 101,429,436

% of 

National Total 6% 7% 24% 13% 15% 32%
9A-9

i 2010 Census Summary File 1 Delivered via FTP, http://www2.census.gov/census_2010/04-Summary_File_1/

ii U.S. Census Bureau 2006-2010 American Community Survey 5-Year Estimates, 

http://www.census.gov/acs/www/data_documentation/2010_release/

iii U.S. Census Bureau 2006-2010 American Community Survey 5-Year Estimates, 

http://www.census.gov/acs/www/data_documentation/2010_release/

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Geography of Social Vulnerability: Race, Class, and Catastrophe. Understanding Katrina. Social Sciences Research 

Council. June 11. http://understandingkatrina.ssrc.org/Cutter/; Gold, Diane R. and Rosalind Wright. 2005. 

Population Disparities in Asthma. Annu. Rev. Public Health 26:89–113; Nelson DA, Johnson CC, Divine GW, 

Strauchman C, Joseph CL, Ownby DR. 1997. Ethnic Differences in the Prevalence of Asthma in Middle Class 

Children. Ann. Allergy Asthma Immunol. 78:21– 26; O’Neill et al., 2003. Health, Wealth, and Air Pollution: 

Advancing Theory and Methods, 111 Environmental Health Perspectives 16: 1861, 1865, 

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Persp. 104:1, January, pp. 22-25.; Gwynn, R. Charon, and George D. Thurston. 2001. The Burden of Air Pollution: 

Impacts among Racial Minorities. Environ Health Perspect. 109 (Suppl 4): 501–506 

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v U.S. Department of Health & Human Services. Frequently Asked Questions Related to the Poverty Guidelines and 

Poverty. http://aspe.hhs.gov/poverty/faq.shtml

vi USEPA. Office of Environmental Justice. 2004. Toolkit for Assessing Potential Allegations of Environmental 

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Jiang Y, Hesser JE. 2009. Using Item Response Theory to Analyze the Relationship Between Health-Related 

Quality of Life and Health Risk Factors. Prev Chronic Dis. 6(1); Brulle, Robert J. and David N. Pellow. 2006. 

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Health Persp. 104:1, January, pp. 22-25; Gwynn, R. Charon, and George D. Thurston. 2001. The Burden of Air 

Pollution: Impacts among Racial Minorities. Environ Health Perspect. August; 109 (Suppl 4):501–506 

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1240572/pdf/ehp109s-000501.pdf; USEPA. 2008. Assessment and 

Standards Division, Office of Transportation and Air Quality. Regulatory Impact Analysis: Control of Emissions of 

Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder. 

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vii American Lung Association. 2009. Protect the Air we Breathe. February. 

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viii USEPA. 2004. Air Quality Criteria for Particulate Matter. RTP, NC. EPA/600/P-99/002aF-bF. 

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Disproportionate Environmental Health Impacts; USEPA. Framework for Cumulative Risk Assessment; National 

Resources Defense Council. Our Children at Risk: The 5 Worst Environmental Threats to Their Health. 

http://www.nrdc.org/health/kids/ocar/chap4.asp

ix USEPA. 2004. Air Quality Criteria for Particulate Matter. Research Triangle Park, NC. EPA/600/P-99/002aF-bF. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=87903; Host S; et al. 2007. Short-term Associations Between 

Fine and Coarse Particles and Cardiorespiratory Hospitalizations in Six French Cities. Occup Environ Med; Mar TF; 

et al. 2004. An Analysis of the Association Between Respiratory Symptoms in Subjects with Asthma and Daily Air 

Pollution in Spokane, Washington. Inhal Toxicol 16:809-815; Mauad T; et al. 2008. Chronic Exposure to Ambient 

Levels of Urban Particles Affects Mouse Lung Development. Am J Respir Crit Care Med 178: 721-728; Peel JL; et 

al. 2005. Ambient Air Pollution and Respiratory Emergency Department Visits. Epidemiology, 16:164-174; 

Pinkerton KE; et al. 2004. Reduced Lung Cell Proliferation Following Short-Term Exposure to Ultrafine Soot and 

Iron Particles in Neonatal Rats: Key to Impaired Lung Growth? Inhal Toxicol 1:73-81; National Resources Defense 

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http://www.nejm.org/doi/full/10.1056/NEJMoa040610?ck=nck&#articleTop; Neidell, Matthew J. 2004. Air 

Pollution, Health, and Socio-economic Status: the Effect of Outdoor Air Quality on Childhood Asthma. Journal of 

Health Economics 23: pp.1209–1236.

x O’Neill et al. 2003. Health, Wealth, and Air Pollution: Advancing Theory and Methods. Environmental Health 

Perspectives 16: pp.1861, 1865. http://www.ehponline.org/docs/2003/6334/abstract.html; Gold, et al. 1993. Race 

and Gender Differences in Respiratory Illness Prevalence and Their Relationship to Environmental Exposures in 

Children 7 to 14 Years of Age. Am Rev Respir Dis.148(1):10-8; Neidell, Matthew J. 2004. Air Pollution, Health, 

and Socio-economic Status: the Effect of Outdoor Air Quality on Childhood Asthma. Journal of Health Economics 

23: pp.1209–1236.

xi American Lung Association. 2009. Protect the Air we Breathe. February.

xii USEPA. Framework for Cumulative Risk Assessment.

xiii Gerber et al. 2010. Cumulative Exposure to Air Pollution and long term outcomes after first acute myocardial 

infarction: A population-based cohort study. BMC Public Health 10:369. http://www.biomedcentral.com/1471-

2458/10/369; American Heart Association. Air Pollution, Heart Disease and Stroke. 

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Sciences. Elderly Have Higher Risk for Cardiovascular, Respiratory Disease From Fine Particle Pollution. 

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Zeka A; Schwartz J. 2007. Association Between PM2.5 and All-Cause and Specific-Cause Mortality in 27 US 

Communities. Journal of Exposure Science and Environmental Epidemiology, 17:279-87.; 

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xiv USEPA. 2004. National Environmental Justice Advisory Council. Ensuring Risk Reduction in Communities with 

Multiple Stressors: Environmental Justice and Cumulative Risks/Impacts.

xv DeWalt, Darren A., Marylee H. Dilling, Marjorie S. Rosenthal, and Michael P. Pignone. 2007. Low Parental 

Literacy Associated with Worse Asthma Care Measures in Children. Ambul Pediatr 7(1): 25–31. 

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10-1

CHAPTER 10: QUALITATIVE DISCUSSION OF EMPLOYMENT IMPACTS OF AIR 

QUALITY

Overview

Executive Order 13563 directs federal agencies to consider regulatory impacts on job 

creation and employment: “our regulatory system must protect public health, welfare, safety, and 

our environment while promoting economic growth, innovation, competitiveness, and job 

creation. It must be based on the best available science”. Although benefit-cost analyses do not 

typically include a separate analysis of regulation-induced employment impacts,144 during 

periods of sustained high unemployment, such impacts are of particular concern and questions 

may arise about their existence and magnitude. This chapter discusses some, but not all, possible 

types of labor impacts that may result from measures to decrease NOx emissions. 

Section 10.1 describes the theoretical framework used to analyze regulation-induced 

employment impacts, discussing how economic theory alone cannot predict whether such 

impacts are positive or negative. Section 10.2 presents an overview of the peer-reviewed 

literature relevant to evaluating the effect of environmental regulation on employment. Section 

10.3 discusses employment related to installation of NOx controls on coal and gas-fired electric 

generating units, industrial boilers, and cement kilns. 

10.1 Economic Theory and Employment

Regulatory employment impacts are difficult to disentangle from other economic changes 

affecting employment decisions over time and across regions and industries. Labor market 

responses to regulation are complex. They depend on labor demand and supply elasticities and 

possible labor market imperfections (e.g., wage stickiness, long-term unemployment, etc). The 

unit of measurement (e.g., number of jobs, types of job hours worked, and earnings) may affect 

observability of that response. Net employment impacts are composed of a mix of potential 

declines and gains in different areas of the economy (the directly regulated sector, upstream and 

 

144 Labor expenses do, however, contribute toward total costs in the EPA’s standard benefit-cost analyses.
10-2

downstream sectors, etc.) over time. In light of these difficulties, economic theory provides a 

constructive framework for analysis.

Microeconomic theory describes how firms adjust input use in response to changes in 

economic conditions.145 Labor is one of many inputs to production, along with capital, energy, 

and materials. In competitive markets, firms choose inputs and outputs to maximize profit as a 

function of market prices and technological constraints.146,147

Berman and Bui (2001) and Morgenstern, Pizer, and Shih (2002), adapt this model to 

analyze how environmental regulations affect labor demand.148 They model environmental 

regulation as effectively requiring certain factors of production, such as pollution abatement 

capital, at levels that firms would not otherwise choose.

Berman and Bui (2001, pp. 274-75) model two components that drive changes in firmlevel labor demand: output effects and substitution effects.149 Regulation affects the profitmaximizing quantity of output by changing the marginal cost of production. If regulation causes 

marginal cost to increase, it will place upward pressure on output prices, leading to a decrease in 

demand, and resulting in a decrease in production. The output effect describes how, holding 

labor intensity constant, a decrease in production causes a decrease in labor demand. As noted by 

Berman and Bui, although many assume that regulation increases marginal cost, it need not be 

the case. A regulation could induce a firm to upgrade to less polluting and more efficient 

equipment that lowers marginal production costs. In such a case, output could increase for 

facilities that do not exit the industry. For example, improving the heat rate of a utility boiler 

increases fuel efficiency, lowering marginal production costs, and thereby potentially increasing 

 

145 See Layard and Walters (1978), a standard microeconomic theory textbook, for a discussion, in Chapter 9. 

146 See Hamermesh (1993), Ch. 2, for a derivation of the firm’s labor demand function from cost-minimization. 

147 In this framework, labor demand is a function of quantity of output and prices (of both outputs and inputs).

148 Berman and Bui (2001) and Morgenstern, Pizer, and Shih (2002) use a cost-minimization framework, which is a 

special case of profit-maximization with fixed output quantities.

149 The authors also discuss a third component, the impact of regulation on factor prices, but conclude that this effect 

is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer and 

Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) a demand effect; 2) a

cost effect; and 3) a factor-shift effect.
10-3

the boiler’s generation. An unregulated profit-maximizing firm may not have chosen to install 

such an efficiency-improving technology if the investment cost were too high.

The substitution effect describes how, holding output constant, regulation affects laborintensity of production. Although stricter environmental regulation may increase use of pollution 

control equipment and energy to operate that equipment, the impact on labor demand is 

ambiguous. Equipment inspection requirements, specialized waste handling, or pollution 

technologies that alter the production process may affect the number of workers necessary to 

produce a unit of output. Berman and Bui (2001) model the substitution effect as the effect of 

regulation on pollution control equipment and expenditures required by the regulation and the 

corresponding change in labor-intensity of production. 

In summary, as output and substitution effects may be positive or negative, theory cannot 

predict the direction of the net effect of regulation on labor demand at the level of the regulated 

firm. Operating within the bounds of standard economic theory, however, empirical estimation 

of net employment effects on regulated firms is possible when data and methods of sufficient 

detail and quality are available. The literature, however, illustrates difficulties with empirical 

estimation. For example, studies sometimes rely on confidential plant-level employment data 

from the U.S. Census Bureau, possibly combined with pollution abatement expenditure data that 

are too dated to be reliably informative. In addition, the most commonly used empirical methods 

do not permit estimation of net national effects.

The conceptual framework described thus far focused on regulatory effects on plant-level 

decisions within a regulated industry. Employment impacts at an individual plant do not 

necessarily represent impacts for the sector as a whole. The approach must be modified when 

applied at the industry level.

At the industry-level, labor demand is more responsive if: (1) the price elasticity of 

demand for the product is high, (2) other factors of production can be easily substituted for labor, 

(3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total 
10-4

production costs.150 For example, if all firms in an industry are faced with the same regulatory 

compliance costs and product demand is inelastic, then industry output may not change much, 

and output of individual firms may change slightly.151 In this case the output effect may be small, 

while the substitution effect depends on input substitutability. Suppose, for example, that new 

equipment for heat rate improvements requires labor to install and operate. In this case the 

substitution effect may be positive, and with a small output effect, the total effect may be 

positive. As with potential effects for an individual firm, theory cannot determine the sign or 

magnitude of industry-level regulatory effects on labor demand. Determining these signs and 

magnitudes requires additional sector-specific empirical study. For environmental rules, much of 

the data needed for these empirical studies are not publicly available, would require significant 

time and resources in order to access confidential U.S. Census data for research, and also would 

not be necessary for other components of a typical RIA. 

In addition to changes to labor demand in the regulated industry, net employment impacts 

encompass changes in other related sectors. For example, the proposed guidelines may increase 

demand for pollution control equipment and services. This increased demand may increase 

revenue and employment in the firms supporting this technology. At the same time, the regulated 

industry is purchasing the equipment and these costs may impact labor demand at regulated 

firms. Therefore, it is important to consider the net effect of compliance actions on employment 

across multiple sectors or industries.

If the U.S. economy is at full employment, even a large-scale environmental regulation is 

unlikely to have a noticeable impact on aggregate net national employment.152 Instead, labor 

would primarily be reallocated from one productive use to another (e.g., from producing 

electricity or steel to producing high efficiency equipment), and net national employment effects 

 

150 See Ehrenberg & Smith, p. 108. 

151 This discussion draws from Berman and Bui (2001), pp. 293. 

152 Full employment is a conceptual target for the economy where everyone who wants to work and is available to 

do so at prevailing wages is actively employed. The unemployment rate at full employment is not zero. 
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from environmental regulation would be small and transitory (e.g., as workers move from one 

job to another).153

Affected sectors may experience transitory effects as workers change jobs. Some workers 

may retrain or relocate in anticipation of new requirements or require time to search for new 

jobs, while shortages in some sectors or regions could bid up wages to attract workers. These 

adjustment costs can lead to local labor disruptions. Although the net change in the national 

workforce is expected to be small, localized reductions in employment may adversely impact 

individuals and communities just as localized increases may have positive impacts.

If the economy is operating at less than full employment, economic theory does not clearly 

indicate the direction or magnitude of the net impact of environmental regulation on 

employment; it could cause either a short-run net increase or short-run net decrease 

(Schmalansee and Stavins, 2011). An important research question is how to accommodate 

unemployment as a structural feature in economic models. This feature may be important in 

assessing large-scale regulatory impacts on employment (Smith 2012).

Environmental regulation may also affect labor supply. In particular, pollution and other 

environmental risks may impact labor productivity or employees’ ability to work.154 While the 

theoretical framework for analyzing labor supply effects is analogous to that for labor demand, it 

is more difficult to study empirically. There is a small emerging literature, described in the next 

section that uses detailed labor and environmental data to assess these impacts.

To summarize, economic theory provides a framework for analyzing the impacts of 

environmental regulation on employment. The net employment effect incorporates expected 

employment changes (both positive and negative) in the regulated sector and elsewhere. Labor 

demand impacts for regulated firms, and also for the regulated industry, can be decomposed into 

output and substitution effects which may be either negative or positive. Estimation of net 

employment effects for regulated sectors is possible when data of sufficient detail and quality are 

 

153 Arrow et. al. 1996; see discussion on bottom of p. 8. In practice, distributional impacts on individual workers can 

be important, as discussed in later paragraphs of this section.

154 E.g. Graff Zivin and Neidell (2012).
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available. Finally, economic theory suggests that labor supply effects are also possible. In the 

next section, we discuss the empirical literature.

10.2 Current State of Knowledge Based on the Peer-Reviewed Literature

The labor economics literature contains an extensive body of peer-reviewed empirical 

work analyzing various aspects of labor demand, relying on the theoretical framework discussed 

in the preceding section.155 This work focuses primarily on effects of employment policies such 

as labor taxes and minimum wages.156 In contrast, the peer-reviewed empirical literature 

specifically estimating employment effects of environmental regulations is more limited. 

Empirical studies, such as Berman and Bui (2001), suggest that net employment impacts 

were not statistically different from zero in the regulated sector. Other research suggests that 

more highly regulated counties may generate fewer jobs than less regulated ones (Greenstone 

2002). Environmental regulations may affect sectors that support pollution reduction earlier than 

the regulated industry. Rules are usually announced well in advance of their effective dates and 

then typically provide a period of time for firms to invest in technologies and process changes to 

meet the new requirements. When a regulation is promulgated, the initial response of firms is 

often to order pollution control equipment and services to enable compliance when the regulation 

becomes effective. Estimates of short-term increases in demand for specialized labor within the

environmental protection sector have been prepared for several EPA regulations in the past, 

including the Mercury and Air Toxics Standards (MATS).157 Overall, the peer-reviewed 

literature does not contain evidence that environmental regulation has a large impact on net 

employment (either negative or positive) in the long run across the whole economy. 

10.2.1 Regulated Sectors 

Berman and Bui (2001) examine how an increase in local air quality regulation affects 

manufacturing employment in the South Coast Air Quality Management District (SCAQMD), 

which includes Los Angeles and its suburbs. From 1979 to 1992 the SCAQMD enacted some of 

 

155 Again, see Hamermesh (1993) for a detailed treatment. 

156 See Ehrenberg & Smith (2000), Chapter 4: “Employment Effects: Empirical Estimates” for a concise overview. 

157 U.S. EPA (2011b).
10-7

the country’s most stringent air quality regulations. Using SCAQMD’s local air quality 

regulations, Berman and Bui identify the effect of environmental regulations on net employment 

in regulated manufacturing industries relative to other plants in the same 4-digit SIC industries 

but in regions not subject to local regulations.158 The authors find that “while regulations do 

impose large costs, they have a limited effect on employment” (Berman and Bui, 2001, p. 269). 

Their conclusion is that local air quality regulation “probably increased labor demand slightly” 

but that “the employment effects of both compliance and increased stringency are fairly precisely 

estimated zeros, even when exit and dissuaded entry effects are included” (Berman and Bui, 

2001, p. 269).159

A small literature examines impacts of environmental regulations on manufacturing 

employment. Kahn and Mansur (2013) study environmental regulatory impacts on geographic 

distribution of manufacturing employment, controlling for electricity prices and labor regulation 

(right to work laws). Their methodology identifies employment impacts by focusing on 

neighboring counties with different ozone regulations. They find limited evidence that 

environmental regulations may cause employment to be lower within “county-border-pairs.” 

This result suggests that regulation may cause an effective relocation of labor across a county 

border, but since one county’s loss may be another’s gain, such shifts cannot be transformed into 

an estimate of a national net effect on employment. Moreover this result is sensitive to model 

specification choices.

10.2.2 Labor Supply Impacts

The empirical literature on environmental regulatory employment impacts focuses 

primarily on labor demand. However, there is a nascent literature focusing on regulation-induced 

effects on labor supply.160 Although this literature is limited by empirical challenges, researchers 

have found that air quality improvements lead to reductions in lost work days (e.g., Ostro 1987). 

Limited evidence suggests worker productivity may also improve when pollution is reduced. 

 

158 Berman and Bui include over 40 4-digit SIC industries in their sample. They do not estimate the number of jobs 

created in the environmental protection sector.

159 Including the employment effect of existing plants and plants dissuaded from opening will increase the estimated 

impact of regulation on employment.

160 For a recent review see Graff-Zivin and Neidell (2013).
10-8

Graff Zivin and Neidell (2012) used detailed worker-level productivity data from 2009 and 2010, 

paired with local ozone air quality monitoring data for one large California farm growing 

multiple crops, with a piece-rate payment structure. Their quasi-experimental structure identifies 

an effect of daily variation in monitored ozone levels on productivity. They find “ozone levels 

well below federal air quality standards have a significant impact on productivity: a 10 parts per 

billion (ppb) decreases in ozone concentrations increases worker productivity by 5.5 percent.” 

(Graff Zivin and Neidell, 2012, p. 3654).161

This section has outlined the challenges associated with estimating regulatory effects on 

both labor demand and supply for specific sectors. These challenges make it difficult to estimate 

net national employment estimates that would appropriately capture the way in which costs, 

compliance spending, and environmental benefits propagate through the economy. Quantitative 

estimates are further complicated by the fact that macroeconomic models often have little 

sectoral detail and usually assume that the economy is at full employment. The EPA is currently 

seeking input from an independent expert panel on modeling economy-wide regulatory impacts, 

including employment effects.162

10.3 Employment Related to Installation and Maintenance of NOx Control Equipment

This section discusses employment related to installation of NOx controls on coal and 

gas-fired electric generating units, industrial boilers, and cement kilns, which are among the 

highest NOx-emitting source categories in EPA’s emissions inventory (see chapter 3 for more 

detail on emissions). Sections 10.3.1 and 10.3.2 below contain estimates of the number of direct 

short-term and long-term jobs that would be created by addition of NOx controls at these three 

categories of emissions sources, for various size units. Because the apportionment of emissions 

control across emissions sources in this RIA analysis is illustrative and not necessarily 

representative of the controls that will be required in individual state SIPs, EPA did not estimate 

 

161 The EPA is not quantifying productivity impacts of reduced pollution in this rulemaking using this study. In light 

of this recent research, however, the EPA is considering how best to incorporate possible productivity effects in the 

future.

162 For further information see: <https://www.federalregister.gov/articles/2014/02/05/2014-02471/draft-supportingmaterials-for-the-science-advisory-board-panel-on-the-role-of-economy-wide-modeling>.
10-9

short-term or long-term employment that would result from addition of NOx controls at these 

three source categories at the national level. 

10.3.1 Employment Resulting from Addition of NOx Controls at EGUs

Coal-fired EGUs are likely to apply additional NOx controls in response to State 

Implementation Plans (SIPs) approved pursuant to a revised ozone standard. While many EGUs 

have already installed and operate various NOx control devices, there are additional existing 

coal-fired EGUs that could decrease NOx emissions by installing or upgrading their NOx 

reducing systems. While all existing coal-fired EGUs already have low NOx burners, there are 

EGUs that could have a selective catalytic reduction (SCR) system installed, or could improve 

their NOx emissions by replacing an existing selective non-catalytic reduction (SNCR) system 

with an SCR system. The EPA identified 145 existing coal-fired EGUs, with a total of 51.0 GW 

of capacity, that (1) are in areas anticipated to need additional NOx reductions under an 

alternative ozone standard of 65 ppb , and (b) do not already have an SCR emission control 

system. (For an alternative ozone standard of 70 ppb, there are 15 EGUs so identified, with a 

total of 7.4 GW of capacity.) While there are currently SNCR systems in use that could be 

upgraded to an SCR system, the EPA’s 2025 baseline analysis163 estimates that the remaining 

SNCR systems will already be upgraded by 2025 in response to existing emission control 

programs.

The EPA used a bottom up engineering analysis using data on labor productivity, 

engineering estimates of the types of labor needed to manufacture, construct and operate SCRs 

on EGUs. The EPA’s labor estimates include not only labor directly involved with installing 

SCRs on EGUs and on-site labor used to operate the SCRs once they become operational, but 

also include the labor requirements in selected major upstream sectors directly involved 

manufacturing the materials used in SCR systems (steel), as well as the chemicals used to 

operate an SCR system (ammonia and the catalyst used to in the construction and operation of 

SCR systems, including such as steel, concrete, or chemicals used to manufacture NOx controls.

 

163 The 2025 baseline used in this illustrative analysis incorporates the “state only” implementation option used in 

the proposed carbon pollution guidelines for existing power plants and emission standards for modified and 

reconstructed power plants (a.k.a. the proposed Clean Power Plan, June, 2013).
10-10

This section presents an illustrative analysis of the direct labor needs to install and 

operate SCRs at 3 common sizes of coal-fired EGUs: 300 MW, 500 MW and 1000 MW. As 

discussed below, the illustrative analysis is for a “model plant” of each size, using consistent 

assumptions about the plant’s operation that impact the material and labor needs of an 

representative plant such as the capacity factor, heat rate, and type of coal. The analysis does not 

include an estimate of the aggregate total of the labor needed for installing and running SCRs in 

any particular level of the revised ozone standard, nor does it reflect plant-specific variations in 

labor needs due to regional differences in prices and labor availability, existing control 

technology at the plant, etc.

The analysis draws on information from four primary sources:

 Documentation for EPA Base Case v.5.13 Using the Integrated Planning Model. 

November, 2013

 “ENGINEERING AND ECONOMIC FACTORS AFFECTING THE INSTALLATION 

OF CONTROL TECHNOLOGIES: An Update”. By James E. Staudt, Andover 

Technology Partners. December, 2011.

 “Regulatory Impact Analysis (RIA) for the final Transport Rule”. June 2011

 “Regulatory Impact Analysis for the Proposed Carbon Pollution Guidelines for Existing 

Power Plants and Emission Standards for Modified and Reconstructed Power Plants”. 

June 2013

10.3.1.1 Existing EGUs Without SCR Systems

The EPA identified 145 existing coal-fired EGU units that are estimated to continue to be 

in operation in 2025 in the baseline that are located in areas considered likely to be affected by 

State Implementation Plans developed for a 65 ppb alternative ozone standard. The size 

distribution of the 145 units is shown in Figure 10-1. The 145 units have a total generating 

capacity of 51.0 GW and are anticipated to generate 282,000 GWh of electricity in 2025. With 

the current level of NOx controls installed (or anticipated to be installed by 2025 to meet existing 

environmental regulations), these 145 units are estimated to emit 290.7 tons of NOx in 2025.

The following key assumptions are used to estimate the amount of labor needed to install 

and operate individual SCR systems of various sizes.
10-11

Figure 10-1. Size Distribution of 145 Existing Coal-Fired EGU Units without SCR NOx 

Controls

10.3.1.2 Labor Estimates for Installing and Operating Individual SCR Systems 

All labor estimates in this illustrative analysis are in terms of person-years (i.e., full time 

equivalents, or FTEs).

The labor involved with manufacturing and installing the SCRs is a one-time labor need, 

and occurs over a 2 to 3 year construction period; the estimated FTEs during the construction 

phase are presented as the cumulative amount of labor over the multi-year period. The 

construction phase labor includes both labor directly involved with installing the SCR on site 

(including boiler makers, general labor and engineering).

There are three types of annual labor estimated to operate an SCR, and will be needed 

each year the EGU is in operation. The largest category is on-site labor at the EGU. The 

estimated amounts of direct labor involved with installing SCR systems is shown in Table 10-1.

Table 10-1. Summary of Direct Labor Impacts for SCR Installation at EGUs

Plant Size

300 MW 500 MW 1000 MW

Construction Phase 

(One time, Total Labor over 2-3 Year Period)

Direct Construction-related 

Employment 158.7 264.4 528.8

Operation Phase (Annual Operations)

5.5

0.8

0

5

10

15

20

25

30

35

40

# of Units

Size Distribution of 145 Existing Coal-Fired EGU

Units Without SCR NOx Controls
10-12

Plant Size

Operation and Maintenance 1.9 2.8 4.6

The key assumptions used in the labor analysis are presented in Table 10-2.

Table 10-2. Key Assumptions in Labor Analysis for EGUs

Assumptions Key Factor Source 300 MW 500 MW 1000 MW

Capital Investment 

to Install SCR

Utility-owned Capital 

Recovery Rate for 

Environmental 

Retrofits (12.1%)

IPM 5/13 Base 

Case 

Documentation

$86.1 

million $133 million $244 million

Result: FTEs to 

Install an SCR 1,100 hours/MW Staudt, 2011

158.65 264.42 528.85

Labor Cost (fixed 

O&M) per Year

IPM analysis 

of CPP 

baseline 

$218,000 $310,500 $513,000

Result: FTEs per 

Year 8.9 FTEs per $1 

million of Fixed O&M CSAPR RIA

1.95 2.76 4.57

Result: Total 

FTES to Operate

an SCR Annually

1.95 2.76 4.57

10.3.2 Assessment of Employment Impacts for Individual Industrial, Commercial, and 

Institutional (ICI) Boilers and Cement Kilns

Facilities other than electric power generators are likely to apply NOx controls in 

response to State Implementation Plans (SIPs) approved pursuant to a revised ozone standard. In 

addition to EGUs, the EPA estimated the amount and types of direct labor that might be used to 

apply and operate NOX controls for ICI boilers and for cement kilns. As with EGUs, the EPA 

used a bottom up engineering analysis using data on labor productivity, engineering estimates of 

the types of labor needed to manufacture, construct and operate NOx controls on ICI boilers and 

cement kilns. No estimates were made for labor requirements in upstream sectors such as steel, 

concrete, or chemicals used to manufacture or search as inputs to NOx controls. In addition, the 

numbers presented in this section are only indicative of the relative number and types of labor 
10-13

that might be used at these two categories of plants, without calculating an estimate of the labor 

that would be required by them in the aggregate (SC&A, 2014).

10.3.2.1 ICI Boilers

There are a number of control technologies available to reduce NOx emissions from ICI 

boilers. The EPA anticipates that the most commonly applied control technology for ICI boilers 

that could require NOx reductions as part of an ozone SIP will be selective catalytic reduction 

(SCR). The analysis calculates s labor requirements to fabricate, install, and operate different 

sizes of SCR for coal, oil and natural gas ICI boilers. Estimated total labor costs are a function 

of total capital costs and boiler size in EPA’s Coal Utility Environmental Cost (CUECost) 

model. Total SCR capital costs of ICI boilers was estimated using the EPA’s Control Strategy 

tool (CoST) model. Labor is estimated to be about 50% of the total capital costs of an SCR. 

(SC&A, 2014).

Just over 24% of total capital costs are for labor used in SCR fabrication. This 

percentage was multiplied by the total capital cost, and the resulting dollar amount was 

converted into full time equivalents (FTE) based on the average annual salary of workers (as 

outlined in IEC, 2011). The annual compensation came from the Bureau of Labor Statistics 

(BLS). This salary number was adjusted to account for benefits also based on BLS data. The 

total fabrication expenditures were divided by the average fabrication labor compensation to 

estimate the number of full time equivalent workers in SCR fabrication. 

The calculation of construction or installation labor is based on previous research on 

labor required for SCR installation at utility boilers. (Staudt 2011). Based on that, we estimate 

that 27% of SCR capital costs are spent on installation labor. We applied that percentage to the 

estimates of the capital costs of SCR for ICI boilers to give us the total labor expenditures, which 

we then converted to FTE based on average annual compensation provided by BLS. 

Operation and Maintenance labor was estimated using the CUECost model. Maintenance 

and administrative labor for SCR is estimated to be small in relation to fabrication and 

construction, with the caveat that available information on which to base an estimate is sparse. 

According to the approach used in the CUECost model, most utility boilers require a full time 

worker to operate and maintain the equipment. ICI boilers are much smaller and so are likely to 

require less than one FTE. Table 10-3 below provides summary labor estimates for SCR at 

varying sized ICI boilers.
10-14

Table 10-3. Summary of Direct Labor Impacts for Individual ICI Boilers

Plant Type Boiler Size 

(MMBtu/hr)

One-Time Employment 

Impacts1(Annual 

FTEs)

Recurring Annual 

Employment Impacts2

(FTEs per year)

Coal-fired 750 19.5 1.2

500 15.2 1.1

400 13.6 1.0

250 10.7 0.9

Oil-fired 250 9.8 0.9

150 7.3 0.9

100 5.5 0.8

50 3.2 0.8

Natural Gas-fired 250 10.5 0.9

150 11.0 0.9

100 8.4 0.9

50 6.5 0.8

1. Includes Fabrication and Installation Labor

2. Includes Operations, Maintenance, and Administrative Support

10.3.2.2 Cement Kilns

There are a number of technologies that can be used to control NOx emissions at cement 

kilns. The analysis focused on synthetic non-catalytic reduction (SNCR) as the most likely 

choice for future NOx controls at cement kilns affected by requirements in ozone SIPs. 

Although SNCR is not considered an appropriate technology for wet and long dry kilns, most 

new or recently constructed kilns will likely be preheater and precalciner kilns, and these kilns 

will likely operate using SNCR as a control technology. 

Fabrication capital cost was estimated for an SNCR system for a mid-sized preheater and 

precalciner kiln (125 to 208 tons of clinker per hour). The percent of capital cost of these 

systems attributable to labor is 44% based on vendor supplied estimates. (Wojichowski, 2014). 

This labor cost was converted to FTE using BLS data. A similar methodology was used to 

estimate installation labor. Labor costs for SNCR installation was estimated by the vendor to be 

17% of the capital cost. That was converted to FTE using BLS data. This information is 

summarized in Table 10-4.
10-15

Table 10-4. Estimated Direct Labor Impacts for Individual SNCR Applied to a MidSized Cement Kiln (125-208 tons clinker/hr)

Kiln Type Preheater / Precalciner

Manufacturing FTE 1.5

Installation FTE 0.9

O&M Annual Recurring FTE 13

10.4 References

Bureau of Labor Statistics, 2011. “National Compensation Survey: Occupational Earnings in the United States, 

2010.” Located online at: http://www.bls.gov/ncs/ncswage2010.htm#Wage_Tables

Bureau of Labor Statistics, 2014. “Employer Costs for Employee Compensation”. Released September 10, 2014. 

Located online at: http://www.bls.gov/news.release/ecec.toc.htm 

U.S. Environmental Protection Agency (U.S. EPA), 2002: USEPA Office of Air Quality Planning and Standards, 

EPA Air Pollution Control Cost Manual, Sixth Edition, EPA/452/B-02-001, Research Triangle Park, NC, 

January 2002.

U.S. Environmental Protection Agency (U.S. EPA), 2007: Alternative Control Techniques Document Update –

NOx Emissions from New Cement Kilns, EPA-453/R-07-006, prepared by EPA and Research Triangle Institute 

for EPA Office of Air Quality Planning and Standards, Sector Policies and Programs Division, November 2007.

U.S. Environmental Protection Agency (U.S. EPA). 2011. Regulatory Impact Assessment for the Cross State Air 

Pollution Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at 

http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2014. Regulatory Impact Analysis for the Clean Power Plan. 

Office of Air and Radiation, Washington, D.C. Available at: 

http://www.epa.gov/ttn/ecas/regdata/RIAs/111dproposalRIAfinal0602.pdf

SC&A,2014:, Assessment of Employment Impacts of NOx Controls. Memorandum prepared to Jason Price, IEc and 

Ellen Kurlansky, US EPA., prepared by J. Wilson; M Mullen; and j Schreiber, SC&A, Inc. September 2014

Staudt, 2011: Staudt, James E., Engineering and Economic Factors Affecting the Installation of Control 

Technologies—An Update, Andover Technology Partners, North Andover, MA, December 15, 2011.

Wojichowski, 2014: David Wojichowski, De-Nox Technologies, LLC, SNCR cost estimates provided to Maureen 

Mullen, SC&A via email, September 25, 2014.
United States

Environmental Protection 

Agency

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

Research Triangle Park, NC

Publication No. 

EPA-452/P-14-006

November 2014