Regulatory Impact Analysis of the Final 

Revisions to the National Ambient Air 

Quality Standards for Ground-Level Ozone 

 
ii 

EPA-452/R-15-007 

September 2015 

Regulatory Impact Analysis of the Final 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 
iii 

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). The docket number for this Regulatory Impact Analysis is EPA-HQOAR-2013-0169. 

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. 

 
iv 

TABLE OF CONTENTS 

page 

LIST OF TABLES ......................................................................................................................... xi

LIST OF FIGURES .......................................................................................................................xx

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

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

ES.1 Overview of Analytical Approach ....................................................................ES-4

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 Identified Controls in 2025 ....................ES-7

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

ES.1.2.3 Emissions Reductions beyond Identified Controls for Post-2025 ....ES-10

ES.1.3 Human Health Benefits ..............................................................................ES-11

ES.1.4 Welfare Benefits of Meeting the Primary and Secondary Standards .........ES-13

ES.2 Results of Benefit-Cost Analysis ....................................................................ES-14

ES.3 Improvements between the Proposal and Final RIAs .....................................ES-19

ES.4 Uncertainty .....................................................................................................ES-21

ES.5 References .......................................................................................................ES-21

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

Introduction ........................................................................................................................... 1-1

1.1 Background ................................................................................................................ 1-3

1.1.1 National Ambient Air Quality Standards ....................................................... 1-3

1.1.2 Role of Executive Orders in the Regulatory Impact Analysis ........................ 1-3

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

1.2 The Need for National Ambient Air Quality Standards ............................................ 1-5

1.3 Overview and Design of the RIA ............................................................................... 1-6

1.3.1 Establishing Attainment with the Current Ozone National Ambient Air 

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

1.3.2 Establishing the Baseline for Evaluation of Revised and Alternative 

Standards .................................................................................................................. 1-10

1.3.3 Cost Analysis Approach ............................................................................... 1-12

1.3.4 Human Health Benefits ................................................................................ 1-13

1.3.5 Welfare Benefits of Meeting the Primary and Secondary Standards ........... 1-13

1.4 Updates between the Proposal and Final RIAs ........................................................ 1-13

1.5 Organization of the Regulatory Impact Analysis ..................................................... 1-15

1.6 References ................................................................................................................ 1-16

CHAPTER 2: EMISSIONS, AIR QUALITY MODELING AND ANALYTIC 

METHODOLOGIES ............................................................................................................ 2-1

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

2.1 Emissions and Air Quality Modeling Platform ......................................................... 2-4

2.2 Projecting Ozone Levels into the Future ................................................................... 2-6

2.2.1 Methods for Calculating Future Year Ozone Design Values ......................... 2-6

2.2.2 Emissions Sensitivity Simulations ................................................................. 2-8

2.2.3 Determining Ozone Response Factors from Emissions Sensitivity 

Simulations .............................................................................................................. 2-13

2.2.4 Combining Response from Multiple Sensitivity Runs to Determine Tons 

of Emissions Reductions to Meet Various NAAQS Levels .................................... 2-16

2.2.5 Monitoring Sites Excluded from Quantitative Analysis............................... 2-18

2.3 Creating Spatial Surfaces for BenMap .................................................................... 2-20

2.4 Improvements in Emissions and Air Quality for the Final RIA .............................. 2-24

2.4.1 Improvement in Emissions ........................................................................... 2-24

2.4.2 Improvements in Air Quality Modeling ....................................................... 2-26

2.5 References ................................................................................................................ 2-27

APPENDIX 2A: ADDITIONAL AIR QUALITY ANALYSIS AND RESULTS ................. 2A-1

2A.1 2011 Emissions and Air Quality Modeling Platform ...................................... 2A-1

2A.1.1 Photochemical Model Description and Modeling Domain ......................... 2A-1

2A.1.2 Meteorological Inputs, Initial Conditions, and Boundary Conditions ........ 2A-2

2A.1.3 2025 Base Case Emissions Inputs ............................................................... 2A-4

2A.1.4 2011 Model Evaluation for Ozone .............................................................. 2A-8

2A.2 VOC Impact Regions ..................................................................................... 2A-30

2A.3 Monitors Excluded from the Quantitative Analysis ...................................... 2A-30

2A.3.1 Sites without Projections Due to Insufficient Days ................................... 2A-31

2A.3.2 Winter Ozone ............................................................................................. 2A-32

2A.3.3 Monitoring Sites in Rural/Remote Areas of the West and Southwest ...... 2A-33

2A.4 Design Values for All Monitors Included in the Quantitative Analysis ........ 2A-36

2A.5 References ...................................................................................................... 2A-65

CHAPTER 3: CONTROL STRATEGIES AND EMISSIONS REDUCTIONS ....................... 3-1

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

3.1 The 2025 Control Strategy Scenarios ........................................................................ 3-2

3.1.1 Approach for the Revised Standard of 70 ppb and Alternative Standard of 

65 ppb ........................................................................................................................ 3-2

3.1.2 Identified Control Measures ........................................................................... 3-8

3.1.3 Results .......................................................................................................... 3-10

3.2 The Post-2025 Scenario for California .................................................................... 3-17

3.2.1 Creation of the Post-2025 Baseline Scenario for California ........................ 3-17

3.2.2 Approach for Revised Standard of 70 ppb and Alternative Standard of 65 

ppb for California ..................................................................................................... 3-20

3.2.3 Results for California.................................................................................... 3-20

3.3 Improvements and Refinements since the Proposal RIA ........................................ 3-24

3.4 Limitations and Uncertainties .................................................................................. 3-26

3.5 References ................................................................................................................ 3-29
vi 

APPENDIX 3A: CONTROL STRATEGIES AND EMISSIONS REDUCTIONS ................ 3A-1

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

3A.1 Target Emissions Reductions Needed to Create the Baseline, Post-2025 

Baseline and Alternatives ............................................................................................... 3A-1

3A.2 Numeric Examples of Calculation Methodology for Changes in Design 

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

3A.3 Types of Control Measures .............................................................................. 3A-4

3A.4 Application of Control Measures in Geographic Areas .................................. 3A-5

3A.5 NOx Control Measures for Non-EGU Point Sources ...................................... 3A-8

3A.6 VOC Control Measures for Non-EGU Point Sources ................................... 3A-14

3A.7 NOx Control Measures for Nonpoint (Area) and Nonroad Sources .............. 3A-15

3A.8 VOC Control Measures for Nonpoint (Area) Sources .................................. 3A-15

CHAPTER 4: ENGINEERING COST ANALYSIS AND ECONOMIC IMPACTS ................ 4-1

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

4.1 Estimating Engineering Costs .................................................................................... 4-2

4.1.1 Methods and Data ........................................................................................... 4-3

4.1.2 Engineering Cost Estimates for Identified Controls ..................................... 4-10

4.2 The Challenges of Estimating Costs for Unidentified Control Measures ................ 4-16

4.2.1 Impact of Technological Innovation and Diffusion ..................................... 4-18

4.2.2 Learning by Doing ........................................................................................ 4-22

4.2.3 Incomplete Characterization of Available NOx Control Technologies ........ 4-24

4.2.4 Comparing Baseline Emissions and Controls across Ozone NAAQS RIAs 

from 1997 to 2014 .................................................................................................... 4-30

4.2.5 Possible Alternative Approaches to Estimate Costs of Unidentified 

Control Measures ..................................................................................................... 4-31

4.2.6 Conclusion .................................................................................................... 4-35

4.3 Compliance Cost Estimates for Unidentified Emissions Controls .......................... 4-35

4.3.1 Methods ........................................................................................................ 4-36

4.3.2 Compliance Cost Estimates from Unidentified Controls ............................. 4-40

4.4 Total Compliance Cost Estimates ............................................................................ 4-41

4.5 Economic Impacts .................................................................................................... 4-42

4.5.1 Introduction .................................................................................................. 4-42

4.5.2 Summary of Market Impacts ........................................................................ 4-44

4.6 Differences between the Proposal and Final RIAs .................................................. 4-45

4.7 Uncertainties and Limitations .................................................................................. 4-46

4.8 References ................................................................................................................ 4-48

APPENDIX 4A: ENGINEERING COST ANALYSIS ........................................................... 4A-1

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

4A.1 Cost of Identified Controls in Alternative Standards Analyses ....................... 4A-1

4A.2 Alternative Estimates of Costs Associated with Emissions Reductions from 

Unidentified Controls ..................................................................................................... 4A-5
vii 

4A.3 Alternative Approaches to Estimating the Costs Associated with Emissions 

Reductions from Unidentified Controls ......................................................................... 4A-7

4A.3.1 Regression Approach ...................................................................................... 4A-7

4A.3.2 Simulation Approach ...................................................................................... 4A-9

CHAPTER 5: QUALITATIVE DISCUSSION OF EMPLOYMENT IMPACTS OF AIR 

QUALITY ............................................................................................................................. 5-1

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

5.1 Economic Theory and Employment .......................................................................... 5-1

5.2 Current State of Knowledge Based on the Peer-Reviewed Literature ....................... 5-6

5.2.1 Regulated Sectors ........................................................................................... 5-7

5.2.2 Economy-Wide ............................................................................................... 5-8

5.2.3 Labor Supply Impacts ..................................................................................... 5-8

5.3 Employment Related to Installation and Maintenance of NOx Control Equipment .. 5-9

5.3.1 Employment Resulting from Addition of NOx Controls at EGUs ................. 5-9

5.3.2 Assessment of Employment Impacts for Individual Industrial, 

Commercial, and Institutional (ICI) Boilers and Cement Kilns .............................. 5-15

5.4 Conclusion ............................................................................................................... 5-18

5.5 References ................................................................................................................ 5-19

CHAPTER 6: HUMAN HEALTH BENEFITS ANALYSIS APPROACH AND RESULTS .. 6-1

6.1 Summary .................................................................................................................... 6-1

6.2 Overview .................................................................................................................... 6-3

6.3 Updated Methodology Presented in the Proposal and Final RIAs ............................ 6-7

6.4 Human Health Benefits Analysis Methods ................................................................ 6-9

6.4.1 Health Impact Assessment............................................................................ 6-10

6.4.2 Economic Valuation of Health Impacts ........................................................ 6-12

6.4.3 Estimating Benefits for 2025 and Post-2025 Analysis Years....................... 6-13

6.4.4 Benefit-per-ton Estimates for PM2.5 ............................................................. 6-14

6.5 Characterizing Uncertainty ...................................................................................... 6-16

6.5.1 Monte Carlo Assessment .............................................................................. 6-17

6.5.2 Quantitative Analyses Supporting Uncertainty Characterization ................. 6-18

6.5.3 Qualitative Assessment of Uncertainty and Other Analysis Limitations ..... 6-20

6.6 Benefits Analysis Data Inputs .................................................................................. 6-20

6.6.1 Demographic Data ........................................................................................ 6-21

6.6.2 Baseline Incidence and Prevalence Estimates .............................................. 6-21

6.6.3 Effect Coefficients ........................................................................................ 6-25

6.6.3.1 Ozone Exposure Metric ......................................................................... 6-32

6.6.3.2 Ozone Premature Mortality Effect Coefficients .................................... 6-33

6.6.3.3 PM2.5 Premature Mortality Coefficients ................................................ 6-39

6.6.3.4 Hospital Admissions and Emergency Department Visits ...................... 6-44

6.6.3.5 Acute Health Events .............................................................................. 6-47

6.6.3.6 Nonfatal Acute Myocardial Infarctions (AMI) (Heart Attacks) ............ 6-51

6.6.3.7 Worker Productivity .............................................................................. 6-53

6.6.3.8 Unquantified Human Health Effects ..................................................... 6-53
viii 

6.6.4 Economic Valuation Estimates ..................................................................... 6-54

6.6.4.1 Mortality Valuation ............................................................................... 6-55

6.6.4.2 Hospital Admissions and Emergency Department Valuation ............... 6-66

6.6.4.3 Nonfatal Myocardial Infarctions Valuation ........................................... 6-67

6.6.4.4 Valuation of Acute Health Events ......................................................... 6-69

6.6.4.5 Growth in WTP Reflecting National Income Growth over Time ......... 6-71

6.6.5 Benefit per Ton Estimates Used in Modeling PM2.5-Related Co-benefits ... 6-75

6.7 Benefits Results ....................................................................................................... 6-77

6.7.1 Benefits of Attaining a Revised Ozone Standard in 2025 ............................ 6-77

6.7.2 Benefits of the Post-2025 Scenario .............................................................. 6-84

6.7.3 Uncertainty in Benefits Results (including Results of Quantitative 

Uncertainty Analyses) .............................................................................................. 6-89

6.8 Discussion ................................................................................................................ 6-92

6.9 References ................................................................................................................ 6-95

APPENDIX 6A: COMPREHENSIVE CHARACTERIZATION OF UNCERTAINTY IN 

OZONE BENEFITS ANALYSIS ..................................................................................... 6A-1

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

6A.1 Description of Classifications Applied in the Uncertainty Characterization ... 6A-1

6A.1.1 Direction of Bias .......................................................................................... 6A-2

6A.1.2 Magnitude of Impact ................................................................................... 6A-2

6A.1.3 Confidence in Analytic Approach ............................................................... 6A-3

6A.1.4 Uncertainty Quantification .......................................................................... 6A-4

6A.2 Organization of the Qualitative Uncertainty Table ......................................... 6A-5

6A.3 References ...................................................................................................... 6A-13

Appendix 6B: QUANTITATIVE ANALYSES COMPLETED IN SUPPORT OF 

UNCERTAINTY CHARACTERIZATION ......................................................................6B-1

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

6B.1 Alternative C-R Functions for Short-term Exposure to Ozone ........................6B-2

6B.2 Monetized Benefits for Premature Mortality from Long-term Exposure to 

Ozone ..........................................................................................................................6B-3

6B.3 Threshold Analysis for Premature Mortality Incidence and Benefits from 

Long-term Exposure to Ozone ........................................................................................6B-4

6B.4 Alternative C-R Functions for PM2.5–Related Mortality ..................................6B-6

6B.5 Income Elasticity of Willingness-to-Pay ........................................................6B-11

6B.6 Age Group-Differentiated Aspects of Short-Term Ozone Exposure-Related 

Mortality ........................................................................................................................6B-12

6B.7 Evaluation of Mortality Impacts Relative to the Baseline Pollutant 

Concentrations for both Short-Term Ozone Exposure-Related Mortality and LongTerm PM2.5 Exposure-Related Mortality ......................................................................6B-17

6B.8 Ozone-related Impacts on Outdoor Worker Productivity ...............................6B-24

6B.9 References .......................................................................................................6B-26
ix 

CHAPTER 7: IMPACTS ON PUBLIC WELFARE OF ATTAINMENT STRATEGIES TO 

MEET PRIMARY AND SECONDARY OZONE NAAQS ................................................ 7-1

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

7.1 Welfare Benefits of Strategies to Attain Primary and Secondary Ozone Standards . 7-2

7.2 Welfare Benefits of Reducing Ozone ........................................................................ 7-3

7.3 Additional Welfare Benefits of Strategies to Meet the Ozone NAAQS .................... 7-6

7.4 References .................................................................................................................. 7-8

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

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

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

8.2 Improvements between the Proposal and Final RIAs ................................................ 8-9

8.2.1 Relative Contribution of PM Benefits to Total Benefits .............................. 8-11

8.2.2 Developing Future Control Strategies with Limited Data ............................ 8-12

8.3 Net Present Value of a Stream of Costs and Benefits .............................................. 8-14

8.4 Framing Uncertainty ................................................................................................ 8-16

8.5 Key Observations from the Analysis ....................................................................... 8-19

8.6 References ................................................................................................................ 8-20

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

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

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

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

9.3 Regulatory Flexibility Act ......................................................................................... 9-3

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

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

9.6 Executive Order 13175: Consultation and Coordination with Indian Tribal 

Governments ..................................................................................................................... 9-4

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

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

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

Distribution, or Use ........................................................................................................... 9-5

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

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

Minority Populations and Low-Income Populations ........................................................ 9-6

9.11 Congressional Review Act (CRA) ............................................................................. 9-8

APPENDIX 9A: SOCIO-DEMOGRAPHIC CHARACTERISTICS OF POPULATIONS IN 

CORE BASED STATISTICAL AREAS WITH OZONE MONITORS EXCEEDING 

REVISED AND ALTERNATIVE OZONE STANDARDS ............................................ 9A-1

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

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

9A.1.1 Demographic Variables Included in Analysis ............................................. 9A-3
x 

9A.2 Considerations in Evaluating and Interpreting Results ................................... 9A-4

9A.3 Presentation of Results .................................................................................... 9A-5

 
xi 

LIST OF TABLES 

Table ES-1. Summary of Emissions Reductions by Sector for the Identified Control 

Strategy for the Revised Standard Level of 70 ppb for 2025, except California (1,000 

tons/year)........................................................................................................................ES-9

Table ES-2. Summary of Emissions Reductions by Sector for the Identified Control 

Strategy for Alternative Standard Level of 65 ppb for 2025, except California (1,000 

tons/year)........................................................................................................................ES-9

Table ES-3. Summary of Emissions Reductions from the Unidentified Control Strategies 

for the Revised and Alternative Standard Levels for 2025, except California (1,000 

tons/year)......................................................................................................................ES-10

Table ES-4. Summary of Emissions Reductions from the Unidentified Control Strategies 

for the Revised and Alternative Standard Levels for Post-2025 - California (1,000 

tons/year)......................................................................................................................ES-11

Table ES-5. Total Annual Costs and Benefits for U.S., except California in 2025 (billions 

of 2011$, 7% Discount Rate) .......................................................................................ES-15

Table ES-6. Summary of Total Number of Annual Ozone and PM-Related Premature 

Mortalities and Premature Morbidity: 2025 National Benefits ..................................ES-16

Table ES-7. Summary of Total Control Costs (Identified + Unidentified Control 

Strategies) by Revised and Alternative Standard Levels for 2025 - U.S., except 

California (billions of 2011$, 7% Discount Rate) .......................................................ES-16

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

2025 (Nationwide Benefits of Attaining the Revised and Alternative Standard Levels 

Everywhere in the U.S., except California) ................................................................ES-17

Table ES-9. Total Annual Costs and Benefits of the Identified + Unidentified Control 

Strategies Applied in California, Post-2025 (billions of 2011$, 7% Discount Rate) ..ES-18

Table ES-10. Summary of Total Number of Annual Ozone and PM-Related Premature 

Mortalities and Premature Morbidity: Post-2025 ........................................................ES-18

Table ES-11. Summary of Total Control Costs (Identified + Unidentified Control 

Strategies) by Revised and Alternative Standards for Post-2025 - California (billions 

of 2011$, 7% Discount Rate) .......................................................................................ES-19

Table ES-12. Regional Breakdown of Monetized Ozone-Specific Benefits Results for 

Post-2025 (Nationwide Benefits of Attaining Revised and Alternative Standards just 

in California) ................................................................................................................ES-19
xii 

Table 2-1. Terms Describing Different Scenarios Discussed in This Analysis ................... 2-4

Table 2-2. List of Emissions Sensitivity Modeling Runs Modeled in CAMx to 

Determine Ozone Response Factors ................................................................................ 2-8

Table 2A-1. 2011 and 2025 Base Case NOx and VOC Emissions by Sector (thousand 

tons) ......................................................................................................................... 2A-8

Table 2A-2. MDA8 Ozone Performance Statistics Greater than or Equal to 60 Ppb for 

May through September by Climate Region, by Network ......................................... 2A-15

Table 2A-3. Key Monitoring Sites Used for the Ozone Time Series Analysis ................. 2A-23

Table 2A-4. Monitors without Projections due to Insufficient High Modeling Days to 

Meet EPA Guidance for Projecting Design Values .................................................... 2A-31

Table 2A-5. Monitors Determined to Have Design Values Affected by Winter Ozone 

Events ....................................................................................................................... 2A-33

Table 2A-6. Monitors with Limited Response to Regional NOx and National VOC 

Emissions Reductions in the 2025 and Post-2025 Baselines ...................................... 2A-34

Table 2A-7. Design Values (ppb) for California Monitors ................................................ 2A-36

Table 2A-8. Design Values (ppb) for Continental U.S. Monitors outside of California ... 2A-40

Table 3-1. Identified Controls Applied for the Revised and Alternative Standard 

Analyses Strategies ........................................................................................................ 3-10

Table 3-2. Number of Counties with Exceedances and Number of Additional Counties 

Where Reductions Were Applied for the 2025 Revised and Alternative Standards 

Analyses - U.S., except California ................................................................................. 3-12

Table 3-3. 2011 and 2025 Base Case NOx and VOC Emissions by Sector (1000 tons) ... 3-14

Table 3-4. Summary of Emissions Reductions by Sector for the Identified Control 

Strategies Applied for the Revised 70 ppb Ozone Standard in 2025, except 

California (1,000 tons/year) ........................................................................................... 3-14

Table 3-5. Summary of Emissions Reductions by Sector for the Identified Control 

Strategies for the Alternative 65 ppb Ozone Standard in 2025 - except California 

(1,000 tons/year) ............................................................................................................ 3-15

Table 3-6. Summary of Emissions Reductions for the Revised and Alternative 

Standards for the Unidentified Control Strategies for 2025 - except California (1,000 

tons/year)........................................................................................................................ 3-16
xiii 

Table 3-7. Summary of Emissions Reductions from the Identified + Unidentified 

Control Strategies by Alternative Standard Levels in 2025, Except California (1,000 

tons/year)........................................................................................................................ 3-16

Table 3-8. Summary of Emissions Reductions (Identified + Unidentified Controls) 

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

tons/year)........................................................................................................................ 3-22

Table 3-9. Summary of Emissions Reductions from Unidentified Control Strategy for 

the Revised and Alternative Standard Levels for Post-2025 - California (1,000 

tons/year)........................................................................................................................ 3-24

Table 3-10. Summary of Emissions Reductions from the Identified + Unidentified 

Control Strategy by the Revised and Alternative Standard Levels for Post-2025 - 

California (1,000 tons/year) ........................................................................................... 3-24

Table 3A-1. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 

70 ppb Scenario............................................................................................................. 3A-2

Table 3A-2. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 

65 ppb Scenario............................................................................................................. 3A-2

Table 3A-3. Emissions Reductions Applied to Create the Post-2025 Baseline Scenario .... 3A-2

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

Alternative Standard Analyses - U.S., except California .............................................. 3A-6

Table 3A-5. Geographic Areas for Application of VOC Controls in the Baseline and 

Alternative Standard Analyses - U.S., except California .............................................. 3A-7

Table 3A-6. Geographic Areas for Application of NOx Controls in the Baseline and 

Alternative Standard Analyses – California ................................................................. 3A-8

Table 3A-7. Geographic Areas for Application of VOC Controls in the Baseline and 

Alternative Standard Analyses – California ................................................................. 3A-8

Table 3A-8. NOx Control Measures Applied in the 70 ppb Analysis .................................. 3A-9

Table 3A-9. VOC Control Measures Applied in the 70 ppb Analysis .............................. 3A-11

Table 3A-10. NOx Control Measures Applied in the 65 ppb Alternative 

 Standard Analysis ....................................................................................................... 3A-12 

Table 3A-11. VOC Control Measures Applied in the 65 ppb Alternative 

 Standard Analysis ....................................................................................................... 3A-14 

Table 4-1. Summary of Identified Annualized Control Costs by Sector for 70 ppb and 

65 ppb for 2025 - U.S., except California (millions of 2011$) ...................................... 4-11
xiv 

Table 4-2. NOx and VOC Control Costs Applied for 70 ppb in 2025 – Average, 

Median, Minimum, Maximum, and Emissions Weighted Average Values ($/ton) ...... 4-12

Table 4-3. By Sector, Discount Rates Used for Annualized Control Costs Estimates and 

Percent of Total Identified Control Costs ...................................................................... 4-16

Table 4-4. Control Measures in Dallas-Fort Worth SIP Not Reflected in the 1997 

Ozone NAAQS RIA ...................................................................................................... 4-27

Table 4-5. Non-End-of-Pipe Control Measures from SIPs ................................................ 4-29

Table 4-6. Non-End-of-Pipe Measures in California ......................................................... 4-30

Table 4-7. Average NOx Offset Prices for Four Areas (2011$, perpetual tpy) ................ 4-33

Table 4-8. Annualized NOx Offset Prices for Four Areas (2011$, tons) ......................... 4-34

Table 4-9. Unidentified Control Costs in 2025 by Alternative Standard for 2025 – U.S., 

except California (7 percent discount rate, millions of 2011$) ..................................... 4-40

Table 4-10. Unidentified Control Costs in 2025 by Alternative Standard for Post-2025 –

California (7 percent discount rate, millions of 2011$) ................................................. 4-41

Table 4-11. Summary of Total Control Costs (Identified and Unidentified) by 

Alternative Level for 2025 - U.S., except California (millions of 2011$, 7% Discount 

Rate) .......................................................................................................................... 4-41

Table 4-12. Summary of Total Control Costs (Identified and Unidentified) by 

Alternative Level for Post-2025 - California (millions of 2011$, 7% Discount Rate) .. 4-42

Table 4A-1. Costs for Identified NOx Controls in the 70 ppb Analysis (2011$) ................. 4A-1

Table 4A-2. Costs for Identified VOC Controls in the 70 ppb Analysis (2011$) ............... 4A-3

Table 4A-3. Costs for Identified NOx Controls in the 65 ppb Analysis (2011$) ................. 4A-3

Table 4A-4. Costs for Identified VOC Controls in the 65 ppb Analysis (2011$) ............... 4A-5

Table 4A-5. Summary of Total Control Costs (Identified and Unidentified) by 

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

Unidentified Control Costs, except California (millions of 2011$) ............................. 4A-6

Table 4A-6. Summary of Total Control Costs (Identified and Unidentified) by 

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

for Unidentified Control Costs (millions of 2011$) ..................................................... 4A-6

Table 4A-7. Costs and Number of Identified NOx Controls by Sector in the CoST 

Database (2011$) ........................................................................................................ 4A-10
xv 

Table 4A-8. Simulation Percentage of NOx Controls from Sectors Based on Application 

of Identified Controls .................................................................................................. 4A-11

Table 4A-9. Simulation Percentage of NOx Controls from Sectors Based on Remaining 

Emissions in Sectors ................................................................................................... 4A-11

Table 4A-10. Unidentified NOx Control Costs by Alternative Standard using Alternative 

Methods for Estimation of Costs from Unidentified Controls (total costs in millions 

of 2011$, cost per ton in parentheses in $2011) ......................................................... 4A-12

Table 5-1. Summary of Direct Labor Impacts for SCR Installation at EGUs (FTEs) ....... 5-14

Table 5-2. Key Assumptions in Labor Analysis for EGUs ................................................ 5-14

Table 5-3. Summary of Direct Labor Impacts for Individual ICI Boilers ......................... 5-16

Table 5-4. Estimated Direct Labor Impacts for Individual SNCR Applied to a MidSized Cement Kiln (125-208 tons clinker/hr) ................................................................ 5-17

Table 6-1. Estimated Monetized Benefits of Attainment of the Revised and Alternative 

Ozone Standards for 2025 (nationwide benefits of attaining the standards everywhere 

in the U.S. except California) (billions of 2011$) ........................................................... 6-2

Table 6-2. Estimated Monetized Benefits of Attainment of the Revised and Alternative 

Ozone Standards for post-2025 (nationwide benefits of attaining the standards just in 

California) (billions of 2011$) ......................................................................................... 6-3

Table 6-3. Human Health Effects of Pollutants Potentially Affected by Strategies to 

Attain the Primary Ozone Standards ............................................................................... 6-5

Table 6-4. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population ....................................................................................... 6-24

Table 6-5. Asthma Prevalence Rates ................................................................................. 6-25

Table 6-6. Criteria Used When Selecting C-R Functions .................................................. 6-27

Table 6-7. Health Endpoints and Epidemiological Studies Used to Quantify OzoneRelated Health Impacts .................................................................................................. 6-29

Table 6-8. Health Endpoints and Epidemiological Studies Used to Quantify PM2.5-

Related Health Impacts .................................................................................................. 6-30

Table 6-9. Health Endpoints and Epidemiological Studies Used to Quantify OzoneRelated Health Impacts in Quantitative Analyses Supporting Uncertainty 

Characterization ............................................................................................................ 6-31

Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$) ................ 6-57
xvi 

Table 6-11. Influence of Applied VSL Attributes on the Size of the Economic Benefits 

of Reductions in the Risk of Premature Mortality (U.S. EPA, 2006a) .......................... 6-62

Table 6-12. Unit Values for Hospital Admissions .............................................................. 6-67

Table 6-13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart 

Attacksa

 .......................................................................................................................... 6-69

Table 6-14. Estimated Costs Over a 5-Year Period of a Nonfatal Myocardial Infarction 

(in 2011$) ....................................................................................................................... 6-69

Table 6-15. Elasticity Values Used to Account for Projected Real Income Growth ........... 6-73

Table 6-16. Adjustment Factors Used to Account for Projected Real Income Growth ....... 6-75

Table 6-17. Summary of PM2.5 Benefit-per-ton Estimates .................................................. 6-76

Table 6-18. Population-Weighted Air Quality Change for the Revised and Alternative 

Annual Primary Ozone Standards Relative to the Analytical Baseline in 2025 ............ 6-78

Table 6-19. Sector-Specific NOx Emissions Reductions for the Revised and Alternative 

Standard Levels .............................................................................................................. 6-78

Table 6-20. Estimated Number of Avoided Ozone-Related Health Impacts for the 

Revised and Alternative Standard Levels (Incremental to the Baseline) for the 2025 

Scenario (nationwide benefits of attaining the standards in the U.S. except 

California) ..................................................................................................................... 6-79

Table 6-21. Total Monetized Ozone-Related Benefits for the Revised and Alternative 

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

(nationwide benefits of attaining the standards everywhere in the U.S. except 

California) (millions of 2011$) ...................................................................................... 6-80

Table 6-22. Estimated Number of Avoided PM2.5-Related Health Impacts for the 

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

the 2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. 

except California)........................................................................................................... 6-81

Table 6-23. Monetized PM2.5-Related Health Co-Benefits for the Revised and 

Alternative Annual Ozone Standards (Incremental to Baseline) for the 2025 Scenario 

(Nationwide Benefits of Attaining the Standards in the U.S. except California) 

(millions of 2011$) ....................................................................................................... 6-82

Table 6-24. Estimated Monetized Ozone and PM2.5 Benefits for Revised and Alternative 

Annual Ozone Standards Incremental to the Baseline for the 2025 Scenario 

(Nationwide Benefits of Attaining the Standards in the U.S. Except California) –

Identified + Unidentified Control Strategies (combined) and Identified Control 

Strategies Only (billions of 2011$) ................................................................................ 6-83
xvii 

Table 6-25. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. except 

California) – Identified + Unidentified Control Strategies ........................................... 6-83

Table 6-26. Population-Weighted Air Quality Change for the Revised and Alternative 

Annual Primary Ozone Standards Relative to Baseline for Post-2025 ......................... 6-84

Table 6-27. Estimated Number of Avoided Ozone-Related Health Impacts for the 

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

the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in 

California) ..................................................................................................................... 6-85

Table 6-28. Total Monetized Ozone-Only Benefits for the Revised and Alternative 

Annual Ozone Standards (Incremental to the Baseline) for the Post-2025 Scenario 

(Nationwide Benefits of Attaining the Standards just in California) (millions of 

2011) .......................................................................................................................... 6-86

Table 6-29. Estimated Number of Avoided PM2.5-Related Health Impacts for the 

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

the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in 

California) ...................................................................................................................... 6-87

Table 6-30. Monetized PM2.5-Related Health Co-Benefits for the Revised and 

Alternative Annual Ozone Standards (Incremental to Baseline) for the Post-2025 

Scenario (Nationwide Benefits of Attaining the Standards just in California) 

(millions of 2011$) ....................................................................................................... 6-88

Table 6-31. Estimate of Monetized Ozone and PM2.5 Benefits for Revised and 

Alternative Annual Ozone Standards Incremental to the Baseline for the Post-2025 

Scenario (Nationwide Benefits of Attaining the Standards just in California) – 

Identified + Unidentified Control Strategies (billions of 2011$) .................................. 6-88

Table 6-32. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in 

California) – Identified + Unidentified Control Strategies ........................................... 6-89

Table 6A-1. Summary of Qualitative Uncertainty for Key Modeling Elements in Ozone 

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

Table 6B-1. Quantitative Analysis for Alternative C-R Functions for Short-term 

Exposure to Ozone .........................................................................................................6B-2

Table 6B-2. Monetized Benefits for Mortality from Long-term Exposure to Ozone 

(millions of 2011$) (2025 and post-2025 scenarios) .....................................................6B-3

Table 6B-3. Long-term Ozone Mortality Incidence at Various Assumed Thresholds .........6B-5
xviii 

Table 6B-4. Summary of Effect Estimates from Recent Cohort Studies in North America 

Associated with Change in Long-Term Exposure to PM2.5 ...........................................6B-7

Table 6B-5. PM2.5 Co-benefit Estimates using Two Epidemiology Studies and Functions 

Supplied from the Expert Elicitation ...........................................................................6B-10

Table 6B-6. Ranges of Elasticity Values Used to Account for Projected Real Income 

Growth ........................................................................................................................6B-11

Table 6B-7. Ranges of Adjustment Factors Used to Account for Projected Real Income 

Growth to 2024 ............................................................................................................6B-11

Table 6B-8. Sensitivity of Monetized Ozone Benefits to Alternative Income Elasticities 

in 2025 (Millions of 2011$) ........................................................................................6B-12

Table 6B-9. Potential Reduction in Premature Mortality by Age Range from Attaining 

the Revised and Alternative Ozone Standards (2025 scenario) ..................................6B-15

Table 6B-10. Potential Years of Life Gained by Age Range from Attaining the Revised 

and Alternative Ozone Standards (2025 Scenario) .....................................................6B-15

Table 6B-11. Estimated Percent Reduction in All-Cause Mortality Attributed to the 

Proposed Primary Ozone Standards (2025 Scenario) .................................................6B-16

Table 6B-12. Population Exposure in the Baseline Sector Modeling (used to generate the 

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

the Underlying Epidemiology Studies ........................................................................6B-23

Table 6B-13. Definitions of Variables Used to Calculate Changes in 

 Worker Productivity.....................................................................................................6B-26 

Table 6B-14. Population Estimated Economic Value of Increased Productivity among 

Outdoor Agricultural Workers from Attaining the Revised and Alternative Ozone 

Standards in 2025 (millions of 2011$) .........................................................................6B-26

Table 7-1. Welfare Effects of NOx and VOC Emissions ..................................................... 7-3

Table 8-1. Total Costs, Total Monetized Benefits, and Net Benefits of Control 

Strategies in 2025 for U.S., except California (billions of 2011$) .................................. 8-4

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

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

Table 8-3. Summary of Identified Control Strategies Annualized Control Costs by 

Sector for 70 ppb and 65 ppb for 2025 - U.S., except California (millions of 2011$) .... 8-5

Table 8-4. Estimated Monetized Ozone and PM2.5 Benefits for Revised and Alternative 

Annual Ozone Standards Incremental to the Baseline for the 2025 Scenario 
xix 

(Nationwide Benefits of Attaining the Standards in the U.S. except California) – 

Identified Control Strategies (billions of 2011$) ............................................................ 8-6

Table 8-5. Summary of Total Control Costs (Identified + Unidentified) by Revised and 

Alternative Standard Level for 2025 - U.S., except California (millions of 2011$, 7% 

Discount Rate) ................................................................................................................. 8-6

Table 8-6. Summary of Total Control Costs (Unidentified Control Strategies) by 

Revised and Alternative Level for Post-2025 - California (millions of 2011$, 7% 

Discount Rate) ................................................................................................................. 8-7

Table 8-7. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

2025 Scenario (nationwide benefits of attaining revised and alternative standard 

levels everywhere in the U.S. except California) – Identified + Unidentified Control 

Strategies .......................................................................................................................... 8-7

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

Post-2025 Scenario (nationwide benefits of attaining revised and alternative standard 

levels just in California) – Identified + Unidentified Control Strategies ......................... 8-7

Table 8-9. Human Health Effects of Pollutants Potentially Affected by Strategies to 

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

Table 8-10. Relevant Factors and Their Potential Implications for Attainment .................. 8-17

Table 9A-1. Census Derived Demographic Data ................................................................. 9A-3

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

Interest and National Perspective .................................................................................. 9A-6

 
xx 

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-8

Figure ES-3. Projected Ozone Design Values in the Post-2025 Baseline ...........................ES-11

Figure 2-1. Process to Determine Emissions Reductions Needed to Meet Baseline and 

Alternative Standards Analyzed ...................................................................................... 2-3

Figure 2-2. Across-the-Board Emissions Reduction and Combination Sensitivity 

Regions .......................................................................................................................... 2-12

Figure 2-3. Map of 200 km Buffer Regions in California, East Texas and the Northeast 

Created as Part of the Analysis for the November 2014 Proposal RIA ......................... 2-13

Figure 2-4. Map of VOC Impact Regions ........................................................................... 2-16

Figure 2-5. Process Used to Create Spatial Surfaces for BenMap ...................................... 2-24

Figure 2A-1. Map of the CAMx Modeling Domain Used for Ozone NAAQS RIA ............ 2A-2

Figure 2A-2a.    AQS Ozone Monitoring Sites ....................................................................... 2A-14

Figure 2A-2b.    CASTNet Ozone Monitoring Sites ............................................................... 2A-14

Figure 2A-3. NOAA Nine Climate Regions ...................................................................... 2A-15

Figure 2A-4. Density Scatter Plots of Observed/Predicted MDA8 Ozone for the Northeast, 

Ohio River Valley, Upper Midwest, Southeast, South, Southwest, Northern Rockies, 

Northwest and West Regions ...................................................................................... 2A-16

Figure 2A-5. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Northeast Region, (a) AQS Network and (b) 

CASTNet Network ..................................................................................................... 2A-17

Figure 2A-6. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Ohio Valley Region, (a) AQS Network and 

(b) CASTNet Network ................................................................................................ 2A-17

Figure 2A-7. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Upper Midwest Region, (a) AQS Network 

and (b) CASTNet Network ......................................................................................... 2A-18
xxi 

Figure 2A-8. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Southeast Region, (a) AQS Network and (b) 

CASTNet Network ...................................................................................................... 2A-18

Figure 2A-9. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the South Region, (a) AQS Network and (b) 

CASTNet Network ...................................................................................................... 2A-19

Figure 2A-10. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Southwest Region, (a) AQS Network and (b) 

CASTNet Network ...................................................................................................... 2A-19

Figure 2A-11. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Northern Rockies Region, (a) AQS Network 

and (b) CASTNet Network ......................................................................................... 2A-20

Figure 2A-12. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Northwest Region, (a) AQS Network and (b) 

CASTNet Network ...................................................................................................... 2A-20

Figure 2A-13. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the West Region, (a) AQS Network and (b) 

CASTNet Network ...................................................................................................... 2A-21

Figure 2A-14. Mean Bias (ppb) of MDA8 Ozone Greater than or Equal to 60 ppb over 

the Period May-September 2011 at AQS and CASTNet Monitoring ......................... 2A-21

Figure 2A-15. Normalized Mean Bias (%) of MDA8 Ozone Greater than or Equal to 60 

ppb over the Period May-September 2011 at AQS and CASTNet Monitoring Sites . 2A-22

Figure 2A-16. Mean Error (ppb) of MDA8 Ozone Greater than or Equal to 60 ppb over 

the Period May-September 2011 at AQS and CASTNet Monitoring Sites ................ 2A-22

Figure 2A-17. Normalized Mean Error (%) of MDA8 Ozone Greater than or Equal to 60 

ppb over the Period May-September 2011 at AQS and CASTNet Monitoring Sites . 2A-23

Figure 2A-18a. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for 

May through September 2011 at Site 360810124 in Queens, New York ................... 2A-24

Figure 2A-18b. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for 

May through September 2011 at Site 361030002 in Suffolk County, New York ...... 2A-24

Figure 2A-18c. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for 

May through September 2011 at Site 240251001 in Harford Co., Maryland ............. 2A-25

Figure 2A-18d. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for 

May through September 2011 at Site 420031005 in Allegheny Co., Pennsylvania ... 2A-25
xxii 

Figure 2A-18e. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for 

May through September 2011 at Site 211110067 in Jefferson Co., Kentucky ........... 2A-26

Figure 2A-18f. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for 

May through September 2011 at Site 261630019 in Wayne Co., Michigan .............. 2A-26

Figure 2A-18g. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for 

May through September 2011 at Site 551170006 in Sheboygan Co., Wisconsin ...... 2A-27

Figure 2A-18h. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for 

May through September 2011 at Site 484392003 in Tarrant Co., Texas ................... 2A-27

Figure 2A-18i. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for 

May through September 2011 at Site 480391004 in Brazoria Co., Texas ................. 2A-28

Figure 2A-18j. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for 

May through September 2011 at Site 80350004 in Douglas Co., Colorado ............... 2A-28

Figure 2A-18k. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for 

May through September 2011 at Site 60195001 in Fresno Co., California ................ 2A-29

Figure 2A-18l. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for 

May through September 2011 at Site 60710005 in San Bernardino Co., California .. 2A-29

Figure 2A-19. Change in July Average of 8-hr Daily Maximum Ozone Concentration 

(ppb) Due to 50% Cut in U.S. Anthropogenic VOC Emissions ................................. 2A-30

Figure 2A-20. Location of Sites Identified in Table 2A-6 ............................................... 2A-35

Figure 3-1. Process to Find Needed Reductions to Reach the Revised and Alternative 

Standards .......................................................................................................................... 3-4

Figure 3-2. Buffers of 200 km for NOx Emissions Reductions around Projected 

Exceedance Areas ............................................................................................................ 3-6

Figure 3-3. Buffers of 100 km for VOC Emissions Reductions around Projected 

Exceedance Areas ............................................................................................................ 3-7

Figure 3-4. Process to Estimate the Control Strategies for the Revised and Alternative 

Standards .......................................................................................................................... 3-8

Figure 3-5. Projected Ozone Design Values in the 2025 Baseline Scenario ....................... 3-11

Figure 3-6. Counties Where NOX Emissions Reductions Were Applied to Simulate 

Attainment with the Revised and Alternative Ozone Standards in the 2025 Analysis .. 3-12

Figure 3-7. Counties Where VOC Emissions Reductions Were Applied to Simulate 

Attainment with the Revised and Alternative Ozone Standards in the 2025 Analyses . 3-13
xxiii 

Figure 3-8. Steps to Create the Post-2025 Baseline for California ..................................... 3-18

Figure 3-9. Counties Projected to Exceed 75 ppb in the Post-2025 Baseline Scenario ...... 3-21

Figure 3-10. Counties Where Emissions Reductions Were Applied to Demonstrate 

Attainment with the Current Standard ........................................................................... 3-22

Figure 3-11. Projected Ozone Design Values in the Post-2025 Baseline Scenario .............. 3-23

Figure 4-1. Identified Control Cost Curve for 2025 for All Identified NOx Controls for 

All Source Sectors (EGU, non-EGU Point, Nonpoint, and Nonroad) ............................. 4-7

Figure 4-2. Identified Control Cost Curve for 2025 for All Identified VOC Controls for 

All Source Sectors (EGU, non-EGU Point, Nonpoint, and Nonroad) ........................... 4-10

Figure 4-3. Regions Used to Present Emissions Reductions and Cost Results ................... 4-13

Figure 4-4. Observed but Incomplete MACC (Solid Line) Based on Identified Controls 

in Current Tools and Complete MACC (dashed line) where Gaps Indicate Abatement 

Opportunities Not Identified by Current Tools .............................................................. 4-25

Figure 4A-1. Marginal Costs for Identified NOx Controls for All Source Sectors with 

Regression Line for Unidentified Control Measures .................................................... 4A-8

Figure 4A-2. Marginal Costs for Identified NOx Controls for All Source Sectors with 

Unidentified Control Measures from Regression Line Included .................................. 4A-9

Figure 5-1. Size Distribution of Identified 319 Existing Coal-Fired EGU Units 

Nationwide without SCR NOx Controls (or with SCRs Operated Less Than the 

Maximum Possible Amount of Time) ........................................................................... 5-13

Figure 5-2. Size Distribution of 37 Existing Coal-Fired EGU Units without SCR NOx 

Controls (or with SCRs Operated Less Than the Maximum Possible Amount of 

Time) in Areas Anticipated to Need Additional NOx Controls With the Alternative 

65 ppb Ozone Standard Level ........................................................................................ 5-13

Figure 6-1. Illustration of BenMAP-CE Approach ............................................................. 6-11

Figure 6-2. Data Inputs and Outputs for the BenMAP-CE Program .................................. 6-13

Figure 6-3. Procedure for Generating Benefits Estimates for the 2025 and Post-2025 

Scenarios ........................................................................................................................ 6-14

Figure 6-4. Quantitative Uncertainty Analysis for Short-Term Ozone-Related Mortality 

Benefits .......................................................................................................................... 6-80

Figure 6-5. Quantitative Uncertainty Analysis Long-Term PM2.5-Related Mortality CoBenefits .......................................................................................................................... 6-82
xxiv 

Figure 6B-1. Premature Ozone-related Deaths Avoided for the Revised and Alternative 

Standards (2025 scenario) According to the Baseline Ozone Concentrations ............6B-18

Figure 6B-2. Cumulative Probability Plot of Premature Ozone-related Deaths Avoided for 

the Revised and Alternative Standards (2025 scenario) According to the Baseline 

Ozone Concentrations ..................................................................................................6B-19

Figure 6B-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) ......6B-23

Figure 6B-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) .....................................................................................................................6B-24
ES-1 

EXECUTIVE SUMMARY

Overview 

In setting primary and secondary national ambient air quality standards (NAAQS), the 

EPA’s responsibility under the law is to establish standards that protect public health and 

welfare. 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” and public welfare from 

“any known or anticipated adverse effects.” As interpreted by the Agency and the courts, the Act 

requires the EPA to base the decision for the primary standard 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 Administrator concluded that the current primary standard for ozone does not 

provide requisite protection to public health with an adequate margin of safety, and that it should 

be revised to provide increased public health protection. Specifically, the EPA is retaining the 

indicator (ozone), averaging time (8-hour) and form (annual fourth-highest daily maximum, 

averaged over 3 years) of the existing primary standard and is revising the level of that standard 

to 70 ppb. The EPA has also concluded that the current secondary standard for ozone, set at a 

level of 75 ppb, is not requisite to protect public welfare from known or anticipated adverse 

effects, and is revising the standard to provide increased protection against vegetation-related 

effects on public welfare. Specifically, the EPA is retaining the indicator (ozone), averaging time 

(8-hour) and form (annual fourth-highest daily maximum, averaged over 3 years) of the existing 

secondary standard and is revising the level of that standard to 70 ppb.1

 

 

1

 The EPA has concluded that this revision will effectively curtail cumulative seasonal ozone exposures above 17 

ppm-hrs in terms of a three-year average seasonal W126 index value, based on the three consecutive month period 

within the growing season with the maximum index value, with daily exposures cumulated for the 12-hour period 

from 8:00 am to 8:00 pm. 
ES-2 

The EPA performed an illustrative analysis of the potential costs, human health benefits, 

and welfare benefits of nationally attaining a revised primary ozone standard of 70 ppb and a 

primary alternative ozone standard level of 65 ppb. Because there are not additional costs and 

benefits of attaining the secondary standard, the EPA did not need to estimate any incremental 

costs and benefits associated with attaining a revised secondary standard. Per Executive Orders 

12866 and 13563 and the guidelines of OMB Circular A-4, this Regulatory Impact Analysis 

(RIA) presents the analyses of the revised standard level of 70 ppb and an alternative standard 

level of 65 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 NAAQS (75 

ppb). The 2025 baseline reflects, among other existing regulations, the 2017 and Later Model 

Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy 

Standards, Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and 

Heavy-Duty Engines and Vehicles, the Tier 3 Motor Vehicle Emission and Fuel Standards, the 

Clean Power Plan, the Mercury and Air Toxics Standards,2

 and the Cross-State Air Pollution 

Rule, all of which will help many areas move toward attainment of the existing ozone standard 

(see Appendix 2, Section 2A.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, populations, and premature mortality baseline rates 

for 2025. We recognize that there are 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. Because of data and resource constraints, we were not able to 

project emissions and air quality beyond 2025 for California; however, we adjusted baseline air 

 

2

 On June 29, 2015, the United States Supreme Court reversed the D.C. Circuit opinion affirming the Mercury and 

Air Toxics Standards (MATS). The EPA is reviewing the decision and will determine any appropriate next steps 

once the review is complete, however, MATS is still currently in effect. The first compliance date was April 2015, 

and many facilities have installed controls for compliance with MATS. MATS is included in the baseline for this 

analysis, and the EPA does not believe including MATS substantially alters the results of this analysis. 
ES-3 

quality to reflect mobile source emissions reductions for California that would occur between 

2025 and 2030.3 These emissions reductions were the result of mobile source regulations 

expected to be fully implemented by 2030. 

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. Further, Serious nonattainment areas will likely have to 

attain in late 2026 or early 2027. As such, 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. 

While there is uncertainty about the precise timing of emissions reductions and related 

costs for California, we assume costs associated with the installation of controls occur through 

the end of 2037 and beginning of 2038. In addition, we estimate benefits for California using 

projected population demographics and baseline mortality rates 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 estimated using population 

demographics and baseline mortality rates for 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 

 

3

 At the time of this analysis, there were no future year emissions for California beyond 2030, and projecting 

emissions beyond 2030 could introduce additional uncertainty. 
ES-4 

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 estimates of current and future 

emissions of relevant precursors (i.e., NOx and VOC) that contribute to the air quality problem 

and estimates of current and future ozone concentrations (Chapter 2 – Emissions, Air Quality 

Modeling and Analytic Methodologies); development of illustrative control strategies to attain 

the revised standard of 70 ppb and an alternative primary standard level of 65 ppb (Chapter 3 – 

Control Strategies and Emissions Reductions); estimates of the incremental costs of attaining the 

revised and alternative standard levels (Chapter 4 – Engineering Cost Analysis and Economic 

Impacts); 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 5 – Qualitative Discussion of Employment Impacts of Air Quality); estimates of the 

incremental benefits of attaining the revised and alternative standard levels (Chapter 6 – Human 

Health Benefits Analysis Approach and Results); a qualitative discussion of the welfare benefits 

of attaining the revised standards (Chapter 7 – Impacts on Public Welfare of Attainment 

Strategies to Meeting Primary and Secondary Ozone NAAQS); a comparison and discussion of 

the benefits and costs (Chapter 8 – Comparison of Costs and Benefits); and an analysis of the 

impacts in the context of the relevant statutory and executive order requirements (Chapter 9 – 

Statutory and Executive Order Impact Analyses). 

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 potential costs and benefits of the illustrative attainment strategies to meet the 

revised and alternative standard levels. The flowchart below (Figure ES-1) outlines the analytical 

steps taken to illustrate attainment with the revised and alternative standard levels, and the 

following discussion describes each of the major steps in the process. 
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 Appendix 2, Section 2A.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 proposed Clean Power Plan (U.S. 

EPA, 2014b).4

 

4

 The impact of these forecast changes in NOx emissions between the proposed and final CPP on ozone 

concentrations in specific locations is uncertain. There is no clear spatial pattern of where emissions are forecast to 

be higher or lower in the final CPP relative to the proposed CPP. Furthermore, states have flexibility in the form of 
ES-6 

We performed a national scale air quality modeling analysis to estimate ozone 

concentrations for the future base case year of 2025. In addition, we modeled fifteen 2025 

emissions sensitivity simulations.5

 The emissions sensitivity simulations were used to develop 

ozone response factors (ppb/ton) from the modeled response of ozone to changes in NOx and 

VOC emissions from various sources and locations. These ozone response factors were then used 

to determine the amount of emissions reductions needed to reach the 2025 baseline and to 

evaluate the revised and alternative standard levels of 70 and 65 ppb incremental to the 

baseline. We used the estimated emissions reductions needed to reach the revised and alternative 

standard levels to analyze the costs and benefits. 

ES.1.2 Control Strategies and Emissions Reductions 

The EPA used the Control Strategy Tool (CoST) to estimate engineering control costs. 

We estimated costs for non-electric generating unit point (non-EGU point), nonpoint, and mobile 

nonroad sources. Some electric generating units (EGUs) run their control equipment part of the 

year. To estimate the costs for EGUs, we assumed they ran their control equipment all year, and 

we estimated the costs of additional inputs needed. CoST calculates engineering costs using one 

of two different methods: (1) an equation that incorporates key operating unit information, such 

as unit design capacity or stack flow rate, or (2) an average annualized cost-per-ton factor 

multiplied by the total tons of reduction of a pollutant. The engineering cost analysis uses the 

equivalent uniform annual costs (EUAC) method, in which annualized costs are calculated based 

on the equipment life for the control measure and the interest rate incorporated into a capital 

recovery factor. Annualized costs represent an equal stream of yearly costs over the period the 

control technology is expected to operate. 

The EPA analyzed illustrative control strategies that areas across the U.S. might employ 

to attain the revised primary ozone standard level of 70 ppb and an alternative standard level of 

65 ppb. The EPA analyzed the impact that additional emissions control technologies and 

 

their plans that implement the CPP and therefore the specific impact of the CPP on NOx emissions in any state is 

uncertain. 

5

 The approach of using emissions sensitivity simulations to determine the response of ozone at monitor locations to 

emissions changes in specific regions is similar to the approach used in the November 2014 proposal RIA. 

However, in the final RIA we conducted sensitivity simulations using ten regions compared to five much larger 

regions in the proposal RIA. 
ES-7 

measures, across numerous sectors, would have on predicted ambient ozone concentrations 

incremental to the baseline. These control measures, also referred to as identified controls, are 

based on information available at the time of this analysis and include primarily end-of-pipe 

control technologies. In addition, to attain the revised and alternative primary standard levels 

analyzed, some areas needed additional emissions reductions beyond the identified controls, and 

we refer to these as unidentified controls or measures (see Chapter 3, Sections 3.1 and 3.2 for 

additional information).6 

Using the ozone response factors mentioned above, we estimated the emissions 

reductions over and above the baseline that were needed to meet the revised standard of 70 ppb 

and an alternative standard level of 65 ppb. Costs of controls incremental to baseline emissions 

reductions are attributed to the costs of meeting the revised and alternative standard levels. These 

emissions reductions can come from specific identified controls, as well as unidentified controls 

in some areas. The baseline shows that by 2025, ozone concentrations would be significantly 

better than today under current requirements, and depending on the standard level analyzed, 

some areas in the Eastern, Central, and Western U.S. would need to develop and adopt additional 

controls to attain the revised and alternative standard levels (see Chapter 3, Section 3.1.3 and 

Figure 3-5 for additional details on the areas that would need to develop and adopt controls). 

ES.1.2.1 Emissions Reductions from Identified Controls in 2025 

Figure ES-2 shows the counties projected to exceed the revised and alternative standard 

levels analyzed for 2025 for areas other than California. For the revised standard of 70 ppb, 

emissions reductions were required for monitors in the Colorado, Great Lakes, North East, Ohio 

River Valley and East Texas regions (see Chapter 2, Figure 2-2 for a map of the regions). For the 

65 ppb alternative standard level, in addition to the regions listed above, NOx emissions 

reductions were required in the Arizona-New Mexico, Nevada, and Oklahoma-ArkansasLouisiana regions. VOC emissions reductions were required in Denver, Houston, Louisville, 

 

6

 In the proposal RIA we discuss emissions reductions resulting from the application of known controls, as well as 

emissions reductions beyond known controls, using the terminology of “known controls” and “unknown controls.” 

In the final RIA, we have used slightly different terminology, consistent with past NAAQS RIAs. Here we refer to 

emissions reductions and controls as either “identified” controls or measures or “unidentified” controls or measures 

reflecting that unidentified controls or measures can include existing controls or measures for which the EPA does 

not have sufficient data to accurately estimate their costs. 
ES-8 

Chicago and New York City. Tables ES-1 and ES-2 show the emissions reductions from 

identified controls for the revised and alternative standard levels analyzed. We aggregate results 

by region – East and West, except California – to present cost and benefits estimates. See 

Chapter 4, Figure 4.3 for a representation of the East and West regions. 

 

Figure ES-2. Projected Ozone Design Values in the 2025 Baseline Scenario 
ES-9 

Table ES-1. Summary of Emissions Reductions by Sector for the Identified Control 

Strategy for the Revised Standard Level of 70 ppb for 2025, except California 

(1,000 tons/year)a 

Geographic Area Emissions Sector NOx VOC

East 

EGU 45 - 

Non-EGU Point 85 1 

Nonpoint 100 19 

Nonroad 3 - 

Onroad - - 

Total 230 20 

West 

EGU - - 

Non-EGU Point 6 - 

Nonpoint 1 - 

Nonroad - - 

Onroad - - 

Total 7 - 

a

 Emissions reduction estimates are rounded to two significant figures. 

Table ES-2. Summary of Emissions Reductions by Sector for the Identified Control 

Strategy for Alternative Standard Level of 65 ppb for 2025, except California 

(1,000 tons/year)a 

Geographic Area Emissions Sector NOx VOC 

East 

EGU 110 - 

Non-EGU Point 220 5 

Nonpoint 160 100 

Nonroad 8 - 

Total 500 100 

West 

EGU 0 - 

Non-EGU Point 33 - 

Nonpoint 22 5 

Nonroad 1 - 

Total 56 5 

a

 Emissions reduction estimates are rounded to two significant figures. 

ES.1.2.2 Emissions Reductions beyond Identified Controls in 2025 

There were several areas where identified controls did not achieve enough emissions 

reductions to attain the revised standard level of 70 ppb or alternative standard level of 65 ppb. 

The EPA then estimated the additional emissions reductions beyond identified controls needed to 

reach attainment (i.e., unidentified controls). The EPA’s application of unidentified control 

measures does not mean the Agency has concluded that all unidentified control measures are 

currently not commercially available or do not exist. Unidentified control technologies or 

measures can include existing controls or measures for which the EPA does not have sufficient 

data to accurately estimate engineering costs. Likewise, the control measures in the CoST 
ES-10 

database do not include abatement possibilities from energy efficiency measures, fuel switching, 

input or process changes, or other abatement strategies that are non-traditional in the sense that 

they are not the application of an end-of-pipe control. Table ES-3 shows the emissions 

reductions needed from unidentified controls in 2025 for the U.S., except California, for the 

revised and alternative standard levels analyzed. 

Table ES-3. Summary of Emissions Reductions from the Unidentified Control Strategies 

for the Revised and Alternative Standard Levels for 2025, except California 

(1,000 tons/year)a 

Revised and 

Alternative Standard 

Levels 

Region NOx VOC 

70 ppb East 

West 

47 

- 

- 

- 

65 ppb East 

West 

820 

40 

- 

- a

 Estimates are rounded to two significant figures. 

ES.1.2.3 Emissions Reductions beyond Identified Controls for Post-2025 

Figure ES-3 shows the counties projected to exceed the revised and alternative standard 

levels analyzed for the post-2025 analysis for California. For the California post-2025 revised 

and alternative standard level analyses, all identified controls were applied in the baseline, so 

incremental emissions reductions to demonstrate attainment of the revised and alternative 

standards were from unidentified controls. Table ES-4 shows the emissions reductions needed 

from unidentified controls for post-2025 for California for the revised and alternative standard 

levels analyzed. 
ES-11 

Figure ES-3. Projected Ozone Design Values in the Post-2025 Baseline 

Table ES-4. Summary of Emissions Reductions from the Unidentified Control Strategies 

for the Revised and Alternative Standard Levels for Post-2025 - California 

(1,000 tons/year)a

Revised and 

Alternative Standard 

Levels 

Region NOx VOC 

70 ppb CA 51 - 

65 ppb CA 100 - 

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 
ES-12 

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). 

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 later assigned 

dollar values. For this RIA, the health impacts analysis is limited to those health effects that are 

directly linked to changes in ambient levels of ozone and PM2.5 due to reductions in ozone 

precursor emissions. Emissions reductions of NOx or VOC to attain the ozone standards would 

simultaneously reduce ambient PM2.5 concentrations. 

Benefits estimates for ozone were generated using the damage-function approach outlined 

above wherein changes in ambient ozone concentrations were translated into reductions in the 

incidence of specific health endpoints (e.g., premature mortality or hospital admissions) using 

the environmental Benefits Mapping and Analysis Program – Community Edition (BenMAPCE). 

In contrast to ozone, we used a benefit-per-ton approach to estimate PM2.5 co-benefits. 

With this approach, we use the results of previous air quality modeling to derive benefit-per-ton 

estimates for NOx. These benefit-per-ton estimates provide the monetized human health cobenefits (the sum of premature mortality and premature morbidity) of reducing one ton of a 

PM2.5 precursor (such as NOX) from a specified source. We then combine these benefit-per-ton 

estimates with reductions in NOx emissions associated with meeting the revised and alternative 

standard levels. We acknowledge increased uncertainty associated with the benefit-per-ton 

approach, relative to using scenario-specific air quality modeling to estimate the PM2.5 cobenefits. 

In addition to ozone and PM2.5 benefits, implementing emissions controls to attain the 

revised and alternative ozone standard levels would reduce exposure to other ambient pollutants 
ES-13 

(e.g., NO2). However, we were not able to quantify the co-benefits of reduced exposure to these 

pollutants, nor were we able to estimate some anticipated health benefits associated with 

exposure to ozone and PM2.5 due to data and methodology limitations. 

ES.1.4 Welfare Benefits of Meeting the Primary and Secondary Standards 

Section 302(h) of the Clean Air Act states that effects on welfare include, but are not 

limited to, “effects on soils, water, crops, vegetation, man-made 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.” 

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 the RIA for the proposal, we quantified a small portion of the welfare impacts associated 

with reductions in ozone concentrations to meet the alternative ozone standard levels analyzed. 

Using a model of commercial agriculture and forest markets, we analyzed the effects on 

consumers and producers of forest and agricultural products of changes in the W126 index 

resulting from meeting alternative standards levels. We also assessed the effects of those 

changes in commercial agricultural and forest yields on carbon sequestration and storage. The 

analysis provided limited quantitative information on the welfare benefits of meeting alternative 

secondary standard levels, focusing only on one subset of ecosystem services. Commercial and 

non-commercial forests provide a number of additional services, including medicinal uses, noncommercial food and fiber production, arts and crafts uses, habitat, recreational uses, and cultural 

uses for Native American tribes. In this final RIA, we did not update this analysis and refer to 

the analysis conducted in the proposal RIA (U.S. EPA, 2014c). We did not update the analysis 

from the proposal RIA because the welfare benefits estimates (i) in the proposal analysis were 

small, and we anticipated that the estimates in the final analysis would be even smaller, and (ii) 

are not added to the human health benefits estimates. 
ES-14 

ES.2 Results of Benefit-Cost Analysis 

Below in Table ES-5, we present the primary costs and benefits estimates for 2025 for all 

areas except California. We anticipate that benefits and costs will likely begin occurring earlier 

than 2025, as states begin implementing control measures to show progress towards attainment. 

In these tables, ranges within the total benefits rows reflect multiple studies upon which the 

estimates associated with premature mortality were derived. PM2.5 co-benefits account for 

approximately 60 to 70 percent of the estimated benefits, depending on the standard analyzed 

and on the choice of ozone and PM mortality functions used. Assuming a 7 percent discount 

rate, for a standard of 70 ppb the total health benefits are comprised of between 29 and 34 

percent ozone benefits and between 66 and 71 percent PM2.5 co-benefits. Assuming a 7 percent 

discount rate, for a standard of 65 ppb the total health benefits are comprised of between 29 and 

35 percent ozone benefits and between 62 and 70 percent PM2.5 co-benefits. In addition for 

2025, Table ES-6 presents the numbers of premature deaths avoided for the revised and 

alternative standard levels analyzed, as well as the other health effects avoided. Table ES-7 

provides information on the costs by geographic region for the U.S., except California in 2025, 

and Table ES-8 provides a regional breakdown of benefits for 2025. See the tables in Chapter 6 

for additional characterizations of the monetized benefits. 

In the RIA we provide estimates of the costs of emissions reductions to attain the revised 

and alternative standard levels 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 both control strategies applied within the region and reductions in 

transport of ozone associated with emissions reductions in other regions. 

The net benefits of emissions reductions strategies in a specific region reflect 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 was conducted at the national level, we 

do not estimate separately the nationwide benefits associated with the emissions reductions 

occurring in any specific region.7

 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 

 

7

 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 analysis for California. 
ES-15 

accruing to that region is not an estimate of net benefits of the emissions reductions in that 

region. 

Table ES-5. Total Annual Costs and Benefitsa,b for U.S., except California in 2025 

(billions of 2011$, 7% Discount Rate)c

 

 Revised and Alternative Standard Levels 

 70 ppb 65 ppb 

Total Costsd $1.4 $16 

Total Health Benefits $2.9 to $5.9e, f $15 to $30e, f

Net Benefits $1.5 to $4.5 -$1.0 to $14 

a

 All values are rounded to two significant figures. 

b

 Benefits are nationwide benefits of attainment everywhere except California. c

 The tables in Chapter 6 provide additional characterizations of the monetized benefits, including benefits estimated 

at a 3 percent discount rate. 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. 

d

 The engineering costs in this table are annualized at a 7 percent discount rate to the extent possible. See Chapter 4 

for more discussions. 

e

 Assuming a 7 percent discount rate, for a standard of 70 ppb the total health benefits are comprised of between 29 

and 34 percent ozone benefits and between 66 and 71 percent PM2.5 co-benefits. Assuming a 7 percent discount 

rate, for a standard of 65 ppb the total health benefits are comprised of between 29 and 35 percent ozone benefits 

and between 62 and 70 percent PM2.5 co-benefits. f Excludes additional health and welfare benefits that could not be quantified (see Chapter 6, Section 6.6.3.8).

The guidelines of OMB Circular A-4 require providing comparisons of social costs and 

social benefits at discount rates of 3 and 7 percent. 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 nonfatal 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. 
ES-16 

Table ES-6. Summary of Total Number of Annual Ozone and PM-Related Premature 

Mortalities and Premature Morbidity: 2025 National Benefits a

 

Revised and Alternative Standard Levels 

 70 ppb 65 ppb 

Ozone-related premature deaths avoided (all ages) 96 to 160 490 to 820 

PM2.5-related premature deaths avoided (age 30+) 220 to 500 1,100 to 2,500 

Other health effects avoided 

Non-fatal heart attacks (age 18-99) (5 studies) PM 28 to 260 140 to 1,300 

Respiratory hospital admissions (age 0-99)O3, PM 250 1,200 

Cardiovascular hospital admissions (age 18-99) PM 80 400 

Asthma emergency department visits (age 0-99) O3, PM 630 3,300 

Acute bronchitis (age 8-12) PM 340 1,700 

Asthma exacerbation (age 6-18) O3, PM 230,000 1,100,000 

Lost work days (age 18-65) PM 28,000 140,000 

Minor restricted activity days (age 18-65) O3, PM 620,000 3,100,000 

Upper & lower respiratory symptoms (children 7-14) 

PM 11,000 53,000 

School loss days (age 5-17) O3 160,000 790,000 

a

 Nationwide benefits of attainment everywhere except California. All values are rounded to two significant figures. 

Additional information on confidence intervals are available in the tables in Chapter 6. 

Table ES-7. Summary of Total Control Costs (Identified + Unidentified Control 

Strategies) by Revised and Alternative Standard Levels for 2025 - U.S., except 

California (billions of 2011$, 7% Discount Rate)a

Revised and Alternative 

Standards Levels 

Geographic Area Total Control Costs 

(Identified and 

Unidentified) 

70 ppb 

East 1.4 

West <0.05 

Total $1.4 

65 ppb 

East 15 

West <0.75 

Total $16 

a

 All values are rounded to two significant figures. Costs are annualized at a 7 percent discount rate to the extent 

possible. Costs associated with unidentified controls are based on an average cost-per-ton methodology (see 

Chapter 4, Section 4.3 for more discussion on the average-cost methodology). 
ES-17 

Table ES-8. Regional Breakdown of Monetized Ozone-Specific Benefits Results for 2025 

(Nationwide Benefits of Attaining the Revised and Alternative Standard Levels 

Everywhere in the U.S., except California) a

Revised and Alternative Standard Levels 

Region 70 ppb 65 ppb 

East b 98% 96% 

California 0% 0% 

Rest of West 2% 4% 

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 states to the north and east. 

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-9. 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-10 presents the 

numbers of premature deaths avoided for the revised and alternative standard levels analyzed, as 

well as the other health effects avoided. Table ES-11 provides information on the costs for 

California for post-2025, and Table ES-12 provides a regional breakdown of benefits for post2025. 

The EPA presents separate costs and benefits results for California because assuming 

attainment in an earlier year than would be required under the Clean Air Act would likely lead to 

an overstatement of costs and benefits 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 timeframe are likely to be relatively more uncertain than the national 

attainment estimates for 2025. 
ES-18 

Table ES-9. Total Annual Costs and Benefitsa of the Identified + Unidentified Control 

Strategies Applied in California, Post-2025 (billions of 2011$, 7% Discount 

Rate)b 

 Revised and Alternative Standard Levels 

 70 ppb 65 ppb 

Total Costsc $0.80 $1.5

Total Health Benefits $1.2 to $2.1d

 $2.3 to $4.2d

Net Benefits $0.4 to $1.3 $0.8 to $2.7 

a

 Benefits are nationwide benefits of attainment in California. 

b

 The guidelines of OMB Circular A-4 require providing comparisons of social costs and social benefits at discount 

rates of 3 and 7 percent. The tables in Chapter 6 provide additional characterizations of the monetized benefits, 

including benefits estimated at a 3 percent discount rate. 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 engineering costs in this table are annualized at a 7 percent discount rate to the extent possible. See Chapter 4 

for more discussions. 

d

 Excludes additional health and welfare benefits that could not be quantified (see Chapter 6, Section 6.6.3.8). 

Table ES-10. Summary of Total Number of Annual Ozone and PM-Related Premature 

Mortalities and Premature Morbidity: Post-2025a 

Revised and Alternative Standard Levels 

 70 ppb 65 ppb 

Ozone-related premature deaths avoided (all ages) 72 to 120 150 to 240 

PM2.5-related premature deaths avoided (age 30+) 43 to 98 84 to 190 

Other health effects avoided 

Non-fatal heart attacks (age 18-99) (5 studies) PM 6 to 51 11 to 100 

Respiratory hospital admissions (age 0-99)O3, PM 150 300 

Cardiovascular hospital admissions (age 18-99) PM 16 31 

Asthma emergency department visits (age 0-99) O3, PM 380 760 

Acute bronchitis (age 8-12) PM 64 130 

Asthma exacerbation (age 6-18) O3, PM 160,000 330,000 

Lost work days (age 18-65) PM 5,300 10,000 

Minor restricted activity days (age 18-65) O3, PM 360,000 720,000 

Upper & lower respiratory symptoms (children 7-14) 

PM 2,000 3,900 

School loss days (age 5-17) O3 120,000 240,000 

a

 Nationwide benefits of attainment in California. All values are rounded to two significant figures. Additional 

information on confidence intervals are available in the tables in Chapter 6.
ES-19 

Table ES-11. Summary of Total Control Costs (Identified + Unidentified Control 

Strategies) by Revised and Alternative Standards for Post-2025 - California 

(billions of 2011$, 7% Discount Rate)a

Revised and Alternative 

Standard Level Geographic Area 

Total Control Costs 

(Identified and 

Unidentified) 

70 ppb California $0.80 

65 ppb California $1.5 

a

 All values are rounded to two significant figures. Costs are annualized at a 7 percent discount rate to the extent 

possible. Costs associated with unidentified controls are based on an average cost-per-ton methodology. 

Table ES-12. Regional Breakdown of Monetized Ozone-Specific Benefits Results for Post2025 (Nationwide Benefits of Attaining Revised and Alternative Standards just 

in California)a

Revised and Alternative Standard Levels 

Region 70 ppb 65 ppb 

East b 3% 2% 

California 90% 91% 

Rest of West 7% 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 states to the north and east. 

ES.3 Improvements between the Proposal and Final RIAs 

In the regulatory impact analyses for both the proposed and final ozone NAAQS, there 

were two geographic areas outside of California where the majority of emissions reductions were 

needed to meet the revised standard level of 70 ppb – Texas and the Northeast. In analyzing 70 

ppb in the final RIA, there were approximately 50 percent fewer emissions reductions needed in 

these two geographic areas. For an alternative standard of 65 ppb in the final RIA, emissions 

reductions needed nationwide were approximately 20 percent lower than at proposal. The 

primary reason for the difference in emissions reductions estimated for attainment is that in the 

final RIA we conducted more geographically-refined air quality sensitivity modeling to develop 

improved ozone response factors (see Chapter 2, Section 2.2.2 for a more detailed discussion of 

the air quality modeling) and focused the emissions reduction strategies on geographic areas 

closer to the monitors with the highest design values (see Chapter 3, Section 3.1.1 for a more 

detailed discussion of the emissions reduction strategies). The improvements in air quality 

modeling and emissions reduction strategies account for about 80 percent of the difference in 

needed emissions reductions between the proposal and final RIAs. 
ES-20 

In Texas and the Northeast, the updated response factors and more focused emissions 

reduction strategies resulted in larger changes in ozone concentrations in response to more 

geographically focused emissions reductions. In east Texas, the ppb/ton ozone response factors 

used in the final RIA were 2 to 3 times more responsive than the factors used in the proposal 

RIA at controlling monitors in Houston and Dallas. In the Northeast, the ppb/ton ozone response 

factors used in the final RIA were 2.5 times more responsive than the factors used in the proposal 

RIA at the controlling monitor on Long Island, NY. 

A secondary reason for the difference is that between the proposal and final RIAs we 

updated emissions inventories, models and model inputs for the base year of 2011. See 

Appendix 2, Section 2A.1.3 for additional discussion of the updated emissions inventories, 

models and model inputs. When projected to 2025, these changes in inventories, models and 

inputs had compounding effects for year 2025, and in some areas resulted in lower projected 

base case design values for 2025. The updated emissions inventories, models, and model inputs 

account for about 20 percent of the difference in needed emissions reductions between the 

proposal and final RIAs. 

These differences in the estimates of emissions reductions needed to attain the revised 

and alternative standard levels affect the estimates for the costs and benefits in this RIA. For a 

revised standard of 70 ppb, the costs were 60 percent lower than at proposal and the benefits 

were 55 percent lower than at proposal. The percent decrease in costs is slightly more than the 

percent decrease in emissions reductions because a larger number of lower cost identified 

controls were available to bring areas into attainment with 70 ppb.8

 The percent decrease in 

benefits is similar to the percent decrease in emissions reductions. For an alternative standard 

level of 65 ppb, the costs were less than three percent more than those estimated at proposal and 

the benefits were 22 percent lower than at proposal. The percent change in costs was less than 

the percent decrease in emissions reductions because in the final analysis we applied identified 

controls in smaller geographic areas, resulting in fewer identified controls available within those 

 

8

 In the final RIA, outside of California all areas were projected to meet the current standard of 75 ppb. As such, no 

identified controls were used to bring areas into attainment with 75 ppb. In the proposal RIA, some of these lower 

cost controls were used to bring areas into attainment with 75 ppb, making them unavailable for application in the 

analysis of 70 ppb. 
ES-21 

areas and an increase in higher cost unidentified controls being applied to bring areas into 

attainment with 65 ppb. The percent decrease in benefits is similar to the percent decrease in 

emissions reductions. 

ES.4 Uncertainty 

Despite uncertainties inherent in any complex, quantitative analysis, the underlying tools 

and models (CoST and BenMAP) have been peer-reviewed and the analytical methods are 

consistent with standard economic practice. For a detailed discussion on uncertainty associated 

with developing illustrative control strategies to attain the alternative standard levels, see Chapter 

3, Section 3.4. For a description of the key assumptions and uncertainties related to ozone 

benefits, see Chapter 6, Section 6.5, and for an additional qualitative discussion of sources of 

uncertainty associated with both ozone-related benefits and PM2.5-related co-benefits, see 

Appendix 6A. For a discussion of the limitations and uncertainties in the engineering cost 

analyses, see Chapter 4, Section 4.7. For a general discussion about key factors that could 

impact how air quality changes over time, see Chapter 8, Section 8.3. 

ES.5 References 

De Steiguer, J., Pye, J., Love, C. 1990. Air Pollution Damage to U.S. Forests. Journal of Forestry, 88(8), 17-22. 

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 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>. 
ES-22 

U.S. Environmental Protection Agency (U.S. EPA). 2014c. Regulatory Impact Analysis of the Proposed Revisions 

to the National Ambient Air Quality Standards for Ground-Level Ozone. Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. November. Available at 

<http://www.epa.gov/ttnecas1/regdata/RIAs/20141125ria.pdf >.
1-1 

CHAPTER 1: INTRODUCTION AND BACKGROUND 

Introduction 

The Environmental Protection Agency (EPA) initiated the current ozone National 

Ambient Air Quality Standards (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. In addition, as required by the Clean 

Air Act (CAA), the documents were peer-reviewed by the Clean Air Scientific Advisory 

Committee (CASAC), the Administrator’s independent advisory committee established by the 

CAA. The final documents for this review reflect the EPA staff’s consideration of the comments 

and recommendations made by the CASAC and the public on draft versions of these documents. 

The EPA has concluded that the current primary standard for ozone, set at a level of 75 

ppb, is not requisite to protect public health with an adequate margin of safety, and is revising 

the standard to provide increased public health protection. Specifically, the EPA is retaining the 

indicator (ozone), averaging time (8-hour) and form (annual fourth-highest daily maximum, 

averaged over 3 years) of the existing primary standard and is revising the level of that standard 

to 70 ppb. The EPA is making 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 non-accidental) 

mortality. For long-term ozone exposures, these health effects include a variety of respiratory 

morbidity effects and respiratory mortality. 

The EPA has also concluded that the current secondary standard for ozone, set at a level 

of 75 ppb, is not requisite to protect public welfare from known or anticipated adverse effects, 

and is revising the standard to provide increased protection against vegetation-related effects on 

public welfare. Specifically, the EPA is retaining the indicator (ozone), averaging time (8-hour) 
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and form (annual fourth-highest daily maximum, averaged over 3 years) of the existing 

secondary standard and is revising the level of that standard to 70 ppb. The EPA has concluded 

that this revision will effectively curtail cumulative seasonal ozone exposures above 17 ppm-hrs, 

in terms of a three-year average seasonal W126 index value, based on the three consecutive 

month period within the growing season with the maximum index value, with daily exposures 

cumulated for the 12-hour period from 8:00 am to 8:00 pm. Thus, the EPA has concluded that 

this revision will provide the requisite protection against known or anticipated adverse effects to 

the public welfare. 

This Regulatory Impact Analysis (RIA) analyzes the human health benefits and costs and 

welfare cobenefits of the revised standard of 70 ppb as well as a more stringent alternative level 

of 65 ppb. In setting primary ambient air quality standards, the EPA’s responsibility under the 

law is to establish standards that protect public health, regardless of the costs of implementing 

those standards. As interpreted by the Agency and the courts, the CAA requires the EPA to 

create standards based on health considerations only. 

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

standards, however, does not mean that costs or other economic 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 the implementation process, as they decide what 

timelines, strategies, and policies are appropriate for their circumstances. 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 part of setting the standards. 

This chapter summarizes provides a brief background on NAAQS, the need for NAAQS, 

and an overview of this RIA, including a discussion of its design. The EPA prepared this RIA 

both to provide the public with information on the benefits and costs of meeting a revised ozone 

NAAQS and to meet the requirements of Executive Orders 12866 and 13563. 
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1.1 Background 

1.1.1 National Ambient Air Quality Standards 

Sections 108 and 109 of the CAA govern the establishment and revision of the NAAQS. 

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

reasonably be anticipated to endanger public health or welfare” and to issue air quality criteria 

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

knowledge useful in indicating the kind and extent of all identifiable effects on public health or 

welfare which may be expected from the presence of [a] pollutant in the ambient air.” 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 

public welfare from any known or anticipated adverse effects associated with the presence of 

[the] pollutant in the ambient air.” Welfare effects as defined in section 302(h) [42 U.S.C. 

7602(h)] include but are not limited to “effects on soils, water, crops, vegetation, manmade 

materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of 

property, and hazards to transportation, as well as effects on economic values and on personal 

comfort and well-being.” 

Section 109(d) of the CAA directs the Administrator to review existing criteria and 

standards at 5-year intervals. When warranted by such review, the Administrator is to retain or 

revise the NAAQS. After promulgation or revision of the NAAQS, the standards are 

implemented by the states. 

1.1.2 Role of Executive Orders in the Regulatory Impact Analysis 

While this RIA is separate from the NAAQS decision-making process, several statutes and 

executive orders still apply to any public documentation. The analyses required by these statutes 
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and executive orders are presented in detail in Chapter 9, and below we briefly discuss 

requirements of Orders 12866 and 13563 and the guidelines of the Office of Management and 

Budget (OMB) Circular A-4 (U.S. OMB, 2003). 

In accordance with Executive Orders 12866 and 13563 and the guidelines of OMB 

Circular A-4, the RIA analyzes the benefits and costs associated with emissions controls to attain 

the revised 8-hour ozone standard of 70 ppb in ambient air, incremental to a baseline of attaining 

the existing standard (8-hour ozone standard of 75 ppb).9 OMB Circular A-4 requires analysis of 

one potential alternative standard level more stringent than the revised standard and one less 

stringent than the revised standard. In this RIA, we analyze a more stringent alternative standard 

level of 65 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). Further, as discussed in the notice of final rulemaking, the available scientific 

evidence and quantitative risk and exposure information on the health effects of ozone exposure 

provide strong support for a revised standard of 70 ppb, but do not identify a bright line for 

identifying any specific standard level between 70 and 75 ppb for analysis in the RIA. As such, 

we did not analyze a standard between 70 and 75 ppb in this RIA. 

1.1.3 Illustrative Nature of the Analysis 

The control strategies presented in this RIA are an illustration of one possible set of control 

strategies states might choose to implement to meet the revised standards. States—not the 

EPA—will implement the revised NAAQS and will ultimately determine appropriate emissions 

control strategies and measures. State Implementation Plans (SIPs) will likely vary from the 

EPA’s estimates provided in this analysis due to differences in the data and assumptions that 

states use to develop these plans. Because states are ultimately responsible for implementing 

strategies to meet the revised standards, the control strategies in this RIA are considered 

hypothetical. The hypothetical strategies were constructed with the understanding that there are 

 

9

 On April 30, 2012 the EPA issued final designations for the 2008 ozone NAAQS. After final designations, areas 

have up to three years to submit attainment SIPs. Because of the timing of these SIP submittals, the EPA does not 

have the most current information on control measures and emissions reductions needed to meet the current standard 

of 75 ppb. To account for potential emissions reductions associated with meeting the current standard, we estimate 

these emissions reductions in defining the baseline. 
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inherent uncertainties in projecting emissions and control applications. Additional important 

uncertainties and limitations are documented in the relevant portions of the RIA. 

The EPA’s national program rules require technology application or emissions limits for a 

specific set of sources or source groups. In contrast, a NAAQS establishes a standard level and 

requires states to identify and secure emissions reductions to meet the standard level from any set 

of sources or source groups. To avoid double counting the impacts of NAAQS and other 

national program rules, the EPA includes federal regulations and enforcement actions in its 

baseline for this analysis (See Section 1.3.1 for additional discussion of the baseline). The 

benefits and costs of the revised standards will not be realized until specific control measures are 

mandated by SIPs or other federal regulations. 

1.2 The Need for National Ambient Air Quality Standards 

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

be issued is to address a market failure. The major types of market failure include: externality, 

market power, and inadequate or asymmetric information. Correcting market failures is one 

reason for regulation, but it is not the only reason. Other possible justifications include 

improving the function of government, removing distributional unfairness, or promoting privacy 

and personal freedom. 

Environmental problems are classic examples of externalities -- uncompensated benefits or 

costs imposed on another party as a results of one’s actions. For example, the smoke from a 

factory may adversely affect the health of local residents and soil the property in nearby 

neighborhoods. If bargaining was costless and all property rights were well defined, people 

would eliminate externalities through bargaining without the need for government regulation. 

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

to address an externality in which firms emit pollutants, resulting in health and environmental 

problems without compensation for those incurring the problems. Setting a standard with a 

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

and lessens the impact on those who suffer the health and environmental problems from higher 

levels of pollution. For additional discussion on the ozone air quality problem, see Chapter 2 of 
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the Policy Assessment for the Review of the Ozone National Ambient Air Quality Standards (US 

EPA, 2014). 

1.3 Overview and Design of the RIA 

The RIA evaluates the costs and benefits of hypothetical national control strategies to 

attain the revised ozone standard of 70 ppb and an alternative ozone standard level of 65 ppb. 

1.3.1 Establishing Attainment with the Current Ozone National Ambient Air Quality Standard 

The RIA is intended to evaluate the overall potential costs and benefits of reaching 

attainment with the revised and alternative ozone standard levels. To develop and evaluate 

control strategies for attaining a more stringent primary standard, it is important to estimate 

ozone levels in the future after attaining the current NAAQS of 75 ppb, and taking into account 

projections of future air quality reflecting on-the-books Federal regulations, substantial federal 

regulatory proposals, enforcement actions, state regulations, population and where possible, 

economic growth. Establishing this baseline for the analysis then allows us to estimate the 

incremental costs and benefits of attaining the revised and alternative standard levels. 

Attaining 75 ppb reflects emissions reductions (i) already achieved as a result of national 

regulations, (ii) 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 current ozone standard), and (iii) from additional controls that the EPA 

estimates need to be included to attain the current standard. Additional 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 for this analysis, a baseline 

that reflects attainment of 75 ppb. First, national ozone concentrations were projected to the 

analysis year (2025) based on forecasts of population and where possible, economic growth and 

the application of emissions controls resulting from national rules promulgated prior to this 

analysis, as well as state programs and enforcement actions. Second, we estimated additional 

emissions reductions needed to meet the current standard of 75 ppb and make adjustments for the 

proposed Clean Power Plan. 
1-7 

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 6.2, 2011 Emissions Modeling Platform (US EPA, 2015). If the national rules reflected 

in the baseline result in changes in ozone concentrations or actual emissions reductions that are 

lower or higher than those estimated, the costs and benefits estimated in this final RIA would be 

higher or lower, respectively. 

 Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility 

Generating Units (Proposed Rule) (U.S. EPA, 2014a) 

 Tier 3 Motor Vehicle Emission and Fuel Standards (U.S. EPA, 2014c) 

 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and 

Corporate Average Fuel Economy Standards (U.S. EPA, 2012) 

 Cross State Air Pollution Rule (CSAPR) (U.S. EPA, 2011) 

 Mercury and Air Toxics Standards (U.S. EPA, 2011a)10

 Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and 

Heavy-Duty Engines and Vehicles (U.S. EPA, 2011d)11

 C3 Oceangoing Vessels (U.S. EPA, 2010) 

 Reciprocating Internal Combustion Engines (RICE) NESHAPs (U.S. EPA, 2010a) 

 Regulation of Fuels and Fuel Additives: Modifications to Renewable Fuel Standard 

Program (RFS2) (U.S. EPA, 2010b) 

 Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel 

Economy Standards; Final Rule for Model-Year 2012-2016 (U.S. EPA, 2010c) 

 Hospital/Medical/Infectious Waste Incinerators: New Source Performance Standards and 

Emission Guidelines: Final Rule Amendments (U.S. EPA, 2009) 

 

10 On June 29, 2015, the United States Supreme Court reversed the D.C. Circuit opinion affirming the Mercury and 

Air Toxics Standards (MATS). The EPA is reviewing the decision and will determine any appropriate next steps 

once the review is complete, however, MATS is still currently in effect. The first compliance date was April 2015, 

and many facilities have installed controls for compliance with MATS. MATS is included in the baseline for this 

analysis, and the EPA does not believe including MATS substantially alters the results of this analysis. 11 This rule is Phase 1 of the Heavy Duty Greenhouse Gas Standards for New Vehicles and Engines (76 FR 57106, 

September 15, 2011) and is included in the 2025 base case. Phase 2 of the Heavy Duty Greenhouse Gas Standards 

for New Vehicles and Engines (80 FR 40138, July 13, 2015) is not included in the 2025 base case because the 

rulemaking was not finalized in time to include in this analysis. If the emissions reductions from Phase 1 were not 

included in the baseline in this analysis, the estimated costs and benefits of achieving the revised and alternative 

standards analyzed would be higher because more emissions reductions would be needed. 
1-8 

 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, 2005a) 

 NOx Emission Standard for New Commercial Aircraft Engines (U.S. EPA, 2005) 

To define the baseline in the ozone NAAQS final RIA, we adjusted the 2025 final ozone 

NAAQS base case air quality to reflect the proposed Clean Power Plan (CPP) using the Option 1 

State illustrative compliance approach from the CPP proposal RIA. We recognize that the 

difference in forecast NOx emissions from the electricity sector between the CPP proposal and 

final likely has some effect on baseline ozone concentrations, and therefore on estimated NOx

emissions reductions needed to meet the ozone standards analyzed in the NAAQS final RIA. 

The power sector modeling for the final CPP reflected updated inputs including lower 

costs for new renewable energy resources and changes in the composition of electric generating 

resources relative to the baseline used for the proposed CPP. These updated inputs resulted in 

changes in the baseline level and spatial distribution of NOx emissions in the final CPP. In 

addition, in the final CPP the CO2 emissions goals for states and compliance timing changed 

from the proposal, which further changed the level and spatial distribution of NOx emissions. 

The net effect of these changes is that total forecast annual NOx emissions in 2025 for the 

electricity sector were between 13,000 and 51,000 tons lower under the final CPP than under the 

proposed CPP. 

The impact of these forecast changes in NOx emissions on ozone concentrations in specific 

locations is uncertain. There is no clear spatial pattern of where emissions are forecast to be 

higher or lower in the final CPP relative to the proposed CPP. Furthermore, states have 

flexibility in the form of their plans that implement the CPP and therefore the specific impact of 

the CPP on NOx emissions in any state is uncertain. Finally, because no air quality modeling was 

done for the final CPP, we are not able to implement the same approach to reflect the impact of 
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the final CPP on ozone air quality in the NAAQS baseline that we used to account for the 

proposed CPP in the baseline. 

We recognize that not accounting for the final CPP in the baseline introduces additional 

uncertainty into the NAAQS final RIA. However, in the final CPP EPA recommends that states 

take a multipollutant planning approach that recognizes co-pollutant impacts of CO2 compliance 

decisions and takes into account local air quality impacts. Given the flexibility that states have 

in addressing both their CO2 and air quality requirements, EPA expects that states will design 

strategies to meet both the CPP and NAAQS in the most cost-effective manner12, and thus costs 

for the combined set of actions will likely differ from the combined costs provided in the 

separate RIAs. 

The baseline for this analysis does not assume emissions controls that might be 

implemented to meet the current PM2.5, NO2, or SO2 NAAQS. For the current PM2.5 and SO2

NAAQS, the Agency has not issued final designations and does not have information on what 

areas would need emissions controls; for the current NO2 NAAQS there are no nonattainment 

areas. 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.13

More importantly, all control strategies analyzed in NAAQS RIAs are hypothetical. 

 

12 “...the EPA believes that the Clean Power Plan provides an opportunity for states to consider strategies for 

meeting future CAA planning obligations as they develop their plans under this rulemaking. Multi-pollutant 

strategies that incorporate criteria pollutant reductions over the planning horizons specific to particular states, 

jointly with strategies for reducing CO2 emissions from affected EGUs needed to meet Clean Power Plan 

requirements over the time horizon of this rule, may accomplish greater environmental results with lower longterm costs.” Page 1333 of the Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric 

Utility Generating Units, currently available at the following link: 

http://www2.epa.gov/sites/production/files/2015-08/documents/cpp-final-rule.pdf. In the future, please refer to 

the official version in a forthcoming FR publication, which will appear on the Government Printing Office's 

FDSys website 

(http://gpo.gov/fdsys/search/home.action) and on Regulations.gov (http://www.regulations.gov) in Docket No. 

EPA–HQ–OAR–2013-0602. 13 There were no additional NOx controls applied in the 2012 PM2.5 NAAQS RIA, and therefore there would be little 

to no impact on the controls selected in 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 controls on a $/ug basis, states may choose to adopt different control options. These options could 
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1.3.2 Establishing the Baseline for Evaluation of Revised and Alternative Standards 

The RIA evaluates, to the extent possible, the costs and benefits of attaining the revised 

and alternative ozone standards incremental to attaining the current 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 

attain the revised standard by 2025. As such, we developed our projected baselines for 

emissions, air quality, and populations and present the primary costs and benefits estimates for 

2025. 

The selection of 2025 as the analysis year in the RIA does not predict or prejudge 

attainment dates that will ultimately be assigned to individual areas under the CAA. The CAA 

contains a variety of potential attainment dates and flexibility to move to later dates (up to 20 

years), provided that the date is as expeditious as practicable. 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 provides 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. 

The EPA recognizes that areas designated nonattainment for the revised ozone NAAQS 

and classified as Marginal or Moderate will likely incur some costs prior to the 2025 analysis 

year. The Agency, however, anticipates that on-the-books federal emissions control measures14

will be sufficient to bring the majority of these areas into attainment by 2025. Areas designated 

 include NOx controls, and it is difficult to determine the impact on costs and benefits for this RIA because it 

depends highly on the control measures that would be chosen and the costs of these measures. 14 These federal control measures are listed above in section 1.3.1. 
1-11 

nonattainment and classified as Marginal are required to develop emission inventories, emission 

statements, and produce a CAA section 110 infrastructure SIP. These areas are not required to 

develop any control measures aside from the federal emissions control measures reflected in the 

baseline. As a result, the Agency anticipates that costs in these Marginal areas will be minimal. 

In addition to the federal control measures and the requirements for Marginal nonattainment 

areas, states with nonattainment areas designated as Moderate are required by the CAA to 

develop state implementation plans (SIPs) demonstrating attainment by no later than the assigned 

attainment date. The CAA also requires these states to address Reasonably Available Control 

Technologies (RACT) for sources in the Moderate nonattainment area, which could lead to 

additional point source controls in an area beyond the federal emissions control measures. 

Additionally, the CAA requires some Moderate areas with larger populations to implement basic 

vehicle inspection and maintenance (I/M) in the area. Should these federal programs and CAA 

required programs prove inadequate for the area to attain the revised standard by the attainment 

date, the state would need to identify additional emissions control measures in its SIP to meet 

attainment requirements. 

In addition, in estimating the incremental costs and benefits of the revised and alternative 

standards, we recognize that there are areas that are not required to meet the existing ozone 

standard by 2025 -- the CAA 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 2037.15,16 Because of data and resource constraints, 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 

 

15 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 by December 31, 2032 and nonattainment areas classified as Extreme will likely have to attain by December 

31, 2037. 

16 In this RIA before deciding to continue to analyze California beyond the future analysis year of 2025, we 

reviewed California’s NOx and VOC emissions within existing nonattainment areas. The vast majority of these 

emissions come from emissions sources located in existing nonattainment areas that would likely have to attain the 

final standard sometime between 2032 and 2037. As a result, we concluded that analyzing California separately and 

after 2025 continued to be an appropriate analytical decision. 
1-12 

expected to be fully implemented by 2030.17 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 difference in 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. 

1.3.3 Cost Analysis Approach 

 The EPA estimated total costs under partial and full attainment of the revised and 

alternative ozone standard levels analyzed. These cost estimates reflect only engineering costs, 

which generally includes the costs of purchasing, installing, and operating the referenced control 

technologies. The technologies and control strategies selected for analysis illustrate one way in 

which nonattainment areas could 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 identified controls. Costs for full attainment include estimates for the 

costs associated with the additional emissions reductions that are needed beyond identified 

 

17 At the time of this analysis, there were no future year emissions for California beyond 2030, and projecting 

emissions beyond 2030 could introduce additional uncertainty. 
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controls. The EPA recognizes that the portion of the cost estimates from emissions reductions 

beyond identified controls reflects substantial uncertainty about which sectors and which 

technologies might become available for cost-effective application in the future. 

1.3.4 Human Health Benefits 

The EPA estimated human health (i.e., mortality and morbidity effects) under both partial 

and full attainment of the two alternative ozone standard levels analyzed. 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, which has been used in many recent RIAs (e.g., U.S. EPA, 

2011a, 2011c) and The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011b). 

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 6). In addition, unquantified health benefits are also discussed in Chapter 6. 

1.3.5 Welfare Benefits of Meeting the Primary and Secondary Standards 

Even though the primary standards are designed to protect against adverse effects to 

human health, the emissions reductions would have welfare 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. Welfare co-benefits are discussed further in Chapter 7. 

1.4 Updates between the Proposal and Final RIAs 

For NAAQS RIAs, the Agency always reviews the underlying data used and makes 

methodological and model improvements both between proposal and final analyses and between 

different NAAQS analyses. For this final RIA, we made updates to the emissions inventory 

based on public comments and input from the states, updated the oil and gas sector emissions 

projections based on input from the states, and used updated versions of IPM and the onroad 
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mobile source model.18 For a detailed discussion of these emissions inventory, model, and 

model input updates, see Appendix 2, Section 2A.1.3. In addition, based on the analyses in the 

proposal RIA, in this final NAAQS RIA the EPA decided to conduct more refined air quality 

modeling to assess emissions changes closer to monitors in certain areas, specifically Texas and 

the Northeast. 

 The net effects of the emissions inventory, model, and model input updates are changes in 

projected 2025 ozone air quality design values (DVs)19 in many areas. These new projected DVs 

were higher than previously modeled for the proposal RIA in some locations and lower in others. 

The new projections show lower 2025 DVs in Central Texas from Houston to Dallas, the El Paso 

area (NM and TX) and Big Bend, Texas, and several states in the central U.S., including 

Oklahoma, Kansas, Missouri, Arkansas, Mississippi, Tennessee, and southern Kentucky. The 

new projections also show higher 2025 DVs in Denver, Las Vegas, Phoenix, Charlotte, the upper 

Midwest, and parts of the New York/New Jersey areas. See Appendix 2A, Section 2A.4 for 

detailed information on the updated DVs. 

We also conducted additional air quality modeling runs to provide more spatially resolved 

air quality response factors, allowing us to more appropriately represent the effectiveness of 

emissions reductions from sources closer to receptor monitors compared to the regional response 

factors used for the November 2014 proposal RIA (see Chapter 2, Section 2.2.2 for a discussion 

of the additional air quality modeling). In the final RIA, there were approximately 50 percent 

fewer emissions reductions needed in Texas and the Northeast to reach a revised standard of 70 

ppb. For an alternative standard of 65 ppb in the final RIA, emissions reductions needed 

nationwide were approximately 20 percent lower than at proposal. 

The primary reasons for the difference in emissions reductions estimated in the final RIA 

are the more spatially resolved air quality modeling and resulting improved ozone response 

 

18 Based on the timing associated with both preparing an updated 2025 base case and completing the analyses in this 

final RIA, we used the IPM v5.14 base case because the IPM v5.15 base case was not available. 19 The DV is calculated as the 3-year average of the annual 4th highest daily maximum 8-hour ozone concentration in 

parts per billion, with decimal digits truncated. The DV is a metric that is compared to the standard level to 

determine whether a monitor is violating the NAAQS. The ozone DV is described in more detail in Chapter 2, 

Section 2.2. 
1-15 

factors, as well as the focus of the emissions reduction strategies on geographic areas closer to 

the monitors with the highest design values (see Chapter 3, Section 3.1.1 for a more detailed 

discussion of the emissions reduction strategies). The improvements in air quality modeling and 

emissions reduction strategies account for about 80 percent of the difference in estimated needed 

emissions reductions between the proposal and final RIAs. 

For example, in analyzing the revised standard of 70 ppb, in Texas and the Northeast the 

updated response factors and more focused emissions reduction strategies resulted in larger 

changes in ozone concentrations in response to more geographically focused emissions 

reductions. In east Texas, the air quality response factors used in the final RIA were 2 to 3 times 

more responsive than the factors used in the proposal RIA at controlling monitors in Houston 

and Dallas. In the Northeast, the air quality response factors used in the final RIA were 2.5 times 

more responsive than the factors used in the proposal RIA at the controlling monitor on Long 

Island, NY. 

The updates made to the emissions inventories, models, and model inputs for the base 

year of 2011 account for the remaining 20 percent of the difference in estimated emissions 

reductions needed between the proposal and final RIAs. When projected to 2025, these changes 

in inventories, models and inputs had compounding effects for year 2025, and in some areas 

resulted in lower projected base case DVs for 2025. 

For additional information on how the revised emissions reduction estimates affect the 

cost estimates, see Chapter 4, Section 4.6. For additional information on how the revised 

emissions reduction estimates affect the benefits estimates, see Chapter 6, Section 6.1.

1.5 Organization of the Regulatory Impact Analysis 

This RIA is organized into the following remaining chapters: 

 Chapter 2: Emissions, Air Quality Modeling and Analytic Methodologies. 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 benefits and costs. 

 Chapter 3: Control Strategies and Emissions Reductions. The chapter presents the 

hypothetical control strategies, the geographic areas where controls were applied, and the 
1-16 

results of the modeling that predicted ozone concentrations in 2025 after applying the 

control strategies.

 Chapter 4: Engineering Cost Analysis and Economic Impacts. The chapter summarizes 

the data sources and methodology used to estimate the engineering costs of partial and 

full attainment of the three alternative standard levels analyzed. 

 Chapter 5: Qualitative Discussion of Employment Impacts of Air Quality. The chapter 

provides a discussion of some possible types of employment impacts of reducing 

emissions of ozone precursors.

 Chapter 6: Human Health Benefits Analysis Approach and Results. The chapter 

quantifies the health-related benefits of the ozone-related air quality improvements 

associated with the three alternative standard levels analyzed. 

 Chapter 7: Impacts on Public Welfare of Attainment Strategies to Meet the Primary and 

Secondary Ozone NAAQS. The chapter includes a discussion of the welfare-related 

benefits of meeting alternative primary and secondary ozone standards and a limited 

quantitative analysis for effects associated with changes in yields of commercial forests 

and agriculture, and associated changes in carbon sequestration and storage. 

 Chapter 8: Comparison of Benefits and Costs. The chapter compares estimates of the 

total benefits with total costs and summarizes the net benefits of the three alternative 

standards analyzed. 

 Chapter 9: Statutory and Executive Order Impact Analyses. The chapter summarizes the 

Statutory and Executive Order impact analyses. 

1.6 References 

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

&contentType=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. 

U.S. Environmental Protection Agency (U.S. EPA). 2005a. 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). 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). 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. 
1-17 

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). 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

&contentType=pdf. 

U.S. Environmental Protection Agency (U.S. EPA). 2010a. 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). 2010b. Regulation of Fuels and Fuel Additives: Modifications 

to Renewable Fuel Standard Program (RFS2). Available at http://www.gpo.gov/fdsys/pkg/FR-2010-12-

21/pdf/2010-31910.pdf. 

U.S. Environmental Protection Agency (U.S. EPA). 2010c. Light-Duty Vehicle Greenhouse Gas Emission 

Standards and Corporate Average Fuel Economy Standards; Final Rule for Model-Year 2012-2016. Available at 

http://www.gpo.gov/fdsys/pkg/FR-2010-05-07/pdf/2010-8159.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). 2011a. 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). 2011b. 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. 

U.S. Environmental Protection Agency (U.S. EPA). 2011c. 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 (U.S. EPA). 2011d. Greenhouse Gas Emissions Standards and Fuel 

Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles. Available at 

http://www.gpo.gov/fdsys/pkg/FR-2011-09-15/pdf/2011-20740.pdf. 

U.S. Environmental Protection Agency (U.S. EPA). 2012. 2017 and Later Model Year Light-Duty Vehicle 

Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards. Available at 

http://www.gpo.gov/fdsys/pkg/FR-2012-10-15/pdf/2012-21972.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 (U.S. EPA). 2014a. Carbon Pollution Emission Guidelines for Existing 

Stationary Sources: Electric Utility Generating Units (Proposed Rule). US EPA, OAQPS, 2014, RTP, NC, 79 FR 

34829, EPA-HQ-OAR-2013-0602. 
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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. Environmental Protection Agency, 2015. Preparation of Emissions Inventories for the Version 6.2, 2011 

Emissions Modeling Platform. Available at: http://www.epa.gov/ttn/chief/emch/index.html#2011. 

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

http://www.whitehouse.gov/omb/circulars/a004/-.pdf. 
2-1 

CHAPTER 2: EMISSIONS, AIR QUALITY MODELING AND ANALYTIC 

METHODOLOGIES

Overview 

This regulatory impacts analysis (RIA) evaluates the costs as well as the health and 

environmental benefits associated with complying with the revised (70 ppb) and alternative (65 

ppb) 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 modeling conducted with emissions 

projected to the year 2025 including all current on-the-books federal regulations.20 As described 

in section 2.1, the emissions inputs for the 2011 base year and 2025 base case simulations were 

updated between the November 2014 proposal RIA (EPA, 2014a) and this final analysis. These 

updates were made in response to comments provided by states and newly available emissions 

models and projection information. In the following sections, the 2025 base case from the 

November 2014 proposal RIA will be referred to as the “proposal 2025 base case” while the 

updated 2025 base case will be referred to as the “final 2025 base case”. In addition, a series of 

emissions sensitivity21 modeling runs were conducted to determine the response of ozone to 

changes in 2025 emissions. These sensitivity runs were used to develop ozone response factors 

(ppb/ton) that represent the modeled response of ozone to changes in NOx and VOC emissions 

from various sources and locations.22 

The following scenarios were developed based on applying the ozone response factors to 

the final 2025 base case ozone concentrations: (1) the baseline scenario (a scenario that includes 

 

20 Emissions reductions to attain the 2012 PM2.5 NAAQS are not included in the proposal or final 2025 base case 

because the scenarios modeled in the 2012 PM2.5 NAAQS RIA did not reflect any NOx emissions reductions (US 

EPA, 2012). 

21 Sensitivity refers to modeling simulations designed to capture the response of ozone concentrations to changes in 

emissions. 

22 All emissions sensitivity model runs were created with reductions incremental to the proposal 2025 base case 

scenario. 
2-2 

attainment of the current standard of 75 ppb)23 and (2) the revised standard level scenario and an 

alternative standard level scenario that both represent incremental emissions reductions beyond 

the baseline to meet levels of 70 and 65 ppb respectively.24 For each scenario we calculated 

emissions reductions necessary to meet the target standard level and resulting ozone 

concentrations at ozone monitoring locations. We used the emissions reductions as inputs in the 

estimation of control strategies (Chapter 3) and costs (Chapter 4) associated with attaining the 

revised and alternative ozone standard levels. The emissions reductions were also used to 

estimate changes in health-related ozone concentration metrics under each scenario allowing us 

to calculate the health-related benefits that would result from the reductions in emission and 

ozone concentrations associated with meeting various standard levels (Chapter 6). Figure 2-1 

below outlines these general steps and Table 2-1 lists all of the scenarios discussed above with 

their respective definitions. 

 

23 As described in chapter 1, section 1.3.2, we use a “2025 baseline scenario” for areas of the contiguous U.S. 

outside of California and a “post-2025 baseline scenario” for California due to the later attainment dates for some 

areas in that state. 

24 For the revised standard and the alternative standard we present both a scenario which represents only the portion 

of emissions reductions that come from identified controls (identified control strategies) and one that represents total 

emissions reductions necessary to attain the respective standard levels (identified + unidentified control strategies). 
2-3 

Figure 2-1. Process to Determine Emissions Reductions Needed to Meet Baseline and 

Alternative Standards Analyzed 
2-4 

Table 2-1. Terms Describing Different Scenarios Discussed in This Analysis 

Scenario name Definition 

Base year Photochemical model simulations for 2011 using best estimates or actual meteorology, 

emissions and resulting ozone concentrations

2025 base case Modeling conducted with emissions projected to the year 2025 including all current 

on-the-books federal regulations and using 2011 meteorology

Proposal 2025 base case The 2025 base case from the November 2014 proposal RIA

Final 2025 base case The updated 2025 base case that includes improvements to 2011 emissions and 2025 

emissions projections described in section 2.1

2025 baseline 

2025 ozone concentrations from the final 2025 base case that have been adjusted to 

account for potential impacts from the proposed Clean Power Plan.25 Costs and 

benefits of revised and alternative standard levels for all areas of the contiguous U.S. 

outside of California are calculated incremental to this scenario.

Post-2025 baseline 

2025 ozone concentrations from the final 2025 base case that have been adjusted to 

account for potential impacts from the Clean Power Plan plus additional emissions 

reductions in California to attain of the current (75 ppb) ozone standard sometime after 

2025. Costs and benefits of revised and alternative standard levels for California are 

calculated incremental to this scenario.

Revised standard Emissions reductions and resulting ozone concentrations incremental to the baseline 

scenario needed to reach attainment of the 70 ppb ozone standard.

Alternative standard Emissions reductions and resulting ozone concentrations incremental to the baseline 

scenario that would be needed to reach attainment of a 65 ppb ozone standard.

Emissions reductions 

from identified control 

strategies

The portion of emissions reductions and resulting ozone concentrations that come 

from identified emissions controls described in chapter 3

Emissions reductions 

from identified + 

unidentified control 

strategies

Total emissions reductions and resulting ozone concentrations that are applied to reach 

either the revised or alternative ozone standard

 

The remainder of the chapter is organized as follows: Section 2.1 describes the 2025 

base case emissions and air quality modeling simulation; Section 2.2 describes how we project 

ozone levels into the future including the methodology for constructing the baseline, revised 

standard, and alternative standard scenarios (this methodology is applied in chapter 3 sections 

3.1 and 3.2); and Section 2.3 describes the creation of spatial surfaces that serve as inputs to 

health benefits calculations discussed in Chapter 6. 

2.1 Emissions and Air Quality Modeling Platform 

The 2011-based modeling platform was used to provide emissions, meteorology and 

other inputs to the 2011 and 2025 air quality model simulations. This platform was chosen 

 

25 No additional reductions to meet the current (75 ppb) standard are applied since no areas outside of California are 

projected to violate the current standard once ozone adjustments for the Clean Power Plan are made. 
2-5 

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 6.11, 

Environ, 2014) for photochemical model simulations performed for the RIA. CAMx requires a 

variety of input files that contain information pertaining to the modeling domain and simulation 

period. These files include gridded, hourly emissions estimates and meteorological data, and 

initial and boundary conditions. Separate emissions inventories were prepared for the final 2011 

base year, the final 2025 base case, the proposal 2025 base case and the 2025 emissions 

sensitivity simulations. 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 time and space depending on the sampling period of measured data. 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 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. 

Information on the components of the 2011-based modeling platform, including 

information on the 2011 base year and 2025 base case emission inventories, and the model 

evaluation methodology and results are provided in Appendix 2A. Additional details on the final 

2011 base year and 2025 base case emissions inventories can also be found in the Technical 

Support Document (TSD): Preparation of Emissions Inventories for the Version 6.2, 2011 

Emissions Modeling Platform (US EPA, 2015). Section 4 of the TSD summarizes the control 

and growth assumptions by source type that were used to create the U.S. final 2025 base case 

emissions inventory and includes a table of those assumptions for each major source sector. 
2-6 

Section 2.4 of this document summarizes the changes to the emissions inventories used in the 

final modeling as compared to the November 2014 proposal modeling. 

2.2 Projecting Ozone Levels into the Future 

In this section we present the methods used to create the future baseline and the two 

scenarios that demonstrate attainment of the revised and alternative NAAQS levels analyzed in 

this RIA. First, in section 2.2.1, we describe the procedures for projecting ozone “design values” 

into the future. In section 2.2.2, we present the development of 15 emissions sensitivity 

simulations and in section 2.2.3 we show how to calculate ppb/ton ozone response factors from 

these sensitivity simulations. Next, in section 2.2.4, we describe the approach for using this 

information to construct the baseline, revised standard and alternative standard scenarios. The 

implementation of these methods using the 2025 base case ozone levels together with the ozone 

response factors and the resulting emissions scenarios and associated ozone levels is presented in 

Chapter 3. Finally, in section 2.2.5 we discuss a small subset of monitoring sites that were not 

included in the quantitative analysis. 

2.2.1 Methods for Calculating Future Year Ozone Design Values 

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 being analyzed. 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 finalized for the new NAAQS in Appendix U to 40 

CFR Part 50 – Interpretation of the Primary and Secondary National Ambient Air Quality 

Standards for Ozone. For the purpose of this analysis, ozone DVs were derived from data 

reported in EPA’s air quality system (AQS) for the years 2009-2013. The base period 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, 2014c) because it tends to minimize the year-to-year meteorologicallydriven variability in ozone concentrations given that the future year meteorology is unknown. 
2-7 

For sites with fewer than five years of valid monitoring data available, the current year DV was 

calculated using a minimum of 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. 

Future year ozone design values were calculated at monitor locations using the Model 

Attainment Test Software program (Abt Associates, 2014). This program 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 2-1 describes the recommended 

model attainment test in its simplest form, as applied for monitoring site i: 

ሺDVFሻ୧ ൌ ሺܴܴܨሻ௜ ൈ ሺܦܸܤሻ௜ Equation 2-1 

DVF୧ is the estimated design value for the future year in which attainment is required at 

monitoring site i; RRF୧ is the relative response factor at monitoring site i; and DVB୧ is the base 

design value monitored at site i. The relative response factor for each monitoring site ሺܴܴܨሻ௜ is 

the fractional change of ozone in the vicinity of the monitor that is simulated on high ozone days. 

The recently released draft version of EPA’s ozone and PM2.5 photochemical modeling guidance 

(US EPA, 2014c) 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 is calculated based on the 10 highest days in the base year modeling in the 

vicinity of the monitor location when the base 8-hr daily maximum ozone values were greater 

than or equal to 60 ppb for that day.26 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 

 

26 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, 2014c). The RRF was based on a 3 x 3 array of 12 km grid cells centered on 

the location of the grid cell containing 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. 
2-8 

was greater than or equal to 60 ppb, as long as there were at least 5 days that met that criteria. At 

monitor locations with fewer than 5 days with ozone greater than or equal to 60 ppb, no RRF or 

DVF was calculated for the site and the monitor in question was not included in this analysis. 

2.2.2 Emissions Sensitivity Simulations 

A total of fifteen emissions sensitivity modeling runs were conducted to determine ozone 

response to reductions of NOx and VOC emissions in different areas. (See Table 2-2 for a list of 

the sensitivity runs). The sensitivity modeling provides an efficient and flexible approach that 

allowed us to evaluate ozone responses from multiple source regions and several levels of 

emissions reductions simultaneously. All emissions sensitivity simulations included emissions 

reductions incremental to the proposal 2025 base case.27 Ozone response factors (ppb/ton) were 

created by comparing changes in projected ozone levels between the proposal 2025 base case 

and the individual emission sensitivity simulations. These response factors were then applied to 

the final 2025 base case design values. There were three types of sensitivity runs, each of which 

is described in more detail below: (1) explicit emissions control cases; (2) across-the-board 

reductions in anthropogenic emissions in different areas; and (3) combination cases that included 

both explicit emissions controls and across-the-board reductions. 

Table 2-2. List of Emissions Sensitivity Modeling Runs Modeled in CAMx to Determine 

Ozone Response Factors 

 

27 Modeling incremental changes from the proposal 2025 base case provided consistency with sensitivity 

simulations performed for the proposal and allowed us to leverage a subset of sensitivity simulations created as part 

of that proposal. This was necessary due to timing and resource constraints. Since the sensitivity simulations are 

used to create relative ppb/ton response factors, it is appropriate to apply changes derived from these sensitivities to 

the final 2025 base case modeling since atmospheric chemistry regimes are not likely to have changed substantially 

between the proposal and final 2025 base case simulations. 

Emissions 

Sensitivity 

Simulation 

Region Pollutant Emissions Change Types 

1 National All Clean Power Plan Explicit control 

2 National VOC 50% VOC cut Across-the-board 

3 California NOx CA explicit emissions control Explicit control 

4 N. California NOx Sensitivity 3 + 50% NOx cut in N. CA Combination 

5 N. California NOx Sensitivity3 + 90% NOx cut in N. CA Combination 

6 S. California NOx Sensitivity 3 + 50% NOx cut in S. CA Combination 

7 S. California NOx Sensitivity 3 + 90% NOx cut in S. CA Combination 
2-9 

Explicit Emissions Controls: Two explicit emissions control sensitivity modeling runs 

were conducted. These emissions control sensitivity runs are referred to as “explicit emissions 

control” runs because they represent the impact of sets of specific controls rather than 

sensitivities to all anthropogenic emissions. First, we modeled one possible representation of 

implementing the EPA’s proposed carbon pollution guidelines under section 111(d) of the Clean 

Air Act (CAA) (i.e., option 1 state; hereafter referred to as the Clean Power Plan sensitivity). 

Emissions for this simulation are described in the regulatory impact analysis for that proposed 

rule (EPA, 2014d). Second, we conducted an additional emissions control sensitivity run that 

included NOx emissions reductions from controls applied to specific sources in California. 

Based on analysis conducted for the November 2014 proposal RIA (EPA, 2014a) and projected 

design values (DVs)28 from the final 2025 base case, it was determined that California was the 

only region for which all identified controls would be exhausted before reaching the baseline. 

Therefore, we created a sensitivity run in which all identified NOx emissions controls below 

$15,000/ton were applied in California. The explicit controls were only applied in a 200 km 

buffer area around counties in California projected to violate 70 ppb in the proposal 2025 base 

case. The EPA’s Control Strategy Tool (CoST) (EPA, 2014e) was used to determine the 

potential reductions in this area. NOx controls were identified for all nonpoint, non-EGU point, 

and nonroad sources. This emissions sensitivity was created as part of the analysis for the 

November 2014 proposal RIA (EPA, 2014a). The assumptions about which sources were 

available for controls in California are the same as those described in Chapter 3, with the 

exception that for proposal, only controls with a cost under $15,000 per ton were considered. In 

 

28 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 2.2. 

8 Nevada NOx 50% NOx cut Across-the-board 

9 Arizona/New Mexico NOx 50% NOx cut Across-the-board 

10 Colorado NOx 50% NOx cut Across-the-board 

11 E. Texas NOx 50% NOx cut Across-the-board 

12 Oklahoma/Arkansas/Louisiana NOx 50% NOx cut Across-the-board 

13 Great Lakes NOx 50% NOx cut Across-the-board 

14 Ohio River Valley NOx 50% NOx cut Across-the-board 

15 Northeast Corridor NOx 50% NOx cut Across-the-board 
2-10 

the final rule we have identified additional controls in California (i.e. controls with a cost 

between $15,000 per ton and $19,000 per ton: the thresholds applied for proposal and final RIA 

respectively) which were not included in the California explicit emissions control case that we 

modeled but which were accounted for using ppb/ton response factors from combination 

emissions sensitivities described below. 

Across-the-board Emissions Reductions: We performed across-the-board sensitivity 

modeling for areas of the U.S. projected to contain monitors with ozone design values greater 

than 65 ppb in the proposal 2025 base case. We created 8 regions that contain these monitoring 

sites, as shown in Figure 2-2. The boundaries of these regions were generally defined in terms of 

the borders of a single state or a small group of adjacent states. In addition, we also used the two 

“buffer regions” (one in East Texas and the other in the Northeastern U.S.) that were created in 

the analysis for the November 2014 proposal RIA for areas with 2025 baseline DVs above 70 

ppb and were not updated using the final 2025 base case modeling. These buffers around 

counties projected to violate 70 ppb allowed us to target reductions in locations close to the 

highest ozone monitors, an approach that is likely to be most effective at reducing ozone 

concentrations for these relatively isolated violations.29 The two buffer regions were determined 

based on 200 km buffers around all monitors projected to be above 70 ppb in the proposal 2025 

base case. In Texas, the buffer region was restricted to counties within state boundaries. In the 

Northeast, the buffer was restricted to a subset of the states/counties that are currently under the 

jurisdiction of the Ozone Transport Commission (OTC), a multistate region that already has 

interstate cooperation for air quality planning. The Texas and Northeast buffer areas are shown 

in Figure 2-3.30 Unlike in California, it was not clear that all identified controls would be 

required in any one region to meet the 65 and/or 70 ppb standard levels. Therefore, in these two 

regions we generated more general emissions response factors using an across-the-board 50% 

 

29 Note that counties projected to violate the alternative 65 ppb standard are more broadly distributed throughout the 

U.S. and less isolated in nature. Therefore it may be less important to differentiate between impacts from very local 

emissions within 200 km of a violating county compared to impacts from emissions across a statewide or multistate 

region in designing control strategies for those areas. 

30 The 200 km buffers are shaded in orange and counties that contained one or more monitors projected to be above 

70 ppb in the proposal 2025 base case modeling are shaded in blue. 
2-11 

reduction in U.S. anthropogenic NOx emissions. We also performed a VOC sensitivity run with 

a 50% cut in anthropogenic VOC emissions across the 48 contiguous states. 

Combination Emissions Sensitivities: We conducted four additional emissions sensitivity 

modeling runs that combined the explicit emissions controls with across-the-board reductions in 

California. Based on a previous EPA analysis (EPA, 2014a; EPA, 2014f) we identified 

California as the region most likely to need NOx reductions beyond 50% to reach the revised and 

alternative standard levels. Therefore, we modeled both a 50% and a 90% NOx emissions 

reduction in California to capture nonlinearities in ozone response to large NOx emissions 

changes. The 50% and 90% NOx reductions were applied in Northern and Southern California 

separately recognizing that the topography in California effectively isolates the air shed in the 

San Joaquin Valley from the southernmost portion of the state which has the effect of limiting 

the impact of emissions from Southern California on ozone in Northern California and vice 

versa. The geographic delineation of Northern and Southern California for these emissions 

sensitivity simulations is shown in Figure 2-2. In all four California emissions sensitivities, the 

50% and 90% NOx reductions were applied on top of the California explicit controls sensitivity 

run (sensitivity simulation #3). 
2-12 

Figure 2-2. Across-the-Board Emissions Reduction and Combination Sensitivity 

Regions31 

 

31 Combination Sensitivities were used for the two California regions whereas, Across-the-Board Sensitivities were 

used in all other regions. 
2-13 

Figure 2-3. Map of 200 km Buffer Regions in California, East Texas and the Northeast 

Created as Part of the Analysis for the November 2014 Proposal RIA32

2.2.3 Determining Ozone Response Factors from Emissions Sensitivity Simulations 

Section 2.2.1 describes, in general terms, how the 2025 projections for ozone DVs were 

computed. This procedure was followed for the proposal and final 2025 base case modeling and 

for each of the fifteen emissions sensitivity modeling simulations. Using the projected DVs and 

corresponding emissions changes, a unique ozone response factor (ppb/ton) was calculated for 

each emissions sensitivity at each ozone monitor using equation 2-2: 

 

32 The California buffer was used to determine the area over which explicit controls were applied in the California 

explicit control sensitivity simulation (sensitivity simulation #3). The Texas and Northeast buffers were used to 

delineate the areas over which across-the-board anthropogenic NOx emissions reductions were applied in sensitivity 

simulations #11 and #15 respectively. 
2-14 

ܴ௜,௝ ൌ ஽௏೔,ೕష஽௏మబమఱ್ೌೞ೐,ೕ

∆ா೔

 Equation 2-2

In equation 2-2, Ri,j represents the ozone response at monitor j to emissions changes between the 

2025 proposal base case and the sensitivity simulation i; DVi,j represents the DV at monitor j for 

emissions sensitivity i; DV2025base,j represents the DV at monitor j in the proposal 2025 base case; 

and ΔEi represents the difference in NOx or VOC emissions (tons) between the proposal 2025 

base case and emissions sensitivity run i. 

In California where emissions reductions in four sensitivity runs (i) were incremental to 

emissions reductions in another run (k), the following equation was used: 

ܴ௜,௝ ൌ ஽௏೔,ೕష஽௏ೖ,ೕ

∆ா೔ೖ

 Equation 2-3

in which ΔEik represents the difference in NOx emissions (tons) between the emissions run k and 

emissions run i. For emissions sensitivity simulations #4 and #6 (50% NOx reductions), k 

represented emissions sensitivity #3 (California explicit control). For emissions sensitivity 

simulations #5 and #7 (90% NOx reductions), k represented emissions sensitivities #4 and #6 

respectively. 

For the VOC emissions sensitivity run, we determined it was appropriate to compute 

response factors for smaller geographic areas than were modeled in the emissions sensitivity 

simulations shown in Figure 2-2. Past work has shown that impacts of anthropogenic VOC 

emissions on ozone DVs in the U.S. tend to be much more localized than reductions in NOx (Jin 

et al., 2008). Consistent with past analyses (US EPA, 2008) we made the simplifying 

assumption that VOC reductions do not affect ozone at distances more than 100 km from the 

emissions source. Consequently, we created a series of VOC impact regions in 7 areas (Figure 

2-4) for which our modeling showed that ozone is responsive to VOC emissions reductions and 

which had the highest ozone DVs in the NOx sensitivity regions: New York City, Chicago, 

Louisville, Houston, Denver, and Northern and Southern California.33 VOC impact regions were 

 

33 The following additional local VOC areas were also explored but were found not to be helpful in reaching the 

revised or alternative NAAQS levels in this analysis: Dallas, Detroit, Pittsburgh, and Baltimore. This may be due to 

the construct of the attainment scenarios analyzed and does not mean that VOC controls would not be effective in 

these areas under alternative assumptions about regional NOx controls. 
2-15 

delineated by creating a 100km buffer around counties containing monitors violating 60 ppb in 

the proposal 2025 base case modeling. In addition, VOC impact regions were constrained by 

state boundaries except in cases where a current nonattainment area straddled multiple states 

(e.g., 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 affect locations in Wisconsin, Illinois, Indiana, and Michigan that border Lake 

Michigan (Dye et al., 1995). For California, the VOC impact regions were delineated identically 

to the Northern and Southern California regions used in the NOx emissions sensitivity runs 

except that the Northern California region did not extend beyond the 200 km buffer shown in 

Figure 2-3. To create the ozone response factors to VOC for each monitoring site within a VOC 

impact region, 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 emissions sensitivity 

modeling run. 
2-16 

Figure 2-4. Map of VOC Impact Regions 

2.2.4 Combining Response from Multiple Sensitivity Runs to Determine Tons of Emissions 

Reductions to Meet Various NAAQS Levels 

 Ozone DVs were calculated for the baseline scenario as well as for the revised and 

alternative standards using Equation 2-4 in which ܦܸଶ଴ଶହ,௝ is the ozone DV at monitor j in the 

final 2025 base case, ܴ௡,௝ is the ozone response factor for sensitivity n at monitor j, and ∆ܧ ௡is 

the tons of emissions reductions from region n being applied to reach the desired standard level: 

ܦܸ ௝ൌ ܦܸଶ଴ଶହ,௝ ൅ ൫ܴଵ,௝ ൈ ∆ܧଵ൯ ൅ ൫ܴଶ,௝ ൈ ∆ܧଶ൯ ൅ ൫ܴଷ,௝ ൈ ∆ܧଷ൯൅⋯ Equation 2-4 

For the baseline as well as the two alternative standards analyzed, we determine the least amount 

of emissions reductions (tons) needed in each region (∆ܧ (௡to bring the ozone DVs at all 
2-17 

monitors down to the particular standard level being analyzed. Note that California was analyzed 

independent of the rest of the country due to the later attainment dates in many California 

counties. Therefore, in determining the necessary emissions reductions, we did not account for 

any impacts of California reductions on other areas of the U.S. and vice versa. The application of 

equation 2-4 to determine emissions reductions necessary to meet the various standard levels at 

U.S. locations outside of California is presented in chapter 3, section 3.2. 

Because California included multiple incremental sensitivity simulations, Equation 2-4 

had to be slightly modified for calculating DV changes to emissions reductions in that state. The 

modeled impacts from multiple California sensitivity simulations were combined in a linear 

manner to estimate the overall impacts. For example, at any monitor in California we could use 

the following equation to determine the DVs that would result from a 75% reduction in Northern 

California emissions beyond the explicit emissions control sensitivity simulation: 

ܦܸ଻ହ%஼஺,௝ ൌ ܦܸଶ଴ଶହ,௝ ൅ ቀܴ஼஺೐ೣ೛೗೔೎೔೟೎೚೙೟ೝ೚೗,௝ ൈ ∆ܧ೐஺஼ೣ೗೚ೝ೟೙೚೎೟೔೎೔೗೛ቁ ൅ ൫ܴே஼஺ହ଴ேை௫,௝ ൈ ∆ܧହ଴ேை௫൯ ൅

൫ܴே஼஺ଽ଴ேை௑,௝ ൈ ∆ܧ଻ହேை௑൯ Equation 2-5 

In equation 2-5, ܦܸଶ଴ଶହ,௝ represents the projected DV from the final 2025 base case at 

monitor j, ∆ܧோ_௘௫௣௟௜௖௜௧௖௢௡௧௥௢௟ represents the difference in NOx emissions between the proposal 

2025 base case and the 2025 California explicit emissions control sensitivity; ∆ܧହ଴ேை௫ represents 

the difference in NOx emissions between the 2025 California explicit emissions control 

sensitivity and the combined California explicit emissions control with 50% Northern California 

NOx cuts sensitivity; and ∆ܧ଻ହேை௑ represents the additional emissions reductions needed to 

reach a 75% NOx cut in Northern California above and beyond the emissions reductions in the 

combined California explicit emissions control run with 50% Northern California NOx cuts run. 

Note that in this equation, emission reductions in Northern California impact monitors (j) in both 

the Northern and Southern California regions. Similar to the methods applied in other regions, 

we determine the smallest amount of emissions reductions (tons) in northern and southern 

California regions necessary to decrease all ozone DVs in each region to the standard level being 

analyzed. The application of equation 2-5 to determine emissions reductions necessary to meet 

the various standard levels in California is presented in chapter 3, section 3.3. 
2-18 

 While ozone responses can be nonlinear and vary by emissions source type and location, 

in this analysis we make several simplifying assumptions. First, we assume that every ton of 

NOx or VOC reduced within a region results in the same ozone response regardless of where the 

emissions reductions come from within the region because we do not have any information on 

the differential ozone response from emissions changes at different locations within the region. 

However, the somewhat smaller emissions sensitivity regions used in this analysis compared to 

the November 2014 proposal RIA provide a more spatially resolved representation of the ozone 

response to emissions changes and thus reduces but does not eliminate this uncertainty. Second, 

we assume that NOx and VOC responses are additive. Third, we assume that the responses from 

multiple regions are additive. Fourth, 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 California where we have multiple levels of 

emissions reductions, we assume linearity within each simulation, but we are able to capture 

discrete shifts in ozone response based on the multiple sensitivity simulations (i.e., one response 

for explicit emissions control run reductions, another response level up to 50% NOx emissions 

reductions beyond the explicit emissions control run, and a third level of response between 50% 

and 90% NOx emissions reductions beyond the explicit emissions control run). Finally, outside 

of California, the ozone response to NOx reductions greater than 50% is based on an 

extrapolation beyond the modeled emissions reductions. However, only East Texas and the 

Northeast require NOx reductions greater than 50% in the 65 ppb scenario and in both cases the 

NOx reductions are not substantially greater than 50% (52% and 56% respectively), so we 

expect that the ozone response from the 50% sensitivity is appropriate for extrapolation to 52% 

and 56% with only a small amount of additional uncertainty. 

2.2.5 Monitoring Sites Excluded from Quantitative Analysis 

There were 1,225 ozone monitors with complete ozone data for at least one DV period 

covering the years 2009-2013. We included 1,165, or 95% of these sites in the analysis to 

determine the tons of emissions reductions necessary for each of the three scenarios (i.e., the 

baseline and two alternative standard level scenarios). However, there were three types of sites 

that were excluded from this analysis. First, we did not analyze the baseline or attainment levels 

at each of the 41 sites that did not have a valid projected final 2025 base case DV because there 
2-19 

were fewer than 5 modeled days above 60 ppb in the 2011 CAMx simulation, as required in the 

EPA SIP modeling guidance (US EPA, 2014c). It is unlikely that these sites would have any 

substantial impact on costs and benefits because the reason that projections could not be made is 

that they have no more than 4 modeled days above 60 ppb. Only one of these 41 sites (site 

311079991 in Knox County, NE) has a base year DV greater than 65 ppb. These sites are listed 

in Appendix 2A. 

Second, seven 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 here based on summertime conditions to a wintertime ozone 

event, which is driven by different characterizations 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 2A 

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 12 relatively remote, rural sites in the 

Western U.S. with projected baseline DVs between 66 and 69 ppb that showed limited response 

to the NOx and VOC emissions sensitivities we modeled. Air agencies responsible for 

attainment at 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) 
2-20 

 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) 

 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. These sites were initially identified in the 

November 2014 RIA proposal (EPA, 2014a) and more detailed discussion of their characteristics 

were provided in Appendix 3A of that document. Only the subset of those sites with DVs 

greater than 65 ppb in the 2025 baseline scenario are excluded in this analysis since sites 

projected to have DVs at or below 65 ppb would not incur any additional costs or benefits. 

2.3 Creating Spatial Surfaces for BenMap 

The emissions reductions for attainment of the current, revised, and an alternative NAAQS 

level determined in chapter 3 were used to create spatial fields of ozone concentrations (i.e., 

spatial surfaces) for input into the calculation of health benefits associated with attainment of 

each NAAQS level, incremental to the baseline. The spatial surfaces used to calculate ozonerelated health benefits with the BenMap tool (Chapter 6) are described below. 

Health benefits associated with meeting different ozone standard levels were calculated 

based on the following three ozone metrics, as described in more detail in Chapter 6: May-Sep 

seasonal mean of 8-hr daily maximum ozone, Apr-Sep seasonal mean of 1-hr daily maximum 

ozone, and May-Sep seasonal mean of 9-hr daily average ozone (6am-3pm). For each metric, 

spatial fields (i.e., gridded surfaces) were created for a total of 8 scenarios, including: 

 2025 baseline 

 post-2025 baseline 

 2025 70 ppb identified control strategies 

 2025 70 ppb identified + unidentified control strategies 

 post-2025 70 ppb identified + unidentified control strategies 

 2025 65 ppb identified control strategies 

 2025 65 ppb identified + unidentified control strategies 
2-21 

 post-2025 65 ppb identified + unidentified control strategies 

The surfaces created for the 2025 scenarios represent attainment at all contiguous U.S. 

monitors outside of California, while the surfaces for the post-2025 scenarios represent all 

contiguous U.S. monitors including those in California meeting the standard being evaluated. 

The effects due only to California meeting the standard are isolated in Chapter 6 through a series 

of BenMap simulations using these surfaces and varying assumptions about population 

demographics. In addition, for the 2025 scenarios we include “identified control” and “identified 

+ unidentified control” strategies in which the identified control strategies only include ozone 

changes resulting from emissions reductions from identified control measures, while the 

identified + unidentified controls strategies include ozone changes resulting from all emissions 

reductions necessary to attain the standard from both identified controls and unidentified 

measures. 

The ozone surfaces were created using the following steps, which are described in more 

detail below and depicted in Figure 2-5. 

 Step 1: Create spatial fields of gridded ozone concentrations for each of the three 

seasonal metrics using the model-predicted hourly ozone concentrations. 

 Step 2: Create spatial fields of gridded ozone response factors for each seasonal 

metric. 

 Step 3: Create spatial field of gridded ozone concentrations for baseline, revised 

standard, and alternative standard scenarios and each seasonal ozone metric 

 Step 4: Create 2011 enhanced Voronoi Neighbor Averaging (eVNA) fused surface 

of 2011 modeled and 2010-2012 observed values for each seasonal ozone metric 

 Step 5: Create eVNA fused modeled/monitored surface for each attainment 

scenario and each seasonal ozone metric 

Step 1: Create spatial fields of seasonal ozone metrics for each model simulation 

 Inputs: Hourly gridded model concentrations for final 2011 base year, proposal and 

final 2025 base cases, and the fifteen 2025 emissions sensitivity simulations 

detailed in Section 2.2.2 

 Outputs: Seasonal ozone metrics for 2011, proposal and final 2025 base cases, and 

fifteen 2025 emissions sensitivity simulations (18 total spatial fields for each 

metric) 
2-22 

Step 2: Create spatial fields of ppb/ton ozone response factors 

 Inputs: Seasonal ozone metrics for proposal 2025 base case and fifteen 2025 

emissions sensitivity simulations (from Step 1); Amount of emissions reductions 

(tons) modeled in each emissions sensitivity 

 Outputs: Gridded ozone response factor (ppb/ton) for each seasonal ozone metric 

from each emissions sensitivity simulation 

 Methods: 

 Calculate the change in the seasonal ozone metrics between each emissions 

sensitivity simulation (i) and the proposal 2025 base case. This step results 

in 15 spatial fields of gridded ozone changes (∆ܱଷ) for each seasonal ozone 

metric. 

 Divide each of the spatial fields of ozone changes by the tons of emissions 

reductions applied in that emissions sensitivity simulation compared to the 

proposal 2025 base case: (∆ைయ

∆ா ). This step results in 15 spatial fields of 

gridded ozone response factors (ppb/ton) for each seasonal ozone metric. 

Step 3: Create spatial field seasonal ozone metrics for baseline, revised standard, and alternative 

standard scenarios 

 Inputs: Gridded ozone response factor for each seasonal ozone metric from each 

emissions sensitivity simulation (from Step 2); Amount of emissions reductions 

from each region (from Appendix 3A); Gridded ozone surface for each seasonal 

metric from the final 2025 base case (from Step 1). 

 Outputs: Gridded seasonal ozone metrics for each attainment scenario 

 Methods: 

 The gridded ozone response factors from Step 2 were multiplied by the 

relevant tons of emissions reductions for each sensitivity and then summed 

to create a gridded field representing the scenario using question (Equation 

2-6) 

ܱ3௫௬,௦,௠ ൌ ܱ3௫௬,ଶ଴ଶହ,௠ ൅ ൫ܴ௫௬,ଵ,௠ ൈ ∆ܧଵ,௦൯ ൅ ൫ܴ௫௬,ଶ,௠ ൈ ∆ܧଶ,௦൯ ൅

൫ܴ௫௬,ଶ,௠ ൈ ∆ܧଷ,௦൯൅⋯ Equation 2-6 
2-23 

In equation 2-6, 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 final 2025 base case simulation at grid cell x,y

aggregated to metric m. Rxy,1,m represents the ozone 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 that were found to be necessary for scenario s. 

Identified control strategy ozone surfaces at each standard level were 

created by only including ΔE values for emissions coming from identified 

controls as described in Chapter 3. Post-2025 surfaces include all 

emissions reductions outside of California that we estimate in 2025 plus 

additional reductions in California which would occur after 2025.34 

Step 4: Create 2011 enhanced Voronoi Neighbor Averaging (eVNA) fused surface of 2011 

modeled and observed values for each seasonal ozone metric 

 Inputs: 2010-2012 observed ozone values (seasonal ozone metrics at each monitor 

location); 2011 modeled ozone (seasonal ozone metrics at each grid cell) (from 

Step 1)

 Outputs: 2011 fused modeled/monitored surfaces for each seasonal ozone metric 

 Methods: 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: Create eVNA fused modeled/monitored surfaces for baseline, revised standard, and 

alternative standard scenarios 

 

34 A small error was discovered in the post-2025 surfaces in that the baseline surface included 202,000 tons of NOx 

emission reductions in California rather than the actual 206,000 tons of NOx emissions reductions applied to reach 

75 ppb in California. This error was carried through to the 70 ppb and 65 ppb surfaces for the post-2025 scenarios 

so the incremental changes in ozone between the baseline and alternative NAAQS level surfaces should not be 

significantly impacted. 
2-24 

 Inputs: 2011 fused model/observed surfaces for each seasonal ozone metric (from 

Step 4); modeled seasonal ozone metrics (gridded fields) for 2011 (from Step 1)

and each attainment scenario (from Step 3). 

 Outputs: Fused modeled/monitored surface for each attainment scenario and each 

seasonal ozone metric 

 Methods: The 24 model-based surfaces (i.e., 8 scenarios and 3 metrics) were used 

as inputs in the MATS tool along with the gridded 2011 base year and 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 base year 2011 model 

field. This RRF field was then multiplied by the 2011 eVNA field to create a 

gridded eVNA field for each scenario. 

Figure 2-5. Process Used to Create Spatial Surfaces for BenMap 

2.4 Improvements in Emissions and Air Quality for the Final RIA 

2.4.1 Improvement in Emissions

Between proposal and the final rule, improvements were implemented in both the base 

(2011) and future year (2025) emissions scenarios. The proposal emissions are documented in 

the Version 6.1, 2011 Emissions Modeling Platform (US EPA, 2014b) TSD. Many 
2-25 

improvements to the inventories resulted from the Federal Register notices for the 2011 and 2018 

Emissions Modeling platforms released in November 2013 and June 2014. Comments on these 

notices were received from states, industry, and other organizations. Although the 2025 

emissions were not specifically released for comment, improved methodologies and data were 

also applied to the updated 2025 emissions wherever possible. For example, many 

improvements were made on the National Electric Energy Data System database that is a key 

input in the preparation of future year EGU inventories; state agencies and regional planning 

organizations provided specific growth and control factors for stationary sources; and 

improvements were made to the modeling of onroad mobile sources in the base and future years. 

Most updates to the 2011 emissions are reflected in the 2011 National Emissions Inventory 

(NEI) version 2. These updates included 1) the use of the 2014 version of the Motor Vehicle 

Emissions Simulator (MOVES2014) for onroad mobile source emissions, along with many 

upgrades to the input databases used by MOVES; 2) updated oil and gas emissions based on the 

Oil and Gas Emissions Estimate Tool version 2.0; 3) version 3.6.1 of the Biogenic Emission 

Inventory System along with improved land use data; 4) many updates to point and nonpoint 

source emissions submitted directly into the Emission Inventory System (EIS) by states; 5) 

improved temporal allocation of electric generating unit (EGU) and onroad mobile source 

emissions; 6) upgraded VOC speciation to be consistent with the most recent chemical 

mechanism available in CAMx (i.e., CB6); and 7) improved spatial surrogates for heavy-duty 

trucks, buses, and other types of vehicles. In addition, Canadian emissions were upgraded to the 

latest available data from Environment Canada for the year 2010 and Mexican emissions were 

upgraded to use the 2008 Inventario Nacional de Emisiones de Mexico, whereas for the proposal 

modeling the Mexican emissions had been based on those developed for 1999. The cumulative 

national impact of the changes to 2011 emissions between the proposal and final RIA resulted in 

a 1% increase in NOx emissions and no change in VOC emissions, although local changes were 

larger. 

Improvements to the 2025 emissions included 1) using the Integrated Planning Model 

(IPM) version 5.14 with associated input databases and a representation of the Cross-State Air 

Pollution Rule (CSAPR); 2) using MOVES2014 to represent emission reductions from the Tier 3 

Final rulemaking and recent light and heavy duty greenhouse gas mobile source rules; 3) use of 
2-26 

Annual Energy Outlook (AEO) 2014 for projections of vehicle miles traveled, oil and gas 

growth, and growth in other categories; and 4) improved representation of growth and controls 

for non-EGU stationary source emissions. The cumulative national impact of the changes to the 

2025 emissions between the proposal and final RIAs resulted in a 2% reduction in NOx 

emissions and a 1% increase in VOC emissions, although localized changes were larger. For 

more information on the improvements to the 2025 emissions, see Appendix 2A and the 

Emissions Modeling TSD. 

The net effects of the emissions inventory, model, and model input updates are changes in 

projected 2025 ozone air quality design values (DVs) in many areas. These new projected DVs 

were higher than previously modeled for the proposal RIA in some locations and lower in others. 

The new projections show lower 2025 DVs in Central Texas from Houston to Dallas, the El Paso 

area (NM and TX) and Big Bend, Texas, and several states in the central U.S., including 

Oklahoma, Kansas, Missouri, Arkansas, Mississippi, Tennessee, and southern Kentucky. The 

new projections also show higher 2025 DVs in Denver, Las Vegas, Phoenix, Charlotte, the upper 

Midwest, and some parts of the New York/New Jersey areas. See Appendix 2A, Section 2A.4 

for detailed information on the updated DVs. 

2.4.2 Improvements in Air Quality Modeling

In this final RIA, we used emissions sensitivity simulations to determine the response of 

ozone at monitor locations to emissions changes in specific regions, similar to the approach used 

in the November 2014 proposal RIA (EPA, 2014a). However, when we reviewed the analysis 

for the November 2014 proposal RIA we determined that in certain locations (e.g., Texas and the 

Northeast) where violations of the 70 ppb scenario were limited to fairly localized areas, the 

analysis could be improved by using more geographically refined ozone response factors. In 

addition, we determined that smaller regions would also provide more refined ozone responses 

across the rest of the U.S. As a result, in this final RIA we designed 10 smaller regions to 

determine ozone response factors (see Figure 2-2), compared to the 5 larger regions used in the 

proposal RIA (see Figure 3-3 in EPA, 2014a). This more geographically refined resolution 

allows us to more accurately represent the increased effectiveness of emissions reductions closer 

to monitor locations compared to emissions reductions from sources that are further away. For 

example, in the proposal RIA, we analyzed one large Southwest region and made no 
2-27 

differentiation between the impacts of emissions from Nevada, Utah, New Mexico, Arizona, or 

Colorado on the monitors in Denver. In the final RIA, the smaller regions allow us to 

differentiate the impact of NOx emissions reductions in Colorado on ozone concentrations in 

Denver compared to NOx emissions reductions in Arizona and New Mexico on ozone in Denver. 

Similarly, in the final RIA we differentiate the impacts of east Texas emissions on ozone at 

Dallas and Houston monitors from impacts of emissions in west Texas, Louisiana, Oklahoma, 

Mississippi, Arkansas, Kansas and Missouri on those same monitors (in the proposal RIA, we 

used one large central U.S. region that did not differentiate these impacts). 

In Texas and the Northeast, the improved response factors resulted in larger changes in 

ozone concentrations in response to the more geographically focused emissions reductions. For 

example, in east Texas, emissions reductions were 2 to 3 times more effective at reducing ozone 

concentrations at controlling monitors in Houston and Dallas than equivalent regional emissions 

reductions used in the proposal. In the Northeast, local emissions reductions were 2.5 times 

more effective at reducing ozone concentrations at the controlling monitor on Long Island, NY 

than the equivalent regional emissions reductions used in the proposal. The more geographically 

refined modeling and improved ozone response factors resulted in fewer emissions reductions 

needed to meet a revised standard of 70 ppb and an alternative standard level of 65 ppb. For 

additional discussion on how these improved response factors affect emissions reductions needed 

to reach a revised standard of 70 ppb and an alternative standard level of 65 ppb, see Chapter 3, 

Section 3.3. For additional discussion on how the improved response factors and reduced 

emissions reductions impact cost estimates, see Chapter 4, Section 4.6, and for additional 

discussion on how this impacts benefits estimates, see Chapter 6, Section 6.1. 

2.5 References 

Abt Associates, 2014. User’s Guide: Modeled Attainment Test Software. 

http://www.epa.gov/scram001/modelingapps_mats.htm 

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. 

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. 
2-28 

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. 

National Research Council (NRC), 2002. Estimating the Public Health Benefits of Proposed Air Pollution 

Regulations, Washington, DC: National Academies Press. 

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 (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) Regulatory Impact Analysis of the Proposed Revisions to the 

National Ambient Air Quality Standards for Ground-Level Ozone, US EPA, OAQPS, 2014, RTP, EPA-452/P-14-

006, http://www.epa.gov/ttn/ecas/regdata/RIAs/20141125ria.pdf 

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) 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 (2014d) 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 

U.S. Environmental Protection Agency (2014e) 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 (2014f) 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 (2015) Preparation of Emissions Inventories for the Version 6.2, 2011 

Emissions Modeling Platform (http://www.epa.gov/ttn/chief/emch) 
2A-1 

APPENDIX 2A: ADDITIONAL AIR QUALITY ANALYSIS AND RESULTS

2A.1 2011 Emissions and Air Quality Modeling Platform 

2A.1.1 Photochemical Model Description and Modeling Domain 

CAMx is a three-dimensional grid-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 2A-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. 
2A-2 

Figure 2A-1. Map of the CAMx Modeling Domain Used for Ozone NAAQS RIA 

2A.1.2 Meteorological Inputs, Initial Conditions, and Boundary Conditions 

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, 
2A-3 

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. 

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 standard version 8-03-02 

(Yantosca and Carouge, 2010) 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/GEOS-5). This model was run for 2011 with a 

grid resolution of 2.0 degrees x 2.5 degrees (latitude-longitude). The predictions were used to 

provide one-way dynamic boundary conditions at one-hour 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 GEOS-Chem 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. 
2A-4 

2A.1.3 2025 Base Case Emissions Inputs 

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 were preprocessed into CAMx-ready inputs using the Sparse Matrix 

Operator Kernel Emissions (SMOKE) modeling system (Houyoux et al., 2000). 

The 2025 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 IPM35 version 5.14 

(http://epa.gov/powersectormodeling/psmodel514.html). 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. Improvements to the National Electric Energy Data System 

database, a key input in the preparation of future year EGU inventories, were implemented as a 

result of updated information becoming available and based on comments submitted in response 

the January 2014 Federal Register notice. 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 CSAPR issued July 6, 2011.36 

Projections for most stationary emissions sources other than EGUs (i.e., non-EGUs) were 

developed by using the EPA Control Strategy Tool (CoST) to create post-controls future year 

inventories. CoST is described in chapter 4 (section 4.1.1) and 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 received by EPA in response to the January 2014 Federal 

 

35 IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector. 

36 An emissions modeling sensitivity run described in Section 2.2.2 also includes a representation of EPA’s 

proposed carbon pollution guidelines under section 111(d) of the Clean Air Act (CAA). 
2A-5 

Register Notice, along with emissions reductions due to national and local rules, control 

programs, plant closures, consent decrees and settlements. Some improvements made based on 

comments included the use of growth and control factors provided by states and by regional 

organizations on behalf of states. Reductions to criteria air pollutant (CAP) emissions from 

stationary engines resulting as cobenefits to the Reciprocating Internal Combustion Engines 

(RICE) National Emission Standard for Hazardous Air Pollutants (NESHAP) are included. 

Reductions due to the New Source Performance Standards (NSPS) VOC controls for oil and gas 

sources, and the NSPS for process heaters, internal combustion engines, and natural gas turbines 

are also included. 

Regional projection factors for point and nonpoint oil and gas emissions were developed 

using Annual Energy Outlook (AEO) 2014 projections from year 2011 to year 2025 

(http://www.eia.gov/forecasts/aeo/). Projected emissions for corn ethanol, cellulosic 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). Airport-specific terminal area forecast (TAF) data were used for aircraft to 

account for projected changes in landing/takeoff activity. 

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. Fugitive dust for paved and 

unpaved roads take growth in VMT and population into account. 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. Impacts from the New Source 

Performance Standards (NSPS) for wood burning devices are also included. 

Projection factors for the remaining nonpoint sources such as stationary source fuel 

combustion, industrial processes, solvent utilization, and waste disposal, reflect emissions 

reductions due to control programs along with comments on the growth and control of these 
2A-6 

sources as a result of the January 2014 Federal Register notice and information gathered from 

prior rulemakings and outreach to states on emission inventories. Future year portable fuel 

container (PFC) inventories reflect the impact of the final Mobile Source Air Toxics (MSAT2). 

The MOVES2014-based 2025 onroad mobile source 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 2017 and Later Model Year Light-Duty Vehicle 

Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards (LD GHG), the 

Renewable Fuel Standard (RFS2), the Mobile Source Air Toxics Rule, the Light Duty Green 

House Gas/Corporate Average Fuel Efficiency (CAFE) standards for 2012-2016, the Greenhouse 

Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines 

and Vehicles, the Light-Duty Vehicle Tier 2 Rule, and the Heavy-Duty Diesel Rule. 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 

emissions sectors because that information was not included in the provided inventories. The 

input databases for MOVES, the methods for projecting activity data, and the emissions 

estimation methods implemented with MOVES were improved from those used in the proposal 

modeling with some improvements based on comments received via the January 2014 Federal 

Register notice. 

The nonroad mobile 2025 emissions, including railroads and commercial marine vessel 

emissions also include all national control programs. These control programs include the Clean 

Air Nonroad Diesel Rule – Tier 4, the Nonroad Spark Ignition rules, and the Locomotive-Marine 

Engine rule. For ocean-going vessels (Class 3 marine), 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 
2A-7 

the 2016 International Marine Organization (IMO) Tier 3 NOx controls. Control and growthrelated 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. 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. 

All modeled 2011 and 2025 emissions cases use the 2010 Canada emissions data. Note 

that 2010 is the latest year for which Environment 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, emissions compiled from the 

Inventario Nacional de Emisiones de Mexico, 2008 were used for 2011, as that was the latest 

complete inventory available. For 2025, projected emissions for the year 2025 based on the 

2008 inventory were used (ERG, 2014). Offshore oil platform emissions for the United States 

represent the year 2011 and are consistent with those in the 2011 National Emissions Inventory, 

version 2. Biogenic and fire emissions were held constant for all emissions cases and were based 

on 2011-specific data. Table 2A-1 shows the modeled 2011 and 2025 NOX and VOC emissions 

by sector. Additional details on the emissions by state are given in the emissions modeling TSD. 
2A-8 

Table 2A-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 2,000 1,400 36 42 

NonEGU-point 1,200 1,200 800 830 

Point oil and gas 500 460 160 190 

Wild and Prescribed Fires 330 330 4,700 4,700 

Nonpoint oil and gas 650 720 2,600 3,500 

Residential wood 

combustion 34 35 440 410 

Other nonpoint 760 790 3,700 3,500 

Nonroad 1,600 800 2,000 1,200 

Onroad 5,700 1,700 2,700 910 

C3 Commercial marine 

vessel (CMV) 130 100 5 9 

Locomotive and C1/C2 

CMV 1,100 680 48 24 

Biogenics 1,000 1,000 41,000 41,000 

TOTAL 15,000 9,300 58,000 56,000 

2A.1.4 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. The purpose of this 

evaluation is to examine the ability of the 2011 air quality modeling platform to represent the 

magnitude and spatial and temporal variability of measured (i.e., observed) ozone concentrations 

within the modeling domain. The evaluation presented here is based on model simulations using 

the v2 version of the 2011 emissions platform (i.e., case name 2011eh_cb6v2_v6_11g, also 

called the “final RIA 2011 base year” in chapter 2)37. The model evaluation for ozone focuses on 

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) (Figures 2A-2a and 2A-2b). 

Included in the evaluation are statistical measures of model performance based upon 

model-predicted versus observed concentrations that were paired in space and time. Model 

performance statistics were calculated for several spatial scales and temporal periods. Statistics 

were calculated for individual monitoring sites and for each of nine climate regions of the 12-km 

U.S. modeling domain. The regions include the Northeast, Ohio Valley, Upper Midwest, 

 

37 For an evaluation of the proposal RIA 2011 base year modeling, please see appendix 3-A of EPA, 2014d. 
2A-9 

Southeast, South, Southwest, Northern Rockies, Northwest and West38,39, which are defined 

based upon the states contained within the National Oceanic and Atmospheric Administration 

(NOAA) climate regions (Figure 2A-3)40 as were originally identified in Karl and Koss (1984). 

For maximum daily average 8-hour (MDA8) ozone, model performance statistics were 

created for each climate region for the May through September ozone season.41 In addition to the 

performance statistics, we prepared several graphical presentations of model performance for 

MDA8 ozone. These graphical presentations include: 

(1) density scatter plots of observed AQS data and predicted MDA8 ozone concentrations 

for May through September; 

(2) regional maps that 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 AQS and 

CASTNet monitoring sites; 

(3) bar and whisker plots that show the distribution of the predicted and observed MDA8 

ozone concentrations by month (May through September) and by region and by network; and 

(4) time series plots (May through September) of observed and predicted MDA8 ozone 

concentrations for 12 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 evaluation of the 

ozone predictions in the 2011 CAMx modeling platform, we have selected the mean bias, mean 

 

38 The nine climate regions are defined by States where: Northeast includes CT, DE, ME, MA, MD, NH, NJ, NY, 

PA, RI, and VT; Ohio Valley includes IL, IN, KY, MO, OH, TN, and WV; Upper Midwest includes IA, MI, MN, 

and WI; Southeast includes AL, FL, GA, NC, SC, and VA; South includes AR, KS, LA, MS, OK, and TX; 

Southwest includes AZ, CO, NM, and UT; Northern Rockies includes MT, NE, ND, SD, WY; Northwest includes 

ID, OR, and WA; and West includes CA and NV. 

39 Note most monitoring sites in the West region are located in California (see Figures 2A-2a and 2A-2b), therefore 

statistics for the West will be mostly representative of California ozone air quality. 

40 NOAA, National Centers for Environmental Information scientists have identified nine climatically consistent 

regions within the contiguous U.S., http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php. 41 In calculating the ozone season statistics we limited the data to those observed and predicted pairs with 

observations that are greater than or equal 60 ppb in order to focus on concentrations at the upper portion of the 

distribution of values. 
2A-10 

error, normalized mean bias, and normalized mean error to characterize model performance, 

statistics which are consistent with the recommendations in Simon et al. (2012) and the draft 

photochemical modeling guidance (US EPA, 2014c). As noted above, we calculated the 

performance statistics by climate region for the period of 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 = ଵ

௡ ∑ ሺܲെܱሻ ௡

ଵ , 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 = ଵ

௡ ∑ |ܲ െ ܱ| ௡

ଵ

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 

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 percentage units and is defined as: 

NMB = ∑ ሺ௉ିைሻ ೙

భ

∑ ሺைሻ ೙

భ

∗ 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 

percentage units and is defined as: 

NME = ∑ |௉ିை| ೙

భ

∑ ሺைሻ ೙

భ

∗ 100

As described in more detail below, the model performance statistics indicate that the 8-

hour daily maximum ozone concentrations predicted by the 2011 CAMx modeling platform 
2A-11 

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., 2012; 

US EPA, 2005; US EPA, 2009; US EPA, 2011). These other modeling studies represent a wide 

range of modeling analyses that 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 are within the range found in other recent 

peer-reviewed and regulatory applications. The model performance results, as described in this 

document, demonstrate that the predictions from the 2011 modeling platform closely replicate 

the corresponding observed concentrations in terms of the magnitude, temporal fluctuations, and 

spatial differences for 8-hour daily maximum ozone. 

The density scatter plots of MDA8 ozone are provided Figure 2A-4. 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 2A-2. 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 2A-5 through 2A-13. Spatial plots of the mean bias and error as well as the normalized 

mean bias and error for individual monitors are shown in Figures 2A-14 through 2A-17. The 

statistics shown in these two sets of 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 12 representative high ozone monitoring 

sites are provided in Figure 2A-18, (a) through (l). These sites are listed in Table 2A-3. 

The density scatter plots in Figure 2A-4 provide a qualitative comparison of modelpredicted and observed MDA8 ozone concentrations. In these plots the intensity of the colors 

indicates the density of individual observed/predicted paired values. The greatest number of 

individual paired values is denoted by the core area in white. The plots indicate that the 

predictions correspond closely to the observations in that a large number of observed/predicted 

paired values lie along or close to the 1:1 line shown on each plot. Overall, performance is best 

for observed values ≥ 60. The model tends to over-predict the observed values to some extent 
2A-12 

particularly at low and mid-range concentrations generally < 60 ppb in each of the regions. This 

feature is most evident in the South and Southeast states. In the West, high concentrations are 

under-predicted and low and mid-range concentrations are over-predicted. Observed and 

predicted values are in close agreement in the Southwest and Northwest regions. 

As indicated by the statistics in Table 2A-2, 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 at AQS sites in the Western region and at rural CASTNet 

sites in the South, Southwest and Western regions 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 

2A-5 through 2A-13. The distribution of predicted concentrations tends to be close to that of the 

observed data at the 25th percentile, median and 75th percentile values for each region, although 

there is a small persistent overestimation bias for these metrics in the Northeast, Southeast, and 

Ohio Valley regions, and under-prediction at CASTNet sites in the West and Southwest42. The 

CAMx model, as applied here, also has a tendency to under-predict the highest observational 

concentrations at both the AQS and CASTNet network sites. 

Figures 2A-14 through 2A-17 show the spatial variability in bias and error at monitor 

locations. Mean bias, as seen from Figure 2A-14, is less than 5 ppb at many sites across the East 

with over-prediction of 5 to 10 ppb at some sites from the Southeast into the Northeast. 

Elsewhere, mean bias is generally in the range of -5 to -10 ppb. Figure 2A-15 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 majority of monitoring sites across the modeling 

domain. There are regional differences in model performance, where the model tends to overpredict from the Southeast into the Northeast and generally under predict in the Southwest, 

Northern Rockies, Northwest and West. Model performance in the Ohio Valley and Upper 

Midwest states shows both under and over predictions. 

 

42 The over-prediction at CASTNet sites in the Northwest may not be representative of performance in rural areas of 

this region because there are so few observed and predicted data pairs in this region. 
2A-13 

Model error, as seen from Figure 2A-16, is 10 ppb or less at most of the sites across the 

modeling domain. Figure 2A-17 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 (i.e., greater 

than 15 percent) is evident at sites in several areas most notably along portions of the Northeast 

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 12 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 because they are high ozone 

sites in those urban areas with the highest projected ozone levels in the 2025 base case 

simulation. The results, as shown in Figures 2A-18 (a) through (l), 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 the day-to-day 

variability in the observations: Alleghany County, PA; 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; Queens 

County, NY; Suffolk County, NY; Sheboygan County, WI. Note that at the site in Brazoria 

County, TX there is an extended period from mid-July to mid-August with very low observed 

ozone concentrations, mostly in the range of 30 to 40 ppb. The model predicted values during 

this period in the range of 40 to 60 ppb which is not quite as low as the observed values. The 

sites in Douglas County, CO and Harford County, MD closely track the day-to-day variability in 

the observed MDA8 values, but some days are over predicted while other days are under 

predicted to some extent. Finally, the daily modeled ozone at the two California sites evaluated 

correlates well with observations but has a persistent low bias. Looking across all 12 sites 

indicates that the modeling platform is able to capture the site to site differences in the short-term 

variability of ozone concentrations. 
2A-14 

Figure 2A-2a. AQS Ozone Monitoring Sites 

Figure 2A-2b. CASTNet Ozone Monitoring Sites 
2A-15 

Figure 2A-3. NOAA Nine Climate Regions (source: http://www.ncdc.noaa.gov/monitoringreferences/maps/us-climate-regions.php#references) 

Table 2A-2. MDA8 Ozone Performance Statistics Greater than or Equal to 60 Ppb for 

May through September by Climate Region, by Network 

Network Climate region 

No. of 

Obs MB ME 

NMB 

(%) 

NME 

(%) 

AQS 

Northeast 3,998 2.2 7.4 3.2 10.8 

Ohio Valley 6,325 0.3 7.6 0.4 11.3 

Upper Midwest 1,162 -3.0 7.5 -4.4 11.0 

Southeast 37,280 -2.5 8.1 -3.6 11.9 

South 5,694 -3.7 8.1 -5.4 11.7 

Southwest 6,033 -5.2 7.9 -7.8 12.0 

Northern Rockies 380 -5.9 7.4 -9.4 11.7 

Northwest 79 -5.4 8.1 -8.5 12.6 

West 8,665 -7.3 9.5 -10.3 13.5 

CASTNet 

Northeast 264 2.3 6.1 3.4 9.1 

Ohio Valley 107 -2.3 6.2 -3.4 9.4 

Upper Midwest 38 -3.9 5.9 -5.8 8.8 

Southeast 2,068 -5.0 8.2 -7.5 12.1 

South 215 -7.1 8.0 -10.7 12.0 

Southwest 382 -7.7 8.6 -11.7 13.1 

Northern Rockies 110 -7.8 8.1 -12.2 12.8 

Northwest - - - - - 

West 425 -12.1 12.5 -16.6 17.1 
2A-16 

Figure 2A-4. Density Scatter Plots of Observed/Predicted MDA8 Ozone for the Northeast, 

Ohio River Valley, Upper Midwest, Southeast, South, Southwest, Northern 

Rockies, Northwest and West Regions 
2A-17 

Figure 2A-5. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Northeast Region, (a) AQS Network 

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

percentiles; top/bottom line = max/min values] 

Figure 2A-6. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Ohio Valley Region, (a) AQS Network 

and (b) CASTNet Network 

a b 

a b 
2A-18 

Figure 2A-7. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Upper Midwest Region, (a) AQS 

Network and (b) CASTNet Network 

Figure 2A-8. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Southeast Region, (a) AQS Network and 

(b) CASTNet Network 

a b 

a b 
2A-19 

 

Figure 2A-9. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the South Region, (a) AQS Network and (b) 

CASTNet Network 

Figure 2A-10. Distribution of Observed and Predicted MDA8 Ozone by Month for 

the Period May through September for the Southwest Region, (a) AQS Network 

and (b) CASTNet Network 

a b 

a b 
2A-20 

Figure 2A-11. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Northern Rockies Region, (a) AQS 

Network and (b) CASTNet Network 

Figure 2A-12. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the Northwest Region, (a) AQS Network and 

(b) CASTNet Network 

a b 

a b 
2A-21 

Figure 2A-13. Distribution of Observed and Predicted MDA8 Ozone by Month for the 

Period May through September for the West Region, (a) AQS Network and (b) 

CASTNet Network 

 

Figure 2A-14. Mean Bias (ppb) of MDA8 Ozone Greater than or Equal to 60 ppb over the 

Period May-September 2011 at AQS and CASTNet Monitoring 

a b 
2A-22 

Figure 2A-15. Normalized Mean Bias (%) of MDA8 Ozone Greater than or Equal to 60 ppb 

over the Period May-September 2011 at AQS and CASTNet Monitoring Sites 

Figure 2A-16. Mean Error (ppb) of MDA8 Ozone Greater than or Equal to 60 ppb over the 

Period May-September 2011 at AQS and CASTNet Monitoring Sites 
2A-23 

Figure 2A-17. Normalized Mean Error (%) of MDA8 Ozone Greater than or Equal to 60 

ppb over the Period May-September 2011 at AQS and CASTNet Monitoring 

Sites 

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

County State Monitoring

Site ID

Queens New York 360810124 

Suffolk New York 361030002 

Harford Maryland 240251001 

Allegheny Pennsylvania 420031005 

Jefferson Kentucky 211110067 

Wayne Michigan 261630019 

Sheboygan Wisconsin 551170006 

Tarrant Texas 484392003 

Brazoria Texas 480391004 

Douglas Colorado 80350004 

Fresno California 60195001 

San Bernardino California 60710005 
2A-24 

Figure 2A-18a. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May 

through September 2011 at Site 360810124 in Queens, New York 

Figure 2A-18b. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May 

through September 2011 at Site 361030002 in Suffolk County, New York 
2A-25 

Figure 2A-18c. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May 

through September 2011 at Site 240251001 in Harford Co., Maryland 

Figure 2A-18d. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May 

through September 2011 at Site 420031005 in Allegheny Co., Pennsylvania 
2A-26 

Figure 2A-18e. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May 

through September 2011 at Site 211110067 in Jefferson Co., Kentucky 

Figure 2A-18f. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May 

through September 2011 at Site 261630019 in Wayne Co., Michigan 
2A-27 

Figure 2A-18g. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May 

through September 2011 at Site 551170006 in Sheboygan Co., Wisconsin 

Figure 2A-18h. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May 

through September 2011 at Site 484392003 in Tarrant Co., Texas 
2A-28 

Figure 2A-18i. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May 

through September 2011 at Site 480391004 in Brazoria Co., Texas 

Figure 2A-18j. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May 

through September 2011 at Site 80350004 in Douglas Co., Colorado 
2A-29 

Figure 2A-18k. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May 

through September 2011 at Site 60195001 in Fresno Co., California 

Figure 2A-18l. Time Series of Observed (black) and Predicted (red) MDA8 Ozone for May 

through September 2011 at Site 60710005 in San Bernardino Co., 

California 
2A-30 

2A.2 VOC Impact Regions 

As described in Chapter 2, we defined VOC impact regions for the following urban areas: New 

York City, Chicago, Louisville, Houston, Denver, Northern California and Southern California.43 

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 2A-19 shows the 

impact of 50% U.S. anthropogenic VOC cuts on July monthly average 8-hour daily maximum 

ozone concentrations across the US. Ozone in each of the areas listed above is shown to have at 

least 0.2 ppb response to VOC emissions cuts.

Figure 2A-19. Change in July Average of 8-hr Daily Maximum Ozone Concentration 

(ppb) Due to 50% Cut in U.S. Anthropogenic VOC Emissions 

2A.3 Monitors Excluded from the Quantitative Analysis 

There were 1,225 ozone monitors with complete ozone data for at least one DV period 

covering the years 2009-2013. Of those sites, we quantitatively analyzed 1,165 in this analysis. 

As discussed in Chapter 2, 60 sites were excluded from the quantitative analysis of emissions 

 

43 Other local VOC areas that had similar levels of ozone response to the 50% VOC reduction were also explored 

but were found not to be helpful in reaching alternative NAAQS levels in this analysis: Dallas, Detroit, Pittsburgh, 

and Baltimore. This may be due to the construct of the attainment scenarios explored here and does not mean that 

VOC controls might not be effective in these areas under alternate assumptions about regional NOx controls.
2A-31 

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. 

2A.3.1 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 

fewer) at the site to compute a design value according to EPA SIP modeling guidance (US EPA, 

2014c). A list of the 41 sites in this category is given in Table 2A-4. 

Table 2A-4. Monitors without Projections due to Insufficient High Modeling Days to 

Meet EPA Guidance for Projecting Design Values 

Site ID Lat Long State County 

60010009 37.74307 -122.17 California Alameda 

60010011 37.81478 -122.282 California Alameda 

60131004 37.9604 -122.357 California Contra Costa 

60231004 40.77694 -124.178 California Humboldt 

60450008 39.14566 -123.203 California Mendocino 

60750005 37.76595 -122.399 California San Francisco 

60811001 37.48293 -122.203 California San Mateo 

60932001 41.72689 -122.634 California Siskiyou 

160230101 43.46056 -113.562 Idaho Butte 

230031100 46.69643 -68.033 Maine Aroostook 

260330901 46.49361 -84.3642 Michigan Chippewa 

270052013 46.85181 -95.8463 Minnesota Becker 

270177416 46.70527 -92.5238 Minnesota Carlton 

270750005 47.94862 -91.4956 Minnesota Lake 

270834210 44.4438 -95.8179 Minnesota Lyon 

271370034 48.41333 -92.8306 Minnesota Saint Louis 

300298001 48.51017 -113.997 Montana Flathead 

300490004 46.8505 -111.987 Montana Lewis and Clark 

311079991 42.8292 -97.854 Nebraska Knox 

380070002 46.8943 -103.379 North Dakota Billings 

380130004 48.64193 -102.402 North Dakota Burke 

380150003 46.82543 -100.768 North Dakota Burleigh 

380171004 46.93375 -96.8554 North Dakota Cass 

380250003 47.3132 -102.527 North Dakota Dunn 

380530002 47.5812 -103.3 North Dakota McKenzie 

380570004 47.29861 -101.767 North Dakota Mercer 

380650002 47.18583 -101.428 North Dakota Oliver 

410170122 44.0219 -121.26 Oregon Deschutes 
2A-32 

Site ID Lat Long State County 

410290201 42.22989 -122.788 Oregon Jackson 

410591003 45.82897 -119.263 Oregon Umatilla 

460110003 44.3486 -96.8073 South Dakota Brookings 

530090013 48.29786 -124.625 Washington Clallam 

530330080 47.56824 -122.309 Washington King 

530530012 46.7841 -121.74 Washington Pierce 

530531010 46.75833 -122.124 Washington Pierce 

530570020 48.39779 -122.505 Washington Skagit 

530730005 48.95074 -122.554 Washington Whatcom 

550030010 46.602 -90.656 Wisconsin Ashland 

551250001 46.052 -89.653 Wisconsin Vilas 

560390008 43.67083 -110.599 Wyoming Teton 

560391011 44.55972 -110.401 Wyoming Teton 

2A.3.2 Winter Ozone 

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 conducive to 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 
2A-33 

(November-March). The seven sites identified as being affected by wintertime ozone events are 

listed in Table 2A-5. 

Table 2A-5. 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 

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). 

2A.3.3 Monitoring Sites in Rural/Remote Areas of the West and Southwest 

As mentioned in Chapter 2, model-predicted ozone concentrations at 12 sites in 

rural/remote areas in the West and Southwest were excluded from the quantitative analysis. 

These 12 sites are a subset of 26 sites identified in the November 2014 RIA proposal (US EPA, 

2014d). The original 26 sites had two common characteristics. First, they had small modeled 

responses 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. All of these 12 

monitoring sites have 2025 baseline concentrations below 70 ppb. Therefore, no emissions 

reductions would be required for these sites to meet a primary standard of 70 ppb in 2025. Of 

the 26 sites identified in the RIA proposal, only the sites with 2025 baseline DVs (or post-2025 

baseline DVs for CA) above 65 ppb are excluded from this analysis. More details on these 12 

sites are provided in Table 2A-6. We have qualitatively characterized the predominant ozone 

influence for each site in Table 2A-6. 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 trans-border pollution) and altitude (e.g., potential influence of 

ozone transported from the free troposphere: stratospheric intrusions or long range transport of 
2A-34 

international anthropogenic ozone). Figure 2A-20 shows the location of all sites listed in Table 

2A-7 and for demonstrative purposes assigns each site to a category based on the predominant 

source of ozone in that location. The table and figure indicate that all 12 sites have 2025 or post2025 baseline design values below 70 ppb as mentioned above. Of the 12 sites, 5 sites are 

characterized as border sites, 5 sites are characterized as being strongly influenced by California 

emissions, and 2 sites are influenced by other ozone sources. 

Table 2A-6. Monitors with Limited Response to Regional NOx and National VOC 

Emissions Reductions in the 2025 and Post-2025 Baselines 

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 

Yuma Supersite 40278011 Arizona Yuma 51 SLAMS Mexican border 

+ California 75 66 

El Centro-9th st 60251003 California Imperial - SLAMS California + 

Mexican Border 81 68 

Yosemite NP 60430003 California Mariposa 5265 CASTNET California + 

Other sources 77 67 

Sequoia and Kings 

Canyon NP 61070006 California Tulare 1890 

Non-EPA 

Federal 

(NPS) 

California + 

Other sources 81 69 

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 

BLM land near 

Carlsbad 350151005 New 

Mexico Eddy 780 SLAMS 

Central region + 

Southwest 

region + 

Mexican border 

70 67 

Big Bend NP 480430101 Texas Brewster 1052 CASTNET Mexican border 70 68 

BLM 

Land/Carlsbad 483819991 Texas Randall 780 SLAMS 

Central region + 

Mexican border 

+ Other sources 

73 66 

Zion NP 490530130 Utah Washington 1213 

Non-EPA 

Federal 

(NPS) 

California + 

Other sources 71 66 
2A-35 

Figure 2A-20. Location of Sites Identified in Table 2A-6 

In Figure 2A-20, 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 2A-6 are shown as small diamonds. Gray 

diamonds represent sites that had DVs less than or equal to 65 ppb in the 2025 baseline (or post2025 baseline for California sites). Black diamonds represent sites that had DVs greater than 65 

ppb in the 2025 baseline (or post-2025 baseline for California sites). 
2A-36 

2A.4 Design Values for All Monitors Included in the Quantitative Analysis 

In addition to other information, Tables 2A-7 and 2A-8 provide baseline design values 

corresponding to the information presented in the maps in Figures 3-5 and 3-11. Note that some 

counties contain more than one monitor and the highest monitor is used for the map. 

Table 2A-7. Design Values (ppb) for California Monitors 

Site ID Lat Long State County

O3 DV for Scenario:

Base 

Case Baseline 70 65 

60010007 37.68753 -121.784 California Alameda 67 62 58 54 

60012001 37.65446 -122.032 California Alameda 54 50 48 45 

60050002 38.33991 -120.764 California Amador 60 55 51 47 

60070007 39.71404 -121.619 California Butte 61 56 52 48 

60070008 39.76154 -121.842 California Butte 53 49 46 43 

60090001 38.20185 -120.682 California Calaveras 63 57 54 50 

60111002 39.20294 -122.018 California Colusa 53 49 46 43 

60130002 37.93601 -122.026 California Contra Costa 66 61 57 53 

60131002 38.00631 -121.642 California Contra Costa 64 58 55 51 

60170010 38.72528 -120.822 California El Dorado 66 60 55 50 

60170012 38.81161 -120.033 California El Dorado 62 59 57 56 

60170020 38.89094 -121.003 California El Dorado 66 60 55 50 

60190007 36.70551 -119.742 California Fresno 82 74 70 65 

60190011 36.78532 -119.774 California Fresno 81 73 69 64 

60190242 36.84139 -119.874 California Fresno 81 75 70 66 

60192009 36.63423 -120.382 California Fresno 64 59 55 52 

60194001 36.5975 -119.504 California Fresno 77 69 65 60 

60195001 36.81911 -119.717 California Fresno 83 75 70 65 

60210003 39.53376 -122.192 California Glenn 56 52 49 46 

60250005 32.67619 -115.484 California Imperial 71 63 62 61 

60254003 33.0325 -115.624 California Imperial 66 56 54 53 

60254004 33.21361 -115.545 California Imperial 64 53 52 50 

60270101 36.50861 -116.848 California Inyo 67 64 63 63 

60290007 35.34609 -118.852 California Kern 80 74 69 64 

60290008 35.05444 -119.404 California Kern 75 69 65 61 

60290011 35.05055 -118.147 California Kern 71 62 60 57 

60290014 35.35609 -119.041 California Kern 77 70 66 61 

60290232 35.43887 -119.017 California Kern 76 70 66 61 

60295002 35.23668 -118.789 California Kern 74 67 63 59 

60296001 35.50359 -119.273 California Kern 74 68 65 61 

60311004 36.3144 -119.645 California Kings 74 68 63 59 

60333001 39.0327 -122.922 California Lake 50 47 44 42 
2A-37 

Site ID Lat Long State County

O3 DV for Scenario:

Base 

Case Baseline 70 65 

60370002 34.1365 -117.924 California Los Angeles 75 56 52 48 

60370016 34.14435 -117.85 California Los Angeles 88 65 61 56 

60370113 34.05111 -118.456 California Los Angeles 62 49 46 42 

60371002 34.17605 -118.317 California Los Angeles 72 53 49 45 

60371103 34.06659 -118.227 California Los Angeles 61 46 42 39 

60371201 34.19925 -118.533 California Los Angeles 83 64 60 56 

60371302 33.90139 -118.205 California Los Angeles 58 54 52 50 

60371602 34.01194 -118.07 California Los Angeles 63 52 48 44 

60371701 34.06703 -117.751 California Los Angeles 79 61 57 52 

60372005 34.1326 -118.127 California Los Angeles 73 54 50 46 

60374002 33.82376 -118.189 California Los Angeles 57 52 50 49 

60376012 34.38344 -118.528 California Los Angeles 89 66 61 57 

60379033 34.67139 -118.131 California Los Angeles 80 62 59 55 

60390004 36.86667 -120.01 California Madera 70 64 61 57 

60392010 36.95326 -120.034 California Madera 74 68 64 60 

60410001 37.97231 -122.52 California Marin 47 44 42 39 

60430006 37.54993 -119.845 California Mariposa 65 61 58 55 

60470003 37.2816 -120.435 California Merced 72 66 62 58 

60530002 36.49577 -121.732 California Monterey 50 44 42 40 

60530008 36.20929 -121.126 California Monterey 50 44 42 40 

60531003 36.69676 -121.637 California Monterey 46 40 38 36 

60550003 38.31094 -122.296 California Napa 53 49 46 43 

60570005 39.23433 -121.057 California Nevada 62 57 53 49 

60570007 39.31656 -120.845 California Nevada 60 55 51 47 

60590007 33.83062 -117.938 California Orange 62 51 48 46 

60591003 33.67464 -117.926 California Orange 60 50 47 45 

60592022 33.63003 -117.676 California Orange 62 46 43 41 

60595001 33.92513 -117.953 California Orange 68 55 52 49 

60610003 38.93568 -121.1 California Placer 67 60 55 50 

60610004 39.10028 -120.953 California Placer 60 54 50 47 

60610006 38.74573 -121.266 California Placer 70 64 58 53 

60650004 34.007 -117.521 California Riverside 78 61 57 54 

60650008 33.7411 -115.821 California Riverside 56 47 46 45 

60650009 33.44787 -117.089 California Riverside 60 45 43 41 

60650012 33.92086 -116.858 California Riverside 87 65 61 57 

60650016 33.58333 -117.083 California Riverside 64 48 45 42 

60651016 33.945 -116.83 California Riverside 88 66 62 58 

60652002 33.70853 -116.215 California Riverside 74 60 58 55 

60655001 33.85275 -116.541 California Riverside 81 63 60 57 

60656001 33.78942 -117.228 California Riverside 80 59 55 52 
2A-38 

Site ID Lat Long State County

O3 DV for Scenario:

Base 

Case Baseline 70 65 

60658001 33.99958 -117.416 California Riverside 88 67 63 58 

60658005 33.99564 -117.493 California Riverside 84 64 60 56 

60659001 33.67649 -117.331 California Riverside 75 55 52 49 

60659003 33.61241 -114.603 California Riverside 60 54 53 52 

60670002 38.71209 -121.381 California Sacramento 66 60 55 50 

60670006 38.61378 -121.368 California Sacramento 67 61 56 51 

60670010 38.55823 -121.493 California Sacramento 62 57 52 48 

60670011 38.30259 -121.421 California Sacramento 63 57 53 49 

60670012 38.6833 -121.164 California Sacramento 76 69 63 58 

60670014 38.65078 -121.507 California Sacramento 60 55 51 47 

60675003 38.49448 -121.211 California Sacramento 72 66 60 55 

60690002 36.8441 -121.362 California San Benito 54 47 45 43 

60690003 36.48522 -121.157 California San Benito 62 55 52 50 

60710001 34.89501 -117.024 California San Bernardino 70 58 56 53 

60710005 34.2431 -117.272 California San Bernardino 100 75 70 65 

60710012 34.42613 -117.564 California San Bernardino 86 66 63 59 

60710306 34.51001 -117.331 California San Bernardino 77 61 57 54 

60711004 34.10374 -117.629 California San Bernardino 91 69 65 60 

60711234 35.76387 -117.397 California San Bernardino 64 61 60 59 

60712002 34.10002 -117.492 California San Bernardino 97 74 69 64 

60714001 34.41807 -117.286 California San Bernardino 89 68 64 59 

60714003 34.05977 -117.147 California San Bernardino 96 72 67 62 

60719002 34.07139 -116.391 California San Bernardino 82 66 63 61 

60719004 34.10688 -117.274 California San Bernardino 91 67 63 58 

60730001 32.63123 -117.059 California San Diego 59 52 51 50 

60730003 32.79119 -116.942 California San Diego 63 50 48 46 

60730006 32.83646 -117.129 California San Diego 63 51 49 47 

60731001 32.95212 -117.264 California San Diego 58 49 47 46 

60731002 33.12771 -117.075 California San Diego 58 45 43 41 

60731006 32.84224 -116.768 California San Diego 71 55 53 51 

60731008 33.21703 -117.396 California San Diego 57 44 42 41 

60731010 32.70149 -117.15 California San Diego 54 48 47 46 

60731016 32.84547 -117.124 California San Diego 59 48 46 44 

60731201 33.36259 -117.09 California San Diego 59 45 43 41 

60732007 32.55216 -116.938 California San Diego 54 48 47 46 

60771002 37.95074 -121.269 California San Joaquin 59 54 50 46 

60773005 37.6825 -121.441 California San Joaquin 71 65 61 57 

60790005 35.63163 -120.691 California San Luis Obispo 56 51 49 47 

60792006 35.25658 -120.67 California San Luis Obispo 47 42 41 39 

60793001 35.36631 -120.843 California San Luis Obispo 47 42 41 40 
2A-39 

Site ID Lat Long State County

O3 DV for Scenario:

Base 

Case Baseline 70 65 

60794002 35.03146 -120.501 California San Luis Obispo 51 45 43 41 

60798001 35.49158 -120.668 California San Luis Obispo 54 49 47 45 

60798005 35.64368 -120.231 California San Luis Obispo 68 62 59 56 

60798006 35.35472 -120.04 California San Luis Obispo 65 60 57 54 

60830008 34.46245 -120.026 California Santa Barbara 52 46 45 44 

60830011 34.42778 -119.691 California Santa Barbara 50 43 42 41 

60831008 34.94915 -120.438 California Santa Barbara 43 38 36 35 

60831013 34.72556 -120.428 California Santa Barbara 55 48 46 45 

60831014 34.54166 -119.791 California Santa Barbara 59 51 50 48 

60831018 34.52744 -120.197 California Santa Barbara 49 45 44 43 

60831021 34.40278 -119.458 California Santa Barbara 59 52 50 49 

60831025 34.48974 -120.047 California Santa Barbara 61 54 53 52 

60832004 34.63782 -120.458 California Santa Barbara 47 42 41 40 

60832011 34.44551 -119.828 California Santa Barbara 50 44 43 42 

60833001 34.60582 -120.075 California Santa Barbara 53 46 45 43 

60834003 34.59611 -120.63 California Santa Barbara 54 49 48 46 

60850002 36.99957 -121.575 California Santa Clara 60 54 50 47 

60850005 37.3485 -121.895 California Santa Clara 58 54 51 48 

60851001 37.22686 -121.98 California Santa Clara 61 56 53 49 

60852006 37.07938 -121.6 California Santa Clara 63 58 54 51 

60852009 37.31844 -122.07 California Santa Clara 58 54 51 47 

60870007 36.98392 -121.989 California Santa Cruz 48 44 41 39 

60890004 40.54958 -122.38 California Shasta 51 46 43 40 

60890007 40.45291 -122.299 California Shasta 57 52 48 45 

60890009 40.68925 -122.402 California Shasta 59 54 50 47 

60893003 40.53681 -121.574 California Shasta 58 55 53 51 

60950004 38.10251 -122.238 California Solano 53 49 47 43 

60950005 38.22707 -122.076 California Solano 57 52 49 45 

60953003 38.35837 -121.95 California Solano 58 54 50 47 

60970003 38.4435 -122.71 California Sonoma 39 37 35 33 

60990005 37.64158 -120.995 California Stanislaus 67 61 57 53 

60990006 37.48798 -120.837 California Stanislaus 77 70 65 60 

61010003 39.13877 -121.619 California Sutter 54 50 46 43 

61010004 39.20557 -121.82 California Sutter 63 58 54 51 

61030004 40.26208 -122.094 California Tehama 64 59 55 52 

61030005 40.17583 -122.237 California Tehama 62 57 54 51 

61070009 36.48944 -118.829 California Tulare 79 73 69 65 

61072002 36.33218 -119.291 California Tulare 71 65 61 57 

61072010 36.03183 -119.055 California Tulare 76 70 66 62 

61090005 37.98158 -120.38 California Tuolumne 61 57 53 50 
2A-40 

Site ID Lat Long State County

O3 DV for Scenario:

Base 

Case Baseline 70 65 

61110007 34.20824 -118.869 California Ventura 65 51 48 46 

61110009 34.40285 -118.81 California Ventura 65 51 49 46 

61111004 34.44657 -119.23 California Ventura 67 58 56 54 

61112002 34.27574 -118.685 California Ventura 73 57 54 51 

61113001 34.25324 -119.143 California Ventura 55 46 44 42 

61130004 38.53445 -121.773 California Yolo 58 53 50 47 

61131003 38.66121 -121.733 California Yolo 60 55 51 48 

Table 2A-8. Design Values (ppb) for Continental U.S. Monitors outside of California 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

10030010 30.498 -87.8814 Alabama Baldwin 53 52 52 51 

10331002 34.75878 -87.6506 Alabama Colbert 47 45 45 43 

10499991 34.2888 -85.9698 Alabama DeKalb 51 50 50 47 

10510001 32.49857 -86.1366 Alabama Elmore 50 48 48 47 

10550011 33.90404 -86.0539 Alabama Etowah 47 46 46 45 

10690004 31.19066 -85.4231 Alabama Houston 50 49 49 48 

10730023 33.55306 -86.815 Alabama Jefferson 55 54 54 53 

10731003 33.48556 -86.915 Alabama Jefferson 56 55 54 54 

10731005 33.33111 -87.0036 Alabama Jefferson 57 55 55 54 

10731009 33.45972 -87.3056 Alabama Jefferson 56 55 55 54 

10731010 33.54528 -86.5492 Alabama Jefferson 56 55 54 54 

10732006 33.38639 -86.8167 Alabama Jefferson 56 55 55 54 

10735002 33.70472 -86.6692 Alabama Jefferson 54 53 53 52 

10735003 33.80167 -86.9425 Alabama Jefferson 55 54 53 53 

10736002 33.57833 -86.7739 Alabama Jefferson 59 57 57 56 

10890014 34.68767 -86.5864 Alabama Madison 54 53 53 51 

10890022 34.77273 -86.7562 Alabama Madison 51 50 50 48 

10970003 30.76994 -88.0875 Alabama Mobile 53 51 51 51 

10972005 30.47467 -88.1411 Alabama Mobile 53 52 52 51 

11011002 32.40712 -86.2564 Alabama Montgomery 50 49 49 48 

11030011 34.51874 -86.9769 Alabama Morgan 54 54 54 52 

11130002 32.46797 -85.0838 Alabama Russell 50 49 49 48 

11170004 33.31732 -86.8251 Alabama Shelby 55 54 54 53 

11190002 32.36401 -88.2019 Alabama Sumter 50 47 47 46 

11250010 33.0896 -87.4597 Alabama Tuscaloosa 46 45 45 44 

40051008 35.20611 -111.653 Arizona Coconino 63 63 63 63 

40070010 33.6547 -111.107 Arizona Gila 63 63 63 61 
2A-41 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

40128000 34.2319 -113.58 Arizona La Paz 65 65 65 65 

40130019 33.48385 -112.143 Arizona Maricopa 67 67 67 65 

40131004 33.56033 -112.066 Arizona Maricopa 69 68 68 65 

40131010 33.45223 -111.733 Arizona Maricopa 59 59 59 56 

40132001 33.57454 -112.192 Arizona Maricopa 65 65 65 62 

40132005 33.70633 -111.856 Arizona Maricopa 65 65 65 62 

40133002 33.45793 -112.046 Arizona Maricopa 65 64 64 62 

40133003 33.47968 -111.917 Arizona Maricopa 66 65 65 63 

40134003 33.40316 -112.075 Arizona Maricopa 67 67 67 64 

40134004 33.29898 -111.884 Arizona Maricopa 63 62 62 60 

40134005 33.4124 -111.935 Arizona Maricopa 60 60 60 58 

40134008 33.82169 -112.017 Arizona Maricopa 64 64 64 61 

40134010 33.63713 -112.342 Arizona Maricopa 60 60 60 57 

40134011 33.37005 -112.621 Arizona Maricopa 57 57 57 55 

40137003 33.29023 -112.161 Arizona Maricopa 61 61 61 59 

40137020 33.48824 -111.856 Arizona Maricopa 64 63 63 61 

40137021 33.50799 -111.755 Arizona Maricopa 66 65 65 63 

40137022 33.47461 -111.806 Arizona Maricopa 63 62 62 60 

40137024 33.50813 -111.839 Arizona Maricopa 63 63 63 61 

40139508 33.9828 -111.799 Arizona Maricopa 61 61 61 59 

40139702 33.54549 -111.609 Arizona Maricopa 64 63 63 61 

40139704 33.61103 -111.725 Arizona Maricopa 64 63 63 61 

40139706 33.71881 -111.672 Arizona Maricopa 63 63 63 60 

40139997 33.50383 -112.096 Arizona Maricopa 67 67 67 64 

40170119 34.8225 -109.892 Arizona Navajo 61 59 59 58 

40190021 32.17454 -110.737 Arizona Pima 61 58 58 57 

40191011 32.20441 -110.878 Arizona Pima 57 55 55 53 

40191018 32.42526 -111.064 Arizona Pima 59 58 58 56 

40191020 32.04767 -110.774 Arizona Pima 60 56 56 54 

40191028 32.29515 -110.982 Arizona Pima 57 55 55 54 

40191030 31.87952 -110.996 Arizona Pima 59 56 56 55 

40191032 32.173 -110.98 Arizona Pima 57 54 54 53 

40191034 32.38082 -111.127 Arizona Pima 56 55 54 53 

40213001 33.4214 -111.544 Arizona Pinal 62 62 62 59 

40213003 32.95436 -111.762 Arizona Pinal 59 59 59 57 

40213007 32.50831 -111.308 Arizona Pinal 61 60 60 59 

40217001 33.08009 -111.74 Arizona Pinal 61 61 61 59 

40218001 33.29347 -111.286 Arizona Pinal 65 64 64 62 

40258033 34.5467 -112.476 Arizona Yavapai 63 63 63 63 

50199991 34.1795 -93.0988 Arkansas Clark 55 53 52 49 
2A-42 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

50350005 35.19729 -90.1931 Arkansas Crittenden 61 60 60 54 

51010002 35.83273 -93.2083 Arkansas Newton 55 53 53 52 

51130003 34.45441 -94.1433 Arkansas Polk 64 62 60 57 

51190007 34.75619 -92.2813 Arkansas Pulaski 53 50 49 48 

51191002 34.83572 -92.2606 Arkansas Pulaski 56 52 52 50 

51191008 34.68134 -92.3287 Arkansas Pulaski 56 53 52 51 

51430005 36.1797 -94.1168 Arkansas Washington 60 58 57 56 

80013001 39.83812 -104.95 Colorado Adams 66 66 65 60 

80050002 39.56789 -104.957 Colorado Arapahoe 70 70 69 64 

80050006 39.63852 -104.569 Colorado Arapahoe 64 64 63 59 

80130011 39.95721 -105.238 Colorado Boulder 65 65 64 60 

80310014 39.75176 -105.031 Colorado Denver 63 63 62 58 

80310025 39.70401 -104.998 Colorado Denver 62 62 61 57 

80350004 39.53449 -105.07 Colorado Douglas 70 70 69 64 

80410013 38.95834 -104.817 Colorado El Paso 64 64 63 61 

80410016 38.8531 -104.901 Colorado El Paso 65 65 65 63 

80450012 39.54182 -107.784 Colorado Garfield 63 63 63 60 

80519991 38.9564 -106.986 Colorado Gunnison 64 64 64 63 

80590002 39.80033 -105.1 Colorado Jefferson 62 62 62 57 

80590005 39.63878 -105.139 Colorado Jefferson 66 67 66 61 

80590006 39.9128 -105.189 Colorado Jefferson 71 71 70 65 

80590011 39.74372 -105.178 Colorado Jefferson 71 71 70 65 

80590013 39.54152 -105.298 Colorado Jefferson 63 63 62 58 

80677001 37.13678 -107.629 Colorado La Plata 64 63 63 63 

80677003 37.10258 -107.87 Colorado La Plata 62 62 62 61 

80690007 40.2772 -105.546 Colorado Larimer 66 66 66 61 

80690011 40.59254 -105.141 Colorado Larimer 71 71 70 65 

80690012 40.6421 -105.275 Colorado Larimer 64 64 63 59 

80691004 40.57747 -105.079 Colorado Larimer 64 64 63 58 

80770020 39.13058 -108.314 Colorado Mesa 64 64 64 62 

80810002 40.50695 -107.891 Colorado Moffat 60 59 59 58 

80830006 37.35005 -108.592 Colorado Montezuma 61 61 61 60 

80830101 37.19833 -108.49 Colorado Montezuma 60 60 60 59 

81030005 40.03889 -107.848 Colorado Rio Blanco 60 60 59 58 

81230009 40.38637 -104.737 Colorado Weld 70 70 69 64 

90010017 41.00361 -73.585 Connecticut Fairfield 70 70 67 59 

90011123 41.39917 -73.4431 Connecticut Fairfield 65 65 62 54 

90013007 41.1525 -73.1031 Connecticut Fairfield 71 70 68 60 

90019003 41.11833 -73.3367 Connecticut Fairfield 73 72 70 62 

90031003 41.78472 -72.6317 Connecticut Hartford 61 61 58 51 
2A-43 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

90050005 41.82134 -73.2973 Connecticut Litchfield 57 56 54 47 

90070007 41.55222 -72.63 Connecticut Middlesex 65 64 61 53 

90090027 41.3014 -72.9029 Connecticut New Haven 63 63 61 54 

90099002 41.26083 -72.55 Connecticut New Haven 71 71 68 60 

90110124 41.35362 -72.0788 Connecticut New London 66 65 63 56 

90131001 41.97639 -72.3881 Connecticut Tolland 62 62 59 51 

90159991 41.8402 -72.01 Connecticut Windham 57 56 54 47 

100010002 38.98475 -75.5552 Delaware Kent 59 58 56 49 

100031007 39.55111 -75.7308 Delaware New Castle 60 59 57 49 

100031010 39.81722 -75.5639 Delaware New Castle 61 59 57 49 

100031013 39.77389 -75.4964 Delaware New Castle 62 61 58 50 

100032004 39.73944 -75.5581 Delaware New Castle 60 59 56 49 

100051002 38.64448 -75.6127 Delaware Sussex 61 60 58 51 

100051003 38.7792 -75.1627 Delaware Sussex 64 63 61 55 

110010041 38.89722 -76.9528 District Of 

Columbia 

District of 

Columbia 58 57 55 46 

110010043 38.92185 -77.0132 District Of 

Columbia 

District of 

Columbia 62 61 58 49 

120013011 29.54472 -82.2961 Florida Alachua 50 50 50 49 

120030002 30.20111 -82.4411 Florida Baker 52 51 50 50 

120050006 30.13043 -85.7315 Florida Bay 52 51 50 50 

120090007 28.05361 -80.6286 Florida Brevard 53 52 52 52 

120094001 28.31056 -80.6156 Florida Brevard 54 53 53 53 

120110033 26.07354 -80.3385 Florida Broward 52 51 51 51 

120112003 26.29203 -80.0965 Florida Broward 50 50 50 50 

120118002 26.087 -80.111 Florida Broward 53 53 53 53 

120210004 26.27 -81.711 Florida Collier 49 48 48 48 

120230002 30.17806 -82.6192 Florida Columbia 52 51 51 50 

120310077 30.47773 -81.5873 Florida Duval 52 50 50 49 

120310100 30.261 -81.454 Florida Duval 53 51 51 50 

120310106 30.37822 -81.8409 Florida Duval 52 50 50 50 

120330004 30.52537 -87.2036 Florida Escambia 56 53 52 52 

120330018 30.36805 -87.271 Florida Escambia 58 55 55 54 

120550003 27.18889 -81.3406 Florida Highlands 53 52 51 51 

120570081 27.74003 -82.4651 Florida Hillsborough 60 57 57 57 

120571035 27.92806 -82.4547 Florida Hillsborough 56 54 54 54 

120571065 27.89222 -82.5386 Florida Hillsborough 60 58 58 58 

120573002 27.96565 -82.2304 Florida Hillsborough 56 55 55 54 

120590004 30.84861 -85.6039 Florida Holmes 49 48 48 46 

120619991 27.8492 -80.4554 Florida Indian River 54 53 53 53 

120690002 28.525 -81.7233 Florida Lake 54 52 52 52 
2A-44 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

120712002 26.54786 -81.98 Florida Lee 52 51 51 51 

120713002 26.44889 -81.9394 Florida Lee 50 49 48 48 

120730012 30.43972 -84.3464 Florida Leon 48 48 48 47 

120730013 30.48444 -84.1994 Florida Leon 48 48 48 47 

120813002 27.63278 -82.5461 Florida Manatee 53 51 51 51 

120814012 27.48056 -82.6189 Florida Manatee 53 52 51 51 

120814013 27.44944 -82.5222 Florida Manatee 51 49 49 49 

120830003 29.17028 -82.1008 Florida Marion 52 51 51 51 

120830004 29.1925 -82.1733 Florida Marion 50 49 49 49 

120850007 27.17246 -80.2407 Florida Martin 51 50 50 50 

120860027 25.73338 -80.1618 Florida Miami-Dade 58 58 58 58 

120860029 25.58638 -80.3268 Florida Miami-Dade 56 56 56 56 

120910002 30.42653 -86.6662 Florida Okaloosa 52 50 50 49 

120950008 28.45417 -81.3814 Florida Orange 58 56 56 56 

120952002 28.59639 -81.3625 Florida Orange 59 58 58 58 

120972002 28.34722 -81.6367 Florida Osceola 52 51 51 51 

120990009 26.73083 -80.2339 Florida Palm Beach 55 54 54 54 

120990020 26.59123 -80.0609 Florida Palm Beach 54 53 53 53 

121010005 28.33194 -82.3058 Florida Pasco 53 51 51 51 

121012001 28.195 -82.7581 Florida Pasco 54 53 53 52 

121030004 27.94639 -82.7319 Florida Pinellas 55 54 54 54 

121030018 27.78587 -82.7399 Florida Pinellas 55 53 53 53 

121035002 28.09 -82.7008 Florida Pinellas 53 52 52 52 

121056005 27.93944 -82.0003 Florida Polk 54 52 52 51 

121056006 28.02889 -81.9722 Florida Polk 55 53 52 52 

121130015 30.39413 -87.008 Florida Santa Rosa 56 54 53 53 

121151005 27.30694 -82.5706 Florida Sarasota 57 56 56 55 

121151006 27.35028 -82.48 Florida Sarasota 55 53 53 53 

121152002 27.08919 -82.3626 Florida Sarasota 54 52 52 52 

121171002 28.74611 -81.3106 Florida Seminole 55 53 53 52 

121272001 29.10889 -80.9939 Florida Volusia 47 45 45 45 

121275002 29.20667 -81.0525 Florida Volusia 51 49 49 49 

121290001 30.0925 -84.1611 Florida Wakulla 53 52 51 51 

130210012 32.80541 -83.5435 Georgia Bibb 53 49 49 47 

130510021 32.06923 -81.0488 Georgia Chatham 51 50 50 49 

130550001 34.47429 -85.408 Georgia Chattooga 51 49 49 47 

130590002 33.91807 -83.3445 Georgia Clarke 51 50 50 49 

130670003 34.01548 -84.6074 Georgia Cobb 55 54 54 53 

130730001 33.58214 -82.1312 Georgia Columbia 51 51 50 49 

130770002 33.40404 -84.746 Georgia Coweta 49 48 48 47 
2A-45 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

130850001 34.37632 -84.0598 Georgia Dawson 49 48 48 47 

130890002 33.68797 -84.2905 Georgia DeKalb 56 55 55 54 

130970004 33.74366 -84.7792 Georgia Douglas 52 51 51 50 

131210055 33.72019 -84.3571 Georgia Fulton 59 58 58 57 

131270006 31.16974 -81.4959 Georgia Glynn 48 47 47 47 

131350002 33.96127 -84.069 Georgia Gwinnett 55 54 54 53 

131510002 33.43358 -84.1617 Georgia Henry 59 58 58 57 

132130003 34.7852 -84.6264 Georgia Murray 52 51 51 48 

132150008 32.5213 -84.9448 Georgia Muscogee 50 50 49 49 

132230003 33.9285 -85.0453 Georgia Paulding 53 51 51 49 

132319991 33.1787 -84.4052 Georgia Pike 52 51 51 50 

132450091 33.43335 -82.0222 Georgia Richmond 53 52 51 50 

132470001 33.59108 -84.0653 Georgia Rockdale 55 54 54 53 

132611001 31.9543 -84.0811 Georgia Sumter 53 52 52 51 

160010010 43.6007 -116.348 Idaho Ada 60 59 59 59 

160010017 43.5776 -116.178 Idaho Ada 60 60 60 60 

160010019 43.63459 -116.234 Idaho Ada 54 53 53 53 

160550003 47.78891 -116.805 Idaho Kootenai 47 47 47 47 

170010007 39.91541 -91.3359 Illinois Adams 57 56 55 54 

170190007 40.24491 -88.1885 Illinois Champaign 60 59 59 56 

170191001 40.05224 -88.3725 Illinois Champaign 61 60 60 57 

170230001 39.21086 -87.6683 Illinois Clark 58 58 58 53 

170310001 41.67099 -87.7325 Illinois Cook 64 63 63 58 

170310032 41.75583 -87.5454 Illinois Cook 57 57 57 57 

170310064 41.79079 -87.6016 Illinois Cook 53 52 52 52 

170310076 41.7514 -87.7135 Illinois Cook 63 63 62 58 

170311003 41.98433 -87.792 Illinois Cook 49 49 49 50 

170311601 41.66812 -87.9906 Illinois Cook 62 62 61 57 

170314002 41.85524 -87.7525 Illinois Cook 52 51 51 51 

170314007 42.06029 -87.8632 Illinois Cook 48 48 48 49 

170314201 42.14 -87.7992 Illinois Cook 56 55 55 57 

170317002 42.06186 -87.6742 Illinois Cook 54 54 54 56 

170436001 41.81305 -88.0728 Illinois DuPage 58 58 57 53 

170491001 39.06716 -88.5489 Illinois Effingham 58 58 57 54 

170650002 38.08216 -88.6249 Illinois Hamilton 64 65 65 61 

170831001 39.11054 -90.3241 Illinois Jersey 61 60 60 59 

170859991 42.2869 -89.9997 Illinois Jo Daviess 58 57 56 55 

170890005 42.04915 -88.273 Illinois Kane 63 62 62 58 

170971007 42.46757 -87.81 Illinois Lake 57 57 57 58 

171110001 42.22144 -88.2422 Illinois McHenry 61 60 60 55 
2A-46 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

171132003 40.51874 -88.9969 Illinois McLean 59 57 56 53 

171150013 39.86683 -88.9256 Illinois Macon 59 59 58 56 

171170002 39.39608 -89.8097 Illinois Macoupin 57 56 55 54 

171190008 38.89019 -90.148 Illinois Madison 62 61 61 59 

171191009 38.72657 -89.96 Illinois Madison 63 62 62 60 

171193007 38.86067 -90.1059 Illinois Madison 62 61 60 59 

171199991 38.869 -89.6228 Illinois Madison 60 59 59 58 

171430024 40.68742 -89.6069 Illinois Peoria 53 50 50 47 

171431001 40.7455 -89.5859 Illinois Peoria 61 58 57 54 

171570001 38.17628 -89.7885 Illinois Randolph 58 57 57 55 

171613002 41.51473 -90.5174 Illinois Rock Island 50 49 49 47 

171630010 38.61203 -90.1605 Illinois Saint Clair 62 61 61 59 

171670014 39.83152 -89.6409 Illinois Sangamon 59 58 57 56 

171971011 41.22154 -88.191 Illinois Will 55 55 54 50 

172012001 42.33498 -89.0378 Illinois Winnebago 58 57 57 53 

180030002 41.22142 -85.0168 Indiana Allen 57 56 56 53 

180030004 41.09497 -85.1018 Indiana Allen 58 57 57 53 

180110001 39.99748 -86.3952 Indiana Boone 60 60 60 55 

180150002 40.54046 -86.553 Indiana Carroll 58 58 57 54 

180190008 38.39383 -85.6642 Indiana Clark 65 65 64 58 

180350010 40.30002 -85.2454 Indiana Delaware 56 56 55 52 

180390007 41.71805 -85.8306 Indiana Elkhart 56 55 55 50 

180431004 38.30806 -85.8342 Indiana Floyd 65 65 64 58 

180550001 38.98558 -86.9901 Indiana Greene 68 68 67 62 

180570006 40.0683 -85.9925 Indiana Hamilton 59 58 58 54 

180590003 39.93504 -85.8405 Indiana Hancock 55 55 54 50 

180630004 39.759 -86.3971 Indiana Hendricks 57 56 56 52 

180690002 40.96071 -85.3798 Indiana Huntington 55 54 54 50 

180710001 38.92084 -86.0805 Indiana Jackson 57 57 57 51 

180810002 39.41724 -86.1524 Indiana Johnson 58 58 58 53 

180839991 38.7408 -87.4853 Indiana Knox 65 65 64 59 

180890022 41.60668 -87.3047 Indiana Lake 55 55 55 53 

180890030 41.6814 -87.4947 Indiana Lake 57 56 56 54 

180892008 41.63946 -87.4936 Indiana Lake 57 56 56 54 

180910005 41.71702 -86.9077 Indiana LaPorte 66 65 65 61 

180910010 41.6291 -86.6846 Indiana LaPorte 59 59 59 55 

180950010 40.00255 -85.6569 Indiana Madison 55 55 54 50 

180970050 39.85892 -86.0213 Indiana Marion 60 59 59 54 

180970057 39.74902 -86.1863 Indiana Marion 59 58 58 53 

180970073 39.78949 -86.0609 Indiana Marion 60 60 59 55 
2A-47 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

180970078 39.8111 -86.1145 Indiana Marion 59 59 58 54 

181090005 39.57563 -86.4779 Indiana Morgan 56 56 56 51 

181230009 38.11316 -86.6036 Indiana Perry 65 65 65 59 

181270024 41.61756 -87.1992 Indiana Porter 57 57 57 55 

181270026 41.51029 -87.0385 Indiana Porter 55 54 54 51 

181290003 38.00529 -87.7184 Indiana Posey 61 61 61 56 

181410010 41.5517 -86.3706 Indiana St. Joseph 52 52 51 48 

181410015 41.69669 -86.2147 Indiana St. Joseph 58 57 57 52 

181411007 41.7426 -86.1105 Indiana St. Joseph 53 53 52 48 

181450001 39.61342 -85.8706 Indiana Shelby 62 61 61 56 

181630013 38.11395 -87.537 Indiana Vanderburgh 63 63 62 58 

181630021 38.01325 -87.5779 Indiana Vanderburgh 63 63 62 58 

181670018 39.48615 -87.4014 Indiana Vigo 55 54 54 50 

181670024 39.56056 -87.3131 Indiana Vigo 55 55 54 50 

181699991 40.816 -85.6611 Indiana Wabash 61 61 61 57 

181730008 38.052 -87.2783 Indiana Warrick 63 63 63 58 

181730009 38.1945 -87.3414 Indiana Warrick 61 61 60 55 

181730011 37.95451 -87.3219 Indiana Warrick 64 64 63 58 

190170011 42.74306 -92.5131 Iowa Bremer 53 52 52 51 

190450021 41.875 -90.1776 Iowa Clinton 57 56 55 53 

190850007 41.83226 -95.9282 Iowa Harrison 55 54 54 53 

190851101 41.78026 -95.9484 Iowa Harrison 56 55 55 54 

191130028 41.91056 -91.6519 Iowa Linn 55 54 54 53 

191130033 42.28101 -91.5269 Iowa Linn 53 53 53 52 

191130040 41.97677 -91.6877 Iowa Linn 53 53 53 52 

191370002 40.96911 -95.045 Iowa Montgomery 56 55 55 54 

191471002 43.1237 -94.6935 Iowa Palo Alto 57 56 55 55 

191530030 41.60316 -93.6431 Iowa Polk 49 48 48 47 

191630014 41.69917 -90.5219 Iowa Scott 55 54 53 52 

191630015 41.53001 -90.5876 Iowa Scott 56 56 55 53 

191690011 41.88287 -93.6878 Iowa Story 50 49 49 48 

191770006 40.69508 -92.0063 Iowa Van Buren 55 54 53 51 

191810022 41.28553 -93.584 Iowa Warren 53 52 51 50 

200910010 38.83858 -94.7464 Kansas Johnson 61 60 60 59 

201030003 39.32739 -94.951 Kansas Leavenworth 58 57 57 56 

201070002 38.13588 -94.732 Kansas Linn 59 58 58 57 

201619991 39.1021 -96.6096 Kansas Riley 62 61 61 60 

201730001 37.78139 -97.3372 Kansas Sedgwick 55 54 54 53 

201730010 37.70207 -97.3148 Kansas Sedgwick 64 63 62 61 

201730018 37.89751 -97.4921 Kansas Sedgwick 62 61 61 60 
2A-48 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

201770013 39.02427 -95.7113 Kansas Shawnee 62 61 61 61 

201910002 37.47689 -97.3664 Kansas Sumner 65 64 64 63 

201950001 38.77008 -99.7634 Kansas Trego 65 65 65 64 

202090021 39.11722 -94.6356 Kansas Wyandotte 55 54 54 53 

210130002 36.60843 -83.7369 Kentucky Bell 50 49 49 45 

210150003 38.91833 -84.8526 Kentucky Boone 59 58 57 51 

210190017 38.45934 -82.6404 Kentucky Boyd 58 58 57 50 

210290006 37.98629 -85.7119 Kentucky Bullitt 62 62 61 56 

210373002 39.02188 -84.4745 Kentucky Campbell 66 66 65 58 

210430500 38.23887 -82.9881 Kentucky Carter 56 56 55 49 

210470006 36.91171 -87.3233 Kentucky Christian 53 53 52 49 

210590005 37.78078 -87.0753 Kentucky Daviess 67 67 67 61 

210610501 37.13194 -86.1478 Kentucky Edmonson 57 57 57 53 

210670012 38.06503 -84.4976 Kentucky Fayette 58 58 58 52 

210890007 38.54814 -82.7312 Kentucky Greenup 59 59 58 51 

210910012 37.93829 -86.8972 Kentucky Hancock 66 66 66 60 

210930006 37.70561 -85.8526 Kentucky Hardin 59 59 58 53 

211010014 37.8712 -87.4638 Kentucky Henderson 68 68 68 63 

211110027 38.13784 -85.5765 Kentucky Jefferson 66 65 65 59 

211110051 38.06091 -85.898 Kentucky Jefferson 68 68 67 61 

211110067 38.22876 -85.6545 Kentucky Jefferson 71 71 70 63 

211130001 37.89147 -84.5883 Kentucky Jessamine 56 57 57 52 

211390003 37.15539 -88.394 Kentucky Livingston 61 65 65 60 

211451024 37.05822 -88.5725 Kentucky McCracken 64 69 68 64 

211759991 37.9214 -83.0662 Kentucky Morgan 57 56 56 49 

211850004 38.4002 -85.4443 Kentucky Oldham 68 68 67 60 

211930003 37.28329 -83.2093 Kentucky Perry 56 56 55 49 

211950002 37.4826 -82.5353 Kentucky Pike 56 56 55 48 

211990003 37.09798 -84.6115 Kentucky Pulaski 51 51 50 46 

212130004 36.70861 -86.5663 Kentucky Simpson 53 53 53 48 

212218001 36.78389 -87.8519 Kentucky Trigg 56 57 56 51 

212219991 36.7841 -87.8499 Kentucky Trigg 57 58 57 52 

212270008 37.03544 -86.2506 Kentucky Warren 51 50 50 46 

212299991 37.7046 -85.0485 Kentucky Washington 57 57 57 52 

220050004 30.23389 -90.9683 Louisiana Ascension 63 62 62 61 

220150008 32.53626 -93.7489 Louisiana Bossier 66 64 62 59 

220170001 32.67639 -93.8597 Louisiana Caddo 64 62 60 56 

220190002 30.14333 -93.3719 Louisiana Calcasieu 66 66 65 64 

220190008 30.26167 -93.2842 Louisiana Calcasieu 60 60 59 58 

220190009 30.22778 -93.5783 Louisiana Calcasieu 63 62 60 57 
2A-49 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

220330003 30.41976 -91.182 Louisiana East Baton 

Rouge 67 67 67 65 

220330009 30.46198 -91.1792 Louisiana East Baton 

Rouge 64 63 63 62 

220330013 30.70092 -91.0561 Louisiana East Baton 

Rouge 60 59 59 58 

220470009 30.22056 -91.3161 Louisiana Iberville 62 62 61 60 

220470012 30.20699 -91.1299 Louisiana Iberville 65 65 64 63 

220511001 30.04357 -90.2751 Louisiana Jefferson 64 64 63 63 

220550007 30.2175 -92.0514 Louisiana Lafayette 60 60 59 58 

220570004 29.76389 -90.7652 Louisiana Lafourche 62 61 61 60 

220630002 30.3125 -90.8125 Louisiana Livingston 62 62 62 61 

220710012 29.99444 -90.1028 Louisiana Orleans 60 59 58 58 

220730004 32.50971 -92.0461 Louisiana Ouachita 55 55 55 54 

220770001 30.68174 -91.3662 Louisiana Pointe Coupee 63 62 62 61 

220870004 29.93961 -89.9239 Louisiana St. Bernard 59 58 58 57 

220890003 29.98417 -90.4106 Louisiana St. Charles 61 60 60 59 

220930002 29.99444 -90.82 Louisiana St. James 58 58 58 57 

220950002 30.05833 -90.6083 Louisiana St. John the 

Baptist 63 62 62 61 

221030002 30.4293 -90.1997 Louisiana St. Tammany 63 62 62 61 

221210001 30.50064 -91.2136 Louisiana West Baton 

Rouge 59 59 59 58 

230010014 43.97462 -70.1246 Maine Androscoggin 50 49 48 44 

230052003 43.56104 -70.2073 Maine Cumberland 57 57 55 50 

230090102 44.3517 -68.227 Maine Hancock 58 57 56 52 

230090103 44.37705 -68.2609 Maine Hancock 55 54 53 49 

230112005 44.23062 -69.785 Maine Kennebec 50 50 49 45 

230130004 43.91796 -69.2606 Maine Knox 55 55 53 49 

230173001 44.25092 -70.8606 Maine Oxford 46 45 45 42 

230194008 44.73598 -68.6708 Maine Penobscot 47 46 45 42 

230230006 44.005 -69.8278 Maine Sagadahoc 49 49 47 43 

230290019 44.53191 -67.5959 Maine Washington 49 49 48 44 

230290032 44.96363 -67.0607 Maine Washington 46 46 45 42 

230310038 43.65676 -70.6291 Maine York 49 48 47 43 

230310040 43.58889 -70.8773 Maine York 52 51 50 46 

230312002 43.34317 -70.471 Maine York 60 59 57 52 

240030014 38.9025 -76.6531 Maryland Anne Arundel 64 63 61 51 

240051007 39.46202 -76.6313 Maryland Baltimore 65 63 61 53 

240053001 39.31083 -76.4744 Maryland Baltimore 67 66 63 53 

240090011 38.53672 -76.6172 Maryland Calvert 63 63 60 51 

240130001 39.44417 -77.0417 Maryland Carroll 61 60 59 51 
2A-50 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

240150003 39.70111 -75.86 Maryland Cecil 66 65 62 54 

240170010 38.50417 -76.8119 Maryland Charles 61 60 58 50 

240199991 38.445 -76.1114 Maryland Dorchester 61 60 58 52 

240210037 39.42276 -77.3752 Maryland Frederick 62 62 60 52 

240230002 39.70595 -79.012 Maryland Garrett 59 59 58 52 

240251001 39.41 -76.2967 Maryland Harford 74 73 70 59 

240259001 39.56333 -76.2039 Maryland Harford 63 61 59 49 

240290002 39.3052 -75.7972 Maryland Kent 62 61 58 50 

240313001 39.11444 -77.1069 Maryland Montgomery 60 59 57 49 

240330030 39.05528 -76.8783 Maryland Prince George's 61 60 58 49 

240338003 38.81194 -76.7442 Maryland Prince George's 63 62 60 50 

240339991 39.0284 -76.8171 Maryland Prince George's 62 61 58 50 

240430009 39.56558 -77.7216 Maryland Washington 60 59 58 52 

245100054 39.32889 -76.5525 Maryland Baltimore (City) 62 62 59 50 

250010002 41.9758 -70.0236 Massachusetts Barnstable 59 59 57 51 

250034002 42.63668 -73.1674 Massachusetts Berkshire 57 57 55 50 

250051002 41.63328 -70.8792 Massachusetts Bristol 59 59 57 50 

250070001 41.33047 -70.7852 Massachusetts Dukes 64 64 62 54 

250092006 42.47464 -70.9708 Massachusetts Essex 58 57 56 52 

250094005 42.81441 -70.8178 Massachusetts Essex 57 56 55 50 

250095005 42.77084 -71.1023 Massachusetts Essex 56 56 54 49 

250130008 42.19438 -72.5551 Massachusetts Hampden 59 59 56 49 

250150103 42.40058 -72.5231 Massachusetts Hampshire 52 52 50 44 

250154002 42.29849 -72.3341 Massachusetts Hampshire 57 56 54 47 

250170009 42.62668 -71.3621 Massachusetts Middlesex 55 54 52 46 

250171102 42.41357 -71.4828 Massachusetts Middlesex 54 53 51 45 

250213003 42.21177 -71.114 Massachusetts Norfolk 59 59 57 52 

250250041 42.31737 -70.9684 Massachusetts Suffolk 56 55 54 50 

250250042 42.3295 -71.0826 Massachusetts Suffolk 49 49 48 44 

250270015 42.27432 -71.8755 Massachusetts Worcester 55 55 53 47 

250270024 42.0997 -71.6194 Massachusetts Worcester 55 54 53 47 

260050003 42.76779 -86.1486 Michigan Allegan 70 69 69 63 

260190003 44.61694 -86.1094 Michigan Benzie 62 61 61 56 

260210014 42.19779 -86.3097 Michigan Berrien 68 68 67 62 

260270003 41.89557 -86.0016 Michigan Cass 63 62 62 57 

260370001 42.79834 -84.3938 Michigan Clinton 57 56 55 51 

260490021 43.04722 -83.6702 Michigan Genesee 61 60 60 56 

260492001 43.16834 -83.4615 Michigan Genesee 60 60 59 55 

260630007 43.83639 -82.6429 Michigan Huron 61 61 60 57 

260650012 42.73862 -84.5346 Michigan Ingham 57 56 56 52 
2A-51 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

260770008 42.27807 -85.5419 Michigan Kalamazoo 61 60 59 55 

260810020 42.98417 -85.6713 Michigan Kent 60 59 59 54 

260810022 43.17667 -85.4166 Michigan Kent 59 58 58 53 

260910007 41.99557 -83.9466 Michigan Lenawee 60 60 59 55 

260990009 42.73139 -82.7935 Michigan Macomb 67 67 66 62 

260991003 42.51334 -83.006 Michigan Macomb 69 68 68 63 

261010922 44.307 -86.2426 Michigan Manistee 61 60 60 55 

261050007 43.95333 -86.2944 Michigan Mason 62 61 60 56 

261130001 44.31056 -84.8919 Michigan Missaukee 58 57 57 53 

261210039 43.27806 -86.3111 Michigan Muskegon 66 66 65 60 

261250001 42.46306 -83.1832 Michigan Oakland 66 65 65 60 

261390005 42.89445 -85.8527 Michigan Ottawa 63 62 62 57 

261470005 42.95334 -82.4562 Michigan St. Clair 65 65 65 60 

261530001 46.28888 -85.9502 Michigan Schoolcraft 60 60 59 55 

261579991 43.6138 -83.3591 Michigan Tuscola 58 57 57 52 

261610008 42.24057 -83.5996 Michigan Washtenaw 62 62 62 58 

261619991 42.4165 -83.902 Michigan Washtenaw 60 60 59 55 

261630001 42.22862 -83.2082 Michigan Wayne 61 61 61 57 

261630019 42.43084 -83.0001 Michigan Wayne 70 70 69 65 

261659991 44.1809 -85.739 Michigan Wexford 56 55 55 51 

270031001 45.40184 -93.2031 Minnesota Anoka 53 53 53 52 

270031002 45.13768 -93.2076 Minnesota Anoka 57 56 56 56 

270353204 46.39674 -94.1303 Minnesota Crow Wing 51 49 49 49 

270495302 44.47375 -93.0126 Minnesota Goodhue 53 53 53 52 

270953051 46.2053 -93.7595 Minnesota Mille Lacs 48 47 47 47 

271095008 43.99691 -92.4504 Minnesota Olmsted 53 53 53 52 

271377550 46.81826 -92.0894 Minnesota Saint Louis 42 41 41 40 

271390505 44.79144 -93.5125 Minnesota Scott 54 53 53 53 

271453052 45.54984 -94.1335 Minnesota Stearns 53 50 50 50 

271636015 45.11728 -92.8553 Minnesota Washington 52 52 51 51 

271713201 45.20916 -93.6692 Minnesota Wright 55 52 52 52 

280010004 31.56075 -91.3904 Mississippi Adams 55 54 54 53 

280110001 33.74606 -90.723 Mississippi Bolivar 61 60 60 59 

280330002 34.82166 -89.9878 Mississippi DeSoto 57 55 55 51 

280450003 30.30083 -89.3959 Mississippi Hancock 53 50 50 49 

280470008 30.39037 -89.0498 Mississippi Harrison 56 51 51 50 

280490010 32.38573 -90.1412 Mississippi Hinds 49 48 48 47 

280590006 30.37829 -88.5339 Mississippi Jackson 59 58 58 57 

280750003 32.36457 -88.7315 Mississippi Lauderdale 50 49 49 47 

280810005 34.26492 -88.7662 Mississippi Lee 51 50 50 48 
2A-52 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

281619991 34.0026 -89.799 Mississippi Yalobusha 52 51 51 49 

290030001 39.9544 -94.849 Missouri Andrew 60 59 58 57 

290190011 39.0786 -92.3152 Missouri Boone 56 56 56 55 

290270002 38.70608 -92.0931 Missouri Callaway 56 55 55 54 

290370003 38.75976 -94.58 Missouri Cass 57 57 57 56 

290390001 37.69 -94.035 Missouri Cedar 61 60 59 57 

290470003 39.40745 -94.2654 Missouri Clay 63 62 62 61 

290470005 39.30309 -94.3766 Missouri Clay 62 61 61 60 

290470006 39.33191 -94.5808 Missouri Clay 64 63 63 62 

290490001 39.5306 -94.556 Missouri Clinton 64 63 63 62 

290770036 37.25614 -93.2999 Missouri Greene 56 55 55 54 

290770042 37.31951 -93.2046 Missouri Greene 58 57 57 56 

290970004 37.2385 -94.4247 Missouri Jasper 65 62 61 58 

290990019 38.44863 -90.3985 Missouri Jefferson 64 63 63 62 

291130003 39.0447 -90.8647 Missouri Lincoln 63 62 62 61 

291370001 39.47514 -91.7891 Missouri Monroe 58 57 57 55 

291570001 37.70264 -89.6986 Missouri Perry 62 62 62 59 

291831002 38.87255 -90.2265 Missouri Saint Charles 66 65 65 64 

291831004 38.8994 -90.4492 Missouri Saint Charles 65 64 64 62 

291860005 37.90084 -90.4239 Missouri Sainte 

Genevieve 61 60 60 59 

291890005 38.4902 -90.7052 Missouri Saint Louis 59 59 58 57 

291890014 38.7109 -90.4759 Missouri Saint Louis 65 64 64 63 

292130004 36.70773 -93.222 Missouri Taney 59 57 57 55 

295100085 38.6565 -90.1986 Missouri St. Louis City 63 63 62 61 

300870001 45.36615 -106.49 Montana Rosebud 53 52 52 52 

310550019 41.24749 -95.9731 Nebraska Douglas 58 57 57 57 

310550028 41.20796 -95.9459 Nebraska Douglas 51 50 50 50 

310550035 41.30676 -95.961 Nebraska Douglas 54 53 53 53 

311090016 40.98472 -96.6772 Nebraska Lancaster 47 47 46 46 

320010002 39.47247 -118.784 Nevada Churchill 52 52 52 52 

320030022 36.39101 -114.907 Nevada Clark 63 62 62 60 

320030023 36.80791 -114.061 Nevada Clark 57 57 57 56 

320030043 36.10637 -115.253 Nevada Clark 69 69 69 65 

320030071 36.16975 -115.263 Nevada Clark 69 69 69 65 

320030073 36.17342 -115.333 Nevada Clark 68 68 68 65 

320030075 36.27058 -115.238 Nevada Clark 68 67 67 64 

320030538 36.14296 -115.056 Nevada Clark 63 63 63 60 

320030540 36.1419 -115.079 Nevada Clark 63 63 63 60 

320030601 35.97813 -114.846 Nevada Clark 65 65 65 63 

320031019 35.78567 -115.357 Nevada Clark 67 67 67 65 
2A-53 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

320032002 36.19126 -115.123 Nevada Clark 63 63 63 60 

320190006 39.60279 -119.248 Nevada Lyon 60 60 60 60 

320310016 39.52508 -119.808 Nevada Washoe 59 58 58 58 

320310020 39.46922 -119.775 Nevada Washoe 59 59 59 59 

320310025 39.39984 -119.74 Nevada Washoe 59 59 59 58 

320311005 39.54092 -119.747 Nevada Washoe 59 59 59 59 

320312002 39.25041 -119.957 Nevada Washoe 53 53 53 53 

320312009 39.64526 -119.84 Nevada Washoe 59 59 59 58 

325100002 39.16725 -119.732 Nevada Carson City 59 59 59 59 

330012004 43.56611 -71.4964 New Hampshire Belknap 51 51 50 46 

330050007 42.93047 -72.2724 New Hampshire Cheshire 50 50 48 44 

330074001 44.27017 -71.3038 New Hampshire Coos 58 57 57 54 

330074002 44.30817 -71.2177 New Hampshire Coos 51 51 50 48 

330090010 43.62961 -72.3096 New Hampshire Grafton 50 49 48 45 

330111011 42.71866 -71.5224 New Hampshire Hillsborough 54 53 51 46 

330115001 42.86175 -71.8784 New Hampshire Hillsborough 57 56 55 49 

330131007 43.2185 -71.5145 New Hampshire Merrimack 52 52 50 46 

330150014 43.07533 -70.748 New Hampshire Rockingham 54 53 52 47 

330150016 43.04528 -70.7138 New Hampshire Rockingham 54 54 52 48 

330150018 42.86254 -71.3802 New Hampshire Rockingham 55 55 53 47 

340010006 39.46487 -74.4487 New Jersey Atlantic 60 59 57 50 

340030006 40.87044 -73.992 New Jersey Bergen 64 63 61 53 

340071001 39.68425 -74.8615 New Jersey Camden 68 67 64 55 

340110007 39.42227 -75.0252 New Jersey Cumberland 59 57 55 47 

340130003 40.72099 -74.1929 New Jersey Essex 65 64 61 53 

340150002 39.80034 -75.2121 New Jersey Gloucester 69 68 65 56 

340170006 40.67025 -74.1261 New Jersey Hudson 64 63 61 53 

340190001 40.51526 -74.8067 New Jersey Hunterdon 63 62 60 52 

340210005 40.28309 -74.7426 New Jersey Mercer 64 63 61 53 

340219991 40.3125 -74.8729 New Jersey Mercer 62 61 59 51 

340230011 40.46218 -74.4294 New Jersey Middlesex 66 65 62 53 

340250005 40.27765 -74.0051 New Jersey Monmouth 67 65 63 54 

340273001 40.78763 -74.6763 New Jersey Morris 63 62 60 52 

340290006 40.06483 -74.4441 New Jersey Ocean 67 66 63 54 

340315001 41.05862 -74.2555 New Jersey Passaic 62 61 59 52 

340410007 40.92458 -75.0678 New Jersey Warren 52 52 50 44 

350010023 35.1343 -106.585 New Mexico Bernalillo 58 58 58 57 

350010024 35.0631 -106.579 New Mexico Bernalillo 59 59 59 58 

350010027 35.1539 -106.697 New Mexico Bernalillo 62 62 62 61 

350010029 35.01708 -106.657 New Mexico Bernalillo 59 59 59 58 
2A-54 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

350010032 35.06407 -106.762 New Mexico Bernalillo 58 57 57 56 

350011012 35.1852 -106.508 New Mexico Bernalillo 63 62 62 61 

350011013 35.19324 -106.614 New Mexico Bernalillo 60 60 60 58 

350130008 31.93056 -106.631 New Mexico Dona Ana 57 56 56 56 

350130017 31.79583 -106.558 New Mexico Dona Ana 58 58 58 57 

350130020 32.04111 -106.409 New Mexico Dona Ana 59 58 58 58 

350130021 31.79611 -106.584 New Mexico Dona Ana 62 61 61 61 

350130022 31.78778 -106.683 New Mexico Dona Ana 62 61 61 61 

350130023 32.3175 -106.768 New Mexico Dona Ana 57 57 56 56 

350171003 32.69194 -108.124 New Mexico Grant 61 61 61 60 

350250008 32.72666 -103.123 New Mexico Lea 61 61 61 60 

350290003 32.2558 -107.723 New Mexico Luna 58 57 57 56 

350431001 35.29944 -106.548 New Mexico Sandoval 55 55 55 54 

350439004 35.61528 -106.724 New Mexico Sandoval 58 58 58 58 

350450009 36.74222 -107.977 New Mexico San Juan 57 57 57 56 

350450018 36.80973 -107.652 New Mexico San Juan 62 62 62 61 

350451005 36.79667 -108.473 New Mexico San Juan 56 55 55 54 

350451233 36.8071 -108.695 New Mexico San Juan 56 55 55 54 

350490021 35.61975 -106.08 New Mexico Santa Fe 60 59 59 59 

350610008 34.8147 -106.74 New Mexico Valencia 58 58 58 56 

360010012 42.68075 -73.7573 New York Albany 57 56 54 49 

360050133 40.8679 -73.8781 New York Bronx 67 66 64 57 

360130006 42.49963 -79.3188 New York Chautauqua 61 61 60 55 

360130011 42.29071 -79.5896 New York Chautauqua 61 61 60 55 

360150003 42.11096 -76.8022 New York Chemung 57 57 56 53 

360270007 41.78555 -73.7414 New York Dutchess 58 57 55 48 

360290002 42.99328 -78.7715 New York Erie 61 61 60 56 

360310002 44.36608 -73.9031 New York Essex 57 57 56 54 

360310003 44.39308 -73.8589 New York Essex 57 57 56 53 

360410005 43.44957 -74.5163 New York Hamilton 57 56 55 52 

360430005 43.68578 -74.9854 New York Herkimer 56 55 54 52 

360450002 44.08747 -75.9732 New York Jefferson 62 62 61 60 

360530006 42.73046 -75.7844 New York Madison 55 54 53 50 

360551007 43.14618 -77.5482 New York Monroe 60 59 59 57 

360610135 40.81976 -73.9483 New York New York 65 64 63 58 

360631006 43.22386 -78.4789 New York Niagara 64 64 64 61 

360650004 43.30268 -75.7198 New York Oneida 53 52 52 49 

360671015 43.05235 -76.0592 New York Onondaga 59 59 58 56 

360715001 41.52375 -74.2153 New York Orange 56 55 53 46 

360750003 43.28428 -76.4632 New York Oswego 58 58 58 56 
2A-55 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

360790005 41.45589 -73.7098 New York Putnam 57 56 54 47 

360810124 40.73614 -73.8215 New York Queens 72 71 70 65 

360830004 42.78189 -73.4636 New York Rensselaer 57 56 55 49 

360850067 40.59664 -74.1253 New York Richmond 73 72 70 63 

360870005 41.18208 -74.0282 New York Rockland 62 61 59 51 

360910004 43.01209 -73.6489 New York Saratoga 56 55 54 49 

360930003 42.79901 -73.9389 New York Schenectady 54 53 52 47 

361010003 42.09142 -77.2098 New York Steuben 57 56 55 52 

361030002 40.74529 -73.4192 New York Suffolk 74 73 70 62 

361030004 40.96078 -72.7124 New York Suffolk 67 66 63 54 

361030009 40.82799 -73.0575 New York Suffolk 71 70 68 59 

361099991 42.4006 -76.6538 New York Tompkins 58 58 57 54 

361111005 42.14403 -74.4943 New York Ulster 58 58 56 51 

361173001 43.23086 -77.1714 New York Wayne 56 56 56 54 

361192004 41.05192 -73.7637 New York Westchester 65 64 62 54 

370030004 35.929 -81.1898 North Carolina Alexander 52 51 50 49 

370110002 35.97222 -81.9331 North Carolina Avery 50 49 49 46 

370119991 36.1058 -82.0454 North Carolina Avery 50 49 49 44 

370210030 35.5001 -82.5999 North Carolina Buncombe 52 51 51 49 

370270003 35.93583 -81.5303 North Carolina Caldwell 51 50 50 49 

370319991 34.8848 -76.6203 North Carolina Carteret 51 50 50 48 

370330001 36.30703 -79.4674 North Carolina Caswell 55 54 54 53 

370370004 35.75722 -79.1597 North Carolina Chatham 49 48 47 46 

370510008 35.15869 -78.728 North Carolina Cumberland 54 53 53 51 

370511003 34.96889 -78.9625 North Carolina Cumberland 55 54 54 52 

370590003 35.89707 -80.5573 North Carolina Davie 54 53 53 51 

370630015 36.03294 -78.9054 North Carolina Durham 52 51 51 50 

370650099 35.98833 -77.5828 North Carolina Edgecombe 56 54 54 52 

370670022 36.11056 -80.2267 North Carolina Forsyth 58 57 57 55 

370670028 36.20306 -80.2158 North Carolina Forsyth 54 53 53 51 

370670030 36.026 -80.342 North Carolina Forsyth 56 55 55 53 

370671008 36.05083 -80.1439 North Carolina Forsyth 55 55 55 53 

370690001 36.09619 -78.4637 North Carolina Franklin 52 52 51 50 

370750001 35.25793 -83.7956 North Carolina Graham 54 54 54 51 

370770001 36.14111 -78.7681 North Carolina Granville 56 54 54 53 

370810013 36.10071 -79.8105 North Carolina Guilford 57 56 56 55 

370870008 35.50716 -82.9634 North Carolina Haywood 50 49 49 47 

370870035 35.37917 -82.7925 North Carolina Haywood 55 54 54 52 

370870036 35.59 -83.0775 North Carolina Haywood 55 54 54 52 

370990005 35.52444 -83.2361 North Carolina Jackson 55 54 54 51 
2A-56 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

371010002 35.59083 -78.4619 North Carolina Johnston 55 54 54 52 

371070004 35.23146 -77.5688 North Carolina Lenoir 54 52 52 50 

371090004 35.43856 -81.2768 North Carolina Lincoln 57 55 55 53 

371139991 35.0608 -83.4306 North Carolina Macon 50 49 49 48 

371170001 35.81069 -76.8978 North Carolina Martin 51 51 50 48 

371190041 35.2401 -80.7857 North Carolina Mecklenburg 65 64 64 63 

371191005 35.11316 -80.9195 North Carolina Mecklenburg 60 60 60 59 

371191009 35.34722 -80.695 North Carolina Mecklenburg 62 61 61 60 

371239991 35.2632 -79.8365 North Carolina Montgomery 50 49 49 48 

371290002 34.36417 -77.8386 North Carolina New Hanover 50 49 49 48 

371450003 36.30697 -79.092 North Carolina Person 63 56 56 54 

371470006 35.63861 -77.3581 North Carolina Pitt 55 54 54 52 

371570099 36.30889 -79.8592 North Carolina Rockingham 57 56 56 55 

371590021 35.55187 -80.395 North Carolina Rowan 57 56 55 54 

371590022 35.53448 -80.6676 North Carolina Rowan 57 57 56 55 

371730002 35.43551 -83.4437 North Carolina Swain 49 49 48 46 

371790003 34.97389 -80.5408 North Carolina Union 54 53 53 52 

371830014 35.85611 -78.5742 North Carolina Wake 54 53 53 52 

371830016 35.59694 -78.7925 North Carolina Wake 57 56 56 54 

371990004 35.76541 -82.2649 North Carolina Yancey 54 53 53 51 

390030009 40.77094 -84.0539 Ohio Allen 61 61 60 56 

390071001 41.9597 -80.5728 Ohio Ashtabula 62 62 61 55 

390090004 39.30798 -82.1182 Ohio Athens 58 58 57 52 

390170004 39.38338 -84.5444 Ohio Butler 67 66 66 59 

390170018 39.52948 -84.3934 Ohio Butler 67 67 66 59 

390179991 39.5327 -84.7286 Ohio Butler 65 64 64 58 

390230001 40.00103 -83.8046 Ohio Clark 61 61 60 55 

390230003 39.85567 -83.9977 Ohio Clark 61 60 60 54 

390250022 39.0828 -84.1441 Ohio Clermont 65 65 64 57 

390271002 39.43004 -83.7885 Ohio Clinton 63 63 62 56 

390350034 41.55523 -81.5753 Ohio Cuyahoga 58 57 57 56 

390350060 41.49212 -81.6784 Ohio Cuyahoga 52 52 52 52 

390350064 41.36189 -81.8646 Ohio Cuyahoga 56 56 56 55 

390355002 41.53734 -81.4588 Ohio Cuyahoga 57 57 57 56 

390410002 40.35669 -83.064 Ohio Delaware 60 59 59 54 

390479991 39.6359 -83.2605 Ohio Fayette 58 57 57 52 

390490029 40.0845 -82.8155 Ohio Franklin 67 66 65 59 

390490037 39.96523 -82.9555 Ohio Franklin 61 61 60 54 

390490081 40.0877 -82.9598 Ohio Franklin 58 58 57 52 

390550004 41.51505 -81.2499 Ohio Geauga 60 60 59 54 
2A-57 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

390570006 39.66575 -83.9429 Ohio Greene 59 59 58 52 

390610006 39.2787 -84.3661 Ohio Hamilton 70 70 69 62 

390610010 39.21494 -84.6909 Ohio Hamilton 66 65 65 58 

390610040 39.12886 -84.504 Ohio Hamilton 68 67 67 60 

390810017 40.36644 -80.6156 Ohio Jefferson 61 60 59 54 

390830002 40.31003 -82.6917 Ohio Knox 60 59 59 53 

390850003 41.67301 -81.4225 Ohio Lake 58 58 58 57 

390850007 41.72681 -81.2422 Ohio Lake 53 53 53 52 

390870011 38.62901 -82.4589 Ohio Lawrence 54 54 53 47 

390870012 38.50811 -82.6593 Ohio Lawrence 59 59 58 51 

390890005 40.02604 -82.433 Ohio Licking 59 58 58 52 

390930018 41.42088 -82.0957 Ohio Lorain 54 54 54 54 

390950024 41.64407 -83.5463 Ohio Lucas 55 55 55 53 

390950027 41.49417 -83.7189 Ohio Lucas 59 58 58 54 

390950034 41.67521 -83.3069 Ohio Lucas 61 61 60 58 

390970007 39.78819 -83.4761 Ohio Madison 60 59 59 53 

390990013 41.09614 -80.6589 Ohio Mahoning 58 58 57 51 

391030004 41.0604 -81.9239 Ohio Medina 57 57 57 52 

391090005 40.08455 -84.1141 Ohio Miami 59 59 58 53 

391130037 39.78563 -84.1344 Ohio Montgomery 63 62 61 55 

391219991 39.9428 -81.3373 Ohio Noble 52 52 51 47 

391331001 41.18247 -81.3305 Ohio Portage 56 56 55 50 

391351001 39.83562 -84.7205 Ohio Preble 59 59 58 54 

391510016 40.82805 -81.3783 Ohio Stark 62 61 61 55 

391510022 40.71278 -81.5983 Ohio Stark 58 57 57 52 

391514005 40.9314 -81.1235 Ohio Stark 59 58 58 53 

391530020 41.10649 -81.5035 Ohio Summit 60 59 59 53 

391550009 41.45424 -80.591 Ohio Trumbull 57 56 56 51 

391550011 41.24046 -80.6626 Ohio Trumbull 62 62 61 55 

391650007 39.42689 -84.2008 Ohio Warren 64 64 63 57 

391670004 39.43212 -81.4604 Ohio Washington 60 60 59 53 

391730003 41.37769 -83.6111 Ohio Wood 61 60 60 56 

400019009 35.75074 -94.6697 Oklahoma Adair 64 61 60 58 

400159008 35.11194 -98.2528 Oklahoma Caddo 63 61 60 59 

400170101 35.47922 -97.7515 Oklahoma Canadian 62 61 61 59 

400219002 35.85408 -94.986 Oklahoma Cherokee 65 61 60 59 

400270049 35.32011 -97.4841 Oklahoma Cleveland 63 62 61 59 

400310651 34.63298 -98.4288 Oklahoma Comanche 64 64 63 60 

400370144 36.10548 -96.3612 Oklahoma Creek 62 59 59 58 

400430860 36.15841 -98.932 Oklahoma Dewey 65 65 64 63 
2A-58 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

400719010 36.95622 -97.0314 Oklahoma Kay 63 62 61 60 

400871073 35.15965 -97.4738 Oklahoma McClain 62 61 60 59 

400892001 34.477 -94.656 Oklahoma McCurtain 61 59 58 56 

400979014 36.22841 -95.2499 Oklahoma Mayes 67 63 62 60 

401090033 35.47704 -97.4943 Oklahoma Oklahoma 65 64 64 62 

401090096 35.4778 -97.303 Oklahoma Oklahoma 63 63 62 61 

401091037 35.61413 -97.4751 Oklahoma Oklahoma 66 65 64 63 

401159004 36.92222 -94.8389 Oklahoma Ottawa 63 61 60 58 

401210415 34.90227 -95.7844 Oklahoma Pittsburg 66 64 63 60 

401359021 35.40814 -94.5244 Oklahoma Sequoyah 62 60 59 57 

401430137 36.35744 -95.9992 Oklahoma Tulsa 65 63 62 61 

401430174 35.95371 -96.005 Oklahoma Tulsa 64 60 60 59 

401430178 36.1338 -95.7645 Oklahoma Tulsa 65 62 62 60 

401431127 36.2049 -95.9765 Oklahoma Tulsa 66 63 62 61 

410050004 45.25928 -122.588 Oregon Clackamas 54 54 54 54 

410090004 45.76853 -122.772 Oregon Columbia 45 45 45 45 

410390060 44.02631 -123.084 Oregon Lane 48 48 48 48 

410391007 43.8345 -123.035 Oregon Lane 49 49 49 49 

410470004 44.81029 -122.915 Oregon Marion 49 49 49 49 

410510080 45.49664 -122.603 Oregon Multnomah 51 51 51 51 

410671004 45.40245 -122.854 Oregon Washington 50 50 50 50 

420010002 39.93 -77.25 Pennsylvania Adams 58 56 55 49 

420019991 39.9231 -77.3078 Pennsylvania Adams 59 58 56 50 

420030008 40.46542 -79.9608 Pennsylvania Allegheny 67 67 65 59 

420030010 40.44558 -80.0162 Pennsylvania Allegheny 65 64 63 57 

420030067 40.37564 -80.1699 Pennsylvania Allegheny 65 64 63 57 

420031005 40.61395 -79.7294 Pennsylvania Allegheny 71 71 69 62 

420050001 40.81418 -79.5648 Pennsylvania Armstrong 65 64 63 56 

420070002 40.56252 -80.5039 Pennsylvania Beaver 62 62 61 58 

420070005 40.68472 -80.3597 Pennsylvania Beaver 66 66 65 59 

420070014 40.7478 -80.3164 Pennsylvania Beaver 65 64 63 58 

420110006 40.51408 -75.7897 Pennsylvania Berks 59 57 55 48 

420110011 40.38335 -75.9686 Pennsylvania Berks 63 61 59 52 

420130801 40.53528 -78.3708 Pennsylvania Blair 66 64 62 55 

420170012 40.10722 -74.8822 Pennsylvania Bucks 66 65 62 54 

420210011 40.30972 -78.915 Pennsylvania Cambria 61 60 58 52 

420270100 40.81139 -77.877 Pennsylvania Centre 63 62 61 54 

420279991 40.7208 -77.9319 Pennsylvania Centre 65 64 62 55 

420290100 39.83446 -75.7682 Pennsylvania Chester 62 59 57 49 

420334000 41.1175 -78.5262 Pennsylvania Clearfield 63 63 61 54 
2A-59 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

420430401 40.24699 -76.847 Pennsylvania Dauphin 59 57 55 49 

420431100 40.27222 -76.6814 Pennsylvania Dauphin 63 60 58 50 

420450002 39.83556 -75.3725 Pennsylvania Delaware 61 60 58 50 

420479991 41.598 -78.7674 Pennsylvania Elk 55 55 54 48 

420490003 42.14175 -80.0386 Pennsylvania Erie 60 60 59 54 

420550001 39.96111 -77.4756 Pennsylvania Franklin 56 55 54 48 

420590002 39.80933 -80.2657 Pennsylvania Greene 58 57 56 50 

420630004 40.56333 -78.92 Pennsylvania Indiana 66 65 63 57 

420690101 41.47912 -75.5782 Pennsylvania Lackawanna 61 60 58 52 

420692006 41.44278 -75.6231 Pennsylvania Lackawanna 59 58 56 50 

420710007 40.04667 -76.2833 Pennsylvania Lancaster 66 61 58 51 

420710012 40.04383 -76.1124 Pennsylvania Lancaster 65 61 59 51 

420730015 40.99585 -80.3464 Pennsylvania Lawrence 61 60 59 53 

420750100 40.33733 -76.3834 Pennsylvania Lebanon 63 61 59 52 

420770004 40.61194 -75.4325 Pennsylvania Lehigh 62 61 59 52 

420791100 41.20917 -76.0033 Pennsylvania Luzerne 55 54 52 46 

420791101 41.26556 -75.8464 Pennsylvania Luzerne 55 54 52 46 

420810100 41.2508 -76.9238 Pennsylvania Lycoming 58 56 55 50 

420850100 41.21501 -80.4848 Pennsylvania Mercer 62 61 60 54 

420859991 41.4271 -80.1451 Pennsylvania Mercer 55 54 54 49 

420890002 41.08306 -75.3233 Pennsylvania Monroe 54 53 51 45 

420910013 40.11222 -75.3092 Pennsylvania Montgomery 63 61 59 51 

420950025 40.62806 -75.3411 Pennsylvania Northampton 61 59 57 50 

420958000 40.69222 -75.2372 Pennsylvania Northampton 56 55 53 47 

420990301 40.45694 -77.1656 Pennsylvania Perry 59 58 57 51 

421010004 40.00889 -75.0978 Pennsylvania Philadelphia 55 54 52 45 

421010024 40.0764 -75.0115 Pennsylvania Philadelphia 69 68 65 56 

421011002 40.03599 -75.0024 Pennsylvania Philadelphia 66 65 63 54 

421119991 39.9878 -79.2515 Pennsylvania Somerset 54 53 52 46 

421174000 41.64472 -76.9392 Pennsylvania Tioga 59 58 57 52 

421250005 40.14667 -79.9022 Pennsylvania Washington 60 60 58 52 

421250200 40.17056 -80.2614 Pennsylvania Washington 60 59 58 52 

421255001 40.44528 -80.4208 Pennsylvania Washington 62 61 60 55 

421290006 40.42808 -79.6928 Pennsylvania Westmoreland 62 62 60 54 

421290008 40.30469 -79.5057 Pennsylvania Westmoreland 60 60 58 51 

421330008 39.96528 -76.6994 Pennsylvania York 62 57 55 48 

421330011 39.86097 -76.4621 Pennsylvania York 62 58 56 48 

440030002 41.61524 -71.72 Rhode Island Kent 60 59 57 50 

440071010 41.84157 -71.3608 Rhode Island Providence 59 59 57 50 

440090007 41.49511 -71.4237 Rhode Island Washington 63 63 60 53 
2A-60 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

450010001 34.32532 -82.3864 South Carolina Abbeville 47 46 46 44 

450030003 33.34223 -81.7887 South Carolina Aiken 49 48 48 47 

450070005 34.62324 -82.5321 South Carolina Anderson 53 52 52 51 

450150002 32.98725 -79.9367 South Carolina Berkeley 49 49 49 48 

450190046 32.94102 -79.6572 South Carolina Charleston 51 50 50 49 

450250001 34.61537 -80.1988 South Carolina Chesterfield 50 50 49 48 

450290002 33.00787 -80.965 South Carolina Colleton 48 47 47 45 

450310003 34.2857 -79.7449 South Carolina Darlington 53 52 52 50 

450370001 33.73996 -81.8536 South Carolina Edgefield 46 45 45 44 

450450016 34.75185 -82.2567 South Carolina Greenville 52 51 51 50 

450451003 35.0574 -82.3729 South Carolina Greenville 50 49 49 48 

450770002 34.65361 -82.8387 South Carolina Pickens 54 53 53 51 

450770003 34.85154 -82.7446 South Carolina Pickens 50 50 49 48 

450790007 34.09396 -80.9623 South Carolina Richland 51 50 50 49 

450790021 33.81468 -80.7811 South Carolina Richland 46 45 44 43 

450791001 34.13126 -80.8683 South Carolina Richland 54 53 53 52 

450830009 34.98871 -82.0758 South Carolina Spartanburg 56 55 55 54 

450910006 34.93582 -81.2284 South Carolina York 50 49 49 48 

460330132 43.5578 -103.484 South Dakota Custer 58 58 57 57 

460710001 43.74561 -101.941 South Dakota Jackson 52 52 52 51 

460930001 44.15564 -103.316 South Dakota Meade 53 53 52 52 

460990008 43.54792 -96.7008 South Dakota Minnehaha 56 56 55 55 

461270003 42.88021 -96.7853 South Dakota Union 54 53 53 52 

470010101 35.96522 -84.2232 Tennessee Anderson 55 55 54 48 

470090101 35.63149 -83.9435 Tennessee Blount 59 59 58 52 

470090102 35.60306 -83.7836 Tennessee Blount 51 50 50 45 

470259991 36.47 -83.8268 Tennessee Claiborne 48 47 47 43 

470370011 36.205 -86.7447 Tennessee Davidson 52 52 51 46 

470370026 36.15074 -86.6233 Tennessee Davidson 56 55 55 49 

470419991 36.0388 -85.7331 Tennessee DeKalb 54 54 53 49 

470651011 35.23348 -85.1816 Tennessee Hamilton 55 55 54 50 

470654003 35.10264 -85.1622 Tennessee Hamilton 56 55 54 50 

470890002 36.10563 -83.6021 Tennessee Jefferson 58 57 56 51 

470930021 36.08551 -83.7648 Tennessee Knox 53 53 52 47 

470931020 36.01919 -83.8738 Tennessee Knox 55 54 54 48 

471050109 35.72089 -84.3422 Tennessee Loudon 57 56 55 49 

471210104 35.28938 -84.9461 Tennessee Meigs 55 54 54 50 

471490101 35.73288 -86.5989 Tennessee Rutherford 53 53 52 47 

471550101 35.69667 -83.6097 Tennessee Sevier 57 57 56 52 

471550102 35.56278 -83.4981 Tennessee Sevier 57 56 56 52 
2A-61 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

471570021 35.2175 -90.0197 Tennessee Shelby 60 59 58 52 

471570075 35.1517 -89.8502 Tennessee Shelby 61 60 59 53 

471571004 35.37815 -89.8345 Tennessee Shelby 58 57 56 51 

471632002 36.54144 -82.4248 Tennessee Sullivan 60 60 59 53 

471632003 36.58211 -82.4857 Tennessee Sullivan 59 59 59 53 

471650007 36.29756 -86.6531 Tennessee Sumner 60 60 59 54 

471650101 36.45398 -86.5641 Tennessee Sumner 57 56 56 51 

471870106 35.95153 -87.137 Tennessee Williamson 55 55 54 48 

471890103 36.06083 -86.2863 Tennessee Wilson 57 57 56 52 

480271047 31.088 -97.6797 Texas Bell 63 62 60 56 

480290032 29.51509 -98.6202 Texas Bexar 67 66 63 59 

480290052 29.63206 -98.5649 Texas Bexar 68 67 64 60 

480290059 29.27538 -98.3117 Texas Bexar 60 59 57 53 

480391004 29.52044 -95.3925 Texas Brazoria 76 75 70 64 

480391016 29.04376 -95.4729 Texas Brazoria 64 63 61 57 

480610006 25.8925 -97.4938 Texas Cameron 57 57 56 54 

480850005 33.13242 -96.7864 Texas Collin 69 68 64 58 

481130069 32.81995 -96.8601 Texas Dallas 69 68 64 58 

481130075 32.91921 -96.8085 Texas Dallas 70 69 65 59 

481130087 32.67645 -96.8721 Texas Dallas 69 68 64 58 

481210034 33.21906 -97.1963 Texas Denton 71 70 66 60 

481211032 33.41064 -96.9446 Texas Denton 70 69 65 59 

481390016 32.48208 -97.0269 Texas Ellis 66 65 62 57 

481391044 32.17543 -96.8702 Texas Ellis 61 60 57 53 

481410029 31.78577 -106.324 Texas El Paso 54 54 54 54 

481410037 31.76829 -106.501 Texas El Paso 62 62 62 61 

481410044 31.7657 -106.455 Texas El Paso 60 60 60 60 

481410055 31.74674 -106.403 Texas El Paso 58 58 58 58 

481410057 31.6675 -106.288 Texas El Paso 57 57 57 57 

481410058 31.89391 -106.426 Texas El Paso 61 60 60 60 

481671034 29.25447 -94.8613 Texas Galveston 70 69 67 64 

481830001 32.37868 -94.7118 Texas Gregg 70 66 62 55 

482010024 29.90104 -95.3261 Texas Harris 71 70 66 60 

482010026 29.80271 -95.1255 Texas Harris 70 69 66 61 

482010029 30.03953 -95.6739 Texas Harris 69 68 64 59 

482010046 29.82809 -95.2841 Texas Harris 67 66 62 57 

482010047 29.83472 -95.4892 Texas Harris 67 66 62 55 

482010051 29.62361 -95.4736 Texas Harris 69 68 64 58 

482010055 29.69574 -95.4993 Texas Harris 70 69 65 58 

482010062 29.62583 -95.2675 Texas Harris 68 67 63 57 
2A-62 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

482010066 29.72472 -95.5036 Texas Harris 66 66 62 55 

482010070 29.73513 -95.3156 Texas Harris 67 66 62 56 

482010075 29.75278 -95.3503 Texas Harris 68 67 63 57 

482010416 29.68639 -95.2947 Texas Harris 69 68 64 58 

482011015 29.76165 -95.0814 Texas Harris 67 66 63 59 

482011034 29.76797 -95.2206 Texas Harris 73 72 67 61 

482011035 29.73373 -95.2576 Texas Harris 70 69 65 59 

482011039 29.67003 -95.1285 Texas Harris 75 74 70 65 

482011050 29.58305 -95.0155 Texas Harris 72 71 68 64 

482030002 32.66899 -94.1675 Texas Harrison 63 61 59 55 

482150043 26.22623 -98.2911 Texas Hidalgo 56 55 55 53 

482151048 26.13108 -97.9373 Texas Hidalgo 55 54 53 52 

482210001 32.44231 -97.8035 Texas Hood 65 64 61 56 

482311006 33.15308 -96.1156 Texas Hunt 61 60 57 52 

482450009 30.03644 -94.0711 Texas Jefferson 64 63 60 56 

482450011 29.8975 -93.9911 Texas Jefferson 65 64 62 58 

482450022 29.86395 -94.3178 Texas Jefferson 62 61 58 54 

482450101 29.728 -93.894 Texas Jefferson 69 69 67 64 

482450102 29.9425 -94.0006 Texas Jefferson 62 61 58 55 

482450628 29.865 -93.955 Texas Jefferson 63 62 60 57 

482451035 29.97892 -94.0109 Texas Jefferson 63 62 60 56 

482510003 32.35359 -97.4367 Texas Johnson 68 67 64 59 

482570005 32.56495 -96.3177 Texas Kaufman 62 60 57 53 

483091037 31.65307 -97.0707 Texas McLennan 64 63 60 56 

483390078 30.3503 -95.4251 Texas Montgomery 66 66 62 57 

483491051 32.03194 -96.3991 Texas Navarro 63 61 58 54 

483550025 27.76534 -97.4342 Texas Nueces 64 63 62 59 

483550026 27.83241 -97.5554 Texas Nueces 64 63 61 59 

483611001 30.08526 -93.7613 Texas Orange 64 63 61 57 

483611100 30.19417 -93.8669 Texas Orange 61 59 57 53 

483670081 32.86878 -97.9059 Texas Parker 68 67 64 59 

483739991 30.7017 -94.6742 Texas Polk 60 60 58 55 

483970001 32.93652 -96.4592 Texas Rockwall 66 65 62 56 

484230007 32.34401 -95.4158 Texas Smith 65 62 60 55 

484390075 32.98789 -97.4772 Texas Tarrant 70 69 66 60 

484391002 32.80582 -97.3566 Texas Tarrant 69 68 65 59 

484392003 32.9225 -97.2821 Texas Tarrant 74 73 69 62 

484393009 32.98426 -97.0637 Texas Tarrant 73 72 68 61 

484393011 32.65637 -97.0886 Texas Tarrant 69 68 65 59 

484530014 30.35442 -97.7603 Texas Travis 64 63 60 56 
2A-63 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

484530020 30.48317 -97.8723 Texas Travis 61 60 58 54 

484690003 28.83617 -97.0055 Texas Victoria 62 60 58 54 

484790016 27.51127 -99.5203 Texas Webb 59 59 58 56 

490030003 41.49271 -112.019 Utah Box Elder 61 60 60 60 

490037001 41.94595 -112.233 Utah Box Elder 61 60 60 60 

490050004 41.73111 -111.838 Utah Cache 59 59 59 58 

490071003 39.60996 -110.801 Utah Carbon 65 62 62 62 

490110004 40.90297 -111.884 Utah Davis 62 61 61 61 

490131001 40.20865 -110.841 Utah Duchesne 63 63 63 62 

490352004 40.73639 -112.21 Utah Salt Lake 66 65 65 65 

490353006 40.73639 -111.872 Utah Salt Lake 66 65 65 64 

490370101 38.45861 -109.821 Utah San Juan 64 64 64 64 

490450003 40.54331 -112.3 Utah Tooele 65 65 65 64 

490490002 40.25361 -111.663 Utah Utah 63 63 63 62 

490495008 40.43028 -111.804 Utah Utah 60 59 59 59 

490495010 40.13634 -111.661 Utah Utah 63 63 63 62 

490530006 37.129 -113.637 Utah Washington 62 62 62 62 

490570002 41.20632 -111.976 Utah Weber 65 64 64 64 

490571003 41.30361 -111.988 Utah Weber 65 65 65 64 

500030004 42.88759 -73.2498 Vermont Bennington 53 53 52 47 

500070007 44.52839 -72.8688 Vermont Chittenden 53 52 52 50 

510030001 38.07657 -78.504 Virginia Albemarle 54 54 53 49 

510130020 38.8577 -77.0592 Virginia Arlington 63 63 60 51 

510330001 38.20087 -77.3774 Virginia Caroline 56 55 53 47 

510360002 37.34438 -77.2593 Virginia Charles 61 59 58 56 

510410004 37.35748 -77.5936 Virginia Chesterfield 58 56 55 51 

510590030 38.77335 -77.1047 Virginia Fairfax 63 62 59 50 

510610002 38.47367 -77.7677 Virginia Fauquier 50 49 48 43 

510690010 39.28102 -78.0816 Virginia Frederick 55 54 53 48 

510719991 37.3297 -80.5578 Virginia Giles 48 48 47 44 

510850003 37.60613 -77.2188 Virginia Hanover 59 57 56 54 

510870014 37.55652 -77.4003 Virginia Henrico 61 58 58 55 

511071005 39.02473 -77.4893 Virginia Loudoun 60 59 57 49 

511130003 38.52199 -78.4358 Virginia Madison 60 59 58 54 

511390004 38.66373 -78.5044 Virginia Page 56 55 55 50 

511479991 37.1655 -78.3069 Virginia Prince Edward 54 50 50 48 

511530009 38.85287 -77.6346 Virginia Prince William 58 58 56 49 

511611004 37.28342 -79.8845 Virginia Roanoke 54 53 53 51 

511630003 37.62668 -79.5126 Virginia Rockbridge 52 51 51 48 

511650003 38.47753 -78.8195 Virginia Rockingham 55 55 54 50 
2A-64 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

511790001 38.48123 -77.3704 Virginia Stafford 54 53 51 43 

511970002 36.89117 -81.2542 Virginia Wythe 54 53 53 49 

515100009 38.8104 -77.0444 Virginia Alexandria City 62 61 59 50 

516500008 37.10373 -76.387 Virginia Hampton City 60 59 59 57 

518000004 36.90118 -76.4381 Virginia Suffolk City 60 59 59 57 

518000005 36.66525 -76.7308 Virginia Suffolk City 56 55 54 52 

530110011 45.61667 -122.517 Washington Clark 50 50 50 50 

530330010 47.5525 -122.065 Washington King 50 50 50 50 

530330017 47.49022 -121.773 Washington King 49 49 49 49 

530330023 47.1411 -121.938 Washington King 55 55 55 55 

530630001 47.41645 -117.53 Washington Spokane 51 51 51 51 

530630021 47.67248 -117.365 Washington Spokane 51 51 51 51 

530630046 47.82728 -117.274 Washington Spokane 50 50 50 50 

530670005 46.95256 -122.595 Washington Thurston 48 47 47 47 

540030003 39.44801 -77.9641 West Virginia Berkeley 56 56 54 49 

540110006 38.42413 -82.4259 West Virginia Cabell 58 57 57 50 

540219991 38.8795 -80.8477 West Virginia Gilmer 52 52 51 45 

540250003 37.90853 -80.6326 West Virginia Greenbrier 54 53 53 48 

540291004 40.42154 -80.5807 West Virginia Hancock 63 63 62 57 

540390010 38.3456 -81.6283 West Virginia Kanawha 64 64 63 55 

540610003 39.64937 -79.9209 West Virginia Monongalia 63 62 61 54 

540690010 40.11488 -80.701 West Virginia Ohio 61 60 59 53 

540939991 39.0905 -79.6617 West Virginia Tucker 56 55 55 49 

541071002 39.32353 -81.5524 West Virginia Wood 57 57 56 50 

550090026 44.53098 -87.908 Wisconsin Brown 57 56 55 52 

550210015 43.3156 -89.1089 Wisconsin Columbia 57 56 55 52 

550250041 43.10084 -89.3573 Wisconsin Dane 56 56 55 52 

550270001 43.46611 -88.6211 Wisconsin Dodge 62 61 61 57 

550290004 45.237 -86.993 Wisconsin Door 64 63 63 58 

550350014 44.761 -91.143 Wisconsin Eau Claire 51 51 50 49 

550390006 43.6874 -88.422 Wisconsin Fond du Lac 61 60 60 56 

550410007 45.563 -88.8088 Wisconsin Forest 53 53 52 50 

550550002 43.002 -88.8186 Wisconsin Jefferson 58 58 57 54 

550590019 42.50472 -87.8093 Wisconsin Kenosha 59 58 59 59 

550610002 44.44312 -87.5052 Wisconsin Kewaunee 63 62 62 57 

550630012 43.7775 -91.2269 Wisconsin La Crosse 53 52 52 51 

550710007 44.13862 -87.6161 Wisconsin Manitowoc 66 66 65 60 

550730012 44.70735 -89.7718 Wisconsin Marathon 53 52 52 49 

550790010 43.01667 -87.9333 Wisconsin Milwaukee 56 56 55 53 

550790026 43.06098 -87.9135 Wisconsin Milwaukee 60 60 60 57 
2A-65 

Site ID Lat Long State County 

O3 DV for Scenario: 

Base 

Case Baseline 70 65 

550790085 43.181 -87.9 Wisconsin Milwaukee 64 64 63 59 

550870009 44.30738 -88.3951 Wisconsin Outagamie 60 59 59 56 

550890008 43.343 -87.92 Wisconsin Ozaukee 66 65 65 61 

550890009 43.49806 -87.81 Wisconsin Ozaukee 62 61 61 57 

551010017 42.7139 -87.7986 Wisconsin Racine 57 57 57 56 

551050024 42.50908 -89.0628 Wisconsin Rock 60 60 59 55 

551110007 43.4351 -89.6797 Wisconsin Sauk 55 53 53 50 

551170006 43.679 -87.716 Wisconsin Sheboygan 71 71 70 65 

551199991 45.2066 -90.5969 Wisconsin Taylor 53 53 52 51 

551270005 42.58001 -88.499 Wisconsin Walworth 60 60 59 56 

551330027 43.02008 -88.2151 Wisconsin Waukesha 58 57 57 53 

560019991 41.3642 -106.24 Wyoming Albany 65 65 65 64 

560050123 44.6522 -105.29 Wyoming Campbell 60 59 59 59 

560050456 44.14696 -105.53 Wyoming Campbell 59 59 59 59 

560070100 41.38694 -107.617 Wyoming Carbon 60 59 59 58 

560130232 43.08167 -107.549 Wyoming Fremont 60 59 59 59 

560210100 41.18223 -104.778 Wyoming Laramie 63 62 62 60 

560350700 42.48636 -110.099 Wyoming Sublette 60 60 60 60 

560359991 42.9288 -109.788 Wyoming Sublette 62 62 62 62 

560370077 41.158 -108.619 Wyoming Sweetwater 59 58 58 58 

560370200 41.67745 -108.025 Wyoming Sweetwater 57 56 56 56 

560370300 41.75056 -109.788 Wyoming Sweetwater 60 60 60 60 

560410101 41.3731 -111.042 Wyoming Uinta 58 58 58 58 

2A.5 References 

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. 

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. 

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.or 

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. 
2A-66 

Karl, T. R. and Koss, W. J., 1984: "Regional and National Monthly, Seasonal, and Annual Temperature Weighted 

by Area, 1895-1983." Historical Climatology Series 4-3, National Climatic Data Center, Asheville, NC, 38 

pp. 

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. 

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. 

Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang, W., Powers, 

J.G., 2008, A description of the advanced research WRF version 3, National Center for Atmospheric Research, 

Boulder, CO, USA, NCAR/TN-475+STR 

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 (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 (US EPA) 2014c; 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 (US EPA), 2014d; Regulatory Impact Analysis of the Proposed Revisions to 

the National Ambient Air Quality Standards for Ground-Level Ozone, Office of Air Quality Planning and 

Standards; RTP, NC, EPA-452/P-14-006 

Yantosca, B. and Carouge, C., 2010, GEOS-Chem v8-03-01 User’s Guide, Atmospheric Chemistry Modeling 

Group, Harvard University, Cambridge, MA, http://acmg.seas.harvard.edu/geos/doc/archive/man.v8-03-

02/index.html 
3-1 

CHAPTER 3 : CONTROL STRATEGIES AND EMISSIONS REDUCTIONS 

Overview 

To estimate the costs and benefits of alternative ozone standard levels, the EPA has 

analyzed hypothetical control strategies that areas across the country might employ to attain the 

revised ozone standard level of 70 ppb and a more stringent alternative standard of 65 ppb. The 

future year for analyzing the incremental costs and benefits of meeting a revised ozone standard 

is 2025.44 This analysis year was chosen because most areas of the U.S. will be required to meet 

a revised ozone standard by 2025. California was analyzed independently from the rest of the 

U.S. because of the potential for longer compliance timelines in many areas. Consequently, we 

created two baseline scenarios, a 2025 baseline for all areas outside of California and a post-2025 

baseline for California. 

This chapter documents the (i) emissions control measures EPA applied to illustrate 

attainment with the revised ozone standard of 70 ppb and the alternative standard of 65 ppb and 

(ii) projected emissions reductions associated with the measures. The chapter is organized into 

five sections. Section 3.1 provides a summary of the steps that we took to determine necessary 

emissions reductions to create the 2025 baseline and the control strategies to reach the revised 

standard level of 70 ppb and an alternative standard of 65 ppb in the continental U.S. outside of 

California. Section 3.2 describes the steps we took to determine necessary emissions reductions 

to create the post 2025-baseline and the control strategy to reach 70 ppb and 65 ppb for 

California. In Section 3.3 we discuss key differences between the results from the analysis 

conducted for the proposal RIA and this analysis. In Section 3.4 we list the key limitations and 

uncertainties associated with the control strategy analysis. And finally, Section 3.5 includes the 

references for the chapter. 

To conduct the control strategy analyses, we first require information on total emissions 

reductions needed to simulate attainment. For that purpose we need (i) projected future design 

value and design value (DV) targets for each area, (ii) the sensitivity of ozone DVs to the NOx 

 

44 Please see Chapter 1, Section 1.3.2 for a detailed discussion of the potential nonattainment designations and their 

timing. 
3-2 

and VOC emissions reductions, and (iii) available NOx and VOC reductions from identified 

controls (as will be described in section 3.1.1). Second, to find an illustrative control strategy to 

achieve the emissions reductions needed, we need information about available identified 

controls45 for specific sources and associated emissions reductions. More details on air quality 

modeling and information about projected future DVs, DV targets, and ppb/ton ozone response 

factors are provided in Chapter 2. In this chapter we calculate the necessary emissions 

reductions and describe the creation of hypothetical control strategies for the post-2025 baseline 

and for the revised and alternative standard levels analyzed. 

3.1 The 2025 Control Strategy Scenarios 

To create the baseline, we projected 2025 ozone DVs for the base case scenario as 

described in Section 2.2 of Chapter 2. We adjusted the 2025 base case for all areas of the U.S. to 

account for emissions reductions from the Clean Power Plan in creating the 2025 baseline. In 

addition, because in the final 2025 base case projections no monitors outside of California were 

projected to violate the current standard of 75 ppb, no additional controls were applied to create 

the 2025 baseline. 

3.1.1 Approach for the Revised Standard of 70 ppb and Alternative Standard of 65 ppb 

 The control strategies applied to illustrate attainment of the revised and alternative 

standards analyzed involved several steps. We applied regional and local ppb/ton ozone 

response factors to estimate resulting ozone DVs at air quality monitor locations to find the 

target emissions levels. Then we applied controls to reach those targets levels. These steps are 

described in this section. 

As described in Chapter 2, we performed a series of photochemical modeling simulations 

to determine the response of ozone DVs at monitor locations to emissions reductions in specific 

 

45 In the proposal RIA we discuss emissions reductions resulting from the application of known controls, as well as 

emissions reductions beyond known controls, using the terminology of “known controls” and “unknown controls.” 

In the final RIA, we have used slightly different terminology, consistent with past NAAQS RIAs. Here we refer to 

emissions reductions and controls as either “identified” controls or measures or “unidentified” controls or measures 

reflecting that unidentified controls or measures can include existing controls or measures for which the EPA does 

not have sufficient data to accurately estimate their costs. 

 
3-3 

regions (NOx emissions reductions) and urban areas (VOC emissions reductions). We estimated 

the necessary emissions reductions sequentially, one region at a time. For each air quality 

sensitivity region (see Chapter 2, Figure 2-2 for a map of the sensitivity regions), we determined 

the amount of emissions reductions necessary for all monitors within the region to meet the 

standard level analyzed. 

To implement this approach, we ranked the monitors in descending order by baseline 

DV, and the region that included the monitor with the highest projected baseline DV (East 

Texas) was analyzed first. We estimated the emissions reductions to decrease ozone 

concentrations to the level needed for that region. We then estimated the impact that those 

emissions reductions would have on all other remaining regions. After emissions reductions 

were estimated for each region, the remaining monitors were re-ordered based on the resulting 

DVs and the next region with the highest baseline DV was targeted for emissions reductions, if 

needed, and the impact of its reductions were estimated for the remaining regions. We repeated 

this process until all regions had been analyzed. For each region analyzed, we determined (i) the 

quantity of emissions reductions from available identified NOx & VOC controls, and (ii) the 

impact of these controls on ozone concentrations. If additional decreases in ozone 

concentrations were needed in the region being analyzed, additional emissions reductions would 

have to come from unidentified controls. Figure 3-1 shows a summary of this process. A 

numeric example of the calculation methodology is provided in Appendix 3A. In addition, 

ozone DVs at all evaluated monitors are provided for each scenario in Appendix 2A, Section 

2A.4. 
3-4 

Figure 3-1. Process to Find Needed Reductions to Reach the Revised and Alternative 

Standards 

Because emissions reductions in NOx and VOC have different resulting air quality 

impacts on ozone and because different combinations of reductions from these pollutants could 

potentially render the same reduction in ozone, it is important to know for each region, a-priori, 

the total potential available reductions of these two pollutants from identified controls. To find 

these potentially available tons of NOx and VOC emissions reductions, we ran a maximum 

emissions reductions run using CoST (Control Strategy Tool) (a description of CoST, its 

algorithms, and the control strategy applied to obtain the necessary reductions follows in section 

3.1.2), and applied the reductions from these controls as part of the process described above to 

obtain the total needed emissions reductions. First, we estimated the available NOx and VOC 

emissions reductions from identified controls to determine the reductions in ozone 

concentrations. In this analysis, identified VOC controls are generally more expensive than 

identified NOx controls and are only effective at reducing ozone in a limited number of 

locations. For completeness, we applied the more expensive identified VOC controls in these 

locations before applying any unidentified controls. Then we estimated any additional NOX

emissions reductions needed from unidentified controls to achieve the target reduction. We did 

not apply any unidentified VOC controls.46 States will likely pursue the most effective controls 

 

46 Past air quality modeling experience has indicated that in most areas NOx emissions reductions are more effective 

at reducing ozone concentrations at the monitor with the highest DV. 
3-5 

for reducing ozone concentrations. In this analysis, overall NOx controls are more effective at 

reducing high ozone concentrations, so we applied unidentified NOx controls. 

To define the geographic areas within which we would obtain NOX emissions reductions, 

we created a 200 kilometer buffer around each county with a DV projected to exceed the 

standard level being analyzed, but we limited the buffer to within the borders of the state 

containing the exceeding county. The area outside the buffer but within the air quality modeling 

sensitivity region was also identified. To define the geographic areas within which we would 

obtain VOC emissions reductions, we created a 100 kilometer buffer around the county with the 

projected monitor exceedance in areas where the modeling showed that ozone concentrations are 

responsive to VOC emissions reductions. Figures 3-2 and 3-3 are maps displaying the NOX and 

VOC buffers respectively. We used these buffers in estimating available emissions reductions to 

target the application of identified controls as close to the projected exceeding monitors as 

possible, within each region. 
3-6 

Figure 3-2. Buffers of 200 km for NOx Emissions Reductions around Projected 

Exceedance Areas 
3-7 

Figure 3-3. Buffers of 100 km for VOC Emissions Reductions around Projected 

Exceedance Areas 

Once we completed the process of estimating the necessary emissions reductions to meet 

the revised standard of 70 ppb and alternative standard of 65 ppb for each region, we applied 

control strategies to simulate attainment with them. For each air quality sensitivity region 

containing a monitor projected to exceed either the revised or the alternative standard, we 

applied NOX controls to simulate attainment with the respective standard. If these controls did 

not bring the area into attainment and VOC reductions were needed, then we applied a control 

strategy within the 100 km buffer to reach the VOC target reductions. If the quantity of 

emissions reductions needed were greater than the available emissions reductions from NOX and 

VOC controls within the buffer, additional identified controls were applied within the remaining 

air quality sensitivity region outside the buffer. If further emission reductions were needed 

within the region then we assumed that unidentified controls would be used for that region to 

meet the standard analyzed. Figure 3-4 illustrates this process. 
3-8 

Figure 3-4. Process to Estimate the Control Strategies for the Revised and Alternative 

Standards 

3.1.2 Identified Control Measures 

Control measures applied to meet the revised and alternative standards were identified for 

four emissions sectors: Electric Generating Units (EGUs), Non-Electric Generating Unit Point 

Sources (Non-EGUs), Nonpoint (Area) Sources, and Nonroad Mobile Sources. Onroad mobile 

source controls were not applied because they are largely addressed in existing rules such as the 

Tier 3 rule (U.S. EPA, 2014a). Controls applied for the revised and alternative standard analyses 

are listed in Table 3-1. 

The control measures we applied were identified using the EPA’s Control Strategy Tool 

(CoST) (U.S. EPA, 2014b), the NONROAD Model (U.S. EPA, 2005) and the Integrated 

Planning Model (IPM) (U.S. EPA, 2015).47 CoST models emissions reductions and engineering 

costs associated with control strategies applied to non-EGUs, area, and mobile sources of air 

pollutant emissions by matching control measures to emissions sources using algorithms such as 

 

47 For the final RIA, an updated version of IPM was used. As a result of the updated version of IPM, after 

accounting for emissions reductions from the proposed Clean Power Plan we applied fewer controls to EGU sources 

than we applied in the proposal RIA. 
3-9 

"maximum emissions reduction", "least cost", and "apply measures in series". For this control 

strategy analysis, we applied both the maximum emissions reduction (when all available 

reductions were needed) and least cost algorithms48 (when not all available reductions were 

needed). These controls are described further in Appendix 3A. 

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 non-EGU 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. VOC controls applied included 

surface coating, solvents, and fuel storage tanks. 

To more accurately depict available controls, the EPA employed a decision rule in which 

controls were not applied to any non-EGU point or nonpoint sources with less than 25 tons/year 

of emissions per pollutant for NOX and 10 tons/year for VOC. This decision rule is more 

inclusive of sources than the decision rule employed in the previous Ozone and PM2.5 NAAQS 

RIAs where we applied a minimum of 50 tons/year for each pollutant. The reason for not 

applying controls to sources below these levels is that many of these sources likely already have 

controls in place that may not be reflected in the emissions inventory inputs, and we don’t 

believe it is cost effective to apply an additional control device (see Chapter 4, Section 4.1.1 for a 

brief discussion of the emissions inventory inputs). Furthermore, controls were not applied if 

their cost per ton exceeded $19,000/ton for NOx or $33,000/ton for VOC (see Chapter 4, Section 

4.1.1 for a discussion about these cutoff values). In addition, we only apply controls that replace 

existing controls if replacement controls are at least 10% more effective than the existing control. 

This is because we assume that replacement below that level would not be cost effective. 

 

48 A maximum emissions reductions run in CoST will yield the same result as a least cost control strategy run with 

100% control. 
3-10 

Table 3-1. Identified Controls Applied for the Revised and Alternative Standard Analyses 

Strategies 

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

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

Biosolid Injection Technology Reduced Solvent Utilization

Episodic Burn Ban Gas Recovery in Landfills

EGU SCR and SNCR 

Nonroad Diesel Retrofits & Engine Rebuilds

3.1.3 Results 

Figure 3-5 shows the counties projected to exceed the revised standard and alternative 

standard analyzed for the 2025 baseline for areas other than California. For the 70 ppb control 

strategy, NOX emissions reductions were required for monitors in the following regions: 

Colorado, Great Lakes, North East, Ohio River Valley and East Texas (see Chapter 2, Figure 2-2 

for a depiction of the sensitivity regions). VOC reductions were required in Houston (see 

Chapter 2, Figure 2-4 for a depiction of the VOC impact regions). For the 65 ppb alternative 

standard, in addition to the regions listed above, NOx reductions were also applied in the 

Arizona-New Mexico, Nevada, and Oklahoma-Arkansas-Louisiana regions. VOC reductions 
3-11 

were required in Denver, Houston, Louisville, Chicago and New York City.49 It is important to 

note that for both the 70 ppb revised standard as well as the 65 ppb alternative standard when 

VOC reductions were needed all available reductions from identified controls were applied using 

the maximum emissions reductions algorithm. In all of these areas, we used all of the available 

identified VOC controls, and for remaining reductions in ozone concentrations we applied 

unidentified NOx controls. Summaries of the emissions reductions are presented by region and 

source category in Appendix 3A. 

Figure 3-5. Projected Ozone Design Values in the 2025 Baseline Scenario 

Table 3-2 shows the number of exceeding counties and the number of neighboring 

counties to which controls were applied for the revised and alternative standards analyzed. 

Figure 3-6 shows counties where NOx controls were applied for the revised and alternative 

 

49 These five urban areas were determined to have ozone that was sensitive to reductions of VOC emissions in some 

locations and were the areas with the highest ozone DVs in their respective regions. See Chapter 2, section 2.3 and 

Appendix 2A. 
3-12 

standards, and Figure 3-7 depicts counties where VOC controls were applied for the revised and 

alternative standards analyzed. For a complete list of geographic areas for the revised and 

alternative standards analyzed see Appendix 3A. 

Table 3-2. Number of Counties with Exceedances and Number of Additional Counties 

Where Reductions Were Applied for the 2025 Revised and Alternative 

Standards Analyses - U.S., except California 

Revised and 

Alternative 

Standards 

Number of Counties with 

Exceedances 

Number of Additional Counties Where Reductions 

Were Applied 

70 ppb 14 663 

65 ppb 50 1,170 

Figure 3-6. Counties Where NOX Emissions Reductions Were Applied to Simulate 

Attainment with the Revised and Alternative Ozone Standards in the 2025 

Analysis 
3-13 

Figure 3-7. Counties Where VOC Emissions Reductions Were Applied to Simulate 

Attainment with the Revised and Alternative Ozone Standards in the 2025 

Analyses 

Table 3-3 shows the modeled 2011 and 2025 base case NOx and VOC emissions by sector 

(this table is also Table 2A-1 in Appendix 2A). Additional details on the emissions by state are 

given in the emissions modeling TSD. Tables 3-4 and 3-5 show the emissions reductions from 

identified controls for the revised and alternative standard analyzed. The largest emission 

reductions were in the non-EGU point source and nonpoint source sectors. For details regarding 

emissions reductions by control measure see Appendix 3.A 
3-14 

Table 3-3. 2011 and 2025 Base Case NOx and VOC Emissions by Sector (1000 tons) 

Sector 2011 NOx 2025 NOx 2011 VOC 2025 VOC 

EGU-point 2,000 1,400 36 42 

NonEGU-point 1,200 1,200 800 830 

Point oil and gas 500 460 160 190 

Wild and Prescribed Fires 330 330 4,700 4,700 

Nonpoint oil and gas 650 720 2,600 3,500 

Residential wood 

combustion 34 35 440 410 

Other nonpoint 760 790 3,700 3,500 

Nonroad 1,600 800 2,000 1,200 

Onroad 5,700 1,700 2,700 910 

C3 Commercial marine 

vessel (CMV) 130 100 5 9 

Locomotive and C1/C2 

CMV 1,100 680 48 24 

Biogenics 1,000 1,000 41,000 41,000 

TOTAL 15,000 9,300 58,000 56,000 

Table 3-4. Summary of Emissions Reductions by Sector for the Identified Control 

Strategies Applied for the Revised 70 ppb Ozone Standard in 2025, except 

California (1,000 tons/year)a

Geographic Area Emissions Sector NOx VOC

East 

EGU 45 - 

Non-EGU Point 85 1 

Nonpoint 100 19 

Nonroad 3 - 

Onroad - - 

Total 230 20 

West 

EGU - - 

Non-EGU Point 6 - 

Nonpoint 1 - 

Nonroad - - 

Onroad - - 

Total 7 - 

a

 Emissions reduction estimates are rounded to two significant figures. 
3-15 

Table 3-5. Summary of Emissions Reductions by Sector for the Identified Control 

Strategies for the Alternative 65 ppb Ozone Standard in 2025 - except California 

(1,000 tons/year)a 

Geographic Area Emissions Sector NOx VOC 

East 

EGU 110 - 

Non-EGU Point 220 5 

Nonpoint 160 100 

Nonroad 8 - 

Total 500 100 

West 

EGU 0 - 

Non-EGU Point 33 - 

Nonpoint 22 5 

Nonroad 1 - 

Total 56 5 

a

 Emissions reduction estimates are rounded to two significant figures. 

As mentioned previously, there were several areas where identified controls did not 

achieve enough emissions reductions to meet the revised and alternative standards of 70 and 65 

ppb. Texas East was the only area where identified controls were not enough to get the needed 

emissions reductions for 70 ppb. Great Lakes, Colorado, Texas East, Ohio River Valley, 

Northeast and Nevada were the areas where identified controls were not enough to get the 

needed emissions reductions for 65 ppb. See Chapter 2, Figure 2-2 for a map showing these 

areas. To complete the analysis, the EPA then assumed that the remaining reductions needed to 

meet the standard would be obtained from unidentified controls. Table 3-6 shows the emissions 

reductions needed from unidentified controls in 2025 for the U.S., except California, for the 

revised and alternative standards analyzed. 
3-16 

Table 3-6. Summary of Emissions Reductions for the Revised and Alternative Standards 

for the Unidentified Control Strategies for 2025 - except California (1,000 

tons/year)a 

Revised and 

Alternative Standards 

Region NOx VOC 

70 ppbb East 

West 

47 

- 

- 

- 

65 ppbc East 

West 

820 

40 

- 

- a

 Estimates are rounded to two significant figures. 

b

 Unidentified controls for the revised standard of 70 ppb are needed in the Texas East (see Chapter 2, Figure 2-2 for 

a description of these regions). c

 Unidentified controls for the 65 ppb alternative standard are needed in Nevada, Colorado, Texas East, Great Lakes, 

Ohio River Valley and North East (see Chapter 2, Figure 2-2 for a description of these regions). 

Table 3-7 summarizes the total (identified and unidentified) emissions reductions needed 

to meet the revised and alternative standard levels in 2025 for the East and West, except 

California (see Chapter 4, Figure 4-3 for a map depicting the East and West regions). In the East 

for 2025, the unidentified NOx emissions reductions needed as percentage of the total reductions 

increases from 17 percent to 62 percent as the standard level analyzed decreases from 70 ppb to 

65 ppb. In the West, unidentified NOX emissions reductions are only needed for the 65 ppb 

alternative standard and account for 42 percent of the total reductions needed. No unidentified 

VOC reductions are needed in the East or West for the 70 ppb and 65 ppb standard levels. 

Table 3-7. Summary of Emissions Reductions from the Identified + Unidentified Control 

Strategies by Alternative Standard Levels in 2025, Except California (1,000 

tons/year)a

 Alternative Standard 

Geographic Area Emissions Reductions 70 ppb 65 ppb 

East 

NOx Identified 230 500 

NOx Unidentified 50 820 

% NOx Unidentified 17% 62% 

VOC Identified 20 100 

VOC Unidentified 0 0 

% VOC Unidentified 0% 0% 

West 

NOx Identified 7 56 

NOx Unidentified 0 40 

% NOx Unidentified 0% 42% 

VOC Identified 0 5 

VOC Unidentified 0 0 

% VOC Unidentified 0% 0% 

a

 Estimates are rounded to two significant figures. 
3-17 

3.2 The Post-2025 Scenario for California 

The post-2025 baseline and alternative standard level scenarios for California were created 

using similar methods to those described above in Section 3.1. However, in contrast to the rest 

of the U.S., substantial emissions reductions were needed in California to meet the current 

standard of 75 ppb. All identified controls were used to meet the current standard in this process, 

so the revised and alternative standards analyzed in California relied entirely on unidentified 

measures. 

3.2.1 Creation of the Post-2025 Baseline Scenario for California 

The final 2025 base case projections predict several areas of California would have ozone 

DVs above the current standard level of 75 ppb. Therefore, we estimated emissions reductions 

in the following order to construct the post-2025 baseline scenario for California: (1) emissions 

changes from the Clean Power Plan, (2) mobile source emissions changes between 2025 and 

2030, (3) identified controls of NOx emissions from nonpoint, non-EGU point, and nonroad 

sources, (4) identified controls of VOC emissions, and (5) additional NOx reductions beyond 

identified controls (i.e., unidentified controls). All controls applied to these sources were above 

and beyond reductions from on-the-books regulations that were included in the final 2025 base 

case modeling. The following paragraphs and Figure 3-8 outline these steps. 
3-18 

Figure 3-8. Steps to Create the Post-2025 Baseline for California 

To create the post-2025 baseline, in Step 1 we accounted for emissions reductions from 

the Clean Power Plan.50 In Step 2 we applied the 2025 to 2030 mobile source emissions 

reductions because many locations in California will likely have attainment dates farther into the 

future than 2025. Although emissions projections years beyond 2025 were not available, 

California provided emissions projections in the year 2030 of both VOC and NOx 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 

 

50 We adjusted the 2025 base case to reflect emissions reductions from the Clean Power Plan to create the post-2025 

baseline. 
3-19 

these mobile source changes resulted in: (1) VOC emissions that were 1% less than those 

modeled in the California base case in both the Northern and Southern California sub-regions 

(see Chapter 2, Figure 2-2 for a depiction of the California sub-regions), and (2) NOx emissions 

that were 4% less than those modeled in the California base case in the Northern California subregion 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 post-2025 baseline scenario in 

California using the response ratios developed from the air quality sensitivity simulations as 

described in Chapter 2. In Step 3 available NOx reductions from identified control measures 

were applied from three sectors:51 Non-Electric Generating Unit Point Sources (Non-EGUs), 

Nonpoint (Area) Sources, and Nonroad Mobile Sources. No controls for EGUs within the 

parameters of size (25 tpy) and dollar per ton control costs less than $19,000 per ton were 

available for California. Table 3-1 above also includes identified controls that we applied in 

California. In Step 4, the ppb/ton from the sensitivities were applied to determine ozone 

reductions. Then, in Step 5, VOC controls were applied in the California counties indicated in 

Figure 3-10. 

In Step 6, we used additional reductions (assumed to come from unidentified NOX

controls) in Southern California and associated regional ppb/ton response factors from the 

Southern California combined sensitivity simulations to reduce DVs at Southern California 

monitors to reach the current standard of 75 ppb. As described in Chapter 2 and shown in the 

example calculation in Appendix 3-A, we applied emissions responses derived from multiple 

emissions sensitivity simulations to capture the nonlinear response of large emissions reductions 

in Southern California. Similarly, we used unidentified reductions and associated ppb/ton 

response factors from Northern California to reduce DVs at Northern California monitors. Since 

the highest projected DVs occurred in Southern California, we first quantified necessary 

emissions reductions from unidentified NOx reduction measures to reduce all Southern 

California monitors to 75 ppb or lower. We then recalculated the resulting Northern California 

DVs before determining how many additional emissions reductions from unidentified NOx 

 

51 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 because states will ultimately determine controls as 

part of the SIP process. 
3-20 

control measures in Northern California would be necessary to bring all Northern California 

monitors into attainment with the current standard of 75 ppb. Summaries of the emissions 

reductions are presented for the post-2025 baseline in Appendix 3A. The resulting ozone DVs at 

all evaluated monitors are also provided in Appendix 2A, Section 2A.4. 

The post-2025 baseline for this analysis presents one scenario of future year air quality 

based upon specific control measures, additional emissions reductions beyond identified 

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 approach relying 

on the identified federal measures and other strategies that states may employ. California may 

ultimately employ other strategies and/or other federal rules may be adopted that would also help 

in achieving attainment with the current standard. 

3.2.2 Approach for Revised Standard of 70 ppb and Alternative Standard of 65 ppb for 

California 

We created the post-2025 70 ppb and 65 ppb scenarios by applying emissions reductions 

incrementally to the post-2025 baseline. As mentioned above, all identified measures in 

California were exhausted in reaching the post-2025 baseline. We started with the post-2025 

baseline and then applied NOx from unidentified controls to meet the revised and alternative 

standard levels. As with the baseline, we first identified the NOx reductions in Southern 

California that would be required to bring Southern California monitors down to the revised and 

alternative standard levels. We then recalculated the Northern California DVs that would result 

from the Southern California emissions reductions and applied additional Northern California 

unidentified NOx emissions reductions to bring all Northern California monitors down to the 

revised and alternative standard levels. Also, as was done for the baseline, we applied ppb/ton 

response levels that were derived from multiple emissions sensitivities to capture nonlinear 

responses of ozone to large emissions reductions in California (see example calculation in 

Appendix 3-A). 

3.2.3 Results for California 

Nine counties in California were projected to exceed the current ozone standard of 75 ppb 

in the post-2025 baseline scenario (see Figure 3-9). Figure 3-10 shows areas where identified 
3-21 

control measures were applied to bring ozone DVs in those counties into attainment with the 

current standard and establish the baseline. Table 3-8 includes a summary of NOx and VOC 

emissions reductions needed to demonstrate attainment of the current ozone standard of 75 ppb. 

Figure 3-9. Counties Projected to Exceed 75 ppb in the Post-2025 Baseline Scenario 
3-22 

Figure 3-10. Counties Where Emissions Reductions Were Applied to Demonstrate 

Attainment with the Current Standard 

Table 3-8. Summary of Emissions Reductions (Identified + Unidentified Controls) Applied 

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

tons/year)a 

 Emissions Sector NOx VOC 

Identified Controls 

EGU - - 

Non-EGU Point 14 1 

Nonpoint 14 54 

Nonroad 4 - 

Onroad - - 

Total 32 55 

Unidentified Controls All 160 - 

Total 190 55 

Percent Unidentified 84% 0% 

a

 Emission reduction estimates are rounded to two significant figures. 
3-23 

Figure 3-11 shows the California counties projected to exceed the revised and alternative 

standards analyzed for the post-2025 baseline analysis. Table 3-9 shows the emissions reductions 

needed from unidentified controls to meet the revised standard level of 70 ppb and alternative 

standard level of 65 ppb in those counties for the post-2025 analysis. Table 3-10 highlights that 

there were no identified NOx emissions reductions available for meeting the revised and 

alternative standard levels for post-2025 California and that 100 percent of the NOx emissions 

reductions needed were unidentified controls. 

Figure 3-11. Projected Ozone Design Values in the Post-2025 Baseline Scenario 
3-24 

Table 3-9. Summary of Emissions Reductions from Unidentified Control Strategy for the 

Revised and Alternative Standard Levels for Post-2025 - California (1,000 

tons/year)a

Alternative Standard Region NOx VOC 

70 ppb CA 51 - 

65 ppb CA 100 - 

a

 Estimates are rounded to two significant figures. 

Table 3-10. Summary of Emissions Reductions from the Identified + Unidentified 

Control Strategy by the Revised and Alternative Standard Levels for Post-2025 - 

California (1,000 tons/year)a

 Alternative Standard 

Geographic Area Emissions Reductions 70 ppb 65 ppb 

California 

NOx Identified 0 0 

NOx Unidentified 51 100 

% NOx Unidentified 100% 100% 

VOC Identified 0 0 

VOC Unidentified 0 0 

% VOC Unidentified 0% 0% 

a

 Estimates are rounded to two significant figures. 

3.3 Improvements and Refinements since the Proposal RIA 

In the regulatory impact analyses for both the ozone NAAQS proposal and final, there 

were two geographic areas outside of California where the majority of emissions reductions were 

needed to meet an alternative standard level of 70 ppb – Texas and the Northeast. In analyzing 

the revised standard of 70 ppb for the final RIA, there were approximately 50 percent fewer 

emissions reductions needed in these two areas. For an alternative standard of 65 ppb, emissions 

reductions needed nationwide were approximately 20 percent lower than at proposal.

The primary reason for the difference in emissions reductions needed for both 70 and 65 

ppb is that in the final RIA we conducted more geographically-refined air quality sensitivity 

modeling to develop improved response factors (i.e., changes in ozone concentrations in 

response to emissions reductions). More detailed air quality modeling and improved response 

factors account for 80 percent of the difference in needed emissions reductions between proposal 

and final. See Chapter 2, Section 2.4.2 for a discussion of the air quality modeling. 

For the analysis of the revised standard of 70 ppb, in Texas and the Northeast, the 

improved and refined response factors and more geographically focused emissions reductions 
3-25 

strategies resulted in larger changes in ozone concentrations. In east Texas, the ppb/ton response 

factors used in the final RIA were 2 to 3 times more responsive than the factors used in the 

proposal RIA at controlling monitors in Houston and Dallas. In the Northeast, the ppb/ton 

response factors used in the final RIA were 2.5 times more responsive than the factors used in 

the proposal RIA at the controlling monitor on Long Island, NY. 

A secondary reason for the difference is that between the proposal and final RIAs we 

updated models and model inputs for the base year of 2011. See Appendix 2, Section 2A.1.3 for 

additional discussion of the updated models and model inputs. When projected to 2025, these 

changes in models and inputs had compounding effects for year 2025, and in some areas resulted 

in lower projected base case design values for 2025. In these areas, the difference between the 

base case design values and a standard of 70 ppb was smaller, thus requiring fewer emissions 

reductions to attain the 70 ppb revised standard. 

Note that the more spatially refined emissions sensitivity modeling had more impact on the 

results at 70 ppb than it did on the results at 65 ppb due to the more localized nature of projected 

exceedances at 70 ppb. For example, as described above, the new sensitivity regions showed 

that emissions reductions in eastern Texas would have a larger impact on ozone in Houston and 

Dallas than the same emissions reductions would have if they were spread over the central U.S. 

states used in the proposal RIA. Conversely, these same east Texas emissions reductions would 

have less impact on violating monitors in Louisiana or Oklahoma. Therefore, for the 65 ppb 

scenario, additional local controls were necessary in Louisiana and Oklahoma. 

As a consequence of the use of more geographically refined sensitivity regions, emissions 

reductions control strategies were also applied in geographic areas closer to the monitors of 

projected exceedances. For example, in the proposal RIA, the Central region included Texas, 

Oklahoma, Kansas, Missouri, Arkansas, Louisiana and Mississippi, meaning that controls could 

be applied anywhere in those states after identified controls had been exhausted within the 200 

km buffer. But in the final RIA, the only geographic area where we applied controls was East 

Texas. Thus, once identified control measures were exhausted there, we had to obtain remaining 

reductions from unidentified control measures. While the total amount of emissions needed to 
3-26 

meet the 65 ppb alternative standard is lower than it was in the proposal RIA, the fraction of 

emissions reductions from identified controls was smaller. 

3.4 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 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 

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. 
3-27 

 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 there is 

significant flexibility for states to determine which measures to apply to comply 

with the standard. 

 Sequential processing of regional emission reductions: Because this method 

prioritizes emissions reductions in the regions with the highest ozone values first 

but then does not go back and re-evaluate the amount of reduction in the higher 

priority region after emissions reductions have been applied in lower-priority 

regions, there is the potential to reduce a greater quantity of emissions at monitors 

in the higher priority regions. For instance, in the 65 ppb scenario, in the 

Northeast, the monitor which required the largest emissions reductions to reach 65 

ppb was located in Queens, NY. After identifying necessary emissions reductions 

in the Northeast region, that monitor had a projected DV of 65.996 ppb (which 

truncates to 65 ppb). Additional reductions from lower priority regions such as the 

Ohio River Valley and the Great Lakes, brought the DV at that site down to 65.002 

ppb. In theory, fewer tons of emissions reductions could then have been applied in 

the Northeast to reach a DV less than 66 ppb. However, if emissions reductions in 

the Northeast were rolled back, then necessary reductions in all lower priority 

regions would need to be recalculated and consequently the degree to which the 

Northeast emissions reductions were rolled back would also need to be 

recalculated. This could be quantified either in an iterative process or through a 

linear programming model that found a least cost solution based on all response 

factors and associated costs. Neither of these options were available for this 

analysis, but it should be noted that this likely leads to some overestimate in our 
3-28 

calculation of tons of emissions reductions necessary to meet the 70 and 65 ppb 

standard levels and in the resulting costs and benefits. 

 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: As we focus on the advances that might be 

expected in existing pollution control technologies, we recognize that the control 

measures applied do not reflect potential effects of technological change that may 

be available in future years. 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 or costs. 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 impacts of technological change. We 

believe the effect of including these impacts may change 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 (see Chapter 4, Section 4.2 for additional 

discussion of the Phase 2 Heavy Duty Greenhouse Gas Standards for New Vehicles 

and Engines). These regulations could reduce the current baseline levels of 

emissions. 
3-29 

3.5 References 

U.S. Environmental Protection Agency (U.S. EPA). User’s Guide for the Final NONROAD 2005 Model. Available 

at http://www.epa.gov/otaq/models/nonrdmdl/nonrdmdl2005/420r05013.pdf. 

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. 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). 2015. EPA Base Case 

v.5.14 Using the Integrated Planning Model, Incremental Documentation. Available at 

http://www.epa.gov/airmarkets/documents/ipm/EPA_Base_Case_v514_Incremental_Documentation.pdf 
3A-1 

APPENDIX 3A: CONTROL STRATEGIES AND EMISSIONS REDUCTIONS

Overview 

Chapter 3 describes the approach that EPA used in applying control measures to 

demonstrate attainment of alternative ozone standard levels of 70. This Appendix contains more 

detailed information about the control strategy analyses, including numerical examples of the 

calculation methods for changes in ozone DVs, the control measures that were applied and the 

geographic areas in which they were applied. 

3A.1 Target Emissions Reductions Needed to Create the Baseline, Post-2025 Baseline and 

Alternatives 

Tables 3A-1 to 3A-3 depict emissions reductions required in each region to reach the 

alternative standard level scenarios for the U.S. except California, and the post-2025 Baseline 

and alternative standard levels for California. These emissions reductions were determined using 

the methodology described in Chapter 3, Sections 3.1 and 3.2 and illustrated in the numerical 

example in section 3A.2 of this Appendix. Sector-specific controls used for these reductions are 

discussed in more detail in Chapter 3. These emissions reductions were used to create the ozone 

surfaces described in Chapter 2, Section 2.4. 

 
3A-2 

Table 3A-1. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 70 

ppb Scenario 

Emissions reductions (thousand tons) applied from 

NOx reductions from 

identified controls 

VOC reductions from 

identified controls 

Additional NOx 

reductions from 

unidentified measures 

Northeast 111 - - 

Ohio River Valley 27 - - 

Great Lakes 18 - - 

East Texas 123 20 (Houston) - 

Colorado 7 - - 

N. California Exhausted in baseline 

scenario 

Exhausted in baseline 

scenario 35 

S. California Exhausted in baseline 

scenario 

Exhausted in baseline 

scenario 16 

Table 3A-2. Emissions Reductions Applied Beyond the Baseline Scenario to Create the 65 

ppb Scenario 

Emissions reductions (thousand tons) applied from 

NOx reductions from 

identified controls 

VOC reductions from 

identified controls 

Additional NOx 

reductions from 

unidentified measures 

Northeast 163 41 (NY area) 285 

Ohio River Valley 169 7 (Louisville area) 112 

Great Lakes 197 39 (Chicago area) 56 

OK/AR/LA 24 - - 

E. Texas 123 20 (Houston area) 188 

AZ/NM 29 - - 

Colorado 36 5 (Denver area) 20 

Nevada 10 - - 

N. California Exhausted in baseline 

scenario 

Exhausted in baseline 

scenario 65.5 

S. California Exhausted in baseline 

scenario 

Exhausted in baseline 

scenario 32 

Table 3A-3. Emissions Reductions Applied to Create the Post-2025 Baseline Scenario* 

Emissions reductions (thousand tons) applied from 

2025-2030 

California 

mobile source 

changes 

NOx reductions 

from identified 

controls 

VOC reductions 

from identified 

controls 

Additional NOx 

reductions from 

unidentified 

measures 

N. California 8 (NOx) 

3 (VOC) 16 27 24 

S. California 6 (NOx) 

3 (VOC) 16 29 136 

*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. 
3A-3 

3A.2 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 Sections 3.1 and 3.2. For each monitor, numerical 

examples are given for calculating the emissions reductions necessary to attain the current 

standard of 75 ppb (i.e., the baseline scenario) as well as the 70 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 70.9 would meet an alternative standard 

level of 70. 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 2-5 from Chapter 

2. 

ܦܸ ௝ൌ ܦܸଶ଴ଶହ,௝ ൅ ൫ܴଵ,௝ ൈ ∆ܧଵ൯ ൅ ൫ܴଶ,௝ ൈ ∆ܧଶ൯ ൅ ൫ܴଷ,௝ ൈ ∆ܧଷ൯൅⋯ Eq 2-5 

Example 1. Fresno California monitor 60195001 (baseline): 

ܦܸ଴଺ଵଽହ଴଴ଵ,௕௔௦௘௟௜௡௘

ൌ 83.4 ถ஽௏లబభవఱబబభ,మబమఱ൅ െ0.7 ถ∆஽௏లబభవఱబబభ,భభభ೏

ᇩᇭᇭᇭᇪᇭᇭᇭᇫ

ேை௫ା௏ை஼

൅ ൭ െ5.0 ൈ 10 ᇣᇧᇧᇧᇤᇧᇧᇧିହᇥ

ோలబభవఱబబభ,಴ಲ೎೚೙೟ೝ೗

ൈ 32,000 ᇣᇧᇤᇧᇥ

∆ா಴ಲ೎೚೙೟ೝ೗

൱

ᇩᇭᇭᇭᇭᇭᇭᇭᇭᇪᇭᇭᇭᇭᇭᇭᇭᇭᇫ

ேை௫

൅ ൭ െ1.4 ൈ 10 ᇣᇧᇧᇧᇤᇧᇧᇧିସᇥ

ோలబభవఱబబభ,಴ಲ೎೚೙೟ೝ೚೗శఱబಿೀೣ,ಿ಴ಲ

ൈ 8000 ൅ 24,000	 ᇣᇧᇧᇧᇧᇤᇧᇧᇧᇧᇥ

∆ா೘೚್೔೗೐,మబయబା∆ா ൱

ᇩᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇪᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇫ

ேை௫

൅ ൭ െ9.9 ൈ 10 ᇣᇧᇧᇧᇤᇧᇧᇧି଺ᇥ

ோలబభవఱబబభ,ೇೀ಴_ఱబ,ಿ಴ಲ

ൈ 3000 ൅ 27,000 ᇣᇧᇧᇧᇧᇤᇧᇧᇧᇧᇥ

∆ா೘೚್೔೗೐,మబయబା∆ா ൱

ᇩᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇪᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇫ

௏ை஼

൅ ൭ െ2.3 ൈ 10 ᇣᇧᇧᇧᇤᇧᇧᇧି଺ᇥ

ோలబభవఱబబభ,಴ಲ೎೚೙೟ೝ೚೗శఱబಿೀೣ,ೄ಴ಲ

ൈ 6000 ൅ 100,000	 ᇣᇧᇧᇧᇧᇤᇧᇧᇧᇧᇥ

∆ா೘೚್೔೗೐,మబయబା∆ா ൱

൅ ൭ െ2.6 ൈ 10 ᇣᇧᇧᇧᇤᇧᇧᇧି଺ᇥ

ோలబభవఱబబభ,಴ಲ೎೚೙೟ೝ೚೗శవబಿೀೣ,ೄ಴ಲ

ൈ 36,000	 ᇣᇧᇤᇧᇥ

∆ா ൱ ൌ 75.9	݌݌ܾ
3A-4 

Example 2. Fresno California monitor 60195001 (65 ppb scenario): 

ܦܸ௝,଺ହ ൌ 75.9 ถ஽௏ೕ,್ೌೞ೐೗೔೙೐൅ ൭ െ1.4 ൈ 10 ᇣᇧᇧᇧᇤᇧᇧᇧିସᇥ

ோలబభవఱబబభ,಴ಲ೎೚೙೟ೝ೚೗శఱబಿೀೣ,ಿ಴ಲ

ൈ 35,000	 ᇣᇧᇤᇧᇥ

∆ா ൱

ᇩᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇪᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇫ

ேை௫	௨௣	௧௢	ହ଴%	௢௙	஼஺	௠௢ௗ௘௟௘ௗ	௖௢௡௧௥௢௟	௦௘௡௦௜௧௜௩௜௧௬

൅ ൭ െ2.6 ൈ 10 ᇣᇧᇧᇧᇤᇧᇧᇧି଺ᇥ

ோలబభవఱబబభ,಴ಲ೎೚೙೟ೝ೚೗శవబಿೀೣ,ೄ಴ಲ

ൈ 16,000	 ᇣᇧᇤᇧᇥ

∆ா ൱ ൌ 70.9	݌݌ܾ

Example 3. Dallas monitor 484392003 (baseline): 

ܦܸସ଼ସଷଽଶ଴଴ଷ,௕௔௦௘௟௜௡௘ ൌ 74.3 ถ஽௏రఴరయవమబబయ,మబమఱ൅ െ1.0 ถ ∆஽௏రఴరయవమబబయ,భభభ೏

ᇩᇭᇭᇭᇪᇭᇭᇭᇫ

ேை௫ା௏ை஼

ܾ݌݌	73.3 ൌ

Example 4. Dallas monitor 484392003 (65 ppb scenario): 

ܦܸସ଼ସଷଽଶ଴଴ଷ,଺ହ ൌ 73.3 ถ ஽௏రఴరయవమబబయ,್ೌೞ೐೗೔೙೐൅ ൭ െ3.3 ൈ 10 ᇣᇧᇧᇧᇤᇧᇧᇧିହᇥ

ோరఴరయవమబబయ,ಶ.೅೐ೣೌೞ

ൈ 123,000 ᇣᇧᇤᇧᇥ

∆ா ൱

ᇩᇭᇭᇭᇭᇭᇭᇭᇭᇪᇭᇭᇭᇭᇭᇭᇭᇭᇫ

ேை௫

൅ ൭ െ4.1 ൈ 10 ᇣᇧᇧᇧᇤᇧᇧᇧି଼ᇥ

ோరఴరయవమబబయ,ಿ೚ೝ೟೓೐ೌೞ೟

ൈ 111,000 ᇣᇧᇤᇧᇥ

∆ா ൱

ᇩᇭᇭᇭᇭᇭᇭᇭᇭᇭᇪᇭᇭᇭᇭᇭᇭᇭᇭᇭᇫ

ேை௫

൅ ൭ െ5.1 ൈ 10 ᇣᇧᇧᇧᇤᇧᇧᇧି଻ᇥ

ோరఴరయవమబబయ,಴೚೗೚ೝೌ೏೚

ൈ 7000 ᇣᇤᇥ

∆ா ൱

ᇩᇭᇭᇭᇭᇭᇭᇭᇪᇭᇭᇭᇭᇭᇭᇭᇫ

ேை௫

൅ ൭ െ2.6 ൈ 10 ᇣᇧᇧᇧᇤᇧᇧᇧି଻ᇥ

ோరఴరయవమబబయ,ೀ೓೔೚ೃ೔ೡ೐ೝೇೌ೗೗೐೤

ൈ 27,000 ᇣᇧᇤᇧᇥ

∆ா ൱

ᇩᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇪᇭᇭᇭᇭᇭᇭᇭᇭᇭᇭᇫ

ேை௫

൅ ൭ െ1.9 ൈ 10 ᇣᇧᇧᇧᇤᇧᇧᇧି଻ᇥ

ோరఴరయవమబబయ,ಸೝ೐ೌ೟ಽೌೖ೐ೞ

ൈ 18,000 ᇣᇧᇤᇧᇥ

∆ா ൱

ᇩᇭᇭᇭᇭᇭᇭᇭᇭᇭᇪᇭᇭᇭᇭᇭᇭᇭᇭᇭᇫ

ேை௫

ܾ݌݌	69.3 ൌ

3A.3 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: 
3A-5 

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 25 tons/yr of NOx (see description of controls on smaller 

sources below); 

 any control for nonEGU 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 and SNCRs – applied to coal-fired EGUs where they 

are in place but are idle. 

VOC Reductions – VOC control measures for nonEGU and nonpoint sources that: 

 emit at least 10 tons/yr of VOC; 

 any control must result in a reduction of VOC of at least 1 ton/yr; and 

 any replacement control must achieve 10% more reduction than the existing control. 

3A.4 Application of Control Measures in Geographic Areas 

Control measures were applied, to obtain the emissions reductions described in Section 

3A.1 of this Appendix, to geographic areas including or adjacent to areas that were projected to 

exceed the baseline and alternative standards. If all non-EGU NOx reductions were needed, then 

the maximum emissions reductions algorithm in CoST was used. Where less non-EGU NOx

reductions were needed than were available, these were obtained using the least cost algorithm. 

Where VOC reductions were needed, all potentially available VOC reductions were needed so 

these were identified using the maximum emissions reduction algorithm. No unidentified 

controls were needed for VOC emissions reductions. Tables 3A-4 and 3A-5 show where 

controls were applied and where unidentified controls were needed in the U.S. except California. 

Tables 3A-6 and 3A-7 show where controls were applied and where unidentified controls were 

needed in California. 

 
3A-6 

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

Alternative Standard Analyses - U.S., except Californiaa 

Geographic Areas and Controls Baseline 70 ppb 65 ppb 

EAST 

North East 

Inside buffer 

Non-EGU x x 

EGU x x 

Outside buffer x x 

Unidentified U 

OK + AR + LA 

Inside buffer 

Non-EGU x 

EGU 

Outside buffer 

Unidentified 

Ohio River Valley 

Inside buffer 

Non-EGU x x 

EGU x x 

Outside buffer x x 

Unidentified U 

TX East 

Inside buffer 

Non-EGU x x 

EGU 

Outside buffer x x 

Unidentified U U 

 

WEST 

AZ + NM 

Inside buffer 

Non-EGU x 

EGU 

Outside buffer x 

Unidentified 

Colorado 

Inside buffer 

Non-EGU x x 

EGU 

Outside buffer x 

Unidentified U 
3A-7 

Geographic Areas and Controls Baseline 70 ppb 65 ppb 

Great Lakes 

Inside buffer 

Non-EGU x x 

EGU x 

Outside buffer x x 

Unidentified U 

Nevada 

Inside buffer 

Non-EGU x 

EGU 

Outside buffer x 

Unidentified U 

a

 “x” indicates known controls were applied; “U” indicates unknown control reductions. 

Table 3A-5. Geographic Areas for Application of VOCa Controls in the Baseline and 

Alternative Standard Analyses - U.S., except Californiab 

Geographic Area Baseline 70 ppb 65 ppb 

EAST x 

North East 

New York, New Jersey, Long Island, NY-NJ-CT 

 

Ohio River Valley 

Louisville, KY x 

 

TX East 

Houston-Galveston-Brazoria, TX x x 

 

WEST 

Colorado 

Denver-Boulder-Greeley-Ft.Collins-Loveland, CO x 

 

Great Lakes 

Chicago-Lake Michigan, WI-IL-IN-MI x 

 a No unidentified VOC controls were needed to attain any of the standards; b

 “x” indicates known controls were 

applied 

 
3A-8 

Table 3A-6. Geographic Areas for Application of NOx

a

 Controls in the Baseline and 

Alternative Standard Analyses – Californiab

Geographic Areas and Control Groups Baseline 70 ppb 65 ppb 

California 

California North Identified x 

California North Unidentified U U U 

California South Identified x 

California South Unidentified U U U 

a All reductions were calculated using the maximum reductions algorithm b “x” indicates known controls were 

applied; “U” indicates unknown control reductions. 

Table 3A-7. Geographic Areas for Application of VOCa Controls in the Baseline and 

Alternative Standard Analyses – Californiab

Geographic Areas and Control Groups Baseline 70 ppb 65 ppb 

California 

California North – San Joaquin x 

Identified U U U 

Unidentified 

California South – Los Angeles 

Identified x 

Unidentified U U U 

a All reductions were calculated using the maximum reductions algorithm b “x” indicates known controls were 

applied; “U” indicates unknown control reductions. 

 3A.5 NOx Control Measures for Non-EGU 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 
3A-9 

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 3A-8 through 3A-11 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 

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 3A-8. NOx Control Measures Applied in the 70 ppb Analysis 

NOx Control Measure Reductions (tons/year) 

Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 8,723 

Biosolid Injection Technology - Cement Kilns 5,383 

EGU SCR & SNCR 44,951 

Episodic Burn Ban 2,797 

Excess O3 Control 229 

Ignition Retard - IC Engines 618 

Low Emission Combustion - Gas Fired Lean Burn IC Engines 17,676 

Low NOx Burner - Coal Cleaning 270 

Low NOx Burner - Commercial/Institutional Boilers & IC Engines 21,417 

Low NOx Burner - Gas-Fired Combustion 9,237 

Low NOx Burner - Glass Manufacturing 247 

Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 5,580 

Low NOx Burner - Industrial Combustion 25 

Low NOx Burner - Lime Kilns 2,433 

Low NOx Burner - Natural Gas-Fired Turbines 6,276 

Low NOx Burner - Residential Water Heaters & Space Heaters 19,900 

Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 359 

Low NOx Burner and Flue Gas Recirculation - Fluid Catalytic Cracking Units 84 

Low NOx Burner and Flue Gas Recirculation - Iron & Steel 399 

Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 8,408 

Mid-Kiln Firing - Cement Manufacturing 1,241 
3A-10 

NOx Control Measure Reductions (tons/year) 

Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 39,258 

Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 2,832 

OXY-Firing - Glass Manufacturing 11,984 

Replacement of Residential & Commercial/Institutional Water Heaters 8,641 

Selective Catalytic Reduction (SCR) - Cement Kilns 10,176 

Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 1,709 

Selective Catalytic Reduction (SCR) - Glass Manufacturing 3,481 

Selective Catalytic Reduction (SCR) - IC Engines, Diesel 863 

Selective Catalytic Reduction (SCR) - ICI Boilers 4,618 

Selective Catalytic Reduction (SCR) - Industrial Incinerators 1,384 

Selective Catalytic Reduction (SCR) - Iron & Steel 155 

Selective Catalytic Reduction (SCR) - Process Heaters 784 

Selective Catalytic Reduction (SCR) - Sludge Incinerators 100 

Selective Catalytic Reduction (SCR) - Space Heaters 24 

Selective Catalytic Reduction (SCR) - Utility Boilers 1,391 

Selective Non-Catalytic Reduction (SNCR) - Cement Manufacturing 2,405 

Selective Non-Catalytic Reduction (SNCR) - Coke Manufacturing 1,589 

Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 58 

Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 365 

Selective Non-Catalytic Reduction (SNCR) - Sludge Incinerators 33 

Ultra-Low NOx Burner - Process Heaters 329 

 
3A-11 

Table 3A-9. VOC Control Measures Applied in the 70 ppb Analysis 

VOC Control Measure Reductions (tons/year) 

Control Technology Guidelines - Wood Furniture Surface Coating 272 

Control of Fugitive Releases - Oil & Natural Gas Production 9 

Flare - Petroleum Flare 94 

Incineration - Other 10,717 

LPV Relief Valve - Underground Tanks 1,299 

MACT - Motor Vehicle Coating 10 

Permanent Total Enclosure (PTE) - Surface Coating 369 

RACT - Graphic Arts 260 

Reduced Solvent Utilization - Surface Coating 27 

Reformulation - Architectural Coatings 5,246 

Reformulation - Pesticides Application 171 

Reformulation-Process Modification - Automobile Refinishing 220 

Reformulation-Process Modification - Cutback Asphalt 655 

Reformulation-Process Modification - Other 113 

Reformulation-Process Modification - Surface Coating 178 

Solvent Recovery System - Printing/Publishing 13 

Wastewater Treatment Controls- POTWs 207 

 
3A-12 

Table 3A-10. NOx Control Measures Applied in the 65 ppb Alternative Standard Analysis 

NOx Control Measure Reductions (tons/year) 

Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 16,423 

Biosolid Injection Technology - Cement Kilns 5,907 

EGU SCR & SNCR 109,503 

Episodic Burn Ban 3,283 

Ignition Retard - IC Engines 575 

Low Emission Combustion - Gas Fired Lean Burn IC Engines 75,724 

Low NOx Burner - Coal Cleaning 475 

Low NOx Burner - Commercial/Institutional Boilers & IC Engines 36,210 

Low NOx Burner - Fiberglass Manufacturing 65 

Low NOx Burner - Gas-Fired Combustion 11,889 

Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 21,918 

Low NOx Burner - Industrial Combustion 25 

Low NOx Burner - Lime Kilns 4,616 

Low NOx Burner - Natural Gas-Fired Turbines 12,109 

Low NOx Burner - Residential Water Heaters & Space Heaters 51,703 

Low NOx Burner - Steel Foundry Furnaces 294 

Low NOx Burner - Surface Coating Ovens 26 

Low NOx Burner and Flue Gas Recirculation - (ICI) Boilers 477 

Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 429 

Low NOx Burner and Flue Gas Recirculation - Fluid Catalytic Cracking Units 59 

Low NOx Burner and Flue Gas Recirculation - Iron & Steel 781 

Low NOx Burner and Flue Gas Recirculation - Process Heaters 548 

Low NOx Burner and Flue Gas Recirculation - Starch Manufacturing 67 

Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 24,281 

Low NOx Burner and SNCR - Industr/Commercial/Institutional Boilers 482 

Natural Gas Reburn - Natural Gas-Fired EGU Boilers 590 

Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 70,008 

Non-Selective Catalytic Reduction - Nitric Acid Manufacturing 491 

Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 8,791 

OXY-Firing - Glass Manufacturing 27,100 

Replacement of Residential Water Heaters 133 

Selective Catalytic Reduction (SCR) - Ammonia Mfg 2,336 

Selective Catalytic Reduction (SCR) - Cement Kilns 26,144 

Selective Catalytic Reduction (SCR) - Coke Ovens 1,243 

Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 4,078 

Selective Catalytic Reduction (SCR) - IC Engines, Diesel 3,574 

Selective Catalytic Reduction (SCR) - ICI Boilers 9,963 

Selective Catalytic Reduction (SCR) - Industrial Incinerators 1,723 

Selective Catalytic Reduction (SCR) - Iron & Steel 1,777 

Selective Catalytic Reduction (SCR) - Process Heaters 2,744 
3A-13 

NOx Control Measure Reductions (tons/year) 

Selective Catalytic Reduction (SCR) - Sludge Incinerators 1,771 

Selective Catalytic Reduction (SCR) - Space Heaters 286 

Selective Catalytic Reduction (SCR) - Taconite 4,248 

Selective Catalytic Reduction (SCR) - Utility Boilers 1,391 

Selective Non-Catalytic Reduction (SNCR) - Coke Mfg 2,880 

Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 159 

Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 1,057 

Selective Non-Catalytic Reduction (SNCR) - Municipal Waste Combustors 67 

Selective Non-Catalytic Reduction (SNCR) - Sludge Incinerators 113 

Selective Non-Catalytic Reduction (SNCR) - Utility Boilers 235 

Ultra-Low NOx Burner - Process Heaters 854 

 
3A-14 

Table 3A-11. VOC Control Measures Applied in the 65 ppb Alternative Standard Analysis 

VOC Control Measure Reductions (tons/year) 

Control Technology Guidelines - Wood Furniture Surface Coating 2,988 

Control of Fugitive Releases - Oil & Natural Gas Production 30 

Flare - Petroleum Flare 108 

Gas Recovery - Municipal Solid Waste Landfill 290 

Improved Work Practices, Material Substitution, Add-On Controls - Printing 8 

Improved Work Practices, Material Substitution, Add-On Controls 

-Industrial Cleaning Solvents 248 

Incineration - Other 16,710 

LPV Relief Valve - Underground Tanks 4,871 

Low VOC Adhesives and Improved Application Methods - Industrial Adhesives 237 

Low-VOC Coatings and Add-On Controls - Surface Coating 274 

MACT - Motor Vehicle Coating 1,934 

Permanent Total Enclosure (PTE) - Surface Coating 3,286 

Petroleum and Solvent Evaporation - Surface Coating Operations 250 

RACT - Graphic Arts 5,586 

Reduced Solvent Utilization - Surface Coating 3,047 

Reformulation - Architectural Coatings 52,378 

Reformulation - Industrial Adhesives 1,110 

Reformulation - Pesticides Application 3,957 

Reformulation-Process Modification - Automobile Refinishing 4,879 

Reformulation-Process Modification - Cutback Asphalt 2,555 

Reformulation-Process Modification - Oil & Natural Gas Production 291 

Reformulation-Process Modification - Other 546 

Reformulation-Process Modification - Surface Coating 5,622 

Solvent Recovery System - Printing/Publishing 854 

Solvent Substitution and Improved Application Methods - Fiberglass Boat Mfg 14 

Wastewater Treatment Controls- POTWs 234 

3A.6 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. 
3A-15 

3A.7 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., 

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. 

3A.8 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 
3A-16 

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 

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. 
4-1 

CHAPTER 4: ENGINEERING COST ANALYSIS AND ECONOMIC IMPACTS

Overview 

This chapter provides estimates of the engineering costs of the control strategies 

presented in Chapter 3 for the revised primary standard of 70 ppb and an alternative standard 

level of 65 ppb and summarizes the data sources and methodologies used to estimate the 

engineering costs presented in this regulatory impact analysis (RIA). As discussed in Chapter 3, 

identified control measures were applied to EGU, non-EGU point, nonpoint (area), and nonroad 

mobile sources to demonstrate attainment with the revised and alternative standards analyzed.52 

In several areas identified controls did not achieve the emissions reductions needed to attain the 

revised and alternative standards analyzed. In these areas, the EPA assumed that further controls 

would be applied to reach attainment. These additional controls are referred to as unidentified 

controls. 

The total cost estimates include the costs of both identified and unidentified control 

technologies and measures. The estimated total costs of attaining the revised and alternative 

standards are partly a function of (1) assumptions used in the analysis, including assumptions 

about which areas will require emissions controls and the sources and controls available in those 

areas; (2) the level of sufficient, detailed information on identified control measures needed to 

estimate engineering costs; and (3) the future year baseline emissions from which the emissions 

reductions needed to attain are measured. 

The remainder of the chapter is organized as follows. Section 4.1 presents the 

engineering costs associated with the application of identified controls. Section 4.2 discusses the 

challenges associated with estimating costs for unidentified controls, including a brief discussion 

 

52 In Chapter 3, Table 3-7 lists the specific control technologies applied in the identified control measures analysis. 

In addition, in the proposal RIA we discuss emissions reductions resulting from the application of known controls, 

as well as emissions reductions beyond known controls, or in short, known controls and unknown controls. In the 

final RIA we refer to those sets of emissions reductions and controls as identified controls or measures and 

unidentified controls or measures. This terminology has been used in prior NAAQS RIAs and reflects that we have 

illustrated control strategies primarily using end-of-pipe controls and many additional controls that are not end-ofpipe (e.g., energy efficiency) that we have not identified here could also be part of a states’ strategies to reduce 

emissions. 
4-2 

of some of the limitations of EPA’s control strategy tools and available data on NOx control 

technologies, and a brief discussion of the challenges in estimating baseline emissions over time. 

Section 4.3 presents the estimated costs associated with unidentified controls. Section 4.4 

provides the total compliance cost estimates. Section 4.5 includes a discussion of potential 

economic impacts. Section 4.6 concludes with a discussion of the uncertainties and limitations 

associated with these components of the RIA. 

4.1 Estimating Engineering Costs 

The engineering costs described in this chapter generally include the costs of purchasing, 

installing, operating, and maintaining the technologies applied. The costs associated with 

monitoring, testing, reporting, and recordkeeping for affected sources are not included in the 

annualized cost estimates as this data is not generally available and can vary substantially from 

one facility to another. For a variety of reasons, actual control costs may vary from the estimates 

the EPA presents. As discussed throughout this analysis, the technologies and control strategies 

selected for analysis illustrate 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 a revised standard, and the EPA anticipates that state and 

local governments will consider programs best suited for local conditions. In addition, the EPA 

recognizes that there is substantial uncertainty in the portion of the engineering cost estimates 

associated with unidentified controls. The estimates presented herein are based on assumptions 

about the sectors and technologies that might become available for cost-effective control 

application in the future. 

The engineering cost estimates are limited in their scope. This analysis focuses on the 

emissions reductions needed for attainment of the revised standard and an alternative standard 

analyzed. The EPA understands that some states will incur costs both designing State 

Implementation Plans (SIPs) and implementing new control strategies to meet final revised 

standards. However, the EPA 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 
4-3 

implementation of specific technologies, especially for technologies that are not necessarily 

market driven. 

4.1.1 Methods and Data 

The EPA uses the Control Strategy Tool (CoST) (U.S. EPA, 2014a) to estimate 

engineering control costs. CoST was used in two parts of the analysis. First, CoST was applied 

to help determine potential NOx and VOC emissions reductions for each of the emissions 

sensitivity regions (see Chapter 2 Figure 2.2 for a map of these regions). Secondly, CoST was 

used to estimate the identified controls costs for the measures identified in Chapter 3. We 

estimated costs for non-electric generating unit point (non-EGU 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 much 

more detailed data, and parameters used in these equations are not 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, including cost-per-ton factors 

and cost equations, can be found in the tool documentation.53 Costs for selective reduction 

catalysts (SCR) applied as part of the analysis for reducing NOx emissions at coal-fired electric 

generating units (EGUs) were estimated using documentation for the Integrated Planning Model 

(IPM) (Sargent & Lundy, 2013). 

When sufficient information is available to estimate a control cost using equations, the 

capital costs of the control equipment must be annualized. Capital costs are converted to annual 

costs using the capital recovery factor (CRF).54 The engineering cost analysis uses the 

 

53 CoST documentation is available at: http://www.epa.gov/ttnecas1/cost.htm 54 The capital recovery factor incorporates the interest rate and equipment life (in years) of the control equipment. 

The capital recovery factor formula is expressed as r*(1+r)^n/[(1+r)^n -1]. Where r is the real rate of interest and n 

is the number of time periods. Using engineering convention, the annualized costs assume a 7 percent interest rate 
4-4 

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. Where possible, calculations are used to calculate total annual control cost 

(TACC), which is a function of capital costs (CC) and O&M costs. 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 the EUAC method and the TACC, 

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, and 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$).55 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 (i) we do not know what 

all firms making capital investments for control measures will do and when they will do it and 

(ii) we do not have nor know of a better data source with possible capital investment schedules. 

The estimates of annualized costs include annualized capital and annual O&M costs for those 

controls included in the identified control strategy analysis. We make no assumptions about 

capital investments prior to 2025 or additional capital investment in years beyond 2025. The 

controls applied and their respective engineering costs are described in the Chapter 4 Appendix. 

CoST relies on detailed data from the National Emissions Inventory (NEI), including 

detailed information by source on emissions, installed control devices, and control device 

efficiency. Much of this underlying NEI data serves as key inputs into the control strategy 

analysis. The EPA receives NEI submissions from state, local, and tribal (SLT) air agencies. 

Information on whether a source is currently controlled, by what control device, and control 

device efficiency, is required under the Air Emissions Reporting Rule (AERR) used to collect 

 

for non-EGU point sources, nonpoint sources, and nonroad mobile sources. For EGU sources the annualized costs 

assume a rate of 4.77 percent. For additional discussion please see Section 4.1.2. 

55 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. 
4-5 

the NEI data. This information is only required to be provided when controls are present for the 

sources. Since controls are not present on every source, it is not possible for the EPA to enforce 

systematically (i.e., through electronic reporting) the requirement to report control devices. As a 

result, control information may not be fully reported by SLT agencies and would therefore not be 

available for purposes of the control strategy analysis. 

As indicated earlier, EPA needed to determine the universe of potential NOx and VOC 

controls and emissions reductions for each of the emissions sensitivity regions. To accomplish 

this, the EPA reviewed the emissions inventory and universe of potential control information 

from CoST to identify and employ (i) size thresholds for minimum emissions reductions (e.g., 

applying a control device should result in a minimum of 5 tons of NOx emissions reductions), 

(ii) size thresholds for application of control devices (e.g., apply a control device to sources of 25 

tons of NOx emissions or more), and (iii) cost-per-ton thresholds for applying controls from the 

CoST database (e.g., do not apply controls that cost more than $19,000/ton to reduce NOx 

emissions). The above steps are taken to mitigate potential double counting of controls due to 

possible missing control measure information in the NEI and to reduce the number of cases 

where additional control measures are applied in impractical circumstances. 

The highest cost-per-ton estimates are often associated with controls that reduce very 

small increments of NOx emissions or are unique applications of a particular control. For 

example, in some cases, controls that were developed primarily to address other pollutant 

emissions, such as SO2, also achieve NOX reductions and could be applied for this purpose. 

These controls are well characterized in the CoST database because they have been used for SO2

control, but the degree to which sources would adopt these controls specifically to obtain NOX

reductions is uncertain. To reduce the number of cases where additional control measures are 

applied in impractical circumstances, we selected cost-per-ton thresholds for applying both NOx 

and VOC controls from the CoST database. We aggregated the raw data on all identified 

controls for NOx in the control measures database by cost per ton and plotted an identified 

control cost curve. It is important to note that this identified control cost curve is not a complete 

representation of the marginal abatement cost curve. A marginal abatement cost curve presents 

the least-cost approach to achieving any specific level of emissions reduction. In contrast, the 

identified control cost curve is a series of cost-per-ton estimates based on a specific emissions 
4-6 

inventory combined with details from CoST about possible control measures that could be 

applied. The identified control cost curve defines how many tons of emissions reductions can be 

achieved at various cost levels from identified control technologies. While emissions reductions 

and their associated costs may be available for many different control measures, not all of these 

measures will be the most cost-effective way of achieving a given level of abatement, and 

therefore should not be used to construct the marginal abatement cost curve. In addition, we lack 

information on the control measures and costs for the remaining uncontrolled NOx emissions 

(see more detailed discussion on incomplete representation of marginal abatement cost curve in 

section 4.2). 

Because the identified control cost curve reflects incomplete information, it is necessary 

to take steps to identify likely impractical control applications and to remove them from the 

analysis.56 We determined that applying an exponential trend line would produce a reasonable 

cost threshold for identified controls, and we used the assumption in this analysis. To determine 

a cost threshold for identified NOx controls, we used the full dataset on NOx control measures 

and plotted an exponential trend line through the identified control cost curve.57 Figure 4-1 

shows the identified control cost curve for all the NOx control measures contained in the CoST 

database, aggregated by cost per ton, and the exponential trend line. As the figure indicates, the 

curves intersect at $19,000 per ton, meaning control costs above $19,000 per ton begin 

increasing at more than an exponential rate. We selected $19,000 per ton as the control cost 

value above which we would not apply additional identified NOx controls because controls 

above this value are not likely to be cost-effective. In the control strategy analysis for 70 ppb, 

there are a total of only eight control applications in three geographic areas where identified NOx 

controls are applied at a cost of $19,000/ton. In addition, for a standard of 70 ppb, in east Texas, 

the Northeast, the Great Lakes, and the Ohio River Valley there are a total of 25 control 

applications between $15,000/ton and $19,000/ton, representing approximately 5 percent of the 

 

56 Examples of control applications that could be removed from the analysis include: (i) applying SCR to small lean 

burn natural gas-fired reciprocating internal combustion engines to reduce NOx emissions -- these units are often in 

very remote locations and the requirements for ammonia or urea storage and replenishment are not practical, and ii) 

retrofit controls on small ICI boilers with space limitations that make the retrofit too difficult, 57 The full dataset on NOx control measures includes approximately 120,000 individual observations, and when 

aggregated by cost per ton, the dataset includes 1,500 observations. 
4-7 

total cost of identified NOx controls and approximately 1 percent of the total NOx emissions 

reductions from identified controls. 

Figure 4-1. Identified Control Cost Curve for 2025 for All Identified NOx Controls for 

All Source Sectors (EGU, non-EGU Point, Nonpoint, and Nonroad) 

In Section 4.3 we present an average cost-per-ton approach to estimate the costs of 

achieving any additional NOx emission reductions that may be needed after the application of 

the identified controls discussed above.58 That is, we apply a constant, average cost per ton of 

$15,000/ton to capture total costs associated with the NOx emissions reductions achieved 

through unidentified controls. The process for determining threshold values for applying 

identified NOx controls and the determination of a cost for valuing unidentified NOx controls are 

independent decisions. As discussed earlier, to determine threshold values for applying 

 

58 We do not apply unidentified VOC control measures in the control strategy analyses. 
4-8 

identified NOx controls, we review the entire data set of potential identified controls and remove 

likely impractical control applications. The control cost data used in Figure 4-1 reflects the 

entire data set of potential NOx controls from CoST prior to removing any control applications 

or applying any thresholds. This raw data has a median control cost of $10,400/ton and an 

emissions-weighted average cost of $3,000/ton; 97 percent of the emissions reductions from 

these controls are available at a cost less than $15,000/ton.59 In addition, the alternative 

approaches for estimating costs for unidentified controls presented in Appendix 4A generated 

unit estimates ranging from $2,500/ton to $14,000/ton for a standard of 70 ppb and from 

$2,800/ton to $14,000/ton for a standard of 65 ppb. Given that both the statistics on the entire 

data set for identified NOx controls and the results of the alternative approaches for valuing 

unidentified controls provide costs below $15,000/ton, the decision to value unidentified NOx 

controls at $15,000/ton is both appropriate and conservative. The value of $15,000/ton captures 

the potential for unidentified controls to cost both above and below this value. Currently 

identified controls that may be applied to additional sources would likely cost less than 

$15,000/ton, while newly developed technologies or technologies that may be developed in the 

future may cost more than $15,000/ton. The assumption of an average cost of $15,000/ton does 

not reflect an assumption that all controls will be available at this cost. Rather, it reflects a belief 

that a mixture of less expensive and more expensive controls will lead to an average cost of 

$15,000/ton. 

In the control strategy analyses, identified VOC controls are applied in the non-EGU 

point and nonpoint emissions sectors and in (i) fewer locations than identified NOx controls, and 

(ii) specific locations where the relative effectiveness of VOC controls will have a greater effect 

on ozone concentrations. For example, in analyzing emissions reductions needed for a standard 

of 70 ppb, we applied identified VOC controls only in a portion of the Houston buffer region, 

while we applied identified NOx controls in five larger geographic locations. Because identified 

VOC controls are generally more expensive than identified NOx controls and are only effective 

in a limited number of locations, it is reasonable to define a separate and higher cost threshold 

for applying VOC controls (for a detailed discussion of the contribution of VOC emissions to 

 

59 In the raw data, the average control cost is $17,800/ton. This average control cost is influenced by a few very 

high cost control applications that we do not apply in the identified control strategy analyses. 
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ozone formation, see Chapter 2, Section 2.1 of the November 2014 proposal RIA). We 

aggregated the raw data on all available identified measures for VOC in the control measures 

database by cost per ton and plotted an identified control cost curve for VOC controls. The 

dataset on VOC controls is significantly less robust with approximately 14,000 individual 

observations and 100 observations when aggregated by cost per ton, and the identified control 

cost curve revealed a clear point -- $33,000 per ton -- above which costs began increasing at 

more than an exponential rate. Therefore, we selected $33,000 per ton as the control cost value 

above which we would not apply additional identified VOC controls. In the control strategy 

analysis for 70 ppb, there are a total of only six applications in one geographic area (Houston) 

where identified VOC controls are applied at a cost of $33,000/ton. Figure 4-2 represents the 

identified control cost curve for all VOC control measures contained in the CoST control 

measures database, aggregated by cost per ton. As with the NOx identified control cost curve, it 

is important to note that this curve provides an incomplete representation of the marginal 

abatement cost curve for all VOC abatement because we do not have information on the control 

measures and costs for the remaining uncontrolled VOC emissions (see more detailed discussion 

on incomplete representation of marginal abatement cost curve in section 4.2). 
4-10 

Figure 4-2. Identified Control Cost Curve for 2025 for All Identified VOC Controls for 

All Source Sectors (EGU, non-EGU Point, Nonpoint, and Nonroad) 

4.1.2 Engineering Cost Estimates for Identified Controls 

In this section, we provide engineering cost estimates for the identified controls detailed 

in Chapter 3 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 revised ozone standard of 70 ppb 

and alternative standard of 65 ppb and additional emission reductions beyond identified controls 

are needed (unidentified controls). 
4-11 

See Table 4-1 for summaries of control costs from the application of identified controls 

for the final standard of 70 ppb and an alternative standard of 65 ppb. Costs are listed by sector 

for both the eastern and western U.S., except California. Note that any incremental costs for 

identified controls for California (post-2025) for the revised standard of 70 ppb and an 

alternative standard of 65 ppb are zero because all identified controls for California were applied 

in the demonstration of attainment for the current standard of 75 ppb (baseline). We aggregate 

results by region – East and West, except California – to present cost and benefits estimates. See 

Figure 4.3 for a representation of these regions. 

Table 4-1. Summary of Identified Annualized Control Costs by Sector for 70 ppb and 

65 ppb for 2025 - U.S., except California (millions of 2011$)a

Geographic 

Area Emissions Sector Identified Control Costs for 70 ppb 

Identified Control 

Costs for 65 ppb 

 

7 Percent 

Discount Rateb

7 Percent 

Discount Rateb

East 

EGU 52c 130c 

Non-EGU Point 260d 750d 

Nonpoint 360 1,500 

Nonroad 13e 36e 

Total 690 2,400 

West 

EGU - - 

Non-EGU Point 4d 49d 

Nonpoint <1 88 

Nonroad - 4e 

Total 4 140 

Total Identified Control Costs 690 2,600 

a

 All values are rounded to two significant figures. 

b

 The numbers presented in this table reflect the engineering costs annualized at a 7 percent discount rate, to the 

extent possible. 

c

 EGU sector control cost data is calculated using a capital charge rate between 7 and 12 percent for retrofit controls 

depending on the type of equipment. 

d A share of the non-EGU point source sector costs can be calculated using both 3 and 7 percent discount rates. 

When applying a 3 percent discount rate where possible, the total non-EGU point source sector costs are $250 

million for 70 ppb and $740 million for 65 ppb. 

e Nonroad sector control cost data is calculated using a 3 percent discount rate.

The total annualized engineering costs associated with the application of identified 

controls, using a 7 percent discount rate, are approximately $690 million for the final annual 

standard of 70 ppb and $2.6 billion for a 65 ppb alternative standard. Table 4-2 below provides 

summary statistics by emissions source category of the NOx and VOC control cost data from the 
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identified control strategy for the revised standard of 70 ppb.60 The costs of NOx controls, in 

terms of dollars per ton of NOx reduction for the standards analyzed were approximately 

$1,200/ton on average for the EGU sector.61 The costs of NOx controls were $2,600/ton for the 

non-EGU point sector on average, with a range of $0/ton to $19,000/ton, a median of $960/ton, 

and an emissions weighted average of $2,800/ton; $760/ton for the nonpoint sector on average, 

with a range of $0 to $2,000/ton, a median of $970/ton, and an emissions weighted average of 

$1,000/ton; and $4,600/ton for the nonroad sector on average, with a range of $3,300/ton to 

$5,300/ton, a median of $4,600/ton, and an emissions weighted average of $4,500/ton. The costs 

of VOC controls in terms of dollars per ton of VOC reduction for the standards analyzed were 

approximately $11,000/ton for the non-EGU point sector on average, with a range of $1,200/ton 

to $25,000/ton, a median of $9,800/ton, and an emissions weighted average of $8,100/ton; and 

$11,000/ton for the nonpoint sector on average, with a range of $24 to $33,000/ton, a median of 

$15,000/ton, and an emissions weighted average of $14,000/ton 

Table 4-2. NOx and VOC Control Costs Applied for 70 ppb in 2025 – Average, Median, 

Minimum, Maximum, and Emissions Weighted Average Values ($/ton)a

Emissions Sector 

Average 

Cost/Ton 

Median 

Cost/Ton 

Minimum 

Cost/Ton 

Maximum 

Cost/Ton 

Emissions 

Weighted 

Average 

Cost/Ton 

NOx Controls 

EGU 1,200 1,200 1,200 1,200 1,200 

Non-EGU 

Point 2,600 960 0 19,000 2,800 

Nonpoint 760 970 0 2,000 1,000 

Nonroad 4,600 4,600 3,300 5,300 4,500 

VOC Controls 

Non-EGU 

Point 11,000 9,800 1,200 25,000 8,100 

Nonpoint 11,000 15,000 24 33,000 14,000 a

 The numbers presented in this table reflect the engineering costs annualized at a 7 percent discount rate to the 

extent possible. EGU control cost data is calculated using a capital charge rate between 7 and 12 percent for retrofit 

controls depending on the type of equipment. Nonroad control cost data is calculated using a 3 percent discount rate.

 

60 Across all of the data in the control strategy analysis for a standard of 70 ppb, the average control cost is 

$5,000/ton and the emissions-weighted average cost is $2,000/ton. 

61 After accounting for the Clean Power Plan in the Baseline (see Chapters 2 and 3), remaining EGUs not affected 

by the Clean Power Plan were plants where NOX controls existed, but had not been dispatched. This dollar per ton 

value represents the average operation and maintenance cost of running such controls. 
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Figure 4-3. Regions Used to Present Emissions Reductions and Cost Results 

The numbers presented in Tables 4-1 and 4-2 reflect the engineering costs annualized at 

the different discount rates discussed below and include 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.62 A higher discount, or interest, rate results in a larger annualized cost of capital 

 

62 In the cost 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. 

In benefits analyses, the discount rate is used to discount benefits that occur in time periods after the year in which 

emissions reductions take place. As a result, the way the discount rate is used in the cost analysis is different from 

the way it is used in the benefits analysis. For an explanation of the benefits calculations, see Chapter 6. In both 

cases, the values at different discount rates do not indicate that the value is the present value of a stream of 

annualized benefits or costs. 
4-14 

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.63 If disaggregated control cost data is not available (i.e., where capital, equipment 

life value, and O&M costs are not separated out and where we only have a $/ton value), EPA 

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 separated out) we can and do recalculate costs using a 3 percent discount rate. For the 

engineering costs provided in this analysis, we estimate costs for the sectors as follows: 

 For EGU controls, the annualized EGU control costs were not estimated for 

either 3 or 7 percent. This is due to the complexity of investment decisions in the 

EGU sector. Decisions about investments in control equipment are not uniform 

across the sector, are made in different time frames, with different loan rates and 

thus, ultimately different capital recovery factors. Equipment pay off times, 

depreciation rates and capacities that factor into the capital charge rate 

vary. According to the IPM v5.13 documentation (U.S. EPA, 2013 Chapters 5 

and 8), capital charge rates can vary from 7 percent to 12 percent depending on 

the type of equipment. See the IPM v5.13 documentation cited for a more in 

depth discussion. EGU control costs represent 8 percent and 5 percent of the 

compliance cost estimates for identified controls for the final standard of 70 ppb 

and an alternative standard level of 65 ppb, respectively. 

 For non-EGU point source controls, some disaggregated data are available, and 

we were able to calculate costs at both 3 and 7 percent discount rates for those 

controls. For the final and alternative standards analyzed in this RIA, 

approximately 29 and 24 percent, respectively, of identified control costs for nonEGU point sources are disaggregated at a level that could be recalculated at a 3 

percent discount rate. Non-EGU point source control costs represent 38 percent 

 

63 Data sources can include state and technical studies, which frequently do not include the original data source. 
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and 31 percent, respectively, of the compliance cost estimates for identified 

controls for the final standard of 70 ppb and alternative standard level of 65 ppb. 

 For nonpoint source controls, because we do not have disaggregated control 

cost data total annualized costs for these sectors are assumed to be calculated 

using a 7 percent discount rate. Nonpoint source control costs represent 53 

percent and 62 percent, respectively, of the compliance cost estimates for 

identified controls for the final standard of 70 ppb and an alternative standard 

level of 65 ppb. 

 For nonroad mobile source controls, the cost estimates for control of emissions 

from nonroad diesel engines are prepared using a net present value (NPV) 

approach, which is different from the approach applied for other sources whose 

emissions are controlled in the illustrative control strategies applied in the RIA 

(U.S. EPA, 2007). To be consistent with the engineering cost estimates for other 

emissions sources, we would need to use the EUAC method to calculate control 

costs for nonroad diesel engines. To use the EUAC method we need information 

on the portion of annual costs that is from the annualization of the original capital 

expense for these nonroad controls and the portion that is from annual operation 

and maintenance. The cost estimates for the nonroad diesel engine retrofit 

controls did not include estimates for operating costs, and we do not have 

sufficient information to determine if the annual cost estimates reflect only capital 

costs. As a result, we are unable to estimate annual costs at interest rates of 3 

percent and 7 percent for these controls. The nonroad diesel engine retrofit costs 

are estimated using a 3 percent interest rate.64  Nonroad mobile source control 

costs represent 2 percent of the compliance cost estimates for identified controls 

for the final standard of 70 ppb and an alternative standard level of 65 ppb. 

 

64 The capital recovery factor, used to convert capital costs to annual costs, requires both an interest rate and an 

equipment life. While we do have the expected lifetime for these controls, we are not able to estimate these costs at 

a different interest rate using the EUAC based on the lack of annualized capital cost data. 
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Table 4-3 summarizes the discount rates discussed above and the percent of total 

identified control costs for each emissions sector for the final standard of 70 ppb and an 

alternative standard of 65 ppb. Because we do not have a full set of costs at the 3 percent 

discount rate or the 7 percent discount rate and because we believe the majority of the identified 

control costs is calculated at a 7 percent discount rate, Table 4-1 presents engineering cost 

estimates based on a 7 percent discount rate. 

Table 4-3. By Sector, Discount Rates Used for Annualized Control Costs Estimates and 

Percent of Total Identified Control Costs 

Emissions Sector 

Discount 

Rate 

Percent of Total Identified 

Control Costs for 70 ppb 

Percent of Total Identified 

Control Costs for 65 ppb

EGU 7 – 12% 8 5 

Non-EGU Point 3 and 7% 38 31 

Nonpoint 7% 53 62 

Nonroad 3% 2 2 

Total 100% 100% 

4.2 The Challenges of Estimating Costs for Unidentified Control Measures 

Some areas are unable to attain the revised and alternative levels of the standard using 

only identified controls. In these areas, it is necessary to assume the application of currently 

unidentified control measures to estimate the full cost of attaining the standards analyzed. The 

EPA’s application of unidentified control measures does not mean the Agency has concluded 

that all unidentified control measures are currently not commercially available or do not exist. 

Unidentified control technologies or measures can include existing controls or measures for 

which the EPA does not have sufficient data to accurately estimate engineering costs. Likewise, 

the control measures in the CoST database do not include abatement possibilities from energy 

efficiency measures, fuel switching, input or process changes, or other abatement strategies that 

are non-traditional in the sense that they are not the application of an end-of-pipe control. In 

addition, there will likely be some emissions reductions from currently unidentified control 

technologies as a result of state-specific rules that are not in the future year baseline emissions 

projections or are not yet finalized. See the discussion in Section 4.2.3 for examples of existing 

control measures for which the EPA does not have sufficient data to estimate engineering costs, 

as well as state-specific rules that are not in the future year baseline emissions projections. 
4-17 

The EPA’s application of unidentified control measures does reflect the Agency’s 

experience that some portion of controls to be applied in the future may not be currently 

available but will be deployed or developed over time. The EPA believes that a portion of the 

estimated emissions reductions needed to comply with a revised standard can be secured through 

future technologies, national regulatory programs, and/or state regulatory programs or measures 

for which information is either not currently complete or not currently available. As an example, 

in the 1997 ozone NAAQS RIA, NOx emissions reductions that were estimated from the mobile 

source Tier 2 standards were not considered as part of the “known” controls, even though the 

RIA acknowledged the potential for these mobile source standards to provide substantial costeffective controls and emissions reductions. While in 1997 these emissions reductions were 

considered to come from “unknown”, or unidentified, controls, in retrospect, they were achieved 

through mobile source controls. Looking forward, the EPA estimates that the Phase 2 of the 

Heavy Duty Greenhouse Gas Standards for New Vehicles and Engines65 will provide additional 

NOx emissions reductions.66

The remainder of this section presents and discusses various factors that should be 

considered when estimating the costs of applying costs to emission reductions from unidentified 

control measures for the future using only information on a limited set of today’s available 

control technologies or measures. We start with discussions about the role of technological 

innovation and change from the economics literature: Section 4.2.1 discusses the impact of 

technological innovation and diffusion on available control technologies; and Section 4.2.2 

presents information on improvements in control technologies over time through learning by 

doing. 

 

65 Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and 

Vehicles (Phase 1 of the Heavy Duty Greenhouse Gas Standards for New Vehicles and Engines) was included in the 

2025 base case (see Chapter 1, Section 1.3.1 for a list of rules in the base case). 

66 The focus of the Phase 1 (76 FR 57106, September 15, 2011) and Phase 2 (80 FR 40138, July 13, 2015) Heavy 

Duty GHG rules is to reduce GHG emissions and fuel consumption, but there can also be NOx reductions that stem 

largely from a switch in using the on-road engine to using an auxiliary power unit (APU) during extended 

idling. Because the Heavy Duty GHG standards are performance-based and manufacturers can choose their own 

mix of technologies to meet the standards, the standards provide an incentive for APU use but do not require it. 

Thus, the impact on NOx emissions depends on the assumptions and projections for APU use. The EPA expects 

increased APU usage would result from the Phase 2 rule. After considering the revised APU projections, the EPA 

estimates that the two Heavy Duty GHG rules combined would reduce NOx emissions by up to 120,000 tons in 

2025 and 450,000 tons in 2050. 
4-18 

Following the sections on the role of technological innovation and change, we include the 

following discussions related to limitations in the currently available information on traditional 

end-of-pipe technologies or measures and on projecting the future air quality problem being 

analyzed: Section 4.2.3 discusses the incomplete characterization of the supply of available 

control technologies and why the abatement supply curve from identified controls presented in 

the previous section provides an incomplete picture of all currently available pollution abatement 

opportunities; Section 4.2.4 discusses how over time as EPA reviews NAAQS standards, 

relevant information about future year baseline emissions and possible control technologies is 

revealed in the current RIA development process that was not available to analysts for previous 

RIAs; and Section 4.2.5 includes information on how NOx offset prices and Section 185 fees 

could serve as reasonable proxies for the costs associated with emissions reductions from 

unidentified controls. Finally, we describe how we use this information to help inform the 

unidentified control cost methodology applied in section 4.3. 

4.2.1 Impact of Technological Innovation and Diffusion 

In general, the marginal abatement cost curve (MACC) at any particular point in time for a 

defined set of emitting sectors will be an increasing function of the level of abatement.67 That is, 

marginal costs are increasing as the amount of emissions are reduced. However, it is important to 

note that the MACC is not just the relationship between marginal cost and abatement, but also 

should be constructed as the envelope of least cost approaches for any given level of abatement. 

As previously noted, the identified control cost curve derived from data in CoST may include 

measures that may not be the most cost-effective way of achieving the emissions reduction, and 

as a result the cost curve derived using that data may not represent the complete MACC. The 

aggregated MACC is the horizontal summation of individual firms/sectors marginal abatement 

curves, and is generally thought to reflect the overall marginal and total abatement costs when a 

least cost approach is implemented. This aggregate MACC gives the efficient MAC level for 

each firm/sector for any aggregate emissions target for a given time period. However, the 

MACC represents the efficient MAC level only under some fairly restrictive conditions, 

including 1) all abatement opportunities across all sectors and locations have been identified and 

 

67 The marginal abatement cost curve (MACC) is a representation of how the marginal cost of additional emissions 

abatement changes with increasing levels of abatement. 
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included in the cost curve, and 2) information about applicability of controls is available with no 

uncertainty. In addition, the MACC for a current time period will only hold for a future time 

period if no technical change (either introduction of new technologies or reduction in cost of 

existing technologies) or learning by doing occurs between the present and future time periods. 

In regulatory analyses of NAAQS, we typically assess costs of abatement in a future year 

or years selected to represent implementation of the standards. The focus has typically been on 

the application of existing technologies and the evolution of those technologies over time rather 

than on innovations that may lead to development of new pollution control technologies. As 

such, a MACC constructed based on currently available information on abatement opportunities 

will not be the best representation of a future MACC. A future MACC will likely reflect 

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). Because we are unable to predict technological 

advances that may occur in the future, the discussion in this section focuses on the advances that 

might be expected in existing pollution control technologies. 

Technological innovation and diffusion can affect the MACC in several ways. Some 

examples of the potential effects of technical change are: 

1. New control technologies may be developed that cost less than existing technologies. 

2. A new control technology may be developed to address an uncontrolled emissions 

source. 

3. The efficiency of an existing control measure may increase. In some cases, the control 

efficiency of a measure can be improved through technological advances. 

4. The cost of an existing control measure may decrease. 

5. The applicability of an existing control measure to other emissions sources may increase. 
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Overall, these five examples describe ways that technological change can reduce both the 

amount of unidentified abatement needed, shift the MACC, decrease the MAC, decrease average 

costs, and decrease total costs relative to the case where it is assumed that the current MACC 

reflects all possible abatement opportunities both in the present and future. It is also possible in 

cases where there is a strictly binding emissions reduction target that new control technologies 

can be introduced and adopted with much higher marginal costs. However, if there are cost offramps, such as those provided by Section 185 of the CAA, those higher cost technologies may 

not be adopted (see Section 4.2.5 for a brief discussion of Section 185 fees). 

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 examples 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: 

1. Selective catalytic reduction (SCR) and ultra-low NOx burners for NOx 

emissions; 

2. Scrubbers that achieve 95 percent or greater SO2 control on boilers; 

3. Sophisticated new valve seals and leak detection equipment for refineries and 

chemical plants to reduce VOC and HAP emissions; 

4. Low or zero VOC paints, consumer products and cleaning processes; 

5. Chlorofluorocarbon (CFC) free air conditioners, refrigerators, and solvents; 
4-21 

6. Water and powder-based coatings to replace petroleum-based formulations to 

reduce VOC and HAP emissions; 

7. Vehicles with lower NOx emissions than believed possible in the late 1980s due 

to improvements in evaporative controls, catalyst design and fuel control systems 

for light-duty vehicles; and treatment devices and retrofit technologies for heavyduty engines; 

8. Idle-reduction technologies for engines to reduce NOx and PM2.5 emissions, 

including truck stop electrification efforts; and 

9. Improvements in gas-electric hybrid vehicles and cleaner fuels to reduce NOx 

emissions. 

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) 

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 
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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. 

4.2.2 Learning by Doing 

As experience is gained in the application of control technologies or pollution control 

practices, firms learn how to operate the controls more efficiently and learn how to apply 

controls to additional sources. 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. Such impacts have been 

identified to occur in a number of studies conducted for various production processes. These 

impacts 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) discuss how the cost of control technologies can decline over time 

using the example of post-combustion SO2 and NOx combustion systems. After an increase in 
4-23 

costs during an initial commercialization period, costs decreased by at least 50 percent over the 

course of two decades. The 1997 Ozone NAAQS RIA includes information on 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. Other discrete examples include the dramatic 85 

percent decline in prices of the catalyst used in operating SCR between 1980 and 2005 

(Cichanowicz, 2010). In addition, analyses performed for the 2008 Ozone NAAQS RIA found 

controls originally developed for one source type were being applied to new source categories. 

For example, SCR, originally developed for use by EGUs, is now used in the cement 

manufacturing sector, and SNCR is now pertinent to a large number of additional boiler source 

categories. In some cases, these newly found controls proved to be more effective than what had 

been applied in the past. 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. These examples serve as evidence of a learning 

effect – production and implementation costs decrease as learning and repetitive use occurs. 

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. The magnitude of 

learning curve impacts on pollution control costs was estimated for a variety of sectors as part of 

the cost analyses done for the Direct Cost Estimates Report for the Second EPA Section 812 

Prospective Analysis of the Clean Air Act Amendments of 1990.68 In the Report, learning curve 

adjustments were included for those sectors and technologies for which learning curve data were 

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 

 

68 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. 
4-24 

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. 

Learning by doing can reduce costs in a number of ways: through the reduction of 

operating and maintenance costs, finding new ways to use existing technologies, etc. 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 and resources to properly generate control costs that 

reflect learning curve impacts. 

4.2.3 Incomplete Characterization of Available NOx Control Technologies 

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, or 

abatement, 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.69 Although exogenous factors, such as 

changes in economic conditions, may have 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. 

 

69 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. 
4-25 

Underlying the selection of controls described in Appendix 3A is the concept of the 

MACC. Adding newly developed control technologies, or changing either the abatement amount 

or cost of the technology, will change the shape of the overall MACC. The engineering cost 

estimates in section 4.1 are estimated primarily from end-of-pipe controls and only included 

limited process-oriented control measures, such as switching to lower-emitting fuel or energy 

sources and installing energy efficiency measures. As a result, the MACC derived in the 

previous section from identified controls represents an incomplete supply curve that only 

partially captures the abatement supply. An illustrative depiction of an “observed but 

incomplete” MACC and the complete underlying MACC is presented below in Figure 4-4. 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 increases the supply of abatement. 

Figure 4-4. Observed but Incomplete MACC (Solid Line) Based on Identified Controls 

in Current Tools and Complete MACC (dashed line) where Gaps Indicate 

Abatement Opportunities Not Identified by Current Tools 
4-26 

Because of the incomplete characterization of the full range of NOx abatement 

possibilities, it is important to understand the composition of the cost information EPA has 

available and uses to construct the partial MACC. The nature of available information on the 

cost of NOx abatement measures is somewhat complex. EPA’s control strategy tools undergo 

continuous improvement, and as the need for additional abatement opportunities increases, 

additional evaluation of uncontrolled emissions takes place. During these evaluations, additional 

abatement opportunities from applying identified controls typically are found. These abatement 

opportunities or additional controls are added to the CoST database and will be available for 

future analyses. In addition, in some cases we may have specific knowledge of potential 

additional control measures due to an impending regulation (e.g., Tier 3), but until a regulation is 

finalized those identified controls are not included in any concurrent analyses. 

It is also important to understand that EPA’s control strategy tools largely focus on end-ofpipe controls and a limited set of emissions inventory sectors, whereas opportunities for 

emissions reductions through non-end-of-pipe controls or measures exist. For example, we 

reviewed the existing control strategies indicated in the SIP for the Dallas-Fort Worth area for 

the 1997 ozone NAAQS, and we compared the strategies and measures in that SIP to the 

measures the EPA analyzed in the 1997 ozone NAAQS regulatory impact analysis. The EPA 

analyzed several industrial source categories and measures that were reflected in the 1997 

Dallas-Fort Worth SIP, including existing control measures for stationary sources such as cement 

kilns, industrial boilers, iron and steel mills, as well as enhanced inspection and maintenance 

programs for mobile sources.70 The Dallas-Fort Worth SIP recognized the need for additional 

control strategies and measures to achieve further emissions reductions – strategies and measures 

that were not reflected in EPA’s 1997 control strategy analysis. These additional control 

measures included transportation control measures, additional voluntary mobile emission 

reduction programs, and energy efficiency/renewable energy measures. Table 4-4 below 

includes examples of each of these types of programs or measures. 

 

70 The Dallas-Fort Worth SIP also reflected the following existing voluntary mobile emission reduction programs: 

alternative fuel vehicle program; employee trip reduction program; and vehicle retirement program. Information on 

the Dallas-Fort Worth SIP is available at http://www.nctcog.org/trans/air/sip/future/lists.asp. 
4-27 

Table 4-4. Control Measures in Dallas-Fort Worth SIP Not Reflected in the 1997 Ozone 

NAAQS RIA 

Transportation Control Measures 

 Bicycle/Pedestrian Projects 

 Grade Separation Projects 

 HOV/Managed Lane Projects 

 Intersection Improvement Projects 

 Park and Ride Projects 

 Rail Transit Projects 

 Vanpool Projects 

Voluntary Mobile Source Emission Reduction 

Programs 

 Clean Vehicle Program 

 Employee Trip Reduction 

 Locally Enforced Idling Restriction 

 Diesel Freight Idling Reduction Program 

 

Other State and Local Programs: Energy 

Efficiency/Renewable Energy 

 Residential Building Code 

 Commercial Building Code 

 Federal Facilities Projects 

 Political Subdivision Projects71

 Electric Utility-Sponsored Programs72

 Wind Power Projects 

Additional Measures 

 Clean School Bus Program 

 Texas Low Emission Diesel 

 Stationary Diesel and Dual-Fired Engine 

Control Measures 

Further, Table 4-5 includes specific non-end-of-pipe control measures from approved SIPs 

in Texas and Louisiana, including measures from the Dallas-Fort Worth SIP (i.e., energy 

efficiency measures). The approved, non-end-of-pipe control measures include local 

transportation measures, local building energy efficiency requirements, and mobile source sector 

measures. In addition, Table 4-6 includes examples of approved non-end-of-pipe control 

measures in California. California is also currently developing the following additional 

measures: 

 

71 These projects are typically building system retrofits, non-building lighting projects, and other mechanical and 

electrical systems retrofits, such as municipal water and waste water treatment systems. 72 These programs include air conditioner replacements, ventilation duct tightening, and commercial and industrial 

equipment replacement. 
4-28 

 Encouraging Use of Warm Mix Asphalt over Hot Mix Asphalt - European and 

American companies have developed several techniques, collectively known as 

warm-mix asphalt (WMA), to increase the workability of asphalt by lowering the 

viscosity at temperatures as much as 100°F below that of hot-mix asphalt (HMA). 

WMA was introduced in Europe in 1997 and in the United States in 2002. WMA 

has shown potential for reducing emissions associated with the production of 

asphalt for paving projects when compared to HMA. Lower temperatures required 

for production, storage, transport, and application translates to lower fuel 

consumption, which in turn reduces the criteria air pollutant emissions associated 

with combustion.73 

 Replacement of gas-powered leaf blowers and mowers – The South Coast Air 

Quality Management District has a program that subsidizes the replacement of 

existing two-stroke backpack blowers currently used by commercial 

landscapers/gardeners with new four-stroke backpack blowers that have 

significantly reduced emissions and noise levels.74 

All of these additional and non-end-of-pipe measures and associated emissions reductions are not 

reflected in EPA’s control strategy tools and represent additional abatement opportunities not 

accurately captured in this analysis. 

 

73 For additional information see http://www.fhwa.dot.gov/everydaycounts/technology/asphalt/intro.cfm ; slide 31 

aci-na.org/static/entransit/sunday_warmmix_logan.pdf. FHWA estimates that warm mix asphalt uses 20 percent 

less energy than hot mix asphalt. Also see http://www.fhwa.dot.gov/everydaycounts/technology/asphalt/intro.cfm. 

Airports Council International reports 10-55 percent lower energy consumption and 20-55 percent reduction in 

emissions. (See http://aci-na.org/static/entransit/sunday_warmmix_logan.pdf, Slide 31)). 

74 Typically, ten exchange events are set up across the District, and for the convenience of the participants, the 

exchange events take place during consecutive weekdays. At the event site, the old leaf blowers will be tested for 

operation and then drained of all fluids in a responsible manner and collected for scrapping. The vendor will haul the 

traded-in blowers to a scrapping yard where they are crushed and recycled. The vendor will also provide training for 

the proper use of the equipment at each of the exchange sites. (http://www.aqmd.gov/docs/default-source/LawnEquipment/leafblower-brochure.pdf?sfvrsn=9) 
4-29 

Table 4-5. Non-End-of-Pipe Control Measures from SIPs 

Measure Projected Emissions 

Reductions 

(tons per day) 

Area Citation 

Energy Efficiency75 0.04 (NOx) Shreveport/Bossier City 

Area, Louisiana 

70 FR 48880 

August 22, 2005 

Energy Efficiency76 0.72 (NOx) Dallas/Fort Worth 

(DFW), Texas 

73 FR 47835 

August 15, 2008 

Transportation Emission 

Reduction Measures77

0.72 (NOx) 

0.83 (VOC) 

Austin - Early Action 

Compact Area, Texas 

70 FR 48640 

August 19, 2005 

Voluntary Mobile 

Emission Reduction 

Program (VMEP) 

2.63 (NOx) 

0.61 (VOC) 

DFW area 74 FR 1906 

January 14, 2009 

Texas Emission Reduction 

Plan (TERP)78, 79

14.2 (NOx) DFW area 74 FR 1906 

January 14, 2009 

Texas Low Emission 

Diesel (TxLED)80

Up to 6 (NOx); on-going 

and varies annually 

East and Central Texas 

(includes DFW area) 

79 FR 67068 

November 12, 2014 

 

 

75 The measures involved installing energy conservation equipment in 33 city buildings. Measures included 

upgrades to the lighting, mechanical and control systems, water conservation upgrades, and other miscellaneous 

activities. This was an Early Action Compact (EAC) area SIP. 

76 The NOx emissions reductions in the DFW area were due to energy efficiency measures in new construction for 

single and multi-family residences. This measure was initially submitted within a SIP to provide emissions 

reductions of 5 percent in the DFW area; actual reductions fell short of the 5 percent goal, but this measure was 

eventually approved with other measures that, by providing additional emissions reductions, improved air quality in 

the area. 

77 The transportation projects were to reduce vehicle use, improve traffic flow, and/or reduce congested conditions 

throughout the Austin EAC area (this was an EAC SIP). The Austin EAC area included Bastrop, Caldwell, Hays, 

Travis, and Williamson counties and the cities of Austin, Bastrop, Elgin, Lockhart, Luling, Round Rock, and San 

Marcos. 

78 TERP is a discretionary economic incentive program: economic incentives to reduce emissions. The approved 

TERP is a grant program, unique to Texas, that provides funds through the Texas Commission on Environmental 

Quality in a variety of categories, including emissions reduction incentive grants, rebate grants (including grants for 

small businesses), and heavy and light duty motor vehicle purchase or lease programs, all with the goal of improving 

air quality in Texas. Examples of TERP programs include assisting small businesses in purchasing lower-emission 

diesel vehicles, helping school districts to reduce emissions from school buses, and providing funds to support 

research and development of pollution-reducing technology. TERP is available to all public and private fleet 

operators that operate qualifying equipment in any of the ozone nonattainment counties within Texas. TERP was 

also approved into the Texas EAC SIPs, providing at least 2 tons per day in NOx reductions in each of three areas 

(Austin, Tyler/Longview, and San Antonio). 

79 The State of Texas reports on the TERP every other year and the emissions reductions cited here were based on an 

estimated $6,000 per ton. 

80 The TxLED fuel program was initially approved by EPA on November 14, 2001 (66 FR 57196) and has 

undergone subsequent revisions, the latest on May 5, 2013 (78 FR 26255). TxLED fuel is required for use by onhighway vehicles and non-road equipment (including marine vessels) in 110 counties in eastern and central Texas. 

Use of this boutique fuel reduces NOx emissions. 
4-30 

Table 4-6. Non-End-of-Pipe Measures in California 

Program or Standard Area Citation Estimated 

Emissions 

Reductions 

Carl Moyer Memorial Air Quality 

Standards Attainment Program (selected 

project types)* 

San Joaquin Valley, 

California 

79 FR 29327 (May 

22, 2014) 

3.78 tpd NOx

credited in San

Joaquin Valley

Proposition 1B: Goods Movement 

Emission Reduction Program (selected 

project types)* 

San Joaquin Valley, 

California 

79 FR 29327 (May 

22, 2014) 

1.23 tpd NOx

credited in San

Joaquin Valley

California Reformulated Gasoline (RFG) 

Program 

California 

(statewide) 

60 FR 43379 (August 

21, 1995), revised 75 

FR 26653 (May 12, 

2010) 

California Diesel Fuel Program California 

(statewide) 

60 FR 43379 (August 

21, 1995), revised 75 

FR 26653 (May 12, 

2010) 

Regional Clean Air Incentives Market 

(RECLAIM) 

South Coast, 

California 

63 FR 32621 (June 

15, 1998), revised 71 

FR 51120 (August 29, 

2006) and 76 FR 

50128 (August 12, 

2011) 

* Program not approved into SIP but relied upon for emission reduction credit through state commitment. 

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. However, other sectors may not be as well-controlled, and lower cost controls may 

be available. 

4.2.4 Comparing Baseline Emissions and Controls across Ozone NAAQS RIAs from 1997 to 

2014 

Many factors affect the future year baseline emissions used in a NAAQS analysis, 

including the future year being analyzed, the projected air quality in that year, the emissions 

inventories used, emissions projections methodologies, and any federal and/or state regulatory 

programs or measures that are reflected in the emissions projections. The EPA believes that 

while these factors and changes are difficult to track individually, additional federal and/or state 
4-31 

regulatory programs promulgated and the new data on available control technologies or measures 

can, over time, result in emissions reductions and control measures being reclassified from 

unidentified to identified measures. Each ozone NAAQS analysis since 1997 has required at 

least some emissions reductions from controls that were considered unidentified at the time of 

analysis, but evidence indicates that over time new information becomes available that changes 

the characterization of these emissions reductions from unidentified measures to identified 

measures. For example, in the 1997 ozone NAAQS RIA, NOx emissions reductions that were 

expected to result from the at that time upcoming mobile source Tier 2 standards were not 

characterized as resulting from identified controls, even though the RIA acknowledged the 

potential for these standards to provide substantial cost-effective controls and emissions 

reductions. As a result, in 1997 these cost-effective emissions reductions were considered to be 

from unidentified controls, while in retrospect they were actually from identified controls. 

Likewise, the 2008 ozone NAAQS RIA did not include controls on EGUs that would later be 

predicted to result from the Mercury and Air Toxics Standards or the Clean Power Plan. As a 

result, in 2008 emissions reductions needed from unidentified controls were estimated to be 

higher in some regions of the U.S. than those estimated in this RIA. In general, during the time 

between the promulgation of a NAAQS and the required date of attainment, additional rules may 

be developed and additional analyses performed that shed light on how emissions reductions that 

were once thought to be unavailable from identified control measures are obtained through 

tangible means. Improvements over time, both in information and engineering, lead to an 

increase in identified controls and as a result emission reductions obtained only through 

unidentified controls in one analysis may be realized through identified controls in subsequent 

analyses. 

4.2.5 Possible Alternative Approaches to Estimate Costs of Unidentified Control Measures 

In determining how to estimate the costs of achieving the emission reductions needed 

from unidentified control measures (see Section 4.3), we examined what information could be 

gleaned from existing regional NOx offset prices. 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 
4-32 

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 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 2013.81 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.

82 Lastly, we collected 

information on NOx offset prices in the Houston-Galveston nonattainment area for 2010 through 

2013 from the Trade Report83 and the New York-New Jersey-Connecticut nonattainment area 

from 2000 through 2013 from industry representatives. 

Table 4-7 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, and represent a one-time 

payment or cost, not an annual payment or cost. The prices constitute average of the trades in 

the regions for the year given. The data series for the California regions are more complete than 

those for Houston and the New York region. 

 

81 http://www.arb.ca.gov/nsr/erco/erco.htm 

82 http://www.aqmd.gov/home/programs/business/about-reclaim/reclaim-trading-credits 83 http://www.tceq.state.tx.us/airquality/banking/mass_ect_prog.html 
4-33 

Table 4-7. Average NOx Offset Prices for Four Areas (2011$, perpetual tpy) a

 NOx Offset Prices ($/ perpetual tpy) 

 

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.

To more directly compare offset prices to potential annual costs for unidentified 

emissions controls, we annualized the perpetual, 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 cost by using the capital recovery factor (CRF) discussed in Section 

4.1.1. 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 4-8 presents the average and 

maximum annualized NOx offset prices in 2011 dollars. 
4-34 

Table 4-8. Annualized NOx Offset Prices for Four Areas (2011$, tons)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 since the purchaser of the offset chose to purchase the offset rather than curtail 

their business activities or purchase some other pollution control technology. It is possible that 

these offset prices could serve as reasonable proxies for the costs associated with emissions 

reductions from unidentified measures or controls. The cost information informing the identified 

control strategy traces out an incomplete marginal abatement cost curve in that, as discussed in 

Chapter 3, the controls used in the identified control analysis are primarily end-of-pipe 

technologies. The identified control estimates for NOx do not account for other forms of 

abatement, e.g., switching to lower emitting fuels or increasing energy efficiency. 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 emissions and air quality profiles. In 

each region, offset supply is a function of the emissions inventory and offset demand is a 

function of economic growth, and neither offset supply nor demand is infinite. 

An alternative proxy for estimating the costs of unidentified control measures is using the 

current annualized section 185 fee rate. The section 185 fee program requirement applies to any 

ozone nonattainment area that is classified as Severe or Extreme under the NAAQS. If a Severe 

or Extreme nonattainment area fails to attain the ozone NAAQS by the required date, section 185 

of the Clean Air Act requires each major stationary source of VOC and NOx located in such area 

to pay a fee to the state for each calendar year following the attainment year for emissions above 
4-35 

a baseline amount and until the area reaches attainment.84,85 The fee was set in the 1990 Clean 

Air Act at $5,000 per ton of VOC and NOx emissions above the baseline amount and is adjusted 

annually for inflation based on the Consumer Price Index. The 2013 annualized section 185 fee 

rate was $9,398.67 per ton. Examples of states or areas that have adopted section 185 fee 

programs include: (a) Texas for the Houston-Galveston nonattainment area, which adopted its 

fee program in May 2013,86 and (b) the South Coast Air Quality Management District, which 

amended Rule 317 that governs its fee program in February 2011. 

4.2.6 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.87 In addition, while we 

examined other approaches to estimate the costs of unidentified measures, we concluded that 

these approaches could potentially undervalue the costs of unidentified controls. As the EPA 

continuously improves its data and tools, we expect to better characterize the currently 

unobserved pieces of the MACC. 

4.3 Compliance Cost Estimates for Unidentified Emissions Controls 

This section presents the methodology and results for the costs of emissions reductions 

from unidentified control measures needed to demonstrate full attainment of the revised and 

alternative standards analyzed. We refer to the costs of emissions reductions from unidentified 

 

84 For additional information on developing fee programs required by Clean Air Act Section 185, see the January 5, 

2010 memorandum from the EPA’s Office of Air Quality Planning and Standards, available at: 

http://www.epa.gov/ttn/naaqs/aqmguide/collection/cp2/bakup/20100105_page_section_185_fee_programs.pdf. 85 In 1990, the Clean Air Act set the fee at $5,000/ton of VOC and NOx emitted by the source during the calendar 

year in excess of 80 percent of the baseline amount. A source’s baseline amount is the lower of the amount of actual 

or allowable emissions under the permit for the source during the attainment year. 

86 Additional information on Texas’s actions is available at http://www.tceq.texas.gov/airquality/point-sourceei/sipsection185.html 

87 See Chapter 3, Section 3.4 for additional discussion of uncertainties associated with predicting technological 

advancements that may occur between now and 2025. 
4-36 

controls as unidentified control costs.88 As discussed in Chapter 3, the application of the 

identified control strategies was not sufficient in reaching full, nationwide attainment of the 

revised standard of 70 ppb and the alternative standard of 65 ppb analyzed. Therefore, the 

engineering costs detailed in Section 4.1 represent only the costs of partial attainment. 

4.3.1 Methods 

On the issue of estimating the costs of unidentified control measures, in 2007 the EPA’s 

Science Advisory Board offered the following advice: 

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 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.89

While we have considered alternative methodologies to predict future abatement supply 

curves, we are currently unable to quantitatively predict future shifts in the abatement 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: 

a “fixed cost” approach, following the SAB advice, and a “hybrid” approach that has not yet 

 

88 In previous analyses, these costs were referred to as extrapolated costs. 89 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. 
4-37 

been reviewed by the SAB.90 We refer to the fixed cost approach as the “average cost” approach 

here. The average cost approach 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 (Appendix 4A). The range of estimates reflects different assumptions about 

the cost of additional emissions reductions beyond those in the identified control strategies. 

While we use a constant, average cost per ton to estimate the costs of the emissions reductions 

beyond identified controls, this does not imply that the MACC is not upward sloping. The 

constant, average cost per ton is designed to capture total costs associated with the abatement of 

the emissions reductions from unidentified controls – because of the incomplete information 

available to inform the characterization of the MACC, a portion of those total costs is likely at a 

value below the average cost per ton and a portion is likely at a value above the average cost per 

ton. 

The alternative values used in the sensitivity analysis implicitly reflect different 

assumptions about the amount of technological progress and innovation in emissions reduction 

strategies that may be expected in the future. The average cost approach reflects a view that 

because we have incomplete data on existing control technologies, and because no cost data 

exists for unidentified future technologies, measures, or strategies, it is unclear whether 

approaches using hypothetical cost curves will be more accurate or less accurate in forecasting 

total national costs of unidentified controls than an average cost approach that uses a range of 

national cost-per-ton values. 

The hybrid approach assumed increasing marginal costs of control along an upwardsloping marginal cost curve. The hybrid approach assumed the rate of increase 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.91 Under this 

approach, the relative costs of unidentified controls in different geographic areas reflected the 

 

90 The three approaches mentioned above, outlined for SAB review, for assigning costs to unidentified measures 

included: (1) the fixed cost approach that assigns all unidentified measures a fixed cost per ton; (2) an approach 

based on an upward sloping cost curve that uses information from the identified control measure analysis on an areaspecific basis; and (3) an approach that adjusts the upward sloping cost curve projections using information about 

cost changes over time to reflect factors such as learning by doing and induced innovation. 91 See, for example, Section 7.2 and Appendix 7.A.2 in the December 2012 RIA for the final PM2.5 NAAQS, 

available at http://www.epa.gov/ttn/ecas/regdata/RIAs/finalria.pdf. 
4-38 

expectation that average per-ton control costs are likely to be higher in areas needing a higher 

ratio of emissions reductions from unidentified and identified 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 approach 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. 

For areas needing significant additional emission reductions, much pollution abatement is 

likely needed from sectors that historically have not been intensively regulated and thus have 

relatively more available potential emissions reductions. 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. 

As noted in previous NAAQS analyses, the EPA continues to explore other sources of 

information to inform the estimates of costs associated with unidentified controls. For this RIA 

we examined the full set of identified controls, examined evidence that suggests that over time 

new information and data emerges that shifts emissions reductions from the unidentified to the 

identified 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 Science Advisory Board 

Council Advisory’s advice, and the requirements of E.O. 12866 and OMB circular A-4, which 

provide guidance on the estimation of benefits and costs of regulations, in this RIA, we follow 
4-39 

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 approach 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. 

 As discussed in Section 4.1.1, we apply a constant, average cost per ton of $15,000/ton to 

capture total costs associated with the NOx emissions reductions achieved through unidentified 

controls. To explore how sensitive total costs are to this assumption, we also use alternative 

assumptions of the average cost. Specifically, we conduct 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. This range is inclusive of the annualized NOx offset prices observed in recent years in the 

areas likely to need unidentified controls to achieve the standard (Table 4-8), and if anything, 

suggests the central estimate of $15,000/ton is conservative. In the RIA for the ozone NAAQS 

proposal, EPA requested comments on the methods presented to estimate emissions reductions 

needed beyond identified controls, including the parameter estimate of $15,000/ton. We 

received comments that the parameter estimate of $15,000/ton (2011$) was low and should be 

adjusted to reflect inflation. The EPA has elected to retain the parameter estimate of $15,000/ton 

(2011$) because inflation was low between 2006 and 2011. While the Agency received 

comments on its methods, alternative approaches have not been subjected to peer review and 

therefore could not be applied here. 

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 4-7, regional 

factors may play a significant role in the estimation of control costs. In the RIA for proposal, 

EPA stated it may review alternative methodologies and sources of regional information that 

could result in the average cost methodology being applied more regionally. The EPA reviewed 

data on end-of-pipe controls more closely and worked on identifying information from measures 

used in SIPs. The data in CoST on end-of-pipe technologies was not sufficiently robust to 

generate regional data, and obtaining detailed cost information from state or local SIPs requires a 
4-40 

longer-term effort. Despite the regional variation in offset prices, we believe the $15,000/ton 

estimate represents a conservative value because it is higher than the majority of the annualized 

offset values in Table 4-8, and because the values we use in the sensitivity analyses include the 

highest annualized offset value. 

4.3.2 Compliance Cost Estimates from Unidentified Controls 

Table 4-9 presents the control cost estimates for unidentified controls for the East and 

West in 2025, except for California for the final standard of 70 ppb and an alternative standard of 

65 ppb using an assumed average cost of $15,000/ton, as well as values of $10,000/ton and 

$20,000/ton. Appendix 4A includes potential alternative methods for either assigning an average 

cost value or for determining values used in sensitivity analyses. 

Table 4-9. Unidentified Control Costs in 2025 by Alternative Standard for 2025 – U.S., 

except California (7 percent discount rate, millions of 2011$) 

Alternative Level Geographic Area Unidentified Control Cost 

$10,000/ton $15,000/ton $20,000/ton 

70 ppb 

East 470 700 930 

West - - - 

Total 470 700 930 

65 ppb East 8,200 12,000 16,500 

West 400 610 810 

Total 8,600 13,000 17,000 

a

 All values are rounded to two significant figures. 

Table 4-10 presents the unidentified control cost estimates for post-2025 for California 

for the final standard of 70 ppb and an alternative standard level of 65 ppb using an assumed 

average cost of $15,000/ton, as well as values of $10,000/ton and $20,000/ton. 
4-41 

Table 4-10. Unidentified Control Costs in 2025 by Alternative Standard for Post-2025 –

California (7 percent discount rate, millions of 2011$) 

Alternative Level Geographic Area Unidentified Control Cost 

$10,000/ton $15,000/ton $20,000/ton 

70 ppb California 510 800 1,020 

65 ppb California 1,000 1,500 2,000 

a

 All values are rounded to two significant figures. 

4.4 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. Please 

see Chapter 1, Section 1.3.2 for additional detailed discussion on potential nonattainment 

designations and timing. 

Tables 4-11 and 4-12 present summaries of the total national annual costs (identified and 

unidentified) of attaining the revised standard of 70 ppb and alternative standard of 65 ppb – 

Table 4-11 presents the total national annual costs by alternative standard for 2025 for all of the 

U.S., except California and Table 4-12 presents the total national annual costs by alternative 

standard for post-2025 for California. As discussed in Section 4.1.2, because we do not have a 

full set of costs at the 3 percent discount rate or the 7 percent discount rate and because we 

believe the majority of the identified control costs is calculated at a 7 percent discount rate, 

Tables 4-11 and 4-12 present engineering cost estimates based on a 7 percent discount rate. 

Table 4-11. Summary of Total Control Costs (Identified and Unidentified) by Alternative 

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

Alternative Level Geographic Area Total Control Costs 

(Identified and 

Unidentified) 

70 ppb 

East 1,400 

West <5 

Total $1,400 

65 ppb 

East 15,000 

West 750 

Total $16,000 

a

 All values are rounded to two significant figures. Unidentified control costs are based on the average cost 

approach. 
4-42 

Table 4-12. Summary of Total Control Costs (Identified and Unidentified) by Alternative 

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

Alternative Level Geographic Area 

Total Control Costs 

(Identified and 

Unidentified) 

70 ppb California 800 

65 ppb California 1,500 

a

 All values are rounded to two significant figures. Unidentified control costs are based on the average cost 

approach. 

4.5 Economic Impacts 

4.5.1 Introduction 

This section addresses the potential economic impacts of the illustrative control strategies 

for the 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 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 from unidentified controls 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 alternative ozone standards are anticipated to impact multiple markets in many times 

and places. Computable General Equilibrium (CGE) models are one possible tool for evaluating 
4-43 

the impacts of a regulation on the broader economy because this class of models explicitly 

captures interactions between markets across the entire economy. 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 identified 

controls (excluding the unidentified control costs). EMPAX is a dynamic computable general 

equilibrium (CGE) model that forecasts a new equilibrium for the entire economy after a policy 

intervention. While a CGE model captures the effects of behavioral responses on the part of 

consumers or other producers to changes in price that are missed by an engineering estimate of 

compliance costs, most CGE models do not model the environmental externality - or the benefits 

that accrue to society from mitigating it. When benefits from a regulation are expected to be 

substantial, social cost cannot be interpreted as a complete characterization of economic welfare. 

To the extent that the benefits affect behavioral responses in markets, the social cost measure 

may also be potentially biased. 

EPA included specific types of health benefits in a CGE model for the prospective 

analysis, The Benefits and Costs of the Clean Air Act from 1990 to 2020 (EPA 2011), and 

demonstrated the importance of their inclusion when evaluating the economic welfare effects of 

policy. However, while the external Council on Clean Air Compliance Analysis (Council) peer 

review of this EPA report (Hammitt 2010) stated that inclusion of benefits in an economy-wide 

model, specifically adapted for use in that study, “represent[ed] a significant step forward in 

benefit-cost analysis”, serious technical challenges remain when attempting to evaluate the 

benefits and costs of potential regulatory actions using economy-wide models. 

To begin to address these technical challenges, the EPA has established a new Science 

Advisory Board (SAB) panel on economy-wide modeling to consider the technical merits and 

challenges of using CGE and other economy-wide modeling tools to evaluate costs, benefits, and 

economic impacts of air regulations. 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. 
4-44 

The advice from the SAB panel formed specifically to address the subject of economywide modeling was not available in time for this analysis. Given the ongoing SAB panel on 

economy-wide modeling, the uncertain nature of costs, the Council’s advice regarding the 

importance of including benefit-side effects, and the lack of available multi-year air quality 

projections needed to include these benefit-side effects, EPA has not conducted CGE modeling 

for this analysis. Instead, this section proceeds with a qualitative discussion of market impacts. 

4.5.2 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 

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 

to a change in price, 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. 
4-45 

4.6 Differences between the Proposal and Final RIAs 

Several changes in the analysis for this final RIA have resulted in lower control costs 

compared to the proposal RIA. As discussed in Chapter 3, Section 3.3, improved emissions 

inventory and model inputs as well as more refined air quality modeling resulted in 

approximately 50 percent fewer emissions reductions needed in Texas and the Northeast to reach 

the revised standard of 70 ppb compared to the proposal RIA. For an alternative standard of 65 

ppb, we needed approximately 20 percent fewer emissions reductions nationwide than at 

proposal. In addition, because of the more refined air quality modeling, control strategies were 

applied in smaller geographic areas closer to monitors projected to exceed 70 and 65 ppb. Also, 

in the proposal RIA we applied controls to reach the current standard of 75 ppb in Texas and the 

Northeast, and in this final RIA these controls were not needed in these areas to reach the current 

standard. Not applying controls to reach the current standard, as well as applying controls in 

smaller geographic areas had impacts on the cost estimates that are discussed below. 

In the final RIA, to reach a revised standard of 70 ppb in 2025 we applied a larger number 

of lower cost identified controls because (i) fewer emissions reductions were needed overall, and 

(ii) we did not apply any identified controls to reach 75 ppb. This meant that the cost of 

additional reductions could be estimated from lower cost identified controls in the final RIA. As 

a result, total estimated costs to reach 70 ppb were lower than costs estimates in the proposal 

RIA by 55 percent. 

In the final RIA, to reach an alternative standard of 65 ppb, while fewer reductions were 

needed, the area where we applied identified controls was smaller than in the proposal RIA, 

resulting in exhausting the supply of identified controls available in these areas. We needed 

additional, higher-cost unidentified controls to being these areas into attainment. For example, in 

the proposal RIA, in analyzing 65 ppb we applied controls across the state of Texas as well as in 

surrounding states inside the “Central” region (Oklahoma, Kansas, Missouri, Arkansas, 

Louisiana and Mississippi). Because of the size of the area in which controls were applied for 

proposal, there were more identified controls from which to choose. In the final RIA, we applied 

controls only in east Texas, which meant there were fewer identified controls available and we 

relied more heavily on unidentified controls. Overall, because we applied all available identified 

controls in the final RIA, we relied on unidentified controls for 57 percent of the emissions 
4-46 

reductions needed, whereas in the proposal RIA we relied on unidentified controls for 40 percent 

of the emissions reductions needed to reach 65 ppb. Because unidentified controls are more 

expensive than identified controls and we relied on more unidentified controls in the final RIA, 

the estimated costs did not decrease in proportion to the decrease in needed emissions reductions 

and are about the same as in the proposal RIA. 

4.7 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 

control assumptions. In addition, our estimates of control efficiencies for the identified 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 not 

available (i.e., where capital, equipment life value, and operation and maintenance [O&M] costs 

are not separated out), the EPA 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, 
4-47 

equipment life value, and O&M costs are separated out), we can and do recalculate costs using a 

3 percent discount rate. In general, we have some disaggregated data available for non-EGU 

point source controls, but we do not have any disaggregated control cost data for nonpoint (area) 

source controls. In addition, while these discount rates are consistent with OMB guidance, the 

actual real discount rates may vary regionally or locally. 

Identified 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.92 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 may differ from 

actual 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 

EPA rules where per-unit costs were specifically evaluated, costs of regulations were 

overestimated in five cases, underestimated in four cases, and accurately estimated in four cases 

(Harrington et al. 2000). The collection of literature regarding the accuracy of cost estimates 

seems to reflect these splits. A recent EPA report, the “Retrospective Study of the Costs of EPA 

Regulations” that examined the compliance costs of five EPA regulations in four case studies,93

found that several of the case studies suggested that cost estimates were over-estimated ex ante, 

 

92 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. 93 The four case studies in the 2014 Retrospective Study of the Costs of EPA Regulations examine five EPA 

regulations: the 2001/2004 National Emission Standards for Hazardous Air Pollutants and Effluent Limitations 

Guidelines, Pretreatment Standards, and New Source Performance Standards on the Pulp and Paper Industry; 

Critical Use Exemptions for Use of Methyl Bromide for Growing Open Field Fresh Strawberries in California for 

the 2004-2008 Seasons; the 2001 National Primary Drinking Water Regulations for Arsenic; and the 1998 

Locomotive Emission Standards. 
4-48 

but did not find the evidence to be conclusive. The EPA stated in the report that the small 

number of regulatory actions covered, as well as significant data and analytical challenges 

associated with the case studies limited the certainty of this conclusion.

Costs of unidentified controls: In addition to the application of identified controls, the EPA 

assumes the application of unidentified controls for attainment in the projection year for this 

analysis. 

4.8 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. 

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 
4-49 

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). 2011. The Benefits and Costs of the Clean Air Act from 1990 to 

2020. Final Report. Office of Air and Radiation, Washington, DC. Available at: 

http://www.epa.gov/air/sect812/feb11/fullreport_rev_a.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. 
4A-1 

APPENDIX 4A: ENGINEERING COST ANALYSIS

Overview 

Chapter 4 describes the engineering cost analysis approach that EPA used to demonstrate 

attainment of the revised standard of 70 ppb and an alternative ozone standard level of 65 ppb. 

This Appendix contains more detailed information about the control costs of the identified 

control strategy analyses by control measure as well as sensitivity analyses for the average cost 

approach used to estimate costs for the unidentified emissions controls. Specifically, results 

using two alternative cost assumptions for unidentified controls are presented, and in addition the 

findings from six alternative approaches for estimating these costs are described and presented. 

These include a regression-based approach and five simulation-based variations. Table 4A-10 at 

the end of this Appendix provides a summary of these alternative approaches. 

4A.1 Cost of Identified Controls in Alternative Standards Analyses 

This section presents costs of identified 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 identified controls for California were applied as 

part of the baseline analysis, no identified controls were available for the alternative standards 

analyses in California. The costs for the standards analyzed do not include any identified control 

costs for California. Tables 4A-1 and 4A-2 present the costs for identified controls by measure 

for the 70 ppb alternative standard analysis for NOx and VOC respectively. Tables 4A-3 and 

4A-4 present the costs for identified controls by measure for the 65 ppb alternative standard 

analysis. 

Table 4A-1. Costs for Identified NOx Controls in the 70 ppb Analysis (2011$) 

NOx Control Measure Costa

 (million) 

Average 

$/ton 

Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 3.49 

 

400 

Biosolid Injection Technology - Cement Kilns 2.22 413 

EGU SCR & SNCR 51.69 1,150 

Episodic Burn Ban - - 94

 

94 An ozone season episodic burn ban is a daily ban of open burning of yard/agricultural waste on an ozone season 

day where ozone exceedances are predicted. There are minimal administrative costs associated with this measure, 

and we have not quantified those costs in this or previous analyses. For additional information on these measures go 
4A-2 

NOx Control Measure Costa

 (million) 

Average 

$/ton 

Excess O3 Control 0.01 24 

Ignition Retard - IC Engines 0.73 1,185 

Low Emission Combustion - Gas Fired Lean Burn IC Engines 14.66 829 

Low NOx Burner - Coal Cleaning 0.30 1,125 

Low NOx Burner - Commercial/Institutional Boilers & IC Engines 23.78 1,110 

Low NOx Burner - Gas-Fired Combustion 8.96 970 

Low NOx Burner - Glass Manufacturing 0.28 1,141 

Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 6.33 1,135 

Low NOx Burner - Industrial Combustion 0.03 1,255 

Low NOx Burner - Lime Kilns 2.22 913 

Low NOx Burner - Natural Gas-Fired Turbines 12.27 1,955 

Low NOx Burner - Residential Water Heaters & Space Heaters 22.57 

 

1,134 

Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 1.87 5,199 

Low NOx Burner and Flue Gas Recirculation - Fluid Catalytic Cracking Units 0.17 2,091 

Low NOx Burner and Flue Gas Recirculation - Iron & Steel 0.25 619 

Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 62.83 7,473 

Mid-Kiln Firing - Cement Manufacturing 0.09 73 

Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 41.27 1,051 

Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 12.87 4,545 

OXY-Firing - Glass Manufacturing 44.23 3,691 

Replacement of Residential & Commercial/Institutional Water Heaters -- --95

Selective Catalytic Reduction (SCR) - Cement Kilns 54.22 5,328 

Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 7.54 4,414 

Selective Catalytic Reduction (SCR) - Glass Manufacturing 4.03 1,157 

Selective Catalytic Reduction (SCR) - IC Engines, Diesel 3.29 3,814 

Selective Catalytic Reduction (SCR) - ICI Boilers 17.35 3,756 

Selective Catalytic Reduction (SCR) - Industrial Incinerators 5.27 3,805 

Selective Catalytic Reduction (SCR) - Iron & Steel 0.80 5,180 

Selective Catalytic Reduction (SCR) - Process Heaters 6.65 8,483 

Selective Catalytic Reduction (SCR) - Sludge Incinerators 0.38 3,805 

Selective Catalytic Reduction (SCR) - Space Heaters 0.11 4,661 

Selective Catalytic Reduction (SCR) - Utility Boilers 0.18 128 

Selective Non-Catalytic Reduction (SNCR) - Cement Manufacturing 3.13 1,303 

Selective Non-Catalytic Reduction (SNCR) - Coke Manufacturing 4.25 2,673 

Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 0.11 1,842 

Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 0.60 1,645 

Selective Non-Catalytic Reduction (SNCR) - Sludge Incinerators 0.06 1,842 

 

to http://www.epa.gov/ttn/ecas/cost.htm. The EPA will continue to conduct research on possible costs for this 

measure, and if applicable update costs for this measure in future analyses. 95 We have not quantified specific costs for this measure in this or previous analyses. For additional information on 

these measures go to http://www.epa.gov/ttn/ecas/cost.htm. The EPA will continue to conduct research on possible 

costs for this measure, and if applicable update costs for this measure in future analyses. 
4A-3 

NOx Control Measure Costa

 (million) 

Average 

$/ton 

Ultra-Low NOx Burner - Process Heaters 0.14 420 

a

 All values are rounded to two significant figures. 

Table 4A-2. Costs for Identified VOC Controls in the 70 ppb Analysis (2011$) 

VOC Control Measure Costa

 (million) Average $/ton 

Control Technology Guidelines - Wood Furniture Surface Coating 8.87 32,595 

Control of Fugitive Releases - Oil & Natural Gas Production 0.02 2,689 

Flare - Petroleum Flare 0.31 3,305 

Incineration - Other 155.85 14,543 

LPV Relief Valve - Underground Tanks 2.29 1,763 

MACT - Motor Vehicle Coating 0.00 192 

Permanent Total Enclosure (PTE) - Surface Coating 4.53 12,289 

RACT - Graphic Arts 1.66 6,386 

Reduced Solvent Utilization - Surface Coating 0.03 1,232 

Reformulation - Architectural Coatings 86.00 16,394 

Reformulation - Pesticides Application 2.59 15,157 

Reformulation-Process Modification - Automobile Refinishing 2.58 11,734 

Reformulation-Process Modification - Cutback Asphalt 0.02 24 

Reformulation-Process Modification - Other 0.35 3,102 

Reformulation-Process Modification - Surface Coating 0.59 3,304 

Solvent Recovery System - Printing/Publishing 0.02 1,232 

Wastewater Treatment Controls- POTWs 0.70 3,366 

a

 All values are rounded to two significant figures. 

Table 4A-3. Costs for Identified NOx Controls in the 65 ppb Analysis (2011$)

NOx Control Measure Costa

 (million) Average $/ton 

Adjust Air to Fuel Ratio and Ignition Retard - Gas Fired IC Engines 7.25 441 

Biosolid Injection Technology - Cement Kilns 2.44 413 

EGU SCR & SNCR 125.93 1,150 

Episodic Burn Ban - -96

Ignition Retard - IC Engines 0.73 1,262 

Low Emission Combustion - Gas Fired Lean Burn IC Engines 57.85 764 

Low NOx Burner - Coal Cleaning 0.69 1,451 

Low NOx Burner - Commercial/Institutional Boilers & IC Engines 38.59 1,066 

Low NOx Burner - Fiberglass Manufacturing 0.10 1,522 

Low NOx Burner - Gas-Fired Combustion 11.53 970 

Low NOx Burner - Industr/Commercial/Institutional (ICI) Boilers 56.57 2,581 

 

96An ozone season episodic burn ban is a daily ban of open burning of yard/agricultural waste on an ozone season 

day where ozone exceedances are predicted. There are minimal administrative costs associated with this measure, 

and we have not quantified those costs in this or previous analyses. For additional information on these measures go 

to http://www.epa.gov/ttn/ecas/cost.htm. The EPA will continue to conduct research on possible costs for this 

measure, and if applicable update costs for this measure in future analyses. 
4A-4 

NOx Control Measure Costa

 (million) Average $/ton 

Low NOx Burner - Industrial Combustion 0.03 1,255 

Low NOx Burner - Lime Kilns 4.21 913 

Low NOx Burner - Natural Gas-Fired Turbines 25.63 2,117 

Low NOx Burner - Residential Water Heaters & Space Heaters 103.38 1,999 

Low NOx Burner - Steel Foundry Furnaces 0.27 929 

Low NOx Burner - Surface Coating Ovens 0.09 3,585 

Low NOx Burner and Flue Gas Recirculation - (ICI) Boilers 2.00 4,197 

Low NOx Burner and Flue Gas Recirculation - Coke Oven/Blast Furnace 2.23 5,199 

Low NOx Burner and Flue Gas Recirculation - Fluid Catalytic Cracking Units 0.26 4,347 

Low NOx Burner and Flue Gas Recirculation - Iron & Steel 0.48 619 

Low NOx Burner and Flue Gas Recirculation - Process Heaters 2.85 5,199 

Low NOx Burner and Flue Gas Recirculation - Starch Manufacturing 0.35 5,199 

Low NOx Burner and SCR - Industr/Commercial/Institutional Boilers 151.27 6,230 

Low NOx Burner and SNCR - Industr/Commercial/Institutional Boilers 1.93 3,997 

Natural Gas Reburn - Natural Gas-Fired EGU Boilers 1.41 2,388 

Non-Selective Catalytic Reduction (NSCR) - 4 Cycle Rich Burn IC Engines 54.24 775 

Non-Selective Catalytic Reduction - Nitric Acid Manufacturing 0.94 1,905 

Nonroad Diesel Retrofits & Engine Rebuilds - e.g., Construction Equipment 39.78 4,525 

OXY-Firing - Glass Manufacturing 110.92 4,093 

Replacement of Residential Water Heaters -- --97

Selective Catalytic Reduction (SCR) - Ammonia Mfg 6.77 2,896 

Selective Catalytic Reduction (SCR) - Cement Kilns 161.93 6,194 

Selective Catalytic Reduction (SCR) - Coke Ovens 9.70 7,798 

Selective Catalytic Reduction (SCR) - Fluid Catalytic Cracking Units 18.56 4,551 

Selective Catalytic Reduction (SCR) - IC Engines, Diesel 13.10 3,664 

Selective Catalytic Reduction (SCR) - ICI Boilers 36.23 3,636 

Selective Catalytic Reduction (SCR) - Industrial Incinerators 6.56 3,805 

Selective Catalytic Reduction (SCR) - Iron & Steel 6.81 3,834 

Selective Catalytic Reduction (SCR) - Process Heaters 20.24 7,376 

Selective Catalytic Reduction (SCR) - Sludge Incinerators 10.26 5,796 

Selective Catalytic Reduction (SCR) - Space Heaters 1.33 4,631 

Selective Catalytic Reduction (SCR) - Taconite 27.40 6,449 

Selective Catalytic Reduction (SCR) - Utility Boilers 0.18 128 

Selective Non-Catalytic Reduction (SNCR) - Coke Mfg 7.70 2,673 

Selective Non-Catalytic Reduction (SNCR) - Comm./Inst. Incinerators 0.29 1,842 

Selective Non-Catalytic Reduction (SNCR) - Industrial Incinerators 1.97 1,861 

Selective Non-Catalytic Reduction (SNCR) - Municipal Waste Combustors 0.12 1,842 

Selective Non-Catalytic Reduction (SNCR) - Sludge Incinerators 0.21 1,863 

Selective Non-Catalytic Reduction (SNCR) - Utility Boilers 0.33 1,390 

 

97 We have not quantified specific costs for this measure in this or previous analyses. For additional information on 

these measures go to http://www.epa.gov/ttn/ecas/cost.htm. The EPA will continue to conduct research on possible 

costs for this measure, and if applicable update costs for this measure in future analyses. 
4A-5 

NOx Control Measure Costa

 (million) Average $/ton 

Ultra-Low NOx Burner - Process Heaters 1.24 1,447 

a

 All values are rounded to two significant figures. 

Table 4A-4. Costs for Identified VOC Controls in the 65 ppb Analysis (2011$)

VOC Control Measure 

Costa

(million) 

Average 

($/ton) 

Control Technology Guidelines - Wood Furniture Surface Coating 97.41 32,595 

Control of Fugitive Releases - Oil & Natural Gas Production 0.08 2,689 

Flare - Petroleum Flare 0.36 3,305 

Gas Recovery - Municipal Solid Waste Landfill 0.32 1,106 

Improved Work Practices, Material Substitution, Add-On Controls - Printing 0.00 159 

Improved Work Practices, Material Substitution, Add-On Controls -Industrial 

Cleaning Solvents (0.34) (1,360) 

Incineration - Other 240.54 14,395 

LPV Relief Valve - Underground Tanks 8.59 1,763 

Low VOC Adhesives and Improved Application Methods - Industrial Adhesives 0.06 270 

Low-VOC Coatings and Add-On Controls - Surface Coating 0.73 2,668 

MACT - Motor Vehicle Coating 0.37 192 

Permanent Total Enclosure (PTE) - Surface Coating 45.92 13,973 

Petroleum and Solvent Evaporation - Surface Coating Operations 0.09 376 

RACT - Graphic Arts 35.67 6,386 

Reduced Solvent Utilization - Surface Coating 5.36 1,758 

Reformulation - Architectural Coatings 858.67 16,394 

Reformulation - Industrial Adhesives 13.34 12,017 

Reformulation - Pesticides Application 59.97 15,157 

Reformulation-Process Modification - Automobile Refinishing 57.25 11,734 

Reformulation-Process Modification - Cutback Asphalt 0.06 24 

Reformulation-Process Modification - Oil & Natural Gas Production 0.19 641 

Reformulation-Process Modification - Other 1.94 3,548 

Reformulation-Process Modification - Surface Coating 21.00 3,736 

Solvent Recovery System - Printing/Publishing 1.05 1,232 

Solvent Substitution and Improved Application Methods - Fiberglass Boat Mfg 0.06 4,310 

Wastewater Treatment Controls- POTWs 0.79 3,366 a

 All values are rounded to two significant figures.

4A.2 Alternative Estimates of Costs Associated with Emissions Reductions from 

Unidentified Controls 

This section presents alternative estimates of the unidentified control costs using 

alternative average cost per ton of emissions reductions from unidentified controls of $10,000 
4A-6 

per ton and $20,000/ton.98 Table 4A-5 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 unidentified controls. Table 4A-6 presents the estimates of the total 

control costs for post-2025 California when using the alternative per ton cost assumptions for 

emissions reductions from unidentified controls. 

Table 4A-5. Summary of Total Control Costs (Identified and Unidentified) by Alternative 

Level for 2025 - U.S. using Alternative Cost Assumption for Unidentified 

Control Costs, except California (millions of 2011$)a

Alternative Level Geographic Area 

Total Control Costs (Identified and Unidentified) 

Unidentified 

Control Cost = 

$10,000/ton 

Unidentified 

Control Cost = 

$15,000/ton 

Unidentified 

Control Cost = 

$20,000/ton 

70 ppb 

East 1,100 1,400 1,600 

West <5 <5 <5 

Total 1,200 1,400 1,600 

65 ppb 

East 11,000 15,000 19,000 

West 550 750 950 

Total 11,000 16,000 20,000 

a All values are rounded to two significant figures.

Table 4A-6. Summary of Total Control Costs (Identified and Unidentified) by Alternative 

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

Unidentified Control Costs (millions of 2011$)a

Alternative Level Geographic Area 

Total Control Cost 

Unidentified 

Control Cost = 

$10,000/ton 

Unidentified 

Control Cost = 

$15,000/ton 

Unidentified 

Control Cost = 

$20,000/ton 

70 ppb California 510 800 1,020 

65 ppb California 1,000 1,500 2,000 

a All values are rounded to two significant figures. 

 

98 The EPA decided to use the alternative values of $10,000 per ton and $20,000 per ton because these values were 

used in the following recent RIAs, and we did not identify other more appropriate alternative values: the November 

2014 proposal RIA, the December 2012 Regulatory Impact Analysis for the Final Revisions to the National Ambient 

Air Quality Standards for Particulate Matter and the June 2012 Regulatory Impact Analysis for the Proposed 

Revisions to the National Ambient Air Quality Standards for Particulate Matter. 
4A-7 

4A.3 Alternative Approaches to Estimating the Costs Associated with Emissions 

Reductions from Unidentified Controls 

4A.3.1 Regression Approach 

Using all observations under the cost per ton threshold for identified controls ($19,000/ton 

for NOx), a linear regression is estimated and used to predict the price of the additional 

unidentified controls required to attain a particular level of the standard. That is, to meet a 

particular level of the standard, it is assumed that all reductions that can be achieved at a cost less 

than the cost threshold will first be exhausted and any additional tons required can be achieved at 

a cost determined by the value of the regression line at those tons. Hence, the total cost of the 

tons of reductions for which controls are unidentified is the area under the regression line for 

tons between the total identified tons and the total tons of reduction required to meet the level of 

the standard being analyzed (see Figure 4A-1 below). Using this methodology, the light gray 

shaded area under the regression line represents the cost of unidentified controls needed to attain 

the 70 ppb level of the standard ($960 million), while the darker gray shaded area represents the 

additional cost of unidentified controls needed to attain the 65 ppb level of the standard (an 

additional $13 billion, for a total of $14 billion in unidentified control costs). 
4A-8 

Figure 4A-1. Marginal Costs for Identified NOx Controls for All Source Sectors with 

Regression Line for Unidentified Control Measures 

An alternative interpretation of this approach that is in line with the discussion of the 

incomplete characterization of the MACC (Chapter 4, Section 4.2.3) is that the points on the 

regression line represent potential controls that could be applied to sectors or processes that are 

not characterized in the CoST tool.99 The MACC can then be redrawn including these points, as 

appears below in Figure 4A-2. In this curve, the dark red line represents the expanded MACC 

curve including controls needed to attain the 70 ppb level of the standard. The blue portion of the 

line includes additional controls needed to attain the 65 ppb level of the standard. As this is 

simply a way of visualizing how controls estimated from the regression line could be 

incorporated into the MACC, the resulting unidentified control cost estimates ($960 million for 

 

99 Examples of controls or measures that are not in the CoST tool include local transportation measures, energy 

efficiency measures, or fuel switching applications. 
4A-9 

the 70 ppb level of the standard and an additional $13 billion to attain the 65 ppb level of the 

standard) are unchanged. 

Figure 4A-2. Marginal Costs for Identified NOx Controls for All Source Sectors with 

Unidentified Control Measures from Regression Line Included 

4A.3.2 Simulation Approach 

Another approach to estimate the cost of unidentified controls is to randomly sample from 

the complete MACC (without a cost threshold) to “fill in” the tons of reduction on the MACC 

needed for attainment. Sampling is done with replacement, and since the sampling is random, it 

is repeated 1000 times to limit the influence of any particular simulation. The mean of the total 

cost estimates is then the estimate of the cost of the unidentified controls. Alternatively, the mean 

cost per ton of the estimates can be interpreted as the cost per ton estimate for the unidentified 

controls. 
4A-10 

The implicit assumption when applying this simulation approach is that the controls in the 

incomplete MACC curve are representative of the types of controls (both in cost and 

effectiveness) that could be applied to alternative sources not yet controlled or adequately 

characterized in the CoST database, and also that controls that may be developed in the future 

(prior to the attainment date) will be similar in cost and effectiveness to controls currently 

available. While we do not believe that controls with costs greater than the cost threshold are 

likely to be applied, they are not removed from the simulation dataset in order to provide a more 

complete set of identified abatement possibilities. 

The CoST database used for this analysis contains approximately 120,000 individual 

controls applicable to five broad sectors. While there is geographic specificity in the applicability 

of the controls, the simulation is currently being performed on a national scale. The cost per ton 

and number of available controls differs considerably between sectors, as shown below in Table 

4A-7, and for this reason it is important to determine how broadly to sample when selecting 

controls to attain a particular level of the standard. 

Table 4A-7. Costs and Number of Identified NOx Controls by Sector in the CoST 

Database (2011$) 

Sector 

Number 

of 

Controls 

Cost per Ton 

Minimum Median Mean Maximum 

nonpt 2,069 $413 $1,008 $1,455 $2,005 

nonroad 111,943 $3,330 $4,618 $4,619 $5,300 

np_oilgas 772 $78 $649 $747 $2,019 

pt_oilgas 3,892 $12 $649 $1,120 $44,860 

ptnonipm 2,756 $18 $3,814 $12,147 $354,974 

In this simulation approach, we investigate three methods for selecting available controls 

to expand the MACC to simulate attainment. First, the sectors are aggregated into three broad 

sectors (nonroad, nonpoint, and non-EGU point sources) and controls are selected from these 

sectors in the same proportion as the control strategy discussed in Chapter 3 for these three 

sectors for each level of the standard. These percentages appear below in Table 4A-8. 
4A-11 

Table 4A-8. Simulation Percentage of NOx Controls from Sectors Based on Application of 

Identified Controls 

Sector 70 ppb 65 ppb 

non-EGU point 46% 56% 

nonpoint 52% 42% 

nonroad 2% 2% 

Using this method, since only 2 percent of the controls to meet any of the standards were 

applied to nonroad sources, only 2 percent of the tons from random draws are allowed to come 

from this sector. The majority of simulated controls are then drawn from the nonpoint and nonEGU point sectors. 

A second method of assigning the proportions is based upon the number of available tons 

of reduction remaining in each sector after the application of the controls discussed in Chapter 3. 

To calculate this, the tons of reduction from the control scenarios are subtracted from the 

projected inventories, and then the percentages used for the simulation exercise are based upon 

the proportion of remaining emissions in the three sectors. These percentages appear below in 

Table 4A-9. 

Table 4A-9. Simulation Percentage of NOx Controls from Sectors Based on Remaining 

Emissions in Sectors 

Sector 70 ppb 65 ppb 

non-EGU point 33% 30% 

nonpoint 36% 36% 

nonroad 31% 34% 

This method leads to a larger proportion of the simulated controls being selected from the 

nonroad sector, because this sector has a relatively large quantity of remaining emissions in 

2025. Accordingly, the proportion of simulated controls drawn from the non-EGU point and 

nonpoint sectors is lower using this method. A third method imposes no restrictions on the 

selection of controls, so controls are randomly selected from the complete set regardless of the 

sector. 

Instead of randomly selecting from the MACC, it is also possible to simulate attainment by 

selecting controls from along the regression line shown in Figure 4A-1. For the purposes of this 
4A-12 

exercise, controls are first selected from the point where identified controls exceed the cost 

threshold up to the total tons of reduction necessary to meet a 65 ppb ozone standard. Each 

control is assumed to provide one ton of reduction at a cost calculated using the regression 

equation. This is different from using the area under the regression line to calculate the cost of 

unidentified controls, because in this case controls can be selected from any point along the 

regression line beyond the point where identified controls cross the cost threshold. As a result, 

the cost of the first additional ton of control as identified by the regression line is approximately 

$9,300 and this value rises to slightly more than $19,000 for the last ton required to attain the 65 

ppb standard. A second variation on this approach selects tons of control from any point along 

the regression line. The results of the simulations appear in Table 4A-10. 

Table 4A-10. Unidentified NOx Control Costs by Alternative Standard using Alternative 

Methods for Estimation of Costs from Unidentified Controls (total costs in 

millions of 2011$, cost per ton in parentheses in $2011) 

Level of 

Standard 

Tons of 

Unidentified 

NOx

reductions 

Regression 

Approach 

Simulation Approach 

Sector 

Percentages 

from 

Applied 

Controls 

Sector 

Percentages 

from 

Remaining 

Emissions 

Random 

Draws from 

all Identified 

Controls 

Random 

Draws from 

Regression 

Line Beyond 

Identified 

Controls 

Random 

Draws from 

Entire 

Regression 

Line 

70 ppb 97,000b $960 $250 $310 $290 $1,400 $940 

($9,800) ($2,500) ($3,100) ($3,000) ($14,000) ($9,600) 

65 ppb 960,000c $14,000 $2,700 $3,000 $2,900 $14,000 $9,300 

($14,000) ($2,800) ($3,100) ($3,000) ($14,000) ($9,600) 

a All values are rounded to two significant figures. 

b

 Total tons of NOx reductions required includes 51,000 tons for Post-2025 California and 46,000 tons for the rest of 

the United States in 2025. Because these simulations are designed to be proof of concept, we combined the 

emissions reductions needed and controls applied in these analyses. c

 Total tons of NOx reductions required includes 100,000 tons for Post-2025 California and 860,000 tons for the rest of 

the United States in 2025. Because these simulations are designed to be proof of concept, we combined the 

emissions reductions needed and controls applied in these analyses. 

Cost estimates based on the regression approach or sampling from the regression line are 

consistently higher than those based on sampling from the identified controls, as should be 

expected. While the simulations that sampled from identified controls were allowed to select 

controls beyond the cost per ton threshold applied in the identified control strategy described in 

Chapter 3, there are a limited number of controls above the cost per ton threshold in the database. 

As a result, the simulation is far more likely to select cheaper controls simply because of their 

prevalence in the data. Another observation that can be made is that requiring a certain 
4A-13 

percentage of controls from particular sectors does affect the results, but not to the extent that 

might be expected. While the cost of controls vary between sectors, the simulations produced 

similar results regardless of restrictions on the distribution of controls across sectors. Finally, 

while the cost per ton estimates varied considerably across the approaches, all cost per ton 

estimates were below the $15,000/ton estimate used as the primary estimate of the cost of 

unidentified controls. 

The EPA continues to investigate methods to better estimate the cost of currently 

unidentified controls. While we have reason to believe that technological advances over the 

coming years will both lead to new types of controls as well as reduce the cost of currently 

available controls, we do not presently possess the capability to accurately predict the rate at 

which technological progress will occur or the potential impacts such progress will have on the 

cost of controls. As a result, the simulations presented herein draw upon currently identified 

controls and their current costs, or a linear regression of these data. The results of the simulations 

are sensitive to the percentage of unidentified controls required from each sector, and for this 

reason we plan to continue investigating methods for assigning these percentages based upon the 

degree to which sectors have already been controlled. Furthermore, while the simulations in this 

appendix were performed at the national level, we recognize that areas differ greatly in their 

industrial base, and for this reason the simulations should be performed at a more disaggregated 

level using data about the emissions sources in each area, available controls, and the degree to 

which sources in the area have already been controlled. Because these simulations are designed 

to be proof of concept, we combined the emissions reductions needed and controls applied in 

these analyses. 
5-1 

CHAPTER 5: QUALITATIVE DISCUSSION OF EMPLOYMENT IMPACTS OF AIR 

QUALITY 

Overview 

 Executive Order 13563 directs federal agencies to consider regulatory impacts on job 

creation and employment. According to the Executive Order, “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” 

(Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically 

included a separate analysis of regulation-induced employment impacts,100 we typically conduct 

employment analyses for economically significant rules. While the economy continues moving 

toward full employment, employment 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.101

Section 5.1 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 5.2 presents an overview of the peer-reviewed literature 

relevant to evaluating the effect of environmental regulation on employment. Section 5.3 

discusses employment related to installation of NOx controls on coal and gas-fired electric 

generating units, industrial boilers, and cement kilns. 

5.1 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 

 

100 Labor expenses do, however, contribute toward total costs in the EPA’s standard benefit-cost analyses. 101 The employment analysis in this RIA is part of EPA’s ongoing effort to “conduct continuing evaluations of 

potential loss or shifts of employment which may result from the administration or enforcement of [the Act]” 

pursuant to CAA section 321(a). 
5-2 

declines and gains in different areas of the economy (the directly regulated sector, upstream and 

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.102 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.103,104

Berman and Bui (2001) adapt this model to analyze how environmental regulations affect 

labor demand.105 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) model two components that drive changes in firm-level labor 

demand: output effects and substitution effects.106 Regulation affects the profit-maximizing 

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 

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. 

 

102 See Layard and Walters (1978), a standard microeconomic theory textbook, for a discussion, in Chapter 9. 103 See Hamermesh (1993), Ch. 2, for a derivation of the firm’s labor demand function from cost-minimization. 104 In this framework, labor demand is a function of quantity of output and prices (of both outputs and inputs). 105 Morgenstern, Pizer, and Shih (2002) develop a similar model. 106 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. 
5-3 

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, 

for example Greenstone (2002), likely overstate employment impacts because they rely on 

relative comparisons between more regulated and less regulated counties, which can lead to 

“double counting” of impacts when production and employment shift from more regulated 

toward less regulated areas. Thus the empirical methods cannot be used to estimate net 

employment effects.107

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, 

 

107 See Greenstone (2002) p. 1212. 
5-4 

(3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total 

production costs.108 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.109 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 regulatory impact analysis (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.110 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 

 

108 See Ehrenberg & Smith, p. 108. 109 This discussion draws from Berman and Bui (2001), pp. 293. 110 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. 
5-5 

from environmental regulation would be small and transitory (e.g., as workers move from one 

job to another).111 

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). For example, the Congressional Budget Office considered 

EPA’s Mercury and Air Toxics Standards and regulations for industrial boilers and process 

heaters as potentially leading to short-run net increases in economic growth and employment, 

driven by capital investments for compliance with the regulations (Congressional Budget Office, 

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.112 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 

 

111 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. 

112 E.g. Graff Zivin and Neidell (2012). 
5-6 

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 

available. Finally, economic theory suggests that labor supply effects are also possible. In the 

next section, we discuss the empirical literature. 

5.2 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.113 This work focuses primarily on effects of employment policies such 

as labor taxes and minimum wages.114 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) and Ferris, Shadbegian, and Wolverton 

(2014), suggest that regulation-induced net employment impacts may be zero or slightly positive, 

but small in the regulated sector. Other research on regulated sectors suggests that employment 

growth may be lower in more regulated areas (Greenstone 2002, Walker 2011, 2013). However 

since these latter studies compare more regulated to less regulated counties, this methodological 

approach likely overstates employment impacts to the extent that regulation causes plants to 

locate in one area of the country rather than another, which would lead to “double counting” of 

the employment impacts. List et al. (2003) find some evidence that this type of geographic 

relocation may be occurring. 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 

 

113 Again, see Hamermesh (1993) for a detailed treatment. 114 See Ehrenberg & Smith (2000), Chapter 4: “Employment Effects: Empirical Estimates” for a concise overview. 
5-7 

Standards (MATS).115 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. 

5.2.1 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 

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.116 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).117 

 A small literature examines impacts of environmental regulations on manufacturing 

employment. Greenstone (2002) and Walker (2011, 2013) study the impact of air quality 

regulations on manufacturing employment, estimating the net effects in nonattainment areas 

relative to attainment areas. 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 

 

115 U.S. EPA (2011b). 116 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. 

117 Including the employment effect of existing plants and plants dissuaded from opening will increase the estimated 

impact of regulation on employment. 
5-8 

an estimate of a national net effect on employment. Moreover this result is sensitive to model 

specification choices. 

5.2.2 Economy-Wide 

As noted above it is very difficult to estimate the net national employment impacts of 

environmental regulation. Given the difficulty with estimating national impacts of regulations, 

EPA has not generally estimated economy-wide employment impacts of its regulations in its 

benefit-cost analyses. However, in its continuing effort to advance the evaluation of costs, 

benefits, and economic impacts associated with environmental regulation, EPA has formed a 

panel of experts as part of EPA’s Science Advisory Board (SAB) to advise EPA on the technical 

merits and challenges of using economy-wide economic models to evaluate the impacts of its 

regulations, including the impact on net national employment.118 Once EPA receives guidance 

from this panel it will carefully consider this input and then decide if and how to proceed on 

economy-wide modeling of employment impacts of its regulations. 

5.2.3 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.119 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. 

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 

 

118 For further information see: 

http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67CF77B54734285257BB0004F87ED?OpenDocument 

119 For a recent review see Graff-Zivin and Neidell (2013). 
5-9 

billion (ppb) decreases in ozone concentrations increases worker productivity by 5.5 percent.” 

(Graff Zivin and Neidell, 2012, p. 3654).120

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 macro-economy.

5.3 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 (EGUs), industrial boilers, and cement kilns, which are among 

the highest NOx-emitting source categories in EPA’s emissions inventory (see Chapter 2 and 

Appendix 2A for more detail on emissions). The employment analysis in this section is an 

illustrative analysis, estimating the amount of labor involved with installing advanced NOx 

emission control systems at each of these three different types of NOx emission sources. The 

analysis also estimates the labor needed to operate existing advanced NOx systems more 

frequently (e.g., year round). Sections 5.3.1 and 5.3.2 below contain estimates of the number of 

direct short-term and long-term jobs that would be created by addition of NOx controls at a 

single facility (or unit) in each of these three categories of emissions sources, for various size 

units. Because the apportionment of emissions control across emissions sources in this RIA 

analysis is an illustrative model plant analysis, and is not necessarily representative of the 

controls that will be required in individual state SIPs, the EPA did not estimate short-term or 

long-term employment that would result from addition of NOx controls at these three source 

categories either everywhere throughout the country, or at facilities located in areas anticipated 

to need additional NOx reductions for the 65 ppb and 70 ppb standard alternatives. 

5.3.1 Employment Resulting from Addition of NOx Controls at EGUs 

This section presents an illustrative analysis of the direct labor needs to install and 

operate SCRs at three common sizes of coal-fired EGUs: 300 MW, 500 MW and 1000 MW. As 

 

120 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. 
5-10 

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 a 

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 at 

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. 

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 further decrease NOx emissions by either (a) upgrading or replacing 

their existing NOx emissions reducing systems, or (b) operating their existing NOx systems for 

more hours in the year than the IPM model predicts they will in the IPM v. 5.14 base case121 for 

2025. While all existing coal-fired EGUs already have low NOx burners, there are EGUs that 

currently have a selective non-catalytic reduction (SNCR) post-combustion NOx control system 

that could be replaced with a selective catalytic reduction (SCR) system installed that would 

reduce their NOx emissions rate (and hence quantity of NOx) at those units. 

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 in 

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, such as steel, concrete, or chemicals used to manufacture NOx controls). 

The analysis draws on information from seven primary sources: 

 Documentation for EPA Base Case v.5.13 Using the Integrated Planning Model. 

November, 2013 

 

121 Note that the IPM v. 5.14 base case is not the base case used in the final Clean Power Plan analysis (using IPM v. 

5.15), which is used for other analyses in this RIA. Furthermore neither the v. 5.14 nor the v 5.15 base cases used in 

this RIA include the illustrative estimated EGU responses to the Clean Power Plan. The base case, however, is only 

used in the labor analysis to select the three illustrative sizes of EGUs in the model plant estimates 
5-11 

 IPM updates included in V. 5.14 EPA Base Case v.5.14 Using IPM: Incremental 

Documentation. March, 2015. 

 “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 Clean Power Plan Final Rule”. August, 2015. 

 The National Electric Energy Data System (NEEDS) Version 5.14. March, 2015. 

 EPA Base Case for IPM v. 5.14 estimates for 2025. March, 2015 

5.3.1.1 Existing EGUs Without SCR Systems (or SCR Systems Operating less than Full Time) 

Using EPA’s National Electric Energy Data System (NEEDS) v. 5.14 and the IPM 2025 

estimates from the v 5.14 base case, the EPA identified all existing coal-fired EGUs in the 

contiguous United States that: 

(1) The IPM 5.14 base case estimates will be operating in 2025. 

(2) Either do not already have an SCR NOx emission control system installed, or have an 

SCR NOx system that could be utilized more to further reduce NOx emissions. 

Coal-fired units that currently have an SNCR system but not an SCR system were 

identified directly from NEEDS 5.14, which includes detailed information on the type of 

emissions control systems that are installed. NEEDS 5.14 also includes the NOx emission rate 

(lbs/MMBtu) that the unit could achieve if the required state of the art (SOA) NOx emission 

controls were operated.122 The 2025 estimates from IPM v. 5.14 base case, in combination with 

the SOA NOx rates from NEEDS, were used to determine which units with an SCR installed 

were capable of lowering their annual NOx emissions by operating the SCR as often as possible. 

If IPM estimated that the predicted NOx annual emissions in 2025 exceeded the amount of NOx 

that would be emitted if the 2025 quantity of coal was consumed and the unit achieved the SOA 

NOx emission rate from NEEDS, then the unit is assumed to be operating in 2025 running the 

SCR to its maximum potential all the time. 

Furthermore, the EPA identified the subset of these EGUs that are in areas anticipated to 

need additional NOx reductions under an alternative ozone standard level of 65 ppb, as well as 

 

122 Mode 4 NOx emission rate. For more details see the NEEDS 5.14 documentation page 6, and IPM v. 5.13 User 

Guide Section 3.9.2. 
5-12 

the smaller subset of the EGUs that are in areas anticipated to need additional NOx reductions 

under a 70 ppb ozone standard. 

The EPA identified 319 existing coal-fired EGUs nationwide (total capacity 122.4 GW) 

that are estimated to continue to be in operation in the 2025 base case that either do not already 

have an SCR system (30 units, 5.4 GW) or have an SCR system that is not working full time 

(289 units, 117.1 GW) . These 319 EGUs include units both in areas anticipated to need to 

reduce NOx emissions (37 units, 8.5 GW) with a 65 ppb ozone NAAQS, as well as the EGUs in 

all other areas (282 units, 114.0 MW). All of the 30 EGUs that do not have an SCR are in areas 

expected to need to reduce NOx emissions with a 65 ppb NAAQS (30 units, 5.4 GW). The areas 

that are expected to need to reduce NOx emissions with a 65 ppb NAAQS also have 7 units (3.1 

GW) have an SCR system not working full time. 

Upgrading the 30 EGUs to an SCR emission control system will reduce NOx emissions 

in areas expected to need additional NOx controls with a 65 ppb NAAQS by a total of 41,400 

tons. Operating the 7 EGUs with SCRs system estimated to not be operated as much as possible 

in the 65 ppb ozone NAAQS areas an additional 18,300 tons, for a combined total of 59,700 tons 

of NOx reduced annually. 

Of the 37 EGUs estimated to be able to reduce NOx emissions in the 65 ppb NAAQS 

areas, 5 EGUs (1.7 GW) are also in the areas needing additional NOx reductions under a revised 

70 ppb ozone NAAQS standard. Installing SCRs on 2 of these units, and running the existing 

SCRs as much as possible on the other 3 units, a total of 11,200 tons of NOx in the 70 ppb ozone 

NAAQS areas. 

Given the nationwide size distribution of the existing EGUs that do not already have an 

SCR, or do not operate the existing SCR system full time, we present the illustrative labor 

analysis for three different sized “model plants”: 300 MW capacity, 500 MW, and 800 MW. 

These three capacity sizes of model plants were selected by examining the distribution of 

existing coal-fired EGUs that can either be upgraded to an SCR, or have the existing SCR 

operated to as much as possible. Because of the relatively small number (37) of the identified 

EGUs in the 65 ppb areas, the distribution of 319 EGUs nationwide better reflects the 

distribution of EGUs identified. Figure 5-1 shows the nationwide capacity size distribution of the 

319 units. For comparison Figure 5-2 shows the size distribution of the 37 units in the 65 ppb 

ozone NAAQS areas. As can be seen in Figure 5-1, the 300 MW capacity “model plant” is the 
5-13 

most common candidate for installing an SCR system in the 65 ppb areas, but 500 MW and 800 

MW units (which are common in the national set of 319 identified units) also exist in the 65 ppb 

areas. 

Figure 5-1. Size Distribution of Identified 319 Existing Coal-Fired EGU Units 

Nationwide without SCR NOx Controls (or with SCRs Operated Less Than the 

Maximum Possible Amount of Time) 

Figure 5-2. Size Distribution of 37 Existing Coal-Fired EGU Units without SCR NOx 

Controls (or with SCRs Operated Less Than the Maximum Possible Amount of 

Time) in Areas Anticipated to Need Additional NOx Controls With the 

Alternative 65 ppb Ozone Standard Level 

0

10

20

30

40

50

60

70

80

90

# of EGUs

0

5

10

15

20

25

30

35

0 ‐ 400 MW 450 ‐ 650 MW 650 ‐ 1350

MW

# of EGUs
5-14 

5.3.1.2 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) and labor to manufacture the SCR . 

 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 5-1. 

Table 5-1. Summary of Direct Labor Impacts for SCR Installation at EGUs (FTEs) 

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

Operation and Maintenance 1.9 2.8 4.6 

The key assumptions used in the labor analysis are presented in Table 5-2. 

Table 5-2. 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 labor hours/MW Staudt, 2011 

158.7 

FTEs 264.4 FTEs 528.9 FTEs 

 
5-15 

Assumptions Key Factor Source 300 MW 500 MW 1000 MW 

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 FTEs 2.76 FTEs 4.57 FTEs 

5.3.2 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, and also in an illustrative analysis, the EPA estimated the amount and types of 

direct labor that might be used to apply and operate NOX controls for representative categories 

of ICI boilers and 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. In addition, 

the numbers presented in this section are only indicative of the relative number and types of 

labor 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). 

5.3.2.1 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. 
5-16 

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 (SC&A, 2014). 

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 

(http://nepis.epa.gov/Adobe/PDF/P1005ODM.pdf). 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, however.. Table 5-3 below provides summary labor 

estimates for SCR at varying sized ICI boilers. 

Table 5-3. 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 
5-17 

Similar to the calculations for SCR applied to EGUs, an illustrative analysis based on 

engineering costs estimates labor requirements in upstream sectors for SCR applied to 

representative ICI boilers. This includes labor requirements in selected major upstream sectors 

directly involved in manufacturing the materials used in SCR systems (steel), as well as the 

chemicals used to operate an SCR system (ammonia and the catalyst used in the construction and 

operation of SCR systems, such as steel, concrete, or chemicals used to manufacture NOx 

controls). For an SCR installed at a representative 750 MMBtu/hr coal boiler, we estimate onetime employment impacts in these related sectors as:of 8.6 FTE, annual recurring impacts of 2.17 

FTE for an SCR operated year-round, and annual recurring impacts of 0.90 FTE for an SCR 

operated during the five-month ozone season (SC&A, 2014). 

5.3.2.2 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. 

The capital costs for equipment supply fabrication were estimated for an SNCR system 

for a mid-sized preheater and precalciner kiln (125 to 208 tons of clinker per hour). The percent 

of equipment supply fabrication costs of these systems attributable to labor is 44%. 

(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 were 

estimated by the vendor to be 17% of the total cost. That was converted to FTE using BLS data. 

This information is summarized in Table 5-4. 

Table 5-4. Estimated Direct Labor Impacts for Individual SNCR Applied to a MidSized Cement Kiln (125-208 tons clinker/hr) 

Kiln Type Preheater / Precalciner 

Equipment Supply Fabrication FTE 1.5 

Installation FTE 0.9 

O&M Annual Recurring FTE 0.1 
5-18 

In addition, an illustrative analysis of labor requirements in upstream sectors for SNCR 

applied to cement kilns include the labor requirements in selected major upstream sectors 

directly involved in manufacturing the materials used in SNCR systems (steel), as well as the 

reagent used to operate an SNCR system. For an SNCR installed at a 3,000 tons clinker per day 

capacity cement kiln, we estimate employment impacts in these related sectors as one-time 

employment impacts of 0.22-0.34 FTE, annual recurring impacts of 0.41 FTE for an SNCR 

operated year-round, and annual recurring impacts of 0.28 FTE for an SNCR operated during the 

five-month ozone season (SC&A, 2014). 

5.4 Conclusion 

This chapter presents qualitative and quantitative discussions of potential employment 

impacts of the Ozone NAAQS. The qualitative discussion identifies challenges associated with 

estimating net employment effects and discusses anticipated impacts related to the rule. It 

includes an in-depth discussion of economic theory underlying analysis of employment impacts. 

The employment impacts for regulated firms can be decomposed into output and substitution 

effects, both of which may be positive or negative. Consequently, economic theory alone cannot 

predict the direction or magnitude of a regulation’s employment impact. It is possible to combine 

theory with empirical studies specific to the regulated firms and other relevant sectors if data and 

methods of sufficient detail and quality are available. Finally, economic theory suggests that 

environmental regulations may have positive impacts on labor supply and productivity as well. 

We examine the peer-reviewed economics literature analyzing various aspects of labor 

demand, relying on the above theoretical framework. Determining the direction of employment 

effects in regulated industries is challenging because of the complexity of the output and 

substitution effects. Complying with a new or more stringent regulation may require additional 

inputs, including labor, and may alter the relative proportions of labor and capital used by 

regulated firms (and firms in other relevant industries) in their production processes. The 

available literature illustrates some of the difficulties for empirical estimation. Econometric 

studies of environmental rules converge on the finding that employment effects, whether positive 

or negative, have been small in regulated sectors. 
5-19 

The illustrative quantitative analysis in this chapter projects a subset of potential 

employment impacts in the electricity generation sector and for ICI boilers and cement kilns. 

States have the responsibility and flexibility to implement plans to meet the Ozone NAAQS. As 

such, given the wide range of approaches that may be used, quantifying the associated 

employment impacts is difficult. This analysis presents employment impact estimates based on 

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 for EGUs, ICI 

boilers and cement kilns. 

5.5 References 

Arrow, K. J.; M. L. Cropper; G. C. Eads; R. W. Hahn; L. B. Lave; R. G. Noll; Paul R. Portney; M. Russell; R. 

Schmalensee; V. K. Smith and R. N. Stavins. 1996. “Benefit-Cost Analysis in Environmental, Health, and 

Safety Regulation: A Statement of Principles.” American Enterprise Institute, the Annapolis Center, and 

Resources for the Future; AEI Press. Available at <http://www.hks.harvard.edu/fs/rstavins/Papers/Benefit Cost 

Analysis in Environmental.AEI.1996.pdf>. Accessed June 5, 2015. 

Berman, E. and L. T. M. Bui. 2001. “Environmental Regulation and Labor Demand: Evidence from the South Coast 

Air Basin.” Journal of Public Economics. 79(2): 265-295. 

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 

Congressional Budget Office (2011), Statement of Douglas W. Elmendorf, Director, before the Senate Budget 

Committee, “Policies for Increasing Economic Growth and Employment in 2012 and 2013” (November 

15)<http://www.cbo.gov/sites/default/files/11-15-Outlook_Stimulus_Testimony.pdf> 

Ehrenberg, R. G. and R. S. Smith. 2000. Modern Labor Economics: Theory and Public Policy. Addison Wesley 

Longman, Inc., Chapter 4. 

Executive Order 13563 (January 21, 2011). “Improving Regulation and Regulatory Review. Section 1. General 

Principles of Regulation.” Federal Register 76(14): 3821-3823. 

Ferris, A. E., R. J. Shadbegian, A. Wolverton. 2014. “The Effect of Environmental Regulation on Power Sector 

Employment: Phase I of the Title IV SO2 Trading Program.” Journal of the Association of Environmental and 

Resource Economists. 1(4): 521-553. 

Greenstone, M. 2002. “The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 1970 

and 1977 Clean Air Act Amendments and the Census of Manufactures.” Journal of Political Economy. 110(6): 

1175-1219. 

Hamermesh, D. S. 1993. Labor Demand. Princeton, NJ: Princeton University Press. Chapter 2. 

Kahn, M.E. and E. T. Mansur. 2013. “Do Local Energy Prices and Regulation Affect the Geographic Concentration 

of Employment?” Journal of Public Economics. 101:105–114. 
5-20 

Layard, P.R.G. and A. A. Walters. 1978. Microeconomic Theory. McGraw-Hill, Inc. Chapter 9. 

List, J. A.; D. L. Millimet; P. G. Fredriksson and W. W. McHone. 2003. “Effects of Environmental Regulations on 

Manufacturing Plant Births: Evidence from a Propensity Score Matching Estimator.” The Review of Economics 

and Statistics. 85(4): 944-952. 

Marshall, A. 1920. Principles of Economics. Library of Economics and Liberty. Available at: 

<http://www.econlib.org/library/Marshall/marP.html> . 

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 

Schmalansee, R. and R. Stavins (2011). “A Guide to Economic and Policy Analysis for the Transport Rule.” White 

Paper. Boston, MA. Exelon Corp. 

Smith, K. 2012. “Reflections: In Search of Crosswalks between Macroeconomics and Environmental Economics.” 

Review of Environmental Economics and Policy. 6(2):298-317. 

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. 

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). 2015. Regulatory Impact Analysis for the Clean Power Plan 

Final Rule. Office of Air and Radiation, Washington, D.C. Available at: http://www.epa.gov/airquality/cpp/cppfinal-rule-ria.pdf. 

Wojichowski, 2014: David Wojichowski, De-Nox Technologies, LLC, SNCR cost estimates provided to Maureen 

Mullen, SC&A via email, September 25, 2014. 
6-1 

CHAPTER 6: HUMAN HEALTH BENEFITS ANALYSIS APPROACH AND RESULTS

6.1 Summary 

This chapter of the Regulatory Impact Analysis (RIA) presents the estimated human 

health benefits for the revised 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 emissions control strategies that reduce emissions of the ozone 

precursor pollutants (i.e., nitrogen oxides (NOx) and volatile organic compounds (VOCs)) to 

reach the revised and alternative ozone NAAQS standard levels. We also estimate the health cobenefits of the fine particulate matter (PM2.5)-related air quality improvements that would occur 

as a result of reducing NOx emissions.123 

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. The benefits of each standard 

alternative are estimated as being incremental to attaining the existing standard of 75 ppb.124 

These estimated benefits are incremental to the benefits estimated for several recent rules (e.g., 

U.S. EPA, 2011c and U.S. EPA, 2014a). 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 later than the rest of the U.S. In this chapter, we refer to estimates of nationwide 

benefits of attaining an alternative standard everywhere in the U.S. except California as the 2025 

scenario. The post-2025 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. 

 Table 6-1 summarizes the estimated monetized benefits (total and ozone only) of 

attaining the revised and alternative ozone standards of 70 ppb and 65 ppb, respectively, in 2025. 

Table 6-2 presents the same types of benefit estimates for the scenario. These estimates reflect 

the sum of the economic value of estimated morbidity and mortality effects related to changes in 

exposure to ozone and PM2.5. Although these tables present ozone and PM2.5-related benefits 

 

123 VOC reductions associated with simulated attainment of the revised and alternative ozone standards also have the 

potential to impact PM2.5 concentrations, but we were not able to estimate those effects. 124 The current standard is the 4th highest daily maximum 8-hour ozone concentration of 75 ppb. 
6-2 

separately, it is not appropriate to compare the ozone-only benefits to total costs. Reduced levels 

of NOx emissions needed to attain a more stringent ozone standard will affect levels of both 

ozone and PM2.5. Following the standard practice for assessing the benefits of air quality rules 

(OMB, 2003; U.S. EPA, 2010e), this RIA quantifies the benefits of reducing both pollutants. For 

this reason, the costs of attaining a tighter standard should be compared against the sum of the 

ozone and PM2.5 benefits. 

Compared with benefit estimates generated in the proposal RIA, the total benefit 

estimates generated for the 2025 scenario are ~55% lower for the revised standard (70 ppb) and 

~22% lower for the alternative standard (65 ppb). Benefit estimates for the post-2025 scenarios 

are slightly higher than those generated at proposal (~6% for the revised standard and ~2% for 

the alternative standard). The proposal and final RIA estimates differ principally because as 

discussed in Chapter 2, Section 2.4.2 and Chapter 4, Section 4.6, the additional emissions 

sensitivity simulations and more refined ozone response factors allowed us to more accurately 

represent the increased effectiveness of emissions reductions closer to some monitor locations. 

The more refined air quality modeling resulted in approximately 50 percent fewer emissions 

reductions needed to reach a revised standard of 70 ppb and approximately 20 percent fewer 

emissions reductions needed to reach an alternative standard of 65 ppb. We have also slightly 

modified our approach to estimating morbidity benefits, which had a negligible (~1%) influence 

on the total monetized benefits in this RIA (see sections 6.3 and 6.6.3). 

Table 6-1. Estimated Monetized Benefits of Attainment of the Revised and Alternative 

Ozone Standards for 2025 (nationwide benefits of attaining the standards 

everywhere in the U.S. except California) (billions of 2011$)a 

Discount 

Rate 70 ppb 65 ppb 

Ozone-only Benefits c b $1.0 to $1.7 $5.3 to $8.7 

PM2.5 Co-benefits of NOx Reductions d 3% $2.1 to $4.7 $10 to $23 

7% $1.9 to $4.2 $9.3 to $21 

Total Benefits 3% $3.1 to $6.4e

 $16 to $32 e

7% $2.9 to $5.9 e $15 to $30 e

a

 Rounded to two significant figures. It was not possible to quantify all benefits in this analysis due to data 

limitations. 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. 

c

 Range reflects application of effect estimates from Smith et al. (2009) and Zanobetti and Schwartz (2008). 

d

 Range reflects application of effect estimates from Krewski et al. (2009) and Lepeule et al. (2012). e

 Excludes additional health and welfare benefits which could not be quantified (see section 6.6.3.8). 
6-3 

 

Table 6-2. Estimated Monetized Benefits of Attainment of the Revised and Alternative 

Ozone Standards for post-2025 (nationwide benefits of attaining the standards 

just in California) (billions of 2011$)a 

Discount 

Rate 70 ppb 65 ppb 

Ozone-only Benefits c b $0.79 to $1.3 $1.6 to $2.6 

PM2.5 Co-benefits of NOx reductions d 3% $0.40 to $0.91 $0.79 to $1.8 

7% $0.37 to $0.82 $0.71 to $1.6 

Total Benefits 3% $1.2 to $2.2 e $2.4 to $4.4 e

7% $1.2 to $2.1 e $2.3 to $4.2 e

a

 Rounded to two significant figures. It was not possible to quantify all benefits in this analysis due to data 

limitations. These estimates reflect the economic value of avoided morbidities and premature mortalities 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. c

 Range reflects application of effect estimates from Smith et al. (2009) and Zanobetti and Schwartz (2008). 

d

 Range reflects application of effect estimates from Krewski et al. (2009) and Lepeule et al. (2012). e

 Excludes additional health and welfare benefits which could not be quantified (see section 6.6.3.8). 

The control measures (identified and unidentified) applied to reach the revised and 

alternative ozone standards would reduce other ambient pollutants, including VOCs and 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 unable to quantify the co-benefits 

of reduced exposure to these pollutants. Due to limited data and methods, we were unable to 

estimate some anticipated health benefits associated with exposure to ozone and PM2.5. 

6.2 Overview 

This chapter presents estimated health benefits for the revised and alternative ozone 

standards (70 ppb and 65 ppb, respectively) that the EPA could quantify, given the available 

resources, data and methods. This chapter characterizes the benefits of the application of the 

identified and unidentified control strategies identified in Chapter 3 for the revised and 

alternative ozone standards by answering three key questions: 

1. What health effects are estimated to be avoided by reducing ambient ozone levels 

to attain the revised and alternative ozone standards? 

2. What is the estimated economic value of these effects? 
6-4 

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 associated with to ozone 

and PM2.5 that would be avoided by attaining a revised ozone standard. 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 

mortality and a variety of illnesses associated with acute (days-long) 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 mortality and a variety of illnesses associated with acute and chronic 

exposures. Air pollution can affect human health in a variety of ways. In Table 6-3 we 

summarize the “categories” of effects and describe those that we quantified for this analysis and 

those we were unable to quantify due to lack of resources, data, or methods. 

This list of benefit categories is not exhaustive, and we are not always able to quantify 

each effect completely. In this RIA, we only quantify endpoints that are classified in the ozone 

and PM ISAs as being causally related, or likely to be causally related, to each pollutant. 

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. This way of selecting 

endpoints for quantification should not be interpreted as a change in the level of evidence 

regarding the association between these endpoints and ozone (or PM2.5) exposure. 

This benefits analysis relies on an array of data inputs—including emissions estimates, 

modeled ozone concentrations, 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 6.5 and 6.7.3. 
6-5 

Table 6-3. 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 

Source of 

More 

Information 

Improved Human Health 

Reduced incidence 

of premature 

mortality from 

exposure to ozone 

Premature mortality based on short-term 

exposure (all ages) 

 

Section 6.6 

Premature respiratory mortality based on 

long-term exposure (age 30–99) 

b b

Reduced incidence 

of morbidity from 

exposure to ozone 

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 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 ISAd 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) 

 

Section 6.6 

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) 

— — 
6-6 

Benefits Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

Source of 

More 

Information 

Other cardiovascular effects (e.g., other 

ages) 

— — 

Other respiratory effects (e.g., pulmonary PM ISAc

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 Other respiratory effects (e.g., airway c,d

hyperresponsiveness and inflammation, 

lung function, other ages and populations)

— — 

b

 We quantified these benefits, but they are not part of the core monetized benefits. 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. 

The remainder of this chapter is organized as follows: Section 6.3 includes a discussion 

of the methodological updates represented in this analysis; Section 6.4 includes a discussion of 

the methodologies used in the human health benefits analyses; Section 6.5 includes a 

characterization of uncertainty; Section 6.6 details the data inputs used in the analysis, including 

demographic data, baseline incidence and prevalence estimates, effect coefficients, and 

economic valuation estimates; Section 6.7 presents the results; and Section 6.8 includes a brief 

discussion of the results. In addition, the chapter has several appendices that provide more 

details on the following: Appendix 6A includes a detailed characterization of uncertainty in the 

analysis; Appendix 6B includes additional quantitative analyses supporting uncertainty 

characterization. 
6-7 

6.3 Updated Methodology Presented in the Proposal and Final RIAs 

Both the proposed and final RIAs for this ozone standard incorporate an array of policy 

and technical updates to the benefits analysis methods since the previous review the ozone 

standards in 2008 and the proposed reconsideration in 2010. 

1. To be consistent with Agency guidance, EPA revised the Value of Statistical Life (VSL) 

it used to quantify the value of reduced mortality risk (see the NO2 NAAQS final RIA 

(U.S. EPA, 2010a) for further discussion). 

2. The population demographic data in BenMAP-CE (U.S. EPA, 2015a) 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 older demographic 

projection data from Woods and Poole (2007). This update was introduced in the final 

PM NAAQS RIA (U.S. EPA, 2012a). 

3. 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, 2011c). 

4. The cost-of-illness estimates for hospital admissions, including median wages, have been 

updated to reflect 2007 data. This update was introduced in the proposal PM NAAQS 

RIA (U.S. EPA, 2012a). 

5. Updates specific to estimating ozone-related effects: 

a. New studies used to quantify ozone-related premature mortality. 

i. The ozone ISA (U.S. EPA, 2013a) identifies several new epidemiological 

studies examining the association between ozone exposure and premature 

mortality. We include two new multi-city studies to estimate premature 

mortality attributable to short-term exposure (Smith et al., 2009 and Zanobetti 

and Schwartz 2008). We also estimate long-term respiratory premature 

mortality using Jerrett et al. (2009).125 We introduced this update in the 

proposal RIA (U.S. EPA, 2014c). 

ii. Following completion of the proposal RIA (U.S. EPA, 2014c), we slightly 

modified the methods applied in this RIA. As described in sections 6.6.3, we 

modified the set of epidemiology studies and associated effect coefficients 

used in estimating changes in asthma exacerbation and respiratory hospital 

admissions associated with ozone exposure. Because these changes do not 

involve our approach for estimating air pollution-related premature mortality, 

 

125 Because we do not have information on the cessation lag for premature mortality from long-term ozone exposure, 

we do not include the monetized benefits in the core analysis. Instead, monetized benefits associated with long-term 

ozone-related respiratory mortality are included as a sensitivity analysis (see Appendix 6B, section 6B.2). 
6-8 

which largely drives overall monetized benefits, they have a negligible (~1%) 

impact on total monetized benefits (see section 6.6.3 for further discussion). 

b. New studies used to quantify ozone-related morbidity effects. 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. We introduced this update in 

the proposal RIA (U.S. EPA, 2014c). 

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 

introduced this expanded assessment in the proposal RIA (U.S. EPA, 2014c). 

6. Updates specific to estimating PM2.5-related effects: 

d. When estimating PM2.5-related health effects, EPA no longer assumed a minimum 

concentration at which no effects occurred, while still reporting a range of sensitivity 

estimates based on the EPA’s PM2.5 mortality expert elicitation (see the Portland 

Cement NESHAP proposal RIA (U.S. EPA, 2009a) for further discussion). 

e. New studies used to quantify PM2.5-related premature mortality. 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 update for the American 

Cancer Society cohort was introduced in the proposal RIA for the PM NAAQS 

review (U.S. EPA, 2012a) and the update for the Harvard Six Cities cohort was 

introduced in the final RIA for the PM NAAQS review (U.S. EPA, 2012c). 

f. New studies used to quantify PM2.5 morbidity. Based on the PM ISA and PM 

Provisional Assessment, we added several new studies and morbidity endpoints to our 

health impact assessment, including hospital admissions and emergency department 

visits. These updates were introduced in the proposal (U.S. EPA, 2012a) and final 

RIAs for the PM NAAQS review (U.S. EPA, 2012c). 

g. More recent 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 

premature mortality 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, 

2012c). 

h. Expanded uncertainty assessment. We expanded the comprehensive assessment of the 

various uncertain parameters and assumptions within the benefits analysis including 

the evaluation of air quality benchmarks. This update was introduced in the proposed 

CSAPR RIA (U.S. EPA, 2010f) and refined in the final PM NAAQS RIA (U.S. EPA, 

2012c). 
6-9 

6.4 Human Health Benefits Analysis Methods 

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 (i.e., 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 that EPA uses for assessing 

costs and benefits of environmental quality policies and has been used in several recent analyses 

published in the peer reviewed scientific literature as well (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 valued directly, as is the case 

for changes in visibility. In other cases, such as for changes in health outcomes associated with 

reductions in ozone and PM concentrations, 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 (HIA) is limited to those health effects that the ISA 

identified as causally or likely causally linked to ambient levels of 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 being analyzed. 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. 
6-10 

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 

the revised and alternative standard levels) were explicitly modeled and then translated into 

reductions in the incidence of specific health endpoints (see Chapter 2 for more information). In 

contrast, in estimating PM2.5 co-benefits we utilized a reduced form approach. The details of both 

approaches are described in additional detail below. 

6.4.1 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, 2015a). 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, 2014a). 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 applying emissions controls, such as occupational health exposures. 

The HIA approach used in this analysis involves three basic steps: (1) using projections 

of ozone air quality126 and estimating the change in the spatial distribution of the ambient air 

quality; (2) determining the subsequent change in population-level exposure; and (3) calculating 

health impacts by applying concentration-response (C-R) relationships drawn from the 

epidemiological literature to this change in population exposure (Hubbell et al., 2009). 

 
6-11 

A typical health impact function might look as follows: 

∆ݕ ൌ 1 െ ൫݁ఉ∙∆௫൯ݕ ∙ ௢ܲ݌݋) 6.1 (

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 6-1 provides a simplified overview of this approach. 

Additional detail on the specific types of C-R functions (utilizing epidemiology-based effect 

estimates) involved in benefits modeling can be found in the Appendix C of the User’s Manual 

Appendices supporting BenMAP-CE (U.S. EPA, 2015b). Specific effect estimates used in this 

RIA are documented in section 6.6.3. 

Figure 6-1. Illustration of BenMAP-CE Approach 

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
6-12 

6.4.2 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 changes in risk for 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 

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 1.1 (U.S. EPA, 2015a, 2015b) to estimate the health 

impacts and monetized health benefits for the standards evaluated here. The environmental 

Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) is an open-source 

PC-based tool that quantifies the number and economic value of air pollution-related deaths and 

illnesses.127 Figure 6-2 shows the data inputs and outputs for the BenMAP-CE program. 

 

127 As compared to the version that it replaces (BenMAP v4), BenMAP-CE uses the same computational algorithms 

and input data to calculate benefits 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; and (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 Health Risk and 

Exposure Assessment for Ozone (ozone HREA) (U.S. EPA, 2014b). 
6-13 

Figure 6-2. Data Inputs and Outputs for the BenMAP-CE Program 

6.4.3 Estimating Benefits for 2025 and Post-2025 Analysis Years 

Portions of the country with more significant air quality problems (particularly several 

areas in California) may not be required to meet the revised standard until as late as December 

31, 2038. Consequently, for the revised and alternative standards we evaluate a 2025 scenario 

reflecting the nationwide benefits of attaining the standards everywhere in the U.S. except 

California. We then evaluate a post-2025 scenario, which represents nationwide benefits from 

attaining the standards in California. We report the 2025 and 2038 estimates separately because 

deriving a summed estimate would require us to calculate the Present Value (PV) of the stream 

of benefits occurring between those two years, which is not possible with the available data. 

Our approach for estimating the benefits of attaining the revised and alternative ozone 

standards post-2025 is illustrated in Figure 6-3. In this figure, Simulation A represents our 

approach for estimating the benefits of attaining the revised and alternative ozone standards in 

every state except California in 2025. To estimate benefits for the post-2025 scenario, we first 

estimated the benefits occurring in 2038 from all areas (including California) attaining the 

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 Ozone‐

related 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
6-14 

revised and alternative standards (Simulation C). Next, we simulated the nationwide benefits of 

attaining the revised and alternative ozone standards in 2038 for every state except California 

(Simulation B). We then subtracted Simulation B from Simulation C to calculate the benefits of 

attaining the revised and alternative ozone standards after 2025— that is, to calculate the 

nationwide benefits from California alone attaining the standards in 2038. Important caveats are 

associated with this approach mentioned in Section 6.1 and discussed further in section 6.7.3. 

Figure 6-3. Procedure for Generating Benefits Estimates for the 2025 and Post-2025 

Scenarios 

6.4.4 Benefit-per-ton Estimates for PM2.5

We used a reduced form approach to estimate the PM2.5 co-benefits in this RIA due to data 

and resource constraints. Specifically, we used “benefit-per-ton” estimates. EPA has applied this 

approach in several previous RIAs (e.g., U.S. EPA, 2014a). These benefit-per-ton estimates 

provide the total monetized human health co-benefits (reflecting the sum of premature mortality 

and morbidity effects) of reducing one ton of NOx (as a PM2.5 precursor) from a specified source. 

In general, these estimates apply the same benefits methods (e.g., health impact assessment then 

75 ppb baseline (CA does

not attain 75)

70 ppb full

attainment scenario

everywhere but CA

70 ppb full attainment

scenario everywhere

including CA

75 ppb baseline reductions in

(all locations attain 75)

A

C

2025 scenario: Nationwide benefits in 2025

resulting from all areas in the U.S. attaining

the alternative standard under consideration

excluding California (here 70ppb).  This is

Simulation A

70 ppb full

attainment scenario

everywhere but CA

75 ppb baseline (CA does

not attain 75)

B

BenMAP Benefits Simulations

Benefits year = 2025

Cost year = 2025

Benefits year = 2038

Cost year = 2037/2038

Benefits year = 2038

Cost year = 2037/2038

Post‐2025 scenario: Nationwide benefits in

2038 having California attain the

alternative standard (here 70ppb). This is

the difference between Simulation C and

Simulation B
6-15 

economic valuation) 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. 

We used 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 RIA were derived using the approach published in Fann et al. (2012b), but 

have since been updated to reflect the epidemiology studies and Census population data first 

applied in the final PM2.5 NAAQS RIA (U.S. EPA, 2012c). The approach in Fann et al. (2012b) 

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). 

In Section 6.6, we describe all of the data inputs used in deriving the benefit-per-ton values 

for each sector, including the demographic data, baseline incidence, and valuation functions. The 

benefit-per-ton estimates (by sector) that resulted from this modeling are presented in Section 

6.6.5. We then multiply these benefit-per-ton estimates for each sector with the NOx emission 

reductions from that sector to estimate the PM2.5 co-benefits.128 Additional information on the 

source apportionment modeling for each of the sectors can be found in Fann et al. (2012b) and 

the TSD (U.S. EPA, 2013b). 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 

scenarios, respectively.129

Applying benefit-per-ton estimates introduces uncertainty, which we discuss in section 

6.7.3 and Appendix 6A. 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-related health impacts (e.g., population density, 

 

128 For unidentified controls, we use a weighted average of the benefit-per-ton estimates from all of the sectors. 129 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. 
6-16 

baseline health incidence rates). Therefore, using 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. In addition, the use of national benefit-per-ton estimates results in a known 

underestimation bias in California in the post-2025 scenario due to population density. 

6.5 Characterizing Uncertainty 

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty, and this analysis is no exception. This analysis 

includes many data sources as inputs, including emissions inventories, air quality data from 

models (with their associated parameters and inputs), population data, population estimates, 

health effect estimates from epidemiology studies, economic data for monetizing benefits, and 

assumptions regarding the future state of the world (e.g., regulations, technology, and human 

behavior). Each of these inputs may be uncertain and would affect the benefits estimates. 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 

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 continues to improve how it characterizes uncertainty in health incidence and 

benefits estimates. In response to these recommendations, we incorporated additional 

quantitative and qualitative characterizations of uncertainty. Although we are not yet able to 

perform the probabilistic uncertainty assessment the NAS envisioned, we added several 

quantitative and qualitative analyses. These additional analyses characterize uncertainty related 

to estimated premature mortality, since this endpoint is assigned the largest dollar value. For 

other inputs into the benefits analysis, such as the air quality data, it is too difficult to address 

uncertainty probabilistically for this analysis due to the complexity of the underlying air quality 

models and emissions inputs. 
6-17 

To characterize uncertainty and variability, we follow an approach that combines 

elements from two recent analyses by the EPA (U.S. EPA, 2010b; 2014b), and use a tiered 

approach developed by the World Health Organization (WHO) (WHO, 2008). We present this 

assessment in Appendix 6A (results of these assessments are summarized in section 6.7.3). 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: Monte Carlo assessments, and additional quantitative analyses 

characterizing uncertainty (see Appendix 6B). 

6.5.1 Monte Carlo Assessment 

Similar to other recent RIAs, we used Monte Carlo methods for characterizing random 

sampling error associated with the C-R 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. We report confidence intervals for ozone-related 

benefits, but were unable to provide confidence intervals for PM2.5-related co-benefits because 

we used the benefit-per-ton estimates. 
6-18 

6.5.2 Quantitative Analyses Supporting Uncertainty Characterization 

Because over 90% of the monetized benefits are from avoided premature mortality, it is 

particularly important to characterize the uncertainties associated with reductions in premature 

mortality. Each of the quantitative analyses supporting uncertainty characterization for this RIA 

are briefly described below and section 6.7.3 provides a discussion of the results and 

observations stemming from these quantitative analyses. 

 Alternative C-R functions for short-term ozone exposure-related mortality:

Alternative C-R functions are useful for assessing uncertainty beyond random statistical 

error, including uncertainty in the functional form of the model or alternative study 

designs. We used two multi-city studies (Smith et al., 2009; Zanobetti and Schwartz 

2008) to estimate short-term ozone-related mortality in our core estimate. We performed 

a sensitivity analysis using effect coefficients from additional multi-city studies and 

meta-analyses utilized in prior RIAs (Bell et al., 2004; 2005, Huang, 2005; Ito et al., 

2005; Levy et al., 2005), as well as alternative model specifications from the Smith et al. 

(2009) study (see Figure 6-4 and Appendix 6B, section 6B.1). When selecting studies for 

the core and uncertainty-related analyses we considered NAS (p. 80, NRC, 2008) and 

CASAC recommendations (U.S. EPA-SAB, 2012, 2014). 

 Potential thresholds in the long-term ozone exposure-related respiratory mortality 

C-R function: Consistent with the ozone HREA, we estimate premature respiratory 

deaths from long-term exposure to ozone. The Jerrett et al. (2009) study explored 

potential thresholds in the C-R function. We use the results of the threshold analyses 

conducted by Jerrett et al. (2009) to conduct a quantitative uncertainty analysis evaluating 

models with a range of potential thresholds in addition to a non-threshold (see Appendix 

6B, section 6B.3). 

 Alternative C-R functions in estimating long-term PM2.5 exposure-related mortality:

In estimating PM2.5 co-benefits, we use two studies (Krewski et al., 2009; Lepeule et al., 

2012). To better understand the C-R relationship between PM2.5 exposure and premature 

mortality, the EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 

2006).130 We apply the functions from the experts as a characterization of uncertainty 

(see Figure 6-5 and Appendix 6B, section 6B.2). 

 Cessation lag for long-term O3 exposure-related respiratory mortality: We do not 

know how long-term O3 exposure-related respiratory deaths are distributed over time and 

so we use two lag structures originally developed for PM2.5 (the 20-year segmented lag 

used for PM2.5 and an assumption of zero lag) in this quantitative uncertainty analysis 

(see Appendix 6B, section 6B.2). 

 

130 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually 

of multiple experts, are obtained (Ayyub, 2002). 
6-19 

 Income elasticity in the specification of willingness-to-pay (WTP) functions used for 

mortality and morbidity endpoints: The degree to which the WTP function used in 

valuing mortality and some morbidity endpoints changes in proportion to future changes 

in income is uncertain. We evaluated the potential impact of this factor on the monetized 

benefits in a quantitative uncertainty analysis (see Appendix 6B, section 6B.5). 

Even these multiple estimates (including confidence intervals, where available) 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 identifying a reasonable upper and lower bounds for 

input distributions. As a result, the reported confidence intervals and range of estimates give an 

incomplete picture of 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. 

A number of analyses provide additional perspectives on the benefits results, including: 

 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 concentrations used in estimating premature mortality 

associated with short-term ozone concentrations: We characterize the distribution of 

premature mortality attributed to short term ozone exposure with respect to baseline 

ozone concentrations in the subset of 12km grid cells where the analysis predicts the 

premature mortalities will be avoided. 

 Analysis of baseline PM2.5 concentrations used in estimating short-term ozone 

exposure-related mortality: We also include a similar plot of the baseline annual PM2.5

levels used in estimating PM2.5 mortality from the earlier analysis that generated the 

benefit-per-ton values. This analysis is particularly important because, 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 PM2.5 concentration in the 

epidemiological studies that are used to estimate the benefits.

 Outdoor worker productivity: In this analysis, we quantify the economic value of 

improved productivity among outdoor agricultural workers using Graff Zivin and Neidell 

(2012) in our uncertainty analysis. 
6-20 

6.5.3 Qualitative Assessment of Uncertainty and Other Analysis Limitations 

To more fully address uncertainties, including those we cannot quantify, we apply a fourtiered 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, 2011a, 2012a, 2012b). 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 

qualitative uncertainty characterization are available in Appendix 6A. 

6.6 Benefits Analysis Data Inputs 

In Figure 6-2 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). As noted above, only slight modifications 

have been made to the epidemiology studies used to estimate two morbidity endpoints since 

proposal, and those modifications are described in section 6.6.3. 

A brief note regarding the spatial scale associated with benefits modeling completed for 

this RIA: when quantifying health impacts for the ozone RIA, we apply effect coefficients from 

air pollution epidemiology studies among populations of various ages—either the entire 

population (i.e., ages 0-99) or a subset (e.g., ages 65-99). These age ranges generally correspond 

to those reported in the epidemiological study, though (following NRC guidance) we sometimes 

assign these effect coefficients to a slightly broader age range. We apply a single effect 

coefficient to populations throughout the United States and do not differentiate by region. The 

health impact functions used to quantify risk also specify population counts and baseline rates of 

disease or death, and most of these values are age and sex stratified, allowing us to report 

incidence among population subgroups. 
6-21 

6.6.1 Demographic Data 

Quantifying the incidence and dollar value of pollution impacts requires information 

regarding the demographic characteristics of the exposed 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 projected population by age, sex, and race to 2040, relative to a baseline using the 

2010 Census data; the 2008 ozone NAAQS 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: 

 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-based” 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 by race 

for each year through 2040 based on historical rates of mortality, fertility, and migration. 

6.6.2 Baseline Incidence and Prevalence Estimates 

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide an estimate of the relationship of air quality changes to the relative risk 
6-22 

of a health effect, rather than estimating the absolute number of avoided cases. For example, a 5 

ppb decrease in 8-hour maximum daily ozone concentration 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 in that location. 

Table 6-4 summarizes the sources of baseline incidence rates and provides national 

average (where used) 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 C-R 

functions to individual age groups and then summed over the relevant age range to estimate total 

population benefits. In many cases we used a single national incidence rate, due to a lack of more 

spatially disaggregated data; in these cases, whenever possible we used national average rates, 

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 (U.S. EPA, 2015b). 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 (U.S. EPA, 2015b; 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, 2011c). In addition, we 

previously 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 
6-23 

ages, and in different locations, visit the hospital and emergency department for symptoms and 

illnesses identified in the ISA as associated with ozone and PM2.5. Also, the updated 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 the assumption that 

most air pollution-related hospital visits associated with ozone and PM2.5 are likely to be 

unscheduled. If a portion of scheduled hospital admissions are air pollution-related, 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 6-5 lists the prevalence rates used to determine the applicable population for 

asthma symptoms. Note that these reflect recent asthma prevalence and assume no change in 

prevalence rates in future years. We last updated these rates in the CSAPR RIA (U.S. EPA, 

2011c). 
6-24 

Table 6-4. 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 Exacerbationsc

 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 d

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 Days Daily 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 International Classification of Diseases (ICD) codes 

(AHRQ, 2007). 

c

 The incidence of exacerbated asthma was quantified among children of all races, using the baseline incidence rate 

reported in Ostro et al. (2001). 

d

 Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze. 
6-25 

Table 6-5. Asthma Prevalence Rates 

Population Group 

Asthma Prevalence Rates 

Value Source 

All Ages 0.0780 American Lung Association (2010, Table 7) 

< 18 0.0941 

5–17 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). 

6.6.3 Effect Coefficients 

In this section, we describe our general process for selecting effect coefficients from 

epidemiology studies. The first step in selecting effect coefficients is to identify the health 

endpoints to be quantified. We based our selection of health endpoints on consistency with the 

EPA’s ISAs, with input and advice from the SAB-HES.131 In addition, we 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, 2012d).132 In selecting health endpoints for ozone, 

we also considered the suite of endpoints included in core modeling for the ozone HREA, which 

was supported by CASAC (U.S. EPA-SAB, 2012, 2014). In general, we follow a weight-ofevidence approach, based on the biological plausibility of effects, availability of C-R 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 

 

131 The SAB-HES is 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). 132 The peer-reviewed studies in the Provisional Assessment have not yet undergone external review by the SAB. 
6-26 

studies provide direct C-R 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 model 

the relationship between ozone and PM2.5 and adverse human health effects. We evaluated 

epidemiological studies using the selection criteria summarized in Table 6-6. 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, using C-R 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 (with the 

exception of mortality)133 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 (U.S. EPA, 2015b). 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 

 

133 In the case of mortality, we do not pool results. Instead, we provide the results from each study separately. 
6-27 

the random effects model.134 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. 

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 

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. 

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

Consideration Comments 

Peer-Reviewed 

Research 

Peer-reviewed research is exclusively used to select C-R functions. 

Study Type Prospective cohort vs. ecological: 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: Multi-city time series studies have advantages to 

meta-analyses. Multi-city studies use a consistent model structure and can include factors 

that explain differences between effect estimates among the cities. By contrast, metaanalyses can become imprecise and the results difficult to interpret due to the aggregation 

of large sets of studies. In addition, meta-analyses can suffer from publication bias, 

which can result in high-biased effect estimates. Although we generally prefer multi-city 

studies, we may consider meta-analyses if multi-city studies are not available. 

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 would 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. 

Consequently, studies matching the ozone seasons in the air quality modeling are 

preferred. 

 

134 EPA recently changed the algorithm BenMAP used 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. 
6-28 

Consideration Comments 

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. 

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 selection of a large population or through repeated observations 

on a smaller population (e.g., through a symptom diary recorded for a panel of asthmatic 

children). 

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. Depending on the endpoint and the study, we may consider using Canadian 

studies. 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, co-pollutant models are preferred for estimating PM effects because this 

approach controls for the potential ozone effect while not diminishing the effective 

sample size available for specifying the PM effect. However, when estimating the ozone 

effect, the use of co-pollutant models (with PM) can substantially reduce sample size 

since only days with both ozone and PM can be used. While these co-pollutant 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 generally favor co-pollutant models in modeling PM benefits, 

for ozone we generally favor single pollutant models. 

Measure of PM In general, 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. Therefore, we generally do not 

include these effects in benefits analyses. 

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 are drawn are shown in Tables 6-7 and 

6-8. 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 2008 ozone NAAQS RIA (U.S. EPA, 

2008a). In all cases where effect estimates are drawn directly from epidemiological studies, 
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standard errors are used as a partial representation of the uncertainty in the size of the effect 

estimate. Table 6-9 summarizes those health endpoints and studies we have included in 

quantitative analyses supporting uncertainty characterization. 

Table 6-7. Health Endpoints and Epidemiological Studies Used to Quantify Ozone-Related 

Health Impacts a 

Endpoint Study Study 

Population

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE) 

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 Jerrett et al. (2009) >29 years β = 0.003971 (0.00133) 

Hospital Admissions 

Respiratory Pooled estimate: 

    Katsouyanni et al. (2009) > 65 years 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 exacerbation 

Pooled estimate: b

Mortimer et al. (2002)

Schildcrout et al. (2006)

6–18 years β = 0.00929 (0.00387) 

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 The original study populations were 5 to 12 years for Schildcrout et al. (2006) and 5-9 years for the Mortimer et al. 

(2002) study. Based on advice from the SAB-HES, we extended the applied population to 6-18 years for all three 

studies, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. EPASAB (2004a) and NRC (2002). 
6-30 

Table 6-8. Health Endpoints and Epidemiological Studies Used to Quantify PM2.5-Related 

Health Impacts a 

Endpoint Study 

Study 

Population 

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE) 

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
6-31 

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, 2012c). 

b

 The original study populations were 8 to 13 years for the Ostro et al. (2001) study and 7 to 12 years for the Mar et 

al. (2004) study. Based on advice from the SAB-HES, we extended the applied population to 6-18 years, 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 6-9. Health Endpoints and Epidemiological Studies Used to Quantify Ozone-Related 

Health Impacts in Quantitative Analyses Supporting Uncertainty 

Characterization a 

Endpoint Study 

Study 

Population 

Effect Estimate (β) 

(with 95th Percentile 

Confidence Interval) 

Premature Mortality 

Premature respiratory 

mortality - long-term Jerrett et al. (2009)‐based models:

> 29 years

‐ non‐threshold ozone only (86 cities) β=0.00266 (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) (co‐pollutant 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) β=0.00026 (0.00009) 

a

 Studies highlighted in red represent updates incorporated since the 2008 ozone NAAQS RIA (U.S. EPA, 2008a). 

b

 All threshold models are ozone-only and based on the full 96 city dataset. 
6-32 

6.6.3.1 Ozone Exposure Metric 

Both the NMMAPS analysis and the individual time series studies upon which the metaanalyses 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. A more biologically relevant 

metric, and the one used in the ozone NAAQS since 1997, is the maximum daily 8-hour average 

ozone. Thus, we 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 

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.135 The conversion 

ratios are based on observed relationships between the 24-hour average and 8-hour maximum 

ozone values. For example, in the Bell et al., 2004 analysis of ozone-related premature mortality, 

the authors found that the relationship between the 24-hour average, the 8-hour maximum, and 

the 1-hour maximum was 2:1.5:1, so that the derived health impact effect estimate based on the 

1-hour maximum should be half that of the effect estimate based on the 24-hour values (and the 

8-hour maximum three-quarters of the 24-hour effect estimate). 

As part of the quantitative uncertainty 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 

 

135 However, 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 6A). 
6-33 

al. (2005) all provide national conversion ratios between daily average and 8-hour and 1-hour 

maxima, based on national data. 

6.6.3.2 Ozone Premature Mortality Effect Coefficients 

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 mortality, even as we are mindful of the uncertainty associated with the 

shape of the concentration response curve at lower ozone concentrations. (U.S. EPA, 2013a, 

page 1-7). The 2013 ozone ISA concludes that the evidence suggests that ozone effects are 

independent of the relationship between PM and mortality. (U.S. EPA, 2013a). However, the 

ISA notes that the interpretation of the potential confounding effects of PM on ozone-mortality 

risk estimates requires caution due to the PM sampling schedule (in most cities) which limits the 

overall sample size available for evaluating potential confounding of the ozone effect by PM 

(U.S. EPA 2013a).136 Below we describe the evolution of EPA’s understanding of the evidence 

supporting causality related to short-term ozone exposure and mortality (including 

recommendations provided to EPA by the NAS). 

These observations are consistent with prior recommendations to the EPA by the NAS 

regarding the quantification and valuation of ozone-related short-term mortality (NRC, 2008). 

Chief among the NAS recommendations 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 meta-analyses” (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 

 

136 Consequently, as noted later in this section, while we have used a single-pollutant model in generating short-term 

ozone-related mortality benefits for this RIA, we have included a co-pollutants model as a sensitivity analysis to 

address potential uncertainty associated with this assumption of an independent ozone mortality effect. 
6-34 

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 SAB-HES panel, and the 

CASAC panel, we estimate 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 with several additional studies 

as part of the quantitative uncertainty analysis. CASAC supported using the Smith et al. (2009) 

and Zanobetti and Schwartz (2008) studies for the ozone HREA (U.S. EPA-SAB, 2012, 2014), 

and these are multi-city studies published more recently (as compared with other multi-city 

studies or meta-analyses included in the quantitative uncertainty analyses – see discussion 

below). 

Smith et al. (2009) reanalyzed the NMMAPS dataset, evaluating the relationship between 

short-term ozone exposure and mortality.137 While this study reproduces the core national-scale 

estimates presented in Bell et al. (2004), it also explored the sensitivity of the mortality effect to 

different model specifications including (a) regional versus national Bayes-based adjustment,138

(b) co-pollutant models considering PM10, (c) all-year versus ozone-season based estimates, and 

(d) consideration of a range of ozone metrics, including the daily 8-hour max. In addition, the 

Smith et al. (2009) study did not use the trimmed mean approach employed in the Bell et al. 

(2004) study in preparing ozone monitor data.139 In selecting among the effect estimates from 

Smith et al. (2009), we focused on an ozone-only estimate for non-accidental mortality using the 

 

137 In previous RIAs involving ozone, we have used Bell et al. (2004) as the basis for modeling short-term exposurerelated mortality. However, for reasons presented here and outlined in section 7.3.2 of the final REA completed in 

support of the ozone review (U.S. EPA 2014b), we have substituted Smith et al. (2009) as the basis for effect 

estimates used in modeling this endpoint for both the REA and the benefit analysis described here. The Smith et al. 

(2009) effect estimate used in this RIA (0.00032, see Table 6-7) is about 22% larger than the Bell et al. (2004) effect 

estimate (0.000261, BenMAP-CE standard health functions, U.S. EPA 2015a). 

138 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 if there is low overall variation across estimates. City-specific estimates are 

given less adjustment if they are precisely estimated and/or there is greater overall variation across estimates. 139 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. 
6-35 

8-hour max metric for the warmer ozone season.140 For the quantitative uncertainty analysis, we 

included a co-pollutant model (ozone and PM10) from Smith et al. (2009) for all-cause mortality, 

using the 8-hour max ozone metric for the ozone season. Using a single pollutant model for the 

core analysis and the co-pollutant model in the quantitative uncertainty analysis reflects our 

concern that the reduced sampling frequency for days with co-pollutant measurements (1/3 and 

1/6) could affect the ability of the study to characterize the ozone effect. This choice is consistent 

with the ozone ISA, which concludes that ozone effects are likely to be independent of the 

relationship between PM and mortality (U.S. EPA, 2013a). 

The Zanobetti and Smith (2008) study evaluated the relationship between ozone exposure 

(using an 8-hour 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 CR 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. We used the 

shorter day lag based C-R function since this had the strongest effect and tighter confidence 

interval. We converted the effect estimate from an 8-hour mean metric to an equivalent effect 

estimate based on an 8-hour 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.141 

Mortality Effect Coefficient for Long-term Ozone Exposure. Although previous 

advice provided by the SAB-HES was to include long-term ozone exposure-related mortality 

only as part of a quantitative uncertainty analysis, based on the more recent ISA, CASAC advice, 

 

140 The effect estimates used for both the core and uncertainty were obtained from Smith et al., 2009. Specifically, 

for the core analysis, we used the national-scale ozone-only summer 8-hour max based effect estimate and standard 

error (Smith et al., 2009 Table 1, row seven, columns seven and eight) and for the uncertainty analysis, we included 

the two-pollutant model summer 8-hour max effect estimate and standard error (Smith et al., 2009 Table 1, row ten, 

columns seven and eight). The model results presented in Table 1 of Smith et al. (2009) represent percentage rise in 

mortality per 10 ppb rise in the relevant metric of ozone and consequently these had to be adjusted to represent a 

factor increase per unit ozone. 

141 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 8-hour 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., 8-

hour max and 8-hour 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., 24-hour or 1-hour max ratios to 8-hour max 

equivalents). 
6-36 

and HREA, the current RIA estimated long-term ozone exposure-related respiratory mortality 

incidence in the core analysis.142 Support for modeling long-term exposure-related mortality 

incidence comes from the ozone ISA as well as recommendations provided by CASAC in their 

review of the ozone HREA (U.S. EPA-SAB, 2014, p. 3 and 9), despite the lower confidence in 

quantifying this endpoint because the ISA’s consideration of this endpoint is primarily based on 

one study (Jerrett et al, 2009), though that study is well designed, and because of the uncertainty 

in that study about the existence and identification of a potential threshold in the concentrationresponse function. Whereas the ozone ISA concludes that evidence is suggestive of a causal 

association between total mortality and long-term ozone exposure, specifically with regard to 

respiratory health effects (including mortality), the ISA concludes that there is likely to be a 

causal association (U.S. EPA, 2013a). Consistent with the ozone HREA, we use Jerrett et al. 

(2009) to estimate premature respiratory mortality from long-term ozone exposure. In review of 

the ozone HREA, CASAC concluded 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.” (U.S. EPA-SAB, 2014). 

The Jerrett et al. (2009) study was the first to explore the relationship between long-term 

ozone exposure and respiratory mortality (rather than other causes of mortality). Jerrett et al. 

(2009) exhibits a number of strengths including (a) the study was 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 

included co-pollutant models that controlled for PM2.5; and (c) it explored the potential for a 

threshold concentration associated with the long-term mortality endpoint. However, attributes of 

this study affect how we interpret the long-term exposure-related respiratory mortality estimates. 

First, while CASAC notes that Jerrett et al. (2009) was well designed, it is a single study and 

provides the only quantitative basis for estimating this endpoint. By comparison, we estimate 

short-term exposure-related mortality risk using several studies. 

 

142 As explained in section 6.3, because we do not have information on the cessation lag for premature mortality 

from long-term ozone exposure, we do not include the monetized benefits in the core analysis. Instead, monetized 

benefits associated with long-term ozone-related respiratory mortality are included as a sensitivity analysis (see 

Appendix 5B, section 5B.2). 
6-37 

The quantitative uncertainty analysis take into consideration the potential existence and 

location of a threshold in the C-R function relating mortality and long-term ozone 

concentrations, which can greatly affect the results. CASAC concluded, “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” (U.S. EPA-SAB, 2014, p. 13-14) 

Reflecting this CASAC advice in the context of the HREA, we estimate long-term 

exposure-related respiratory mortality using a non-threshold co-pollutant model (with PM2.5) 

from Jerrett et al. (2009). Because the Jerrett et al. (2009) study uses seasonal average metrics 

(rather than shorter single day or multi-day lagged models as with time series studies studies), 

co-pollutants models obtained from Jerrett et al., (2009) are not affected by the lower PM2.5

sampling rates. Using a co-pollutant model is consistent with this study applying seasonal 

average metrics that are insensitive to co-pollutant monitoring for PM2.5. The effect estimates 

used to model long-term ozone-attributable mortality are calculated using a seasonal average of 

peak (1-hour 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 PM benefits. Therefore, combining long-term and short-term 

ozone-attributable mortality estimates could lead to double counting. Estimates of short-term 

ozone mortality are for all-causes, while estimates of long-term ozone mortality are for 

respiratory-related mortality only. 

Quantitative uncertainty Analysis: Alternate Mortality Effect Coefficients for 

Short-term Ozone Exposure. Although we believe the evidence supports an ozone-only effect 

on short-term exposure-related mortality (as supported by the ozone ISA), we recognize 

limitations in the ability of studies to explore copollutants effects due to lower sampling 

frequency for PM relative to ozone. For that reason, we conduct a quantitative uncertainty 

analysis using the co-pollutants model (with PM10) from Smith et al. (2009). 

Quantitative uncertainty Analysis: Threshold-Based Effect Coefficients for Longterm Ozone Exposure. Consistent with the ozone HREA (U.S. EPA, 2014b), we explore the 

sensitivity of estimated ozone-related premature mortality to a concentration threshold using the 
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Jerrett et al. (2009) study.143 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. This supports the authors’ assertion that 

improved predictions of respiratory mortality are generated when ozone is included in the model. 

In exploring different functional forms, they report that the model including a threshold at 56 ppb 

had the lowest log-likelihood 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 quantitative uncertainty analyses. Specifically, we generate additional long-term risk 

results using unique risk models 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.144 In addition to exploring the impact of 

potential thresholds, as part of the quantitative uncertainty analysis we explore the impact of 

 

143 The approach in the ozone HREA to explore the potential for thresholds related to long-term exposure-related 

mortality is described in Sasser (2014). That memorandum also describes additional data obtained from the 

authors of Jerrett et al. (2009) to support modeling potential thresholds. 144 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. 
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using ozone-only (non-threshold) models in estimating long-term exposure-related respiratory 

mortality.145 

6.6.3.3 PM2.5 Premature Mortality Coefficients 

The co-benefits associated with NOx reductions made in the models to achieve ozone 

reductions are estimated in this RIA using methods consistent with those develop for the RIA for 

the final 2012 PM2.5 NAAQS. Below we provide additional background for readers who are not 

familiar with the PM2.5 literature that drives the approach developed for the 2012 PM2.5 NAAQS 

and employed by EPA since then. 

PM2.5 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 NAAQS, which was twice reviewed by the SAB-CASAC (U.S. EPA-SAB, 

2009a, 2009b), concluded that there is a causal relationship between mortality and both longterm 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 exposure to 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 

 

145 The set of ozone-only non-threshold effect estimates include (a) a value based on the 86 cities for which there are 

co-pollutant monitoring data for both ozone and PM2.5 (this is best compared with the core estimate based on the 

co-pollutant 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). 
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using short-term studies or cohort studies for estimating mortality benefits, cohort analyses are 

thought 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 shortterm exposures (Kunzli et al., 2001; NRC, 2002). The National Research Council (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 noted 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 data and a longer followup 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 [is] 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; 
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Lepeule et al., 2012). Presenting results using both ACS and Six Cities is consistent with other 

recent RIAs (e.g., U.S. EPA, 2010c, 2011a, 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) refined the earlier 

ACS studies 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 well-suited 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, 

2010b) used risk coefficients drawn from the Krewski et al. (2009) study, the most recent 

reanalysis of the ACS cohort data. The PM 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, 2010b). The CASAC also provided extensive 

peer review of the PM risk assessment and supported the use of effect estimates from this study 

(U.S. EPA-SAB, 2009a, b, 2010b). 
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Consistent with the PM risk assessment (U.S. EPA, 2010b) which was reviewed by the 

CASAC (U.S. EPA-SAB, 2009a, b), 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 10 μg/m3 increase in PM2.5). The 

relative risk estimate (1.06 per 10 μg/m3 increase 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 C-R 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 all-cause 

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/m3

 increase 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. 

Given that monetized benefits associated with PM2.5 are driven largely by reductions in 

premature mortality, it is important to characterize the uncertainty in this endpoint. In order to do 

so, we utilize the results of an expert elicitation sponsored by the EPA and completed in 2006 

(Roman et al., 2008; IEc, 2006). The results of that expert elicitation can be used as a 

characterization of uncertainty in the C-R functions. The co-benefits results derived from expert 

elicitation is discussed in Appendix 6B (section 6B.4). 

PM2.5 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.146 The PM ISA states that less evidence is 

 

146 For the purposes of this analysis, we only calculate benefits for infants age 0–1, not all children under 5 years old. 
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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 mortality 

estimated for adults, avoided premature mortality 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). With regard to the cohort study 

conducted by Woodruff et al. (1997), the SAB-HES noted 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) but also 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 Benefits and Costs of the Clean Air 

Act 1990 to 2020 (U.S. EPA, 2011a), we continue to rely on the earlier 1997 study in part due to 

the national–scale of the earlier study. 
6-44 

6.6.3.4 Hospital Admissions and Emergency Department Visits 

We pool together the incidence estimates using several different studies for many of the 

hospital admission endpoints. 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. 

The ozone ISA states that studies consistently found positive associations between shortterm ozone exposures and asthma and COPD hospital admissions and ED visits, with more 

limited evidence for pneumonia (U.S. EPA, 2013a). The ISA found no evidence of a threshold 

between short term ozone exposure and respiratory hospital admissions and ED visits, although 

there is increasing uncertainty at lower ozone concentrations particularly at and below 20 ppb 

(U.S. EPA, section 2.5.4.4). The ISA also observes that effect estimates remained robust to 

copollutants (U.S. EPA 2013a). 

Considering these observation from the ISA and a thoroughly reviewing available 

epidemiological studies, we estimate respiratory hospital admissions (for 65-99 year olds) using 

an effect estimate obtained from Katsouyanni et al. (2009) and asthma-related emergency room 

visits (for all ages) using several single-city studies. Although Katsouyanni et al. (2009) provides 

effect estimates specific to the summer season, we adjusted the 1-hour max metric to the 

equivalent 8-hour max effect estimates.147 The study provides summer season single pollutant 

effect estimates based both on natural and penalized splines. We have re-evaluated the choice of 

these models in this final RIA. In contrast to the proposal RIA, where we averaged both the 

penalized and natural spline estimates, we decided to focus on the penalized spline model 

 

147 Given that Katsouyanni et al. (2009) 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 8-hour metric. 
6-45 

because it displays a higher degree of precision, thus less potential for random error. 148 While 

Katsouyanni et al. (2009) included a set of effect estimates based on co-pollutant modeling (with 

PM10), but we could not use them because they were based on the full year rather than the 

summer season. 

A number of studies are available to model respiratory ED visits. Because we do not yet 

have the information needed to value this endpoint, we focused on the narrower category of 

asthma-related ED visits We used 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, Maine,, 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).149 In 

addition, two of the studies required adjustments to reflect the 8-hour max air metric. 

Specifically, Glad et al. (2012) uses the 1-hour max air metric, while Sarnat et al. (2013) used the 

24-hour 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. 

The two main groups of hospital admissions estimated in this analysis for PM2.5 are: 

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 exposure 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 estimates from studies 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, while Glad et al. examined 

populations 0-99 in Pittsburgh, Pennsylvania. Mar and colleagues perform their study in 

 

148 We evaluated the impact from this change (i.e., use of the penalized spline model alone as contrasted with an 

average of estimates generated using both the natural and penalized splines), and this change made a negligible 

(<<1%) difference in monetized ozone benefits due to the similarity in effect coefficients between the two models. 149 While we have included co-pollutant models as sensitivity analyses for mortality, we did not include separate copollutant models for any of the morbidity endpoints as sensitivity analyses because morbidity endpoint represent a 

small fraction of the total monetized benefits. 
6-46 

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 

exposure to 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. Total cardiovascular 

hospital admissions are the sum of the pooled estimate for adults over 64 and the single study 

estimate for adults 20 to 64. 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, the 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 the pooled risk estimate with a baseline incidence rate that excludes cerebroand peripheral-vascular 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 65 and over, 

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. We pool these two studies using equal weights. Moolgavkar et al. (2003) examines 

PM2.5 and chronic lung disease hospital admissions (not including asthma) in Los Angeles, 

California 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 
6-47 

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. 

6.6.3.5 Acute Health Events 

A number of acute health effects not requiring hospitalization are also associated with 

exposure to ozone and PM2.5. The sources for the effect estimates used to quantify these 

endpoints are described below. 

Asthma exacerbations. For this RIA, we 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 and to avoid double counting impacts, incidence estimates derived 

from studies focused only on asthmatics cannot be added to estimates from studies that consider 

the full population. 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 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 (U.S. 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, for the 

proposed RIA, we selected three multi-city studies (Mortimer et al., 2002; O’Connor et al., 2008; 

Schildcrout et al., 2006). All three of these studies required the application of air metric ratios to 
6-48 

adjust effect estimates to represent the 8-hour max metric.150 In this final RIA, following a final 

review of our technical approach, we removed O’Connor (2008) due to concerns about potential 

exposure measurement error and residual confounding from meteorological variables in its 19-

day lag structure. Current evidence in the ozone ISA suggests a more immediate effect with 

exposure to ozone for respiratory-related effects, such as hospital admissions and emergency 

department visits, with additional supporting evidence from studies of respiratory symptoms 

(U.S. EPA, 2013a). Consequently, the 19-day lag structure reflected in the O’Connor (2008) 

study is not as strongly supported by the evidence as the shorter lag structures associated with the 

other two asthma exacerbation studies.151 In generating the asthma exacerbation estimate, we 

pool estimates from Mortimer et al. (2012) and Schildcrout et al. (2006) using a random/fixed 

effects approach applied within BenMAP-CE (U.S. EPA, 2015a).152

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 exposure to 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 asthma exacerbation benefits.153

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 

 

150 Mortimer et al. (2002) had effect estimates based on an 8-hour mean metric, O’Connor et al. (2008) used a 24-

hour metric, and Schildcrout et al. (2006) was based on a 1-hour max metric. 151 We evaluated the impact from excluding O’Connor (2008) in estimating asthma exacerbations, and the change 

made a negligible (<1%) difference in monetized ozone benefits. Furthermore, the combined changes in estimating 

asthma exacerbations and respiratory hospital admissions resulted in only a 1% change in monetized ozone benefits 

compared to the approaches used in the proposal RIA (U.S. EPA, 2014c). 152 BenMAP-CE applies a chi-squared test to determine whether a fixed or random effect pooling approach should 

be used (for additional detail see U.S. EPA (2015b), p. 206). 

153 The random effects pooling used in BenMAP (U.S. EPA, 2015b) to generate a single estimate for asthma 

exacerbations weights estimates based on variance, and consequently those estimates with less statistical 

significance and wider confidence intervals will be down-weighted in generating the total aggregated estimate. 
6-49 

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 exposure to PM for 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 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 one type of acute respiratory symptom 

related to ozone exposure. Minor Restricted Activity Days (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. 

For ozone, we modeled MRADs using Ostro and Rothschild (1989). This study provides 

a co-pollutant model (with PM2.5) based on a national sample of 18-64 year olds. The original 

study used a 24-hour average metric and included control for PM2.5, which necessitated the use 

of an air metric ratio to convert the effect estimate to an 8-hour max equivalent. 

We estimate three types of acute respiratory symptoms related to PM2.5 exposure: lower 

respiratory symptoms, upper respiratory symptoms, and MRAD. Incidences of lower respiratory 
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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 focused on effects in asthmatics. 

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 (Patel et al., 2010) specified a population aged 13–20, which overlaps with the 

population in which we assess 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. 

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 
6-51 

et al. (2000) and Gilliland et al. (2001) and then pooled the results using the random effects 

pooling procedure. 

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,154 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 resulting from exposure 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. 

6.6.3.6 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., 

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 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 

 

154 See http://www.nlm.nih.gov/medlineplus/ency/article/001087.htm, accessed April 2012. 
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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, including: 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 C-R 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 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 replace 

the 7% survival rate previously applied across all age groups from Rosamond et al. (1999). 
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6.6.3.7 Worker Productivity 

The EPA last quantified the effect of ozone on outdoor agricultural worker productivity 

in the final Regulatory Impact Analysis accompanying the Transport Rule (U.S. EPA, 2011c); 

that analysis reported the value of worker productivity in the core benefits analysis. That RIA 

applied information reported in Crocker and Horst (1981), which observed that reducing ozone 

by 10 percent translated to a 1.4 increase in income among outdoor citrus workers. The RIA 

accompanying the proposed Ozone NAAQS (US EPA, 2014c) noted that, due in part to the 

vintage of the data used in this study, the Agency omitted this analysis in subsequent RIA’s. 

A recent study by Graff Zivin, and Neidell (2012) provides new evidence of the effect of 

ozone exposure on productivity among outdoor agricultural workers which we use in a 

quantitative uncertainty analysis examining this endpoint. Specifically, the study combined 

individual-level daily harvest rates for outdoor agricultural workers on a 500-acre farm in the 

Central Valley of California, with ground-level ozone data. The authors observed that a 10 ppb 

increase in work-day ozone concentrations (from 6:00 am to 3:00 pm) was associated with a 

5.5% decrease in productivity among outdoor agricultural workers on a given day. Additional 

detail on the worker productivity uncertainty analysis (including results) is presented in 

Appendix 6B (section 6B.8). 

6.6.3.8 Unquantified Human Health Effects 

Attaining a revised ozone NAAQS would reduce emissions of NOx and VOC. Although 

we have quantified many of the health benefits associated with reducing exposure to ozone and 

PM2.5, as shown in Table 6-3, we are unable to quantify the health benefits associated with 

reducing direct exposure to NO2 or VOC because of the absence of air quality modeling data for 

these pollutants. In addition, we are unable to quantify the effects of VOC emission reductions 

on ambient PM2.5 and associated health effects. 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). 

Epidemiological researchers have associated NO2 exposure with adverse health effects in 

numerous toxicological, clinical and epidemiological studies, as described in the Integrated 
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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. 

6.6.4 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 
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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 6-10. 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, which 

is discussed in further detail below.155 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. 

6.6.4.1 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 

 

155 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 

estimates use income growth 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. 
6-56 

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 EPA 

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. 
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Table 6-10. 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 o

Premature Mortality (Value of a Statistical Life) 

$8,300,000 $10,000,000 The EPA currently recommends a central VS

(1990$, 1990 income) based on a Weibull dipublished VSL estimates (5 contingent valuastudies). The underlying studies, the distribuother useful information are available in AppGuidelines for Preparing Economic Analyse

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. Agevalues reflect lost earnings and direct medica

period following a nonfatal MI. Lost earningCropper and Krupnick (1990). Direct medicasimple average of estimates from Russell et et al. (1990). Lost earnings: Cropper and Krupnick (1990). Present discolost 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 averag1. Wittels et al. (1990) ($100,000—no discou2. Russell et al. (1998), 5-year period ($22,0$21,000 at 7% discount rate) 
6-58

Table 6-10. 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 o

Hospital Admissions Chronic Lung Disease (18–64) $22,000 $22,000 No distributional information available. The 

earnings plus direct medical costs) are based information (e.g., average hospital care costshospital stay, and weighted share of total chrreported in Agency for Healthcare Research (www.ahrq.gov). 

Asthma Admissions (0–64) $16,000 $16,000 No distributional information available. The 

earnings plus direct medical costs) are based information (e.g., average hospital care costshospital stay, and weighted share of total astreported in Agency for Healthcare Research (www.ahrq.gov). 

All Cardiovascular Age 18–64 Age 65–99 

$44,000 $42,000 

$44,000 $42,000 

No distributional information available. The earnings plus direct medical costs) are based information (e.g., average hospital care costshospital stay, and weighted share of total carillnesses) reported in Agency for Healthcare (2007) (www.ahrq.gov). 

All respiratory (ages 65+) $37,000 $37,000 No distributions available. The COI point es

plus direct medical costs) are based on ICD-(e.g., average hospital care costs, average len

weighted share of total respiratory category Agency for Healthcare Research and Quality

Emergency Department Visits for Asthma 

$440 $440 No distributional information available. Sim

COI values (2000$): (1) $310, from Smith et al. (1997) and (2) $260, from Stanford et al. (1999). 
6-59

Table 6-10. 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 o

Respiratory Ailments Not Requiring Hospitalization Upper Respiratory Symptoms (URS) 

$35 $32 Combinations of the three symptoms for wh

available that closely match those listed by Pdifferent “symptom clusters,” each describindollar value was derived for each type of URestimates of WTP (IEc, 1994) to avoid each and assuming additivity of WTPs. In the abssurrounding the frequency with which each ooccurs within the URS symptom complex, wdistribution between $9.2 and $43 (2000$). 

Lower Respiratory Symptoms (LRS) 

$22 $21 Combinations of the four symptoms for whic

available that closely match those listed by Sdifferent “symptom clusters,” each describindollar value was derived for each type of LRestimates of WTP (IEc, 1994) to avoid each and assuming additivity of WTPs. The dollaaverage of the dollar values for the 11 differabsence of information surrounding the frequthe 11 types of LRS occurs within the LRS sassumed a uniform distribution between $6.9 

Asthma Exacerbations $56 $60 Asthma exacerbations are valued at $45 per 

mean of average WTP estimates for the four “bad asthma day,” described in Rowe and Chstudy surveyed asthmatics to estimate WTP asthma day,” as defined by the subjects. For asthma exacerbation is assumed to be equivaasthma is moderate or worse as reported in th(1986) study. The value is assumed to have a between $16 and $71 (2000$). 
6-60

Table 6-10. 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 

Respiratory Ailments Not Requiring Hospitalization (continued) Acute Bronchitis $460 $500 Assumes a 6-day episode, with the distributi

specified as uniform with the low and high vrecommended for related respiratory sympto(1994). The low daily estimate of $10 is the values recommended by IEc (1994) for two associated with acute bronchitis: coughing ahigh daily estimate was taken to be twice the respiratory restricted-activity day, or $110 (2

Work Loss Days (WLDs) Variable 

(U.S. median = $150) 

Variable (U.S. median = $150) 

No distribution available. Point estimate is bmedian annual wages divided by 52 and then daily wage. U.S. Year 2000 Census, compile(Geolytics, 2002) 

School Loss Days $98 $98 No distribution available. Based on (1) the p

child stays home from school, a parent will hwork to care for the child, and (2) the value oproductivity.

Minor Restricted Activity Days (MRADs) 

$64 $68 Median WTP estimate to avoid one MRAD 

Distribution is assumed to be triangular with maximum of $83, with a most likely value obased on assumption that value should exceesymptom (the highest estimate for a single syirritation—is $16) and be less than that for a distribution acknowledges that the actual val

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 use

2015). Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 estimates use incoand are therefore likely underestimates. Currently, BenMAP does not have an inflation adjustment to 2011$. We ran BenMAP for a cur

calculated the benefit-per-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton estimates to 2011$ using the Consume

items). This approach slightly underestimates the inflation for medical index and wage index between 2010 and 2011, which affects CO

based estimates. 
6-61 

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)156 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 $6.3 million (2000$).157 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 

the risk of premature mortality is the subject of continuing discussion within the economics and 

 

156 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. 

157 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. 
6-62 

public policy analysis communities. 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 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 6-11 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 6-11. Influence of Applied VSL Attributes on the Size of the Economic Benefits of 

Reductions in the Risk of Premature Mortality (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 quantitative uncertainty 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 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 

over the 20 years following exposure and discounted over the period between exposure and 
6-63 

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.158 Changes in the cessation lag assumptions 

do not change the total estimated premature mortality 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 that prevented us monetizing these benefits. In the quantitative uncertainty analysis, we 

include both an assumption of zero lag and the PM lag structure (i.e., the SAB 20-year 

segmented lag). Inclusion of the zero lag reflects consideration of the possibility that the longterm respiratory mortality estimate primarily captures an accumulation of short-term mortality 

effects across the ozone season.159 The use of the 20-year segmented lag reflects consideration of 

advice provided by the SAB-HES (U.S. EPA-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.” Monetized benefit estimates 

 

158 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. 

159 The ozone HREA noted,: “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.” (U.S. EPA, 2014b). 
6-64 

generated using both lag assumptions are presented as quantitative uncertainty analyses (see 

section 6.7.3.1). 

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).160 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 along with WTP to improve other individuals’ survival 

rates. 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 

 

160 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. 
6-65 

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 

analysis 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 

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., 
6-66 

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, traumatic 

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 because 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). 

6.6.4.2 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, 
6-67 

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” (U.S. EPA, 2015). 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 6-12. 

Table 6-12. 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 (U.S. EPA, 2015). 

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 is $480 (using the CPI-U for medical care to adjust to 2011$). The second estimate comes 

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 $440 (2011$). 

6.6.4.3 Nonfatal Myocardial Infarctions Valuation 

We were not able to identify a suitable WTP value for reductions in the risk of nonfatal 

heart attacks. 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 
6-68 

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 6-13): 

 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 

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 as a result whose length of stay was substantially 

shorter. 

 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. 
6-69 

Table 6-13. 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 (U.S. EPA, 2015). 

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 6-14. 

Table 6-14. 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 (U.S. EPA, 2015). 

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. 

6.6.4.4 Valuation of Acute Health Events 

Asthma Exacerbation Valuation. 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. 
6-70 

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 

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 
6-71 

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 Valuation. 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). 

6.6.4.5 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 

empirical evidence that the income elasticity161 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 

 

161 Income elasticity is a common economic measure equal to the percentage change in WTP for a 1 percent change 

in income. 
6-72 

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). More recently, in response to questions related to the adjustment for income growth over 

time, the SAB-EEAC noted that “EPA should adjust for changes in income in evaluating benefits 

of risk reduction” (U.S. EPA-SAB, 2011). 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 quantitative uncertainty 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 

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 a peer review in the near future 

of these studies and its approach to adjusting WTP estimates to account for changes in personal 
6-73 

income. 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. 

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 6-15. 

Table 6-15. 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 
6-74 

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 2010.162 We used 

projections of real GDP (in chained 1996 dollars) provided by Standard and Poor’s (2000) for 

the years 2010 to 2024.163

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 6-16. 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. 

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 

 

162 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. 

163 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. 
6-75 

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 6-16. 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 6-15, U.S. Census population projections, and projections of real GDP 

per capita. 

6.6.5 Benefit per Ton Estimates Used in Modeling PM2.5-Related Co-benefits 

This section presents the benefit-per-ton estimates (dimensioned by mortality study and 

simulation year) used as inputs in generating PM2.5 co-benefits estimates including (Table 6-17).

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 section 6.6.3.3). Estimates were available for 2025 and 2030, with 

those being used to model co-benefits for the 2025 scenario and post-2025 scenario, respectively. 

For additional detail on the approach used to generate PM2.5 co-benefits estimates and the role 

played by these two types of inputs, see Section 6.4.4.164

 

164 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-per 

ton values and consequently, the co-benefits PM2.5 estimates are based on simulated benefits for those seven 

sectors.
6-76 

Table 6-17. Summary of PM2.5 Benefit-per-ton Estimates a a Benefit-per-ton estimates reflect application of the 20-year segmented lag at either a 3% or 7% discount rate. In addition, separate sets 

estimates were generate for 2025 and 2030, reflecting application of appropriate projected demographic and baseline incidence data. 

b Benefit-per-ton estimates for the non-specified source category were generated as a weighted average of values for the 17 source categ

based on sector-specific NOx emissions for 2005 obtained from Fann et al. (2012b).

air,

locamotive

and

marine

cement

kilns

coke

ovens

EGU

point

electric

arc

furnaces

ferro

alloys

integrated

iron and

steel

iron and

steel

non‐EGU

point

other

non‐

point

other nonroad onroad

pulp and

paper refineries

reside

al wo

2025 at 7% social discount

Krewski et al., 2009 $7,200 $5,700 $10,000 $5,200 $9,600 $4,400 $13,000 $17,000 $6,300 $7,900 $7,000 $7,600 $3,700 $6,900 $14,0Lepeule et al., 2012 $16,000 $13,000 $24,000 $12,000 $22,000 $9,800 $30,000 $38,000 $14,000 $18,000 $16,000 $17,000 $8,500 $16,000 $31,0

2025 at 3% social discount

Krewski et al., 2009 $8,000 $6,300 $12,000 $5,800 $11,000 $4,800 $15,000 $19,000 $7,000 $8,700 $7,700 $8,400 $4,200 $7,700 $15,0Lepeule et al., 2012 $18,000 $14,000 $26,000 $13,000 $24,000 $11,000 $34,000 $42,000 $16,000 $20,000 $17,000 $19,000 $9,400 $17,000 $34,0

2030 at 7% social discount

Krewski et al., 2009 $7,800 $6,100 $11,000 $5,600 $10,000 $4,600 $14,000 $18,000 $6,800 $8,500 $7,600 $8,200 $4,000 $7,500 $15,0Lepeule et al., 2012 $18,000 $14,000 $25,000 $13,000 $23,000 $10,000 $32,000 $41,000 $15,000 $19,000 $17,000 $19,000 $9,100 $17,000 $33,0

2030 at 3% social discount

Krewski et al., 2009 $8,700 $6,800 $12,000 $6,200 $11,000 $5,100 $16,000 $20,000 $7,600 $9,400 $8,400 $9,100 $4,500 $8,300 $16,0Lepeule et al., 2012 $20,000 $15,000 $28,000 $14,000 $26,000 $12,000 $36,000 $46,000 $17,000 $21,000 $19,000 $21,000 $10,000 $19,000 $37,0

Long‐term mortality

study

Emissions sector
6-77 

6.7 Benefits Results 

We estimated the benefits of attaining the revised and alternative ozone standard levels 

across the U.S. in 2025 except California. We estimated the benefits of attaining these standard 

levels in California in 2038. We report the 2025 and 2038 estimates separately because deriving 

a summed estimate would require us to calculate the Present Value (PV) of the stream of benefits 

occurring between those two years, which is not possible with the available data. Additional 

analyses (section 6.7.3) inform the interpretation of these core analyses. 

Applying the impact and valuation functions described above to the estimated changes in 

ozone concentrations yields estimates of the changes in physical damages (e.g., premature 

deaths, cases of hospital admissions) and the associated monetary values for those changes. Not 

all known ozone and PM health effects could be quantified or monetized, and the monetized 

value of these unquantified effects is represented by adding an unknown “B” to the aggregate 

total. Values are rounded to two significant figures and so totals may not sum across columns or 

rows. 

6.7.1 Benefits of Attaining a Revised Ozone Standard in 2025 

This section presents the avoided health impacts and monetized benefits of attaining a 

more stringent ozone standard in 2025. Table 6-18 shows the population-weighted air quality 

change for the revised and alternative standard levels averaged across the continental U.S. Table 

6-19 summarizes the tons of NOx emissions required to simulate attainment of the revised and 

alternative standard levels (further differentiated by geographic region including east, west and 

California). Tables 6-20 through 6-25 present the benefits results for the standard levels 

analyzed. Table 6-25 summarizes total benefits by geographic region (including east, west minus 

California, and California). 

 In addition, Figure 6-4 presents a quantitative uncertainty analysis for short-term ozonerelated benefits using additional C-R functions for premature mortality, and Figure 6-5 presents a 

quantitative uncertainty analysis for PM2.5-related co-benefits using additional C-R functions for 

premature mortality. See sections 6.6.3.2 and 6.6.3.3, respectively for additional discussion of 

the alternative effect estimates used in each quantitative uncertainty analysis. 
6-78 

Table 6-18. Population-Weighted Air Quality Change for the Revised and Alternative 

Annual Primary Ozone Standards Relative to the Analytical Baseline in 2025a

Standard 

Population-Weighted Summer Season Ozone Concentration Change 

(8-hour max)b

70 ppb 0.2574 

65 ppb 1.278 

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 6-19. Sector-Specific NOx Emissions Reductions for the Revised and Alternative 

Standard Levelsa

a

All values are tons of NOx reductions (75 ppb vs revised and alternative standard levels). 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

Emis Rdxn

nonCA NOX

Emis Rdxn

CA NOX

Emis Rdxn

nonCA NOX

Emis Rdxn

Aircraft, locomotives and marine vessels             ‐ ‐                 ‐              ‐                 

Area sources            ‐          ‐ 32,224                       69,576

Cement kilns            ‐          ‐ 19,285                       31,963

Electricity Generating Units            ‐          ‐ 47,507                     113,678

Industrial pointsources            ‐        ‐ 134,763                     319,484

Non‐road mobile sources            ‐            ‐ 2,832                         8,791

On‐road mobile sources            ‐

Pulp and paper facilities            ‐                  ‐ 42                             265

Refineries            ‐ 3,134                         7,735

Residential wood combustion

Unknown sector     51,000         46,542      99,500        862,803

TOTAL       51,000         286,330       99,500     1,414,296

Emissions Sector

70ppb   65ppb

Revised and Alternative Standard Levels
6-79 

Table 6-20. Estimated Number of Avoided Ozone-Related Health Impacts for the 

Revised and Alternative Standard Levels (Incremental to the Baseline) for the 

2025 Scenario (nationwide benefits of attaining the standards 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. 

d

 The negative estimates at the 5th percentile confidence estimates for these morbidity endpoints reflect the statistical 

power of the studies used to calculate these health impacts. These results do not suggest that reducing air pollution 

results will adversely affect health, but rather, that we are less confident in the magnitude of the expected benefits 

for this endpoint. 

70 ppb 65 ppb

Avoided Short-Term Mortality

96 490

(47 to 140) (240 to 740)

160 820

(86 to 240) (440 to 1,200)

Avoided Long-term Respiratory Mortality

340 1,700

(110 to 560) (580 to 2,800)

Avoided Morbidity

180 920

(-42 to 400) (-220 to 2,000)

510 2,700

(47 to 1,600) (250 to 8,300)

220,000 1,100,000

(-67,000 to 440,000) (-330,000 to 2,100,000)

450,000 2,200,000

(190,000 to 720,000) (920,000 to 3,500,000)

160,000 790,000

(57,000 to 360,000) (280,000 to 1,700,000)

Health Effectb

Revised and Alterative Standard Levels

(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) (30-99yrs) 

copollutants model (PM2.5) 

Hospital admissions - respiratory 

(age 65+)

d

Emergency department visits for 

asthma (all ages)

Asthma exacerbation (age 6-18)d

Minor restricted-activity days 

(age 18-65)

School Loss Days (age 5-17)
6-80 

Table 6-21. Total Monetized Ozone-Related Benefits for the Revised and Alternative 

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

(nationwide benefits of attaining the standards everywhere in the U.S. except 

California) (millions of 2011$) a

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. 

Figure 6-4. Quantitative Uncertainty Analysis for Short-Term Ozone-Related Mortality 

Benefits 

70 ppb 65 ppb

Avoided Short-Term Mortality - Core Analysis

$1,000 $5,300

($99 to $2,900) ($500 to $15,000)

1,700 8,700

($160 to $4,800) ($800 to $24,000)

Health Effectb

(95th percentile confidence intervals)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

Revised and Alterative Standard Levels

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

Millions (2011$)
6-81 

Table 6-22. Estimated Number of Avoided PM2.5-Related Health Impacts for the Revised 

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

2025 Scenario (Nationwide Benefits of Attaining the Standards 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). 

70ppb 65ppb

Avoided PM2.5-related Mortality

Krewski et al. (2009) (adult mortality age 30+) 220 1,100

Lepeule et al. (2012) (adult mortality age 25+) 500 2,500

Woodruff et al. (1997) (infant mortality) <1 2

Avoided PM2.5-related Morbidity

Non-fatal heart attacks

 Peters et al. (2001) (age >18) 260 1,300

 Pooled estimate of 4 studies (age >18) 28 140

Hospital admissions—respiratory (all ages) 66 330

Hospital admissions—cardiovascular (age > 18) 80 400

Emergency department visits for asthma (all ages) 120 600

Acute bronchitis (ages 8–12) 340 1,700

Lower respiratory symptoms (ages 7–14) 4,400 22,000

Upper respiratory symptoms (asthmatics ages 9–11) 6,300 31,000

Asthma exacerbation (asthmatics ages 6–18) 7,000 42,000

Lost work days (ages 18–65) 28,000 140,000

Minor restricted-activity days (ages 18–65) 170,000 830,000

Revised and Alterative Standard Levels

Health Effectb
6-82 

Table 6-23. Monetized PM2.5-Related Health Co-Benefits for the Revised and Alternative 

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

(Nationwide Benefits of Attaining the Standards in the U.S. except California) 

(millions of 2011$) a,b,c 

Monetized Benefits 

Revised and Alternative Standard Levels 

70 ppb 65 ppb 

3% Discount Rate 

Krewski et al. (2009) (adult mortality age 30+) $2,100 $10,000 

Lepeule et al. (2012) (adult mortality age 25+) $4,700 $23,000 

7% Discount Rate 

Krewski et al. (2009) (adult mortality age 30+) $1,900 $9,300 

Lepeule et al. (2012) (adult mortality age 25+) $4,200 $21,000 

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). 

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. 

Figure 6-5. Quantitative Uncertainty Analysis Long-Term PM2.5-Related Mortality CoBenefits 

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

Millions (2011$)

Dollar benefit (7%) Dollar benefit (3%)
6-83 

Table 6-24. Estimated Monetized Ozone and PM2.5 Benefits for Revised and Alternative 

Annual Ozone Standards Incremental to the Baseline for the 2025 Scenario 

(Nationwide Benefits of Attaining the Standards in the U.S. Except California) –

Identified + Unidentified Control Strategies (combined) and Identified Control 

Strategies Only (billions of 2011$) a

Discount 

Rate 

Revised and Alternative Standard Levels 

70 ppb 65 ppb 

Identified + Unidentified Control Strategies

Ozone-only Benefits (range reflects Smith et 

al. (2009) to Zanobetti and Schwartz (2008))

b $1.0 to $1.7 $5.3 to $8.7 

PM2.5 Co-benefits (range reflects Krewski et 

al. (2009) to Lepeule et al. (2012)

3% $2.1 to $4.7 $10 to $23 

7% $1.9 to $4.2 $9.3 to $21 

Total Benefits 3% $3.1 to $6.4 +B $16 to $32 +B 

7% $2.9 to $5.9 +B $15 to $30 +B 

Identified Control Strategies Only 

Ozone-only Benefits (range reflects Smith et 

al. (2009) to Zanobetti and Schwartz (2008))

b $0.86 to $1.4 $2.2 to $3.5 

PM2.5 Co-benefits (range reflects Krewski et 

al. (2009) to Lepeule et al. (2012)

3% $1.7 to $3.9 $4.0 to $9.0 

7% $1.6 to $3.5 $3.6 to $8.1 

Total Benefits 3% $2.6 to $5.3c $6.1 to $12 c

7% $2.4 to $4.9 c $5.7 to $12 c

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. 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. 

c

 Excludes additional health and welfare benefits which could not be quantified (see section 6.6.3.8). 

 

Table 6-25. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. except 

California) – Identified + Unidentified Control Strategies a, b 

Region Revised and Alterative Standard Levels 

70 ppb 65 ppb 

East c 98% 96% 

California ~0% ~0% 

Rest of West 2% 4% 

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 These regional breakdown results reflect application of identified and unidentified control strategies. Regional 

breakdown results are the same for benefits based on application of identified control strategies only. 

c

 Includes Texas and those states to the north and east. 
6-84 

6.7.2 Benefits of the Post-2025 Scenario 

This section presents the estimated number and economic value of avoided ozone- and 

PM2.5-related effects associated with attaining a revised ozone standard after 2025 (note, sectorspecific NOx emissions reductions levels used in modeling benefits for the post-2025 scenario 

are presented earlier in Table 6-19 - see entries under “CA NOx”). In addition, general trends 

and observations drawn from the quantitative uncertainty analyses presented in Figures 6-4 and 

6-5 hold for the post-2025 scenario and for that reason separate plots for these quantitative 

uncertainty analyses (for the post-2025 scenario) are not presented (see section 6.7.3 for further 

discussion). While simulated attainment of standard levels for the 2025 scenario involved 

application of both identified and unidentified controls outside of California, simulated 

attainment of standard levels for the post-2025 scenario involved application exclusively of 

unidentified controls within California (see Table 6-18 and section 2.2.2). For that reason, results 

tables in this section represent exclusively, benefits based on application of unidentified controls. 

Table 6-26. Population-Weighted Air Quality Change for the Revised and Alternative 

Annual Primary Ozone Standards Relative to Baseline for Post-2025 a

Standard 

Population-Weighted Ozone Season Ozone Concentration Change 

(8-hour max)b

70 ppb 0.1708 

65 ppb 0.3464 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. 
6-85 

Table 6-27. Estimated Number of Avoided Ozone-Related Health Impacts for the 

Revised and Alternative Annual Ozone Standards (Incremental to the Baseline) 

for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards 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. 

d

 The negative estimates at the 5th percentile confidence estimates for these morbidity endpoints reflect the statistical 

power of the studies used to calculate these health impacts. These results do not suggest that reducing air pollution 

results in additional health impacts. 

70 ppb 65 ppb

Avoided Short-Term Mortality

72 150

(35 to 110) (71 to 220)

120 240

(64 to 180) (130 to 350)

Avoided Long-term Respiratory Mortality

290 590

(98 to 480) (200 to 970)

Avoided Morbidity

140 270

(-32 to 300) (-65 to 610)

360 720

(33 to 1,100) (67 to 2,200)

160,000 330,000

(-49,000 to 320,000) (-100,000 to 650,000)

320,000 660,000

(130,000 to 510,000) (270,000 to 1,000,000)

120,000 240,000

(42,000 to 260,000) (85,000 to 530,000)

Asthma exacerbation (age 6-18)d

Minor restricted-activity days 

(age 18-65)

School Loss Days (age 5-17)

Hospital admissions - respiratory 

(age 65+)d

Emergency department visits for 

asthma (all ages)

multi-city study

Health Effectb

Revised and Alterative Standard Levels

(95th percentile confidence intervals)

multi-city studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) 

(all ages)

Jerrett et al. (2009) (30-99yrs) 

copollutants model (PM2.5) 
6-86 

Table 6-28. Total Monetized Ozone-Only Benefits for the Revised and Alternative 

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

Scenario (Nationwide Benefits of Attaining the Standards just in California) 

(millions of 2011$) a 

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 

70 ppb 65 ppb

Avoided Short-Term Mortality - Core Analysis

$790 $1,600

($74 to $2,200) ($150 to $4,500)

1,300 2,600

($120 to $3,600) ($240 to $7,200)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

Health Effectb

(95th percentile confidence intervals)

Revised and Alterative Standard Levels
6-87 

Table 6-29. Estimated Number of Avoided PM2.5-Related Health Impacts for the Revised 

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

Post-2025 Scenario (Nationwide Benefits of Attaining the Standards 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

Avoided PM2.5-related Mortality

Krewski et al. (2009) (adult mortality age 30+) 43 84

Lepeule et al. (2012) (adult mortality age 25+) 98 190

Woodruff et al. (1997) (infant mortality) <1 <1

Avoided PM2.5-related Morbidity

Non-fatal heart attacks

 Peters et al. (2001) (age >18) 51 100

 Pooled estimate of 4 studies (age >18) 6 11

Hospital admissions—respiratory (all ages) 13 26

Hospital admissions—cardiovascular (age > 18) 16 31

Emergency department visits for asthma (all ages) 23 44

Acute bronchitis (ages 8–12) 64 130

Lower respiratory symptoms (ages 7–14) 820 1,600

Upper respiratory symptoms (asthmatics ages 9–11) 1,200 2,300

Asthma exacerbation (asthmatics ages 6–18) 1,900 3,600

Lost work days (ages 18–65) 5,300 10,000

Minor restricted-activity days (ages 18–65) 31,000 61,000

Revised and Alterative Standard Levels

Health Effectb
6-88 

Table 6-30. Monetized PM2.5-Related Health Co-Benefits for the Revised and Alternative 

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

Scenario (Nationwide Benefits of Attaining the Standards just in California) 

(millions of 2011$) a,b

 Revised and Alterative Standard Levels

Monetized Benefits 70 ppb 65 ppb 

3% Discount Rate 

Krewski et al. (2009) (adult mortality age 30+) $400 $790 

Lepeule et al. (2012) (adult mortality age 25+) $910 $1,800 

7% Discount Rate 

Krewski et al. (2009) (adult mortality age 30+) $370 $710 

Lepeule et al. (2012) (adult mortality age 25+) $820 $1,600 

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). 

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 6-31. Estimate of Monetized Ozone and PM2.5 Benefits for Revised and Alternative 

Annual Ozone Standards Incremental to the Baseline for the Post-2025 

Scenario (Nationwide Benefits of Attaining the Standards just in California) 

– Identified + Unidentified Control Strategies (billions of 2011$) a 

Discount 

Rate 

Revised and Alternative Standard Levels 

70 ppb 65 ppb 

Identified + Unidentified Control Strategies

Ozone-only Benefits (range reflects Smith 

et al. (2009) to Zanobetti and Schwartz 

(2008))

b $0.79 to $1.3 $1.6 to $2.6 

PM2.5 Co-benefits (range reflects Krewski 

et al. (2009) to Lepeule et al. (2012)

3% $0.40 to $0.91 $0.79 to $1.8 

7% $0.37 to $0.82 $0.71 to $1.6 

Total Benefits 3% $1.2 to $2.2 c $2.4 to $4.4 c

7% $1.2 to $2.1 c $2.3 to $4.2 c

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. 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. 

c

 Excludes additional health and welfare benefits which could not be quantified (see section 6.6.3.8). 
6-89 

Table 6-32. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in 

California) – Identified + Unidentified Control Strategies a

Region Revised and Alterative Standard Levels 

70 ppb 65 ppb 

East b 3% 2% 

California 90% 91% 

Rest of West 7% 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. 

6.7.3 Uncertainty in Benefits Results (including Results of Quantitative Uncertainty Analyses) 

Avoided ozone and PM2.5 related premature deaths account for 94% to 96% 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 monetized benefits; however, 

these outcomes occur among a significantly larger population. Comparing an incidence table to 

the monetized benefits table reveals that the number of incidences avoided and the unit value for 

that endpoint do not always closely correspond. For example, for ozone we estimate almost 

1,000 times more cases of exacerbated ozone would be avoided than premature deaths, yet cases 

of exacerbated asthma account for only a very small fraction (<1%) of total monetized benefits 

(see Table 6-20). This is because 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 co-benefits are discussed qualitatively 

in Appendix 6A. Quantitative analyses completed in support of uncertainty characterization are 

discussed in detail in Appendix 6B. 

Below we address uncertainty associated in modeling benefits for both ozone- and PM2.5, 

including both key assumptions and uncertainty associated with modeling mortality along with 

brief summarizes of the results of the quantitative analyses completed in support of uncertainty 

characterization (presented in detail in Appendix 6B). 
6-90 

Ozone-Related Benefits 

 Key assumption and uncertainties related to modeling of ozone-related premature 

mortality: Ozone-related short-term mortality represents a substantial proportion of total 

monetized benefits (over 94% of the ozone-related-benefits), and these estimates have the 

following key assumptions and uncertainties. We utilize a log-linear impact function 

without a threshold in modeling short-term ozone-related mortality. However, we 

acknowledge reduced confidence in specifying the nature of the C-R function in the 

range of ≤20ppb and below (ozone ISA, section 2.5.4.4). Thus, the estimates include 

health benefits from reducing ozone in areas with varied concentrations of ozone, 

including both areas that do not meet the ozone standard and those areas that are in 

attainment, down to the lowest modeled concentrations. 

 Avoided premature mortality according to baseline pollutant concentrations: We 

recognize that, in estimating short-term ozone-related mortality, we are less confident in 

specifying the shape of the C-R function at lower ambient ozone concentrations (at and 

below 20 ppb, ozone ISA, section 2.5.4.4). As discussed in section 6.7.3.2 and (in greater 

detail) in Appendix 6B, section 6B.7, quantitative uncertainty analyses completed for this 

RIA found that the vast majority (~84%) of the reductions estimated premature mortality 

for simulated attainment of the revised and alternative standards (70 ppb and 65 ppb) 

were associated with grid cells having mean 8-hour max values between 35 and 55 ppb. 

Furthermore, ~100% of mortality reductions occurred above 20 ppb, where we are more 

confident in specifying the nature of the ozone-mortality effect (ozone ISA, section 

2.5.4.4). owever, as discussed in section 6B.7, care must be taken in interpreting these 

results since the ambient air metric used in modeling this endpoint is the mean 8-hour 

max value in each grid cell (and not the full distribution of 8-hour daily max values). Had 

the latter been used, then the distribution would have likely been wider. The use of the 

mean 8-hour max metric in the RIA also means that the graphical distributions referenced 

here and presented in Appendix 6B.7 cannot be readily contrasted with specific ozone 

standard levels (since those are based on 8-hour max daily metrics and not on a seasonalmean of those metrics). 

 Short-term ozone-exposure related premature mortality (alternative 

epidemiological studies and C-R functions): We estimated the number of premature 

deaths using seven additional effect estimates including four multi-city studies and three 

meta-analysis studies. This quantitative uncertainty 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 Figure 6-4). 

 Economic value of avoided premature mortality from long-term exposure to ozone:

We estimated the economic value of long-term ozone mortality using two cessation lag 

structures: 20-year segment lag (as used for PM2.5) and a zero lag. The quantitative 

uncertainty analysis suggests that if included in the core benefit estimate, long-term 

ozone exposure-related mortality could add substantially to the overall benefits. 

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. 
6-91 

 Long-term ozone-exposure related premature respiratory mortality and potential 

thresholds: We evaluated the impact of several assumed thresholds ranging from 40-60 

ppb. This quantitative uncertainty analysis suggested that a threshold of 50 ppb or greater 

could have a substantial impact on estimated benefits, while thresholds below this range 

have a relatively minor impact. 

 Income elasticity for premature mortality and certain morbidity endpoints: 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 quantitative uncertainty 

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. 

 Value of increased productivity among outdoor agricultural workers due to reduced 

exposure to ozone: Using information from the Graff Zivin and Neidell (2012) study, we 

estimate the economic value of improved productivity among outdoor non-livestock 

workers for the ozone standards in 2025 in our uncertainty analysis. We estimate the 

monetized worker productivity benefits of attaining a 70 ppb standard would be about 

$1.7 million and a 65 ppb standard would yield monetized benefits of about $8.9 

million.165 

PM2.5-Related Benefits 

 Key assumption and uncertainties related to modeling of PM2.5-related premature 

mortality 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 further 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. In addition, we assume that there is a “cessation” lag between 

 

165 We recognize that there is significant uncertainty in the generalizability of this study and the need for additional 

research and peer review in guiding the monetization of agriculatural productivity impacts. 
6-92 

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. And finally, we recognize 

uncertainty associated with application of the benefit-per-ton approach used in modeling 

PM2.5 co-benefits. 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. 

(2012b). 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 benefit-per-ton 

values to estimate co-benefits may lead to higher or lower benefit estimates than if cobenefits were calculated based on direct air quality modeling. 

 Avoided premature mortality according to baseline pollutant concentrations: We 

recognize that, in modeling long-term PM2.5-related mortality, we are less confident in 

specifying the shape of the C-R function at levels below the lowest measured level 

(LML) reported in the epidemiology study(s) providing the effect estimates used in 

modeling the mortality endpoint. As discussed in section 6.7.3.2 and (in greater detail) in 

Appendix 6B, section 6B.7, quantitative analyses completed in support of uncertainty 

characterization completed for this RIA found that, depending on the mortality study, 

between 67% and 93% of the long-term PM2.5- related mortality estimate is based on 

modeling involving baseline PM2.5 levels above the LML. This increases our overall 

confidence in the mortality estimates underlying the benefit-per-ton values used in the 

RIA. 

 Long-term PM2.5 exposure-related premature mortality and alternative C-R 

functions (based on the Expert Elicitation): We applied the set of expert elicitationbased functions to generate an alternative set of PM2.5 benefit estimates (see Figure 6-5). 

The estimates based on Krewski et al. (2009) and Lepeule et al. (2012) fall within the 

range of estimates based on the functions from the 2006 expert elicitation. 

6.8 Discussion 

This analysis demonstrates the potential for significant health benefits of the illustrative 

emissions controls applied to simulate attainment with the revised and alternative primary ozone 

standard levels. We estimate that by 2025, the emissions reductions to reach the revised and 

alternative standard levels 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 rule would also 
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yield significant welfare impacts as well (see Chapter 7). Even considering the quantified and 

unquantified uncertainties identified in this chapter, we believe that the revised and alternative 

standards would have substantial public health benefits that are likely to outweigh the costs of 

the control strategies for the revised and alternative standard levels analyzed (see Chapter 4). 

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 C-R relationships 

and our use of alternate mortality functions. Others, including the 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 revised and alternative 

standard levels. 

As discussed in Chapter 1, 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, 2014a). Setting a NAAQS does not directly result in costs or benefits. The 

NAAQS RIAs illustrate the potential costs and benefits of the revised and alternative air quality 

standards nationwide based on an array of emissions 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 emissions reduction 

strategies that States may choose to enact when implementing a revised NAAQS, and as such, by 

contrast, the emissions reductions from implementation rules are generally for specific, wellcharacterized sources, such as the recent MATS rule (U.S. EPA, 2011c). In general, the EPA is 

more confident in the magnitude and location of the emissions reductions for implementation 

rules. As such, emissions reductions achieved under promulgated implementation rules, such as 

MATS, have been reflected in the baseline of this NAAQS analysis.166 For this reason, the 

benefits estimated provided in this RIA and all other NAAQS RIAs should not be added to the 

 

166 The full set of rules reflected in the baseline are presented in Chapter 2, Section 2.1.3. 
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benefits estimated for implementation rules. Subsequent implementation rules will be reflected 

in the baseline for the next ozone NAAQS review. 

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 emissions controls put in place to reduce concentrations at the highest monitor 

will simultaneously result in lower ozone concentrations throughout the entire area. In fact, the 

ozone HREA (U.S. EPA, 2014b) shows how different standard levels would affect the entire 

distribution of ozone concentrations, and thus 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. Given that the 

epidemiological literature in most cases has not provided estimates based on threshold models, 

there would be additional uncertainties imposed by assuming thresholds or other non-linear C-R 

functions for the purposes of benefits analysis. 

The estimated benefits shown here are in addition to the substantial benefits estimated for 

several recent air quality rules (U.S. EPA, 2009a, 2011c, 2014a). Emissions reductions from 

rules such as Tier 3 will have substantially reduced ambient ozone concentrations by 2025 in the 

East, such that few additional controls would be needed to reach 70 ppb. These rules that have 

already been promulgated have tremendous combined benefits that explain why the number of 

avoided premature mortality associated with this NAAQS revision are smaller than were 
6-95 

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). 

6.9 References 

Adams PF, Hendershot GE, Marano MA. 1999. “Current Estimates from the National Health Interview Survey, 

1996.” Vital and Health Statistics 10(200):1-212. 

Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet, Healthcare Cost and Utilization Project. 

Rockville, MD. Available at <http://hcupnet.ahrq.gov>. 

Agency for Healthcare Research and Quality (AHRQ). 2007. HCUPnet, Healthcare Cost and Utilization Project.

Rockville, MD. Available at <http://hcupnet.ahrq.gov>. 

Agency for Healthcare Research and Quality (AHRQ). 2009. HCUPnet, Healthcare Cost and Utilization Project.

Rockville, MD. Available at <http://hcupnet.ahrq.gov>. 

Alberini A, Cropper M, Krupnick A, Simon NB. 2004. “Does the value of a statistical life vary with age and health 

status? Evidence from the US and Canada.” Journal of Environmental Economics and Management 48(1): 769-

792. 

Alberini, Anna & Milan Ščasný. 2011. "Context and the VSL: Evidence from a Stated Preference Study in Italy and 

the Czech Republic." Environmental & Resource Economics 49(4), pages 511-538. 

Aldy JE, Viscusi WK. 2008. “Adjusting the value of a statistical life for age and cohort effects.” The Review of 

Economics and Statistics 90(3):573-581. 

American Lung Association (ALA). 2002. Trends in Asthma Morbidity and Mortality. American Lung Association, 

Best Practices and Program Services, Epidemiology and Statistics Unit. Available at 

<http://www.lungusa.org/data/asthma/ASTHMAdt.pdf>. 

American Lung Association (ALA). 2010. Trends in Asthma Morbidity and Mortality. American Lung Association 

Epidemiology and Statistics Unit Research and Program Services Division. February. Table 7. Based on NHIS 

data (CDC, 2008). Available at http://www.lungusa.org/finding-cures/our-research/trend-reports/asthma-trendreport.pdf. 

Ayyub, B.M. 2002. Elicitation of Expert Opinions for Uncertainty and Risk. CRC Press, Florida. 

Babin, S. M., H. S. Burkom, et al. 2007. “Pediatric Patient Asthma-related Emergency Department Visits and 

Admissions in Washington, DC, from 2001–2004, and Associations with Air Quality, Socio-economic Status 

and Age Group.” Environmental Health 6: 9. 

Barnett AG; Williams GM; Schwartz J; Best TL; Neller AH; Petroeschevsky AL; Simpson RW. 2006. The effects 

of air pollution on hospitalizations for cardiovascular disease in elderly people in Australian and New Zealand 

cities. Environ Health Perspect, 114: 1018-1023. 

Bell ML; Ebisu K; Peng RD; Walker J; Samet JM; Zeger SL; Dominic F. 2008. Seasonal and regional short-term 

effects of fine particles on hospital admissions in 202 U.S. counties, 1999-2005. Am J Epidemiol, 168: 1301-

1310. 
6-96 

Bell, M.L.; A. McDermott; S.L. Zeger; J.M. Samet; F. Dominici. 2004. Ozone and short-term mortality in 95 U.S. 

urban communities, 1987-2000. JAMA. 292:2372-2378. 

Bell, ML; Dominici, F; Samet, JM. 2005. Ameta-analysis of time-series studies of ozone and mortality with 

comparison to the national morbidity, mortality, and air pollution study. Epidemiology 16: 436-445. 

http://dx.doi.org/10.1097/01.ede.0000165817.40152.85 

Bell, ML; McDermott, A; Zeger, SL; Samet, JM; Dominici, F. 2004. Ozone and short-term mortality in 95 US urban 

communities, 1987-2000. JAMA 292: 2372-2378. http://dx.doi.org/10.1001/jama.292.19.2372 

Berger, M.C., G.C. Blomquist, D. Kenkel, and G.S. Tolley. 1987. “Valuing Changes in Health Risks: A Comparison 

of Alternative Measures.” The Southern Economic Journal 53:977-984. 

Centers for Disease Control and Prevention (CDC). 2008. National Center for Health Statistics. National Health 

Interview Survey, 1999-2008. 

Chen L, Jennison BL, Yang W, and Omaye ST. 2000. “Elementary school absenteeism and air pollution.” 

Inhalation Toxicolology 12(11):997-1016. 

Crocker, T. D., & Horst, R. L. 1981. Hours of work, labor productivity, and environmental conditions: A case study. 

The Review of Economics and Statistics, 63(3), 361-368. 

Cropper, M. L. and A. J. Krupnick. 1990. The Social Costs of Chronic Heart and Lung Disease. Resources for the 

Future. Washington, DC. Discussion Paper QE 89-16-REV. 

Dockery DW; Pope CA III; Xu X; Spengler JD; Ware JH; Fay ME; Ferris BG Jr; Speizer FE. 1993. An association 

between air pollution and mortality in six US cities. N Engl J Med, 329: 1753-1759. 

Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis, J.H. Ware, M. Raizenne, 

and F.E. Speizer. 1996. “Health Effects of Acid Aerosols On North American Children-Respiratory 

Symptoms.” Environmental Health Perspectives 104(5):500-505. 

Eisenstein, E.L., L.K. Shaw, K.J. Anstrom, C.L. Nelson, Z. Hakim, V. Hasselblad and D.B. Mark. 2001. “Assessing 

the Clinical and Economic Burden of Coronary Artery Disease: 1986-1998.” Medical Care 39(8):824-35. 

Fann N, Fulcher C.M., Hubbell B.J. 2009. The influence of location, source, and emission type in estimates of the 

human health benefits of reducing a ton of air pollution. Air Qual Atmos Health (2009) 2:169–176 

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. 

Fann N, Baker K R, Fulcher, C M. 2012b. Characterizing the PM2.5-related health benefits of emission reductions 

for 17 industrial, area and mobile emission sectors across the U.S. Environment International 49 (2012) 141–

151. 

Freeman (III), AM. 1993. The Measurement of Environmental and Resource Values: Theory and Methods.

Washington, DC: Resources for the Future. 

Gent, J.F.; E.W. Triche; T.R. Holford; K. Belanger; M.B. Bracken; W.S. Beckett, et al. 2003. Association of lowlevel ozone and fine particles with respiratory symptoms in children with asthma. Journal of the American 

Medical Association. 290(14):1859-1867. 

GeoLytics Inc. 2002. GeoLytics CensusCD® 2000 Short Form Blocks. CD-ROM Release 1.0. GeoLytics, Inc. East 

Brunswick, NJ. Available: http://www.geolytics.com/ [accessed 29 September 2004]. 
6-97 

Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. 2001. “The effects of ambient 

air pollution on school absenteeism due to respiratory illnesses.” Epidemiology 12(1):43-54. 

Glad, J.A., L.L. Brink, E.O. Talbott, P.C. Lee, X. Xu, M. Saul, and J. Rager. 2012. “The Relationship of Ambient 

ozone and PM2.5 Levels and Asthma Emergency Department Visits: Possible Influence of Gender and 

Ethnicity.” Archives of Environmental & Occupational Health 62 (2): 103-108. 

Graff Zivin, J., Neidell, M. 2012. “The impact of pollution on worker productivity.” American Economic Review, 

102, 3652-3673. 

Hall JV, Brajer V, Lurmann FW. 2003. “Economic Valuation of ozone-related School Absences in the South Coast 

Air Basin of California.” Contemporary Economic Policy 21(4):407-417. 

Harrington, W., and P.R. Portney. 1987. “Valuing the Benefits of Health and Safety Regulation.” Journal of Urban 

Economics 22:101-112. 

Hollman, F.W., T.J. Mulder, and J.E. Kallan. 2000. Methodology and Assumptions for the Population Projections of 

the United States: 1999 to 2100. Population Division Working Paper No. 38, Population Projections Branch, 

Population Division, U.S. Census Bureau, Department of Commerce. January. Available at 

<http://www.census.gov/population/www/documentation/twps0038/twps0038.html>. 

Huang, Y., Dominici, F., & Bell, M. L. 2005. Bayesian Hierarchical Distributed Lag Models for Summer Ozone 

Exposure and Cardio-Respiratory Mortality. Environmetrics, 16, 547-562. 

Hubbell B, Fann N, Levy J. 2009. Methodological considerations in developing local-scale health impact 

assessments: balancing national, regional, and local data. Air Qual. Atmos. Heal. 2:99–110; 

doi:10.1007/s11869-009-0037-z. 

Hubbell, B., A. Hallberg, D.R. McCubbin, and E. Post. 2005. “Health-Related Benefits of Attaining the 8-Hr Ozone 

Standard.” Environmental Health Perspectives 113:73–82. ICD, 1979 

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 on 

the Internet at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_tsd_expert_interview_summaries.pdf>. 

Industrial Economics, Incorporated (IEc). 2012. Updating BenMAP Income Elasticity Estimates—Literature Review. 

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/Benefits/IncomeElasticityUpdate_Recommendationswithappendices.pdf> 

Ito, K.; G.D. Thurston and R.A. Silverman. 2007. Characterization of PM2.5, gaseous pollutants, and meteorological 

interactions in the context of time-series health effects models. Journal of Exposure Science and Environmental 

Epidemiology. 17(S2):S45-60. 

Ito, K; De Leon, SF; Lippmann, M. 2005. Associations between ozone and daily mortality, analysis and metaanalysis. Epidemiology 16: 446-457. 

Jerrett M, Burnett RT, Pope CA, III, et al. 2009. “Long-Term Ozone Exposure and Mortality.” New England 

Journal of Medicine 360:1085-95. 

Katsouyanni, K.; J.M. Samet; H.R. Anderson; R. Atkinson; A.L. Tertre; S. Medina, et al. 2009. Air Pollution and 

Health: A European and North American Approach (APHENA). Health Effects Institute. 
6-98 

Kleckner, N., and J. Neumann. 1999. Recommended Approach to Adjusting WTP Estimates to Reflect Changes in 

Real Income. Memorandum to Jim DeMocker, U.S. EPA/OPAR. June 3. Available at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/klecknerandneumann1999.pdf>. 

Kloog, I., B.A. Coull, A. Zanobetti, P. Koutrakis, J.D. Schwartz. 2012. “Acute and Chronic Effects of Particles on 

Hospital Admissions in New England.” PLoS ONE. Vol 7 (4): 1-8. 

Kochi, I., B. Hubbell, and R. Kramer. 2006. “An Empirical Bayes Approach to Combining Estimates of the Value of 

Statistical Life for Environmental Policy Analysis.” Environmental and Resource Economics. 34: 385-406. 

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. 

Kunzli, N., R. Kaiser, S. Medina, M. Studnicka, O. Chanel, P. Filliger, M. Herry, F. Horak Jr., V. PuybonnieuxTexier, P. Quenel, J. Schneider, R. Seethaler, J-C Vergnaud, and H. Sommer. 2000. “Public-Health Impact of 

Outdoor and Traffic-Related Air Pollution: A European Assessment.” The Lancet 356:795-801. 

Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M. Studnicka. 2001. “Assessment of Deaths 

Attributable to Air Pollution: Should We Use Risk Estimates Based on Time Series or on Cohort Studies?” 

American Journal of Epidemiology 153(11):1050-55. 

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air Pollution and 

Mortality.” American Journal of Respiratory and Critical Care Medicine 173:667-672. 

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. 

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. 

Levy, JI; Chemerynski, SM; Sarnat, JA. 2005. Ozone exposure and mortality, an empiric Bayes metaregression 

analysis. Epidemiology 16: 458-468. 

Lichtenstein, S. and P. Slovic, eds. 2006. The construction of preference. New York: Cambridge University Press. 

Mansfield, Carol; Paramita Sinha; Max Henrion. 2009. Influence Analysis in Support of Characterizing Uncertainty 

in Human Health Benefits Analysis: Final Report. Prepared for U.S. EPA, Office of Air Quality Planning and 

Standards. November. Available at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/influence_analysis_final_report_psg.pdf>. 

Mar, T. F., J. Q. Koenig and J. Primomo. 2010. “Associations between asthma emergency visits and particulate 

matter sources, including diesel emissions from stationary generators in Tacoma, Washington.” Inhalation 

Toxicology 22 (6): 445-8. 

Mar, T. F., J. Q. Koenig and J. Primomo. 2010. “Associations between asthma emergency visits and particulate 

matter sources, including diesel emissions from stationary generators in Tacoma, Washington.” Inhal Toxicol. 

Vol. 22 (6): 445-8.Mar, T. F., T. V. Larson, 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(13): 809-

15. 

Mar, T. F., T. V. Larson, et al. 2004. “An analysis of the association between respiratory symptoms in subjects with 

asthma and daily air pollution in Spokane, Washington.” Inhalation Toxicology 16(13): 809-15. 
6-99 

Mar, TF; Koenig, JQ. 2009. Relationship between visits to emergency departments for asthma and ozone exposure 

in greater Seattle, Washington. Ann Allergy Asthma Immunol 103: 474-479.http://dx.doi.org/10.1016/S1081-

1206(10)60263-3 

Moolgavkar, S.H. 2000. “Air Pollution and Hospital Admissions for Diseases of the Circulatory System in Three 

U.S. Metropolitan Areas.” Journal of the Air and Waste Management Association 50:1199-1206. 

Mortimer, KM; Neas, LM; Dockery, DW; Redline, S; Tager, IB. 2002. The effect of air pollution on innercity 

children with asthma. Eur Respir J 19: 699-705. 

Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A Meta-Analysis.” Journal of Policy 

Analysis and Management 21(2):253-270. 

Mustafić H, Jabre P, Caussin C et al. 2012. “Main Air Pollutants and Myocardial Infarction: A Systematic Review 

and Meta-analysis” JAMA. 2012;307(7):713-721. 

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution 

Regulations. Washington, DC: The National Academies Press. Washington, DC. 

National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from 

Controlling Ozone Air Pollution. National Academies Press. Washington, DC. 

Neumann, J.E., M.T. Dickie, and R.E. Unsworth. 1994. Linkage between Health Effects Estimation and Morbidity 

Valuation in the Section 812 Analysis—Draft Valuation Document. Industrial Economics Incorporated (IEc) 

Memorandum to Jim DeMocker, U.S. Environmental Protection Agency, Office of Air and Radiation, Office of 

Policy Analysis and Review. March 31. Available at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/neumannn1994.pdf>. 

O'Connor, GT; Neas, L; Vaughn, B; Kattan, M; Mitchell, H; Crain, EF; III, ER; Gruchalla, R; Morgan, W; Stout, J; 

Adams, GK; Lippmann, M. 2008. Acute respiratory health effects of air pollution on children with asthma in 

US inner cities. J Allergy Clin Immunol 121: 1133-1139. 

Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis. Washington, DC. Available at 

<http://www.whitehouse.gov/omb/circulars/a004/a-4.html.> 

Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. 2001. “Air Pollution and Exacerbation of Asthma 

in African-American Children in Los Angeles.” Epidemiology 12(2):200–208. 

Ostro, B.D. 1987. “Air Pollution and Morbidity Revisited: A Specification Test.” Journal of Environmental 

Economics Management 14:87-98. 

Ostro, B.D. and S. Rothschild. 1989. “Air Pollution and Acute Respiratory Morbidity: An Observational Study of 

Multiple Pollutants.” Environmental Research 50:238-247. 

Patel, M. M., S. N. Chillrud, J. C. Correa, Y. Hazi, M. Feinberg, D. Kc, S. Prakash, J. M. Ross, D. Levy and P. L. 

Kinney. 2010. “Traffic-Related Particulate Matter and Acute Respiratory Symptoms among New York City 

Area Adolescents.” Environ Health Perspect. Sep;118(9):1338-43. 

Peel, J.L., P.E. Tolbert, M. Klein et al. 2005. Ambient air pollution and respiratory emergency department visits. 

Epidemiology. Vol. 16 (2): 164-74. 

Peng, R. D., H. H. Chang, et al. 2008. “Coarse particulate matter air pollution and hospital admissions for 

cardiovascular and respiratory diseases among Medicare patients.” Journal of the American Medical 

Association 299(18): 2172-9. 
6-100 

Peng, R. D., M. L. Bell, et al. 2009. “Emergency admissions for cardiovascular and respiratory diseases and the 

chemical composition of fine particle air pollution.” Environmental Health Perspectives 117(6): 957-63. 

Peters A. 2005. “Particulate matter and heart disease: evidence from epidemiological studies.” Toxicology and 

Applied Pharmacology Sept 1;207(2 Suppl):477-82. 

Peters, A., D. W. Dockery, J. E. Muller and M. A. Mittleman. 2001. “Increased particulate air pollution and the 

triggering of myocardial infarction.” Circulation 103 (23): 2810-5. 

Poloniecki, J.D., R.W. Atkinson., A.P. de Leon., and H.R. Anderson. 1997. “Daily Time Series for Cardiovascular 

Hospital Admissions and Previous Day’s Air Pollution in London, UK.” Occupational and Environmental 

Medicine 54(8):535-540 

Pope CA III; Thun MJ; Namboodiri MM; Dockery DW; Evans JS; Speizer FE; Heath CW Jr (1995). Particulate air 

pollution as a predictor of mortality in a prospective study of US adults. Am J Respir Crit Care Med, 151: 669-

674. 

Pope, C. A., III, J. B. Muhlestein, et al. 2006. “Ischemic heart disease events triggered by short-term exposure to 

fine particulate air pollution.” Circulation 114(23): 2443-8. 

Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. 1991. “Respiratory Health and PM10 Pollution: 

A Daily Time Series Analysis.” American Review of Respiratory Diseases 144:668-674. 

Pope, C.A., III, R.T. Burnett, G.D. Thurston, M.J. Thun, E.E. Calle, D. Krewski, and J.J. Godleski. 2004. 

“Cardiovascular Mortality and Long-term Exposure to Particulate Air Pollution.” Circulation 109: 71-77. 

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, 

Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the 

American Medical Association 287:1132-1141. 

Ransom MR, Pope CA III. 1992. Elementary school absences and PM10 pollution in Utah Valley. Environ Res 

58:204–219 

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. 

Rosamond, W., G. Broda, E. Kawalec, S. Rywik, A. Pajak, L. Cooper, and L. Chambless. 1999. “Comparison of 

Medical Care and Survival of Hospitalized Patients with Acute Myocardial Infarction in Poland and the United 

States.” American Journal of Cardiology 83:1180-1185 

Rowe, R.D., and L.G. Chestnut. 1986. “Oxidants and Asthmatics in Los Angeles: A Benefits Analysis—Executive 

Summary.” Prepared by Energy and Resource Consultants, Inc. Report to the U.S. Environmental Protection 

Agency, Office of Policy Analysis. EPA- 230-09-86-018. Washington, DC. 

Rowlatt, P., Spackman, M., Jones, S., Jones-Lee, M., Loomes, G. 1998. Valuation of deaths from air pollution. A 

Report for the Department of Environment, Transport and the Regions and the Department of Trade and 

Industry. National Economic Research Associates (NERA), London. 

Russell, M.W., D.M. Huse, S. Drowns, E.C. Hamel, and S.C. Hartz. 1998. “Direct Medical Costs of Coronary 

Artery Disease in the United States.” American Journal of Cardiology 81(9):1110-1115. 

Sarnat, J. A., Sarnat, S. E., Flanders, W. D., Chang, H. H., Mulholland, J., Baxter, L., Ozkaynak, H. 2013. 

Spatiotemporally resolved air exchange rate as a modifier of acute air pollution-related morbidity in Atlanta. J 

Expo Sci Environ Epidemiol. doi: 10.1038/jes.2013.32 
6-101 

Sasser, E. 2014. Response to Comments Regarding the Potential Use of a Threshold Model in Estimating the 

Mortality Risks from Long-term Exposure to Ozone in the Health Risk and Exposure Assessment for Ozone, 

Second External Review Draft. Memorandum to Holly Stallworth, Designated Federal Officer, Clean Air 

Scientific Advisory Committee from EPA/OAQPS Health and Environmental Impacts Division. 

Schildcrout, J. S., Sheppard, L., Lumley, T., Slaughter, J. C., Koenig, J. Q., & Shapiro, G. G. (2006). Ambient air 

pollution and asthma exacerbations in children: an eight-city analysis. Am J Epidemiol, 164(6), 505-517. 

Schwartz J, Dockery DW, Neas LM, Wypij D, Ware JH, Spengler JD, Koutrakis P, Speizer FE, Ferris BG Jr. 1994. 

“Acute effects of summer air pollution on respiratory symptom reporting in children.” American Journal of 

Respiratory and Critical Care Medicine 150(5 Pt 1):1234-42. 

Schwartz, J. 2005. How sensitive is the association between ozone and daily deaths to control for temperature? Am J 

Respir Crit Care Med 171: 627-631. 

Schwartz, J., and L.M. Neas. 2000. “Fine Particles are More Strongly Associated than Coarse Particles with Acute 

Respiratory Health Effects in Schoolchildren.” Epidemiology 11:6-10. 

Sheppard, L. 2003. “Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in Seattle, Washington, 

1987-1994.” In Revised Analyses of Time-Series Studies of Air Pollution and Health. Special Report. Boston, 

MA: Health Effects Institute. 

Shogren, J., and T. Stamland. 2002. “Skill and the Value of Life.” Journal of Political Economy 110:1168-1197. 

Silverman, R.A.; and K. Ito. 2010. Age-related association of fine particles and ozone with severe acute asthma in 

New York City. Journal of Allergy Clinical Immunology. 125(2):367-373. 

Slaughter, James C et al. 2005. “Association between particulate matter and emergency room visits, hospital 

admissions and mortality in Spokane, Washington.” Journal of Exposure Analysis and Environmental 

Epidemiology 15, 153–159. 

Smith et al. 2004. “Do the Near Elderly Value Mortality Risks Differently?” The Review of Economics and Statistics 

86(1): 423-429. 

Smith, D.H., D.C. Malone, K.A. Lawson, L.J. Okamoto, C. Battista, and W.B. Saunders. 1997. “A National 

Estimate of the Economic Costs of Asthma.” American Journal of Respiratory and Critical Care Medicine

156(3 Pt 1):787-793. 

Smith, K., Pattanayak SK, Van Houtven G. 2006. “Structural benefit transfer: An example using VSL estimates.” 

Ecological Economics 60: 361-371. 

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. 

Standard and Poor’s. 2000. “The U.S. Economy: The 25 Year Focus.” Winter. Available at 

<http://:www.standardandpoors.com>. 

Stanford, R., T. McLaughlin and L. J. Okamoto. 1999. “The cost of asthma in the emergency department and 

hospital.” American Journal of Respiratory and Critical Care Medicine 160 (1): 211-5. 

Sullivan, J., L. Sheppard, et al. 2005. “Relation between short-term fine-particulate matter exposure and onset of 

myocardial infarction.” Epidemiology 16(1): 41-8. 

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. 
6-102 

Tolley, G.S. et al. 1986. Valuation of Reductions in Human Health Symptoms and Risks. University of Chicago. 

Final Report for the U.S. Environmental Protection Agency, Office of Policy Analysis. January. Available at 

<http://yosemite.epa.gov/ee/epa/eerm.nsf/a7a2ee5c6158cedd852563970080ee30/eaa2842b3d2bc2798525644d0

053bf48!OpenDocument#_Section6>. 

Tsuge T, Kishimot A, Takeuchi K. 2005. “A choice experiment approach to the valuation of mortality.” Journal of 

Risk and Uncertainty 31(1):73-95. 

U.S. Bureau of Economic Analysis (U.S. BEA). 2004. BEA Economic Areas (EAs). U.S. Department of Commerce. 

Available at <http://www.bea.gov/regional/docs/econlist.cfm>. 

U.S. Census Bureau. 2001. Statistical Abstract of the United States, 2001, Section 12: Labor Force, Employment, 

and Earnings, Table No. 521. Washington, DC. 

U.S. Census Bureau. 2000. Component Assumptions of the Resident Population by Age, Sex, Race, and Hispanic 

Origin. Available online at: http://www.census.gov/population/www/projections/natdet-D5.html 

U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses. EPA 240-

R-00-003. National Center for Environmental Economics, Office of Policy Economics and Innovation. 

Washington, DC. September. Available at 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2007. Ozone Health Risk Assessment for Selected Urban Areas. 

Research Triangle Park, NC: EPA Office of Air Quality Planning and Standards. (EPA document number EPA 

452/R-07-009). Available at: <http://www.epa.gov/ttn/naaqs/standards/ozone/s_O3_cr.html>. 

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—

Health Criteria (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. 

July. Available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645 

U.S. Environmental Protection Agency (U.S. EPA). 2009a. 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). 2009b. Integrated Science Assessment for Particulate Matter 

(Final Report). EPA-600-R-08-139F. National Center for Environmental Assessment—RTP Division. 

December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010a. Final Regulatory Impact Analysis (RIA) for the NO2 

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/FinalNO2RIAfulldocument.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Quantitative Health Risk Assessment for Particulate 

Matter—Final Report. EPA-452/R-10-005. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. September. Available at 

<http://www.epa.gov/ttnnaaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf>. 
6-103 

U.S. Environmental Protection Agency (U.S. EPA). 2010c. Final Regulatory Impact Analysis (RIA) for the SO2

National Ambient Air Quality Standards (NAAQS). Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. June. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/fso2ria100602full.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010d. 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). 2010e. Guidelines for Preparing Economic Analyses. EPA 240-

R-10-001. National Center for Environmental Economics, Office of Policy Economics and Innovation. 

Washington, DC. December. Available at <http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0568-

50.pdf/$file/EE-0568- 50.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010f. Proposed Regulatory Impact Analysis (RIA) for the 

Transport Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. January. Available 

at <http://www.epa.gov/ttn/ecas/regdata/RIAs/proposaltrria_final.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2011a. The Benefits and Costs of the Clean Air Act 1990 to 

2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, Washington, DC. March. 

Available at <http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2011b. 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. June. Available at 

<http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2011c. Regulatory Impact Analysis for the Final Mercury and 

Air Toxics Standards. EPA-452/R-11-011. December. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the Proposed 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/ttn/ecas/regdata/RIAs/PMRIACombinedFile_Bookmarked.pdf. 

U.S. Environmental Protection Agency (U.S. EPA). 2012b. Health Risk and Exposure Assessment for Ozone. First 

External Review Draft. Research Triangle Park, NC: U.S. Environmental Protection Agency, Research Triangle 

Park, NC. (EPA document number EPA 452/P-12-001). 

U.S. Environmental Protection Agency (U.S. EPA). 2012c. 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 (U.S. EPA). 2012d. Provisional Assessment of Recent Studies on Health 

Effects of Particulate Matter Exposure. EPA/600/R-12/056F. National Center for Environmental Assessment—

RTP Division. December. Available at < http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247132>. 

U.S. Environmental Protection Agency (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). 
6-104 

U.S. Environmental Protection Agency (U.S. EPA). 2013b. Technical Support Document: Estimating the Benefit per 

ton of Reducing PM2.5 Precursors from 17 sectors. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. February. Available at: 

<http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2014a. Control of Air Pollution from Motor Vehicles: Tier 3 

Motor Vehicle Emission and Fuel Standards Final Rule: Regulatory Impact Analysis. EPA-420-R-14-005. 

Office of Transportation and Air Quality, Assessment and Standards Division. March. Available at: 

<http://www.epa.gov/otaq/documents/tier3/420r14005.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2014b. Health Risk and Exposure Assessment for Ozone. Final 

Report. Research Triangle Park, NC: U.S. Environmental Protection Agency, Research Triangle Park, NC. 

(EPA document number EPA-452/R-14-004a). 

U.S. Environmental Protection Agency (U.S. EPA). 2014c. Regulatory Impact Analysis of the Proposed Revisions to 

the National Ambient Air Quality Standards for Ground-Level Ozone. Research Triangle Park, NC: Office of 

Air Quality Planning and Standards. (EPA document number EPA-452/P-14-006, November 2014). Available 

at < http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_2008_ria.html>. 

U.S. Environmental Protection Agency (U.S. EPA), 2015a. Environmental Benefits Mapping and Analysis 

Program—Community Edition (Version 1.1). Research Triangle Park, NC. Available on the Internet at 

<hhtp://www2.epa.gov/benmap/>. 

U.S. Environmental Protection Agency (U.S. EPA). 2015b. BenMAP-CE User’s Manual Appendices. Office of Air 

Quality Planning and Standards. Research Triangle Park, NC. Available at 

<http://www2.epa.gov/benmap/manual-and-appendices-benmap-ce>. 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2000. An SAB Report on EPA’s 

White Paper Valuing the Benefits of Fatal Cancer Risk Reduction. EPA-SAB-EEAC-00-013. July. Available at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/41334524148BCCD6852571A700516498/$File/eeacf0

13.pdf>. 

U.S. Environmental Protection Agency Science Advisory Board (U.S. 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. EPA-SAB-COUNCIL-ADV-04-002. March. Available at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400433D5D/$File/counc

il_adv_04002.pdf>. 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2004b. Review of the Draft 

Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of the Clean Air Act, 1990-2020: 

An Advisory by the Advisory Council for Clean Air Compliance Analysis. EPA-SAB-COUNCIL-ADV-04-004. 

May. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/7CCBBFE15CD4C8B185256F17005E3079/$File/council_adv_04

004.pdf>. 

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 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). 2007. SAB Advisory on EPA’s 

Issues in Valuing Mortality Risk Reduction. EPA-SAB-08-001. October. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$File/sab-08-001.pdf>. 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2009a. Review of Integrated 

Science Assessment for Particulate Matter (First External Review Draft, May 2009). EPA-CASAC-09-008. 

Available at < 
6-105 

<http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/73ACCA834AB44A10852

575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf >. 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2009b. Review of Integrated 

Science Assessment for Particulate Matter (Second External Review Draft, July 2009). EPA-CASAC-10-001. 

November. Available at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/151B1F83B0231455

85257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>. 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010a. 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 at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/59e06b6c5ca66597852575

e7006c5d09!OpenDocument&TableRow=2.3#2.>. 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010b. Review of Risk 

Assessment to Support the Review of the Particulate Matter (PM) Primary National Ambient Air Quality 

Standards—External Review Draft (September 2009). EPA-CASAC-10-003. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/BC1ECC5D539EF723852

57678006D5754/$File/EPA-CASAC-10-003-unsigned.pdf>. 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2011. Review of Valuing 

Mortality Risk Reductions for Environmental Policy: A White Paper (December 10, 2010). EPA-SAB-11-011 

July. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/$File/EPA-SAB-11-

011-unsigned.pdf>. 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2012. CASAC Review of the 

EPA’s Health Risk and Exposure Assessment for Ozone (First External Review Draft - Updated August 2012) 

and Welfare Risk and Exposure Assessment for Ozone (First External Review Draft - Updated August 2012). 

U.S. Environmental Protection Agency Science Advisory Board. EPA-CASAC-13-002. 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 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. EPA-CASAC-14-005. 

U.S. Environmental Protection Agency, 2006. Final Regulatory Impact Analysis: PM2.5 NAAQS. Prepared by 

Office of Air and Radiation. Available: http://www.epa.gov/ttn/ecas/ria.html [accessed 10 March 2008]. 

Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates 

throughout the World.” Journal of Risk and Uncertainty 27(1):5-76. 

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, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland. 

Wilson, A.M., C.P. Wake, T. Kelly et al. 2005. Air pollution and weather, and respiratory emergency room visits in 

two northern New England cities: an ecological time-series study. Environ Res. Vol. 97 (3): 312-21. 

Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. 1990. “Medical Costs of Coronary Artery Disease in the United 

States.” American Journal of Cardiology 65(7):432-440. 
6-106 

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected of postneonatal infant 

mortality and particulate air pollution in the United States.” Environmental Health Perspectives 105(6): 608-

612. 

Woods & Poole Economics Inc. 2007. Population by Single Year of Age. 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 at 

<www.who.int/ipcs/methods/harmonization/areas/uncertainty%20.pdf>. 

World Health Organization (WHO). 1977. International Classification of Diseases, 9th Revision (ICD-9). Geneva: 

WHO. 

Zanobetti A. and Schwartz, J. 2006. “Air pollution and emergency admissions in Boston, MA.” Journal of 

Epidemiology and Community Health 60(10): 890-5. 

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. 
6A-1 

APPENDIX 6A: COMPREHENSIVE CHARACTERIZATION OF UNCERTAINTY IN 

OZONE BENEFITS ANALYSIS

Overview 

As noted in Chapter 6, 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 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 estimates. 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, 2010a, 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 benefits, 

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 PM2.5 benefits estimates, please see the 2012 

PM2.5 NAAQS RIA (U.S. EPA, 2012). 

6A.1 Description of Classifications Applied in the Uncertainty Characterization 

Table 6A-1 catalogs the most significant sources of uncertainty in the ozone benefits analysis 

and then characterizes four dimensions of that uncertainty briefly described below. The first two 

dimensions focus on the nature of the uncertainty. The third and fourth dimensions focus on the extent 

to which the analytic approach chosen in the benefits analysis either minimizes the impact of the 

uncertainty or quantitatively characterizes its impact. 
6A-2 

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. 

6A.1.1 Direction of Bias 

The “direction of bias” column in Table 6A-1 is an assessment of whether, if left unaddressed, 

an uncertainty would likely lead to an underestimate or overestimate of 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 (Ozone ISA) (U.S. EPA, 2013). Additional sources of information include 

advice from SAB and the National Academies of Science (NAS), as well as studies from the peerreviewed 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.” 

6A.1.2 Magnitude of Impact 

The “magnitude of impact” column in Table 6A-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, 2010a, 2011, 2014), but we have slightly revised the category names and the cutoffs here.167 The definitions used here are provided below. 

 

167 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. 

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. 
6A-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, 2010a, 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 (C-R) function for both categories of ozone-related mortality, and the 

mortality valuation, specifically for long-term exposure-related mortality. 

6A.1.3 Confidence in Analytic Approach 

The “confidence in analytic approach” column of Table 6A-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 

certain are we that EPA’s selected approach is the most plausible of the potential alternatives. Similar 
6A-4 

classifications have been included in previous risk and benefits analyses (U.S. EPA, 2010a, 2011).168

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, 2013) and by 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. 

6A.1.4 Uncertainty Quantification 

The column of Table 6A-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. 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 

development in order to assess quantitatively. Specifically, EPA applied this framework in multiple 

 

168 We have applied the same classification as The Benefits and Costs of the Clean Air Act from 1990 to 2020 (U.S. EPA, 

2011) 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. 
6A-5 

risk and exposure assessments (U.S. EPA, 2010a, 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). 

6A.2 Organization of the Qualitative Uncertainty Table 

Table 6A-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). 
6A-6 

Table 6A-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 Science Underlying the Analytical Approach 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 

Mortality generally dominates monetized benefits, so small uncertainties could have large impacts on the total monetized benefits. 

Our approach is consistent with the O3 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. EPASAB, 2010). 

Either Medium-High Medium 
6A-7 

Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Science Underlying the Analytical Approach Shape of the C-R functions, particularly at low concentrations for short-term ozone exposure-related mortality 

The direction of bias that assuming a 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 U.S., 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 nothreshold model for the C-R functions for 

short-term ozone mortality. However, the ISA notes that there is less certainty in the shape of the C-R function below 20 ppb due to the low density of data in this range (p. 6-254-255). 

Causal relationship between long-term ozone exposure and premature respiratory mortality 

Overestimate, if long-term ozone exposure does not have a causal relationship with premature mortality. 

Potentially High, if included in monetized benefits Medium 

Mortality generally dominates monetized benefits, so small uncertainties could have large impacts on the total monetized benefits. However, we have not included long-term ozone mortality in the monetized benefits for this analysis due to uncertainties in the cessation lag. 

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). 

Shape of the C-R functions, particularly at low concentrations for long-term ozone exposure-related respiratory mortality 

Either Potentially High, if included in monetized benefits Medium 
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Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Science Underlying the Analytical Approach 

The direction of bias that assuming a 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. However, we have not included long-term ozone mortality in the monetized benefits for this analysis due to uncertainties in the cessation lag. 

In their memo (see Sasser 2014) clarifying the results of their study (Jerrett et al., 2009) regarding long-term ozone exposure-related 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 model...”. 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 quantitative analysis supporting uncertainty characterization. 

Exposure error in epidemiology studies 

Underestimate (generally) Medium Low-Medium 

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. 

Unknown Medium Medium 
6A-9 

Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Science Underlying the Analytical Approach Adjustment of risk coefficients to 8-hour maximum from 24-hour average or 1-hour maximum in the epidemiology studies 

We converted these metrics to maximum 8-hour average ozone concentration using standard conversion functions based on observed relationships in the underlying studies. If the relationships between air metrics reported in the studies differ systematically from the relationships seen across the modeling domain, then bias could be introduced. 

This conversion does not affect the relative magnitude of the health impact function. However, the pattern of 8-hour maximum concentrations for a particular location over an ozone season could differ from the pattern of 1-hour max or 24-hour average metrics for that same location. Consequently, monetized benefits could differ for a particular location depending on the metric used in modeling benefits. 

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 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. 

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 

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. 

Confounding and effect modification by copollutants 

Either, depending upon the pollutant. Medium Medium 

Disentangling the health responses of combustion-related pollutants (i.e., PM, SOx, NOx, ozone, and CO) is a challenge. The PM ISA states that co-pollutants may mediate the effects of PM or PM may influence the toxicity of copollutants (U.S. EPA, 2009, p. 1–16). Alternately, effects attributed to one pollutants may be due to another. 

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 multi-pollutant effect 

estimates when those estimates are available to avoid double-counting and satisfy other selection criteria. In addition, we apply multi-city effect estimates when available. 

Application of C-R relationships only to the original study population 

Underestimate Low High 

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. EPASAB, 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. 

Uncertainties Associated with Economic Valuation 

Unknown High Medium 
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Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Science Underlying the Analytical Approach Mortality Risk Valuation/Value-of-aStatistical-Life (VSL) 

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, 2010b, U.S. EPA-SAB, 2011c). 

Cessation lag structure for long-term ozone mortality 

Unknown Medium, if included in the monetized benefits Low 

We included both a zero (no) lag and 20-year segmented lag model in completing the quantitative uncertainty analysis involving dollar benefits for long-term respiratory mortality. Given that available information does not lead to the selection of a particular lag model, we are not in a position to classify the direction of potential bias associated with this source of uncertainty. 

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. However, we have not included long-term ozone mortality in the monetized benefits for this analysis due to uncertainties in the cessation lag. 

As discussed in section 6.7.3.1, in presenting dollar benefit estimates as part of the quantitative analysis supporting 

uncertainty characterization (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 SAB-HES (USEPASAB, 2010, 2004c). 

Income growth adjustments 

Either Medium Medium 

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. It is difficult to forecast future income growth; however, in the absence of readily available income data projections, per capita GDP is the best available option. 

Morbidity valuation Underestimate Low Low 
6A-11 

Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Science Underlying the Analytical Approach 

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 valuations 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). 

Uncertainties Associated with Baseline Incidence and Population Projections Population estimates and projections 

Either Low–Medium Medium 

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 ozone 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 wellestablished 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. 

Uncertainty in projecting baseline incidence rates for mortality 

Unknown Low-Medium Medium 

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. 

Uncertainty in projecting baseline 

Either, depending on the health endpoint Low Low-Medium 
6A-12 

Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Science Underlying the Analytical Approach incidence rates and prevalence rates for morbidity 

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). 

Uncertainties Associated with Omitted Benefits Categories Unquantified ozone health benefit categories, such as worker productivity and long-term mortality 

Underestimate High Low 

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 shortterm 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). 

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 

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. 
6A-13 

6A.3 References 

Alberini, Anna and Alan Krupnick. 2000. “Cost-of-Illness and Willingness-to-Pay Estimates of the Benefits of 

Improved Air Quality: Evidence from Taiwan.” Land Economics 

Vol. 76, No. 1 (Feb), pp. 37-53. 

Centers for Disease Control and Prevention (CDC). 2008. National Center for Health Statistics. National Health 

Interview Survey, 1999-2008. 

Crocker, T. D., & Horst, R. L. (1981). Hours of work, labor productivity, and environmental conditions: A case 

study. The Review of Economics and Statistics, 63(3), 361-368. 

Fann, N.; K.R. Baker and C.M. Fulcher. 2012. “Characterizing the PM2.5-related health benefits of emission 

reductions for 17 industrial, area and mobile emission sectors across the U.S.” Environment International 49 

41–151. 

Graff Zivin, J., Neidell, M. (2012). “The impact of pollution on worker productivity.” American Economic Review, 

102, 3652-3673. 

Industrial Economics, Incorporated (IEc). 2012. Updating BenMAP Income Elasticity Estimates—Literature 

Review. Memo to Neal Fann. March. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/Income%20Elasticity%20Update_Recommendations_noAppD.p

df> 

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. 

Mansfield, Carol; Paramita Sinha; Max Henrion. 2009. Influence Analysis in Support of Characterizing Uncertainty 

in Human Health Benefits Analysis: Final Report. Prepared for U.S. EPA, Office of Air Quality Planning and 

Standards. November. Available on the internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/influence_analysis_final_report_psg.pdf>. 

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution 

Regulations. Washington, DC: The National Academies Press. Washington, DC. 

National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from 

Controlling Ozone Air Pollution. National Academies Press. Washington, DC. 

Sasser, E. 2014. Response to Comments Regarding the Potential Use of a Threshold Model in Estimating the 

Mortality Risks from Long-term Exposure to Ozone in the Health Risk and Exposure Assessment for Ozone, 

Second External Review Draft. Memorandum to Holly Stallworth, Designated Federal Officer, Clean Air 

Scientific Advisory Committee from EPA/OAQPS Health and Environmental Impacts Division. 

U.S. Environmental Protection Agency (U.S. EPA). 2001. Risk assessment guidance for Superfund. Vol. III, Part A. 

Process for conducting probabilistic risk assessment (RAGS 3A). Washington, DC, United States 

Environmental Protection Agency (EPA 540-R-02-002; OSWER 9285.7-45; PB2002 963302. Available on the 

Internet at <http://www.epa.gov/oswer/riskassessment/rags3adt/index.htm>. 

U.S. Environmental Protection Agency (U.S. EPA). 2004. EPA’s risk assessment process for air toxics: History and 

overview. In: Air toxics risk assessment reference library. Vol. 1. Technical resource manual. Washington, DC, 
6A-14 

United States Environmental Protection Agency, pp. 3-1–3-30 (EPA-453-K-04-001A. Available on the Internet 

at <http://www.epa.gov/ttn/fera/data/risk/vol_1/chapter_03.pdf>. 

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 at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010a. Quantitative Health Risk Assessment for Particulate 

Matter—Final Report. EPA-452/R-10-005. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. September. Available on the Internet at 

<http://www.epa.gov/ttnnaaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Valuing Mortality Risk Reductions for Environmental 

Policy: A White Paper (SAB Review Draft). December. Available on the Internet at 

<http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0563-1.pdf/$file/EE-0563-1.pdf>. 

 U.S. Environmental Protection Agency (U.S. EPA). 2011. The Benefits and Costs of the Clean Air Act 1990 to 

2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, Washington, DC. March. 

Available on the Internet at <http://www.epa.gov/oar/sect812/feb11/fullreport.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 (U.S. EPA). 2013. Integrated Science Assessment of Ozone and Related 

Photochemical Oxidants (Final Report). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R10/076F. Available at: <http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>. 

U.S. Environmental Protection Agency (U.S. EPA). 2014. Health Risk and Exposure Assessment for Ozone Final 

Report. EPA-452/R-14-004a. Office of Air Quality Planning and Standards, Health and Environmental Impacts 

Division. August. Available at: < http://www.epa.gov/ttn/naaqs/standards/ozone/data/20140829healthrea.pdf>. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 1999. An SAB Advisory: The 

Clean Air Act Section 812 Prospective Study Health and Ecological Initial Studies. EPA-SAB-COUNCILADV-99-005, Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/1AFDEBB45C57F6E5852571930058C1A5/$File/coua9905.pdf>. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2000. The Clean Air Act 

Amendments (CAAA) Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Advisory 

Council on Clean Air Compliance Analysis: Costs and Benefits of the CAAA. EPA-SAB-COUNCIL-ADV-00-

002. October. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/E992F918DF32131A852571B9004571F7/$File/coua0002.pdf>. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2001. REVIEW OF THE 

DRAFT ANALYTICAL PLAN FOR EPA’S SECOND PROSPECTIVE ANALYSIS - BENEFITS AND 

COSTS OF THE CLEAN AIR ACT 1990- 2020. EPA-SAB-COUNCIL-ADV-01-004, Available on the 

Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/572DC1989A3986CC8525718D006BAB8B/$File/councila01004.

pdf>. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. 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. EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at 
6A-15 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400433D5D/$File/counc

il_adv_04002.pdf>. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004b. Review of the Draft 

Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of the Clean Air Act, 1990-2020: 

An Advisory by the Advisory Council for Clean Air Compliance Analysis. EPA-SAB-COUNCIL-ADV-04-

004. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/7CCBBFE15CD4C8B185256F17005E3079/$File/council_adv_04

004.pdf>. 

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>. 
6B-1 

APPENDIX 6B: QUANTITATIVE ANALYSES COMPLETED IN SUPPORT OF 

UNCERTAINTY CHARACTERIZATION

Overview 

The benefits analysis presented in Chapter 6 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 

uncertainty into the estimates of benefits and that alternative choices exist for some inputs to the 

analysis, such as the concentration-response (C-R) functions for mortality. 

This appendix presents a set of quantitative analysis completed in support of uncertainty 

characterization including exploration of: (a) alternative studies in modeling short-term ozone 

exposure-related mortality (section 6B.1), (b) monetized benefits associated with mortality 

resulting from long-term exposure to ozone (section 6B.2), (c) the potential impact of thresholds 

in long-term ozone exposure-related mortality in incidence and benefits estimates (section 6B.3), 

(d) alternative response functions developed through expert elicitation for long-term PM2.5

exposure-related mortality (section 6B.4), (e) alternative assumptions regarding income elasticity 

on benefits derived using willingness-to-pay (WTP) functions (section 6B.5), (f) age groupdifferentiated aspects of short-term ozone exposure-related mortality (including total avoided 

incidence, life years gained and percent reduction in baseline mortality - Section 6B.6), (g) 

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 (Section 6B.7) and (h) ozone-related impacts on outdoor worker 

productivity (including a detailed discussion of the methodology used in modeling and 

presentation of results, Section 6B.8). 
6B-2 

For the core analyses, we estimated benefits for two scenarios: 2025 and post-2025. 

However, in conducting these quantitative analyses supporting uncertainty characterization, we 

used the 2025 scenario as the basis for making our calculations, since analytical findings for this 

scenario would generally hold for the post-2025 scenario. 

6B.1 Alternative C-R Functions for Short-term Exposure to Ozone 

Table 6B-1 presents the results of applying alternative effect estimates identified for 

modeling short-term exposure-related mortality for ozone, along with the set of core risk 

estimates (these uncertainty characterization results are also reflected in Figure 6-4 presented in 

the body of the document). The suite of effect estimates included in the analysis consideration 

for both meta-analyses and multi-city epidemiology studies. The rationale for the specific mix of 

effect estimates included in the quantitative analysis is presented in section 6.6.3.2. 

Table 6B-1. Quantitative Analysis for Alternative C-R Functions for Short-term 

Exposure to Ozone a

Health Effect 

Revised and Alternative Standards 

(95th percentile confidence intervals) 

70 ppb 65 ppb 

Avoided Short-Term Mortality - Core Analysis 

multi-city studies 

Smith et al. (2009) (all ages) 96 490 

(47 to 140) (240 to 740) 

Zanobetti and Schwartz (2008) (all ages) 160 820 

(86 to 240) (440 to 1,200) 

Avoided Short-Term Mortality - Uncertainty Analysis 

multi-city studies 

Smith et al. (2009) (all ages) 

co-pollutant model with PM10

77 390 

(-21 to 170) (-110 to 890) 

Schwartz (2005) (all ages) 120 610 

(37 to 200) (190 to 1,000) 

Huang et al. (2005) (cardiopulmonary) 110 580 

(42 to 180) (220 to 940) 

Bell et al. (2004) (all ages) 78 400 

(26 to 130) (130 to 660) 

meta- analyses 

Bell et al. (2005) (all ages) 250 1,300 

(120 to 380) (610 to 2,000) 

Ito et al. (2005) (all ages) 350 1,800 

(210 to 490) (1,100 to 2,500) 

Levy et al. (2005) (all ages) 350 1,800 

(240 to 470) (1,200 to 2,400) 

a All estimates are rounded to two significant digits. 
6B-3 

This quantitative analysis showed that the two core incidence estimates fall within (and 

towards the lower end of) the broader range resulting from application of the seven alternative 

effect estimates (note that these observations based on incidence would also hold for the 

matching set of dollar benefit estimates generated for this endpoint category). 

6B.2 Monetized Benefits for Premature Mortality from Long-term Exposure to Ozone 

As discussed in section 6.3, due to uncertainty in specifying the temporal lag structure 

associated with reductions in long-term ozone-related respiratory mortality, we have included 

these estimates as an uncertainty analysis and not as part of the core dollar benefit estimate. 

Table 6B-2 presents the dollar benefits (2011$) associated with modeled reductions in long-term 

ozone-related respiratory mortality. These benefit estimates are generated using non-threshold 

models obtained from Jerrett et al. (2009) because we present the potential threshold analysis in 

the section 6B.3. Additional detail on the effect estimates used in the core analysis and this 

uncertainty analysis is presented in section 6.6.3.2. Section 6.6.4 provides additional detail on 

the approach used in valuing these mortality estimates including how uncertainty related to 

cessation lag was addressed. 

Table 6B-2. Monetized Benefits for Mortality from Long-term Exposure to Ozone 

(millions of 2011$) (2025 and post-2025 scenarios)a, d

70 ppb 65 ppb

Avoided Long-term Respiratory Mortality

2025 scenario

$3,400 $17,000

($280 to $10,000) ($1,400 to $52,000)

$2,800 to $3,100 $14,000 to $16,000

($250 to $8,400) ($1,200 to $47,000)

post-2025 scenario

$3,000 $6,000

($240 to $8,400) ($490 to $18,000)

$2,400 to $2,700 $4,900 to $5,400

($200 to $8,000) ($400 to $18,000)

Revised and Alterative Standard Levels

(95th percentile confidence intervals)

multi-city study

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) no lag b

multi-city study

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) no lag b

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) 20 yr 

segmented lag c

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) 20 yr 

segmented lag c

Health Effectb
6B-4 

a

 See section 6B.3 for the quantitative analysis for potential thresholds in the C-R function for long-term exposurerelated mortality. Observations from that analysis can be applied to these results. 

b

 The zero-lag model is not affected by discounting. The values in parentheses reflect 95th percentile confidence 

intervals. 

c The range (outside of the parentheses) results from application of 7% and 3% discount rates after applying the 20-

year segmented lag (i.e., ranging from the 7% discount rate up to the 3% discount rate). The range in parentheses 

reflects the 95th percentile confidence interval in combination with the discount rate (i.e., ranging from the 2.5th 

percentile and the 7% discount rate up to the 97.5th percentile and the 3% discount rate). 

d

 All estimates rounded to two significant digits. 

This quantitative analysis suggests that if included in the core benefit estimate, long-term 

ozone exposure-related mortality could add substantially to the overall benefits. 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. 

6B.3 Threshold Analysis for Premature Mortality Incidence and Benefits from Longterm Exposure to Ozone 

In estimating long-term ozone mortality (including the benefit estimates presented in the 

last section), we employed a continuous non-threshold C-R function relating ozone exposure to 

premature death. However, as discussed in Section 6.6.3.2, there is uncertainty regarding the 

potential existence and location of a threshold in the C-R function relating mortality and longterm ozone concentrations. Thus, we have included a quantitative 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 the Clean Air 

Scientific Advisory Committee (CASAC) (U.S. EPA-SAB, 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., 

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 
6B-5 

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 coefficients. Specifically, we estimate long-term 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.169 Table 6B-3 provides the results of 

this uncertainty analysis (based on modeling incidence) for 65 ppb and 70 ppb. The same pattern 

in terms of relative reductions across thresholds would also hold for the monetized benefit 

estimates. 

Table 6B-3. Long-term Ozone Mortality Incidence at Various Assumed Thresholds a 

Threshold Concentration 70 ppb 65 ppb 

No threshold 340 1,700 

40 ppb 260 1,300 

45 ppb 200 910 

50 ppb 59 210 

55 ppb 6 66 

56 ppb 5 60 

60 ppb 3 19 

a

 All estimates rounded to two significant digits. 

The results of the uncertainty analysis based on the suite of threshold-based risk 

coefficients suggest that threshold models can substantially lower estimates of ozone-attributable 

long-term mortality. For example, estimated benefits for long-term mortality using a model that 

 

169 There is a separate effect coefficient (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 coefficient applied to ozone concentrations above the threshold. 
6B-6 

includes a 55 ppb threshold are approximately 70% less than long-term mortality benefits 

estimated using the 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. 

6B.4 Alternative C-R Functions for PM2.5–Related Mortality 

In estimating PM2.5 co-benefits, monetized benefits are driven largely by reductions in 

mortality. Therefore, it is particularly important to attempt to characterize the uncertainties 

associated with reductions in premature mortality. In addition to the ACS and Six Cities cohort 

studies, 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, 2009) and the Provisional 

Assessment (U.S. EPA, 2012a).170, Table 6B-4 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 elicitation171 sponsored by the EPA 

(Roman et al., 2008; IEc, 2006) to demonstrate the sensitivity of the benefits estimates to 12 

expert-defined C-R functions. The PM2.5 expert elicitation and the derivation of C-R functions 

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 we 

summarize the key attributes of this study relative to the interpretation of the estimates of PMrelated mortality reported here. We describe also how the epidemiological literature has evolved 

since the expert elicitation was conducted in 2005 and 2006. 

 

170 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. 

171 Expert elicitation is a formal, highly-structured and well-documented process whereby expert judgments, usually 

of multiple experts, are obtained (Ayyub, 2002). 
6B-7 

Table 6B-4. Summary of Effect Estimates from Recent Cohort Studies in North America 

Associated with Change in Long-Term Exposure to PM2.5

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. 

(2012)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. 
6B-8 

Specifically, during their interviews, the experts highlighted several uncertainties inherent within 

the epidemiology literature, such as causality, concentration thresholds, effect modification, role 

of short- and long-term exposures, potential confounding, and exposure misclassification. For 

several of these uncertainties, such as causality, we are able to use the expert-derived 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”172 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 

nature. The experts also noted a series of limitations in these two cohort studies. In the case of 

 

172 These criteria are substantively similar to EPA’s study selection criteria identified in Table 6-5 of Chapter 6. 
6B-9 

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]). 

It is important to 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 6B-4. 

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 

representativeness, these limitations preclude us from using these studies in our core benefits 
6B-10 

results, and we instead present the risk coefficients from these other multi-state cohorts in Table 

6B-4. However, we have completed an uncertainty analysis based on application of the full set of 

expert-derived effect estimates. To preserve the breadth and diversity of opinion on the expert 

panel, we do not combine the expert results (Roman et. al., 2008). This presentation of the 

expert-derived results is generally consistent with SAB advice (U.S. EPA-SAB, 2008), which 

suggested 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. 

Table 6B-5 presents the results of this uncertainty analysis using the expert elicitation 

results for the 2025 scenario for 70 ppb. Overall conclusions from this analysis are also 

applicable to the post-2025 scenario. 

Table 6B-5. PM2.5 Co-benefit Estimates using Two Epidemiology Studies and Functions 

Supplied from the Expert Elicitation 

C-R Function 70 ppb (2025 Scenario) 

Krewski et al. (2009) 220 

Lepeule et al. (2012) 500 

Expert K 50 

Expert G 260 

Expert L 310 

Expert D 320 

Expert H 330 

Expert J 360 

Expert F 430 

Expert C 450 

Expert I 450 

Expert B 460 

Expert A 570 

Expert E 740 

a

 All estimates rounded to two significant digits. 

The values presented in Table 6B-5 indicate that the two core incidence estimate fall 

within the range of alternative C-R function-based estimates obtained through expert elicitation. 

Figure 6-5 in the body of the document reproduces these benefit estimates, but presents them in 

terms of the associated dollar benefits (7% discount rate in 2011$) rather than incidence 
6B-11 

estimates (observations presented here for the uncertainty analysis results reflected modeled 

incidence estimates - i.e., spread in results and relative position of the two core estimates - also 

hold for dollar benefit estimates presented in Figure 4-6. 

6B.5 Income Elasticity of Willingness-to-Pay 

As discussed in Chapter 6, our estimates of monetized benefits account for growth in real 

GDP per capita by adjusting the WTP for individual endpoints based on the central estimate of 

the adjustment factor for each of the categories (minor health effects, severe and chronic health 

effects, premature mortality, and visibility). We examined how sensitive the estimate of total 

benefits is to alternative estimates of the income elasticities. Income growth projections are only 

currently available in BenMAP through 2024, so both the 2025 and post-2025 scenario estimates 

use income growth through 2024 only and are therefore likely underestimates. 

Table 6B-6 lists the ranges of elasticity values used to calculate the income adjustment 

factors, while Table 6B-7 lists the ranges of corresponding adjustment factors. The results of this 

uncertainty analysis for the two benefit categories are presented in Table 6B-8. 

Table 6B-6. 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 6B-7. 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. 
6B-12 

Table 6B-8. 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

 $31 $150 $33 $162 $38 $186 

Premature Mortality c $950 $4,100 $1,000 $5,200 $1,700 $8,500 

a

 All estimates rounded to two significant digits. Only reflects income growth to 2024. 

b For illustrative purposes, we evaluate minor restricted activity days (MRADS) resulting from short-term ozone 

exposure is the minor health effect here. 

c

 Short-term mortality using Smith et al. (2009) and 3% discount rate. Results using other short-term 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 8% 

to 76% greater than the core 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 much less pronounced, ranging from 5% to 21% greater than the core estimate for minor 

effects. These observations (in terms of relative impact from alternative elasticities) hold for the 

revised and alternative standard levels analyzed under both the 2025 and post-205 scenarios. 

6B.6 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, the National Research Council (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, life-years 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, 2004). To address these recommendations, we use simplifying assumptions to 

estimate the number of life 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 revised 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, 
6B-13 

2006, 2010, 2011a), 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, 2011). 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 PM2.5 NAAQs RIA for more information about the avoided life years lost 

from PM2.5 exposure (U.S. EPA, 2012b). The analysis performed for the PM NAAQS RIA 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, that analysis found that the average individual who would otherwise have died 

prematurely from PM exposure would gain 16 additional years of life. 

Estimated Life Years Gained 

To estimate 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. (2012). 

We have not estimated the change in average life expectancy at birth in this RIA. Because life 
6B-14 

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), we would 

expect average life expectancy changes associated with air pollution exposure to always be 

significantly smaller than the average number of life years lost by an individual 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: 

௡ ௜ܯ	ൈ௜ 	ܧܮ ∑	 ൌ ݏݎܻ݁ܽ	݂݅݁ܮ	݈ܽݐ݋ܶ

௜ୀଵ (6.2) 

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 6B-9 

summarizes the number of avoided deaths (by age range) attributable to ozone for the revised 

and alternative standards for the 2025 scenario. Table 6B-10 summarizes the modeled number of 

life years gained (for each age range) by reducing ozone for the revised and alternative standards 

analyzed for the 2025 scenario. We then calculated the average number of life years gained per 

avoided premature mortality. 
6B-15 

Table 6B-9. Potential Reduction in Premature Mortality by Age Range from Attaining 

the Revised and Alternative Ozone Standards (2025 scenario) a, b 

Age Range b Revised and Alternative Standards 

70 ppb 65 ppb 

0-4 0.3 1.5 

5-9 0.068 0.33 

10-14 0.072 0.35 

15-19 0.1 0.5 

20-24 0.14 0.69 

25–29 0.29 1.4 

30–34 0.3 1.5 

35–44 1.6 7.9 

45–54 4.1 20 

55–64 10 52 

65–74 22 110 

75–84 29 150 

85–99 28 150 

Total ozone-attributable mortality 96 490 

a

 Estimates rounded to two significant digits. 

b

 Effects calculated using Smith et al. (2009). 

Table 6B-10. Potential Years of Life Gained by Age Range from Attaining the Revised and 

Alternative Ozone Standards (2025 Scenario) a,b

Age Range b Revised and Alternative Standard 

70 ppb 65 ppb 

0-4 24 110 

5-9 5 23 

10-14 5 23 

15-19 6 30 

20-24 8 38 

25–29 14 70 

30–34 13 66 

35–44 57 280 

45–54 110 550 

55–64 200 990 

65–74 260 1,300 

75–84 190 970 

85–99 65 340 

Total life years gained 960 4,800 

Average life years gained per individual 10 10 

a

 Estimates rounded to two significant digits. 

b

 Effects calculated using Smith et al. (2009). 

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 6B-9), but half of the life years would occur in populations younger than 
6B-16 

65 (see Table 6B-10). 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 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 the revised 

and alternative standards by the total number of deaths in each county. Table 6B-11 shows the 

reduction in all-cause mortality attributed to reducing ozone exposure to the revised and 

alternative primary standards for the 2025 scenario. 

Table 6B-11. Estimated Percent Reduction in All-Cause Mortality Attributed to the 

Proposed Primary Ozone Standards (2025 Scenario) a 

Age Range b Revised and Alternative Standards 

70 ppb 65 ppb 

0-4 0.0085% 0.0409% 

5-9 0.0084% 0.0411% 

10-14 0.0084% 0.0415% 

15-19 0.0086% 0.0419% 

20-24 0.0085% 0.0412% 

25–29 0.0084% 0.0409% 

30–34 0.0083% 0.0408% 

35–44 0.0081% 0.0404% 

45–54 0.0082% 0.0410% 

55–64 0.0085% 0.0429% 

65–74 0.0086% 0.0436% 

75–84 0.0085% 0.0433% 

85–99 0.0079% 0.0415% 

a

 To illustrate the slight variations in percent reductions across age ranges, we present results rounded to three 

significant digits (rather than two as is typically done for other estimates in this RIA). 

Results presented in Table 6B-11 highlight that when reductions in ozone-attributable 

mortality (in going from baseline to the revised or alternative standard level) are considered as a 
6B-17 

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. 

6B.7 Evaluation of Mortality Impacts Relative to the Baseline Pollutant Concentrations 

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, 2013). 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 ozoneattributable 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 semi-quantitatively, along with many other factors, when 

interpreting the risk estimates rather than as a fixed, bright-line). 

Figures 6B-1 and 6B-2 compare the distribution of short-term ozone exposure-related 

mortality to the underlying distribution of the summer season 8-hour maximum ozone 

concentrations. Both figures (probability and cumulative probability) are based on Smith et al. 

(2009) using mortality results at each 12 km grid cell and the associated summer season 8-hour 

maximum concentration in the baseline. In addition, each figure includes separate plots for the 

revised and alternative standard levels. Figure 6B-1 shows that approximately 45% of the 

premature mortalities estimated for the 65 ppb alternative standard is associated with baseline 

ozone concentrations between 40 and 45 ppb. Because this baseline range is represents the mean 
6B-18 

across the ozone season of 8-hour max values within a given grid cell, the actual distribution of 

8-hour max values on a daily basis is likely wider than the 40-45 ppb range. 

Figure 6B-1. Premature Ozone-related Deaths Avoided for the Revised and Alternative 

Standards (2025 scenario) According to the Baseline Ozone Concentrations 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

0 to 15

15 to 2020 to 2525 to 3030 to 3535 to 4040 to 4545 to 50

50 to 5555 to 6060 to 6565 to 7070 to 7575 to 80

Percentage of reduction in short‐term ozone

related mortality

Ozone season mean daily 8hr max baseline (ppb)

75‐70 75‐65
6B-19 

Figure 6B-2. Cumulative Probability Plot of Premature Ozone-related Deaths Avoided for 

the Revised and 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 the revised and 

alternative standards, we adjust the design value at each monitor exceeding the standard 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 

0

0.2

0.4

0.6

0.8

1

1.2

0 to 15

15 to 2020 to 2525 to 30

30 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
6B-20 

simulating attainment can lead to benefits in counties that are below the revised or 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 revised or alternative 

standards because it would omit benefits attributable to emissions reductions in non-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. 

As shown in Figures 6B-1 and 6B-2, the vast majority of reductions in short-term 

exposure-related mortality for ozone occur in grid cells with mean 8-hour max baseline levels 

(across the ozone season) between 35 and 55 ppb. Comparing patterns across the revised and 

alternative standard levels, the upper end of the distribution shifts downwards as increasingly 

lower standard levels are analyzed (see Figure 6B-2). 

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 
6B-21 

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, 2011c). 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.173 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, 2009). 

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, 2012b). 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 

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.174 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 

 

173 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). 

174 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. 
6B-22 

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 the revised or alternative 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 6B-12 provides the percentage of the population exposed above and below two 

concentration benchmarks in the modeled baseline for the sector modeling. Figure 6B-3 shows a 

bar chart of the percentage of the population exposed to various air quality levels in the baseline, 

and Figure 6B-4 shows a cumulative distribution function of the same data. Both figures identify 

the LML for each of the major cohort studies. 
6B-23 

Table 6B-12. Population Exposure in the Baseline Sector Modeling (used to generate the 

benefit-per-ton estimates) Above and Below Various Concentration 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 et al., the LML is 5.8 μg/m3 and one standard deviation below the mean is 11.0 μg/m3. 

For Lepeule et al., the LML is 8 μg/m3

 and 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 6B-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 PopulationExposed

Baseline Annual MeanPM2.5 Level (μg/m3)

LML of Lepeule et al.

(2012)study

LML of Krewski et

al. (2009) study
6B-24 

Figure 6B-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). 

6B.8 Ozone-related Impacts on Outdoor Worker Productivity 

The EPA last quantified the value of ozone-related worker productivity in the final 

Regulatory Impact Analysis supporting the Transport Rule (U.S. EPA, 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 groundAmong 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 populationexposed

Baseline Annual MeanPM2 5 Level(μg/m3)

LML of Krewski et

al. (2009) study

LML of Lepeule et

al. (2012) study
6B-25 

level ozone and the productivity of agricultural workers because of the vintage of the underlying 

data, the Agency subsequently omitted this endpoint. 

In a recent study, Graff Zivin and Neidell (2012) combined data on individual-level daily 

harvest rates for Outdoor Agricultural Workers (OWAs) with ground-level ozone concentrations 

to characterize changes in worker productivity as a result of ozone exposure. The authors used 

data on harvest rates from a 500-acre farm in the Central Valley of California. That farm 

produced three crops (blueberries 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 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 recognize that there is 
6B-26 

significant uncertainty in the generalizability of this study and the need for additional research 

and peer review in guiding the monetization of agriculatural productivity impacts. Because we 

received no comments on this proposed approach, we now include the results as part of our 

uncertainty analysis. 

Below we provide the function, input data and results for the analysis. 

ݓ݋ݎܩ݈݌݉ܧ ∗ ݏݎ݇݁ݎ݋ܹ݃ܣݎ݋݋݀ݐݑܱ ∗ ܹܽ݃݁ݎ݋݋݀ݐݑܱݕ݈ܽ݅ܦ ∗ ܳܣܽݐ݈݁ܦ ∗ ߚ	 ൌ ܻ

Table 6B-13. Definitions of Variables Used to Calculate Changes in Worker Productivity 

Variable Definition 

ߚ Percent change in daily outdoor worker productivity per 1ppb change in ozone 

(Graff Zivin and Neidell, 2012) 

ܦ݈݁ݐܽܣܳ Summer season average of daily 9 hour average (6am to 3pm) 

ܦ݈ܽ݅ݕܱݐݑ݀ݎ݋݋ܹܽ݃݁ Summer season daily wage for agricultural non-livestock workers in 17 

USDA-defined regions (USDA, 2012) 

ܱݐݑ݀ܣݎ݋݋ܹ݃ݎ݋݇݁ݏݎ Summer season number of workers employed in outdoor non-livestock 

agricultural per county (USDA, 2010; US Census, 2010) 

ܧ݉݌݈ݓ݋ݎܩ Growth in agricultural non-livestock workers to 2025 (Woods and Poole, 

2012) 

Table 6B-14. Population Estimated Economic Value of Increased Productivity among 

Outdoor Agricultural Workers from Attaining the Revised and Alternative 

Ozone Standards in 2025 (millions of 2011$) 

Standard Economic Value 

(95th percentile confidence interval) 

70 ppb $1.7 

($0.1 to $3.3) 

65 ppb $8.9 

($0.6 to $17) 

 

6B.9 References 

Centers for Disease Control and Prevention (CDC). 2011. United States Life Tables, 2007 National Vital Statistics 

Reports. Volume 59, Number 9. September. Available at 

<http://www.cdc.gov/nchs/data/nvsr/nvsr59/nvsr59_09.pdf>. 

Centers for Disease Control and Prevention (CDC). 2014. Health, United States. Table 19. Years of Potential Life 

Lost Before Age 75 for Selected Causes of Death, According to Sex, Race and Hispanic Origin: United States, 

Selected Years 1980–2013 (2014). 

Crocker, T. D., & Horst, R. L. 1981. “Hours of work, labor productivity, and environmental conditions: A case 

study.” The Review of Economics and Statistics 63(3), 361-368. 

Crouse, DL; Peters, PA; van Donkelaar, A; Goldberg, MS; Villeneuve, PJ; Brion, O; Khan, S; Atari, DO; Jerrett, M; 

Pope, CA; Brauer, M; Brook, JR; Martin, RV; Stieb, D; Burnett, RT. 2012.” Risk of nonaccidental and 
6B-27 

cardiovascular mortality in relation to long-term exposure to low concentrations of fine particulate matter: a 

Canadian national-level cohort study.” Environ Health Perspect 120: 708-714. 

Eftim SE, Samet JM, Janes H, McDermott A, Dominici F. 2008. “Fine Particulate Matter and Mortality: A 

Comparison of the Six Cities and American Cancer Society Cohorts with a Medicare Cohort.” Epidemiology

19: 209- 216. 

Fann N, Lamson A, Wesson K, Risley D, Anenberg SC, Hubbell BJ. 2012. “Estimating the National Public Health 

Burden Associated with Exposure to Ambient PM2.5 and ozone. Risk Analysis,” Risk Analysis 32(1): 81-95. 

Graff Zivin, J., Neidell, M. (2012). The impact of pollution on worker productivity. American Economic Review

102, 3652-3673. 

Hubbell BL. 2006. “Implementing QALYs in the analysis of air pollution regulations.” Environmental and Resource 

Economics 34:365–384. 

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. 

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>. 

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. 

Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M. Studnicka. 2001. “Assessment of Deaths 

Attributable to Air Pollution: Should We Use Risk Estimates Based on Time Series or on Cohort Studies?” 

American Journal of Epidemiology 153(11):1050-55. 

Laden F; Schwartz J; Speizer FE; Dockery DW. 2006. “Reduction in fine particulate air pollution and mortality: 

extended follow-up of the Harvard Six Cities study.” Am J Respir Crit Care Med, 173: 667-672. 

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. 

Lipfert FW; Baty JD; Miller JP; Wyzga RE (2006). “PM2.5 constituents and related air quality variables as predictors 

of survival in a cohort of U.S. military veterans.” Inhal Toxicol, 18: 645-657. 

Mansfield and Patil, 2006. Peer Review of Expert Elicitation. Memo from RTI International to Ron Evans, EPA. 

September 26. Available at 

<http://www.epa.gov/ttn/ecas/regdata/Uncertainty/peer_review_summary_rti_29sept06.pdf>. 

Miller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan JH, Anderson GL, Kaufman JD. (2007). “Long-term 

exposure to air pollution and incidence of cardiovascular events in women” N Engl J Med, 356: 447-458. 

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution 

Regulations. Washington, DC: The National Academies Press. Washington, DC. 
6B-28 

National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from 

Controlling Ozone Air Pollution. National Academies Press. Washington, DC. 

Pope, C.A. III; Burnett, R.T.; Thun, M.J.; Calle, E.E.; Krewski, D.; Ito, K.; Thurston G.D. 2002. “Lung cancer, 

cardiopulmonary mortality, and long-term exposure to fine particulate air pollution.” JAMA 287(9):1132-1141. 

Puett, RC; Hart, JE; Suh, H; Mittleman, M; Laden, F. (2011). “Particulate matter exposures, mortality and 

cardiovascular disease in the health professionals follow-up study.” Environ Health Perspect 119: 1130-1135. 

http://dx.doi.org/10.1289/ehp.1002921 

Puett, RC; Hart, JE; Yanosky, JD; Paciorek, C; Schwartz, J; Suh, H; Speizer, FE; Laden, F. 2009. “Chronic fine and 

coarse particulate exposure, mortality, and coronary heart disease in the Nurses' Health Study.” Environ Health 

Perspect 117: 1697-1701. http://dx.doi.org/10.1289/ehp.0900572 

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. 

Sasser, E. 2014. Response to Comments Regarding the Potential Use of a Threshold Model in Estimating the 

Mortality Risks from Long-term Exposure to Ozone in the Health Risk and Exposure Assessment for Ozone, 

Second External Review Draft. Memorandum to Holly Stallworth, Designated Federal Officer, Clean Air 

Scientific Advisory Committee from EPA/OAQPS Health and Environmental Impacts Division. 

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). 2006. Final Regulatory Impact Analysis (RIA) for the PM2.5 

National Ambient Air Quality Standards (NAAQS). Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. Available at < http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf >. 

U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter: 

Final. U.S. Environmental Protection Agency, Research Triangle Park, NC, EPA/600/R-08/139F. Available at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010. Final Regulatory Impact Analysis (RIA) for the SO2 

National Ambient Air Quality Standards (NAAQS). Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. June. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/fso2ria100602full.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2011a. The Benefits and Costs of the Clean Air Act 1990 to 

2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, Washington, DC. March. 

Available at <http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Analysis for the Final Mercury and 

Air Toxics Standards. EPA-452/R-11-011. December. Available at: 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2011c. Policy Assessment for the Review of the Particulate 

Matter National Ambient Air Quality Standards. EPA-452/D-11-003. April. Available on the Internet at 

<http://www.epa.gov/ttnnaaqs/standards/pm/s_pm_2007_pa.html>. 

U.S. Environmental Protection Agency (U.S. EPA). 2011d. 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. June. Available at 

<http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf>. 
6B-29 

U.S. Environmental Protection Agency (U.S. EPA). 2012a. Provisional Assessment of Recent Studies on Health 

Effects of Particulate Matter Exposure. Office of Research and Development, Research Triangle Park, NC. 

December 2012, EPA/600/R-12/056F. Available at 

<http://www.epa.gov/ttn/naaqs/standards/pm/data/20121213psa.pdf >. 

U.S. Environmental Protection Agency (U.S. EPA). 2012b. 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 (U.S. EPA). 2013. 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). 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2004. Advisory on Plans for 

Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of 

the Clean Air Act, 1990-2020: Advisory by the Health Effects Subcommittee of the Advisory Council on Clean 

Air Compliance Analysis. EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400433D5D/$File/counc

il_adv_04002.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/9288428b8eeea4c885257242006935a3/59e06b6c5ca66597852575

e7006c5d09!OpenDocument&TableRow=2.3#2.>. 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 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. EPA-CASAC-14-005. 

<http://yosemite.epa.gov/sab/sabproduct.nsf/db6b9452a00aa3bd85257242006901de/5F6FB6DEE3442C948525

7D08004C707A/$File/EPA-CASAC-14-005+unsigned.pdf>. 

Zeger S; Dominici F; McDermott A; Samet J (2008). Mortality in the Medicare population and chronic exposure to 

fine particulate air pollution in urban centers (2000-2005). Environ Health Perspect, 116: 1614-1619. 
7-1 

CHAPTER 7: 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. Although we have not conducted an 

analysis to represent the ozone improvements from emissions reductions estimated in the final 

RIA, we reference the analysis conducted in the proposal RIA (U.S. EPA, 2014b) as an 

indication of the potential magnitude of effects associated with changes in yields of commercial 

forests and agriculture, and carbon sequestration and storage. We did not update the analysis 

from the proposal RIA because the welfare co-benefits estimates (i) in the proposal analysis were 

small, and we anticipated that the estimates in the final analysis would be even smaller, and (ii) 

are not added to the human health benefits estimates. 

The EPA has also concluded that the current secondary standard for ozone, set at a level of 

75 ppb, is not requisite to protect public welfare from known or anticipated adverse effects, and 

is revising the standard to provide increased protection against vegetation-related effects on 

public welfare. Specifically, the EPA is retaining the indicator (ozone), averaging time (8-hour) 

and form (annual fourth-highest daily maximum, averaged over 3 years) of the existing 

secondary standard and is revising the level of that standard to 70 ppb. The EPA has concluded 

that this revision will effectively curtail cumulative seasonal ozone exposures above 17 ppm-hrs, 

in terms of a three-year average seasonal W126 index value, based on the three consecutive 

month period within the growing season with the maximum index value, with daily exposures 

cumulated for the 12-hour period from 8:00 am to 8:00 pm. Thus, the EPA has concluded that 

this revision will provide the requisite protection against known or anticipated adverse effects to 

the public welfare. 

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 
7-2 

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. However, we are not able to quantify or monetize these 

benefits in this RIA. 

7.1 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 7-1 shows the welfare effects associated with emissions of NOx and 

VOC. The following subsections discuss the direct benefits of reducing ambient ozone 
7-3 

concentrations and the additional welfare benefits associated with reduced emissions of NOx and 

VOC. 

Table 7-1. 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     

7.2 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, 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 
7-4 

can lead to secondary impacts that modify plants' responses to other environmental factors. 

Specifically, plants may become more sensitive to other air pollutants, or 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, an essential symbiotic fungus 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. 
7-5 

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 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 2014a). 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, 2014a). 

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, 2014a). 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. 
7-6 

In the proposal RIA (U.S. EPA, 2014b), we were 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 analyzed 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 assessed the effects of those changes in commercial 

agricultural and forest yields on carbon sequestration and storage. This analysis provided 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, 2014a). Because we 

did not update this analysis for this RIA, we refer the reader to the results in the proposal RIA for 

an indication of the potential magnitude of these welfare benefits. 

7.3 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 
7-7 

accumulations of both algae and sediments in estuarine waters. In addition to contributing to 

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 and 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 
7-8 

particulate nitrate needed to calculate changes in light extinction and the resulting changes in 

economic benefits. 

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. 

7.4 References 

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>. 

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>. 
7-9 

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). 2014a. Welfare Risk and Exposure Assessment (Final). 

August. Available on the internet at: 

<http://www.epa.gov/ttn/naaqs/standards/ozone/data/20141021welfarerea.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2014b. Regulatory Impact Analysis of the Proposed Revisions 

to the National Ambient Air Quality Standards for Ground-Level Ozone. Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. November. Available at 

<http://www.epa.gov/ttnecas1/regdata/RIAs/20141125ria.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. 
8-1 

CHAPTER 8: COMPARISON OF COSTS AND BENEFITS

Overview 

The EPA performed an illustrative analysis to estimate the costs and human health 

benefits of nationally attaining revised and alternative ozone standards. The EPA Administrator 

is revising the level of the primary ozone standard to 70 ppb. Per Executive Order 12866 and the 

guidelines of OMB Circular A-4, this Regulatory Impact Analysis (RIA) presents analysis of an 

alternative standard level of 65 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 cost and benefit 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 the revised and 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. Because of data and resource constraints, 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 will be the result of mobile source 
8-2 

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 post-2025 costs and benefits estimates for California should not be 

added to the primary 2025 estimates for the rest of the U.S. 

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 them 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 and benefits due to (1) emissions reductions 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 total costs and benefits of the revised and alternative 

standard levels analyzed, and show the net benefits for each of the levels across a range of 

modeling assumptions related to the calculation of costs and benefits.175 Tables 8-3 and 8-4 

summarize the costs and benefits resulting from identified control strategies and do not include 

the additional costs and benefits associated with the unidentified control strategies. Tables 8-5 

and 8-6 provide information on the total costs by geographic region for the U.S., except 

California in 2025 and on the costs for California for post-2025. Tables 8-7 and 8-8 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 revised and 

alternative 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 both control strategies applied within the region and reductions in transport of 

ozone associated with emissions reductions in other regions. 

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 at the national level, 

we do not estimate separately the nationwide benefits associated with the emissions reductions 

 

175 As discussed in Chapter 2, Section 2.2.5, of the 1,225 ozone monitors with complete ozone data, there were 

seven monitors, or 0.6 percent of the total, for which the DVs were influenced by wintertime ozone episodes. These 

seven monitors were removed from the analysis because the modeling tools are not currently sufficient to properly 

characterize ozone formation during wintertime ozone episodes. Because there was no technically feasible method 

for projecting DVs at these sites, these sites were not included in determining required NOx and VOC emissions 

reductions needed to meet the revised or alternative standard levels. There could be additional emissions reductions 

required to lower ozone at these locations and associated additional costs and benefits not reflected in this analysis. 
8-4 

occurring in any specific region.176 As a result, we are only able to provide net benefits 

estimates at the national level. The difference between the estimated benefits accruing to a 

specific region and the costs for that region is not an estimate of net benefits of the emissions 

reductions in that region because it ignores the benefits occurring outside of that region. 

Table 8-1. Total Costs, Total Monetized Benefits, and Net Benefits of Control Strategies in 

2025 for U.S., except California (billions of 2011$)a,b

Revised and Alternative 

Standard Levels 

Total Costsc

 Monetized Benefits Net Benefits

7% Discount 

Rate 

7% Discount 

Rate 

7% Discount 

Rate 

70 $1.4 $2.9 to $5.9d $1.5 to $4.5 

65 $16 $15 to $30d -$1.0 to $14 a All values are rounded to two significant figures.

b 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 numbers presented in this table reflect engineering costs annualized at a 7 percent discount rate to the extent 

possible. 

d

 Excludes additional health and welfare benefits that could not be quantified (see Chapter 6, Section 6.6.3.8). 

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

Applied in California, Post-2025 (billions of 2011$)a

Revised and Alternative 

Standard Levels 

Total Costs Monetized Benefits Net Benefits

7% Discount 

Rate 

7% Discount 

Rate 

7% Discount 

Rate 

70 $0.8 $1.2 to $2.1c $0.4 to $1.3 

65 $1.5 $2.3 to $4.2c $0.8 to $2.7

a

 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. 

b

 The numbers presented in this table reflect engineering costs annualized at a 7 percent discount rate to the extent 

possible. 

c

 Excludes additional health and welfare benefits that could not be quantified (see Chapter 6, Section 6.6.3.8). 

 

176 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. 
8-5 

Table 8-3. Summary of Identified Control Strategies Annualized Control Costs by 

Sector for 70 ppb and 65 ppb for 2025 - U.S., except California (millions of 2011$)a

Geographic 

Area Emissions Sector Identified Control Costs for 70 ppb 

Identified Control 

Costs for 65 ppb 

 

7 Percent 

Discount Rateb

7 Percent 

Discount Rateb

East 

EGU 52c 130c 

Non-EGU Point 260d 750d 

Nonpoint 360 1,500 

Nonroad 13e 36e 

Total 690 2,400 

West 

EGU - - 

Non-EGU Point 4d 49d 

Nonpoint <1 88 

Nonroad - 4e 

Total 4 140 

Total Identified Control Costs 690 2,600 

a

 All values are rounded to two significant figures. 

b

 The numbers presented in this table reflect engineering costs annualized at a 7 percent discount rate to the extent 

possible. 

c

 EGU control cost data is calculated using a capital charge rate between 7 and 12 percent for retrofit controls 

depending on the type of equipment. 

d A share of the non-EGU point source costs can be calculated using both 3 and 7 percent discount rates. A share of 

the non-EGU point source sector costs can be calculated using both 3 and 7 percent discount rates. When applying a 

3 percent discount rate where possible, the total non-EGU point source sector costs are $250 million for 70 ppb and 

$740 million for 65 ppb. 

e

 Non-EGU point source costs at a 3 percent discount rate are $72 million for 70 ppb and $180 million for 65 ppb. 
8-6 

Table 8-4. Estimated Monetized Ozone and PM2.5 Benefits for Revised and Alternative 

Annual Ozone Standards Incremental to the Baseline for the 2025 Scenario 

(Nationwide Benefits of Attaining the Standards in the U.S. except California) – 

Identified Control Strategies (billions of 2011$) a

Discount 

Rate 

Revised and Alternative Standard Levels 

70 ppb 65 ppb 

Identified Control Strategies 

Ozone-only Benefits (range reflects Smith 

et al. (2009) to Zanobetti and Schwartz 

(2008))

b

 $0.86 to $1.4 $2.2 to $3.5 

PM2.5 Co-benefits (range reflects Krewski 

et al. (2009) to Lepeule et al. (2012)

3% $1.7 to $3.9 $4.0 to $9.0 

7% $1.6 to $3.5 $3.6 to $8.1 

Total Benefits 3% $2.6 to $5.3c

 $6.1 to $12c

7% $2.4 to $4.9c

 $5.7 to $12c

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. 

c

 Excludes additional health and welfare benefits that could not be quantified (see Chapter 6, Section 6.6.3.8). 

 

Table 8-5. Summary of Total Control Costs (Identified + Unidentified) by Revised and 

Alternative Standard Level for 2025 - U.S., except California (millions of 2011$, 

7% Discount Rate)a 

Revised and 

Alternative Level 

Geographic Area 

Identified Control 

Costs 

Unidentified 

Control Costs 

Total Control 

Costs 

(Identified and 

Unidentified) 

70 ppb 

East 690 700 1,400 

West 4 - <5 

Total $690 $700 $1,400 

65 ppb 

East 2,400 12,000 15,000 

West 140 610 750 

Total $2,600 $12,600 $16,000 

a

 All values are rounded to two significant figures. Unidentified control costs are based on an average cost-per-ton 

methodology described in Chapter 4. 
8-7 

Table 8-6. Summary of Total Control Costs (Unidentified Control Strategies) by Revised 

and Alternative Level for Post-2025 - California (millions of 2011$, 7% Discount 

Rate)a

Revised and Alternative Level Geographic Area Total Control Costs 

(Unidentified) 

70 ppb California $800 

65 ppb California $1,500 

a

 All values are rounded to two significant figures. Unidentified control costs are based on an average cost-per-ton 

methodology described in Chapter 4. 

Table 8-7. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 2025 

Scenario (nationwide benefits of attaining revised and alternative standard levels 

everywhere in the U.S. except California) – Identified + Unidentified Control 

Strategiesa

Region 

Alterative Standards 

70 ppb 65 ppb 

East b 98% 96% 

California 0% 0% 

Rest of West 2% 4% 

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. 

Table 8-8. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the Post2025 Scenario (nationwide benefits of attaining revised and alternative standard 

levels just in California) – Identified + Unidentified Control Strategiesa

Region 

Alterative Standards 

70 ppb 65 ppb 

East 3% 2% 

California 90% 91% 

Rest of West 7% 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. 

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 
8-8 

illnesses associated with acute and chronic exposures. Air pollution can affect human health in a 

variety of ways, and in Table 8-9 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 8-9. 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 

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 
8-9 

Benefits Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

More 

Information 

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

Reduced incidence 

of morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — — NO2 ISA d

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 Improvements between the Proposal and Final RIAs 

In the regulatory impact analyses for both the proposed and revised ozone NAAQS, there 

were two geographic areas outside of California where the majority of emissions reductions were 

needed to meet a standard level of 70 ppb – Texas and the Northeast. In analyzing 70 ppb in this 

RIA for the revised NAAQS, there were approximately 50 percent fewer emissions reductions 
8-10 

needed in these two geographic areas. For an alternative standard of 65 ppb, emissions 

reductions needed nationwide were approximately 20 percent lower than at proposal. 

The primary reason for the difference in emissions reductions estimated for attainment is 

that for this RIA we conducted more geographically-refined air quality sensitivity modeling to 

develop improved ozone response factors (see Chapter 2, Section 2.1.4 for a more detailed 

discussion of this air quality modeling) and focused the emissions reduction strategies on 

geographic areas closer to the monitors with the highest design values (see Chapter 3, Section 

3.1.2 for a more detailed discussion of the emissions reduction strategies). The improvements in 

air quality modeling and emissions reduction strategies account for about 80 percent of the 

difference in needed emissions reductions between the two RIAs. 

In Texas and the Northeast, the updated response factors and more focused emissions 

reduction strategies resulted in larger changes in ozone concentrations in response to more 

geographically focused emissions reductions. In east Texas, the ppb/ton ozone response factors 

used in this RIA were 2 to 3 times more responsive than the factors used in the proposal RIA at 

controlling monitors in Houston and Dallas. In the Northeast, the ppb/ton ozone response factors 

used in this RIA were 2.5 times more responsive than the factors used in the proposal RIA at the 

controlling monitor on Long Island, NY. 

A secondary reason for the difference is that in the time between developing the two 

RIAs we updated emissions inventories, models and model inputs for the base year of 2011. See 

Chapter 2, Section 2.1 and 2.2 for additional discussion of the updated emissions inventories, 

models and model inputs. When projected to 2025, these changes in inventories, models and 

inputs had compounding effects for year 2025, and in some areas resulted in lower projected 

base case design values for 2025. The updated emissions inventories, models, and model inputs 

account for about 20 percent of the difference in needed emissions reductions between the two 

RIAs. 

These differences in the estimates of emissions reductions needed to attain the revised 

and alternative standard levels affect the estimates for the costs and benefits in this RIA. For a 

revised standard of 70 ppb, the costs were 60 percent lower than at proposal and the benefits 

were 55 percent lower than at proposal. The percent decrease in costs is slightly more than the 
8-11 

percent decrease in emissions reductions because a larger number of lower cost identified 

controls were available to bring areas into attainment with 70 ppb.177 The percent decrease in 

benefits is similar to the percent decrease in emissions reductions. For an alternative standard 

level of 65 ppb, the costs were less than three percent higher than those estimated at proposal and 

the benefits were 22 percent lower than at proposal.178 The percent change in costs was less than 

the percent decrease in emissions reductions because in this analysis we applied identified 

controls in smaller geographic areas, resulting in fewer identified controls available within those 

areas and an increase in higher cost unidentified controls being applied to bring areas into 

attainment with 65 ppb. The percent decrease in benefits is similar to the percent decrease in 

emissions reductions. 

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 approximately half to three-quarters of the estimated 

benefits, depending on the standard analyzed and on the choice of ozone and PM mortality 

functions used. 

 

177 In the final RIA, outside of California all areas were projected to meet the current standard of 75 ppb. As such, 

no identified controls were used to bring areas into attainment with 75 ppb. In the proposal RIA, some of these 

lower cost controls were used to bring areas into attainment with 75 ppb, making them unavailable for application in 

the analysis of 70 ppb. 

178 We have slightly modified our approach to estimating morbidity benefits since proposal, which had a negligible 

(~1%) influence on the total monetized benefits in this RIA. 
8-12 

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 identified emissions control 

technologies, the EPA recognized that identified 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 level. 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, end-of-pipe emissions controls, and the 

costs and benefits associated with those controls. The second stage took the emissions 

reductions beyond identified controls and used an average cost-per-ton 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 identified controls. We used the 

information currently available to develop reasonable approximations of the costs and benefits of 

the unidentified portion of the emissions reductions necessary to reach attainment. However, 

because of the uncertainty associated with the costs of unidentified controls, we judged it 

appropriate to provide separate estimates of the costs and benefits for partial attainment (based 

on identified controls) and full attainment (based on identified controls and unidentified 

controls), 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 
8-13 

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. 

In many ways, RIAs for proposed and final 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 and revised 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 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 cost-effective identified and available controls in our database. With the more 

stringent NAAQS, more areas will need to employ additional ways of reducing emissions. The 

control measures reflected in our databases include very few abatement possibilities from energy 

efficiency measures, fuel switching, input or process changes, or other abatement strategies that 

are non-traditional in the sense that they are not the application of an end-of-pipe control. So 

while we can speculate on what some of the future emissions reduction strategies 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 

identified 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 
8-14 

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 4, Section 4.2.3 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 Net Present Value of a Stream of Costs and Benefits 

The EPA believes that providing comparisons of social costs and social benefits at 

discount rates of 3 and 7 percent is appropriate. 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. 

The EPA presents annualized costs and benefits in a single year for comparison in this RIA 

because there are a number of methodological complexities associated with calculating the net 

present value (NPV) of a stream of costs and benefits for a NAAQS. While NPV analysis allows 

evaluation of alternatives by summing the present value of all future costs and benefits, insights 

into how costs will occur over time are limited by underlying assumptions and data. Calculating 

a present value (PV) of the stream of future benefits also poses special challenges, which we 

describe below. In addition, the method requires definition of the length of that future time 

period, which is not straightforward for this analysis and subject to uncertainty. 

To estimate engineering costs, the EPA employs the equivalent uniform annual cost 

(EUAC) method, which annualizes costs over varying lifetimes of control measures applied in 
8-15 

the analysis.179 Using the EUAC method results in a stream of annualized costs that is equal for 

each year over the lifetime of control measures, resulting in a value similar to the value 

associated with an amortized mortgage or other loan payment. Control equipment is often 

purchased by incurring debt rather than through a single up-front payment. Recognizing this led 

the EPA to estimate costs using the EUAC method instead of a method that mimics firms paying 

up front for the future costs of installation, maintenance, and operation of pollution control 

devices. 

Further, because we do not know when a facility will stop using a control measure or 

change to another measure based on economic or other reasons, the EPA assumes the control 

equipment and measures applied in the illustrative control strategies remain in service for their 

full useful life. As a result, the annualized cost of controls in a single future year is the same 

throughout the lifetimes of control measures analyzed, allowing the EPA to compare the 

annualized control costs with the benefits in a single year for consistent comparison. 

The EPA’s RIAs for air quality rules generally report the estimated net benefits of 

improved air quality for a single year. The estimated NPV can better characterize the stream of 

benefits and costs over a multi-year period. However, calculating the PV of improved air quality 

is generally quite data-intensive and costly. Further, the results are sensitive to assumptions 

regarding the time period over which the stream of benefits is discounted. 

The theoretically appropriate approach for characterizing the PV of benefits is the life table 

approach. The life table, or dynamic population, approach explicitly models the year-to-year 

influence of air pollution on baseline mortality risk, population growth and the birth rate—

typically for each year over the course of a 50-to-100 year period (U.S. EPA SAB, 2010; Miller, 

2003). In contrast to the pulse approach180, a life table models these variables endogenously by 

following a population cohort over time. For example, a life table will “pass” the air pollutionmodified baseline death rate and population from year to year; impacts estimated in year 50 will 

 

179 See Chapter 4, Section 4.1.1 for additional information on the EUAC method. 180 The pulse approach assumes changes in air pollution in a single year and affects mortality estimates over a 20-

year period. 
8-16 

account for the influence of air pollution on death rates and population growth in the preceding 

49 years. 

Calculating year-to-year changes in mortality risk in a life table requires some estimate of 

the annual change in air quality levels. It is both impractical to model air quality levels for each 

year and challenging to account for changes in federal, state and local policies that will affect the 

annual level and distribution of pollutants. For each of these reasons the EPA has not generally 

reported the PV of benefits for air rules but has instead pursued a pulse approach. While we 

agree that providing the NPV of a stream of costs and benefits could be informative, based on 

these reasons we are not able to provide the NPV of that stream in this RIA. 

8.4 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) 

are difficult to predict and quantify; and (4) introduce additional uncertainty into this analysis.181 

These factors, summarized in Table 8.10 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 affect 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, 

 

181 OMB Circular A-4 indicates that qualitative discussions should be included in analyses whenever there is 

insufficient data to quantify uncertainty. 
8-17 

broader uncertainties about future trends that provide important context for the costs and benefits 

presented in this analysis. 

Table 8-10. 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. 

Recent increases 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 efficiency182 have likely led to a 

reduction of U.S. dependence on imports of 

foreign oil. 

Upward trends that have emerged over the last 

ten years in natural gas production and 

consumption, renewable energy installations, 

and energy efficiency technology installations 

are likely to continue.183

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.184 

Recent trends in VMT illustrate some of the 

uncertainty around future emissions from 

mobile sources.185 In 2006, projections of 

VMT showed a sustained increase,186 yet VMT 

growth slowed in recent years and actually 

declined in 2008 and 2009.187 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 

Affluence leads to increased consumption and 

energy use. However, this increase may not be 

proportional. Energy and materials use is not 

directly proportional to economic growth, but 

decrease or stabilize over time in spite of 

continued economic growth.189

 

182 http://energy.gov/articles/us-domestic-oil-production-exceeds-imports-first-time-18-years 183 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. 

184 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. 185 For example, see the Transportation Research Board’s National Cooperative Highway Research Program 

(NCHRP) 2014. 

186 https://www.fhwa.dot.gov/policy/2006cpr/chap9.htm#body 187 https://www.fhwa.dot.gov/policyinformation/travel_monitoring/13jantvt/page2.cfm 189 UNEP 2011, http://www.unep.org/resourcepanel/decoupling/files/pdf/decoupling_report_english.pdf 
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Individual Factors Potential Implications for NAAQS 

Attainment 

Information on Trends 

emissions making attainment 

potentially less costly.188

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 would likely be less 

costly. If not, attainment would 

likely be more costly. A move 

toward investments in fuel efficiency 

and low emissions fuels could 

decrease emissions and likely lower 

attainment costs. 

State and local policies related to energy 

efficiency, cleaner energy, energy security190, 

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 electric power, 

the use of cleaner fuels and conservation 

measures would likely result in decreased 

emissions and likely decrease attainment 

costs.191 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.192 U.S. productivity 

per energy expended relative to other countries 

suggests that additional efficiency gains are 

possible.193

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.194 

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 use.195 

a Policies refer to any policies or regulations that are not environmental regulations set by U.S. EPA, states, tribes, or 

local authorities. 

 

188 For example, Bo (2011), and http://www.epa.gov/region1/airquality/nox.html for manufactures contributions to 

NOX emissions. 190 For example, http://www2.epa.gov/laws-regulations/summary-energy-independence-and-security-act. 191 For example, see http://www.dsireusa.org/solar/solarpolicyguide/. 192 http://bipartisanpolicy.org/sites/default/files/BPC%20SEPI%20Energy%20Report%202013_0.pdf, p. 5. 193 http://bipartisanpolicy.org/sites/default/files/BPC%20SEPI%20Energy%20Report%202013_0.pdf, p. 69. 194 See Jacobs (2009). 195 http://www.eia.gov/todayinenergy/detail.cfm?id=16211 
8-19 

8.5 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 identified NOx and VOC controls comprise only a small part 

of the estimated costs of full attainment. These estimated costs for the identified set of 

controls are still uncertain, but they are based on the best available information on control 

technologies, and have their basis in real, tested technologies. Estimating costs of full 

attainment 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.

 An 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 fifteen (15) 2025 

emissions sensitivity simulations. The 15 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 revised and alternative standard levels of 70 and 65 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. 

 NOx and VOC emissions reductions quantified from the technologies identified in 

this RIA may not be sufficient to attain alternative ozone NAAQS in some areas. 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 or alternative standards. 

Chapter 4 contains discussion of other emissions reduction measures not quantified in this 

RIA, as well as discussion of technological improvement over time. After applying 
8-20 

existing rules and the illustrative identified controls across the nation (excluding 

California), in order to reach 70 ppb we were able to identify controls that reduce overall 

NOx emissions by 240,000 tons and VOC emissions by 20,000 tons. In order to reach 65 

ppb we were able to identify controls that reduce overall NOx emissions by 560,000 tons 

and VOC emissions by 110,000 tons. After these reductions, in order to reach 70 ppb over 

47,000 tons of NOx emissions remained, and in order to reach 65 ppb over 860,000 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 

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 unidentified 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 some 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 unidentified portion of 

the emissions reductions was too uncertain to be included as part of these estimates. 

Therefore, we did not include the economic impacts of either the identified control costs 

or costs of unidentified controls. 

 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.

8.6 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. 

Miller BG. Life table methods for quantitative impact assessments in chronic mortality. Journal of Epidemiology & 

Community Health, 2003; 57(3):200–206. 

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. 
8-21 

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). 

U.S. EPA Science Advisory Board. Review of EPA’s DRAFT Health Benefits of the Second Section 812 

Prospective Study of the Clean Air Act. Washington, DC, 2010. 
9-1 

CHAPTER 9: STATUTORY AND EXECUTIVE ORDER IMPACT ANALYSES

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 Executive Order 12866: Regulatory Planning and Review 

This action is an economically significant regulatory action that was submitted to the 

Office of Management and Budget (OMB) for review. Any changes made in response to OMB 

recommendations have been documented in the docket. This RIA estimates the costs and 

monetized human health and welfare benefits of attaining two alternative ozone NAAQS 

nationwide. Specifically, the RIA examines the alternatives of 65 ppb and 70 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 CAA and judicial decisions make clear that the economic and technical 

feasibility of attaining ambient standards are not to be considered in setting or revising NAAQS, 

although such factors may be considered in the development of state plans to implement the 

standards. Accordingly, although an RIA has been prepared, the results of the RIA have not been 

considered in issuing the revised NAAQS. 

9.2 Paperwork Reduction Act 

The information collection requirements for the revised NAAQS have been submitted for 

approval to the Office of Management and Budget (OMB) under the Paperwork Reduction Act 

(PRA). The information collection requirements are not enforceable until OMB approves them. 

The Information Collection Request (ICR) document prepared by the EPA for these revisions 

has been assigned EPA ICR #2313.04. 

The information collected and reported under 40 CFR part 58 is needed to determine 

compliance with the NAAQS, to characterize air quality and associated health and ecosystems 

impacts, to develop emission control strategies, and to measure progress for the air pollution 

program. We are extending the length of the required ozone monitoring season in 32 states and 

the District of Columbia and the revised ozone monitoring seasons will become effective on 
9-2 

January 1, 2017. We are also revising the PAMS monitoring requirements to reduce the number 

of required PAMS sites while improving spatial coverage, and requiring states in moderate or 

above ozone non-attainment areas and the ozone transport region to develop an enhanced 

monitoring plan as part of the PAMS requirements. Monitoring agencies will need to comply 

with the PAMS requirements by June 1, 2019. In addition, we are revising the ozone FRM to 

establish a new, additional technique for measuring ozone in the ambient air. It will be 

incorporated into the existing ozone FRM, using the same calibration procedure in Appendix D 

of 40 CFR part 50. We are also making changes to the procedures for testing performance 

characteristics and determining comparability between candidate FEMs and reference methods. 

For the purposes of ICR number 2313.04, the burden figures represent the burden 

estimate based on the requirements contained in this rule. The burden estimates are for the 3-year 

period from 2016 through 2018. The implementation of the PAMS changes will occur beyond 

the time frame of this ICR with implementation occurring in 2019. The cost estimates for the 

PAMS network (including revisions) will be captured in future routine updates to the Ambient 

Air Quality Surveillance ICR that are required every 3 years by OMB. The addition of a new 

FRM in 40 CFR part 50 and revisions to the ozone FEM procedures for testing performance 

characteristics in 40 CFR part 53 does not add any additional information collection 

requirements. 

The ICR burden estimates are associated with the changes to the ozone seasons in the 

revised NAAQS. This information collection is estimated to involve 158 respondents for a total 

cost of approximately $24,597,485 (total capital, labor, and operation and maintenance) 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 $20,209,966. Also included 

in the total are other costs of operations and maintenance of $2,254,334 and equipment and 

contract costs of $2,133,185. The actual labor cost increase to expand the ozone monitoring 

seasons is $2,064,707. 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 $2,670,360. Burden is 

defined at 5 CFR 1320.3(b). State, local, and tribal entities are eligible for state assistance grants 

provided by the Federal government under the CAA which can be used for related activities. An 

agency may not conduct or sponsor, and a person is not required to respond to, a collection of 
9-3 

information unless it displays a currently valid OMB control number. The OMB control numbers 

for EPA’s regulations in 40 CFR are listed in 40 CFR part 9. 

9.3 Regulatory Flexibility Act 

This action will not have a significant economic impact on a substantial number of small 

entities under the RFA. This action will not impose any requirements on small entities. Rather, 

the 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). Similarly, the revisions to 40 CFR part 58 

address the requirements for states to collect information and report compliance with the 

NAAQS and will not impose any requirements on small entities. Similarly, the addition of a new 

FRM in 40 CFR part 50 and revisions to the FEM procedures for testing in 40 CFR part 53 will 

not impose any requirements on small entities. 

9.4 Unfunded Mandates Reform Act 

This action does not contain any unfunded mandate as described in UMRA, 2 U. S. C. 

1531 – 1538, and does not 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 

RIA pursuant to the UMRA would not furnish any information which the court could consider in 

reviewing the NAAQS). 

9.5 Executive Order 13132: Federalism 

This action 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. 
9-4 

9.6 Executive Order 13175: Consultation and Coordination with Indian Tribal 

Governments 

This action 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 as 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 action. 

The EPA specifically solicited comment on this rule from tribal officials. The EPA also 

conducted outreach consistent with the EPA Policy on Consultation and Coordination with 

Indian Tribes. Outreach to tribal environmental professionals was conducted through 

participation in the Tribal Air call, which is sponsored by the National Tribal Air Association. 

Consistent with the EPA Policy on Consultation and Coordination with Indian Tribes, the EPA 

offered formal consultation to the tribes during the public comment period. Consultation was not 

requested. 

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

Safety Risks 

This action is subject to Executive Order 13045 because it is an economically significant 

regulatory action as defined by Executive Order 12866, and the EPA believes that the 

environmental health risk addressed by this action may have a disproportionate effect on 

children. The rule will establish uniform NAAQS for 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 at-risk populations196 such 

as all people with lung disease and people active outdoors, are at increased risk for health effects 

associated with exposure to ozone in ambient air. Because children are considered an at-risk 

lifestage, 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, 

 

196 As used here and similarly throughout this document, the term population refers to people having a quality or 

characteristic in common, including a specific pre-existing illness or a specific age or lifestage. 
9-5 

policy considerations, and the exposure and risk assessments pertaining to children are contained 

in sections II.B and II.C of the preamble. 

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

Distribution, or Use 

This action is not a “significant energy action” because it is not likely to have a 

significant adverse effect on the supply, distribution, or use of energy. The purpose of the rule is 

to establish a revised NAAQS for ozone, establish an additional FRM, revise FEM procedures 

for testing, and revises air quality surveillance requirements. The rule does not prescribe specific 

pollution control strategies by which these ambient standards and monitoring revisions 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 this rule is not likely to have any 

adverse energy effects and does not constitute a significant energy action as defined in Executive 

Order 13211. 

Application of the modeled illustrative control strategy containing identified controls for 

power plants, shown in Chapter 4, means that 4 percent of the total projected coal-fired EGU 

capacity nationwide in 2025 could be affected by controls for the revised standard level of 70 

ppb. Similarly, 13 percent of total projected coal-fired EGU capacity in 2025 could be affected 

by controls for the alternative standard of 65 ppb. In addition, some fuel switching might occur 

that could alter these percentages, although we are not able to estimate the effect on energy 

impacts from fuel switching. In addition, we are not able 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 

controls to sources other than EGUs for the purpose of attaining a more stringent standard. 

9.9 National Technology Transfer and Advancement Act 

This rulemaking involves environmental monitoring and measurement. Consistent with 

the Agency’s Performance Based Measurement System (PBMS), the EPA is not requiring the 

use of specific, prescribed analytical methods. Rather, the Agency is allowing the use of any 

method that meets the prescribed performance criteria. Ambient air concentrations of ozone are 
9-6 

currently measured by the Federal reference method (FRM) in 40 CFR part 50, Appendix D 

(Measurement Principle and Calibration Procedure for the Measurement of Ozone in the 

Atmosphere) or by Federal equivalent methods (FEM) that meet the requirements of 40 CFR part 

53. Procedures are available in part 53 that allow for the approval of an FEM for ozone that is 

similar to the FRM. Any method that meets the performance criteria for a candidate equivalent 

method may be approved for use as an FEM. This approach is consistent with EPA’s PBMS. The 

PBMS approach is intended to be more flexible and cost-effective for the regulated community; 

it is also intended to encourage innovation in analytical technology and improved data quality. 

The EPA is not precluding the use of any method, whether it constitutes a voluntary consensus 

standard or not, as long as it meets the specified performance criteria. 

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

Minority Populations and Low-Income Populations 

The EPA believes that this action will not have disproportionately high and adverse 

human health or environmental effects on minority populations, low-income populations or 

indigenous peoples. The action described in this notice is to strengthen the NAAQS for ozone. 

The primary NAAQS are established at a level that is requisite to protect public health, 

including the health of sensitive or at-risk groups, with an adequate margin of safety. The 

NAAQS decisions are based on an explicit and comprehensive assessment of the current 

scientific evidence and associated exposure/risk analyses. More specifically, EPA expressly 

considers the available information regarding health effects among at-risk populations, including 

that available for low-income populations and minority populations, in decisions on NAAQS. 

Where low-income populations or minority populations are among the at-risk populations, the 

decision on the standard is based on providing protection for these and other at-risk populations 

and lifestages. Where such populations are not identified as at-risk populations, a NAAQS that 

is established to provide protection to the at-risk populations would also be expected to provide 

protection to all other populations, including low-income populations and minority populations. 

The Integrated Science Assessment, the Health Risk and Exposure Assessment, and the 

Policy Assessment for this review, which include identification of populations at risk from ozone 

health effects, are available in the docket EPA-HQ-OAR-2008-0699. The information on at-risk 
9-7 

populations for this NAAQS review is summarized and considered in the rule preamble (see 

section II.A). The final rule increases the level of environmental protection for all affected 

populations without having any disproportionately high and adverse human health or 

environmental effects on any population, including any minority populations, low-income 

populations or indigenous peoples. The rule establishes uniform national standards for ozone in 

ambient air that, in the Administrator’s judgment, protect public health, including the health of 

sensitive groups, with an adequate margin of safety. 

Although it has a separate docket and is not part of the rulemaking record for this action, 

EPA has prepared a RIA of this decision. As part of the RIA, a demographic analysis was 

conducted. While, as noted in the RIA, the demographic analysis is not a full quantitative, sitespecific exposure and risk assessment, that analysis examined demographic characteristics of 

persons living in areas with poor air quality relative to the revised standard. Specifically, 

Appendix 9A describes the proximity and socio-demographic analysis. The analysis found that 

in areas with poor air quality relative to the revised standard,197 the representation of minority 

populations was slightly greater than in the U.S. as a whole. Because the air quality in these areas 

does not currently meet the revised standard, populations in these areas would be expected to 

benefit from implementation of the strengthened standard, and, thus, would be more affected by 

strategies to attain the revised standard. The analysis, which evaluates the potential implications 

for minority populations and low-income populations of future air pollution control actions that 

state and local agencies may consider in implementing the revised ozone NAAQS described in 

the decision notice, is discussed in Appendix 9A. The RIA is available on the Web, through the 

EPA’s Technology Transfer Network website at 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_index.html and in the RIA docket (EPAHQ-OAR-2013-0169). As noted above, although an RIA has been prepared, the results of the 

RIA have not been considered in issuing this final rule. 

 

197 This refers to monitored areas with ozone design values above the revised and alternative standards. 
9-8 

9.11 Congressional Review Act (CRA) 

This action is subject to the CRA, and the EPA will submit a rule report to each House of 

the Congress and to the Comptroller General of the United States. This action is a “major rule” 

as defined by 5 U.S.C. 804(2). 
9A-1 

APPENDIX 9A: SOCIO-DEMOGRAPHIC CHARACTERISTICS OF POPULATIONS 

IN CORE BASED STATISTICAL AREAS WITH OZONE MONITORS EXCEEDING 

REVISED AND ALTERNATIVE OZONE STANDARDS

Overview 

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 revisions to the National Ambient Air Quality Standards (NAAQS) 

for ozone. 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 (2012-2014) 

design value exceeding the revised and alternative ozone standard levels of 70 and 65 parts per 

billion (ppb). This analysis does not include a quantitative assessment of exposure and/or risk for 

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. 

The EPA Administrator is revising the NAAQS for ozone from the current level of 75 

ppb to a level of 70 ppb. The revisions will establish uniform national standards for ozone in 

ambient air and 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 with poor ozone air quality, defined for this analysis as any Core 

Based Statistical Area (CBSA) with at least one county having an ozone monitor with a current 

(2012-2014) design value exceeding the revised and alternative ozone standard levels (70 and 65 

ppb), including individual counties not included in a CBSA with a design value exceeding the 

revised and alternative standards. These areas are called “study areas” for the purposes of this 

analysis. 

9A.1 Design of Analysis 

To gain a better understanding of the populations within the study areas, the EPA 

conducted an analysis at the county level for this ozone NAAQS review. The study areas for 

these analyses were defined as all counties contained within any CBSA with at least one monitor 
9A-2 

with a current (2012-2014) design value above the revised and alternative standard levels (70 and 

65 ppb) as well as counties not in a CBSA with a current (2012-2014) design value above the 

revised and alternative standard levels (70 and 65 ppb). The study areas were designed to capture 

population and communities with poor ozone air quality, and areas most likely to benefit from 

improved air quality following the implementation of the revised ozone NAAQS. 

For the revised standard levels of 70 ppb, 511 counties were analyzed in 143 areas 

exceeding the standard levels, including 495 counties in 127 CBSAs and 16 counties outside 

CBSAs. For the alternative standard levels of 65 ppb, 891 counties were analyzed in 294 areas 

exceeding the standard level, including 847 counties in 250 CBSAs and 44 counties outside 

CBSAs. The population identified within these study areas made up about 52% of the U.S. 

population for the revised standard levels and about 69% of the U.S. population for the 

alternative standard levels. 

Demographic data from the study areas identified for the revised and alternative standard 

levels were aggregated nationally for comparison with the U.S. population demographics. The 

demographic data used in this analysis include race, ethnicity, age, income, and education 

variables. Details on these demographic groups are provided in the following section (9A.1.1). 

The aggregated demographic values across the study areas are compared to the national data in 

Table 9A-2 of Section 9A-3. 

This analysis identifies, on a limited basis, the populations that are most likely to 

experience reductions in ozone concentrations as a result of actions taken to meet the revised 

standard levels, and 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 populations are disproportionately impacted by ozone levels because they reside in a 

study area, that population will also experience increased environmental and health benefits from 

meeting the revised standard levels. 
9A-3 

9A.1.1 Demographic Variables Included in Analysis 

This analysis includes race, ethnicity, and age data derived from the 2010 Census SF1 

dataset198 and income and education data from the Census Bureau’s 2006-2010 American 

Community Survey (ACS) 5-Year Estimates.199 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 American Number of African Americans (may include Hispanics) 

Native American Number of Native Americas (may include Hispanics) 

Other and multiracial Number of other race and multiracial (may include Hispanics) 

Minority Total Population less White Population 

Hispanic Number of Hispanics 

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 

Education level Number of adults age 25 years and up without a high school diploma 

Low Income Number of people living in households with income below twice the 

poverty line 

Linguistic Isolation Number of people linguistically isolated 

*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 

 

198 2010 Census Summary File 1 Delivered via FTP, http://www2.census.gov/census_2010/04-Summary_File_1/ 199 U.S. Census Bureau 2006-2010 American Community Survey 5-Year Estimates, 

http://www.census.gov/acs/www/data_documentation/2010_release/ 
9A-4 

part. For example, a block is assigned the same percentage of people living below the national 

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 provides 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. 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. 

9A.2 Considerations in Evaluating and Interpreting Results 

This analysis characterizes the demographic attributes of populations located in areas 

defined by a county or a CBSA containing a county with a monitored 2012-2014 design value 

greater than the revised standard levels of 70 ppb, or the alternative standard levels of 65 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 revised and alternative ozone 

standard levels, and thus may be more affected by implementation of the revised standards. This 

analysis does not include a quantitative assessment of exposure 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 could result from implementation of the revised ozone standards. 
9A-5 

The analysis simply represents a national depiction of the baseline characteristics of populations 

residing in areas with measured ozone air quality above the revised and alternative standard 

levels. 

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 

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). EPA does not have 

the ability to conduct such a rigorous technical analysis at this time. 

9A.3 Presentation of Results 

This section presents a summary of the demographics of populations in areas with 2012-

2014 design values greater than the revised and alternative ozone standard levels. The results are 

provided in Table 9A-2. As a whole, the demographic distributions within the study areas 

estimated for the revised and alternative standard levels (i.e., 70 ppb and 65 ppb) are similar to 

the national averages. The largest difference is only 5%, between the national and study area 

percentages for the Minority demographic group. Overall, these qualitative results support the 

determination that the revised rule will tend to benefit geographic areas that have a higher 

proportion of minority residents than the national average. 
9A-6 

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

Demographic Summary Population White 

African 

American 

Native 

American 

Other or 

Multiracial Mino

Area Total - 70 ppb 163,900,459 109,711,305 23,448,540 1,196,130 29,544,484 54,18% of Area Total - 70 ppb 67% 14% 1% 18% 33Area Total - 65 ppb 216,932,408 150,701,600 29,091,602 1,699,450 35,439,756 66,23% of Area Total - 65 ppb 69% 13% 1% 16% 31National Total 312,861,256 226,405,205 39,475,216 2,952,087 44,028,748 86,45% of National Total 72% 13% 1% 14% 28

Demographic Summary Population Age 0 to 4 Age 0 to 17 Age 65+ 

No High 

School 

Diploma Low In

Area Total - 70 ppb 163,900,459 11,028,428 40,546,150 19,510,553 15,960,333 49,39% of Area Total - 70 ppb 7% 25% 12% 10% 30Area Total - 65 ppb 216,932,408 14,387,223 52,913,376 26,596,432 20,469,632 65,33% of Area Total - 65 ppb 7% 24% 12% 9% 30National Total 312,861,256 20,465,065 75,217,176 40,830,262 30,952,789 101,42% of National Total 7% 24% 13% 10% 32
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, NC