Final Ozone NAAQS Regulatory Impact 

Analysis 
EPA-452/R-08-003 

March 2008 

Final Ozone NAAQS Regulatory Impact Analysis

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Air Benefit and Cost Group (C439-02) 

Research Triangle Park, North Carolina 
ii

Table of Contents 

Number Page

Executive Summary.............................................................................................................ES-1

Chapter 1: Introduction and Background............................................................................... 1-1 

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

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

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

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

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

1.2.3 Market Failure or Other Social Purpose .................................................. 1-2

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

1.3 Overview and Design of the RIA......................................................................... 1-4

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

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

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

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

1.5 References............................................................................................................ 1-7

Chapter 2: Characterizing Ozone and Modeling Tools Used in This Analysis..................... 2-1

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

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

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

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

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

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

2.3.1 CMAQ Model and Inputs ........................................................................ 2-4

2.3.2 Emissions Inventory................................................................................. 2-6
iii

2.4 References.......................................................................................................... 2-10

Appendix: Chapter 2 Characterizing Ozone and Modeling Tools Used in This 

Analysis............................................................................................................ 2a-1 

Chapter 3: Modeled Control Strategy: Design and Analytical Results ................................. 3-1

Synopsis ........................................................................................................................ 3-1

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

3.1.1 Control Measures Applied in the Baseline for Ozone Precursors ........... 3-4

3.1.2 Ozone Levels for Baseline....................................................................... 3-7

3.1.3 National Baseline Sensitivity Analysis.................................................... 3-9

3.2 Developing the Modeled Control Strategy Analysis ......................................... 3-10

3.2.1 Controls Applied for the Modeled Control Strategy: NonEGU

Point and Area Sectors........................................................................... 3-12

3.2.2 Controls Applied for the Modeled Control Strategy: EGU Sector........ 3-13

3.2.3 Controls Applied for the Modeled Control Strategy: Onroad and 

Nonroad Mobile Sectors ........................................................................ 3-16

3.2.4 Data Quality for this Analysis................................................................ 3-17

3.3 Geographic Distribution of Emissions Reductions............................................ 3-18

3.4 Ozone Design Values for Partial Attainment..................................................... 3-22

3.5 References.......................................................................................................... 3-25

Chapter 4: Approach for Estimating Reductions for Full Attainment Scenario.................... 4-1

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

4.1 Development of Full Attainment Targets for Estimate of Extrapolated 

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

4.1.1 Design of Supplemental Modeling Scenarios.......................................... 4-1

4.1.2 Results of Supplemental Modeling for Phase 1 Areas............................. 4-3

4.1.3 Estimating Attainment of the 0.070 and 0.065 ppm Standards in 

Phase 2 Areas........................................................................................... 4-7

4.1.4 Estimating Attainment of the 0.065 ppm Standard outside of Phase 

1 and 2 Areas ........................................................................................... 4-9

4.1.5 Aggregate Results / Verification Modeling of Extrapolated Targets .... 4-11
iv

4.2 Conversion of Full Attainment Percentage Targets into Extrapolated Tons ..... 4-15

4.3 Methodology Used to Estimate the Amount of “Overcontrolled” 

Emissions in the Modeled Control Strategy..................................................... 4-17

4.4 Conversion of Estimated Percentages of Unnecessary Emission 

Reductions into “Overcontrolled” Tons ........................................................... 4-19

Chapter 5: Engineering Cost Estimates ................................................................................. 5-1

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

5.1 Modeled Controls.................................................................................................5-2

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

5.1.2 Modeled Controls—Engineering Cost by Sector .................................... 5-5

5.1.3 Limitations and Uncertainties Associated with Engineering Cost 

Estimates.................................................................................................. 5-7

5.2 Extrapolated Engineering Costs......................................................................... 5-10

5.2.1 Methodology.......................................................................................... 5-10

5.2.2 Results.................................................................................................... 5-18

5.3 Summary of Costs.............................................................................................. 5-22

5.4 Technology Innovation and Regulatory Cost Estimates....................................5-25

5.4.1 Examples of Technological Advances in Pollution Control.................. 5-26

5.4.2 Influence on Regulatory Cost Estimates................................................ 5-29

5.5 References.......................................................................................................... 5-31

Appendix: Chapter 5 Additional Benefits Information .............................................. 5a-1 

Appendix: Chapter 5b Economic Impact of Modeled Controls ................................. 5b-1 

Chapter 6: Incremental Benefits of Attaining Alternative Ozone Standards Relative to 

the Current 8-hour Standard (0.08 ppm)....................................................................... 6-1

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

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

6.2 Characterizing Uncertainty: Moving Toward a Probabilistic Framework 

for Benefits Assessment ..................................................................................... 6-5
v

6.3 Health Impact Functions...................................................................................... 6-7

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

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

6.3.3 Baseline Incidence Rates ....................................................................... 6-21

6.4 Economic Values for Health Outcomes............................................................. 6-21

6.4.1 Mortality Valuation................................................................................ 6-23

6.4.2 Hospital Admissions Valuation ............................................................. 6-23

6.4.3 Asthma-Related Emergency Room Visits Valuation ............................ 6-27

6.4.4 Minor Restricted Activity Days Valuation ............................................ 6-27

6.4.5 School Absences .................................................................................... 6-27

6.5 Results and Implications .................................................................................. 6-28

6.5.1 Ozone Benefit Estimates........................................................................ 6-28

6.5.2 PM2.5 Co-Benefit Estimation Methodology........................................... 6-29

6.5.3 Estimate of Full Attainment Benefits .................................................... 6-67

6.5.5 Estimates of Visibility Benefits ............................................................. 6-84

6.5.6 Discussion of Results and Uncertainties................................................ 6-85

6.5.7 Summary of Total Benefits.................................................................... 6-87

6.6 References:.........................................................................................................6-95

Appendix Chapter 6a: Additional Benefits Information............................................. 6a-1 

Appendix Chapter 6b: Cost Effectiveness Analysis ................................................... 6b-1 

Appendix Chapter 6c: Additional Sensitivity Analyses Related To the Benefits 

Analysis............................................................................................................ 6c-1 

Appendix Chapter 6d: Exploring the Effects of Changes in Tropospheric Ozone 

on UVB.............................................................................................................6d-1 

Chapter 7: Conclusions and Implications of the Illustrative Benefit-Cost Analysis ............. 7-1

7.1 Synopsis ...............................................................................................................7-1

7.2 Results.................................................................................................................. 7-1

7.2.1 Presentation of Results............................................................................. 7-1

7.3 Discussion of Results........................................................................................... 7-9

7.3.1 Sensitivity of Changes to Costs and Benefits Under an Alternate 

Baseline Scenario..................................................................................... 7-9
vi

7.3.2 Relative Contribution of PM Benefits to Total Benefits ....................... 7-11

7.3.3 Challenges to Modeling Full Attainment in All Areas .......................... 7-11

7.4 What Did We Learn through this Analysis?......................................................7-13

7.5 References.......................................................................................................... 7-16

Appendix Chapter 7a: National Baseline Sensitivity Analysis................................... 7a-1 

Appendix Chapter 7b Post 2020 Attainment Analysis ............................................... 7b-1 

Chapter 8: Statutory and Executive Order Impact Analyses ................................................. 8-1

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

8.1 Executive Order 12866: Regulatory Planning and Review................................ 8-1

8.2 Paperwork Reduction Act .................................................................................. 8-1

8.3 Regulatory Flexibility Act.................................................................................. 8-2

8.4 Unfunded Mandates Reform Act .......................................................................8-2

8.5 Executive Order 13132: Federalism................................................................... 8-3

8.6 Executive Order 13175: Consultation and Coordination with Indian 

Tribal Governments............................................................................................ 8-3

8.7 Executive Order 13045: Protection of Children from Environmental 

Health & Safety Risks ........................................................................................ 8-4

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

Distribution or Use ............................................................................................. 8-4

8.9 National Technology Transfer Advancement Act.............................................. 8-5

8.10 Executive Order 12898: Federal Actions to Address Environmental 

Justice in Minority Populations and Low-Income Populations.......................... 8-6
vii

LIST OF TABLES

Number Page

2.1 Geographic Specifications of Modeling Domains........................................................ 2-6

2.2 Control Strategies and Projection Assumptions in the 2020 Emissions Inventory ...... 2-7

3.1 Controls for Current Ozone Standard by Sector Applied in the Baseline 

Determination for 2020................................................................................................. 3-5

3.2 Controls Applied, by Sector, for the 0.070 ppm Control Strategy (Incremental to 

Baseline) ..................................................................................................................... 3-11

3.3 Emissions and Reductions (2020) From Applying the Modeled Control Strategy 

(Incremental to the Baseline)...................................................................................... 3-24

4.1 Estimated Percentage Reductions of NOx and VOC beyond the RIA Control 

Scenario Necessary to Meet Various Alternate Ozone Standards in the Phase I 

Areas ............................................................................................................................. 4-7

4.2 Estimated Percentage Reductions of NOx beyond the RIA Control Scenario 

Necessary to Meet Various Alternate Ozone Standards in the Phase I Areas .............. 4-7

4.3 Estimated Percentage Reductions of NOx beyond the RIA Control Scenario 

Necessary to Meet the 0.070 ppm Ozone Standard in Phase 2 Areas .......................... 4-9

4.4 Estimated Percentage Reductions of NOx beyond the RIA Control Case 

Necessary to Meet the 0.065 ppm Ozone Standard in Phase 3 Areas ........................ 4-10

4.5a Complete Set of Estimated Percentage Reductions of NOx beyond the RIA 

Control Scenario Necessary to Meet the Various Ozone Standards in 2020.............. 4-11

4.5b Estimated Percentage Reductions of NOx + VOC beyond the RIA Control 

Scenario Necessary to Meet the Various Ozone Standards in 2020........................... 4-12

4.6 Summary of the Verification Modeling Results ......................................................... 4-15

4.7a Complete Set of Estimated Extrapolated Emissions Reductions of NOx Beyond 

the RIA Control Scenario Necessary to Meet the Various Ozone Standards in 

2020............................................................................................................................. 4-16

4.7b Estimated Extrapolated Emissions Reductions of NOx + VOC Beyond the RIA 

Control Scenario Necessary to Meet the Various Ozone Standards in 2020.............. 4-17

4.8 Estimated Percentages of Modeled Control Strategy Emissions Reductions not 

needed to Meet the Various Ozone Standards in 2020............................................... 4-18

4.9 Estimated 2020 Control Case Emission Reductions not needed to Meet the 

Various Ozone Standards in 2020............................................................................... 4-20
viii

LIST OF TABLES (CONTINUED) 

Number Page

5.1 Annual Control Costs by Sector and Region, for the Modeled Control Strategy 

(2006$).......................................................................................................................... 5-6

5.2 Marginal Cost and Average Cost Values Used in Calculating M .............................. 5-16

5.3 Extrapolated Emission Reductions Needed (Post Application of Supplemental 

Controls) to Meet Various Alternate Standards in 2020............................................. 5-18

5.4 Extrapolated Cost by Region to Meet Various Alternate Standards Using Fixed 

Cost Approach ($15,000/ton)...................................................................................... 5-20

5.5 Extrapolated Cost by Region to Meet Various Alternate Standards Using Hybrid 

Approach (Mid) .......................................................................................................... 5-21

5.6 Total Costs of Attainment in 2020 for Alternate Levels of the Ozone Standard ...... 5-22

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

for EPA Fuel Control Rules ....................................................................................... 5-30

6.1 Human Health and Welfare Effects of Ozone and PM2.5 6-9

6.2 Ozone and PM Related Health Endpoints Basis for the Concentration-Response 

Function Associated with that Endpoint, and Sub-Populations for which They 

Were Computed .......................................................................................................... 6-10

6.3 National Average Baseline Incidence Rates............................................................... 6-22

6.4 Unit Values for Economic Valuation of Health Endpoints (2006$)........................... 6-24

6-5 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the 

Incidence of Premature Mortality Associated with Ozone Exposure in 2020 

(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence 

Intervals in Parentheses) ............................................................................................. 6-34

6-6 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the 

Incidence of Morbidity Associated with Ozone Exposure (Incremental to 

Current Ozone Standard, 95% Confidence Intervals in Parentheses)......................... 6-35

6-7 Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Reductions 

in the Incidence of PM Premature Mortality associated with PM co-benefit............. 6-36

6-8 Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Reductions 

in the Incidence of Morbidity Associated with PM Co-benefit.................................. 6-37

6-9 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in the 

Incidence of Premature Mortality Associated with Ozone Exposure in 2020 

(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence 

Intervals in Parentheses) ............................................................................................. 6-38
ix

LIST OF TABLES (CONTINUED) 

Number Page

6-10 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in the 

Incidence of Morbidity Associated with Ozone Exposure (Incremental to 

Current Ozone Standard, 95% Confidence Intervals in Parentheses)......................... 6-39

6-11 Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Reductions 

in the Incidence of PM Premature Mortality associated with PM co-benefit............. 6-40

6-12 Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Reductions 

in the Incidence of Morbidity Associated with PM Co-benefit.................................. 6-41

6-13 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in the 

Incidence of Premature Mortality Associated with Ozone Exposure in 2020 

(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence 

Intervals in Parentheses) ............................................................................................. 6-42

6-14 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in the 

Incidence of Morbidity Associated with Ozone Exposure (Incremental to 

Current Ozone Standard, 95% Confidence Intervals in Parentheses)......................... 6-43

6-15 Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Reductions 

in the Incidence of PM Premature Mortality associated with PM co-benefit............. 6-44

6-16 Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Reductions 

in the Incidence of Morbidity Associated with PM Co-benefit.................................. 6-45

6-17 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in the 

Incidence of Premature Mortality Associated with Ozone Exposure in 2020 

(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence 

Intervals in Parentheses) ............................................................................................. 6-46

6-18 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in the 

Incidence of Morbidity Associated with Ozone Exposure (Incremental to 

Current Ozone Standard, 95% Confidence Intervals in Parentheses)......................... 6-47

6-19 Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Reductions 

in the Incidence of PM Premature Mortality associated with PM co-benefit............. 6-48

6-20 Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Reductions 

in the Incidence of Morbidity Associated with PM Co-benefit.................................. 6-49

6-21 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone 

Exposure (Incremental to Current Ozone Standard, Arithmetic Mean, 95% 

Confidence Intervals in Parentheses, Millions of 2006$)........................................... 6-50

6-22 Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure 

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

Parentheses, Millions of 2006$) ................................................................................. 6-51

6-23 Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Valuation of 

Reductions in the Incidence of PM Premature Mortality associated with PM cobenefit (Millions of 2006$)......................................................................................... 6-52
x

LIST OF TABLES (CONTINUED) 

Number Page

6-24 Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with PM Co-benefit 

(Millions of 2006$)..................................................................................................... 6-53

6-25 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone 

Exposure (Incremental to Current Ozone Standard, Arithmetic Mean, 95% 

Confidence Intervals in Parenthes, Millions of 2006$) .............................................. 6-54

6-26 Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure 

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

Parentheses, Millions of 2006$) ................................................................................. 6-55

6-27 Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Valuation of 

Reductions in the Incidence of PM Premature Mortality associated with PM cobenefit (Millions of 2006$)......................................................................................... 6-56

6-28 Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with PM Co-benefit 

(Millions of 2006$)..................................................................................................... 6-57

6-29 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone 

Exposure (Incremental to Current Ozone Standard, Arithmetic Mean, 95% 

Confidence Intervals in Parenthes, Millions of 2006$) .............................................. 6-58

6-30 Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure 

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

Parentheses, Millions of 2006$) ................................................................................. 6-59

6-31 Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Valuation of 

Reductions in the Incidence of PM Premature Mortality associated with PM cobenefit (Millions of 2006$)......................................................................................... 6-60

6-32 Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with PM Co-benefit 

(Millions of 2006$)..................................................................................................... 6-61

6-33 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone 

Exposure (Incremental to Current Ozone Standard, Arithmetic Mean, 95% 

Confidence Intervals in Parenthes, Millions of 2006$) .............................................. 6-62

6-34 Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure 

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

Parentheses, Millions of 2006$) ................................................................................. 7-63
xi

LIST OF TABLES (CONTINUED) 

Number Page

6-35 Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Valuation of 

Reductions in the Incidence of PM Premature Mortality associated with PM cobenefit (Millions of 2006$)......................................................................................... 6-64

6-36 Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with PM Co-benefit 

(Millions of 2006$)..................................................................................................... 6-65

6-38 Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality 

(Millions of 2006$) for the 0.065 ppm Full Attainment............................................. 6-68

6-39 Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality 

(Millions of 2006$) for the 0.070 ppm Full Attainment............................................. 6-69

6-40 Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality 

(Millions of 2006$) for the 0.075 ppm Full Attainment............................................. 6-70

6-41 Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality 

(Millions of 2006$) for the 0.079 ppm Full Attainment............................................. 6-71

6-42 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

3% Discount Rate) for the 0.065 ppm Alternative Standard ...................................... 6-72

6-43 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

7% Discount Rate) for the 0.065 ppm Alternative Standard ...................................... 6-73

6-44 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

3% Discount Rate) for the 0.070 ppm Alternative Standard ...................................... 6-74

6-45 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

7% Discount Rate) for the 0.070 ppm Alternative Standard ...................................... 6-75

6-46 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

3% Discount Rate) for the 0.075 ppm Alternative Standard ...................................... 6-76

6-47 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

7% Discount Rate) for the 0.075 ppm Alternative Standard ...................................... 6-77

6-48 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

3% Discount Rate) for the 0.079 ppm Alternative Standard ...................................... 6-78

6-49 Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

7% Discount Rate) for the 0.079 ppm Alternative Standard ...................................... 6-79

6-50 Monetary Benefits Associated with Visibility Improvements from the 0.070 

Simulated Ozone Attainment Strategy in Selected Federal Class I Areas in 2020 

(in millions of 2006$) ................................................................................................. 6-84

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

Mortalities and Premature Morbidity Avoided in 2020.............................................. 6-88

6.52 Regional Breakdown of Annual Ozone Benefit Results by Health Endpoint in 

2020 (thousands of 2006$).......................................................................................... 6-90

6.53 Regional Breakdown of Annual PM Benefit Results by Health Endpoint in 2020 

(thousands of 2006$) at 3% ........................................................................................ 6-91
xii

LIST OF TABLES (CONTINUED) 

Number Page

6.54 Regional Breakdown of Annual PM Benefit Results by Health Endpoint in 2020 

(thousands of 2006$) at 7% ........................................................................................ 6-92

6.55 Regional Breakdown of Annual Ozone and PM Benefit Results by Health 

Endpoint in 2020 (3% discount rate, thousands of 2006$)......................................... 6-93

6.56 Regional Breakdown of Annual Ozone and PM Benefit Results by Health 

Endpoint in 2020 (7% discount rate, thousands of 2006$)......................................... 6-94

7.1a Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5 CoBenefits: 0.075 ppm Standard in 2020 in Billions of 2006$......................................... 7-3

7.1b Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5 CoBenefits: 0.079 ppm Standard in 2020 in Billions of 2006$......................................... 7-3

7.1c Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5 CoBenefits: 0.070 ppm Standard in 2020 in Billions of 2006$......................................... 7-3

7.1d Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5 CoBenefits: 0.065 ppm Standard in 2020 in Billions of 2006$......................................... 7-4

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

Mortalities and Premature Morbidity Avoided: 2020 National Benefits...................... 7-9
xiii

LIST OF FIGURES 

Number Page

1.1 The Process Used to Create this RIA............................................................................ 1-5

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

3.1 Counties Where Controls for Nitrogen Oxides (NOx) Were Included for 

NonEGU Point and Area Sources, for the Current Ozone Standard in the 

Baseline......................................................................................................................... 3-6

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

Applied to NonEGU Point and Area Sources for the Current Ozone Standard in 

the Baseline................................................................................................................... 3-7

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

Nonroad Sources in Addition to National Mobile Controls for the Current 

Ozone Standard in the Baseline .................................................................................... 3-8

3.4 Baseline Projected 8-Hour Ozone Air Quality in 2020 ................................................ 3-9

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

NonEGU Point and Areas Sources for the RIA Modeled Control Strategy 

(Incremental to Baseline)............................................................................................ 3-13

3.6 Counties Where VOC Controls Were Applied to NonEGU Point and Areas 

Sources for the Modeled Control (Incremental to Baseline) ...................................... 3-14

3.7 Geographic Areas where NOx Controls were Applied to Electrical Generating 

Units (EGUs) for the Modeled Control Strategy (Incremental to Baseline) .............. 3-15

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

Nonroad Sources in Addition to National Mobile Controls for the Modeled 

Control Strategy (incremental to Baseline)................................................................. 3-17

3.9 Annual Tons of NOx Emission Reductions for the Modeled Control Strategy 

(Incremental to the Baseline)...................................................................................... 3-19

3.10 Percentage of 2020 Annual NOx Emissions Reduced by Sector Incremental to 

the Baseline................................................................................................................. 3-20

3.11 Annual Tons of VOC Emission Reductions for the Modeled Control Strategy 

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

3.12 Percentage of 2020 Annual VOC Emissions Reduced by Sector............................... 3-22

3.13 Projected 8-Hour Ozone Air Quality in 2020 From Applying the Modeled 

Control Strategy.......................................................................................................... 3-23

3.14 National Annual Emissions Remaining (2020) after Application of Controls for 

the Baseline and Modeled Control Strategy ............................................................... 3-24
xiv

LIST OF FIGURES (CONTINUED) 

Number Page

4.1 Counties within which Across-the-Board Emissions Reductions were Applied in 

the Supplemental Modeling Analyses .......................................................................... 4-2

4.2a Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and 

Each of the Six Supplemental Modeling Scenarios for the Highest Three 

Counties within the Houston Area................................................................................ 4-4

4.2b Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and 

Each of the Six Supplemental Modeling Scenarios for the Highest Counties 

within the Eastern Lake Michigan Area ....................................................................... 4-5

4.2c Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and 

Each of the Six Supplemental Modeling Scenarios for the Highest Counties 

within the Northeast Corridor ....................................................................................... 4-5

4.2d Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and 

Each of the Six Supplemental Modeling Scenarios for Three Specific Areas in 

California ...................................................................................................................... 4-6

4.3a Map of Extrapolated Cost Counties for the 0.065 ppm Alternate Standard and 

the Estimated Percent NOx Controls Needed to Meet that Standard ......................... 4-12

4.3b Map of Extrapolated Cost Counties for the 0.070 ppm Alternate Standard and 

the Estimated Percent NOx Controls Needed to Meet that Standard ......................... 4-13

4.3c Map of Extrapolated Cost Counties for the 0.075 ppm Alternate Standard and 

the Estimated Percent NOx Controls Needed to Meet that Standard ......................... 4-13

4.3d Map of Extrapolated Cost Counties for the 0.079 ppm Alternate Standard and 

the Estimated Percent NOx Controls Needed to Meet that Standard ......................... 4-14

5.1 Marginal Cost Curve for Modeled Control Strategy Geographic Areas (NOX 

nonEGU Point and Area Source Controls Prior to Cut Points) .................................... 5-4

5.2 Marginal Cost Curve for Modeled Control Strategy Geographic Areas (VOC 

nonEGU Point and Area Source Controls Prior to Cut Points) .................................... 5-4

5.3 Total Annualized Costs by Emissions Sector and Region for Modeled Control 

Strategy in 2020 ............................................................................................................ 5-8

5.4 Ratio of Unspecified Emission Reductions to Known Emission Reductions 

Across Various Standards for Phase 1 Areas.............................................................. 5-16

5.5 Ranges of Hybrid (Mid) Average Cost/Ton Values across Geographic Areas 

and Standards.............................................................................................................. 5-17

5.6 Extrapolated Cost by Region to Meet Various Alternate Standards Using Fixed 

Cost Approach ($15,000/ton)...................................................................................... 5-20

5.7 Extrapolated Cost by Region to Meet Various Alternate Standards Using Hybrid 

Approach (Mid) .......................................................................................................... 5-21

5.8 Annual Total Costs by Region.................................................................................... 5-23
xv

LIST OF FIGURES (CONTINUED) 

Number Page

5.9 National Known Control Costs and Extrapolated Costs for Various Standards........ 5-24

5.10 Total Cost Ranges for Various Standards................................................................... 5-25

5.11 Technological Innovation Reflected by Marginal Cost Shift ..................................... 5-26

6.1 Valuation of Ozone Morbidity and Mortality Benefits Results by Standard 

Alternative................................................................................................................... 6-29

6.2 Valuation of PM Co-Benefits by Standard Alternative at 3% and 7%....................... 6-30

6.3 Ozone and PM2.5 Benefits by Standard Alternative (3% and 7% Discount Rates) .... 6-81

6.4 Example Combined Ozone and PM2.5 Monetized Benefits Estimates by 

Standard Alternative (3% and 7% Discount Rates).................................................... 6-82

6.5 Ozone and PM Total Benefits including all combinations of Mortality Estimates 

(3% discount rate)....................................................................................................... 6-83

6.6 Ozone and PM Total Benefits including all combinations of Mortality Estimates 

(7% discount rate)....................................................................................................... 6-84

6.7 Total Annual Ozone and PM2.5-Related Premature Mortalities Avoided in 2020 

by Standard Alternative .............................................................................................. 6-89

7.1 Range of Net Benefits (2006$) for All Standard Alternatives (7% discount) .............. 7-6

7.2 Range of Net Benefits (2006$) for Selected Standard.................................................. 7-7

7.3 Range of Net Benefits for Select Combinations at 3% and 7%.................................... 7-8
ES-1

Executive Summary 

Overview 

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

benefits of nationally attaining a new ozone standard of 0.075 ppm. Per Executive Order 12866 

and the guidelines of OMB Circular A-4, this Regulatory Impact Analysis (RIA) also presents 

analyses of three alternative standards, a less stringent 0.079 ppm and two more stringent options 

(0.065 and 0.070 ppm). The benefit and cost estimates below are calculated incremental to a 

2020 baseline that incorporates air quality improvements achieved through the projected 

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

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

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

attainment of the current ozone standard. 

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

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

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

potential dates and flexibility for extensions. For purposes of this analysis, though, we assume 

attainment by 2020 for all areas except for two areas (San Joaquin Valley and South Coast air 

basins) in California. The state has submitted to EPA plans for implementing the current ozone 

standard which propose that these two areas of California meet that standard by 2024. We have 

assumed for analytical purposes that the San Joaquin Valley and South Coast air basin would 

attain a new standard in 2030. The actual attainment year for all areas will be determined through 

the State Implementation Plan process. A separate analysis for the San Joaquin Valley and South 

Coast air basins in California is provided in Appendix 7b.

EPA designed a two-stage approach to estimating costs and benefits, because we recognized that 

some areas with significant ozone problems would need emission controls beyond those 

currently available to meet either the 1997 ozone standards, or alternative, more stringent 

standards. However, as documented in Chapter 5, there are numerous examples of how 

technological innovation has led to the development of new and improved ways of reducing air 

pollution, often at lower cost than estimated at the time a new NAAQS is established. The 

individual chapters of the RIA present more detail regarding estimated costs and benefits based 

on both partial attainment (manageable with current technologies) and full attainment (which in 

some locations will require new or innovative approaches and technology). 

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

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

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

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

on health considerations only; economic factors cannot be considered. 

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

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

unimportant or should be ignored. The Agency believes that consideration of costs and benefits 
ES-2

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

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

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

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

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

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

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

relevant portions of the analysis.

ES.1 Approach to the Analysis 

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

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

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

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

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

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

from the analysis. 

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

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

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

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

would need to develop and adopt additional controls to attain the new standard. After existing 

control technologies have been applied, additional unspecified emission reductions are applied to 

establish attainment. The cost of these unknown controls was extrapolated and is included in the 

total cost numbers. 

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

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

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

illustrative control strategies we analyzed are also expected to reduce ambient PM2.5 levels in 

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

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

beyond those in the regulatory baseline. In moving further down the list of cost-effective known 

and available controls, we deplete our database of available choices of known controls, and are 

left with background emissions and remaining anthropogenic emissions for which we do not 

have enough knowledge to determine how, and at what cost, reductions can be achieved in the 

future when attainment would be required. 

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

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

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

captured by this range of estimates, and that additional research is needed to more fully establish 
ES-3

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

uncertainly associated with some different modeling assumptions. 

ES.2 Results of Benefit-Cost Analysis 

The following is a presentation of the benefits and costs of attaining various Ozone National 

Ambient Air Quality Standards in the year 2020. These estimates only include areas assumed to 

meet the current standard by 2020. As mentioned earlier, they do not include the costs or benefits 

of attaining the alternate standards in San Joaquin Valley and South Coast air basins. Due to the 

differences in attainment year and other assumptions underlying the 2020 analysis presented 

here, and the 2030 analysis in Appendix 7b, it is not appropriate to add the results together to get 

a national “full attainment” scenario. 

In Tables ES.1 through ES.4, the individual row estimates reflect the different studies available 

to describe the ozone premature mortality relationship. Ranges within the total benefits column 

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

were derived. For the 0.075ppm alternative, PM2.5 co-benefits account for between 42 and 99 

percent of total benefits depending upon the study used. Details about these studies are in 

Chapter 6. 

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

discussed in Chapter 5. The low end of the range of net benefits is constructed by subtracting the 

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

the lowest cost from the highest benefit. The presentation of the net benefit estimates represents 

the widest possible range from this analysis. These tables do not include visibility benefits, 

which are estimated at $160 million/yr. 

Table ES.1: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5 CoBenefits: 0.075 ppm Standard in 2020 in Billions of 2006$* 

Ozone 

Mortality

Function or 

Assumption Reference

Total Benefits** 

 3% 7% 

Total

Costs***

7% 

Net Benefits 

 3% 7% 

NMMAPS Bell et al. 2004 2.6 – 17 2.4 – 16 7.6 – 8.8 –6.3 – 9.5 –6.4 – 7.9 

Bell et al. 2005 3.8 – 18 3.6 – 17 7.6 – 8.8 –5.0 – 11 –5.2 – 9.1 

Ito et al. 2005 4.4 – 19 4.3 – 17 7.6 – 8.8 –4.4 – 11 –4.5 – 9.8 Metaanalysis Levy et al. 2005 4.5 – 19 4.4 – 17 7.6 – 8.8 –4.3 – 11 –4.5 – 9.9 

Assumption that association is 

not causal**** 

2.0 – 17 1.8 – 15 7.6 – 8.8 –6.8 – 9 –7.0 – 7.4 
ES-4

Table ES.2: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5 CoBenefits: 0.079 ppm Standard in 2020 in Billions of 2006$* 

Ozone 

Mortality

Function or 

Assumption Reference 

Total Benefits** 

 3% 7% 

Total

Costs***

7% 

Net Benefits 

 3% 7% 

NMMAPS Bell et al. 2004 1.4 – 11 1.3 – 9.9 2.4 – 2.9 –1.5 – 8.5 –1.6 – 7.5 

Bell et al. 2005 1.9 – 11 1.8 – 10 2.4 – 2.9 –1.1 – 8.9 –1.2 – 7.9 

Ito et al. 2005 2.1 – 12 2.0 – 11 2.4 – 2.9 –0.83 – 9.2 –0.9 – 8.1 Metaanalysis Levy et al. 2005 2.1 – 12 2.0 – 11 2.4 – 2.9 –0.80 – 9.2 –0.9 – 8.2 

Assumption that association is 

not causal**** 

1.2 – 11 1.1 – 9.7 2.4 – 2.9 –1.7 – 8.3 –1.8 – 7.3 

Table ES.3: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5 CoBenefits: 0.070 ppm Standard in 2020 in Billions of 2006$* 

Ozone 

Mortality

Function or 

Assumption Reference 

Total Benefits** 

 3% 7% 

Total

Costs***

7% 

Net Benefits 

 3% 7% 

NMMAPS Bell et al. 2004 5.4 – 29 5.1 – 27 19 – 25 –20 – 10 –20 – 7.6 

Bell et al. 2005 9.7 – 34 9.5 – 31 19 – 25 –15 – 15 –16 – 12 

Ito et al. 2005 12 – 36 12 – 33 19 – 25 –13 – 17 –13 – 14 Metaanalysis Levy et al. 2005 12 – 36 12 – 33 19 – 25 –13 – 17 –13 – 14 

Assumption that association is 

not causal**** 

3.5 – 27 3.2 – 25 19 – 25 –22 – 8 –22 – 5.7 
ES-5

Table ES.4: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5 CoBenefits: 0.065 ppm Standard in 2020 in Billions of 2006$* 

Ozone 

Mortality

Function or 

Assumption Reference 

Total Benefits** 

 3% 7% 

Total

Costs***

7% 

Net Benefits 

 3% 7% 

NMMAPS Bell et al. 2004 9.0 – 46 8.6 – 42 32 – 44 –35 – 14 –35 – 9.7 

Bell et al. 2005 17 – 54 16 – 50 32 – 44 –27 – 22 –28 – 18 

Meta-analysis Ito et al. 2005 21 – 58 21 – 54 32 – 44 –23 – 26 –23 – 22 

Levy et al. 2005 21 – 58 21 – 54 32 – 44 –23 – 26 –23 – 22 

Assumption that association is not 

causal****

5.5 – 42 5.1 – 38 32 – 44 –39 – 10 –39 – 6.2 

*All estimates rounded to two significant figures. As such, they may not sum across columns. These estimates do 

not include visibility benefits. Only includes areas required to meet the current standard by 2020, does not include 

San Joaquin and South Coast areas in California. Appendix 7b shows the costs and benefits of attaining alternate 

standards in San Joaquin and South Coast California. 

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

premature mortality function to both the lower and upper ends of the range of the PM2.5 premature mortality 

functions characterized in the expert elicitation. Tables exclude unquantified and nonmonetized benefits. 

***Range reflects lower and upper bound cost estimates. Data for calculating costs at a 3% discount rate was not 

available for all sectors, and therefore total annualized costs at 3% are not presented here. Additionally, these 

estimates assume a particular trajectory of aggressive technological change. An alternative storyline might 

hypothesize a much less optimistic technological trajectory, with increased costs, or with decreased benefits in 

2020 due to a later attainment date. 

****Total includes ozone morbidity benefits and total PM co-benefits only. 

Table ES.5 presents the total number of estimated ozone and PM2.5-related premature mortalities 

and morbidities avoided nationwide in 2020. 
ES-6

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

Mortalities and Premature Morbidity Avoided: 2020 National Benefits* 

Combined Estimate of Mortality

Standard Alternative and 

Model or Assumption 

Combined Range of Ozone Benefits and 

 PM2.5 Co-Benefits** 

 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 

NMMAPS Bell (2004) 140 – 1,300 260 – 2,000 560 – 3,500 940 – 5,500 

Bell (2005) 200 – 1,300 420 – 2,200 560 – 4,100 2,000 – 6,500 

Meta-Analysis Ito (2005) 230 – 1,300 500 – 2,300 1,100 – 4,300 2,500 – 7,000 

Levy (2005) 230 – 1,400 510 – 2,300 1,400 – 4,400 2,500 – 7,100 

Assumption that association is not 

causal*** 120 – 1,200 190 – 2,000 310 – 3,200 490 – 5,000 

Combined Estimate of Morbidity

Acute Myocardial Infarction 570 890 1,500 2,300 

Upper Respiratory Symptoms 3,100 4,900 8,100 13,000

Lower Respiratory Symptoms 4,200 6,700 11,000 17,000 

Chronic Bronchitis 240 380 630 970 

Acute Bronchitis 640 1,000 1,700 2,600 

Asthma Exacerbation 3,900 6,100 10,000 16,000 

Work Loss Days 28,000 43,000 72,000 110,000 

School Loss Days 72,000 200,000 640,000 1,100,000 

Hospital and ER Visits 890 1,900 5,100 9,400 

Minor Restricted Activity Days 340,000 750,000 2,100,000 3,500,000 

*Only includes areas required to meet the current standard by 2020, does not include San Joaquin Valley 

and South Coast air basins in California. Appendix 7b shows the costs and benefits of attaining 

alternate standards in San Joaquin and South Coast California. 

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

ozone premature mortality function to both the lower and upper ends of the range of the PM2.5 

premature mortality functions characterized in the expert elicitation described in Chapter 6. 

***Estimated reduction in premature mortality due to PM2.5 reductions only 

The following set of graphs is included to provide the reader with a richer presentation of the 

range of costs and benefits of the alternative standards. The graphs supplement the tables by 

displaying all possible combinations of net benefits, utilizing the five different ozone functions, 

the fourteen different PM functions, and the two cost methods. Each of the 140 bars in each 

graph represents an independent and equally probably point estimate of net benefits under a 

certain combination of cost and benefit estimation methods. Thus it is not possible to infer the 

likelihood of any single net benefit estimate. The blue bars indicate combinations where the net 

benefits are negative, whereas the green bars indicate combinations where net benefits are 

positive. Figure ES.1 shows all of these combinations for all standards analyzed. Figure ES.2 

shows a close-up of the range of net benefits for the selected standard of 0.075 ppm. 
ES-7

Range of Net Benefits Across Standard Alternatives*

* This graph shows all 140 combinations of the 5 different ozone mortality functions and assumptions, the 14 different PM mortality functions, and 

the 2 cost methods. All combinations are treated as independent and equally probable. 

Figure ES.1: 

Costs exceedBenefits

Costs exceedBenefits

Costs exceedBenefits

Costs exceedBenefits

Benefits exceed 

Costs

Benefits exceed 

Costs

Benefits exceed 

Costs

Benefits exceed 

Costs
ES-8

* This graph shows all 140 combinations of the 5 different ozone mortality functions and assumptions, the 14 different PM mortality functions, and 

the 2 cost methods. All combinations are treated as independent and equally probable. 

For the selected standard of 0.075 ppm, the median value of all of the independent point estimates is $0.8 billion, and the majority (64%) of the 

combinations indicate positive net benefits for this standard. 

*

Figure ES.2: 

Costs exceed Benefits 

Benefits exceed Costs 
ES-9

ES.3 Caveats and Conclusions 

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

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

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

ozone standard, which are fully discussed in Chapters 5, 6, and 7. Analytically, the 

characterization of mortality benefits and the estimation of the costs to the nation of fully 

attaining a tighter standard will be subject to further review by EPA science advisory boards. 

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

of the more significant sources of uncertainty.

x Benefits estimates are influenced by our ability to accurately model relationships 

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

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

chosen for each health benefit.

x EPA has requested advice from the National Academy of Sciences on how best to 

quantify uncertainty in the relationship between ozone exposure and premature 

mortality within the context of quantifying benefits. We expect to receive this advice 

in the spring of 2008 

x As shown in figure ES.1 above, there is a considerable range of costs and benefits 

associated with attainment of a tighter ozone standard, especially in the range of PM 

2.5 benefits. EPA has plans to ask its Science Advisory Board for advice about how 

to best characterize the PM mortality benefits in future analyses. 

x PM co-benefits are derived primarily from reductions in nitrates (associated with 

NOx controls). As such, these estimates are strongly influenced by the assumption 

that all PM components are equally toxic. Co-benefit estimates are also influenced by 

the extent to which a particular area chooses to use NOx controls rather than VOC 

controls.

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

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

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

implemented to reduce ozone concentrations at the highest monitor would likely 

result in some reductions in ozone concentrations at attaining monitors down-wind 

(i.e., the controls would lead to concentrations below the standard in down-wind 

locations).

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

health and agricultural crop production) and disbenefits (e.g., decreases in 
ES-10

tropospheric ozone lead to reduced screening of UV-B rays and reduced nitrogen 

fertilization of forests and cropland) discussed in this analysis in Chapter 6.

x Changes in air quality as a result of controls are not expected to be uniform over 

the country. In our hypothetical control scenario some increases in ozone levels 

occur in areas already in attainment, though not enough to push the areas into 

nonattainment 

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

For example, the states are likely to use different approaches for reducing NOx 

and VOCs in their state implementation plans to reach a tighter standard. In 

addition, since our modeling of known controls does not get all areas into 

attainment, we needed to make assumptions about the costs of control 

technologies that might be developed in the future and used to meet the tighter 

alternative. For the 21 counties (in four geographic areas) that are not expected to 

attain 0.075 ppm1 in 20202, assumed costs of unspecified controls represent a 

substantial fraction, of the costs estimated in this analysis ranging from 50% to 

89% of total costs depending on the standard being analyzed. 

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

questioned the appropriateness of an approach similar to one of those used here 

for estimating extrapolated costs. For balance, EPA also applied a methodology 

recommended by the Science Advisory Board in an effort to best approximate the 

costs of control technologies that might be developed in the future. 

x Both extrapolated costs and benefits have additional uncertainty relative to 

modeled costs and benefits. The extrapolated costs and benefits will only be 

realized to the extent that unknown extrapolated controls are economically 

feasible and are implemented. Technological advances over time will tend to 

increase the economic feasibility of reducing emissions, and will tend to reduce 

the costs of reducing emissions. Our estimates of costs of attainment in 2020 

assume a particular trajectory of aggressive technological change. This trajectory 

leads to a particular level of emissions reductions and costs which we have 

estimated based on two different approaches, the fixed cost and hybrid 

approaches. An alternative storyline might hypothesize a much less optimistic 

technological change path, such that emissions reductions technologies for 

industrial sources would be more expensive or would be unavailable, so that 

emissions reductions from many smaller sources might be required for 2020 

attainment, at a potentially greater cost per ton. Under this alternative storyline, 

two outcomes are hypothetically possible: Under one scenario, total costs 

associated with full attainment might be substantially higher. Under the second 

scenario, states may choose to take advantage of flexibility in the Clean Air Act to 

1

 Areas that do not meet 0.075 ppm are Chicago, Houston, the Northeastern Corridor, and 

Sacramento. For more information see chapter 4 section 4.1.1. 

2

 This list of areas does not include the San Joaquin and South Coast air basins who are not 

expected to attain the current 0.08 ppm standard until 2024. 
ES-11

adopt plan with later attainment dates to allow for additional technologies to be 

developed and for existing programs like EPA’s Onroad Diesel, Nonroad Diesel, 

and Locomotive and Marine rules to be fully implemented. If states were to 

submit plans with attainment dates beyond our 2020 analysis year, benefits would 

clearly be lower than we have estimated under our analytical storyline. However, 

in this case, state decision makers seeking to maximize economic efficiency 

would not impose costs, including potential opportunity costs of not meeting their 

attainment date, when they exceed the expected health benefits that states would 

realize from meeting their modeled 2020 attainment date. In this case, upper 

bound costs are difficult to estimate because we do not have an estimate of the 

point where marginal costs are equal to marginal benefits plus the costs of 

nonattainment. Clearly, the second stage analysis is a highly speculative exercise, 

because it is based on estimating emission reductions and air quality 

improvements without any information about the specific controls that would be 

available to do so. 

x This analysis shows the costs and benefits of a standard of 0.075 ppm and other 

alternate standards of 0.079, 0.070, and 0.065. The costs and benefits are 

incremental to a baseline that assumes some additional technology changes in the 

onroad technology sector. If these changes do not occur, then cost for all 

standards would increase by $1.8 billion and benefits for all standards would 

increase by $360 million to $3.1 billion using 2006$ and a 3% discount rate, and 

$330 million to $2.8 billion when using a 7% discount rate.3 Details about costs 

and benefits using an alternate baseline can be found in Appendix 7a. 

3

 These estimates are highly uncertain and are purely illustrative estimates of the potential costs 

and benefits of these mobile control strategies. We present them only as screening-level 

estimates to provide a bounding estimate of the costs and benefits of including these emissions 

controls in the ozone NAAQS control case for all standards. As such, it would be inappropriate 

to apply these benefit per-ton estimates to other policy contexts, including other regulatory 

impact analyses. Furthermore, the benefits only reflect a partial accounting of the total benefits 

associated with emission reductions related to the mobile controls included in this sensitivity 

analysis. 
1-1

Chapter 1: Introduction and Background 

Synopsis

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

of attaining a revised primary ozone National Ambient Air Quality Standard (NAAQS) 

nationwide. This document contains illustrative analyses that consider limited emission control 

scenarios that states, tribes and regional planning organizations might implement to achieve a 

revised ozone NAAQS. In some cases, EPA weighed the available empirical data to make 

judgments regarding the proposed attainment status of certain urban areas in the future. 

According to the Clean Air Act, EPA must use health-based criteria in setting the NAAQS and 

cannot consider estimates of compliance cost. This Regulatory Impact Analysis (RIA) is 

intended to provide the public a sense of the benefits and costs of meeting new alternative ozone 

NAAQS, and to meet the requirements of Executive Order 12866 and OMB Circular A-4 

(described below in Section 1.2.2).

1.1 Background 

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

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

reasonably be anticipated to endanger public health or welfare,” and to issue air quality criteria 

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

knowledge useful in indicating the kind and extent of all identifiable effects on public health or 

welfare which may be expected from the presence of [a] pollutant in the ambient air.” Ozone is 

one of six pollutants for which EPA has developed air quality criteria.

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

and “secondary” NAAQS for pollutants identified under section 108. Section 109(b)(1) defines a 

primary standard as “the attainment and maintenance of which in the judgment of the 

Administrator, based on [the] criteria and allowing an adequate margin of safety, [are] requisite 

to protect the public health.” A secondary standard, as defined in section 109(b)(2), must 

“specify a level of air quality the attainment and maintenance of which in the judgment of the 

Administrator, based on [the] criteria, [are] requisite to protect the public welfare from any 

known or anticipated adverse effects associated with the presence of [the] pollutant in the 

ambient air.” Welfare effects as defined in section 302(h) [42 U.S.C. 7602(h)] include but are not 

limited to “effects on soils, water, crops, vegetation, manmade materials, animals, wildlife, 

weather, visibility and climate, damage to and deterioration of property, and hazards to 

transportation, as well as effects on economic values and on personal comfort and well-being.”

Section 109(d) of the Act directs the Administrator to review existing criteria and standards at 

5-year intervals. When warranted by such review, the Administrator is to retain or revise the 

NAAQS. After promulgation or revision of the NAAQS, the standards are implemented by the 

States.
1-2

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

1.2.1 Legislative Roles 

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

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

pollutant, to set a standard that protects public health with “an adequate margin of safety.” As 

interpreted by the Agency and the courts, the Act requires EPA to create standards based on 

health considerations only. Economic factors cannot be considered.

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

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

be ignored. The Agency believes that consideration of costs and benefits are essential to making 

efficient, cost effective decisions for implementation of these standards. The impact of cost and 

efficiency are considered by states during this process, as they decide what timelines, strategies, 

and policies make the most sense. This RIA is intended to inform the public about the potential 

costs and benefits that may result when a new ozone standard is implemented, but is not relevant 

to establishing the standards themselves. 

1.2.2 Role of Statutory and Executive Orders 

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

rulemaking and public documents. This document is separate from the NAAQS decision making 

process, but there are several statutes and executive orders that still apply to any public 

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

Chapter 8. 

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

A-4.1

 These documents present guidelines for EPA to assess the benefits and costs of the selected 

regulatory option, as well as one less stringent and one more stringent option. OMB circular A-4 

also requires both a cost-benefit, and a cost-effectiveness analysis for rules where health is the 

primary effect. Within this RIA we provide a cost benefit analysis. We also provide a costeffectiveness analysis which will be jointly presented in Appendix 6b. 

1.2.3 Market Failure or Other Social Purpose 

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

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

market power, and inadequate or asymmetric information. Correcting market failures is one 

reason for regulation, but it is not the only reason. Other possible justifications include 

improving the function of government, removing distributional unfairness, or promoting privacy 

and personal freedom. 

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

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

1

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

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

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

nearby neighborhoods. If bargaining was costless and all property rights were well defined, 

people would eliminate externalities through bargaining without the need for government 

regulation. From this perspective, externalities arise from high transaction costs and/or poorly 

defined property rights that prevent people from reaching efficient outcomes through market 

transactions. 

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

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

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

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

for selected entities should be avoided. However, there are some circumstances in which 

government may choose to validate a monopoly. If a market can be served at lowest cost only 

when production is limited to a single producer of local gas and electricity distribution services, a 

natural monopoly is said to exist. In such cases, the government may choose to approve the 

monopoly and to regulate its prices and/or production decisions. Nevertheless, it should be noted 

that technological advances often affect economies of scale. This can, in turn, transform what 

was once considered a natural monopoly into a market where competition can flourish. 

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

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

more than demonstrate the possible existence of incomplete or asymmetric information. Even 

though the market may supply less than the full amount of information, the amount it does 

supply may be reasonably adequate and therefore not require government regulation. Sellers 

have an incentive to provide information through advertising that can increase sales by 

highlighting distinctive characteristics of their products. Buyers may also obtain reasonably 

adequate information about product characteristics through other channels, such as a seller 

offering a warranty or a third party providing information. 

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

be appropriate when there are clearly identified measures that can make government operate 

more efficiently. In addition, Congress establishes some regulatory programs to redistribute 

resources to select groups. Such regulations should be examined to ensure that they are both 

effective and cost-effective. Congress also authorizes some regulations to prohibit discrimination 

that conflicts with generally accepted norms within our society. Rulemaking may also be 

appropriate to protect privacy, permit more personal freedom or promote other democratic 

aspirations.

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

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

and environmental problems without compensation for those suffering the problems. Setting a 

standard with a reasonable margin of safety attempts to place the cost of control on those who 

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

problems from higher levels of pollution. 
1-4

1.2.4 Illustrative Nature of the Analysis 

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

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

NAAQS. Because states are ultimately responsible for implementing strategies to meet any 

revised standard, the control scenarios in this RIA are necessarily hypothetical in nature. They 

are not forecasts of expected future outcomes. Important uncertainties and limitations are 

documented in the relevant portions of the analysis. 

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

rules, or the implementation plans states develop, and the distinctions are worth brief mention. 

This RIA does not assess the regulatory impact of an EPA-prescribed national or regional rule 

such as the Clean Air Interstate Rule, nor does it attempt to model the specific actions that any 

state would take to implement a revised ozone standard. This analysis attempts to estimate the 

costs and human and welfare benefits of cost-effective implementation strategies which might be 

undertaken to achieve national attainment of new standards. These hypothetical strategies 

represent a scenario where states use one set of cost-effective controls to attain a revised ozone 

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

determine appropriate emissions control scenarios. State implementation plans would likely vary 

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

these plans. 

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

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

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

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

is that this analysis is carried out for the year 2020, before some areas are required to reach the 

current ozone standard. Section 1.3.1 below explains why EPA selected the analysis year of 

2020. Despite these limitations, EPA has used the best available data and methods to produce 

this RIA. 

1.3 Overview and Design of the RIA 

This Regulatory Impact Analysis evaluates the costs and benefits of hypothetical national 

strategies to attain several potential revised primary ozone standards. The document is intended 

to be straightforward and written for the lay person with a minimal background in chemistry, 

economics, and/or epidemiology. Figure 1.1 provides an illustration of the framework of this 

RIA.
1-5

Figure 1.1: The Process Used to Create this RIA 

1.3.1 Baseline and Years of Analysis 

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

ozone RIA analysis on the previously completed PM NAAQS RIA analysis which also used 

2020 as its analysis year. Many areas will reach attainment of the current ozone standard or any 

alternative ozone standard by 2020. For purposes of this analysis, we assume attainment by 2020 

for all areas except for two areas in California with unique circumstances described in Appendix 

7b. Some areas for which we assume 2020 attainment may in fact need more time to meet one or 

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

the attainment dates that will ultimately be assigned to individual areas under the Clean Air Act, 

which contains a variety of potential dates and flexibility to move to later dates (up to 20 years), 

provided that the date is as expeditious as practicable.

The methodology first estimates what baseline ozone levels might look like in 2020 with existing 

Clean Air Act programs, including application of controls to meet the current ozone standard and 

the newly revised PM NAAQS standard and then models how ozone levels would be predicted 

to change following the application of additional controls to reach a tighter standard. This allows 

for an analysis of the incremental change between the current standard and an alternative 

standard. This timeline is also consistent with expected attainment in 2020 of the revised 

Estimate 2020 Emissions 

Model 2020 Baseline Air Quality 

Identify Areas Projected to Exceed 

Alternate Standard and Calculate 

Needed Reduction Targets 

Select Control Strategies 

Estimate Modeled 

Control Costs 

Estimate Modeled Human 

Health Effects and Dollar 

Benefits

Determine Post-Control Air 

Quality and Compare pre- and 

post-control strategy air quality 

(partial attainment in most areas) 

Extrapolate Tons to Reach Full 

Attainment and Compare pre-and 

post- extrapolation air quality 

Estimate Extrapolated 

Control Costs 

Estimate Extrapolated Human 

Health Effects and Dollar Benefits 
1-6

Particulate Matter (PM) NAAQS covered in the PM NAAQS RIA issued in September 2006. As 

explained in Chapter 2, since one of the principal precursors for ozone, NOx, is also a precursor 

for PM, it is important that we account for the impact on ozone concentrations of NOx controls 

used in the hypothetical control scenario used in the PM NAAQS RIA, so as to avoid double 

counting the benefits and costs of these controls. 

1.3.2 Control Scenarios Considered in this RIA 

A hypothetical control strategy was developed for an alternative 8-hr ozone standard of 0.070 

ppm, in order to illustrate one national scenario for how such a tighter standard might be met. 

First, EPA modeled the predicted air quality changes that would result from the application of 

emissions control options that are known to be available to different types of sources in portions 

of the country that were predicted to be in non-attainment with 0.070 ppm in 2020. However, 

given the limitations of current technology and the amount of improvement in air quality needed 

to reach a standard of 0.070 ppm in some areas, it was also expected that modeling these known 

controls would not reduce ozone concentrations sufficiently to allow all areas to reach the more 

stringent standard. We performed air quality sensitivity modeling by reducing the remaining 

NOx and NOx + VOC emissions by 30, 60, and 90% beyond the percentage inventory reductions 

that were achieved by the modeled known control strategy. This enabled us to determine, for an 

extrapolation analysis, the approximate number of tons of additional reductions, beyond those 

achieved by known controls that would be required to meet the alternate standards.

1.3.3 Evaluating Costs and Benefits 

Applying a two step methodology for estimating emission reductions needed to reach full 

attainment enabled EPA to evaluate nationwide costs and benefits of attaining a tighter ozone 

standard, albeit with substantial additional uncertainty regarding the second step estimates. Costs 

and benefits are presented in this RIA in the same two steps that emissions reductions were 

estimated. First, the costs associated with applying known controls were quantified, and 

presented along with an estimate of their economic impact. Second, EPA estimated costs of the 

additional tons of extrapolated emission reductions estimated which were needed to reach full 

attainment. The analysis of the benefits of setting an alternative primary standard included both 

mortality and morbidity calculations matching the costs of applying known controls and then the 

benefits of reaching full attainment. The costs and monetized benefits were then compared to 

provide an estimate of net benefits nationwide. It is important to note that this analysis did not 

estimate any separate costs or benefits of attaining a secondary NAAQS standard due to resource 

and time constraints. Since the secondary is being set to be equivalent to the primary standard, 

few additional costs and benefits are expected. 

To streamline this RIA, this document refers to several previously published documents, 

including two technical documents EPA produced to prepare for the ozone NAAQS proposal. 

The first was a Criteria Document created by EPA’s Office of Research and Development 

(published in 2006), which presented the latest available pertinent information on atmospheric 

science, air quality, exposure, dosimetry, health effect, and environmental effects of ozone. The 

second was a “Staff Paper” (published in 2007) that evaluated the policy implications of the key 

studies and scientific information contained in the Criteria Document, as well as presented a risk 

assessment for various standard levels. The Staff Paper also includes staff conclusions and 
1-7

recommendations to the Administrator regarding potential revisions to the standards. In addition 

to the Criteria Document and Staff Paper, this ozone RIA relies heavily on the 2006 RIA for 

particulate matter (PM). Many of the models and methodology used here are the same as in the 

PM NAAQS RIA. This RIA identifies methodologies used to generate data, but refers readers to 

the PM NAAQS RIA for many technical details. The focus of this RIA is to explain in detail 

how the approach or methodologies have changed from the PM NAAQS RIA analysis, and to 

present the results of the methodologies employed in this analysis, which compares attainment of 

tighter levels of the ozone standard to the baseline of the current standard. 

1.4 Ozone Standard Alternatives Considered 

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

benefits of nationally attaining a new ozone standard of 0.075 ppm. Per Executive Order 12866 

and the guidelines of OMB Circular A-4, this Regulatory Impact Analysis (RIA) also presents 

analyses of three alternative standards, a less stringent 0.079 ppm and two more stringent options 

(0.065 and 0.070 ppm). The benefit and cost estimates below are calculated incremental to a 

2020 baseline that incorporates air quality improvements achieved through the projected 

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

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

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

attainment of the current ozone standard. 

1.5 References 

Henderson, R. 2006. October 24, 2006. Letter from CASAC Chairman Rogene Henderson to 

EPA Administrator Stephen Johnson, EPA-CASAC-07-001. 

U.S. EPA. 1970. Clean Air Act. 40CFR50. 

U.S. EPA. 2006. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-05/004aF-cF. 

U.S. EPA. 2007. Review of the National Ambient Air Quality Standards for Ozone: Policy 

Assessment of Scientific and Technical Information. OAQPS Staff Paper. North Carolina. EPA452/R-07-003
2-1

Chapter 2: Characterizing Ozone and Modeling Tools Used in This Analysis

Synopsis

This chapter describes the chemical and physical properties of ozone, general ozone air quality 

patterns, key health and environmental impacts associated with exposure to ozone, and key 

sources of ozone precursor emissions. In order to evaluate the health and environmental impacts 

of trying to reach a tighter ozone standard in the year 2020, it was necessary to use models to 

predict concentrations in the future. The tools and methodology used for the air quality modeling 

are described in this chapter. Subsequent chapters of this RIA rely heavily on the results of this 

modeling.

2.1 Ozone Chemistry 

Ozone occurs both naturally in the stratosphere to provide a protective layer high above the 

earth, and at ground-level (troposphere) as the prime ingredient of smog. Tropospheric ozone, 

which is regulated by the NAAQS, is formed by both naturally occurring and anthropogenic 

sources. Ozone is not emitted directly into the air, but is created when its two primary 

components, volatile organic compounds (VOC) and oxides of nitrogen (NOx), combine in the 

presence of sunlight. VOC and NOx are often referred to as ozone precursors, which are, for the 

most part, emitted directly into the atmosphere. 

Ambient ozone concentrations are directly affected by temperature, solar radiation, wind speed 

and other meteorological factors. Ultraviolet radiation from the sun plays a key role in initiating 

the processes leading to ozone formation. However, there is little empirical evidence directly 

linking day-to-day variations in observed surface ultraviolet radiation levels with variations in 

tropospheric ozone levels. 

The rate of ozone production can be limited by either VOCs or NOx. In general, ozone formation 

using these two precursors is reliant upon the relative sources of hydroxide (OH) and NOx. 

When the rate of OH production is greater than the rate of production of NOx, indicating that 

NOx is in short supply, the rate of ozone production is NOx-limited. In this situation, ozone 

concentrations are most effectively reduced by lowering current and future NOx emissions, 

rather than lowering emissions of VOCs. When the rate of OH production is less than the rate of 

production of NOx, ozone production is VOC-limited. Here, ozone is most effectively reduced 

by lowering VOCs. Between the NOx- and VOC-limited extremes there is a transitional region 

where ozone is nearly equally sensitive to each species. However ozone is relatively insensitive 

to marginal changes in both NOx and VOC in this situation. In urban areas with a high 

population concentration, ozone is often VOC-limited. Ozone is generally NOx-limited in rural 

areas and downwind suburban areas. Additional information on ozone formation can be found in 

“Atmospheric Chemistry and Physics” (Seinfeld et. al., 1998). 

Due to the complex photochemistry of ozone production, NOx emissions lead to both the 

formation and destruction of ozone, depending on the local quantities of NOx, VOC, and ozone 

catalysts such as the OH and HO2 radicals. In areas dominated by fresh emissions of NOx, ozone 
2-2

catalysts are removed via the production of nitric acid, which slows the ozone formation rate. 

Because NOx is generally depleted more rapidly than VOC, this effect is usually short-lived and 

the emitted NOx can lead to ozone formation later and further downwind. The terms “NOx 

disbenefits” or “ozone disbenefits” refer to the ozone increases that can result from NOx 

emission reductions in these localized areas.1

2.1.1 Temporal Scale 

Ground-level ozone forms readily in the atmosphere, usually during hot weather. The effects of 

sunlight on ozone formation depend on its intensity and its spectral distribution. Ozone levels 

tend to be highest during the daytime, during the summer or warm season. Changing weather 

patterns contribute to day to day and interannual differences in ozone concentrations. Differences 

in climatic regime, amount and mixture of emissions, and the extent of transport contribute to 

variations in ozone from city to city. 

2.1.2 Geographic Scale and Transport 

In many urban areas, ozone nonattainment is not caused by emissions from the local area alone. 

Due to atmospheric transport, contributions of precursors from the surrounding region can also 

be important. Thus, in designing control strategies to reduce ozone concentrations in a local area, 

it is often necessary to account for regional transport within the U.S. 

In some areas, such as California, global transport of ozone from beyond North America can 

contribute to nonattainment areas. In a very limited number of areas, including areas such as 

Buffalo, Detroit and El Paso, which are located near borders, emissions from Canada or Mexico 

may contribute to nonattainment. In these areas, our illustrative implementation strategies may 

have included more controls on domestic sources than would be required if cross-border 

transport did not occur. However, we have not conducted formal analysis, and as such cannot 

determine the contribution of non-U.S. sources to ozone design values. The transport of ozone is 

determined by meteorological and chemical processes which typically extend over spatial scales 

of several hundred kilometers. Additionally, convection is capable of transporting ozone and its 

precursors vertically through the troposphere, with resulting mixing of stratospheric ozone for 

periods of a month or more with tropospheric ozone.

The Technical Support Document (TSD) for the Clean Air Interstate Rule (CAIR) suggests that 

ozone transport constitutes a sizable portion of projected nonattainment in most eastern areas 

based on a 2010 analysis. A listing of Eastern states and the extent of transported ozone they 

receive in the CAIR analysis is located in the CAIR TSD.2

 We used this information to help 

guide the design of emissions control strategies in this analysis. 

1

 U.S. EPA. Final Regulatory Impact Analysis: Control of Emissions from Nonroad Diesel 

Engines. EPA420-R-04-007. May 2004. 

2

http://www.epa.gov/interstateairquality/pdfs/finaltech02.pdf, Table VI-2. 
2-3

2.2 Sources of Ozone 

The anthropogenic precursors of ozone originate from a wide variety of stationary and mobile 

sources. In urban areas, both biogenic (natural) and anthropogenic VOCs are important for ozone 

formation. Hundreds of VOCs are emitted by evaporation and combustion processes from a large 

number of anthropogenic sources. Current data show that solvent use and highway vehicles are 

the two main sources of VOCs, with roughly equal contributions to total emissions. Emissions of 

VOCs from highway vehicles account for roughly two-thirds of the transportation-related 

emissions.3 By 2020, EPA emission projections show that VOC emissions from highway 

vehicles decrease significantly. Solvent use VOC decreases as well, but by 2020 solvent use 

VOC is projected to be a slightly more significant VOC contributor than mobile VOC. On the 

regional and global scales, emissions of VOCs from vegetation are much larger than those from 

anthropogenic sources. 

Anthropogenic NOx emissions are associated with combustion processes. The two largest 

sources of NOx are electric power generation plants (EGUs) and motor vehicles. EGU NOx is 

approximately 40% less than onroad mobile NOx in 2001. Both decrease between 2001 and 

2020, with onroad mobile NOx decreasing more, so that their emissions are similar in 2020. It is 

not possible to make an overall statement about their relative impacts on ozone in all local areas 

because EGUs are more sparse than mobile sources, particularly in the west and south (See 

Chapter 3 for a discussion of emission reductions projected in 2020 for the 8-hr ozone current 

standard baseline and the more stringent alternative control scenario). Natural NOx sources 

include stratospheric intrusions, lightning, soils, and wildfires. Lightning, fertilized soils, and 

wildfires are the major natural sources of NOx in the United States. Uncertainties in natural NOx 

inventories are much larger than for anthropogenic NOx emissions. 

A complete list of emissions source categories, for both NOx and VOCs, is compiled in the final 

ozone Staff Paper (EPA, 2007a, pp. 2-3 to 2-6). 

2.3 Modeling Ozone Levels in the Future 

In order to evaluate the predicted air quality in 2020, it is necessary to use modeling to derive 

estimated air quality concentrations. The modeling analysis uses an emissions inventory and 

historical meteorological conditions to simulate pollutant concentrations. The predictions from 

the modeling are used to (a) project future ozone design values (a representation of the resultant 

air quality concentration in 2020 representing the 4th highest maximum 8-hr concentration) and 

(b) create spatial fields of ozone and PM2.5 for characterizing human health impacts from 

reducing ozone precursors, which in the case of NOx will also affect the formation of PM2.5. The 

air quality model used in this RIA is the Community Multi-Scale Air Quality (CMAQ) model4.

The modeling for ozone and PM2.5 was performed for a one year time period. All controls in the 

illustrative 0.070 scenario were applied similarly to all months. There were no controls applied 

3

 U.S EPA. 2007. Review of the National Ambient Air Quality Standards for Ozone: Policy 

Assessment of Scientific and Technical Information. OAQPS Staff Paper. North Carolina. EPA452/R-07-003.

4

 See CMAQ references listed at end of this chapter. 
2-4

specifically for PM2.5 co-benefits because the controls developed to reduce summer ozone were 

applied to all months (see Chapter 3). 

2.3.1 CMAQ Model and Inputs 

A national scale air quality modeling analysis was performed to estimate future year 

attainment/nonattainment of the current and alternative ozone standards. In addition, the modelbased projections of ozone and PM2.5 were used as inputs to the calculation of expected 

incremental benefits from the alternative ozone standards considered in this assessment. The 

2002-based modeling platform (EPA, 2008) was used as the basis for air quality modeling of the 

future baseline emissions and illustrative control scenario. This modeling platform includes a 

number of updates and improvements to data and tools compared to the 2001-based platform that 

was used for the proposal modeling. For the final rule modeling we used the new 2002 National 

Emissions Inventory along with updated versions of the models used to project future emissions 

from electric generating units (EGUs) and onroad and nonroad vehicles. The proposal modeling 

was based on the 2001 National Emissions Inventory. The new platform also includes 2002 

meteorology and more recent ambient design values which were used as the starting point for 

projecting future air quality. For proposal, we used meteorology for 2001 for modeling the East 

and 2002 for modeling the West. The updates5 to CMAQ between proposal and final include 

(1) an in-cloud sulfate chemistry module that accounts for the nonlinear sensitivity of sulfate 

formation to varying pH; (2) improved vertical convective mixing; (3) heterogeneous reaction 

involving nitrate formation; (4) an updated gas-phase chemistry mechanism, Carbon Bond 2005 

(CB05); and (5) an aqueous chemistry mechanism that provides a comprehensive simulation of 

aerosol precursor oxidants. 

The key non-emissions inputs to the CMAQ model include meteorological data, and initial and 

boundary concentrations. The CMAQ meteorological input files were derived from simulations 

of the Pennsylvania State University/National Center for Atmospheric Research Mesoscale 

Model (Grell, Dudhia, and Stauffer, 1994). This model, commonly referred to as MM5, is a 

limited-area, nonhydrostatic, terrain-following system that solves for the full set of physical and 

thermodynamic equations which govern atmospheric motions. The lateral boundary and initial 

species concentrations for the 36 km continental scale modeling domain, described below, were 

obtained from a three-dimensional global atmospheric chemistry model, the GEOSChem model 

(Yantosca, 2004). The global GEOSChem model simulates atmospheric chemical and physical 

processes driven by assimilated meteorological observations from the NASA’s Goddard Earth 

Observing System (GEOS). We used GEOSChem results for 2002 to provide initial and 

boundary concentrations for our final rule air quality modeling. For proposal we used 

GEOSChem results for 2001. 

EPA performed an extensive evaluation of CMAQ using the 2002 inputs for emissions, 

meteorology, and boundary conditions. Details of the model performance methodology and 

results are described in the 2002-Based Modeling Platform Report (EPA, 2008). As in the 

evaluation for previous model applications, the “acceptability” of model performance for the 

ozone RIA modeling was judged by comparing the results to those found in recent regional 

5

 Additional documentation on the updates in CMAQ version 4.6 can be found at the following 

web site: http://www.cmascenter.org/. 
2-5

ozone model applications for other EPA and non-EPA studies (see Appendix B of EPA, 2007b). 

Overall, the performance for the CMAQ application is generally within the range of these other 

applications. 

Figure 2.1 shows the modeling domains that were used as a part of this analysis. The geographic 

specifications for these domains are provided in Table 2.1. All three modeling domains contain 

14 vertical layers with a top at about 16,200 meters, or 100 mb. Two domains with 12 km 

horizontal resolution were used for modeling the 2002 base year, 2020 baseline and 2020 control 

strategy scenarios. These domains are labeled as the East and West 12 km domains in Figure 2.1. 

Simulations for the 36 km domain were only used to provide initial and boundary concentrations 

for the 12 km domains. As indicated above, the model produces spatial fields of gridded air 

quality concentrations on an hourly basis for the entire modeling domain. These gridded 

concentrations can be processed to produce a number of air quality metrics, including the 8-hr 

ozone design values, and can be used as inputs for the analysis of costs and benefits. The air 

quality modeling results are used in a relative sense to project concentrations for the future year 

scenarios using procedures consistent with EPA guidance (EPA, 2007b). For the final rule 

projections we used ambient design values for the period 2000 through 2004 as the starting point 

for projections. For the proposal, design values from 1999 through 2003 were used. The change 

between proposal and final in terms of the period of design values was made, in accordance with 

EPA guidance, in order to align the central year of design values with the base year of the 

emissions (i.e., 2001 for the proposed rule and 2002 for the final rule). 

For this analysis, predictions from the East domain were used to provide data for all areas that 

are east of approximately 104 degrees longitude. Model predictions from the West domain we 

used for all areas west of this longitude.
2-6

Figure 2.1: Map of the CMAQ Modeling Domains Used for Ozone NAAQS RIA 

36km Domain Boundary

12km East Domain Boundary

12km West Domain Boundary

Table 2.1: Geographic Specifications of Modeling Domains 

36 km Domain 

(148 x 112 Grid Cells) 

12 km East Domain 

(279 x 240 Grid Cells) 

12 km West Domain 

(213 x 192 Grid Cells) 

 Lon lat lon lat lon lat 

SW í121.77 18.17 SW í106.79 24.99 SW í121.65 28.29 

NE í58.54 52.41 NE í65.32 47.63 NE í94.94 51.91 

2.3.2 Emissions Inventory 

The 2020 inventory, projected from the 2002 Version 3 emissions modeling platform (EPA, 

2008), is the starting point for the baseline and control strategy for the Final Ozone NAAQS 

emissions inventory. The 2002 documentation describes the 2002 base year inventory as well as 

the projection methodology and controls applied to create year 2020 emissions. The 2020 

inventory includes activity growth for some sectors, and controls including: the Clean Air 

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

Nonroad Diesel Rule, the Light-Duty Vehicle Tier 2 Rule, the Heavy Duty Diesel Rule, known 

plant closures, and consent decrees and settlements. Table 2.2 provides a comprehensive list of 

the rules/control strategies and projection assumptions in the 2020 inventory; full discussion of 

the 2020 inventory is provided in the 2002 Version 3 emissions modeling platform (EPA, 

2008a). The data for the controls and projection strategies can be found in the Loco-Marine 

docket (EPA, 2008b). 
2-7

Table 2.2: Control Strategies and Projection Assumptions in the 2020 Emissions Inventory 

Control Strategies 

(Grouped by Affected Pollutants or Standard and Approach Used to 

Apply to the Inventory) 

Pollutants 

Affected 

Approach or 

Reference 

Non-EGU Point Controls 

NOx SIP Call (Phase II): 

Cement Manufacturing 

Large Boiler/Turbine Units 

Large IC Engines

NOx 1 

DOJ Settlements: plant SCC controls 

Alcoa, TX 

MOTIVA, DE

NOx, SO2 2 

Refinery Consent Decrees: plant/SCC controls NOx, PM, SO2 3 

Closures, pre-2007: plant control of 100% 

Auto plants 

Pulp and Paper 

Municipal Waste Combustors 

Plants closed in preparation for 2005 inventory

all 4 

Industrial Boiler/Process Heater plant/SCC controls for PM PM 5 

MACT rules, national, VOC: national applied by SCC, MACT 

Boat Manufacturing 

Polymers and Resins III (Phenolic Resins) 

Polymers and Resins IV (Phenolic Resins) 

Wood Building Products Surface Coating 

Generic MACT II: Spandex Production, Ethylene manufacture 

Large Appliances 

Miscellaneous Organic NESHAP (MON): Alkyd Resins, Chelating Agents, 

Explosives, Phthalate Plasicizers, Polyester Resins, Polymerized Vinylidene 

Chloride 

Manufacturing Nutritional Yeast 

Oil and Natural Gas 

Petroleum Refineries—Catalytic Cracking, Catalytic Reforming, & Sulfur 

Plant Units 

Pesticide Active Ingredient Production 

Publicly Owned Treatment Works 

Reinforced Plastics 

Rubber Tire Manufacturing 

Asphalt Processing & Roofing 

Combustion Sources at Kraft, Soda, and Sulfite Paper Mills 

Fabric Printing, Coating and Dyeing 

Iron & Steel Foundries 

Metal: Can, Coil 

Metal Furniture 

Miscellaneous Metal Parts & Products 

Municipal Solid Waste Landfills 

Paper and Other Web 

Plastic Parts 

Plywood and Composite Wood Products 

Wet Formed Fiberglass Production 

Wood Building Products Surface Coating 

Carbon Black Production 

Cellulose Products Manufacturing 

Cyanide Chemical Manufacturing

VOC EPA, 2007f 

(continued)
2-8

Table 2.2: Control Strategies and Projection Assumptions in the 2020 Emissions Inventory 

(continued)

Control Strategies 

(Grouped by Affected Pollutants or Standard and Approach Used to 

Apply to the Inventory) 

Pollutants 

Affected 

Approach or 

Reference 

Friction Products Manufacturing 

Leather Finishing Operations 

Miscellaneous Coating Manufacturing 

Organic Liquids Distribution (Non-Gasoline) 

Refractory Products Manufacturing 

Sites Remediation

Solid Waste Rules (Section 129d/111d) 

Hospital/Medical/Infectious Waste Incinerator Regulations NOx, PM, SO2 EPA, 2005 

MACT rules, national, PM: 

Portland Cement Manufacturing 

Secondary Aluminum

PM 6 

MACT rules, plant-level, VOC: 

Auto Plants VOC 7 

MACT rules, plant-level, PM & SO2:

Lime Manufacturing PM, SO2 8 

MACT rules, plant-level, PM: 

Taconite Ore PM 9 

Stationary Non-point (Area) Assumptions 

Municipal Waste Landfills: projection factor of 0.25 applied VOC EPA, 2007f 

Livestock Emissions Growth NH3, PM 10 

Residential Wood Combustion Growth 

reflects increase in use of lower polluting wood stoves, and decrease in use 

of higher polluting stoves 

all 11 

Gasoline Stage II growth and control 

(also impacts non-EGU point sources in a couple of states) VOC 12 

Portable Fuel Container growth and control VOC 13 

EGU Point Controls 

CAIR/CAMR/CAVR 

IPM Model 3.0 NOx, SO2, PM 14 

Onroad Mobile and Nonroad Mobile Growth and Controls

Onroad and Nonroad Growth: 

Onroad growth is based on VMT growth from Annual Energy Outlook 

(AEO) 2006 estimates of growth by vehicle type. Nonroad growth is based 

on activity increases from NONROAD model default growth estimates

all

National Onroad Rules: 

Tier 2 Rule 

2007 Onroad Heavy-Duty Rule 

Final Mobile Source Air Toxics Rule (MSAT2) 

Renewable Fuel Standard

all

Local Onroad Programs: 

National Low Emission Vehicle Program (NLEV) 

Ozone Transport Commission (OTC) LEV Program 

VOC 15 

(continued)
2-9

Table 2.2: Control Strategies and Projection Assumptions in the 2020 Emissions Inventory 

(continued)

Control Strategies 

(Grouped by Affected Pollutants or Standard and Approach Used to Apply 

to the Inventory) 

Pollutants 

Affected 

Approach or 

Reference 

National Nonroad Controls: 

Clean Air Nonroad Diesel Final Rule—Tier 4 

Control of Emissions from Nonroad Large-Spark Ignition Engines and 

Recreational Engines (Marine and Land Based): “Pentathalon Rule” 

all 16 

Aircraft, Locomotives, and Commercial Marine Assumptions 

Aircraft:

Itinerant (ITN) operations at airports all 17 

Locomotives: 

Energy Information Administration (EIA) fuel consumption projections for 

freight rail 

Clean Air Nonroad Diesel Final Rule—Tier 4 

Locomotive Final Rulemaking, December 17, 1997

all EPA, 2007e, 

18

Commercial Marine: 

EIA fuel consumption projections for diesel-fueled vessels 

Freight-tonnage growth estimates fro residual-fueled vessels 

Clean Air Nonroad Diesel Final Rule—Tier 4 

Emissions Standards for Commercial Marine Diesel Engines, December 29, 1999 

Tier 1 Marine Diesel Engines, February 28, 2003 

all 18, (EPA, 

2007e) 

APPROACHES: 

1. Used Emission Budget Inventories report (EPA, 1999) for list of SCCs for application of controls, and for 

percent reductions (except IC Engines). Used Federal Register on Response to Court decisions (Federal 

Register, 2004) for IC Engine percent reductions and geographic applicability 

2. For ALCOA consent decree, used http:// cfpub.epa.gov/compliance/cases/index.cfm; for MOTIVA: used 

information sent by State of Delaware 

3. Used data provided by Brenda Shine, EPA, OAQPS 

4. Closures obtained from EPA sector leads; most verified using the world wide web. 

5. Used data list of plants provided by project lead from 2001-based platform; required mapping the 2001 plants 

to 2002 NEI plants due to plant id changes across inventory years 

6. Same as used in CAIR, except added SCCs appeared to be covered by the rule: both reductions based on

preamble to final rule. (Portland Cement used a weighted average across two processes ) 

7. Percent reductions recommended and plants to apply to reduction to were based on recommendations by rule 

lead engineer, and are consistent with the reference: EPA, 2007e 

8. Percent reductions recommended are determined from the existing plant estimated baselines and estimated 

reductions as shown in the Federal Register Notice for the rule. SO2 % reduction will therefore be 

6147/30,783 = 20% and PM10 and PM2.5 reductions will both be 3786/13588 = 28% 

9. Same approach used in CAIR: FR notice estimates reductions of “PM emissions by 10,538 tpy, a reduction of 

about 62%.” Used same list of plants as were identified based on tonnage and SCC from CAIR. 

10. Except for dairy cows and turkeys (no growth), based in animal population growth estimates from USDA and 

Food and Agriculture Policy and Research Institute.

11. Expected benefits of woodstoves change-out program: http://www.epa.gov/woodstoves/index.html

12. VOC emission ratios of year 2020 to year 2002 from the National Mobile Inventory Model (NMIM) results 

for onroad refueling including activity growth from VMT, Stage II control programs at gasoline stations, and 

phase in of newer vehicles with onboard Stage II vehicle controls. 

13. VOC emission ratios of year 2020 to year 2002 from MSAT rule (EPA, 2007c, EPA, 2007d) 

14. http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/summary2006.pdf

15. Only for states submitting these inputs: http://www.epa.gov/otaq/lev-nlev.htm

16. http://www.epa.gov/nonroad-diesel/2004fr.htm

17. Federal Aviation Administration (FAA) Terminal Area Forecast (TAF) System, February 2006: 

http://www.apo.data.faa.gov/main/taf.asp

18. http://www.epa.gov/nonroad-diesel/2004fr.htm
2-10

Differences between the 2020 emissions modeling platforms—particularly the inventories—used 

in the Ozone NAAQS Proposal and here in the Ozone NAAQS Final are discussed in the 

Appendix for Chapter 2. 

The development of the 2020 baseline inventory and the modeled control scenarios are discussed 

in Chapter 3. The 2020 baseline inventory includes the same year 2020 Canada and year 1999 

Mexico emissions as the Final PM NAAQS (EPA, 2006b). 

2.4 References 

Amar, P., R. Bornstein, H. Feldman, H. Jeffries, D. Steyn, R. Yamartino, and Y. Zhang. 2004. 

Final Report Summary: December 2003 Peer Review of the CMAQ Model. pp. 7. 

Byun, D.W., and K.L. Schere. 2006. “Review of the Governing Equations, Computational 

Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality 

(CMAQ) Modeling System.” J. Applied Mechanics Reviews 59(2):51-77. 

Dennis, R.L., D.W. Byun, J.H. Novak, K.J. Galluppi, C.J. Coats, and M.A. Vouk. 1996. “The 

next generation of integrated air quality modeling: EPA’s Models-3.” Atmospheric Environment

30:1925-1938.

Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth-Generation Penn 

State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for 

Atmospheric Research, Boulder CO. 

Seinfeld, J.H. and S. N. Pandis, 1998: Atmospheric Chemistry and Physics. John Wiley & Sons, 

New York. 

U.S. Environmental Protection Agency (EPA). 1999. “Science Algorithms of EPA Models-3 

Community Multiscale Air Quality.” (CMAQ Modeling System D.W. Byun and J.K.S. Ching, 

Eds. EPA/600/R-99/030, Office of Research and Development). 

U.S. Environmental Protection Agency (EPA). 2005. Clean Air Interstate Rule Emissions 

Inventory Technical Support Document, U.S. Environmental Protection Agency, Office of Air 

Quality Planning and Standards, March 2005. Available at 

http://www.epa.gov/cair/pdfs/finaltech01.pdf.

U.S. Environmental Protection Agency (EPA). 2006a. Air Quality Criteria for Ozone and 

Related Photochemical Oxidants (Final). U.S. Environmental Protection Agency, Washington, 

DC, EPA/600/R-05/004aF-cF. 

U.S. Environmental Protection Agency (EPA). 2006b. “Regulatory Impact Analysis for the 

Review of the Particulate Matter National Ambient Air Quality Standards (Chapter 2: Defining 

the PM2.5 Air Quality Problem).” EPA-HQ-OAR-2006-0834-0048.3. 
2-11

U.S. Environmental Protection Agency (EPA). 2007a. Review of the National Ambient Air 

Quality Standards for Ozone: Policy Assessment of Scientific and Technical Information. 

OAQPS Staff Paper. North Carolina. EPA-452/R-07-003. 

U.S. Environmental Protection Agency (EPA). 2007b. “Guidance on the Use of Models and 

Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and 

Regional Haze.” EPA-454/B-07-002, http://www.epa.gov/scram001/guidance/guide/final-03-

pm-rh-guidance.pdf.

U.S. Environmental Protection Agency (EPA). 2007c. Regulatory Impact Analysis for Final 

Rule: Control of Hazardous Air Pollutants from Mobile Sources, U.S. Environmental Protection 

Agency, Office of Transportation and Air Quality, Assessment and Standards Division, Ann

Arbor, MI 48105, EPA420-R-07-002, February 2007. Available at 

http://www.epa.gov/otaq/regs/toxics/420r07002.pdf.

U.S. Environmental Protection Agency (EPA). 2007d. National Scale Modeling for the Final 

Mobile Source Air Toxics Rule, Office of Air Quality Planning and Standards, Emissions 

Analysis and Monitoring Division, Research Triangle Park, NC 27711, EPA 454/R-07-002, 

February 2007. Available at http://www.epa.gov/otaq/regs/toxics/454r07002.pdf.

U.S. Environmental Protection Agency (EPA). 2007e. Draft Regulatory Impact Analysis: 

Control of Emissions of Air Pollution from Locomotive Engines and Marine CompressionIgnition Engines Less than 30 Liters per Cylinder, Chapter 3: Emission Inventory, U.S. 

Environmental Protection Agency, Office of Transportation and Air Quality, Assessment and 

Standards Division, Ann Arbor, MI 48105. EPA420-D-07-001, March 2007. Available at 

http://www.epa.gov/otaq/regs/nonroad/420d07001chp3.pdf.

U.S. Environmental Protection Agency (EPA). 2007f. Guidance for Estimating VOC and NOx 

Emission Changes from MACT Rules, U.S. Environmental Protection Agency Office of Air 

Quality Planning and Standards, Air Quality Policy Division, Research Triangle Park, NC 

27711, EPA-457/B-07-001, May 2007. Available at 

http://www.epa.gov/ttn/naaqs/ozone/o3imp8hr/documents/guidance/200705_epa457_b-07-

001_emission_changes_mact_rules.pdf.

U.S. Environmental Projections Agency (EPA). 2008a. Air Quality Modeling Platform for the 

Ozone National Ambient Air Quality Standard Final Rule Regulatory Impact Assessment. 

U.S. Environmental Projections Agency (EPA). 2008b. Technical Support Document: 

Preparation of Emissions Inventories For the 2002-based Platform, Version 3, Criteria Air 

Pollutants, USEPA, January, 2008. 

Yantosca, B. 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry Modeling 

Group, Harvard University, Cambridge, MA, October 15, 2004.
2a-1

Appendix 2: Additional Emissions Modeling Platform Information 

2a.1 Discussion of Similarities and Differences Between Emissions Modeling Platforms 

Used in Ozone NAAQS Proposal and Final 

All emissions modeling in the Ozone NAAQS Proposal was based off the 2001 emissions 

modeling platform. Version 3 of the 2002 emissions modeling platform (EPA, 2008) is used for 

the Final Ozone NAAQS. In both platforms, emissions are first projected to a year 2020 Base 

case. The following discusses similarities and differences in the 2001 and 2002 emission 

platforms, as well as assumptions used to project emissions to the year 2020. 

2a.1.1 Similarities in the 2001 and 2002 Emissions Modeling Platforms 

The 2001 and 2002 emissions platforms share the same Canada, Mexico, and offshore oil 

production emissions. Both platforms also share the same wildfire and prescribed burning 

emissions. Most input ancillary files used in the emissions processor are also unchanged; 

specifically, almost all cross-reference factors used in speciation profile assignments and temporal 

and spatial allocations are the same. The land use data for biogenic emissions (BELD3) is the 

same. The projection approach for stationary non-EGU emissions is also unchanged; however, for 

a couple of source categories, activity growth was slightly modified to account for the change in 

starting year -2002, rather than 2001. This effect on year 2020 activity (growth) factors is very 

small. Plant closures, consent decrees and settlements, and most national programs for stationary 

non-EGUs are applied as consistently as possible in 2002 as in 2001, by which, we used a crossreference file to match controls for plants in the 2001 to the 2002 inventories. 

2a.1.2 Key Changes to the Emissions Modeling Platform 

As discussed in Chapter 2, the Final Ozone NAAQS utilizes the 2020 inventory, projected from 

the 2002 Version 3 emissions modeling platform. The Proposal utilized the 2001-based, 

projected to year 2020, “PM NAAQS” platform (EPA, 2006). The most significant change in the 

emissions modeling platform is the improvements to emissions estimates over multiple inventory 

sectors. See the 2002, Version 3 documentation for detailed information on these improvements. 

The SMOKE input ancillary data was updated to account for new source categories appearing in 

different inventory sectors; examples include farms and airports in the point source inventory 

and the new inclusion of portable fuel container emissions resulting from the Mobile Source Air 

Toxics (MSAT2) Rule (EPA, 2007a and 2007b). Another significant change in the emissions 

modeling platforms is the use of a new chemical mechanism -CB05 (Yarwood, 2005) versus CBIV in the proposal platform. 

Emissions by geographic area and by model platform in the base and future years are shown in 

Figure 2a.1 and Figure 2a.2, for NOx and VOC, respectively. “Northeast” in all figures 

represents the full OTC (Ozone Transport Commission) member states: Maine, New Hampshire, 

Vermont, Massachusetts, Connecticut, Rhode Island, New York, New Jersey, Pennsylvania, 

Maryland, Delaware, and the District of Columbia. Emissions summaries from the northern 

counties of Virginia, while part of the OTC, are included in the “rest of US” geographic area. 

The “Midwest” geographic area includes Illinois, Indiana, Ohio, Michigan, and Wisconsin. 
2a-2

Figure 2a.1: Total Anthropogenic NOx Emissions [tons/year] by Year and Platform 

0

2

4

6

8

10

12

2001 2002 2020 from

2001

2020 from

2002

2001 minus

2020 from

2001

2002 minus

2020 from

2002

Millions

Northeast

Midwest

California

Texas

Rest of US

Figure 2a.2: Total Anthropogenic VOC Emissions [tons/year] by Year and Platform 

0

1

2

3

4

5

6

7

8

9

10

2001 2002 2020 from

2001

2020 from

2002

2001 minus

2020 from

2001

2002 minus

2020 from

2002

Millions

Northeast

Midwest

California

Texas

Rest of US

Figure 2a.1 and Figure 2a.2 demonstrate that total NOx and VOC emissions do not differ 

significantly by geographic area when comparing the inventories used in the proposal (2001) and 

final (2002). Small decreases in NOx and VOC are evident in the Northeast and Midwest, and 

small decreases in NOx are also seen in the rest of the US. In contrast, slight overall increases of 

NOx in Texas and VOC in the rest of the US can be seen. 

Year 2020 emissions, projected from the 2001 and 2002 emission platforms show slightly less 

NOx in 2020 in the 2002-based platform in the Northeast, Midwest, and rest of the US. Perhaps 

most significant from an air quality modeling aspect is the relative change in emissions in 2020 

when migrating from the 2001 to the 2002 emission platforms, represented by the last 2 sets of 
2a-3

columns in Figure 2a.1 and Figure 2a.2. These show slightly less raw reductions in NOx and 

VOC for all regions with the exception of a very slight increase in NOx reductions in 2020-

based-off-2002 in the Northeast and California. The net effect of these emission summaries is 

that large changes in air quality modeling ozone estimates are unlikely to be explained by 

significant changes in the overall emission changes by migrating from the 2001-based emissions 

platform in the proposal to the 2002-based emissions platform used in the final rulemaking. 

Emissions inventory summaries broken down by sectors (e.g., EGU, non-EGU Point, Onroad 

Mobile, Nonroad Mobile...) also do not show any significant differences by geographic area for 

year 2020 between the 2001-based and 2002-based emission modeling platforms. 

2a.2 References 

U.S. Environmental Protection Agency (EPA). 2006. “Regulatory Impact Analysis for the 

Review of the Particulate Matter National Ambient Air Quality Standards (Chapter 2: Defining 

the PM 2.5 Air Quality Problem).” EPA-HQ-OAR-2006-0834-0048.3. 

U.S. Environmental Protection Agency (EPA). 2007a. Regulatory Impact Analysis for Final 

Rule: Control of Hazardous Air Pollutants from Mobile Sources, U.S. Environmental Protection 

Agency, Office of Transportation and Air Quality, Assessment and Standards Division, Ann

Arbor, MI 48105, EPA420-R-07-002, February 2007. Available at 

http://www.epa.gov/otaq/regs/toxics/420r07002.pdf.

U.S. Environmental Protection Agency (EPA). 2007b. National Scale Modeling for the Final 

Mobile Source Air Toxics Rule, Office of Air Quality Planning and Standards, Emissions 

Analysis and Monitoring Division, Research Triangle Park, NC 27711, EPA 454/R-07-002, 

February 2007. Available at http://www.epa.gov/otaq/regs/toxics/454r07002.pdf.

U.S. Environmental Projections Agency (EPA). 2008. Air Quality Modeling Platform for the 

Ozone National Ambient Air Quality Standard Final Rule Regulatory Impact Assessment. 

Yarwood, G., S. Rao, M. Yocke, and G. Whitten, 2005. Updates to the Carbon Bond Chemical 

Mechanism: CB05. Final Report to the U.S. EPA, RT-0400675. Available at 

http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf.
3-1

Chapter 3: Modeled Control Strategy - Design and Analytical Results 

Synopsis

In order to estimate the costs and benefits of alternate ozone standards, EPA has analyzed one 

possible hypothetical scenario to illustrate the control strategies that areas across the country 

might employ to attain an alternative more stringent primary standard of 0.070 ppm. We 

modeled the lower end of the range to capture a larger number of geographic areas that may be 

affected by a new ozone standard. Specifically, EPA has modeled the impact that additional 

emissions controls across numerous sectors would have on predicted ambient ozone 

concentrations, incremental to meeting the current PM2.5 and ozone standards (baseline). Thus, 

the modeled analysis for a revised standard focuses specifically on incremental improvements 

beyond the current standards, and uses control options that might be available to states for

application by 2020. The hypothetical modeled control strategy presented in this RIA is one 

illustrative option for achieving emissions reductions to move towards a national attainment of a 

tighter standard. It is not a recommendation for how a tighter ozone standard should be 

implemented, and states will make all final decisions regarding implementation strategies once a 

final NAAQS has been set.

In order to model a hypothetical control strategy incremental to attainment of the current 

standard, EPA approached the analysis in stages. First, EPA identified controls to be included in 

the baseline. These included current state and federal programs (see) plus controls to attain the 

current ozone standard (Table 3.1) and PM2.5 standards (see http://www.epa.gov/ttnecas1/ria.html

for a complete list of controls). Then, EPA applied additional known controls within geographic 

areas designed to bring areas predicted to exceed 0.070 ppm in 2020 into attainment. This 

chapter presents the hypothetical modeled control strategy, the geographic areas where controls 

were applied, and the results of the modeling which predicted ozone concentrations in 2020 after 

application of the strategy. The strategy to attain a 0.070 ppm level was the only strategy 

modeled for air quality changes by EPA. EPA did not expect the modeled control strategy to 

result in attainment at 0.070 ppm everywhere, and the modeled control strategy did yield only 

partial attainment. Chapter 4 will explain how EPA used additional air quality modeling to 

estimate total annual tons/year of emissions reductions needed to achieve ozone concentrations 

for 0.075 ppm as well as the less stringent option of 0.079 ppm the and the more stringent 

options of 0.070 ppm and 0.065 ppm). Chapters 5 and 6 present the estimated costs and benefits 

of the modeled costs and benefits for partial attainment. 

Because EPA’s baseline indicated that some areas were not likely to be in attainment with the 

current standard by 2020 (0.08 ppm, effectively 0.084 ppm based on current rounding 

conventions)—(Figure 3.4) EPA expected that known controls would not be enough to bring 

those areas, and likely others, into attainment with 0.070 ppm in 2020. Modeling results showed 

that to be the case (see Figure 3.13). 

Because it was impossible to meet either the current or any tighter ozone standard nationwide 

using only known controls, EPA conducted a second step in the analysis, and estimated the 

number of further tons of emission reductions needed to attain an alternate primary ozone 

standard (presented in Chapter 4). It is uncertain what controls States would put in place to attain 
3-2

a tighter standard, since additional control measures are not currently recognized as being 

commercially available. However, existing emissions inventories for the areas that were 

predicted to be in nonattainment after application of all known controls, do indicate that 

substantial amounts of ozone precursor emissions (i.e., tons of NOx or VOC) are available for 

control, pending future technology. Chapter 4 describes the methodology EPA used to estimate 

the amount of extrapolated tons necessary for control to reach attainment, and Chapters 5 and 6 

present the extrapolation-based costs and benefits of achieving the reductions in ozone necessary 

to either fully or partially attain the standards in 2020, except for a few areas in California, which 

will be more fully explained in Chapter 4. 

3.1 Establishing the Baseline 

The regulatory impact analysis (RIA) is intended to evaluate the costs and benefits of reaching 

attainment with potential alternative ozone standards. In order to develop and evaluate a control 

strategy for attaining a more stringent (0.070 ppm) primary standard, it is important to first 

estimate ozone levels in 2020 given the current NAAQS standards and trends (more information 

is provided in Chapter 1). This scenario is known as the baseline. Establishing this baseline 

allows us to estimate the incremental costs and benefits of attaining any alternate primary 

standard.

This focus on the assessment of the incremental costs and benefits of attaining any alternative 

standard is an important difference from the focus of the risk assessment used in developing the 

standard. For purposes of the Staff Paper-risk assessment, risks are estimated associated with just 

meeting recent air quality and upon just meeting the current and alternative standards as well as 

incremental reductions in risks in going from the current standard to more stringent alternative 

standards. When considering risk estimates remaining upon attaining a given standard, EPA is 

only interested in the risks in excess of policy relevant background (PRB). PRB is defined in the 

ozone Criteria Document and Staff Paper as including (1) O3 in the U.S. from natural sources of 

emissions in the U.S., Canada, and Mexico, and (2) O3 in the U.S. from the transport of O3 or 

the transport of emissions from both natural and man-made sources, from outside of the U.S. and 

its neighboring countries (Staff Paper, p.2-54). Emissions of ozone precursors from natural 

sources (e.g., isoprenes emitted from trees) and from sources outside of the U.S. are uncertain, as 

are the specific impacts those emissions will have on ozone concentrations in areas exceeding 

alternative standards. Our models use available information on these emissions in generating 

future projections of baseline ozone concentrations, and our modeled reductions in U.S. 

emissions of NOx and VOC are based on these baseline levels that include the contribution of 

natural and non-U.S. emissions. To the extent that these emissions contribute a greater (lesser) 

proportion of ozone on high ozone days, more (less) reductions in emissions from U.S. sources 

might be required to reduce ozone levels below the analyzed alternative standards. 

In contrast, the RIA only examines the incremental reduction, not the remaining risk, which 

results from changes in U.S. anthropogenic emissions. The air quality modeling used to establish 

the baseline for the RIA explicitly includes contributions from natural and anthropogenic 

emissions in Canada, Mexico, and other countries abroad, as well as the contributions to ozone 

levels from natural sources in the U.S. Since the RIA does not attempt to estimate the risk 

remaining upon meeting a given standard, and the alternative standards are clearly above the 
3-3

Staff Paper estimates of PRB, we do not consider PRB a component of the RIA costs and 

benefits estimates. 

In developing the baseline it was important to recognize that there are several areas that are not 

required to meet the current standard by 2020. The Clean Air Act allows areas with more 

significant air quality problems to take additional time to reach the current standard. Two areas 

in Southern California1

 are not planning to meet the current standard by 2020. 

The baseline includes controls which EPA estimates need to be included to attain the current 

standard (0.08 ppm, effectively 0.084 ppm based on current rounding conventions) for 2020. 

Two steps were used to develop the baseline. First, the reductions expected in national ozone 

concentrations from national rules in effect or proposed today were considered, in addition to the 

controls applied as part of the PM2.5 NAAQS RIA analysis. Second, since these reductions alone 

were not predicted to bring all areas into attainment with the tighter standard, EPA used a 

hypothetical control strategy to apply additional known controls. Additional control measures 

were used in five sectors to establish the baseline:2

 Non-Electricity Generating Unit Point 

Sources (NonEGUs), Non-Point Area Sources (Area), Onroad Mobile Sources and Nonroad 

Mobile Sources. A fifth sector was used in the subsequent control strategy for a tighter 

alternative standard: Electricity Generating Unit Point Sources (EGUs). Each of these sectors is 

defined below for clarity. 

x NonEGU point sources are stationary sources that emit at least one criteria pollutant with 

emissions of 100 tons per year or higher. NonEGU point sources are found across a wide 

variety of industries, such as chemical manufacturing, cement manufacturing, petroleum 

refineries, and iron and steel mills. 

x NonPoint Area Sources3(Area) are stationary sources that are too numerous or whose 

emissions are too small to be individually included in a stationary source emissions 

inventory. Area sources are the activities where aggregated source emissions information 

is maintained for the entire source category instead of each point source, and are reported 

at the county level. 

x Onroad Mobile Sources are mobile sources that travel on roadways. These sources 

include automobiles, buses, trucks, and motorcycles traveling on roads and highways. 

x Nonroad Mobile Sources4 are any combustion engine that travels by other means than 

roadways. These sources include railroad locomotives; marine vessels; aircraft; off-road 

1At the time of this analysis the South Coast and San Joaquin Valley air basins are expected to 

request a redesignation to extreme status for the current ozone standard. 

2

 In establishing the baseline, EPA selected a set of cost-effective controls to simulate attainment 

of the current ozone and PM2.5 standards. These control sets are hypothetical as states will 

ultimately determine controls as part of the SIP process.

3

 Areas Sources include the nonpoint emissions sector only. 

4

 For the purposes of presentation nonroad mobile sources incorporates both the nonroad 

emissions sector and the aircraft, locomotive, and marine vessels emissions sector. 
3-4

motorcycles; snowmobiles; pleasure craft; and farm, construction, industrial and 

lawn/garden equipment. 

x Electricity Generating Unit Point Sources (EGUs) are stationary sources of 25 megawatts 

(MW) capacity or greater producing and selling electricity to the grid, such as fossil-fuelfired boilers and combustion turbines. 

3.1.1 Control Measures Applied in the Baseline for Ozone Precursors 

The purpose of identifying and modeling baseline controls for ozone precursors, NOx and VOC, 

is to reduce ambient ozone concentrations to meet the current ozone standard in this analysis. 

Control measures were applied in the baseline to reduce ozone concentrations in addition to the 

control set developed for the hypothetical national attainment strategy presented in the PM2.5 

NAAQS RIA (for more information, see http://www.epa.gov/ttn/ecas/ria.html). 

The additional known controls included in the baseline to simulate attainment with current ozone 

NAAQS are listed in Table 3.1 and are described below. Details regarding the individual controls 

are provided in Appendix 3. Due to the extensive reductions from EGUs already implemented in 

CAIR/CAMR/CAVR, no additional EGU controls were included for the current ozone standard.

Controls included in the baseline for NonEGU point and Area sources came from a variety of 

geographic areas and scales. Almost all available controls in Chicago, Houston, and California 

were included in the baseline because these areas contain counties that were projected to be 

nonattainment of the current ozone NAAQS in 2020. 

NOx controls from NonEGU point/Area sources were included in two ways. First, controls were 

included in counties with monitors that were projected to violate the current standard in 2020. 

Controls were then applied to all surrounding counties within the same state that were 

completely contained within 200 km5 of the county containing the projected violating monitor 

(Figure 3.1). Second, controls were applied to large nonEGU point sources6 outside the 200km 

buffer zones. The criteria for control was as follows: the plant level emissions exceeded 1,000 

tons of NOx in 2020, the plant was in a county that touches the 200km buffer, and the plant was 

close to a nonattainment county that had difficulty attaining the baseline in the ozone NAAQS 

proposal RIA. VOC controls were applied to select counties where: VOC emissions were high 

(>5,000 tpy or >25tpy/sq. mi), the county design value was projected to be  0.08 ppm in the 

2020 basecase, and the area had some historical evidence that VOC controls would appreciably 

lower ozone in the local region (Figure 3.2). This evidence came from internal EPA modeling or 

State-submitted modeling.

5

 It is a generic approximation used in this analysis for the sphere of possible emissions influence 

on air quality at the violating monitors. The actual area of emissions control is determined by 

states during attainment planning. 

6

 Large point sources, due to the relative magnitude of emissions and high emissions stack 

heights, theoretically may impact air quality at a downwind violating monitor at distances 

beyond 200km. 
3-5

Table 3.1: Controls for Current Ozone Standard by Sector Applied in the Baseline 

Determination for 2020 

Control Measures 

Sector NOx VOC 

NonEGU 

Point 

Biosolid Injection Technology 

LNB (Low NOx Burner) 

LNB + FGR (Flu Gas Recirculation) 

LNB + SCR (Selective Catalytic Reduction) 

NSCR (Non-selective Catalytic Reduction) 

OXY-Firing 

SCR

SCR + Steam Injection 

SCR + Water Injection 

SNCR (Selective Non-catalytic Reduction) 

SNCR—Urea

SNCR—Urea Based 

Permanent Total Enclosure (PTE) 

Work Practices, Use of Low VOC Coatings 

(NonEGU Point Sources) 

Area RACT to 25 tpy (LNB) 

Switch to Low Sulfur Fuel 

Water Heater + LNB Space Heaters 

CARB Long-Term Limits 

Catalytic Oxidizer 

Equipment and Maintenance 

Gas Collection (SCAQMD/BAAQMD) 

Incineration >100,000 lbs bread 

Low Pressure/Vacuum Relief Valve 

OTC Mobile Equipment Repair and Refinishing 

Rule 

OTC Solvent Cleaning Rule 

SCAQMD—Low VOC 

SCAQMD Limits 

SCAQMD Rule 1168 

Work Practices, Use of Low VOC Coatings (Area 

Sources) 

Switch to Emulsified Asphalts 

Onroad 

Mobile 

Diesel Retrofits 

Reduce Gasoline Reid Vapor Pressure (RVP) to 7.0 (EPA, 2005a) 

Elimination of Long Duration Idling 

Continuous Inspection and Maintenance 

Commuter Programs 

Additional Technology Changes in the Onroad Transportation Sector 

Nonroad 

Mobile 

Diesel Retrofits and Engine Rebuilds 

Reduce Gasoline Reid Vapor Pressure (RVP) to 7.0 (EPA, 2005a) 

Aircraft NOx International Standard 

EGU None None 
3-6

Figure 3.1: Counties Where Controls for Nitrogen Oxides (NOx) Were Included for 

NonEGU Point and Area Sources, for the Current Ozone Standard in the Baseline 

For the Onroad and Nonroad Mobile source sectors, some controls were applied nationwide for 

the current ozone standard in the baseline, while others were applied statewide in certain states or 

locally in a limited number of counties (see Figure 3.3). Counties were identified for locally 

applied Mobile source controls as follows: counties projected to have a monitor that exceeded 

the current standard were surrounded by a 200km buffer zone, and controls were included in the 

counties within this buffer that were within the same state as the exceeding monitor. Where some 

control measures overlapped for a given county, controls with the lowest costs were generally 

included first. Both onroad and nonroad diesel retrofits and idling elimination were included in 

California with an assumed 75% market penetration, and in baseline reduction areas outside of 

California with an assumed 25% market penetration. EPA determined that 25% would have a 

significant impact, but was feasible to achieve and was applied for reduction areas outside of 

California. EPA further determined that for southern California a 75% level of reduction could 

be achieved, which was the highest cost-effective penetration rate that EPA felt could be 

reasonably accomplished. 

Nitrogen oxide (NOx) controls applied to NonEGU point and Area sources 
3-7

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

Applied to NonEGU Point and Area Sources for the Current Ozone Standard in the 

Baseline

3.1.2 Ozone Levels for Baseline

Establishing the baseline required design values (predicted concentrations) of ozone across the 

country. Because the intention of this evaluation was to achieve attainment of the current ozone 

standard, controls were included to reduce ambient ozone concentrations to 0.08 ppm 

(effectively 0.084 ppm based on current rounding conventions). A map of the country is 

presented in Figure 3.4, which shows predicted concentrations for the 661 counties with ozone 

monitors. Projections of ozone design values were developed according to procedures outlined in 

EPA modeling guidance.7,8

7

 Available online at: http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf

8

 As part of the procedure for projecting future ozone design values, the guidance recommends 

using a criterion that there be a minimum of 5 modeled days with predicted base year ozone at or 

above 0.070 ppm. This criterion was relaxed to a minimum of 1 day at or above 0.060 ppm for 

the 82 counties with fewer than 5 days with predicted 2002 concentrations at or above 0.070 

ppm. 

VOC Controls applied to NonEGU Point and Area Sources
3-8

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

Nonroad Sources in Addition to National Mobile Controls* for the Current Ozone 

Standard in the Baseline 

* International Aircraft NOx Standard, national control measures applied as part of the PM NAAQS RIA, 

and Additional Technology Changes in the Onroad Transportation Sector. 

**Onroad retrofits, elimination of long duration idling, and lower Reid Vapor Pressure (RVP) gasoline.

***Nonroad retrofits, continuous inspection and maintenance, and commuter programs. 

The baseline shows that 6 counties would not meet the current ozone standard in 2020, even after 

inclusion of all known controls. Of these 6 counties, 5 of them are in portions of California that 

have current state implementation plans that reflect an attainment date of 2024. After including 

known controls as described above, the analysis predicted that the remaining 655 counties would 

attain the current standard by 2020. The baseline forms the foundation for the cost-benefit 

analysis conducted in this RIA, where EPA compares more stringent primary ozone standard 

alternatives incrementally to national attainment of the current standard. 

Statewide + Local controls*** 

Statewide controls** 
3-9

Figure 3.4: Baseline Projected 8-Hour Ozone Air Quality in 2020a, b, c, d 

a

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

Interstate Rule (EPA, 2005b), the Clean Air Mercury Rule (EPA, 2005c), the Clean Air Visibility Rule 

(EPA, 2005d), the Clean Air Nonroad Diesel Rule (EPA, 2004), the Light-Duty Vehicle Tier 2 Rule 

(EPA, 1999), the Heavy Duty Diesel Rule (EPA, 2000), proposed rules for Locomotive and Marine 

Vessels (EPA, 2007a) and for Small Spark-Ignition Engines (EPA, 2007b), and state and local level 

mobile and stationary source controls identified for additional reductions in emissions for the purpose 

of attaining the current PM 2.5 and Ozone standards. 

b

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

implementation. 

c

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

applied.

d

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

3.1.3 National Baseline Sensitivity Analysis

Circular A-4 of the Office of Management and Budget’s (OMB) guidance under Executive Order 

12866 defines a no-action baseline as “what the world will be like if the proposed rule is not 

adopted.” The illustrative analysis in this RIA assesses the costs and benefits of moving from this 

“no-action” baseline to a suite of possible new standards. Circular A-4 states that the choice of 

an appropriate baseline may require consideration of a wide range of potential factors, including: 

x evolution of the market, 

Legend

17 additional counties that exceed 0.075 ppm for a total of 28

6 counties that exceed 0.084 

61 additional counties that exceed 0.070 ppm for a total of 89

142 additional counties that exceed 0.065 ppm for a total of 231

430 counties meet 0.065 ppm 

5 counties that exceed 0.079 ppm for a total of 11
3-10

x changes in external factors affecting expected benefits and costs, 

x changes in regulations promulgated by the agency or other government entities, and 

x the degree of compliance by regulated entities with other regulations (OMB, 2003). 

Circular A-4 also recommends that: 

“When more than one baseline is reasonable and the choice of baseline will significantly 

affect estimated benefits and costs, you should consider measuring benefits and costs 

against alternative baselines. In doing so you can analyze the effects on benefits and costs 

of making different assumptions about other agencies’ regulations, or the degree of 

compliance with your own existing rules.” (OMB 2003) 

In Appendix 7a, we describe a sensitivity analysis that we conducted to provide information 

about how the no-action baseline would differ under different assumptions about mobile 

technologies. It also assesses nationally what the change would be to costs and benefits of a new 

standard of 0.075 ppm and alternate primary standards of 0.079, 0.070, and 0.065 ppm. See 

Appendix 7a for more details. 

3.2 Developing the Modeled Control Strategy Analysis 

After developing the baseline, EPA developed a hypothetical control strategy to illustrate one 

possible national control strategy that could be adopted to reach an alternative primary standard 

by 2020. The stricter standard alternative of 0.070 ppm was chosen as being representative of the 

set of alternatives being considered by EPA in its notice of proposed rulemaking on the ozone 

NAAQS. The 2020 baseline air quality modeling for proposal resulted in 203 counties with 

projected design values exceeding 0.070 ppm. In the final rule modeling of the 2020 baseline 

there are 89 counties projected to exceed 0.070 ppm. The reduction in the number of counties 

projected to exceed 0.070 between proposal and final reflects the net effect of the updates to the 

air quality modeling platform, as described in Chapter 2, and the additional emissions controls in 

the final rule baseline modeling compared to proposal. 

Controls for five sectors were used in developing the control analysis, as discussed previously: 

nonEGU point, Area, onroad mobile and nonroad mobile, along with EGUs. Reductions in both 

NOx and VOC ozone precursors were needed in all sectors to meet a tighter standard. 

As depicted in the flow diagram in Figure 1.1, the control strategy modeled in this RIA first 

applied known controls to reach attainment. For the control strategy, controls for five sectors 

were used in developing the control analysis, as discussed previously: nonEGU point, Area, 

onroad mobile and nonroad mobile, along with EGUs. Reductions in both NOx and VOC ozone 

precursors were needed in all sectors to meet a tighter standard. The emissions for this control 

strategy were input to the CMAQ model as part of the process to project ozone design values for 

the 2020 control strategy. The results of modeling the control strategy indicate that there were 

some areas projected not to attain 0.070 ppm in 2020 using all known control measures. To

complete the analysis, EPA was then required to extrapolate the additional emission reductions 

required to reach attainment. The methodology used to develop those estimates and those 
3-11

calculations are presented in Chapter 4. Appendix 7a presents a sensitivity analysis of three 

mobile source control measures that could be included in the control strategy to illustrate 

attainment. 

Table 3.2: Controls Applied, by Sector, for the 0.070 ppm Control Strategy (Incremental to 

Baseline)

Control Measures 

Sector NOx VOC 

NonEGU 

Point 

Biosolid Injection Technology 

LNB (Low NOx Burner) 

LNB + FGR (Flu Gas Recirculation) 

LNB + SCR (Selective Catalytic Reduction) 

NSCR (Non-selective Catalytic Reduction) 

OXY-Firing 

SCR

SCR + Steam Injection 

SCR + Water Injection 

SNCR (Selective Non-catalytic Reduction) 

SNCR—Urea

SNCR—Urea Based 

Permanent Total Enclosure (PTE) 

Work Practices, Use of Low VOC Coatings 

(NonEGU Point Sources) 

Area RACT to 25 tpy (LNB) 

Switch to Low Sulfur Fuel 

Water Heater + LNB Space Heaters 

CARB Long-Term Limits 

Catalytic Oxidizer 

Equipment and Maintenance 

Gas Collection (SCAQMD/BAAQMD) 

Incineration >100,000 lbs bread 

Low Pressure/Vacuum Relief Valve 

OTC Mobile Equipment Repair and 

Refinishing Rule 

OTC Solvent Cleaning Rule 

SCAQMD—Low VOC 

SCAQMD Limits 

SCAQMD Rule 1168 

Work Practices, Use of Low VOC Coatings 

(Area Sources) 

Switch to Emulsified Asphalts 

Onroad 

Mobilea

Increased Penetration of Onroad SCR and DPF from 25% to 75% 

Continuous Inspection and Maintenance (OBD) 

Nonroad 

Mobilea

Increased Penetration of Nonroad SCR and DPF from 25% to 75% 

EGU -Lower ozone season nested caps in OTC and 

MWRPO states while retaining the current 

CAIR cap and a new cap for Eastern Texas. 

-Application of local controls (SCR and 

SNCR) nationally to coal fired units in and 

around NA counties covering the combination 

of CBSA (Core based Statistical Areas) and 

CSA (Combined Statistical Areas)B outside of 

OTC and, MWRPO, and East Texas. 

None 

a Onroad and Nonroad Mobile Source control measures applied for the Baseline analysis were applied to 

additional geographic areas in the 0.070 ppm analysis. SCR and DPF retrofits market penetration was 

increased from 25% to 75% for all areas outside of California. 

b

For the definition and current lists of CBSA and CSAs, see 

http://www.census.gov/population/www/estimates/metrodef.html
3-12

3.2.1 Controls Applied for the Modeled Control Strategy: NonEGU Point and Area Sectors 

NonEGU point and Area control measures were identified using AirControlNET 4.1.9,10 To 

reduce NOx and VOC emissions, all known control measures, below a cost cap, were applied, 

allowing for the largest emission reduction per source over the widest geographic area. Because 

all available controls up to the cost cap were used in counties needing emission reductions, 

ordering of which controls were applied first was not relevant. In areas where residual 

nonattainment remained after the modeled control strategy, some known controls above the cost 

cap were analyzed and applied to achieve additional emissions reductions as a portion of the 

extrapolated cost analysis. See Chapter 5 for more information on how we selected our cost cap 

and the extrapolated cost analysis. 

Supplemental controls, which estimated additional emissions control based on similar 

technology for NonEGU point and Areas sources were included in the analysis prior to the 

extrapolating costs of unknown controls. Supplemental controls are described in further detail in 

Appendix 3. 

NOx nonEGU point and Area controls were applied to counties that were projected to have 

concentrations of greater than 0.070 ppm in the 2020 baseline. Additional controls were applied 

in surrounding counties within 200 km of the county projected to be out of attainment (at 0.070 

ppm), but not crossing state boundaries. In addition, controls were applied to large nonEGU 

point sources outside the 200km buffer zones. The criteria for control of these large nonEGU 

point sources was as follows: the plant level emissions exceeded 1,000 tons of NOx in 2020, the 

plant was in a county that touches the 200km buffer, and the plant was close to a nonattainment 

county that had difficulty attaining 0.070 ppm in the ozone NAAQS proposal RIA. 

9

 See http://www.epa.gov/ttnecas1/AirControlNET.htm for a description of how AirControlNET 

operates and what data is included in this tool. 

10 While AirControlNET has not undergone a formal peer review, this software tool has 

undergone substantial review within EPA’s OAR and OAQPS, and by technical staff in EPA’s 

Regional offices. Much of the control measure data has been included in a control measure 

database that will be distributed to EPA Regional offices for use by States as they prepare their 

ozone, regional haze, and PM2.5 SIPs over the next 10 months. See 

http://www.epa.gov/particles/measures/pm_control_measures_tables_ver1.pdf for more details 

on this control measures database. In addition, the control measure data within AirControlNET 

has been used by Regional Planning Organizations (RPOs) such as the Lake Michigan Air 

District Commission (LADCO), the Ozone Transport Commission (OTC), and the Visibility 

Improvement State and Tribal Association of the Southeast (VISTAS) as part of their technical 

analyses associated with SIP development over the last 3 years. All of their technical reports 

are available on their web sites. 
3-13

Figure 3.5: Counties Where Controls for Nitrogen Oxides (NOx) Were Applied to NonEGU 

Point and Areas Sources for the RIA Modeled Control Strategy (Incremental to Baseline) 

VOC controls were applied in select counties where the following criteria were met (including 

the counties which included VOC controls in their baselines): VOC emissions were high (>5,000 

tpy or >25tpy/sq. mi), the county design value was projected to be  0.070 ppm in the 2020 (See 

Figure 3.6), and the area had some historical evidence that VOC controls would appreciably 

lower ozone in the local region. This evidence came from internal EPA modeling or Statesubmitted modeling.

3.2.2 Controls Applied for the Modeled Control Strategy: EGU Sector 

In the Proposal RIA, a control strategy was applied for the EGU sector for the East only, (EGU 

controls for the West were already included in the ozone baseline since they were applied for the 

hypothetical national control strategy in the PM NAAQS RIA.) In the proposed RIA, emissions

reductions were targeted in the OTC and MWRPO states through lower “nested caps” and

“command and control” application in the non-attainment counties outside of the OTC and 

MWRPO within CAIR. 

For the Final RIA, we have employed an enhanced strategy, both in terms of the quantity of 

reductions and the geographic extent of the areas covered. Figure 3.7 depicts the areas covered 

for the EGU sector emission reduction strategy.

Nitrogen oxide (NOx) controls applied to NonEGU Point and area sources
3-14

Figure 3.6: Counties Where VOC Controls Were Applied to NonEGU Point and Areas 

Sources for the Modeled Control (Incremental to Baseline) 

Annual and ozone season CAIR caps remained unchanged, but coal-fired units were targeted for 

this shifted strategy within those caps. This strategy was appropriate to consider because 

transport of NOx pollution is more of a concern in the East, and NOx from EGUs still accounts 

for a significant portion of emissions in this region. California, while in need of reductions as 

well, was not included in this strategy because all known controls (including EGU controls) had 

already been applied in the baseline. The development of an EGU-component to this control 

strategy was based exclusively on NOx emissions during the ozone season, although the 

hypothetical controls applied would operate year-round. The EGU sector used the Integrated 

Planning Model (IPM) to evaluate the reductions that are predicted from a specific control 

strategy. Details of this tool and subsequent analysis can be found in Appendix 3.4.

Reductions in the EGU sector are influenced significantly by the 2003 Clean Air Interstate Rule 

(CAIR) (see Appendix 3.4 for more details on CAIR). CAIR will bring significant emission 

reductions in NOx, and a result, ambient ozone concentrations in the eastern U.S. by 2020.11 A 

map of the CAIR region is presented in Appendix 3.4. Emissions and air quality impacts of 

CAIR are documented in detail in the Regulatory Impact Analysis of the Final Clean Air 

Interstate Rule.12

11 See http://www.epa.gov/airmarkets/progress/progress-reports.html for more information 

12 See http://www.epa.gov/CAIR/technical.html

VOC controls applied to NonEGU Point and Area Sources
3-15

Figure 3.7: Geographic Areas where NOx Controls were Applied to Electrical Generating 

Units (EGUs) for the Modeled Control Strategy (Incremental to Baseline) 

To address nonattainment in the CAIR region (especially the Midwest, Mid-Atlantic,, and 

Northeast), and East Texas13 lower nested ozone season caps (a limit lower than the current 

CAIR cap) were applied in these areas for NOx, while holding the CAIR cap unchanged for the 

entire region. This provides an opportunity to reduce emissions in a cost effective manner in 

targeted regions. Three geographic regions were targeted for cap-and-trade type emissions 

reductions: the Midwest Regional Planning Organization (MWRPO) consisting WI, IL, IN, MI, 

and OH; and the Ozone Transport Commission (OTC), consisting of DC, MD, PA, DE, NJ, CT, 

NY, RI, MA, VT, NH, and ME; and East Texas consisting the counties shown in Figure 3.7. 

These areas were chosen because the MWRPO and OTC states are currently investigating ways 

of reducing EGU emissions further in their states and because most of the potential ozone 

nonattainment areas are found within these two regions. East Texas has also non-attainment 

areas, and the state is looking for strategies to reduce emissions. Considering transport, as well as 

the local effects, reducing emissions in these areas is expected to help bringing the Lake 

Michigan and Northeast corridor as well as East Texas non-attainment areas into attainment. 

Lower nested caps were applied in the MWRPO and OTC states and in East Texas, for the ozone 

season only. The caps that were applied lead to reductions that could be obtained by installing 

13 East Texas geographic area was defined to be identical to the geographic area for other sectors. 
3-16

post-combustion controls, such as Selective Catalytic Reduction (SCR) and Selective NonCatalytic Reduction (SNCR), to all of the coal-fired units that were not projected to have 

previously installed post-combustion controls in the base-case. Following this, 75% of the 

reducti14on that could be obtained from these units was subtracted from the sum of State level 

ozone control season NOx caps for the OTC and East Texas regions, and 90% for the MWRPO 

states in CAIR.15 The CAIR cap for the entire region was kept unchanged. 

In order to address nonattainment elsewhere in the West and CAIR region outside of the 

MWRPO, and OTC, and East Texas a “command and control” type strategy for coal-fired units

has been designed. Annual and ozone season CAIR caps remained unchanged in the East, and 

coal-fired units were targeted for this reduction. Preliminary analysis showed that most of the

needed NOx reductions in the EGU sector can be achieved through application of postcombustion controls on coal units that are projected to remain without controls under the 

CAIR/CAMR/CAVR cap-and-trade scheme. All non-attainment areas nationwide, outside of the 

OTC, MWRPO, and East Texas were subject to this local command-and-control strategy, 

covering the CBSA and CSA counties in and around nonattainment counties. 

At this time, we are in the process of improving our ability to achieve additional reductions 

available in NOx emissions from EGUs and corresponding air quality benefits, 

especially on high energy demand days (HEDDs) through energy efficiency measures. 

We were not able to apply such control strategies as part of this RIA. A Technical 

Support Document (TSD) is available summarizing the previous and ongoing work in 

this area.

3.2.3 Controls Applied for the Modeled Control Strategy: Onroad and Nonroad Mobile Sectors 

As in other sectors, there are several mobile source control strategies that have been, or are 

expected to be, implemented through previous national or regional rules. Although many 

expected reductions from these rules are included in the baseline, additional mobile source 

controls were required to illustrate attainment of an alternate primary standard (See Figure 3.8). 

Information on mobile source control measures for the modeled control strategy analysis were 

derived from various EPA studies and from running EPA’s National Mobile Inventory Model 

(NMIM), which includes the MOBILE6 Onroad model and the NONROAD model. See 

www.epa.gov/otaq/nmim.htm for more information on NMIM and see Appendix 3.3 for more 

information on mobile source controls included in the modeled control strategy analysis. 

All of the local mobile source controls included in the ozone baseline were expanded for the 

hypothetical national control strategy to attain an alternate primary standard. In the case of 

onroad and nonroad Selective Catalytic Reduction (SCR) and Diesel Particulate Filters (DPF), 

14 Potential for Reducing NOx Emissions from EGU Sources on High Energy Demand Days with 

Energy Efficiency Measures. Technical Support Document for the Final Ozone NAAQS 

Regulatory Impact Analysis. U.S. Environmental Protection Agency, Office of Air and 

Radiation. March 2008.

15 Detailed analysis showed that 75%–90% reduction provides the most cost-effective way of 

reducing emissions at the targeted non-attainment areas, considering transport, with the most 

air quality impacts. 
3-17

the measure was applied at a greater penetration rate—to 75% of the modeled equipment 

population. 75% was the highest cost-effective penetration rate that EPA felt could be reasonably 

accomplished. All local and statewide measures were applied to sources in additional geographic 

areas beyond the areas controlled in the baseline. Descriptions of the mobile source rules and 

measures can be found in Appendix 3.3. 

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

Nonroad Sources in Addition to National Mobile Controls for the Modeled Control 

Strategy (incremental to Baseline) 

*Onroad retrofits, elimination of long duration idling, and lower Reid Vapor Pressure (RVP) gasoline. 

**Nonroad retrofits, continuous inspection and maintenance, and commuter programs. 

As in the baseline, some mobile source controls were applied statewide for all states with a 

county projected to exceed 0.070 ppm. ‘Local’ controls were applied to counties within a 200 km 

buffer from counties projected to exceed 0.070 ppm with the following exceptions: 

x counties in neighboring states were omitted from the buffer zone 

x controls were applied statewide to Ozone Transport Commission (OTC) states, with the 

exception of Vermont 

As stated at the beginning of this section, additional reductions were needed to complete the 

analysis of the alternate standard. In addition to the emission reductions accounted for in the 

extrapolation approach described in Chapter 4, Appendix 7a presents a sensitivity analysis of 

Statewide controls*

Statewide + Local controls** 
3-18

three mobile source control measures that could be included in the control strategy to illustrate 

attainment of the alternate standard. 

3.2.4 Data Quality for this Analysis 

The estimates of emission reductions associated with our control strategies above are subject to 

important limitations and uncertainties. EPA’s analysis is based on its best judgment for various 

input assumptions that are uncertain. As a general matter, the Agency selects the best available 

information from available engineering studies of air pollution controls and has set up what it 

believes is the most reasonable framework for analyzing the cost, emission changes, and other 

impacts of regulatory controls. EPA is working on approaches to quantify the uncertainties in 

these areas and will incorporate them in future RIAs as appropriate. 

3.3 Geographic Distribution of Emissions Reductions

The following maps break out NOx and VOC reductions into the controlling sectors. The maps 

for NOx and VOC reductions are presented in Figures 3.9 and 3.11, respectively. Figures 3.10 

and 3.12 indicate the emission reductions attributed to each sector. Appendix 3 contains maps of 

emissions reductions by sector, nationwide. 

Prior to reading the maps, there is an important caveat to consider. The control strategy above 

focuses on reducing emissions of VOC and NOx, the two precursors to ozone formation. 

However, in some cases, the application of the control strategy actually increased the level of 

NOx or VOC emissions. This is due to controls that affect multiple pollutants and complex 

interactions between air pollutants, as well as trading aspects under the CAIR rule. 

With respect to the baseline (CAIR/CAMR/CAVR), total emissions of NOx is lower. At the 

same time emissions shift geographically and hence do not decrease everywhere within the capand-trade regions. However, EGU NOx emissions do decrease substantially everywhere 

compared to the pre-CAIR levels. Substantial EGU NOx emission reductions are already being 

achieved through CAIR/CAMR/CAVR. This strategy focuses reductions under trading programs 

where they are needed most, with the result that some areas get less reductions than might have 

been otherwise expected within the. CAIR region. As explained earlier, the NOx EGU control 

strategy was designed to achieve emission reductions specifically in the non-attainment areas, 

while retaining the overall CAIR cap. Application of nested and lower (ozone season) caps (for 

the states in the MWRPO, and OTC, and East Texas) regions and local controls (SCR and

SNCR) on the uncontrolled coal units in the non-attainment counties (and surrounding CBSA 

and CSA) outside of the trading regions OTC and MWRPO within CAIR region result in 

emission shifts increase of emissions elsewhere within or outside of CAIR region compared to 

the base line (CAIR/CAMR/CAVR). While there are substantial total NOx emission reductions 

(roughly 53,000 tons within the OTC, and MWRPO, and East Texas; and roughly 16,000 tons 

nationwide) expected for the 2020 ozone season (roughly 55,500 tons) compared to the base line 

(CAIR/CAMR/CAVR) as a result of cap-and-trade program with lower caps and local 

command-and-control reductions in other non-attainment counties where uncontrolled coal units 

exist, there are emission shifts geographically and there is the possibility of increases in emission 

from the remainder of sources within and outside of the CAIR region. This approach provides a 
3-19

cost effective opportunity for reducing emissions where the reductions are most needed to help 

reach attainment. It is important to recall that this is a hypothetical control strategy, and the states 

or other authorities may take additional steps to minimize these increases if warranted. 

Figure 3.9: Annual Tons of NOx Emission Reductions for the Modeled Control Strategy 

(Incremental to the Baseline)*

*Reductions are negative and increases are positive.

** The í99– +100 range is shown without color because these are small county-level NOx reductions or 

increases that likely had little to no impact on ozone estimates. Most counties in this range had NOx 

differences less than 1 ton. 
3-20

Figure 3.10: Percentage of 2020 Annual NOx Emissions Reduced by Sector Incremental to 

the Baseline 

4% 5%

64%

21%

6%

Area EGU Point NonEGU Point Onroad Nonroad
3-21

Figure 3.11: Annual Tons of VOC Emission Reductions for the Modeled Control Strategy 

(Incremental to the Baseline)* 

*

 Reductions are negative and increases are positive

** The í99–+53 range is shown without color because these are small county-level VOC reductions or 

increases that likely had little to no impact on ozone estimates. 

**
3-22

Figure 3.12: Percentage of 2020 Annual VOC Emissions Reduced by Sector 

70%

2%

24%

4%

Area NonEGU Point Onroad Nonroad

3.4 Ozone Design Values for Partial Attainment 

After determining the emissions reductions from NOx and VOC, we used modeling tools (see 

Section 2.3.2) to determine ozone design values for 2020. Figure 3.13 shows a map of the design 

values after the modeled control strategy. The map legend is broken out to demonstrate under 

this control strategy, with no adjustments, which counties would reach the targeted standard of 

0.070 ppm, the more stringent alternative standard analyzed (0.065 ppm), and the other end of 

the proposal range (0.075 ppm, and 0.079 ppm). It is understood that this illustrative strategy 

would not be the exact hypothetical strategy used to try to attain either of these alternative 

standards, due to over- and under-attainment in many counties. (Chapter 4 describes EPA’s 

methodology for estimating tons of reductions needed to hypothetically attain these other two 

possible alternative standards.) In addition, because ozone formation is dependent on a variety of 

factors, it is not possible to directly attribute changes in predicted ozone concentrations to 

emission reductions of a specific precursor from a specific sector. 

A full listing of the counties and their design values is provided in Appendix 3. 

Table 3.3 shows the tons of emissions reduced from the modeled control strategy, incremental to 

the baseline. Figure 3.14 shows the tons of emissions remaining after application of the 

hypothetical modeled control strategy, by sector.

Using this strategy, it is possible to reach attainment in 600 counties. However, there are still 61 

counties that will remain out of attainment with an alternative standard of 0.070 ppm using this 

control strategy. All known controls were applied to this scenario, but attainment was not 

achieved everywhere. Because of this partial attainment outcome, it will be necessary to identify 
3-23

additional reductions in NOx and VOC. Chapter 4 will address the methodology for determining 

the additional tons that were needed to reach full attainment. 

Figure 3.13: Projected 8-Hour Ozone Air Quality in 2020 From Applying the Modeled 

Control Strategya, b, c, d, e, 

a

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

Interstate Rule (EPA, 2005b), the Clean Air Mercury Rule (EPA, 2005c), the Clean Air Visibility Rule 

(EPA, 2005d), the Clean Air Nonroad Diesel Rule (EPA, 2004), the Light-Duty Vehicle Tier 2 Rule 

(EPA, 1999), the Heavy Duty Diesel Rule (EPA, 2000), Locomotive and Marine Vessels (EPA, 2007a) 

and for Small Spark-Ignition Engines (EPA, 2007b), and state and local level mobile and stationary 

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

PM 2.5 and Ozone standards. 

b

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

implementation. 

c

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

applied.

d

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

Legend 

11 additional counties that exceed 0.075 ppm for a total of 21 

6 counties that exceed 0.084 ppm 

40 additional counties that exceed 0.070 ppm for a total of 61 

105 additional counties that exceed 0.065 ppm for a total of 166 

495 counties meet 0.065 ppm standard 

4 counties that exceed 0.079 ppm for a total of 10 
3-24

Table 3.3: Emissions and Reductions (2020) From Applying the Modeled Control Strategy 

by Region (Incremental to the Baseline) 

Modeled Control Strategy Emission Reductions (annual 

tons/year) 

Baseline Annual 

Emissions

(annual tons/year) East West Californiaa

Emissions

Sector

VOC NOX VOC NOX VOC NOX VOC NOX 

Area 1,700,000 7,900,000 140,000 20,000 15,000 1,100 10,000 35 

NonEGU 

Point 1,900,000 49,000 4,000 350,000 280 19,000 260 1,600

EGU Point 2,000,000 1,100,000 - 7,500 - 19,000 - 1,400 

Onroad 1,700,000 1,800,000 50,000 110,000 10,000 15,000 45 71 

Nonroad 2,600,000 1,500,000 10,000 32,000 1,500 3,300 19 140 

a

 A majority of the control measures were applied for the baseline in California. 

Figure 3.14: National Annual Emissions Remaining (2020) after Application of Controls for 

the Baseline and Modeled Control Strategy 

1.5

1.5

2.6

2.5

1.8

1.8

1.7

1.6

7.9

7.8

1.7

1.7

1.1

1.1

2

1.6 1.9

1.9

0.0 5.0 10.0 15.0 20.0

Baseline

Modeled Control Strategy

Baseline

Modeled Control Strategy

VOC NOX

Emissions (M Tons)

Nonroad Onroad Area NonEGU EGU
3-25

3.5 References 

Michigan Department of Environmental Quality and Southeast Michigan Council of 

Governments. Proposed Revision to State of Michigan State Implementation Plan for 7.0 Low 

Vapor Pressure Gasoline Vapor Request for Southeast Michigan. May 24, 2006. 

National Ambient Air Quality Standards for Particulate Matter, 40 CFR Part 50 (2006). 

Rule To Reduce Interstate Transport of Fine Particulate Matter and Ozone (Clean Air Interstate 

Rule); Revisions to Acid Rain Program; Revisions to the NOX SIP Call; Final Rule, 40 CFR 

Parts 51, 72, 73, 74, 77, 78 and 96 (2005). 

Standards of Performance for New and Existing Stationary Sources: Electric Utility Steam 

Generating Units, 40 CFR Parts 60, 63, 72, and 75 (2005). 

Regional Haze Regulations and Guidelines for Best Available Retrofit Technology (BART) 

Determinations, 40 CFR Part 51 (2005). 

Control of Emissions of Air Pollution from Locomotive Engines and Marine CompressionIgnition Engines Less than 30 Liters per Cylinder, Proposed rule, 40 CFR Parts 92, 94, 1033, 

1039, 1042, 1065 and 1068 (2007). 

Control of Emissions from Nonroad Spark-Ignition Engines and Equipment; proposed rule, 40 

CFR Parts 60, 63, 85, 89, 90, 91, 1027, 1045, 1048, 1051, 1054, 1060, 1065, 1068, and 1074 

(2007).

U.S. Environmental Protection Agency (EPA). 1999. Regulatory Impact Analysis – Control of 

Air Pollution from New Motor Vehicles: Tier 2 Motor Vehicle Emissions Standards and 

Gasoline Sulfur Control, U.S. Environmental Protection Agency, Office of Transportation and 

Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105, EPA420-R-99-023, 

December 1999. Available at http://www.epa.gov/tier2/frm/ria/r99023.pdf.

U.S. Environmental Protection Agency (EPA). 2000. Regulatory Impact Analysis: Heavy-Duty 

Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements, U.S. 

Environmental Protection Agency, Office of Transportation and Air Quality, Assessment and 

Standards Division, Ann Arbor, MI 48105, EPA420-R-00-026, December 2000. Available at 

http://www.epa.gov/otaq/highway-diesel/regs/exec-sum.pdf 

U.S. Environmental Protection Agency (EPA). 2004. Final Regulatory Analysis: Control of 

Emissions from Nonroad Diesel Engines, U.S. Environmental Protection Agency, Office of 

Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105, 

EPA420-R-04-007, May 2004. Available at http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf.

U.S. Environmental Protection Agency (EPA), 2005a. Guide on Federal and State Summer RVP 

Standards for Conventional Gasoline Only. EPA420-B-05-012. November 2005. 
3-26

U.S. Environmental Protection Agency (EPA). 2005b. Clean Air Interstate Rule Emissions 

Inventory Technical Support Document, U.S. Environmental Protection Agency, Office of Air 

Quality Planning and Standards, March 2005. Available at 

http://www.epa.gov/cair/pdfs/finaltech01.pdf.

U.S. Environmental Protection Agency (EPA). 2005c. Emissions Inventory and Emissions 

Processing for the Clean Air Mercury Rule (CAMR), U.S. Environmental Protection Agency, 

Office of Air Quality Planning and Standards, March 2005. Available at 

http://www.epa.gov/ttn/atw/utility/emiss_inv_oar-2002-0056-6129.pdf

U.S. Environmental Protection Agency (EPA). 2005d. Regulatory Impact Analysis for the Final 

Clean Air Visibility Rule or the Guidelines for Best Available Retrofit Technology (BART) 

Determinations Under the Regional Haze Regulations, U.S. Environmental Protection Agency, 

Office of Air and Radiation, June 2005. EPA-452-R-05-004. Available at 

http://www.epa.gov/visibility/pdfs/bart_ria_2005_6_15.pdf

U.S. Environmental Protection Agency (EPA). 2007a, Regulatory Announcement: EPA Proposal 

for More Stringent Emissions Standards for Locomotives and Marine Compression-Ignition 

Engines. EPA420-F-07-015. 

U.S. Environmental Protection Agency (EPA). 2007b, Proposed Emission Standards for New 

Nonroad Spark-Ignition Engines, Equipment, and Vessels. EPA420-F-07-032. 

U.S. Environmental Protection Agency (EPA). 2008a. Air Quality Modeling Platform for the 

Ozone National Ambient Air Quality Standard Final Rule Regulatory Impact Assessment. 

U.S. Environmental Protection Agency (EPA). 2008b. Technical Support Document: Preparation 

of Emissions Inventories For the 2002-based Platform, Version 3, Criteria Air Pollutants, 

USEPA, January, 2008. 

U.S. Office of Management and Budget. September 2003. Circular A-4, Regulatory Analysis 

Guidance sent to the Heads of Executive Agencies and Establishments. Washington, DC. 

http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf.
3a-1

Appendix 3: Additional Control Strategy Information 

3a.1 NonEGU Point and Area Source Controls 

3a.1.1 NonEGU Point and Area Source Control Strategies for Ozone NAAQS Final 

In the NonEGU point and Area Sources portion of the control strategy, maximum control 

scenarios were used from the existing control measure dataset from AirControlNET 4.1 for 2020 

(for geographic areas defined for each level of the standard being analyzed). This existing 

control measure dataset reflects changes and updates made as a result of the reviews performed 

for the final PM2.5 RIA. Following this, an internal review was performed by the OAQPS 

engineers in the Sector Policies and Programs Division (SPPD) to examine the controls applied 

by AirControlNET and decide if these controls were sufficient or could be more aggressive in 

their application, given the 2020 analysis year. This review was performed for nonEGU point 

NOx control measures. The result of this review was an increase in control efficiencies applied 

for many control measures, and more aggressive control measures for over 70 SCC’s. For 

example, SPPD recommended that we apply SCR to cement kilns to reduce NOx emissions in 

2020. Currently, there are no SCRs in operation at cement kilns in the U.S., but there are several 

SCRs in operation at cement kilns in France now. Based on the SCR experience at cement kilns 

in France, SPPD believes SCR could be applied at U.S. cement kilns by 2020. Following this, it 

was recommended that supplemental controls could be applied to 8 additional SCC’s from 

nonEGU point NOx sources. We also looked into sources of controls for highly reactive VOC 

nonEGU point sources. Four additional controls were applied for highly reactive VOC nonEGU 

point sources not in AirControlNET.

3a.1.2 NOx Control Measures for NonEGU Point Sources. 

Several types of NOx control technologies exist for nonEGU point sources: SCR, selective 

noncatalytic reduction (SNCR), natural gas reburn (NGR), coal reburn, and low-NOx burners. In 

some cases, LNB accompanied by flue gas recirculation (FGR) is applicable, such as when fuelborne NOx emissions are expected to be of greater importance than thermal NOx emissions. 

When circumstances suggest that combustion controls do not make sense as a control technology 

(e.g., sintering processes, coke oven batteries, sulfur recovery plants), SNCR or SCR may be an 

appropriate choice. Finally, SCR can be applied along with a combustion control such as LNB 

with overfire air (OFA) to further reduce NOx emissions. All of these control measures are 

available for application on industrial boilers.

Besides industrial boilers, other nonEGU point source categories covered in this RIA include 

petroleum refineries, kraft pulp mills, cement kilns, stationary internal combustion engines, glass 

manufacturing, combustion turbines, and incinerators. NOx control measures available for 

petroleum refineries, particularly process heaters at these plants, include LNB, SNCR, FGR, and 

SCR along with combinations of these technologies. NOx control measures available for kraft 

pulp mills include those available to industrial boilers, namely LNB, SCR, SNCR, along with 

water injection (WI). NOx control measures available for cement kilns include those available to 

industrial boilers, namely LNB, SCR, and SNCR. Non-selective catalytic reduction (NSCR) can 

be used on stationary internal combustion engines. OXY-firing, a technique to modify 
3a-2

combustion at glass manufacturing plants, can be used to reduce NOx at such plants. LNB, SCR, 

and SCR + steam injection (SI) are available measures for combustion turbines. Finally, SNCR 

is an available control technology at incinerators. Table 3a.1 contains a complete list of the NOx 

nonEGU point control measures applied and their associated emission reductions obtained in the 

modeled control strategy for the alternate primary standard. For more information on these 

measures, please refer to the AirControlNET 4.1 control measures documentation report. 

Table 3a.1: NOx NonEGU Point Emission Reductions by Control Measure 

Control Measure Source Type 

Modeled Control 

Strategy Reductions 

(annual tons/year) 

Biosolid Injection 

Technology 

Cement Kilns 1,200 

Asphaltic Conc; Rotary Dryer; Conv Plant 120 

Ceramic Clay Mfg; Drying 370 

Conv Coating of Prod; Acid Cleaning Bath 440 

Fuel Fired Equip; Furnaces; Natural Gas 170 

In-Process Fuel Use; Natural Gas 1,300 

In-Process Fuel Use; Residual Oil 39

In-Process; Process Gas; Coke Oven Gas 190 

Lime Kilns 5,900 

Sec Alum Prod; Smelting Furn 62 

Steel Foundries; Heat Treating 13 

LNB

Surf Coat Oper; Coating Oven Htr; Nat Gas 30 

Fluid Cat Cracking Units 3,600 

Fuel Fired Equip; Process Htrs; Process Gas 700 

In-Process; Process Gas; Coke Oven Gas 880 

Iron & Steel Mills—Galvanizing 35 

Iron & Steel Mills—Reheating 1,100 

Iron Prod; Blast Furn; Blast Htg Stoves 1,000 

Sand/Gravel; Dryer 11 

LNB + FGR 

Steel Prod; Soaking Pits 100 

Iron & Steel Mills—Annealing 270 

Process Heaters—Distillate Oil 2,300 

Process Heaters—Natural Gas 27,000 

Process Heaters—Other Fuel 14 

Process Heaters—Process Gas 4,200 

LNB + SCR 

Process Heaters—Residual Oil 37 

Rich Burn IC Engines—Gas 22,000 

Rich Burn IC Engines—Gas, Diesel, LPG 3,700 

NSCR

Rich Burn Internal Combustion Engines—Oil 11,000 

Glass Manufacturing—Containers 7,600 

Glass Manufacturing—Flat 18,000 

OXY-Firing 

Glass Manufacturing—Pressed 3,900 

Ammonia—NG-Fired Reformers 5,800 

Cement Manufacturing—Dry 25,000 

Cement Manufacturing—Wet 22,000 

IC Engines—Gas 54,000 

ICI Boilers—Coal/Cyclone 2,200 

ICI Boilers—Coal/Wall 22,000 

ICI Boilers—Coke 490 

SCR

ICI Boilers—Distillate Oil 4,800 
3a-3

Control Measure Source Type 

Modeled Control 

Strategy Reductions 

(annual tons/year) 

ICI Boilers—Liquid Waste 730 

ICI Boilers—LPG 280 

ICI Boilers—Natural Gas 36,000 

ICI Boilers—Process Gas 8,600 

ICI Boilers—Residual Oil 17,000 

Natural Gas Prod; Compressors 810 

Space Heaters—Distillate Oil 22 

Space Heaters—Natural Gas 640 

Sulfate Pulping—Recovery Furnaces 9,900 

SCR + Steam Injection Combustion Turbines—Natural Gas 18,000 

Combustion Turbines—Jet Fuel — 

Combustion Turbines—Natural Gas — 

SCR + Water Injection 

Combustion Turbines—Oil 210 

By-Product Coke Mfg; Oven Underfiring 4,300 

Comm./Inst. Incinerators 1,400 

ICI Boilers—Coal/Stoker 7,000 

Indust. Incinerators 250 

Medical Waste Incinerators — 

In-Process Fuel Use; Bituminous Coal 32 

Municipal Waste Combustors 4,400 

Nitric Acid Manufacturing 3,100 

SNCR

Solid Waste Disp; Gov; Other Inc 95 

SNCR—Urea ICI Boilers—MSW/Stoker 120 

ICI Boilers—Coal/FBC 100 

ICI Boilers—Wood/Bark/Stoker—Large 5,500 

In-Process; Bituminous Coal; Cement Kilns 300 

SNCR—Urea Based 

In-Process; Bituminous Coal; Lime Kilns 31 

3a.1.3 VOC Control Measures for NonEGU Point Sources. 

VOC controls were applied to a variety of nonEGU point sources as defined in the emissions 

inventory in this RIA. The first control is: permanent total enclosure (PTE) applied to paper and 

web coating operations and fabric operations, and incinerators or thermal oxidizers applied to 

wood products and marine surface coating operations. A PTE confines VOC emissions to a 

particular area where can be destroyed or used in a way that limits emissions to the outside 

atmosphere, and an incinerator or thermal oxidizer destroys VOC emissions through exposure to 

high temperatures (2,000 degrees Fahrenheit or higher). The second control applied is petroleum 

and solvent evaporation applied to printing and publishing sources as well as to surface coating 

operations. Table 3a.2 contains the emissions reductions for these measures in the modeled 

control strategy for the alternate primary standard. For more information on these measures, refer 

to the AirControlNET 4.1 control measures documentation report. 
3a-4

Table 3a.2: VOC NonEGU Point Emission Reductions by Control Measure 

Control Measure Source Type 

Modeled Control 

Strategy Reductions 

(annual tons/year) 

Permanent Total Enclosure (PTE) Fabric Printing, Coating and Dyeing 43 

Paper and Other Web Coating 490 

Petroleum and Solvent Evaporation Printing and Publishing 3,600 

Surface Coating 400 

3a.1.4 NOx Control Measures for Area Sources 

There were three control measures applied for NOx emissions from area sources. The first is 

RACT (reasonably available control technology) to 25 tpy (LNB). This control is the addition of 

a low NOx burner to reduce NOx emissions. This control is applied to industrial oil, natural gas, 

and coal combustion sources. The second control is water heaters plus LNB space heaters. This 

control is based on the installation of low-NOx space heaters and water heaters in commercial 

and institutional sources for the reduction of NOx emissions. The third control was switching to 

low sulfur fuel for residential home heating. This control is primarily designed to reduce sulfur 

dioxide, but has a co-benefit of reducing NOx. Table 3a.3 contains the listing of control 

measures and associated reductions for the modeled control strategy. For additional information 

regarding these controls please refer to the AirControlNET 4.1 control measures documentation 

report.

Table 3a.3: NOx Area Source Emission Reductions by Control Measure 

Control Measure Source Type 

Modeled Control Strategy 

Reductions 

(annual tons/year) 

Industrial Coal Combustion 5,400 

Industrial NG Combustion 3,000 

RACT to 25 tpy (LNB) 

Industrial Oil Combustion 570 

Switch to Low Sulfur Fuel Residential Home Heating 970 

Water Heater + LNB Space Heaters Commercial/Institutional—NG 4,300 

Residential NG 6,700 

3a.1.5 VOC Control Measures for Area Source. 

The most frequently applied control to reduce VOC emissions from area sources was CARB 

Long-Term Limits. This control, which represents controls available in VOC rules promulgated 

by the California Air Resources Board, applies to commercial solvents and commercial 

adhesives, and depends on future technological innovation and market incentive methods to 

achieve emission reductions. The next most frequently applied control was the use of low or no 

VOC materials for graphic art source categories. The South Coast Air District’s SCAQMD Rule 

1168 control applies to wood furniture and solvent source categories sets limits for adhesive and 

sealant VOC content. The OTC solvent cleaning rule control establishes hardware and operating 

requirements for specified vapor cleaning machines, as well as solvent volatility limits and 

operating practices for cold cleaners. The Low Pressure/Vacuum Relief Valve control measure is 

the addition of low pressure/vacuum (LP/V) relief valves to gasoline storage tanks at service 
3a-5

stations with Stage II control systems. LP/V relief valves prevent breathing emissions from 

gasoline storage tank vent pipes. SCAQMD Limits control establishes VOC content limits for 

metal coatings along with application procedures and equipment requirements. Switch to 

Emulsified Asphalts control is a generic control measure replacing VOC-containing cutback 

asphalt with VOC-free emulsified asphalt. The equipment and maintenance control measure 

applies to oil and natural gas production. The Reformulation—FIP Rule control measure intends 

to reach the VOC limits by switching to and/or encouraging the use of low-VOC pesticides and 

better Integrated Pest Management (IPM) practices. Table 3a.4 contains the control measures and 

associated emission reductions described above for the modeled control strategy. For additional 

information regarding these controls please refer to the AirControlNET 4.1 control measures 

documentation report. 

Table 3a.4: VOC Area Source Emission Reductions by Control Measure 

Control Measure Source Type 

Modeled Control 

Strategy Reductions 

(annual tons/year) 

CARB Long-Term Limits Consumer Solvents 78,000 

Catalytic Oxidizer Conveyorized Charbroilers 250 

Equipment and Maintenance Oil and Natural Gas Production 450 

Gas Collection (SCAQMD/BAAQMD) Municipal Solid Waste Landfill 1,100 

Incineration >100,000 lbs bread Bakery Products 2,700 

Low Pressure/Vacuum Relief Valve Stage II Service Stations 9,900 

Stage II Service Stations—Underground 

Tanks

9,800 

OTC Mobile Equipment Repair and Aircraft Surface Coating 720 

Refinishing Rule Machn, Electric, Railroad Ctng 4,400 

OTC Solvent Cleaning Rule Cold Cleaning 10,000 

SCAQMD—Low VOC Rubber and Plastics Mfg 1,700 

SCAQMD Limits Metal Furniture, Appliances, Parts 6,300 

SCAQMD Rule 1168 Adhesives—Industrial 22,000 

Large Appliances 8,200 

Metal Furniture 7,600 

Solvent Utilization 

Surface Coating 2,900 

Switch to Emulsified Asphalts Cutback Asphalt 3,300

3a.1.6 Supplemental Controls 

Table 3a.5 below summarizes the supplemental control measures added to our control measures 

database by providing the pollutant it controls and its control efficiency (CE). These controls 

were applied not as part of the modeled control strategy, but as supplemental measures prior to 

extrapolating unknown control costs. However, these controls are not currently located in 

AirControlNET. These measures are primarily found in draft SIP technical documents and have 

not been fully assessed for inclusion in AirControlNET. 
3a-6

Table 3a.5: Supplemental Emissions Control Measures Added to the Control Measures 

Database

Poll 

Control 

Technology SCC

SCC

Description 

Percent

Reduction

(%) 

20200252 Internal Comb. Engines/Industrial/ 

Natural Gas/2-cycle Lean Burn 

NOx LEC 87

20200254 Internal Comb. Engines/Industrial/ 

Natural Gas/4-cycle Lean Burn 

87

Enhanced LDAR 3018001- Fugitive Leaks 50

30600701 

30600999 - 

Flares 98

LDAR 3018001 - Fugitive Leaks 80

Monitoring Program 30600702- Cooling towers No general 

estimate 

Inspection and 

Maintenance Program 

(Separators) 

Water Seals (Drains) 

30600503- Wastewater Drains and Separators 65 

Work Practices,

Use of Low VOC 

Coatings 

(Area Sources) 

2401025000 

2401030000 

2401060000 

2425010000 

2425030000 

2425040000 

2461050000 

Solvent Utilization 90 

VOC

Work Practices,

Use of Low VOC 

Coatings 

(NonEGU Point) 

307001199 

Surface Coating 

Operations 

within SCC 

4020000000, 

Printing/Publis

hing processes 

within SCC 

4050000000 

Petroleum and Solvent Evaporation 90 

Low Emission Combustion (LEC)

Overview: LEC technology is defined as the modification of a natural gas fueled, spark ignited, 

reciprocating internal combustion engine to reduce emissions of NOx by utilizing ultra-lean 

air-fuel ratios, high energy ignition systems and/or pre-combustion chambers, increased 

turbocharging or adding a turbocharger, and increased cooling and/or adding an intercooler or 

aftercooler, resulting in an engine that is designed to achieve a consistent NOx emission rate of 

not more than 1.5-3.0 g/bhp-hr at full capacity (usually 100 percent speed and 100 percent load). 

This type of retrofit technology is fairly widely available for stationary internal combustion 

engines.

For CE, EPA estimates that it ranges from 82 to 91 percent for LEC technology applications. The 

EPA believes application of LEC would achieve average NOx emission levels in the range of 

1.5-3.0 g/bhp-hr. This is an 82-91 percent reduction from the average uncontrolled emission 

levels reported in the ACT document. An EPA memorandum summarizing 269 tests shows that 
3a-7

96 percent of IC engines with installed LEC technology achieved emission rates of less than 2.0 

g/bhp-hr.1 The 2000 EC/R report on IC engines summarizes 476 tests and shows that 97% of the 

IC engines with installed LEC technology achieve emission rates of 2.0 g/bhp-hr or less.2

Major Uncertainties: The EPA acknowledges that specific values will vary from engine to 

engine. The amount of control desired and number of operating hours will make a difference in 

terms of the impact had from a LEC retrofit. Also, the use of LEC may yield improved fuel 

economy and power output, both of which may affect the emissions generated by the device. 

Leak Detection and Repair (LDAR) for Fugitive Leaks

Overview: This control measure is a program to reduce leaks of fugitive VOC emissions from 

chemical plants and refineries. The program includes special “sniffer” equipment to detect leaks, 

and maintenance schedules that affected facilities are to adhere to. This program is one that is 

contained within the Houston-Galveston-Brazoria 8-hour Ozone SIP.

Major Uncertainties: The degree of leakage from pipes and processes at chemical plants is 

always difficult to quantify given the large number of such leaks at a typical chemical 

manufacturing plant. There are also growing indications based on tests conducted by TCEQ and 

others in Harris County, Texas that fugitive leaks have been underestimated from chemical 

plants by a factor of 6 to 20 or greater. 3

Enhanced LDAR for Fugitive Leaks

Overview: This control measure is a more stringent program to reduce leaks of fugitive VOC 

emissions from chemical plants and refineries that presumes that an existing LDAR program 

already is in operation.

Major Uncertainties: The calculations of CE and cost presume use of LDAR at a chemical plant. 

This should not be an unreasonable assumption, however, given that most chemical plants are 

under some type of requirement to have an LDAR program. However, as mentioned earlier, 

there is growing evidence that fugitive leak emissions are underestimated from chemical plants 

by a factor of 6 to 20 or greater.4

1

 “Stationary Reciprocating Internal Combustion Engines Technical Support Document for NOx 

SIP Call Proposal,” U.S. Environmental Protection Agency. September 5, 2000. Available on the 

Internet at http://www.epa.gov/ttn/naaqs/ozone/rto/sip/data/tsd9-00.pdf.

2

“Stationary Internal Combustion Engines: Updated Information on NOx Emissions and Control 

Techniques,” Ec/R Incorporated, Chapel Hill, NC. September 1, 2000. Available on the Internet 

at http://www.epa.gov/ttn/naaqs/ozone/ozonetech/ic_engine_nox_update_09012000.pdf.

3

 VOC Fugitive Losses: New Monitors, Emissions Losses, and Potential Policy Gaps. 2006 

International Workshop. U.S. Environmental Protection Agency, Office of Air Quality Planning 

and Standards and Office of Solid Waste and Emergency Response. October 25-27, 2006. 

4

 VOC Fugitive Losses: New Monitors, Emissions Losses, and Potential Policy Gaps. 2006 

International Workshop. U.S. Environmental Protection Agency, Office of Air Quality Planning 

and Standards and Office of Solid Waste and Emergency Response. October 25-27, 2006. 
3a-8

Flare Gas Recovery

Overview: This control measure is a condenser that can recover 98 percent of the VOC emitted 

by flares that emit 20 tons per year or more of the pollutant. 

Major Uncertainties: Flare gas recovery is just gaining commercial acceptance in the US and is 

only in use at a small number of refineries. 

Cooling Towers

Overview: The control measure is continuous monitoring of VOC from the cooling water return 

to a level of 10 ppb. This monitoring is accomplished by using a continuous flow monitor at the 

inlet to each cooling tower. 

There is not a general estimate of CE for this measure; one is to apply a continuous flow monitor 

until VOC emissions have reached a level of 1.7 tons/year for a given cooling tower.5

Major Uncertainties: The amount of VOC leakage from each cooling tower can greatly affect 

the overall cost-effectiveness of this control measure. 

Wastewater Drains and Separators

Overview: This control measure includes an inspection and maintenance program to reduce VOC 

emissions from wastewater drains and water seals on drains. This measure is a more stringent 

version of measures that underlie existing NESHAP requirements for such sources. 

Major Uncertainties: The reference for this control measures notes that the VOC emissions 

inventories for the five San Francisco Bay Area refineries whose data was a centerpiece of this 

report are incomplete. In addition, not all VOC species from these sources were included in the 

VOC data that is a basis for these calculations.6

Work Practices or Use of Low VOC Coatings

Overview: The control measure is either application of work practices (e.g., storing VOCcontaining cleaning materials in closed containers, minimizing spills) or using coatings that have 

much lower VOC content. These measures, which are of relatively low cost compared to other 

VOC area source controls, can apply to a variety of processes, both for non-EGU point and area 

sources, in different industries and is defined in the proposed control techniques guidelines 

(CTG) for paper, film and foil coatings, metal furniture coatings, and large appliance coatings 

published by the US EPA in July 2007.7

5

 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of Regulation 8, 

Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004. 

6

 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of Regulation 8, 

Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004. 

7

 U.S. Environmental Protection Agency. Consumer and Commercial Products: Control 

Techniques Guidelines in Lieu of Regulations for Paper, Film, and Foil Coatings; Metal 
3a-9

The estimated CE expected to be achieved by either of these control measures is 90 percent. 

Major Uncertainties: The greatest uncertainty is in how many potentially affected processes are 

implementing or already implemented these control measures. This may be particularly true in 

California. Also, there are nine States that have many of the above work practices in effect for 

paper, film and foil coatings processes, but the work practices are not meant to achieve a specific 

emissions limit.8

 Hence, it is uncertain how much VOC reduction is occurring from this control 

measure in this case. 

In addition to the new supplemental controls presented above, there were a number of changes 

made to existing AirControlNET controls. These changes were made based upon an internal 

review performed by EPA engineers to examine the controls applied by AirControlNET and 

determine if these controls were sufficient or could be more aggressive in their application, given 

the 2020 analysis year. This review was performed for nonEGU point NOx control measures. 

The result of this review was an increase in control efficiencies applied for many control 

measures, and more aggressive control measures for over 70 SCCs. The changes apply to the 

control strategies performed for the Eastern US only. These changes are listed in Table 3a.6. 

Table 3a.6: Supplemental Emission Control Measures—Changes to Control Technologies 

Currently in our Control Measures Database For Application in 2020 

Poll SCC 

AirControlNET Source 

Description 

AirControlNE

T Control 

Technology 

New Control 

Technology 

New 

CE

(%) 

Old

CE

(%) 

NOX 10200104 

10200204 

10200205 

10300207 

10300209 

10200217 

10300216 

ICI Boilers—Coal-Stoker SNCR SCR 90 40 

NOX 10200901 

10200902 

10200903 

10200907 

10300902 

10300903 

ICI Boilers—Wood/Bark/ 

Waste

SNCR SCR 90 55 

NOX 10200401 

10200402 

10200404 

10200405 

10300401 

ICI Boilers—Residual Oil SCR SCR 90 80 

Furniture Coatings; and Large Appliance Coatings. 40 CFR 59. July 10, 2007. Available on the 

Intenet at http://www.epa.gov/ttncaaa1/t1/fr_notices/ctg_ccp092807.pdf. It should be noted that 

this CTG became final in October 2007. 

8

 U.S. Environmental Protection Agency. Consumer and Commercial Products: Control 

Techniques Guidelines in Lieu of Regulations for Paper, Film, and Foil Coatings; Metal 

Furniture Coatings; and Large Appliance Coatings. 40 CFR 59. July 10, 2007, p. 37597. 

Available on the Intenet at http://www.epa.gov/ttncaaa1/t1/fr_notices/ctg_ccp092807.pdf.
3a-10

Poll SCC 

AirControlNET Source 

Description 

AirControlNE

T Control 

Technology 

New Control 

Technology 

New 

CE

(%) 

Old

CE

(%) 

NOX 10200501 

10200502 

10200504 

ICI Boilers—Distillate Oil SCR SCR 90 80 

NOX 10200601 

10200602 

10200603 

10200604 

10300601 

10300602 

10300603 

10500106 

10500206 

ICI Boilers—Natural Gas SCR SCR 90 80 

NOX 30500606 Cement Manufacturing—Dry SCR SCR 90 80

NOX 30500706 Cement Manufacturing—Wet SCR SCR 90 80

NOX 30300934 Iron & Steel Mills—

Annealing 

SCR SCR 90 85 

NOX 10200701 

10200704 

10200707 

10200710 

10200799 

10201402 

10300701 

10300799 

ICI Boilers—Process Gas SCR SCR 90 80 

NOX 10200802 

10200804 

ICI Boilers—Coke SCR SCR 90 70 

NOX 10201002 ICI Boilers—LPG SCR SCR 90 80 

NOX 10201301 

10201302 

ICI Boilers—Liquid Waste SCR SCR 90 80 

NOX 30700110 Sulfate Pulping—Recovery 

Furnaces

SCR SCR 90 80 

NOX 30100306 Ammonia Production— 

Pri. Reformer, Nat. Gas 

SCR SCR 90 80 

 30500622 

30500623 

Cement Kilns Biosolid 

Injection 

Biosolid 

Injection 

40 23 

NOX 30590013 

30190013 

30190014 

39990013 

Industrial and Manufacturing 

Incinerators 

SNCR SCR 90 45 

NOX 30101301 

30101302 

Nitric Acid Manufacturing SNCR SCR 90 60 to 

98

NOX 30600201 Fluid Cat. Cracking Units LNB + FGR SCR 90 55 

NOX 30590003 Process Heaters—Process 

Gas

LNB + SCR LNB + SCR 90 88 

NOX 30600101 

30600103 

30600111 

Process Heaters—Distillate 

Oil

LNB + SCR LNB + SCR 90 90 

NOX 30600106 

30600199 

Process Heaters—Residual 

Oil

LNB + SCR LNB + SCR 90 80 

NOX 30600102 

30600105 

Process Heaters—Natural 

Gas

LNB + SCR LNB + SCR 90 80 
3a-11

Poll SCC 

AirControlNET Source 

Description 

AirControlNE

T Control 

Technology 

New Control 

Technology 

New 

CE

(%) 

Old

CE

(%) 

NOX 30700104 Sulfate Pulping—Recovery 

Furnaces

SCR SCR 90 80 

NOX 30790013 Pulp and Paper—Natural 

Gas—Incinerators 

SNCR SCR 90 45 

NOX 39000201 In-Process; Bituminous Coal; 

Cement Kiln 

SNCR—urea 

based 

SCR 90 50 

NOX 39000203 In-Process; Bituminous Coal; 

Lime Kiln 

SNCR—urea 

based 

SCR 90 50 

NOX 39000289 In-Process Fuel Use; 

Bituminous Coal; Gen 

SNCR SCR 90 40 

NOX 39000489 In-Process Fuel Use; 

Residual Oil; Gen 

LNB SCR 90 37 

NOX 39000689 In-Process Fuel Use; Natural 

Gas; Gen 

LNB SCR 90 50 

NOX 39000701 In-Proc; Process Gas; Coke 

Oven/Blast Furn 

LNB + FGR SCR 90 55 

NOX 39000789 In-Process; Process Gas; 

Coke Oven Gas 

LNB SCR 90 50 

NOX 50100101 

50100506 

50200506 

50300101 

50300102 

50300104 

50300506 

50100102 

Solid Waste Disp; Gov; 

Other Incin; Sludge 

SNCR SCR 90 45 

The last category of supplemental controls is control technologies currently in our control 

measures database being applied to SCCs not controlled currently in AirControlNET.

Table 3a.7: Supplemental Emission Control Technologies Currently in our Control 

Measures Database Applied to New Source Types 

Pollutant SCC SCC Description 

Control 

Technology CE 

NOX 39000602 Cement Manufacturing—Dry SCR 90 

NOX 30501401 Glass Manufacturing—General OXY-Firing 85 

NOX 30302351 

30302352 

30302359 

Taconite Iron Ore Processing—Induration—Coal or 

Gas

SCR 90 

NOX 10100101 External Combustion Boilers; Electric Generation; 

Anthracite Coal; Pulverized Coal 

SNCR 40 

NOX 10100202 External Combustion Boilers; Electric Generation; 

Bituminous/Subbituminous Coal; Pulverized Coal: 

Dry Bottom (Bituminous Coal) 

SNCR 40 

NOX 10100204 External Combustion Boilers; Electric Generation; 

Bituminous/Subbituminous Coal; Spreader Stoker 

(Bituminous Coal) 

SNCR 40 

NOX 10100212 External Combustion Boilers; Electric Generation; 

Bituminous/Subbituminous Coal; Pulverized Coal: 

Dry Bottom (Tangential) (Bituminous Coal) 

SNCR 40 
3a-12

Pollutant SCC SCC Description 

Control 

Technology CE 

NOX 10100401 External Combustion Boilers; Electric Generation; 

Residual Oil; Grade 6 Oil: Normal Firing 

SNCR 50 

NOX 10100404 External Combustion Boilers; Electric Generation; 

Residual Oil; Grade 6 Oil: Tangential Firing 

SNCR 50 

NOX 10100501 External Combustion Boilers; Electric Generation; 

Distillate Oil; Grades 1 and 2 Oil 

SNCR 50 

NOX 10100601 External Combustion Boilers; Electric Generation; 

Natural Gas; Boilers > 100 Million Btu/hr except 

Tangential 

NGR 50 

NOX 10100602 External Combustion Boilers; Electric Generation; 

Natural Gas; Boilers < 100 Million Btu/hr except 

Tangential 

NGR 50 

NOX 10100604 External Combustion Boilers; Electric Generation; 

Natural Gas; Tangentially Fired Units 

NGR 50 

NOX 10101202 External Combustion Boilers; Electric Generation; 

Solid Waste; Refuse Derived Fuel 

SNCR 50 

NOX 20200253 Internal Comb. Engines/Industrial/Natural Gas/4-cycle 

Rich Burn 

NSCR 90 

3a.2 Mobile Control Measures Used in Control Scenarios 

Tables 3a.8 and 3a.9 summarize the emission reductions for the mobile source control measures 

discussed in this section. 

Table 3a.8: NOx Mobile Emission Reductions by Control Measure 

Sector Control Measure 

Modeled Control Strategy Reductions 

(annual tons/year) 

Eliminate Long Duration Truck Idling 5,800 

Reduce Gasoline RVP 880 

Diesel Retrofits 91,000 

Continuous Inspection and Maintenance 20,000 

Onroad 

Commuter Programs 4,100 

Nonroad Diesel Retrofits and Engine Rebuilds 35,000

Table 3a.9: VOC Mobile Emission Reductions by Control Measure 

Sector Control Measure 

Modeled Control Strategy Reductions

(annual tons/year) 

Reduce Gasoline RVP 17,000 

Diesel Retrofits 8,400 

Continuous Inspection and Maintenance 28,000 

Onroad 

Commuter Programs 7,000 

Nonroad Reduce Gasoline RVP 6,300 

Diesel Retrofits and Engine Rebuilds 5,200 
3a-13

3a.2.1 Diesel Retrofits and Engine Rebuilds 

Retrofitting heavy-duty diesel vehicles and equipment manufactured before stricter standards are 

in place—in 2007–2010 for highway engines and in 2011–2014 for most nonroad equipment—

can provide NOX and HC benefits. The retrofit strategies included in the RIA retrofit measure 

are:

x Installation of emissions after-treatment devices called selective catalytic reduction 

(“SCRs”)

x Rebuilding nonroad engines (“rebuild/upgrade kit”) 

We chose to focus on these strategies due to their high NOx emissions reduction potential and 

widespread application. Additional retrofit strategies include, but are not limited to, lean NOx 

catalyst systems—which are another type of after-treatment device—and alternative fuels. 

Additionally, SCRs are currently the most likely type of control technology to be used to meet 

EPA’s NOx 2007–2010 requirements for HD diesel trucks and 2008–2011 requirements for 

nonroad equipment. Actual emissions reductions may vary significantly by strategy and by the 

type and age of the engine and its application.

To estimate the potential emissions reductions from this measure, we applied a mix of two 

retrofit strategies (SCRs and rebuild/upgrade kits) for the 2020 inventory of: 

x Heavy-duty highway trucks class 6 & above, Model Year 1995–2009 

x All diesel nonroad engines, Model Year 1991–2007, except for locomotive, marine, 

pleasure craft, & aircraft engines 

Class 6 and above trucks comprise the bulk of the NOx emissions inventory from heavy-duty 

highway vehicles, so we did not include trucks below class 6. We chose not to include 

locomotive and marine engines in our analysis since EPA has proposed regulations to address 

these engines, which will significantly impact the emissions inventory and emission reduction 

potential from retrofits in 2020. There was also not enough data available to assess retrofit 

strategies for existing aircraft and pleasure craft engines, so we did not include them in this 

analysis. In addition, EPA is in the process of negotiating standards for new aircraft engines. 

The lower bound in the model year range—1995 for highway vehicles and 1991 for nonroad 

engines—reflects the first model year in which emissions after-treatment devices can be reliably 

applied to the engines. Due to a variety of factors, devices are at a higher risk of failure for 

earlier model years. We expect the engines manufactured before the lower bound year that are 

still in existence in 2020 to be retired quickly due to natural turnover, therefore, we have not 

included strategies for pre-1995/1991 engines because of the strategies’ relatively small impact 

on emissions. The upper bound in the model year range reflects the last year before more 

stringent emissions standards will be fully phased-in. 

We chose the type of strategy to apply to each model year of highway vehicles and nonroad 

equipment based on our technical assessment of which strategies would achieve reliable results 

at the lowest cost. After-treatment devices can be more cost-effective than rebuild and vice versa 
3a-14

depending on the emissions rate, application, usage rates, and expected life of the engine. The 

performance of after-treatment devices, for example, depends heavily upon the model year of the 

engine; some older engines may not be suitable for after-treatment devices and would be better 

candidates for rebuild/upgrade kit. In certain cases, nonroad engines may not be suitable for 

either after-treatment devices or rebuild, which is why we estimate that retrofits are not suitable 

for 5% of the nonroad fleet. The mix of strategies employed in this RIA for highway vehicles 

and nonroad engines are presented in Table 3a.10 and Table 3a.11, respectively. The groupings 

of model years for highway vehicles reflect changes in EPA’s published emissions standards for 

new engines.

Table 3a.10: Application of Retrofit Strategy for Highway Vehicles by Percentage of Fleet 

Model Year SCR 

<1995 0%

1995–2006 100% 

2007–2009 50% 

>2009 0%

Table 3a.11: Application of Retrofit Strategy for Nonroad Equipment by Percentage of 

Fleet

Model Year Rebuild/Upgrade kit SCR 

1991–2007 50% 50% 

The expected emissions reductions from SCR’s are based on data derived from EPA regulations 

(Control of Emissions of Air Pollution from 2004 and Later Model Year Heavy-duty Highway 

Engines and Vehicles published October 2000), interviews with component manufacturers, and 

EPA’s Summary of Potential Retrofit Technologies. This information is available at 

www.epa.gov/otaq/retrofit/retropotentialtech.htm. The estimates for highway vehicles and 

nonroad engines are presented in Table 3a.12 and Table 3a.13, respectively.

Table 3a.12: Percentage Emissions Reduction by Highway Vehicle Retrofit Strategy 

 PM CO HC NOx 

SCR (+DPF) 90% 90% 90% 70% 

Table 3a.13: Percentage Emissions Reduction by Nonroad Equipment Retrofit Strategy 

Strategy PM CO HC NOx 

SCR (+DPF) 90% 90% 90% 70% 

Rebuild/Upgrade Kit 30% 15% 70% 40% 

It is important to note that there is a great deal of variability among types of engines (especially 

nonroad), the applicability of retrofit strategies, and the associated emissions reductions. We 

applied the retrofit emissions reduction estimates to engines across the board (e.g., retrofits for 

bulldozers are estimated to produce the same percentage reduction in emissions as for 

agricultural mowers). We did this in order to simplify model runs, and, in some cases, where we 

did not have enough data to differentiate emissions reductions for different types of highway 

vehicles and nonroad equipment. We believe the estimates used in the RIA, however, reflect the 
3a-15

best available estimates of emissions reductions that can be expected from retrofitting the heavyduty diesel fleet. 

Using the retrofit module in EPA’s National Mobile Inventory Model (NMIM) available at 

http://www.epa.gov/otaq/nmim.htm, we calculated the total percentage reduction in emissions 

(PM, NOx, HC, and CO) from the retrofit measure for each relevant engine category (source 

category code, or SCC) for each county in 2020. To evaluate this change in the emissions 

inventory, we conducted both a baseline and control analysis. Both analyses were based on 

NMIM 2005 (version NMIM20060310), NONROAD2005 (February 2006), and MOBILE6.2.03 

which included the updated diesel PM file PMDZML.csv dated March 17, 2006. 

For the control analysis, we applied the retrofit measure corresponding to the percent reductions 

of the specified pollutants in Tables 3a.12 and 3a.13 to the specified model years in Tables 3a.10 

and 3a.11 of the relevant SCCs. Fleet turnover rates are modeled in the NMIM, so we applied the 

retrofit measure to the 2007 fleet inventory, and then evaluated the resulting emissions inventory 

in 2020. The timing of the application of the retrofit measure is not a factor; retrofits only need to 

take place prior to the attainment date target (2020 for this RIA). For example, if retrofit devices 

are installed on 1995 model year bulldozers in 2007, the only impact on emissions in 2020 will 

be from the expected inventory of 1995 model year bulldozer emissions in 2020. 

We then compared the baseline and control analyses to determine the percent reduction in 

emissions we estimate from this measure for the relevant SCC codes in the targeted 

nonattainment areas. 

3a.2.2 Implement Continuous Inspection and Maintenance Using Remote Onboard Diagnostics 

(OBD) 

Continuous Inspection and Maintenance (I/M) is a new way to check the status of OBD systems 

on light-duty OBD-equipped vehicles. It involves equipping subject vehicles with some type of 

transmitter that attaches to the OBD port. The device transmits the status of the OBD system to 

receivers distributed around the I/M area. Transmission may be through radio-frequency, cellular 

or wi-fi means. Radio frequency and cellular technologies are currently being used in the states 

of Oregon, California and Maryland.

Current I/M programs test light-duty vehicles on a periodic basis—either annually or biennially. 

Emission reduction credit is assigned based on test frequency. Using Continuous I/M, vehicles 

are continuously monitored as they are operated throughout the non-attainment area. When a 

vehicle experiences an OBD failure, the motorist is notified and is required to get repairs within 

the normal grace period—typically about a month. Thus, Continuous I/M will result in repairs 

happening essentially whenever a malfunction occurs that would cause the check engine light to 

illuminate. The continuous I/M program is applied to the same fleet of vehicles as the current 

periodic I/M programs. Currently, MOBILE6 provides an increment of benefit when going from 

a biennial program to an annual program. The same increment of credit applies going from an 

annual program to a continuous program. 

Source Categories Affected by Measure: 
3a-16

x All 1996 and newer light-duty gasoline vehicles and trucks:

x All 1996 and newer (SCC 2201001000) Light Duty Gasoline Vehicles (LDGV), Total: 

All Road Types 

x All 1996 and newer (SCC 2201020000) Light Duty Gasoline Trucks 1 (LDGT1), Total: 

All Road Types 

x All 1996 and newer (SCC 2201040000) Light Duty Gasoline Trucks 2 (LDGT2), Total: 

All Road Types 

OBD systems on light duty vehicles are required to illuminate the malfunction indicator lamp 

whenever emissions of HC, CO or NOx would exceed 1.5 times the vehicle’s certification 

standard. Thus, the benefits of this measure will affect all three criteria pollutants. MOBILE6 

was used to estimate the emission reduction benefits of Continuous I/M, using the methodology 

discussed above.

3a.2.3 Eliminating Long Duration Truck Idling 

Virtually all long duration truck idling—idling that lasts for longer than 15 minutes—from 

heavy-duty diesel class 8a and 8b trucks can be eliminated with two strategies: 

x truck stop & terminal electrification (TSE) 

x mobile idle reduction technologies (MIRTs) such as auxiliary power units, generator sets, 

and direct-fired heaters

TSE can eliminate idling when trucks are resting at truck stops or public rest areas and while 

trucks are waiting to perform a task at private distribution terminals. When truck spaces are 

electrified, truck drivers can shut down their engines and use electricity to power equipment 

which supplies air conditioning, heat, and electrical power for on-board appliances.

MIRTs can eliminate long duration idling from trucks that are stopped away from these central 

sites. For a more complete list of MIRTs see EPA’s Idle Reduction Technology page at 

http://www.epa.gov/otaq/smartway/idlingtechnologies.htm.

This measure demonstrates the potential emissions reductions if every class 8a and 8b truck is 

equipped with a MIRT or has dependable access to sites with TSE in 2020. 

To estimate the potential emissions reduction from this measure, we applied a reduction equal to 

the full amount of the emissions attributed to long duration idling in the MOBILE model, which 

is estimated to be 3.4% of the total NOx emissions from class 8a and 8b heavy duty diesel trucks. 

Since the MOBILE model does not distinguish between idling and operating emissions, EPA 

estimates idling emissions in the inventory based on fuel conversion factors. The inventory in the 

MOBILE model, however, does not fully capture long duration idling emissions. There is 

evidence that idling may represent a much greater share than 3.4% of the real world inventory, 

based on engine control module data from long haul trucking companies. As such, we believe the 

emissions reductions demonstrated from this measure in the RIA represent ambitious but realistic 
3a-17

targets. For more information on determining baseline idling activity see EPA’s “Guidance for 

Quantifying and Using Long-Duration Truck Idling Emission Reductions in State 

Implementation Plans and Transportation Conformity” available at

http://www.epa.gov/smartway/idle-guid.htm.

Pollutants and Source Categories Affected by Measure: NOx

Table 3a.14: Class 8a and 8b Heavy Duty Diesel Trucks (decrease NOx for all SCCs) 

SCC Note: All SCC Descriptions below begin with “Mobile Sources; Highway Vehicles—Diesel” 

2230074110 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Interstate: Total 

2230074130 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Other Principal Arterial: Total 

2230074150 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Minor Arterial: Total 

2230074170 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Major Collector: Total 

2230074190 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Minor Collector: Total 

2230074210 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Local: Total 

2230074230 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Interstate: Total 

2230074250 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Other Freeways and Expressways: 

Total 

2230074270 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Other Principal Arterial: Total 

2230074290 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Minor Arterial: Total 

2230074310 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Collector: Total 

2230074330 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Local: Total 

Estimated Emissions Reduction from Measure (%): 3.4 % decrease in NOx for all SCCs affected 

by measure 

3a.2.4 Commuter Programs 

Commuter programs recognize and support employers who provide incentives to employees to 

reduce light-duty vehicle emissions. Employers implement a wide range of incentives to affect 

change in employee commuting habits including transit subsidies, bike-friendly facilities, 

telecommuting policies, and preferred parking for vanpools and carpools. The commuter 

measure in this RIA reflects a mixed package of incentives. 

This measure demonstrates the potential emissions reductions from providing commuter 

incentives to 10% and 25% of the commuter population in 2020. 

We used the findings from a recent Best Workplaces for Commuters survey, which was an EPA 

sponsored employee trip reduction program, to estimate the potential emissions reductions from 

this measure.9

 The BWC survey found that, on average, employees at workplaces with 

comprehensive commuter programs emit 15% fewer emissions than employees at workplaces 

that do not offer a comprehensive commuter program. 

9

 Herzog, E., Bricka, S., Audette, L., and Rockwell, J., 2005. Do Employee Commuter Benefits 

Reduce Vehicle Emissions and Fuel Consumption? Results of the Fall 2004 Best Workplaces for 

Commuters Survey, Transportation Research Record, Journal of the Transportation Research 

Board: Forthcoming. 
3a-18

We believe that getting 10%–25% of the workforce involved in commuter programs is realistic. 

For modeling purposes, we divided the commuter programs measure into two program 

penetration rates: 10% and 25%. This was meant to provide flexibility to model a lower 

penetration rate for areas that need only low levels of emissions reductions to achieve attainment. 

According to the 2001 National Household Transportation Survey (NHTS) published by DOT, 

commute VMT represents 27% of total VMT. Based on this information, we calculated that 

BWC would reduce light-duty gasoline emissions by 0.4% and 1% with a 10% and 25% program 

penetration rate, respectively. 

Pollutants and Source Categories Affected by Measure (SCC): NOx, and VOC 
3a-19

Table 3a.15: All Light-Duty Gasoline Vehicles and Trucks 

SCC

Note: All SCC Descriptions below begin with “Mobile Sources; Highway Vehicles—

Gasoline” 

2201001110 Light Duty Gasoline Vehicles (LDGV); Rural Interstate: Total 

2201001130 Light Duty Gasoline Vehicles (LDGV); Rural Other Principal Arterial: Total 

2201001150 Light Duty Gasoline Vehicles (LDGV); Rural Minor Arterial: Total 

2201001170 Light Duty Gasoline Vehicles (LDGV); Rural Major Collector: Total 

2201001190 Light Duty Gasoline Vehicles (LDGV); Rural Minor Collector: Total 

2201001210 Light Duty Gasoline Vehicles (LDGV); Rural Local: Total 

2201001230 Light Duty Gasoline Vehicles (LDGV); Urban Interstate: Total 

2201001250 Light Duty Gasoline Vehicles (LDGV); Urban Other Freeways and Expressways: Total 

2201001270 Light Duty Gasoline Vehicles (LDGV); Urban Other Principal Arterial: Total 

2201001290 Light Duty Gasoline Vehicles (LDGV); Urban Minor Arterial: Total 

2201001310 Light Duty Gasoline Vehicles (LDGV); Urban Collector: Total 

2201001330 Light Duty Gasoline Vehicles (LDGV); Urban Local: Total 

2201020110 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Interstate: Total 

2201020130 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Other Principal Arterial: Total 

2201020150 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Minor Arterial: Total 

2201020170 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Major Collector: Total 

2201020190 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Minor Collector: Total 

2201020210 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Local: Total 

2201020230 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Interstate: Total 

2201020250 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Other Freeways and 

Expressways: Total 

2201020270 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Other Principal Arterial: Total 

2201020290 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Minor Arterial: Total 

2201020310 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Collector: Total 

2201020330 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Local: Total 

2201040110 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Interstate: Total 

2201040130 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Other Principal Arterial: Total 

2201040150 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Minor Arterial: Total 

2201040170 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Major Collector: Total 

2201040190 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Minor Collector: Total 

2201040210 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Local: Total 

2201040230 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Interstate: Total 

2201040250 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Other Freeways and 

Expressways: Total 

2201040270 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Other Principal Arterial: Total 

2201040290 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Minor Arterial: Total 

2201040310 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Collector: Total 

2201040330 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Local: Total 

Estimated Emissions Reduction from Measure (%): 

With a 10% program penetration rate: 0.4% 

With a 25% program penetration rate: 1% 

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

Volatility is the property of a liquid fuel that defines its evaporation characteristics. RVP is an 

abbreviation for “Reid vapor pressure,” a common measure of gasoline volatility, as well as a 

generic term for gasoline volatility. EPA regulates the vapor pressure of all gasoline during the 

summer months (June 1 to September 15 at retail stations). Lower RVP helps to reduce VOCs, 
3a-20

which are a precursor to ozone formation. This control measure represents the use of gasoline 

with a RVP limit of 7.0 psi from May through September in counties with an ozone season RVP 

value greater than 7.0 psi. 

Under section 211(c)(4)(C) of the CAA, EPA may approve a non-identical state fuel control as a 

SIP provision, if the state demonstrates that the measure is necessary to achieve the national 

primary or secondary ambient air quality standard (NAAQS) that the plan implements. EPA can 

approve a state fuel requirement as necessary only if no other measures would bring about timely 

attainment, or if other measures exist but are unreasonable or impracticable. 

Source Categories Affected by Measure: 

x All light-duty gasoline vehicles and trucks: Affected SCC:

– 2201001000 Light Duty Gasoline Vehicles (LDGV), Total: All Road Types 

– 2201020000 Light Duty Gasoline Trucks 1 (LDGT1), Total: All Road Types 

– 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Total: All Road Types 

– 2201070000 Heavy Duty Gasoline Vehicles (HDGV), Total: All Road Types 

– 2201080000 Motorcycles (MC), Total: All Road Types 

3a.3 EGU Controls Used in the Control Strategy 

Table 3a.21 contains the ozone season emissions from all fossil EGU sources (greater than 25 

megawatts) for the baseline and the control strategy. 

Table 3a.16: NOx EGU Ozone Season Emissions (All Fossil Units >25MW) (1,000 Tons)a

 OTC MWRPO East TX National 

CAIR 

Region 

CAIR 

Cap 

Baseline 

(CAIR/CAMR/CAVR) 

73 154 43 828 463 485 

Control Strategy 65 

(í11%) 

113 

(í26%) 

33

(í23%) 

812 

(í2%) 

470 482 

a Numbers in parentheses are the percentage change in emissions. 

3a.3.1 CAIR 

The data and projections presented in Section 3.2.2 cover the electric power sector, an industry 

that will achieve significant emission reductions under the Clean Air Interstate Rule (CAIR) over 

the next 10 to 15 years. Based on an assessment of the emissions contributing to interstate 

transport of air pollution and available control measures, EPA determined that achieving 

required reductions in the identified States by controlling emissions from power plants is highly 

cost effective. CAIR will permanently cap emissions of sulfur dioxide (SO2) and nitrogen oxides 

(NOx) in the eastern United States. CAIR achieves large reductions of SO2 and/or NOx emissions 

across 28 eastern states and the District of Columbia.
3a-21

Figure 3a.1: CAIR Affected Region 

States controlled for fine particles (annual SO2and NOx)

States not covered by CAIR

States controlled for ozone (ozone season NOx)

States controlled for both fine particles (annual SO2and NOx) and ozone (ozone season NOx)

When fully implemented, CAIR will reduce SO2 emissions in these states by over 70% and NOx

emissions by over 60% from 2003 levels (some of which are due to NOx SIP Call). This will 

result in significant environmental and health benefits and will substantially reduce premature 

mortality in the eastern United States. The benefits will continue to grow each year with further 

implementation. CAIR was designed with current air quality standard in mind, and requires 

significant emission reductions in the East, where they are needed most and where transport of 

pollution is a major concern. CAIR will bring most areas in the Eastern US into attainment with 

the current ozone and current PM2.5 standards. Some areas will need to adopt additional local 

control measures beyond CAIR. CAIR is a regional solution to address transport, not a solution 

to all local nonattainment issues. The large reductions anticipated with CAIR, in conjunction 

with reasonable additional local control measures for SO2, NOx, and direct PM, will move States 

towards attainment in a deliberate and logical manner.

Based on the final State rules that have been submitted and the proposed State rules that EPA has 

reviewed, EPA believes that all States intend to use the CAIR trading programs as their 

mechanism for meeting the emission reduction requirements of CAIR. 

The analysis in this section reflects these realities and attempts to show, in an illustrative fashion, 

the costs and impacts of meeting a proposed 8-hr ozone standard of 0.070 ppm for the power 

sector.

3a.3.2 Integrated Planning Model and Background 

CAIR was designed to achieve significant emissions reductions in a highly cost-effective manner 

to reduce the transport of fine particles that have been found to contribute to nonattainment. EPA 
3a-22

analysis has found that the most efficient method to achieve the emissions reduction targets is 

through a cap-and-trade system on the power sector that States have the option of adopting. The 

modeling done with IPM assumes a region-wide cap and trade system on the power sector for the 

States covered.

It is important to note that the proposal RIA analysis used the Integrated Planning Model (IPM) 

v2.1.9 to ensure consistency with the analysis presented in 2006 PM NAAQS RIA and report 

incremental results. EPA’s IPM v2.1.9 incorporated Federal and State rules and regulations 

adopted before March 2004 and various NSR settlements. 

Final RIA analysis uses the latest version of IPM (v3.0) as part of the updated modeling 

platform. IPM v3.0 includes input and model assumption updates in modeling the power sector 

and incorporates Federal and State rules and regulations adopted before September 2006 and 

various NSR settlements. A detailed discussion of uncertainties associated with the EGU sector 

modeling can be found in 2006 PM NAAQS RIA (pg. 3-50) 

The economic modeling using IPM presented in this and other chapters has been developed for 

specific analyses of the power sector. EPA’s modeling is based on its best judgment for various 

input assumptions that are uncertain, particularly assumptions for future fuel prices and 

electricity demand growth. To some degree, EPA addresses the uncertainty surrounding these 

two assumptions through sensitivity analyses. More detail on IPM can be found in the model 

documentation, which provides additional information on the assumptions discussed here as well 

as all other assumptions and inputs to the model (http://www.epa.gov/airmarkets/progsregs/epaipm.html).

3a.3.3 EGU NOx Emission Control Technologies 

IPM v3.0 includes SO2, NOx, and mercury (Hg) emission control technology options for meeting 

existing and future federal, regional, and state, SO2, NOx and Hg emission limits. The NOx 

control technology options include Selective Catalytic Reduction (SCR) system and Selective 

Non-Catalytic Reduction (SNCR) systems. It is important to note that beyond these emission 

control options, IPM offers other compliance options for meeting emission limits. These include 

fuel switching, re-powering, and adjustments in the dispatching of electric generating units. 

Table 3a.22 summarizes retrofit NOx emission control performance assumptions. 

Table 3a.17: Summary of Retrofit NOx Emission Control Performance Assumptions 

Selective Catalytic Reduction 

(SCR) 

Selective Non-Catalytic Reduction 

(SNCR) 

Unit Type Coal Oil/Gasa Coal Oil/Gasa

Percent Removal 90% down to 0.06 

lb/mmBtu 

80% 35% 50% 

Size Applicability Units Қ 100 MW Units Қ 25 MW Units Қ 25 MW 

and

Units < 200 MW 

Units Қ 25 MW 

a

 Controls to oil- or gas-fired EGUs are not applied as part of the EGU control strategy included in this 

RIA.

Existing coal-fired units that are retrofit with SCR have a NOx removal efficiency of 90%, with 

a minimum controlled NOx emission rate of 0.06 lb/mmBtu in IPM v2.1.9. Potential (new) coal-
3a-23

fired, combined cycle, and IGCC units are modeled to be constructed with SCR systems and 

designed to have emission rates ranging between 0.02 and 0.06 lb NOx/mmBtu.

Detailed cost and performance derivations for NOx controls are discussed in detail in the EPA’s 

documentation of IPM (http://www.epa.gov/airmarkets/progsregs/epa-ipm/pastmodeling.html).

3a.4 Emissions Reductions by Sector 

Figures 3a.2–3a.6 show the NOx reductions for each sector and Figures 3a.7–3a.10 show the 

VOC reductions for each sector under the modeled control strategy. 

Figure 3a.2: Annual Tons of NOx Emissions Reduced from EGU Sources* 

*Reductions are negative and increases are positive.

** The í99–+100 range is not shown because these are small county-level NOx reductions or increases 

that likely had little to no impact on ozone estimates. Most counties in this range had NOx differences 

of under 1 ton. 
3a-24

Figure 3a.3: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from NonEGU 

Point Sources* 

* Reductions are negative and increases are positive.

** The í99–0 range is not shown because these are small county-level NOx reductions or increases that 

likely had little to no impact on ozone estimates. Most counties in this range had NOx differences of 

under 1 ton. 
3a-25

Figure 3a.4: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from Area 

Sources*

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level NOx reductions or increases that 

likely had little to no impact on ozone estimates. Most counties in this range had NOx differences of 

under 1 ton. 
3a-26

Figure 3a.5: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from Nonroad 

Sources*

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level NOx reductions or increases that 

likely had little to no impact on ozone estimates. Most counties in this range had NOx differences of 

under 1 ton. 
3a-27

Figure 3a.6: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from Onroad 

Sources*

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level NOx reductions or increases that 

likely had little to no impact on ozone estimates. Most counties in this range had NOx differences of 

under 1 ton. 
3a-28

Figure 3a.7: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced 

from NonEGU Point Sources* 

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level VOC reductions or increases that 

likely had little to no impact on ozone estimates 
3a-29

Figure 3a.8: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced 

from Area Sources* 

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level VOC reductions or increases that 

likely had little to no impact on ozone estimates. 
3a-30

Figure 3a.9: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced 

from Nonroad Mobile Sources* 

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level VOC reductions or increases that 

likely had little to no impact on ozone estimates. 
3a-31

Figure 3a.10: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced 

from Onroad Mobile Sources* 

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level VOC reductions or increases that 

likely had little to no impact on ozone estimates. 

3a.5 Change in Ozone Concentrations Between Baseline and Modeled Control Strategy 

Table 3a.23 provides the projected 8-hour ozone design values for the 2020 baseline and 2020 

control strategy scenarios for each monitored county. The changes in ozone in 2020 between the 

baseline and the control strategy are also provided in this table.
3a-32

Table 3a.18: Changes in Ozone Concentrations between Baseline and Modeled Control 

Strategy

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Alabama Baldwin 0.064 0.064 0.000 

Alabama Clay 0.057 0.056 í0.001 

Alabama Elmore 0.055 0.055 0.001 

Alabama Etowah 0.054 0.053 í0.001 

Alabama Jefferson 0.059 0.061 0.001 

Alabama Lawrence 0.055 0.056 0.001 

Alabama Madison 0.057 0.058 0.001 

Alabama Mobile 0.064 0.064 0.000 

Alabama Montgomery 0.055 0.055 0.000 

Alabama Morgan 0.060 0.061 0.001 

Alabama Shelby 0.061 0.063 0.002 

Alabama Sumter 0.051 0.051 0.000 

Alabama Tuscaloosa 0.052 0.052 0.000 

Arizona Cochise 0.065 0.065 0.000 

Arizona Coconino 0.067 0.067 0.000 

Arizona Maricopa 0.070 0.068 í0.002 

Arizona Navajo 0.058 0.058 í0.001 

Arizona Pima 0.064 0.063 í0.001 

Arizona Pinal 0.065 0.063 í0.002 

Arizona Yavapai 0.065 0.065 0.000 

Arkansas Crittenden 0.068 0.069 0.000 

Arkansas Montgomery 0.051 0.051 0.000 

Arkansas Newton 0.060 0.060 0.000 

Arkansas Pulaski 0.061 0.062 0.000 

California Alameda 0.069 0.069 0.000 

California Amador 0.067 0.067 0.000 

California Butte 0.069 0.068 0.000 

California Calaveras 0.072 0.072 0.000 

California Colusa 0.058 0.058 0.000 

California Contra Costa 0.070 0.069 0.000 

California El Dorado 0.081 0.081 0.000 

California Fresno 0.091 0.091 0.000 

California Glenn 0.058 0.058 0.000 

California Imperial 0.071 0.071 0.000 

California Inyo 0.068 0.068 0.000 

California Kern 0.097 0.096 0.000 

California Kings 0.076 0.076 0.000 

California Lake 0.054 0.054 0.000 

California Los Angeles 0.105 0.104 0.000 

California Madera 0.076 0.076 0.000 

California Marin 0.041 0.041 0.000 

California Mariposa 0.072 0.072 0.000 

California Mendocino 0.046 0.046 0.000 

California Merced 0.079 0.079 0.000 

California Monterey 0.055 0.055 0.000 

California Napa 0.051 0.051 0.000 

California Nevada 0.075 0.075 0.000 

California Orange 0.081 0.081 0.000 
3a-33

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

California Placer 0.076 0.076 0.000 

California Riverside 0.102 0.102 0.000 

California Sacramento 0.077 0.077 0.000 

California San Benito 0.066 0.066 0.000 

California San Bernardino 0.123 0.123 0.000 

California San Diego 0.077 0.077 0.000 

California San Francisco 0.046 0.046 0.000 

California San Joaquin 0.067 0.067 0.000 

California San Luis Obispo 0.060 0.060 0.000 

California San Mateo 0.051 0.051 0.000 

California Santa Barbara 0.068 0.068 0.000 

California Santa Clara 0.066 0.066 0.000 

California Santa Cruz 0.055 0.055 0.000 

California Shasta 0.058 0.058 0.000 

California Solano 0.057 0.057 0.000 

California Sonoma 0.048 0.048 0.000 

California Stanislaus 0.077 0.077 0.000 

California Sutter 0.068 0.068 0.000 

California Tehama 0.066 0.065 í0.001 

California Tulare 0.083 0.083 0.000 

California Tuolumne 0.073 0.073 0.000 

California Ventura 0.077 0.077 0.000 

California Yolo 0.065 0.064 0.000 

Colorado Adams 0.057 0.053 í0.004 

Colorado Arapahoe 0.069 0.065 í0.005 

Colorado Boulder 0.063 0.058 í0.004 

Colorado Denver 0.064 0.060 í0.004 

Colorado Douglas 0.072 0.068 í0.005 

Colorado El Paso 0.062 0.060 í0.003 

Colorado Jefferson 0.073 0.068 í0.005 

Colorado La Plata 0.052 0.051 0.000 

Colorado Larimer 0.067 0.062 í0.005 

Colorado Montezuma 0.062 0.062 0.000 

Colorado Weld 0.064 0.060 í0.004 

Connecticut Fairfield 0.079 0.077 í0.002 

Connecticut Hartford 0.066 0.063 í0.003 

Connecticut Litchfield 0.064 0.062 í0.003 

Connecticut Middlesex 0.073 0.071 í0.003 

Connecticut New Haven 0.076 0.074 í0.003 

Connecticut New London 0.068 0.066 í0.002 

Connecticut Tolland 0.068 0.065 í0.003 

Delaware Kent 0.069 0.067 í0.002 

Delaware New Castle 0.071 0.068 í0.003 

Delaware Sussex 0.070 0.068 í0.002 

D.C. Washington 0.069 0.065 í0.004 

Florida Alachua 0.056 0.057 0.000 

Florida Baker 0.055 0.054 í0.001 

Florida Bay 0.061 0.063 0.002 

Florida Brevard 0.051 0.052 0.001 

Florida Broward 0.054 0.054 0.000 
3a-34

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Florida Collier 0.057 0.056 0.000 

Florida Columbia 0.053 0.052 0.000 

Florida Duval 0.053 0.052 0.000 

Florida Escambia 0.065 0.065 0.000 

Florida Highlands 0.054 0.054 0.000 

Florida Hillsborough 0.065 0.065 0.000 

Florida Holmes 0.055 0.055 0.000 

Florida Lake 0.055 0.056 0.001 

Florida Lee 0.056 0.056 0.000 

Florida Leon 0.055 0.055 0.000 

Florida Manatee 0.061 0.061 0.000 

Florida Marion 0.058 0.058 0.000 

Florida Miami-Dade 0.053 0.053 0.000 

Florida Orange 0.056 0.058 0.002 

Florida Osceola 0.053 0.054 0.001 

Florida Palm Beach 0.055 0.054 0.000 

Florida Pasco 0.058 0.058 0.000 

Florida Pinellas 0.061 0.061 0.000 

Florida Polk 0.058 0.059 0.001 

Florida St Lucie 0.052 0.052 0.000 

Florida Santa Rosa 0.063 0.064 0.000 

Florida Sarasota 0.060 0.061 0.000 

Florida Seminole 0.057 0.058 0.001 

Florida Volusia 0.051 0.051 0.000 

Florida Wakulla 0.059 0.059 0.000 

Georgia Bibb 0.065 0.063 í0.001 

Georgia Chatham 0.053 0.052 0.000 

Georgia Cherokee 0.053 0.051 í0.002 

Georgia Clarke 0.054 0.052 í0.002 

Georgia Cobb 0.063 0.061 í0.002 

Georgia Coweta 0.065 0.060 í0.006 

Georgia Dawson 0.056 0.054 í0.002 

Georgia De Kalb 0.067 0.065 í0.002 

Georgia Douglas 0.064 0.062 í0.002 

Georgia Fayette 0.062 0.060 í0.002 

Georgia Fulton 0.070 0.068 í0.002 

Georgia Glynn 0.054 0.054 í0.001 

Georgia Gwinnett 0.061 0.059 í0.002 

Georgia Henry 0.064 0.062 í0.002 

Georgia Murray 0.059 0.058 í0.001 

Georgia Muscogee 0.054 0.052 í0.002 

Georgia Paulding 0.060 0.058 í0.002 

Georgia Richmond 0.064 0.059 í0.005 

Georgia Rockdale 0.064 0.062 í0.002 

Georgia Sumter 0.054 0.053 í0.001 

Idaho Ada 0.069 0.069 0.000 

Idaho Butte 0.065 0.065 0.000 

Idaho Canyon 0.059 0.059 0.000 

Idaho Elmore 0.060 0.060 0.000 

Illinois Adams 0.060 0.056 í0.004 
3a-35

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Illinois Champaign 0.058 0.057 í0.001 

Illinois Clark 0.053 0.053 í0.001 

Illinois Cook 0.074 0.073 í0.001 

Illinois Du Page 0.061 0.059 í0.001 

Illinois Effingham 0.057 0.056 í0.001 

Illinois Hamilton 0.059 0.057 í0.002 

Illinois Jersey 0.067 0.065 í0.002 

Illinois Kane 0.062 0.061 í0.001 

Illinois Lake 0.071 0.070 í0.001 

Illinois McHenry 0.067 0.065 í0.001 

Illinois McLean 0.057 0.056 í0.001 

Illinois Macon 0.056 0.055 í0.001 

Illinois Macoupin 0.057 0.055 í0.002 

Illinois Madison 0.066 0.064 í0.003 

Illinois Peoria 0.063 0.062 í0.001 

Illinois Randolph 0.059 0.058 í0.001 

Illinois Rock Island 0.055 0.054 í0.001 

Illinois St Clair 0.066 0.064 í0.002 

Illinois Sangamon 0.054 0.053 í0.001 

Illinois Will 0.062 0.060 í0.001 

Illinois Winnebago 0.058 0.057 í0.001 

Indiana Allen 0.067 0.065 í0.002 

Indiana Boone 0.067 0.066 í0.002 

Indiana Carroll 0.062 0.061 í0.001 

Indiana Clark 0.068 0.067 í0.002 

Indiana Delaware 0.064 0.063 í0.002 

Indiana Elkhart 0.066 0.064 í0.002 

Indiana Floyd 0.066 0.065 í0.002 

Indiana Gibson 0.051 0.050 í0.001 

Indiana Greene 0.063 0.061 í0.001 

Indiana Hamilton 0.070 0.068 í0.002 

Indiana Hancock 0.067 0.066 í0.002 

Indiana Hendricks 0.065 0.063 í0.002 

Indiana Huntington 0.064 0.062 í0.002 

Indiana Jackson 0.062 0.060 í0.002 

Indiana Johnson 0.064 0.063 í0.002 

Indiana Lake 0.078 0.077 í0.001 

Indiana La Porte 0.074 0.073 í0.001 

Indiana Madison 0.067 0.066 í0.002 

Indiana Marion 0.069 0.067 í0.002 

Indiana Morgan 0.066 0.064 í0.002 

Indiana Porter 0.075 0.074 í0.001 

Indiana Posey 0.061 0.060 í0.002 

Indiana St Joseph 0.068 0.067 í0.002 

Indiana Shelby 0.069 0.067 í0.002 

Indiana Vanderburgh 0.060 0.058 í0.002 

Indiana Vigo 0.066 0.065 í0.002 

Indiana Warrick 0.064 0.061 í0.003 

Iowa Bremer 0.059 0.059 0.000 

Iowa Clinton 0.063 0.062 í0.001 
3a-36

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Iowa Harrison 0.062 0.062 0.000 

Iowa Linn 0.058 0.057 í0.001 

Iowa Montgomery 0.056 0.056 0.000 

Iowa Palo Alto 0.054 0.054 0.000 

Iowa Polk 0.047 0.046 0.000 

Iowa Scott 0.061 0.060 í0.001 

Iowa Story 0.049 0.048 0.000 

Iowa Van Buren 0.059 0.058 í0.001 

Iowa Warren 0.049 0.049 0.000 

Kansas Linn 0.060 0.060 0.000 

Kansas Sedgwick 0.064 0.064 0.000 

Kansas Sumner 0.063 0.062 0.000 

Kansas Trego 0.055 0.055 0.000 

Kansas Wyandotte 0.063 0.062 0.000 

Kentucky Bell 0.056 0.056 í0.001 

Kentucky Boone 0.063 0.061 í0.002 

Kentucky Boyd 0.071 0.069 í0.002 

Kentucky Bullitt 0.062 0.060 í0.002 

Kentucky Campbell 0.070 0.068 í0.003 

Kentucky Carter 0.058 0.057 í0.001 

Kentucky Christian 0.058 0.058 0.000 

Kentucky Daviess 0.059 0.058 í0.001 

Kentucky Edmonson 0.059 0.058 í0.001 

Kentucky Fayette 0.057 0.056 í0.002 

Kentucky Graves 0.060 0.059 í0.001 

Kentucky Greenup 0.065 0.063 í0.001 

Kentucky Hancock 0.063 0.064 0.001 

Kentucky Hardin 0.058 0.056 í0.001 

Kentucky Henderson 0.060 0.058 í0.003 

Kentucky Jefferson 0.065 0.063 í0.002 

Kentucky Jessamine 0.057 0.056 í0.001 

Kentucky Kenton 0.066 0.063 í0.003 

Kentucky Livingston 0.061 0.061 í0.001 

Kentucky McCracken 0.064 0.063 í0.001 

Kentucky McLean 0.059 0.058 í0.001 

Kentucky Oldham 0.063 0.061 í0.002 

Kentucky Perry 0.055 0.055 í0.001 

Kentucky Pike 0.055 0.053 í0.001 

Kentucky Pulaski 0.059 0.061 0.002 

Kentucky Scott 0.050 0.049 í0.001 

Kentucky Simpson 0.057 0.056 0.000 

Kentucky Trigg 0.052 0.053 0.000 

Kentucky Warren 0.060 0.059 í0.001 

Louisiana Ascension 0.069 0.065 í0.004 

Louisiana Beauregard 0.062 0.059 í0.003 

Louisiana Bossier 0.061 0.060 í0.001 

Louisiana Caddo 0.059 0.057 í0.001 

Louisiana Calcasieu 0.066 0.064 í0.002 

Louisiana East Baton Rouge 0.077 0.074 í0.003 

Louisiana Grant 0.060 0.058 í0.002 
3a-37

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Louisiana Iberville 0.073 0.069 í0.004 

Louisiana Jefferson 0.069 0.067 í0.002 

Louisiana Lafayette 0.066 0.061 í0.005 

Louisiana Lafourche 0.065 0.062 í0.003 

Louisiana Livingston 0.069 0.064 í0.004 

Louisiana Orleans 0.058 0.056 í0.001 

Louisiana Ouachita 0.061 0.060 í0.001 

Louisiana Pointe Coupee 0.064 0.057 í0.007 

Louisiana St Bernard 0.063 0.062 í0.001 

Louisiana St Charles 0.066 0.064 í0.002 

Louisiana St James 0.064 0.061 í0.003 

Louisiana St John The Baptist 0.069 0.066 í0.003 

Louisiana St Mary 0.061 0.058 í0.004 

Louisiana West Baton Rouge 0.074 0.070 í0.004 

Maine Cumberland 0.063 0.061 í0.002 

Maine Hancock 0.071 0.069 í0.003 

Maine Kennebec 0.060 0.058 í0.002 

Maine Knox 0.063 0.061 í0.002 

Maine Oxford 0.050 0.049 í0.001 

Maine Penobscot 0.064 0.062 í0.002 

Maine Sagadahoc 0.060 0.057 í0.002 

Maine York 0.067 0.064 í0.002 

Maryland Anne Arundel 0.072 0.069 í0.003 

Maryland Baltimore 0.071 0.068 í0.003 

Maryland Carroll 0.065 0.062 í0.003 

Maryland Cecil 0.071 0.068 í0.003 

Maryland Charles 0.065 0.062 í0.003 

Maryland Frederick 0.066 0.061 í0.004 

Maryland Harford 0.077 0.074 í0.003 

Maryland Kent 0.070 0.067 í0.003 

Maryland Montgomery 0.064 0.061 í0.003 

Maryland Prince Georges 0.069 0.066 í0.003 

Maryland Washington 0.064 0.061 í0.003 

Massachusetts Barnstable 0.071 0.068 í0.002 

Massachusetts Berkshire 0.069 0.067 í0.002 

Massachusetts Bristol 0.069 0.067 í0.003 

Massachusetts Essex 0.070 0.068 í0.002 

Massachusetts Hampden 0.068 0.066 í0.003 

Massachusetts Hampshire 0.066 0.064 í0.002 

Massachusetts Middlesex 0.065 0.062 í0.003 

Massachusetts Norfolk 0.074 0.072 í0.002 

Massachusetts Suffolk 0.069 0.067 í0.002 

Massachusetts Worcester 0.065 0.063 í0.002 

Michigan Allegan 0.073 0.072 í0.001 

Michigan Benzie 0.067 0.065 í0.001 

Michigan Berrien 0.071 0.069 í0.001 

Michigan Cass 0.068 0.067 í0.002 

Michigan Clinton 0.065 0.063 í0.002 

Michigan Genesee 0.066 0.065 í0.002 

Michigan Huron 0.069 0.067 í0.002 
3a-38

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Michigan Ingham 0.064 0.062 í0.002 

Michigan Kalamazoo 0.063 0.061 í0.002 

Michigan Kent 0.065 0.063 í0.002 

Michigan Lenawee 0.067 0.065 í0.002 

Michigan Macomb 0.075 0.073 í0.002 

Michigan Mason 0.066 0.064 í0.001 

Michigan Missaukee 0.062 0.061 í0.001 

Michigan Muskegon 0.070 0.069 í0.001 

Michigan Oakland 0.072 0.071 í0.001 

Michigan Ottawa 0.067 0.065 í0.002 

Michigan St Clair 0.070 0.068 í0.002 

Michigan Schoolcraft 0.063 0.062 í0.001 

Michigan Washtenaw 0.069 0.067 í0.002 

Michigan Wayne 0.071 0.069 í0.002 

Minnesota St Louis 0.059 0.059 0.000 

Mississippi Adams 0.060 0.060 í0.001 

Mississippi Bolivar 0.057 0.057 0.000 

Mississippi De Soto 0.062 0.062 0.000 

Mississippi Hancock 0.063 0.062 í0.001 

Mississippi Harrison 0.063 0.065 0.003 

Mississippi Hinds 0.051 0.050 0.000 

Mississippi Jackson 0.067 0.068 0.000 

Mississippi Lauderdale 0.051 0.051 0.000 

Mississippi Lee 0.056 0.058 0.002 

Mississippi Madison 0.054 0.054 0.000 

Mississippi Warren 0.052 0.052 0.000 

Missouri Cass 0.061 0.061 0.000 

Missouri Cedar 0.064 0.063 í0.001 

Missouri Clay 0.065 0.064 í0.001 

Missouri Greene 0.059 0.058 í0.001 

Missouri Jefferson 0.067 0.064 í0.003 

Missouri Monroe 0.060 0.059 í0.001 

Missouri Platte 0.063 0.063 í0.001 

Missouri St Charles 0.071 0.069 í0.002 

Missouri Ste Genevieve 0.065 0.063 í0.002 

Missouri St Louis 0.070 0.068 í0.003 

Missouri St Louis City 0.071 0.068 í0.002 

Montana Flathead 0.053 0.053 0.000 

Nebraska Douglas 0.056 0.056 0.000 

Nebraska Lancaster 0.046 0.046 0.000 

Nevada Clark 0.072 0.071 í0.001 

Nevada Douglas 0.059 0.059 0.000 

Nevada Washoe 0.064 0.063 0.000 

Nevada White Pine 0.066 0.065 0.000 

Nevada Carson City 0.063 0.063 0.000 

New Hampshire Belknap 0.060 0.058 í0.002 

New Hampshire Carroll 0.055 0.054 í0.001 

New Hampshire Cheshire 0.057 0.055 í0.002 

New Hampshire Grafton 0.058 0.057 í0.001 

New Hampshire Hillsborough 0.065 0.063 í0.002 
3a-39

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

New Hampshire Merrimack 0.058 0.056 í0.002 

New Hampshire Rockingham 0.064 0.061 í0.002 

New Hampshire Strafford 0.060 0.058 í0.002 

New Hampshire Sullivan 0.061 0.060 í0.001 

New Jersey Atlantic 0.067 0.065 í0.002 

New Jersey Bergen 0.074 0.072 í0.002 

New Jersey Camden 0.077 0.075 í0.003 

New Jersey Cumberland 0.072 0.069 í0.003 

New Jersey Essex 0.053 0.051 í0.002 

New Jersey Gloucester 0.076 0.073 í0.003 

New Jersey Hudson 0.066 0.064 í0.002 

New Jersey Hunterdon 0.071 0.068 í0.003 

New Jersey Mercer 0.076 0.073 í0.003 

New Jersey Middlesex 0.073 0.070 í0.003 

New Jersey Monmouth 0.073 0.071 í0.002 

New Jersey Morris 0.071 0.068 í0.003 

New Jersey Ocean 0.080 0.077 í0.003 

New Jersey Passaic 0.067 0.065 í0.003 

New Mexico Bernalillo 0.065 0.065 0.000 

New Mexico Dona Ana 0.069 0.068 í0.001 

New Mexico Eddy 0.064 0.063 0.000 

New Mexico Sandoval 0.064 0.063 0.000 

New Mexico San Juan 0.070 0.069 0.000 

New Mexico Valencia 0.057 0.057 0.000 

New York Albany 0.065 0.061 í0.003 

New York Bronx 0.069 0.067 í0.002 

New York Chautauqua 0.073 0.070 í0.003 

New York Chemung 0.062 0.060 í0.002 

New York Dutchess 0.069 0.066 í0.003 

New York Erie 0.075 0.072 í0.003 

New York Essex 0.069 0.067 í0.002 

New York Hamilton 0.063 0.062 í0.001 

New York Herkimer 0.059 0.058 í0.001 

New York Jefferson 0.073 0.072 í0.002 

New York Madison 0.062 0.061 í0.002 

New York Monroe 0.067 0.065 í0.002 

New York Niagara 0.075 0.074 í0.002 

New York Oneida 0.063 0.061 í0.002 

New York Onondaga 0.068 0.066 í0.002 

New York Orange 0.064 0.061 í0.003 

New York Oswego 0.054 0.052 í0.002 

New York Putnam 0.071 0.068 í0.003 

New York Queens 0.070 0.068 í0.002 

New York Rensselaer 0.067 0.064 í0.003 

New York Richmond 0.074 0.071 í0.002 

New York Saratoga 0.067 0.064 í0.003 

New York Schenectady 0.062 0.059 í0.002 

New York Suffolk 0.080 0.078 í0.002 

New York Ulster 0.064 0.062 í0.002 

New York Wayne 0.066 0.064 í0.002 
3a-40

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

New York Westchester 0.074 0.071 í0.003 

North Carolina Alexander 0.062 0.062 0.000 

North Carolina Avery 0.059 0.058 í0.001 

North Carolina Buncombe 0.061 0.060 í0.001 

North Carolina Caldwell 0.061 0.060 0.000 

North Carolina Caswell 0.061 0.060 í0.001 

North Carolina Chatham 0.059 0.058 í0.001 

North Carolina Cumberland 0.062 0.060 í0.001 

North Carolina Davie 0.064 0.062 í0.002 

North Carolina Duplin 0.060 0.059 í0.001 

North Carolina Durham 0.062 0.060 í0.001 

North Carolina Edgecombe 0.063 0.062 í0.001 

North Carolina Forsyth 0.064 0.062 í0.002 

North Carolina Franklin 0.063 0.062 í0.001 

North Carolina Granville 0.065 0.063 í0.001 

North Carolina Guilford 0.060 0.059 í0.001 

North Carolina Haywood 0.065 0.064 í0.001 

North Carolina Jackson 0.064 0.063 í0.001 

North Carolina Johnston 0.060 0.059 í0.001 

North Carolina Lenoir 0.060 0.060 í0.001 

North Carolina Lincoln 0.065 0.065 0.001 

North Carolina Martin 0.060 0.059 í0.001 

North Carolina Mecklenburg 0.072 0.071 í0.001 

North Carolina New Hanover 0.057 0.057 0.001 

North Carolina Northampton 0.062 0.061 í0.002 

North Carolina Person 0.063 0.062 í0.001 

North Carolina Pitt 0.059 0.058 í0.001 

North Carolina Randolph 0.058 0.057 í0.001 

North Carolina Rockingham 0.062 0.061 í0.001 

North Carolina Rowan 0.069 0.067 í0.002 

North Carolina Swain 0.053 0.053 í0.001 

North Carolina Union 0.062 0.061 í0.001 

North Carolina Wake 0.064 0.063 í0.001 

North Carolina Yancey 0.063 0.062 í0.001 

North Dakota Billings 0.054 0.054 0.000 

North Dakota Cass 0.056 0.055 0.000 

North Dakota Dunn 0.054 0.054 0.000 

North Dakota McKenzie 0.058 0.058 0.000 

North Dakota Mercer 0.055 0.055 0.000 

North Dakota Oliver 0.051 0.051 0.000 

Ohio Allen 0.068 0.066 í0.003 

Ohio Ashtabula 0.076 0.073 í0.003 

Ohio Butler 0.068 0.065 í0.003 

Ohio Clark 0.067 0.063 í0.004 

Ohio Clermont 0.069 0.066 í0.003 

Ohio Clinton 0.069 0.067 í0.003 

Ohio Cuyahoga 0.068 0.066 í0.002 

Ohio Delaware 0.067 0.064 í0.002 

Ohio Franklin 0.069 0.066 í0.002 

Ohio Geauga 0.077 0.074 í0.002 
3a-41

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Ohio Greene 0.066 0.062 í0.004 

Ohio Hamilton 0.069 0.066 í0.003 

Ohio Jefferson 0.064 0.062 í0.002 

Ohio Knox 0.065 0.062 í0.002 

Ohio Lake 0.073 0.070 í0.002 

Ohio Lawrence 0.065 0.064 í0.001 

Ohio Licking 0.065 0.063 í0.002 

Ohio Lorain 0.067 0.065 í0.002 

Ohio Lucas 0.070 0.068 í0.002 

Ohio Madison 0.065 0.062 í0.003 

Ohio Mahoning 0.065 0.063 í0.002 

Ohio Medina 0.067 0.065 í0.002 

Ohio Miami 0.065 0.062 í0.003 

Ohio Montgomery 0.066 0.063 í0.003 

Ohio Portage 0.069 0.066 í0.002 

Ohio Preble 0.060 0.058 í0.003 

Ohio Stark 0.066 0.063 í0.003 

Ohio Summit 0.071 0.069 í0.003 

Ohio Trumbull 0.069 0.066 í0.003 

Ohio Warren 0.069 0.065 í0.003 

Ohio Washington 0.061 0.061 í0.001 

Ohio Wood 0.068 0.065 í0.003 

Oklahoma Canadian 0.057 0.056 í0.001 

Oklahoma Cleveland 0.060 0.059 í0.001 

Oklahoma Comanche 0.061 0.060 í0.002 

Oklahoma Dewey 0.058 0.057 í0.002 

Oklahoma Kay 0.061 0.060 í0.001 

Oklahoma Mc Clain 0.062 0.060 í0.001 

Oklahoma Oklahoma 0.061 0.060 í0.001 

Oklahoma Ottawa 0.063 0.062 í0.001 

Oklahoma Pittsburg 0.061 0.060 0.000 

Oklahoma Tulsa 0.066 0.066 í0.001 

Oregon Clackamas 0.063 0.063 0.000 

Oregon Columbia 0.056 0.056 0.000 

Oregon Jackson 0.061 0.061 0.000 

Oregon Lane 0.060 0.060 0.000 

Oregon Marion 0.055 0.055 0.000 

Pennsylvania Adams 0.060 0.056 í0.003 

Pennsylvania Allegheny 0.072 0.069 í0.003 

Pennsylvania Armstrong 0.068 0.066 í0.003 

Pennsylvania Beaver 0.071 0.069 í0.003 

Pennsylvania Berks 0.066 0.063 í0.003 

Pennsylvania Blair 0.061 0.058 í0.002 

Pennsylvania Bucks 0.078 0.075 í0.003 

Pennsylvania Cambria 0.064 0.061 í0.003 

Pennsylvania Centre 0.062 0.060 í0.002 

Pennsylvania Chester 0.071 0.068 í0.003 

Pennsylvania Clearfield 0.065 0.062 í0.003 

Pennsylvania Dauphin 0.065 0.061 í0.005 

Pennsylvania Delaware 0.071 0.068 í0.003 
3a-42

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Pennsylvania Erie 0.070 0.068 í0.003 

Pennsylvania Franklin 0.067 0.064 í0.003 

Pennsylvania Greene 0.064 0.062 í0.002 

Pennsylvania Lackawanna 0.062 0.060 í0.002 

Pennsylvania Lancaster 0.068 0.063 í0.005 

Pennsylvania Lawrence 0.058 0.055 í0.002 

Pennsylvania Lehigh 0.067 0.064 í0.003 

Pennsylvania Luzerne 0.062 0.060 í0.002 

Pennsylvania Lycoming 0.061 0.059 í0.002 

Pennsylvania Mercer 0.068 0.065 í0.003 

Pennsylvania Montgomery 0.071 0.069 í0.003 

Pennsylvania Northampton 0.067 0.063 í0.004 

Pennsylvania Perry 0.062 0.059 í0.003 

Pennsylvania Philadelphia 0.077 0.075 í0.003 

Pennsylvania Tioga 0.065 0.063 í0.002 

Pennsylvania Washington 0.067 0.064 í0.003 

Pennsylvania Westmoreland 0.069 0.066 í0.003 

Pennsylvania York 0.067 0.062 í0.005 

Rhode Island Kent 0.070 0.067 í0.003 

Rhode Island Providence 0.069 0.067 í0.003 

Rhode Island Washington 0.071 0.068 í0.003 

South Carolina Abbeville 0.060 0.059 í0.001 

South Carolina Aiken 0.062 0.058 í0.003 

South Carolina Anderson 0.064 0.062 í0.001 

South Carolina Barnwell 0.059 0.057 í0.002 

South Carolina Berkeley 0.053 0.053 0.000 

South Carolina Charleston 0.055 0.054 í0.001 

South Carolina Cherokee 0.061 0.060 í0.001 

South Carolina Chester 0.059 0.058 í0.001 

South Carolina Chesterfield 0.059 0.058 í0.001 

South Carolina Colleton 0.058 0.057 í0.001 

South Carolina Darlington 0.061 0.060 í0.001 

South Carolina Edgefield 0.059 0.057 í0.002 

South Carolina Oconee 0.061 0.059 í0.001 

South Carolina Pickens 0.060 0.059 í0.001 

South Carolina Richland 0.066 0.065 í0.002 

South Carolina Spartanburg 0.063 0.061 í0.002 

South Carolina Union 0.059 0.057 í0.001 

South Carolina Williamsburg 0.052 0.052 í0.001 

South Carolina York 0.060 0.059 í0.001 

South Dakota Pennington 0.062 0.062 0.000 

Tennessee Anderson 0.059 0.058 0.000 

Tennessee Blount 0.065 0.064 í0.001 

Tennessee Davidson 0.057 0.057 0.000 

Tennessee Hamilton 0.062 0.062 0.000 

Tennessee Haywood 0.060 0.063 0.003 

Tennessee Jefferson 0.062 0.061 0.000 

Tennessee Knox 0.062 0.061 0.000 

Tennessee Lawrence 0.056 0.059 0.002 

Tennessee Meigs 0.061 0.061 í0.001 
3a-43

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Tennessee Putnam 0.062 0.061 í0.001 

Tennessee Rutherford 0.058 0.058 0.000 

Tennessee Sevier 0.066 0.065 í0.001 

Tennessee Shelby 0.066 0.066 0.000 

Tennessee Sullivan 0.066 0.066 0.000 

Tennessee Sumner 0.062 0.062 0.000 

Tennessee Williamson 0.061 0.060 0.000 

Tennessee Wilson 0.060 0.060 0.000 

Texas Bexar 0.068 0.067 í0.001 

Texas Brazoria 0.074 0.073 í0.001 

Texas Brewster 0.054 0.054 í0.001 

Texas Cameron 0.053 0.052 í0.001 

Texas Collin 0.070 0.068 í0.002 

Texas Dallas 0.069 0.067 í0.002 

Texas Denton 0.075 0.072 í0.002 

Texas Ellis 0.063 0.059 í0.004 

Texas El Paso 0.069 0.068 í0.001 

Texas Galveston 0.074 0.073 í0.002 

Texas Gregg 0.068 0.064 í0.004 

Texas Harris 0.089 0.088 í0.001 

Texas Harrison 0.061 0.059 í0.003 

Texas Hidalgo 0.062 0.062 í0.001 

Texas Hood 0.058 0.057 í0.002 

Texas Jefferson 0.074 0.071 í0.003 

Texas Johnson 0.066 0.063 í0.003 

Texas Kaufman 0.055 0.053 í0.002 

Texas Montgomery 0.074 0.073 í0.001 

Texas Nueces 0.065 0.064 í0.001 

Texas Orange 0.066 0.064 í0.003 

Texas Parker 0.063 0.062 í0.002 

Texas Rockwall 0.062 0.060 í0.002 

Texas Smith 0.064 0.062 í0.002 

Texas Tarrant 0.075 0.073 í0.002 

Texas Travis 0.063 0.062 í0.001 

Texas Victoria 0.061 0.060 í0.001 

Texas Webb 0.054 0.053 í0.001 

Utah Box Elder 0.064 0.062 í0.002 

Utah Cache 0.056 0.055 í0.002 

Utah Davis 0.070 0.068 í0.003 

Utah Salt Lake 0.070 0.067 í0.002 

Utah San Juan 0.064 0.064 0.000 

Utah Utah 0.067 0.065 í0.002 

Utah Weber 0.065 0.063 í0.002 

Vermont Bennington 0.061 0.058 í0.003 

Vermont Chittenden 0.063 0.062 í0.001 

Virginia Arlington 0.072 0.069 í0.004 

Virginia Caroline 0.059 0.057 í0.002 

Virginia Charles City 0.069 0.067 í0.002 

Virginia Chesterfield 0.066 0.064 í0.002 

Virginia Fairfax 0.071 0.068 í0.004 
3a-44

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Virginia Fauquier 0.058 0.057 í0.002 

Virginia Frederick 0.062 0.060 í0.002 

Virginia Hanover 0.070 0.068 í0.002 

Virginia Henrico 0.068 0.066 í0.002 

Virginia Loudoun 0.067 0.063 í0.004 

Virginia Madison 0.063 0.061 í0.002 

Virginia Page 0.058 0.057 í0.002 

Virginia Prince William 0.063 0.060 í0.003 

Virginia Roanoke 0.062 0.061 í0.001 

Virginia Rockbridge 0.057 0.056 í0.001 

Virginia Stafford 0.063 0.060 í0.002 

Virginia Wythe 0.060 0.060 0.000 

Virginia Alexandria City 0.067 0.063 í0.003 

Virginia Hampton City 0.071 0.070 í0.001 

Virginia Suffolk City 0.070 0.069 í0.001 

Washington Clallam 0.041 0.041 0.000 

Washington Clark 0.062 0.062 0.000 

Washington King 0.064 0.064 0.000 

Washington Klickitat 0.062 0.060 í0.002 

Washington Mason 0.050 0.050 0.000 

Washington Pierce 0.066 0.066 0.000 

Washington Skagit 0.045 0.045 0.000 

Washington Spokane 0.060 0.060 0.000 

Washington Thurston 0.059 0.059 0.000 

Washington Whatcom 0.052 0.052 0.000 

West Virginia Berkeley 0.062 0.060 í0.002 

West Virginia Cabell 0.069 0.067 í0.001 

West Virginia Greenbrier 0.060 0.060 í0.001 

West Virginia Hancock 0.064 0.062 í0.003 

West Virginia Kanawha 0.062 0.062 0.000 

West Virginia Monongalia 0.056 0.055 í0.001 

West Virginia Ohio 0.063 0.061 í0.002 

West Virginia Wood 0.062 0.061 í0.001 

Wisconsin Brown 0.065 0.064 í0.001 

Wisconsin Columbia 0.060 0.059 í0.001 

Wisconsin Dane 0.060 0.059 í0.001 

Wisconsin Dodge 0.063 0.062 í0.001 

Wisconsin Door 0.072 0.071 í0.001 

Wisconsin Florence 0.058 0.057 í0.001 

Wisconsin Fond Du Lac 0.061 0.060 í0.001 

Wisconsin Green 0.059 0.059 í0.001 

Wisconsin Jefferson 0.063 0.061 í0.001 

Wisconsin Kenosha 0.081 0.080 í0.001 

Wisconsin Kewaunee 0.071 0.070 í0.001 

Wisconsin Manitowoc 0.069 0.068 í0.001 

Wisconsin Marathon 0.058 0.057 í0.001 

Wisconsin Milwaukee 0.074 0.073 í0.001 

Wisconsin Oneida 0.057 0.056 í0.001 

Wisconsin Outagamie 0.061 0.060 í0.001 

Wisconsin Ozaukee 0.075 0.073 í0.001 
3a-45

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Wisconsin Racine 0.075 0.074 í0.001 

Wisconsin Rock 0.064 0.063 í0.001 

Wisconsin St Croix 0.060 0.060 0.000 

Wisconsin Sauk 0.057 0.057 í0.001 

Wisconsin Sheboygan 0.077 0.076 í0.001 

Wisconsin Vernon 0.060 0.059 í0.001 

Wisconsin Vilas 0.057 0.056 í0.001 

Wisconsin Walworth 0.064 0.063 í0.001 

Wisconsin Washington 0.065 0.064 í0.001 

Wisconsin Waukesha 0.063 0.062 í0.001 

Wisconsin Winnebago 0.066 0.064 í0.001 

Wyoming Campbell 0.067 0.067 0.000 

Wyoming Teton 0.063 0.063 0.000 
3a-1

Appendix 3: Additional Control Strategy Information 

3a.1 NonEGU Point and Area Source Controls 

3a.1.1 NonEGU Point and Area Source Control Strategies for Ozone NAAQS Final 

In the NonEGU point and Area Sources portion of the control strategy, maximum control 

scenarios were used from the existing control measure dataset from AirControlNET 4.1 for 2020 

(for geographic areas defined for each level of the standard being analyzed). This existing 

control measure dataset reflects changes and updates made as a result of the reviews performed 

for the final PM2.5 RIA. Following this, an internal review was performed by the OAQPS 

engineers in the Sector Policies and Programs Division (SPPD) to examine the controls applied 

by AirControlNET and decide if these controls were sufficient or could be more aggressive in 

their application, given the 2020 analysis year. This review was performed for nonEGU point 

NOx control measures. The result of this review was an increase in control efficiencies applied 

for many control measures, and more aggressive control measures for over 70 SCC’s. For 

example, SPPD recommended that we apply SCR to cement kilns to reduce NOx emissions in 

2020. Currently, there are no SCRs in operation at cement kilns in the U.S., but there are several 

SCRs in operation at cement kilns in France now. Based on the SCR experience at cement kilns 

in France, SPPD believes SCR could be applied at U.S. cement kilns by 2020. Following this, it 

was recommended that supplemental controls could be applied to 8 additional SCC’s from 

nonEGU point NOx sources. We also looked into sources of controls for highly reactive VOC 

nonEGU point sources. Four additional controls were applied for highly reactive VOC nonEGU 

point sources not in AirControlNET.

3a.1.2 NOx Control Measures for NonEGU Point Sources. 

Several types of NOx control technologies exist for nonEGU point sources: SCR, selective 

noncatalytic reduction (SNCR), natural gas reburn (NGR), coal reburn, and low-NOx burners. In 

some cases, LNB accompanied by flue gas recirculation (FGR) is applicable, such as when fuelborne NOx emissions are expected to be of greater importance than thermal NOx emissions. 

When circumstances suggest that combustion controls do not make sense as a control technology 

(e.g., sintering processes, coke oven batteries, sulfur recovery plants), SNCR or SCR may be an 

appropriate choice. Finally, SCR can be applied along with a combustion control such as LNB 

with overfire air (OFA) to further reduce NOx emissions. All of these control measures are 

available for application on industrial boilers.

Besides industrial boilers, other nonEGU point source categories covered in this RIA include 

petroleum refineries, kraft pulp mills, cement kilns, stationary internal combustion engines, glass 

manufacturing, combustion turbines, and incinerators. NOx control measures available for 

petroleum refineries, particularly process heaters at these plants, include LNB, SNCR, FGR, and 

SCR along with combinations of these technologies. NOx control measures available for kraft 

pulp mills include those available to industrial boilers, namely LNB, SCR, SNCR, along with 

water injection (WI). NOx control measures available for cement kilns include those available to 

industrial boilers, namely LNB, SCR, and SNCR. Non-selective catalytic reduction (NSCR) can 

be used on stationary internal combustion engines. OXY-firing, a technique to modify 
3a-2

combustion at glass manufacturing plants, can be used to reduce NOx at such plants. LNB, SCR, 

and SCR + steam injection (SI) are available measures for combustion turbines. Finally, SNCR 

is an available control technology at incinerators. Table 3a.1 contains a complete list of the NOx 

nonEGU point control measures applied and their associated emission reductions obtained in the 

modeled control strategy for the alternate primary standard. For more information on these 

measures, please refer to the AirControlNET 4.1 control measures documentation report. 

Table 3a.1: NOx NonEGU Point Emission Reductions by Control Measure 

Control Measure Source Type 

Modeled Control 

Strategy Reductions 

(annual tons/year) 

Biosolid Injection 

Technology 

Cement Kilns 1,200 

Asphaltic Conc; Rotary Dryer; Conv Plant 120 

Ceramic Clay Mfg; Drying 370 

Conv Coating of Prod; Acid Cleaning Bath 440 

Fuel Fired Equip; Furnaces; Natural Gas 170 

In-Process Fuel Use; Natural Gas 1,300 

In-Process Fuel Use; Residual Oil 39

In-Process; Process Gas; Coke Oven Gas 190 

Lime Kilns 5,900 

Sec Alum Prod; Smelting Furn 62 

Steel Foundries; Heat Treating 13 

LNB

Surf Coat Oper; Coating Oven Htr; Nat Gas 30 

Fluid Cat Cracking Units 3,600 

Fuel Fired Equip; Process Htrs; Process Gas 700 

In-Process; Process Gas; Coke Oven Gas 880 

Iron & Steel Mills—Galvanizing 35 

Iron & Steel Mills—Reheating 1,100 

Iron Prod; Blast Furn; Blast Htg Stoves 1,000 

Sand/Gravel; Dryer 11 

LNB + FGR 

Steel Prod; Soaking Pits 100 

Iron & Steel Mills—Annealing 270 

Process Heaters—Distillate Oil 2,300 

Process Heaters—Natural Gas 27,000 

Process Heaters—Other Fuel 14 

Process Heaters—Process Gas 4,200 

LNB + SCR 

Process Heaters—Residual Oil 37 

Rich Burn IC Engines—Gas 22,000 

Rich Burn IC Engines—Gas, Diesel, LPG 3,700 

NSCR

Rich Burn Internal Combustion Engines—Oil 11,000 

Glass Manufacturing—Containers 7,600 

Glass Manufacturing—Flat 18,000 

OXY-Firing 

Glass Manufacturing—Pressed 3,900 

Ammonia—NG-Fired Reformers 5,800 

Cement Manufacturing—Dry 25,000 

Cement Manufacturing—Wet 22,000 

IC Engines—Gas 54,000 

ICI Boilers—Coal/Cyclone 2,200 

ICI Boilers—Coal/Wall 22,000 

ICI Boilers—Coke 490 

SCR

ICI Boilers—Distillate Oil 4,800 
3a-3

Control Measure Source Type 

Modeled Control 

Strategy Reductions 

(annual tons/year) 

ICI Boilers—Liquid Waste 730 

ICI Boilers—LPG 280 

ICI Boilers—Natural Gas 36,000 

ICI Boilers—Process Gas 8,600 

ICI Boilers—Residual Oil 17,000 

Natural Gas Prod; Compressors 810 

Space Heaters—Distillate Oil 22 

Space Heaters—Natural Gas 640 

Sulfate Pulping—Recovery Furnaces 9,900 

SCR + Steam Injection Combustion Turbines—Natural Gas 18,000 

Combustion Turbines—Jet Fuel — 

Combustion Turbines—Natural Gas — 

SCR + Water Injection 

Combustion Turbines—Oil 210 

By-Product Coke Mfg; Oven Underfiring 4,300 

Comm./Inst. Incinerators 1,400 

ICI Boilers—Coal/Stoker 7,000 

Indust. Incinerators 250 

Medical Waste Incinerators — 

In-Process Fuel Use; Bituminous Coal 32 

Municipal Waste Combustors 4,400 

Nitric Acid Manufacturing 3,100 

SNCR

Solid Waste Disp; Gov; Other Inc 95 

SNCR—Urea ICI Boilers—MSW/Stoker 120 

ICI Boilers—Coal/FBC 100 

ICI Boilers—Wood/Bark/Stoker—Large 5,500 

In-Process; Bituminous Coal; Cement Kilns 300 

SNCR—Urea Based 

In-Process; Bituminous Coal; Lime Kilns 31 

3a.1.3 VOC Control Measures for NonEGU Point Sources. 

VOC controls were applied to a variety of nonEGU point sources as defined in the emissions 

inventory in this RIA. The first control is: permanent total enclosure (PTE) applied to paper and 

web coating operations and fabric operations, and incinerators or thermal oxidizers applied to 

wood products and marine surface coating operations. A PTE confines VOC emissions to a 

particular area where can be destroyed or used in a way that limits emissions to the outside 

atmosphere, and an incinerator or thermal oxidizer destroys VOC emissions through exposure to 

high temperatures (2,000 degrees Fahrenheit or higher). The second control applied is petroleum 

and solvent evaporation applied to printing and publishing sources as well as to surface coating 

operations. Table 3a.2 contains the emissions reductions for these measures in the modeled 

control strategy for the alternate primary standard. For more information on these measures, refer 

to the AirControlNET 4.1 control measures documentation report. 
3a-4

Table 3a.2: VOC NonEGU Point Emission Reductions by Control Measure 

Control Measure Source Type 

Modeled Control 

Strategy Reductions 

(annual tons/year) 

Permanent Total Enclosure (PTE) Fabric Printing, Coating and Dyeing 43 

Paper and Other Web Coating 490 

Petroleum and Solvent Evaporation Printing and Publishing 3,600 

Surface Coating 400 

3a.1.4 NOx Control Measures for Area Sources 

There were three control measures applied for NOx emissions from area sources. The first is 

RACT (reasonably available control technology) to 25 tpy (LNB). This control is the addition of 

a low NOx burner to reduce NOx emissions. This control is applied to industrial oil, natural gas, 

and coal combustion sources. The second control is water heaters plus LNB space heaters. This 

control is based on the installation of low-NOx space heaters and water heaters in commercial 

and institutional sources for the reduction of NOx emissions. The third control was switching to 

low sulfur fuel for residential home heating. This control is primarily designed to reduce sulfur 

dioxide, but has a co-benefit of reducing NOx. Table 3a.3 contains the listing of control 

measures and associated reductions for the modeled control strategy. For additional information 

regarding these controls please refer to the AirControlNET 4.1 control measures documentation 

report.

Table 3a.3: NOx Area Source Emission Reductions by Control Measure 

Control Measure Source Type 

Modeled Control Strategy 

Reductions 

(annual tons/year) 

Industrial Coal Combustion 5,400 

Industrial NG Combustion 3,000 

RACT to 25 tpy (LNB) 

Industrial Oil Combustion 570 

Switch to Low Sulfur Fuel Residential Home Heating 970 

Water Heater + LNB Space Heaters Commercial/Institutional—NG 4,300 

Residential NG 6,700 

3a.1.5 VOC Control Measures for Area Source. 

The most frequently applied control to reduce VOC emissions from area sources was CARB 

Long-Term Limits. This control, which represents controls available in VOC rules promulgated 

by the California Air Resources Board, applies to commercial solvents and commercial 

adhesives, and depends on future technological innovation and market incentive methods to 

achieve emission reductions. The next most frequently applied control was the use of low or no 

VOC materials for graphic art source categories. The South Coast Air District’s SCAQMD Rule 

1168 control applies to wood furniture and solvent source categories sets limits for adhesive and 

sealant VOC content. The OTC solvent cleaning rule control establishes hardware and operating 

requirements for specified vapor cleaning machines, as well as solvent volatility limits and 

operating practices for cold cleaners. The Low Pressure/Vacuum Relief Valve control measure is 

the addition of low pressure/vacuum (LP/V) relief valves to gasoline storage tanks at service 
3a-5

stations with Stage II control systems. LP/V relief valves prevent breathing emissions from 

gasoline storage tank vent pipes. SCAQMD Limits control establishes VOC content limits for 

metal coatings along with application procedures and equipment requirements. Switch to 

Emulsified Asphalts control is a generic control measure replacing VOC-containing cutback 

asphalt with VOC-free emulsified asphalt. The equipment and maintenance control measure 

applies to oil and natural gas production. The Reformulation—FIP Rule control measure intends 

to reach the VOC limits by switching to and/or encouraging the use of low-VOC pesticides and 

better Integrated Pest Management (IPM) practices. Table 3a.4 contains the control measures and 

associated emission reductions described above for the modeled control strategy. For additional 

information regarding these controls please refer to the AirControlNET 4.1 control measures 

documentation report. 

Table 3a.4: VOC Area Source Emission Reductions by Control Measure 

Control Measure Source Type 

Modeled Control 

Strategy Reductions 

(annual tons/year) 

CARB Long-Term Limits Consumer Solvents 78,000 

Catalytic Oxidizer Conveyorized Charbroilers 250 

Equipment and Maintenance Oil and Natural Gas Production 450 

Gas Collection (SCAQMD/BAAQMD) Municipal Solid Waste Landfill 1,100 

Incineration >100,000 lbs bread Bakery Products 2,700 

Low Pressure/Vacuum Relief Valve Stage II Service Stations 9,900 

Stage II Service Stations—Underground 

Tanks

9,800 

OTC Mobile Equipment Repair and Aircraft Surface Coating 720 

Refinishing Rule Machn, Electric, Railroad Ctng 4,400 

OTC Solvent Cleaning Rule Cold Cleaning 10,000 

SCAQMD—Low VOC Rubber and Plastics Mfg 1,700 

SCAQMD Limits Metal Furniture, Appliances, Parts 6,300 

SCAQMD Rule 1168 Adhesives—Industrial 22,000 

Large Appliances 8,200 

Metal Furniture 7,600 

Solvent Utilization 

Surface Coating 2,900 

Switch to Emulsified Asphalts Cutback Asphalt 3,300

3a.1.6 Supplemental Controls 

Table 3a.5 below summarizes the supplemental control measures added to our control measures 

database by providing the pollutant it controls and its control efficiency (CE). These controls 

were applied not as part of the modeled control strategy, but as supplemental measures prior to 

extrapolating unknown control costs. However, these controls are not currently located in 

AirControlNET. These measures are primarily found in draft SIP technical documents and have 

not been fully assessed for inclusion in AirControlNET. 
3a-6

Table 3a.5: Supplemental Emissions Control Measures Added to the Control Measures 

Database

Poll 

Control 

Technology SCC

SCC

Description 

Percent

Reduction

(%) 

20200252 Internal Comb. Engines/Industrial/ 

Natural Gas/2-cycle Lean Burn 

NOx LEC 87

20200254 Internal Comb. Engines/Industrial/ 

Natural Gas/4-cycle Lean Burn 

87

Enhanced LDAR 3018001- Fugitive Leaks 50

30600701 

30600999 - 

Flares 98

LDAR 3018001 - Fugitive Leaks 80

Monitoring Program 30600702- Cooling towers No general 

estimate 

Inspection and 

Maintenance Program 

(Separators) 

Water Seals (Drains) 

30600503- Wastewater Drains and Separators 65 

Work Practices,

Use of Low VOC 

Coatings 

(Area Sources) 

2401025000 

2401030000 

2401060000 

2425010000 

2425030000 

2425040000 

2461050000 

Solvent Utilization 90 

VOC

Work Practices,

Use of Low VOC 

Coatings 

(NonEGU Point) 

307001199 

Surface Coating 

Operations 

within SCC 

4020000000, 

Printing/Publis

hing processes 

within SCC 

4050000000 

Petroleum and Solvent Evaporation 90 

Low Emission Combustion (LEC)

Overview: LEC technology is defined as the modification of a natural gas fueled, spark ignited, 

reciprocating internal combustion engine to reduce emissions of NOx by utilizing ultra-lean 

air-fuel ratios, high energy ignition systems and/or pre-combustion chambers, increased 

turbocharging or adding a turbocharger, and increased cooling and/or adding an intercooler or 

aftercooler, resulting in an engine that is designed to achieve a consistent NOx emission rate of 

not more than 1.5-3.0 g/bhp-hr at full capacity (usually 100 percent speed and 100 percent load). 

This type of retrofit technology is fairly widely available for stationary internal combustion 

engines.

For CE, EPA estimates that it ranges from 82 to 91 percent for LEC technology applications. The 

EPA believes application of LEC would achieve average NOx emission levels in the range of 

1.5-3.0 g/bhp-hr. This is an 82-91 percent reduction from the average uncontrolled emission 

levels reported in the ACT document. An EPA memorandum summarizing 269 tests shows that 
3a-7

96 percent of IC engines with installed LEC technology achieved emission rates of less than 2.0 

g/bhp-hr.1 The 2000 EC/R report on IC engines summarizes 476 tests and shows that 97% of the 

IC engines with installed LEC technology achieve emission rates of 2.0 g/bhp-hr or less.2

Major Uncertainties: The EPA acknowledges that specific values will vary from engine to 

engine. The amount of control desired and number of operating hours will make a difference in 

terms of the impact had from a LEC retrofit. Also, the use of LEC may yield improved fuel 

economy and power output, both of which may affect the emissions generated by the device. 

Leak Detection and Repair (LDAR) for Fugitive Leaks

Overview: This control measure is a program to reduce leaks of fugitive VOC emissions from 

chemical plants and refineries. The program includes special “sniffer” equipment to detect leaks, 

and maintenance schedules that affected facilities are to adhere to. This program is one that is 

contained within the Houston-Galveston-Brazoria 8-hour Ozone SIP.

Major Uncertainties: The degree of leakage from pipes and processes at chemical plants is 

always difficult to quantify given the large number of such leaks at a typical chemical 

manufacturing plant. There are also growing indications based on tests conducted by TCEQ and 

others in Harris County, Texas that fugitive leaks have been underestimated from chemical 

plants by a factor of 6 to 20 or greater. 3

Enhanced LDAR for Fugitive Leaks

Overview: This control measure is a more stringent program to reduce leaks of fugitive VOC 

emissions from chemical plants and refineries that presumes that an existing LDAR program 

already is in operation.

Major Uncertainties: The calculations of CE and cost presume use of LDAR at a chemical plant. 

This should not be an unreasonable assumption, however, given that most chemical plants are 

under some type of requirement to have an LDAR program. However, as mentioned earlier, 

there is growing evidence that fugitive leak emissions are underestimated from chemical plants 

by a factor of 6 to 20 or greater.4

1

 “Stationary Reciprocating Internal Combustion Engines Technical Support Document for NOx 

SIP Call Proposal,” U.S. Environmental Protection Agency. September 5, 2000. Available on the 

Internet at http://www.epa.gov/ttn/naaqs/ozone/rto/sip/data/tsd9-00.pdf.

2

“Stationary Internal Combustion Engines: Updated Information on NOx Emissions and Control 

Techniques,” Ec/R Incorporated, Chapel Hill, NC. September 1, 2000. Available on the Internet 

at http://www.epa.gov/ttn/naaqs/ozone/ozonetech/ic_engine_nox_update_09012000.pdf.

3

 VOC Fugitive Losses: New Monitors, Emissions Losses, and Potential Policy Gaps. 2006 

International Workshop. U.S. Environmental Protection Agency, Office of Air Quality Planning 

and Standards and Office of Solid Waste and Emergency Response. October 25-27, 2006. 

4

 VOC Fugitive Losses: New Monitors, Emissions Losses, and Potential Policy Gaps. 2006 

International Workshop. U.S. Environmental Protection Agency, Office of Air Quality Planning 

and Standards and Office of Solid Waste and Emergency Response. October 25-27, 2006. 
3a-8

Flare Gas Recovery

Overview: This control measure is a condenser that can recover 98 percent of the VOC emitted 

by flares that emit 20 tons per year or more of the pollutant. 

Major Uncertainties: Flare gas recovery is just gaining commercial acceptance in the US and is 

only in use at a small number of refineries. 

Cooling Towers

Overview: The control measure is continuous monitoring of VOC from the cooling water return 

to a level of 10 ppb. This monitoring is accomplished by using a continuous flow monitor at the 

inlet to each cooling tower. 

There is not a general estimate of CE for this measure; one is to apply a continuous flow monitor 

until VOC emissions have reached a level of 1.7 tons/year for a given cooling tower.5

Major Uncertainties: The amount of VOC leakage from each cooling tower can greatly affect 

the overall cost-effectiveness of this control measure. 

Wastewater Drains and Separators

Overview: This control measure includes an inspection and maintenance program to reduce VOC 

emissions from wastewater drains and water seals on drains. This measure is a more stringent 

version of measures that underlie existing NESHAP requirements for such sources. 

Major Uncertainties: The reference for this control measures notes that the VOC emissions 

inventories for the five San Francisco Bay Area refineries whose data was a centerpiece of this 

report are incomplete. In addition, not all VOC species from these sources were included in the 

VOC data that is a basis for these calculations.6

Work Practices or Use of Low VOC Coatings

Overview: The control measure is either application of work practices (e.g., storing VOCcontaining cleaning materials in closed containers, minimizing spills) or using coatings that have 

much lower VOC content. These measures, which are of relatively low cost compared to other 

VOC area source controls, can apply to a variety of processes, both for non-EGU point and area 

sources, in different industries and is defined in the proposed control techniques guidelines 

(CTG) for paper, film and foil coatings, metal furniture coatings, and large appliance coatings 

published by the US EPA in July 2007.7

5

 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of Regulation 8, 

Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004. 

6

 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of Regulation 8, 

Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004. 

7

 U.S. Environmental Protection Agency. Consumer and Commercial Products: Control 

Techniques Guidelines in Lieu of Regulations for Paper, Film, and Foil Coatings; Metal 
3a-9

The estimated CE expected to be achieved by either of these control measures is 90 percent. 

Major Uncertainties: The greatest uncertainty is in how many potentially affected processes are 

implementing or already implemented these control measures. This may be particularly true in 

California. Also, there are nine States that have many of the above work practices in effect for 

paper, film and foil coatings processes, but the work practices are not meant to achieve a specific 

emissions limit.8

 Hence, it is uncertain how much VOC reduction is occurring from this control 

measure in this case. 

In addition to the new supplemental controls presented above, there were a number of changes 

made to existing AirControlNET controls. These changes were made based upon an internal 

review performed by EPA engineers to examine the controls applied by AirControlNET and 

determine if these controls were sufficient or could be more aggressive in their application, given 

the 2020 analysis year. This review was performed for nonEGU point NOx control measures. 

The result of this review was an increase in control efficiencies applied for many control 

measures, and more aggressive control measures for over 70 SCCs. The changes apply to the 

control strategies performed for the Eastern US only. These changes are listed in Table 3a.6. 

Table 3a.6: Supplemental Emission Control Measures—Changes to Control Technologies 

Currently in our Control Measures Database For Application in 2020 

Poll SCC 

AirControlNET Source 

Description 

AirControlNE

T Control 

Technology 

New Control 

Technology 

New 

CE

(%) 

Old

CE

(%) 

NOX 10200104 

10200204 

10200205 

10300207 

10300209 

10200217 

10300216 

ICI Boilers—Coal-Stoker SNCR SCR 90 40 

NOX 10200901 

10200902 

10200903 

10200907 

10300902 

10300903 

ICI Boilers—Wood/Bark/ 

Waste

SNCR SCR 90 55 

NOX 10200401 

10200402 

10200404 

10200405 

10300401 

ICI Boilers—Residual Oil SCR SCR 90 80 

Furniture Coatings; and Large Appliance Coatings. 40 CFR 59. July 10, 2007. Available on the 

Intenet at http://www.epa.gov/ttncaaa1/t1/fr_notices/ctg_ccp092807.pdf. It should be noted that 

this CTG became final in October 2007. 

8

 U.S. Environmental Protection Agency. Consumer and Commercial Products: Control 

Techniques Guidelines in Lieu of Regulations for Paper, Film, and Foil Coatings; Metal 

Furniture Coatings; and Large Appliance Coatings. 40 CFR 59. July 10, 2007, p. 37597. 

Available on the Intenet at http://www.epa.gov/ttncaaa1/t1/fr_notices/ctg_ccp092807.pdf.
3a-10

Poll SCC 

AirControlNET Source 

Description 

AirControlNE

T Control 

Technology 

New Control 

Technology 

New 

CE

(%) 

Old

CE

(%) 

NOX 10200501 

10200502 

10200504 

ICI Boilers—Distillate Oil SCR SCR 90 80 

NOX 10200601 

10200602 

10200603 

10200604 

10300601 

10300602 

10300603 

10500106 

10500206 

ICI Boilers—Natural Gas SCR SCR 90 80 

NOX 30500606 Cement Manufacturing—Dry SCR SCR 90 80

NOX 30500706 Cement Manufacturing—Wet SCR SCR 90 80

NOX 30300934 Iron & Steel Mills—

Annealing 

SCR SCR 90 85 

NOX 10200701 

10200704 

10200707 

10200710 

10200799 

10201402 

10300701 

10300799 

ICI Boilers—Process Gas SCR SCR 90 80 

NOX 10200802 

10200804 

ICI Boilers—Coke SCR SCR 90 70 

NOX 10201002 ICI Boilers—LPG SCR SCR 90 80 

NOX 10201301 

10201302 

ICI Boilers—Liquid Waste SCR SCR 90 80 

NOX 30700110 Sulfate Pulping—Recovery 

Furnaces

SCR SCR 90 80 

NOX 30100306 Ammonia Production— 

Pri. Reformer, Nat. Gas 

SCR SCR 90 80 

 30500622 

30500623 

Cement Kilns Biosolid 

Injection 

Biosolid 

Injection 

40 23 

NOX 30590013 

30190013 

30190014 

39990013 

Industrial and Manufacturing 

Incinerators 

SNCR SCR 90 45 

NOX 30101301 

30101302 

Nitric Acid Manufacturing SNCR SCR 90 60 to 

98

NOX 30600201 Fluid Cat. Cracking Units LNB + FGR SCR 90 55 

NOX 30590003 Process Heaters—Process 

Gas

LNB + SCR LNB + SCR 90 88 

NOX 30600101 

30600103 

30600111 

Process Heaters—Distillate 

Oil

LNB + SCR LNB + SCR 90 90 

NOX 30600106 

30600199 

Process Heaters—Residual 

Oil

LNB + SCR LNB + SCR 90 80 

NOX 30600102 

30600105 

Process Heaters—Natural 

Gas

LNB + SCR LNB + SCR 90 80 
3a-11

Poll SCC 

AirControlNET Source 

Description 

AirControlNE

T Control 

Technology 

New Control 

Technology 

New 

CE

(%) 

Old

CE

(%) 

NOX 30700104 Sulfate Pulping—Recovery 

Furnaces

SCR SCR 90 80 

NOX 30790013 Pulp and Paper—Natural 

Gas—Incinerators 

SNCR SCR 90 45 

NOX 39000201 In-Process; Bituminous Coal; 

Cement Kiln 

SNCR—urea 

based 

SCR 90 50 

NOX 39000203 In-Process; Bituminous Coal; 

Lime Kiln 

SNCR—urea 

based 

SCR 90 50 

NOX 39000289 In-Process Fuel Use; 

Bituminous Coal; Gen 

SNCR SCR 90 40 

NOX 39000489 In-Process Fuel Use; 

Residual Oil; Gen 

LNB SCR 90 37 

NOX 39000689 In-Process Fuel Use; Natural 

Gas; Gen 

LNB SCR 90 50 

NOX 39000701 In-Proc; Process Gas; Coke 

Oven/Blast Furn 

LNB + FGR SCR 90 55 

NOX 39000789 In-Process; Process Gas; 

Coke Oven Gas 

LNB SCR 90 50 

NOX 50100101 

50100506 

50200506 

50300101 

50300102 

50300104 

50300506 

50100102 

Solid Waste Disp; Gov; 

Other Incin; Sludge 

SNCR SCR 90 45 

The last category of supplemental controls is control technologies currently in our control 

measures database being applied to SCCs not controlled currently in AirControlNET.

Table 3a.7: Supplemental Emission Control Technologies Currently in our Control 

Measures Database Applied to New Source Types 

Pollutant SCC SCC Description 

Control 

Technology CE 

NOX 39000602 Cement Manufacturing—Dry SCR 90 

NOX 30501401 Glass Manufacturing—General OXY-Firing 85 

NOX 30302351 

30302352 

30302359 

Taconite Iron Ore Processing—Induration—Coal or 

Gas

SCR 90 

NOX 10100101 External Combustion Boilers; Electric Generation; 

Anthracite Coal; Pulverized Coal 

SNCR 40 

NOX 10100202 External Combustion Boilers; Electric Generation; 

Bituminous/Subbituminous Coal; Pulverized Coal: 

Dry Bottom (Bituminous Coal) 

SNCR 40 

NOX 10100204 External Combustion Boilers; Electric Generation; 

Bituminous/Subbituminous Coal; Spreader Stoker 

(Bituminous Coal) 

SNCR 40 

NOX 10100212 External Combustion Boilers; Electric Generation; 

Bituminous/Subbituminous Coal; Pulverized Coal: 

Dry Bottom (Tangential) (Bituminous Coal) 

SNCR 40 
3a-12

Pollutant SCC SCC Description 

Control 

Technology CE 

NOX 10100401 External Combustion Boilers; Electric Generation; 

Residual Oil; Grade 6 Oil: Normal Firing 

SNCR 50 

NOX 10100404 External Combustion Boilers; Electric Generation; 

Residual Oil; Grade 6 Oil: Tangential Firing 

SNCR 50 

NOX 10100501 External Combustion Boilers; Electric Generation; 

Distillate Oil; Grades 1 and 2 Oil 

SNCR 50 

NOX 10100601 External Combustion Boilers; Electric Generation; 

Natural Gas; Boilers > 100 Million Btu/hr except 

Tangential 

NGR 50 

NOX 10100602 External Combustion Boilers; Electric Generation; 

Natural Gas; Boilers < 100 Million Btu/hr except 

Tangential 

NGR 50 

NOX 10100604 External Combustion Boilers; Electric Generation; 

Natural Gas; Tangentially Fired Units 

NGR 50 

NOX 10101202 External Combustion Boilers; Electric Generation; 

Solid Waste; Refuse Derived Fuel 

SNCR 50 

NOX 20200253 Internal Comb. Engines/Industrial/Natural Gas/4-cycle 

Rich Burn 

NSCR 90 

3a.2 Mobile Control Measures Used in Control Scenarios 

Tables 3a.8 and 3a.9 summarize the emission reductions for the mobile source control measures 

discussed in this section. 

Table 3a.8: NOx Mobile Emission Reductions by Control Measure 

Sector Control Measure 

Modeled Control Strategy Reductions 

(annual tons/year) 

Eliminate Long Duration Truck Idling 5,800 

Reduce Gasoline RVP 880 

Diesel Retrofits 91,000 

Continuous Inspection and Maintenance 20,000 

Onroad 

Commuter Programs 4,100 

Nonroad Diesel Retrofits and Engine Rebuilds 35,000

Table 3a.9: VOC Mobile Emission Reductions by Control Measure 

Sector Control Measure 

Modeled Control Strategy Reductions

(annual tons/year) 

Reduce Gasoline RVP 17,000 

Diesel Retrofits 8,400 

Continuous Inspection and Maintenance 28,000 

Onroad 

Commuter Programs 7,000 

Nonroad Reduce Gasoline RVP 6,300 

Diesel Retrofits and Engine Rebuilds 5,200 
3a-13

3a.2.1 Diesel Retrofits and Engine Rebuilds 

Retrofitting heavy-duty diesel vehicles and equipment manufactured before stricter standards are 

in place—in 2007–2010 for highway engines and in 2011–2014 for most nonroad equipment—

can provide NOX and HC benefits. The retrofit strategies included in the RIA retrofit measure 

are:

x Installation of emissions after-treatment devices called selective catalytic reduction 

(“SCRs”)

x Rebuilding nonroad engines (“rebuild/upgrade kit”) 

We chose to focus on these strategies due to their high NOx emissions reduction potential and 

widespread application. Additional retrofit strategies include, but are not limited to, lean NOx 

catalyst systems—which are another type of after-treatment device—and alternative fuels. 

Additionally, SCRs are currently the most likely type of control technology to be used to meet 

EPA’s NOx 2007–2010 requirements for HD diesel trucks and 2008–2011 requirements for 

nonroad equipment. Actual emissions reductions may vary significantly by strategy and by the 

type and age of the engine and its application.

To estimate the potential emissions reductions from this measure, we applied a mix of two 

retrofit strategies (SCRs and rebuild/upgrade kits) for the 2020 inventory of: 

x Heavy-duty highway trucks class 6 & above, Model Year 1995–2009 

x All diesel nonroad engines, Model Year 1991–2007, except for locomotive, marine, 

pleasure craft, & aircraft engines 

Class 6 and above trucks comprise the bulk of the NOx emissions inventory from heavy-duty 

highway vehicles, so we did not include trucks below class 6. We chose not to include 

locomotive and marine engines in our analysis since EPA has proposed regulations to address 

these engines, which will significantly impact the emissions inventory and emission reduction 

potential from retrofits in 2020. There was also not enough data available to assess retrofit 

strategies for existing aircraft and pleasure craft engines, so we did not include them in this 

analysis. In addition, EPA is in the process of negotiating standards for new aircraft engines. 

The lower bound in the model year range—1995 for highway vehicles and 1991 for nonroad 

engines—reflects the first model year in which emissions after-treatment devices can be reliably 

applied to the engines. Due to a variety of factors, devices are at a higher risk of failure for 

earlier model years. We expect the engines manufactured before the lower bound year that are 

still in existence in 2020 to be retired quickly due to natural turnover, therefore, we have not 

included strategies for pre-1995/1991 engines because of the strategies’ relatively small impact 

on emissions. The upper bound in the model year range reflects the last year before more 

stringent emissions standards will be fully phased-in. 

We chose the type of strategy to apply to each model year of highway vehicles and nonroad 

equipment based on our technical assessment of which strategies would achieve reliable results 

at the lowest cost. After-treatment devices can be more cost-effective than rebuild and vice versa 
3a-14

depending on the emissions rate, application, usage rates, and expected life of the engine. The 

performance of after-treatment devices, for example, depends heavily upon the model year of the 

engine; some older engines may not be suitable for after-treatment devices and would be better 

candidates for rebuild/upgrade kit. In certain cases, nonroad engines may not be suitable for 

either after-treatment devices or rebuild, which is why we estimate that retrofits are not suitable 

for 5% of the nonroad fleet. The mix of strategies employed in this RIA for highway vehicles 

and nonroad engines are presented in Table 3a.10 and Table 3a.11, respectively. The groupings 

of model years for highway vehicles reflect changes in EPA’s published emissions standards for 

new engines.

Table 3a.10: Application of Retrofit Strategy for Highway Vehicles by Percentage of Fleet 

Model Year SCR 

<1995 0%

1995–2006 100% 

2007–2009 50% 

>2009 0%

Table 3a.11: Application of Retrofit Strategy for Nonroad Equipment by Percentage of 

Fleet

Model Year Rebuild/Upgrade kit SCR 

1991–2007 50% 50% 

The expected emissions reductions from SCR’s are based on data derived from EPA regulations 

(Control of Emissions of Air Pollution from 2004 and Later Model Year Heavy-duty Highway 

Engines and Vehicles published October 2000), interviews with component manufacturers, and 

EPA’s Summary of Potential Retrofit Technologies. This information is available at 

www.epa.gov/otaq/retrofit/retropotentialtech.htm. The estimates for highway vehicles and 

nonroad engines are presented in Table 3a.12 and Table 3a.13, respectively.

Table 3a.12: Percentage Emissions Reduction by Highway Vehicle Retrofit Strategy 

 PM CO HC NOx 

SCR (+DPF) 90% 90% 90% 70% 

Table 3a.13: Percentage Emissions Reduction by Nonroad Equipment Retrofit Strategy 

Strategy PM CO HC NOx 

SCR (+DPF) 90% 90% 90% 70% 

Rebuild/Upgrade Kit 30% 15% 70% 40% 

It is important to note that there is a great deal of variability among types of engines (especially 

nonroad), the applicability of retrofit strategies, and the associated emissions reductions. We 

applied the retrofit emissions reduction estimates to engines across the board (e.g., retrofits for 

bulldozers are estimated to produce the same percentage reduction in emissions as for 

agricultural mowers). We did this in order to simplify model runs, and, in some cases, where we 

did not have enough data to differentiate emissions reductions for different types of highway 

vehicles and nonroad equipment. We believe the estimates used in the RIA, however, reflect the 
3a-15

best available estimates of emissions reductions that can be expected from retrofitting the heavyduty diesel fleet. 

Using the retrofit module in EPA’s National Mobile Inventory Model (NMIM) available at 

http://www.epa.gov/otaq/nmim.htm, we calculated the total percentage reduction in emissions 

(PM, NOx, HC, and CO) from the retrofit measure for each relevant engine category (source 

category code, or SCC) for each county in 2020. To evaluate this change in the emissions 

inventory, we conducted both a baseline and control analysis. Both analyses were based on 

NMIM 2005 (version NMIM20060310), NONROAD2005 (February 2006), and MOBILE6.2.03 

which included the updated diesel PM file PMDZML.csv dated March 17, 2006. 

For the control analysis, we applied the retrofit measure corresponding to the percent reductions 

of the specified pollutants in Tables 3a.12 and 3a.13 to the specified model years in Tables 3a.10 

and 3a.11 of the relevant SCCs. Fleet turnover rates are modeled in the NMIM, so we applied the 

retrofit measure to the 2007 fleet inventory, and then evaluated the resulting emissions inventory 

in 2020. The timing of the application of the retrofit measure is not a factor; retrofits only need to 

take place prior to the attainment date target (2020 for this RIA). For example, if retrofit devices 

are installed on 1995 model year bulldozers in 2007, the only impact on emissions in 2020 will 

be from the expected inventory of 1995 model year bulldozer emissions in 2020. 

We then compared the baseline and control analyses to determine the percent reduction in 

emissions we estimate from this measure for the relevant SCC codes in the targeted 

nonattainment areas. 

3a.2.2 Implement Continuous Inspection and Maintenance Using Remote Onboard Diagnostics 

(OBD) 

Continuous Inspection and Maintenance (I/M) is a new way to check the status of OBD systems 

on light-duty OBD-equipped vehicles. It involves equipping subject vehicles with some type of 

transmitter that attaches to the OBD port. The device transmits the status of the OBD system to 

receivers distributed around the I/M area. Transmission may be through radio-frequency, cellular 

or wi-fi means. Radio frequency and cellular technologies are currently being used in the states 

of Oregon, California and Maryland.

Current I/M programs test light-duty vehicles on a periodic basis—either annually or biennially. 

Emission reduction credit is assigned based on test frequency. Using Continuous I/M, vehicles 

are continuously monitored as they are operated throughout the non-attainment area. When a 

vehicle experiences an OBD failure, the motorist is notified and is required to get repairs within 

the normal grace period—typically about a month. Thus, Continuous I/M will result in repairs 

happening essentially whenever a malfunction occurs that would cause the check engine light to 

illuminate. The continuous I/M program is applied to the same fleet of vehicles as the current 

periodic I/M programs. Currently, MOBILE6 provides an increment of benefit when going from 

a biennial program to an annual program. The same increment of credit applies going from an 

annual program to a continuous program. 

Source Categories Affected by Measure: 
3a-16

x All 1996 and newer light-duty gasoline vehicles and trucks:

x All 1996 and newer (SCC 2201001000) Light Duty Gasoline Vehicles (LDGV), Total: 

All Road Types 

x All 1996 and newer (SCC 2201020000) Light Duty Gasoline Trucks 1 (LDGT1), Total: 

All Road Types 

x All 1996 and newer (SCC 2201040000) Light Duty Gasoline Trucks 2 (LDGT2), Total: 

All Road Types 

OBD systems on light duty vehicles are required to illuminate the malfunction indicator lamp 

whenever emissions of HC, CO or NOx would exceed 1.5 times the vehicle’s certification 

standard. Thus, the benefits of this measure will affect all three criteria pollutants. MOBILE6 

was used to estimate the emission reduction benefits of Continuous I/M, using the methodology 

discussed above.

3a.2.3 Eliminating Long Duration Truck Idling 

Virtually all long duration truck idling—idling that lasts for longer than 15 minutes—from 

heavy-duty diesel class 8a and 8b trucks can be eliminated with two strategies: 

x truck stop & terminal electrification (TSE) 

x mobile idle reduction technologies (MIRTs) such as auxiliary power units, generator sets, 

and direct-fired heaters

TSE can eliminate idling when trucks are resting at truck stops or public rest areas and while 

trucks are waiting to perform a task at private distribution terminals. When truck spaces are 

electrified, truck drivers can shut down their engines and use electricity to power equipment 

which supplies air conditioning, heat, and electrical power for on-board appliances.

MIRTs can eliminate long duration idling from trucks that are stopped away from these central 

sites. For a more complete list of MIRTs see EPA’s Idle Reduction Technology page at 

http://www.epa.gov/otaq/smartway/idlingtechnologies.htm.

This measure demonstrates the potential emissions reductions if every class 8a and 8b truck is 

equipped with a MIRT or has dependable access to sites with TSE in 2020. 

To estimate the potential emissions reduction from this measure, we applied a reduction equal to 

the full amount of the emissions attributed to long duration idling in the MOBILE model, which 

is estimated to be 3.4% of the total NOx emissions from class 8a and 8b heavy duty diesel trucks. 

Since the MOBILE model does not distinguish between idling and operating emissions, EPA 

estimates idling emissions in the inventory based on fuel conversion factors. The inventory in the 

MOBILE model, however, does not fully capture long duration idling emissions. There is 

evidence that idling may represent a much greater share than 3.4% of the real world inventory, 

based on engine control module data from long haul trucking companies. As such, we believe the 

emissions reductions demonstrated from this measure in the RIA represent ambitious but realistic 
3a-17

targets. For more information on determining baseline idling activity see EPA’s “Guidance for 

Quantifying and Using Long-Duration Truck Idling Emission Reductions in State 

Implementation Plans and Transportation Conformity” available at

http://www.epa.gov/smartway/idle-guid.htm.

Pollutants and Source Categories Affected by Measure: NOx

Table 3a.14: Class 8a and 8b Heavy Duty Diesel Trucks (decrease NOx for all SCCs) 

SCC Note: All SCC Descriptions below begin with “Mobile Sources; Highway Vehicles—Diesel” 

2230074110 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Interstate: Total 

2230074130 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Other Principal Arterial: Total 

2230074150 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Minor Arterial: Total 

2230074170 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Major Collector: Total 

2230074190 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Minor Collector: Total 

2230074210 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Rural Local: Total 

2230074230 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Interstate: Total 

2230074250 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Other Freeways and Expressways: 

Total 

2230074270 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Other Principal Arterial: Total 

2230074290 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Minor Arterial: Total 

2230074310 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Collector: Total 

2230074330 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B; Urban Local: Total 

Estimated Emissions Reduction from Measure (%): 3.4 % decrease in NOx for all SCCs affected 

by measure 

3a.2.4 Commuter Programs 

Commuter programs recognize and support employers who provide incentives to employees to 

reduce light-duty vehicle emissions. Employers implement a wide range of incentives to affect 

change in employee commuting habits including transit subsidies, bike-friendly facilities, 

telecommuting policies, and preferred parking for vanpools and carpools. The commuter 

measure in this RIA reflects a mixed package of incentives. 

This measure demonstrates the potential emissions reductions from providing commuter 

incentives to 10% and 25% of the commuter population in 2020. 

We used the findings from a recent Best Workplaces for Commuters survey, which was an EPA 

sponsored employee trip reduction program, to estimate the potential emissions reductions from 

this measure.9

 The BWC survey found that, on average, employees at workplaces with 

comprehensive commuter programs emit 15% fewer emissions than employees at workplaces 

that do not offer a comprehensive commuter program. 

9

 Herzog, E., Bricka, S., Audette, L., and Rockwell, J., 2005. Do Employee Commuter Benefits 

Reduce Vehicle Emissions and Fuel Consumption? Results of the Fall 2004 Best Workplaces for 

Commuters Survey, Transportation Research Record, Journal of the Transportation Research 

Board: Forthcoming. 
3a-18

We believe that getting 10%–25% of the workforce involved in commuter programs is realistic. 

For modeling purposes, we divided the commuter programs measure into two program 

penetration rates: 10% and 25%. This was meant to provide flexibility to model a lower 

penetration rate for areas that need only low levels of emissions reductions to achieve attainment. 

According to the 2001 National Household Transportation Survey (NHTS) published by DOT, 

commute VMT represents 27% of total VMT. Based on this information, we calculated that 

BWC would reduce light-duty gasoline emissions by 0.4% and 1% with a 10% and 25% program 

penetration rate, respectively. 

Pollutants and Source Categories Affected by Measure (SCC): NOx, and VOC 
3a-19

Table 3a.15: All Light-Duty Gasoline Vehicles and Trucks 

SCC

Note: All SCC Descriptions below begin with “Mobile Sources; Highway Vehicles—

Gasoline” 

2201001110 Light Duty Gasoline Vehicles (LDGV); Rural Interstate: Total 

2201001130 Light Duty Gasoline Vehicles (LDGV); Rural Other Principal Arterial: Total 

2201001150 Light Duty Gasoline Vehicles (LDGV); Rural Minor Arterial: Total 

2201001170 Light Duty Gasoline Vehicles (LDGV); Rural Major Collector: Total 

2201001190 Light Duty Gasoline Vehicles (LDGV); Rural Minor Collector: Total 

2201001210 Light Duty Gasoline Vehicles (LDGV); Rural Local: Total 

2201001230 Light Duty Gasoline Vehicles (LDGV); Urban Interstate: Total 

2201001250 Light Duty Gasoline Vehicles (LDGV); Urban Other Freeways and Expressways: Total 

2201001270 Light Duty Gasoline Vehicles (LDGV); Urban Other Principal Arterial: Total 

2201001290 Light Duty Gasoline Vehicles (LDGV); Urban Minor Arterial: Total 

2201001310 Light Duty Gasoline Vehicles (LDGV); Urban Collector: Total 

2201001330 Light Duty Gasoline Vehicles (LDGV); Urban Local: Total 

2201020110 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Interstate: Total 

2201020130 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Other Principal Arterial: Total 

2201020150 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Minor Arterial: Total 

2201020170 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Major Collector: Total 

2201020190 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Minor Collector: Total 

2201020210 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Rural Local: Total 

2201020230 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Interstate: Total 

2201020250 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Other Freeways and 

Expressways: Total 

2201020270 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Other Principal Arterial: Total 

2201020290 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Minor Arterial: Total 

2201020310 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Collector: Total 

2201020330 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5); Urban Local: Total 

2201040110 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Interstate: Total 

2201040130 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Other Principal Arterial: Total 

2201040150 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Minor Arterial: Total 

2201040170 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Major Collector: Total 

2201040190 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Minor Collector: Total 

2201040210 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Rural Local: Total 

2201040230 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Interstate: Total 

2201040250 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Other Freeways and 

Expressways: Total 

2201040270 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Other Principal Arterial: Total 

2201040290 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Minor Arterial: Total 

2201040310 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Collector: Total 

2201040330 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5); Urban Local: Total 

Estimated Emissions Reduction from Measure (%): 

With a 10% program penetration rate: 0.4% 

With a 25% program penetration rate: 1% 

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

Volatility is the property of a liquid fuel that defines its evaporation characteristics. RVP is an 

abbreviation for “Reid vapor pressure,” a common measure of gasoline volatility, as well as a 

generic term for gasoline volatility. EPA regulates the vapor pressure of all gasoline during the 

summer months (June 1 to September 15 at retail stations). Lower RVP helps to reduce VOCs, 
3a-20

which are a precursor to ozone formation. This control measure represents the use of gasoline 

with a RVP limit of 7.0 psi from May through September in counties with an ozone season RVP 

value greater than 7.0 psi. 

Under section 211(c)(4)(C) of the CAA, EPA may approve a non-identical state fuel control as a 

SIP provision, if the state demonstrates that the measure is necessary to achieve the national 

primary or secondary ambient air quality standard (NAAQS) that the plan implements. EPA can 

approve a state fuel requirement as necessary only if no other measures would bring about timely 

attainment, or if other measures exist but are unreasonable or impracticable. 

Source Categories Affected by Measure: 

x All light-duty gasoline vehicles and trucks: Affected SCC:

– 2201001000 Light Duty Gasoline Vehicles (LDGV), Total: All Road Types 

– 2201020000 Light Duty Gasoline Trucks 1 (LDGT1), Total: All Road Types 

– 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Total: All Road Types 

– 2201070000 Heavy Duty Gasoline Vehicles (HDGV), Total: All Road Types 

– 2201080000 Motorcycles (MC), Total: All Road Types 

3a.3 EGU Controls Used in the Control Strategy 

Table 3a.16 contains the ozone season emissions from all fossil EGU sources (greater than 25 

megawatts) for the baseline and the control strategy. 

Table 3a.16: NOx EGU Ozone Season Emissions (All Fossil Units >25MW) (1,000 Tons)a

 OTC MWRPO East TX National 

CAIR 

Region 

CAIR 

Cap 

Baseline 

(CAIR/CAMR/CAVR) 

73 154 43 828 463 485 

Control Strategy 65 

(í11%) 

113 

(í26%) 

33

(í23%) 

812 

(í2%) 

470 482 

a Numbers in parentheses are the percentage change in emissions. 

3a.3.1 CAIR 

The data and projections presented in Section 3.2.2 cover the electric power sector, an industry 

that will achieve significant emission reductions under the Clean Air Interstate Rule (CAIR) over 

the next 10 to 15 years. Based on an assessment of the emissions contributing to interstate 

transport of air pollution and available control measures, EPA determined that achieving 

required reductions in the identified States by controlling emissions from power plants is highly 

cost effective. CAIR will permanently cap emissions of sulfur dioxide (SO2) and nitrogen oxides 

(NOx) in the eastern United States. CAIR achieves large reductions of SO2 and/or NOx emissions 

across 28 eastern states and the District of Columbia.
3a-21

Figure 3a.1: CAIR Affected Region 

States controlled for fine particles (annual SO2and NOx)

States not covered by CAIR

States controlled for ozone (ozone season NOx)

States controlled for both fine particles (annual SO2and NOx) and ozone (ozone season NOx)

When fully implemented, CAIR will reduce SO2 emissions in these states by over 70% and NOx

emissions by over 60% from 2003 levels (some of which are due to NOx SIP Call). This will 

result in significant environmental and health benefits and will substantially reduce premature 

mortality in the eastern United States. The benefits will continue to grow each year with further 

implementation. CAIR was designed with current air quality standard in mind, and requires 

significant emission reductions in the East, where they are needed most and where transport of 

pollution is a major concern. CAIR will bring most areas in the Eastern US into attainment with 

the current ozone and current PM2.5 standards. Some areas will need to adopt additional local 

control measures beyond CAIR. CAIR is a regional solution to address transport, not a solution 

to all local nonattainment issues. The large reductions anticipated with CAIR, in conjunction 

with reasonable additional local control measures for SO2, NOx, and direct PM, will move States 

towards attainment in a deliberate and logical manner.

Based on the final State rules that have been submitted and the proposed State rules that EPA has 

reviewed, EPA believes that all States intend to use the CAIR trading programs as their 

mechanism for meeting the emission reduction requirements of CAIR. 

The analysis in this section reflects these realities and attempts to show, in an illustrative fashion, 

the costs and impacts of meeting a proposed 8-hr ozone standard of 0.070 ppm for the power 

sector.

3a.3.2 Integrated Planning Model and Background 

CAIR was designed to achieve significant emissions reductions in a highly cost-effective manner 

to reduce the transport of fine particles that have been found to contribute to nonattainment. EPA 
3a-22

analysis has found that the most efficient method to achieve the emissions reduction targets is 

through a cap-and-trade system on the power sector that States have the option of adopting. The 

modeling done with IPM assumes a region-wide cap and trade system on the power sector for the 

States covered.

It is important to note that the proposal RIA analysis used the Integrated Planning Model (IPM) 

v2.1.9 to ensure consistency with the analysis presented in 2006 PM NAAQS RIA and report 

incremental results. EPA’s IPM v2.1.9 incorporated Federal and State rules and regulations 

adopted before March 2004 and various NSR settlements. 

Final RIA analysis uses the latest version of IPM (v3.0) as part of the updated modeling 

platform. IPM v3.0 includes input and model assumption updates in modeling the power sector 

and incorporates Federal and State rules and regulations adopted before September 2006 and 

various NSR settlements. A detailed discussion of uncertainties associated with the EGU sector 

modeling can be found in 2006 PM NAAQS RIA (pg. 3-50) 

The economic modeling using IPM presented in this and other chapters has been developed for 

specific analyses of the power sector. EPA’s modeling is based on its best judgment for various 

input assumptions that are uncertain, particularly assumptions for future fuel prices and 

electricity demand growth. To some degree, EPA addresses the uncertainty surrounding these 

two assumptions through sensitivity analyses. More detail on IPM can be found in the model 

documentation, which provides additional information on the assumptions discussed here as well 

as all other assumptions and inputs to the model (http://www.epa.gov/airmarkets/progsregs/epaipm.html).

3a.3.3 EGU NOx Emission Control Technologies 

IPM v3.0 includes SO2, NOx, and mercury (Hg) emission control technology options for meeting 

existing and future federal, regional, and state, SO2, NOx and Hg emission limits. The NOx 

control technology options include Selective Catalytic Reduction (SCR) system and Selective 

Non-Catalytic Reduction (SNCR) systems. It is important to note that beyond these emission 

control options, IPM offers other compliance options for meeting emission limits. These include 

fuel switching, re-powering, and adjustments in the dispatching of electric generating units. 

Table 3a.17 summarizes retrofit NOx emission control performance assumptions. 

Table 3a.17: Summary of Retrofit NOx Emission Control Performance Assumptions 

Selective Catalytic Reduction 

(SCR) 

Selective Non-Catalytic Reduction 

(SNCR) 

Unit Type Coal Oil/Gasa Coal Oil/Gasa

Percent Removal 90% down to 0.06 

lb/mmBtu 

80% 35% 50% 

Size Applicability Units Қ 100 MW Units Қ 25 MW Units Қ 25 MW 

and

Units < 200 MW 

Units Қ 25 MW 

a

 Controls to oil- or gas-fired EGUs are not applied as part of the EGU control strategy included in this 

RIA.

Existing coal-fired units that are retrofit with SCR have a NOx removal efficiency of 90%, with 

a minimum controlled NOx emission rate of 0.06 lb/mmBtu in IPM v2.1.9. Potential (new) coal-
3a-23

fired, combined cycle, and IGCC units are modeled to be constructed with SCR systems and 

designed to have emission rates ranging between 0.02 and 0.06 lb NOx/mmBtu.

Detailed cost and performance derivations for NOx controls are discussed in detail in the EPA’s 

documentation of IPM (http://www.epa.gov/airmarkets/progsregs/epa-ipm/pastmodeling.html).

3a.4 Emissions Reductions by Sector 

Figures 3a.2–3a.6 show the NOx reductions for each sector and Figures 3a.7–3a.10 show the 

VOC reductions for each sector under the modeled control strategy. 

Figure 3a.2: Annual Tons of NOx Emissions Reduced from EGU Sources* 

*Reductions are negative and increases are positive.

** The í99–+100 range is not shown because these are small county-level NOx reductions or increases 

that likely had little to no impact on ozone estimates. Most counties in this range had NOx differences 

of under 1 ton. 
3a-24

Figure 3a.3: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from NonEGU 

Point Sources* 

* Reductions are negative and increases are positive.

** The í99–0 range is not shown because these are small county-level NOx reductions or increases that 

likely had little to no impact on ozone estimates. Most counties in this range had NOx differences of 

under 1 ton. 
3a-25

Figure 3a.4: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from Area 

Sources*

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level NOx reductions or increases that 

likely had little to no impact on ozone estimates. Most counties in this range had NOx differences of 

under 1 ton. 
3a-26

Figure 3a.5: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from Nonroad 

Sources*

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level NOx reductions or increases that 

likely had little to no impact on ozone estimates. Most counties in this range had NOx differences of 

under 1 ton. 
3a-27

Figure 3a.6: Annual tons/year of Nitrogen Oxide (NOx) Emissions Reduced from Onroad 

Sources*

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level NOx reductions or increases that 

likely had little to no impact on ozone estimates. Most counties in this range had NOx differences of 

under 1 ton. 
3a-28

Figure 3a.7: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced 

from NonEGU Point Sources* 

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level VOC reductions or increases that 

likely had little to no impact on ozone estimates 
3a-29

Figure 3a.8: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced 

from Area Sources* 

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level VOC reductions or increases that 

likely had little to no impact on ozone estimates. 
3a-30

Figure 3a.9: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced 

from Nonroad Mobile Sources* 

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level VOC reductions or increases that 

likely had little to no impact on ozone estimates. 
3a-31

Figure 3a.10: Annual tons/year of Volatile Organic Compounds (VOC) Emissions Reduced 

from Onroad Mobile Sources* 

*Reductions are negative and increases are positive

**The í99–0 range is not shown because these are small county-level VOC reductions or increases that 

likely had little to no impact on ozone estimates. 

3a.5 Change in Ozone Concentrations Between Baseline and Modeled Control Strategy 

Table 3a.18 provides the projected 8-hour ozone design values for the 2020 baseline and 2020 

control strategy scenarios for each monitored county. The changes in ozone in 2020 between the 

baseline and the control strategy are also provided in this table.

Table 3a.18: Changes in Ozone Concentrations between Baseline and Modeled Control 

Strategy

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Alabama Baldwin 0.063 0.063 0.000 

Alabama Clay 0.056 0.055 -0.001 

Alabama Elmore 0.054 0.055 0.001 

Alabama Etowah 0.054 0.052 -0.002 

Alabama Jefferson 0.059 0.060 0.001 

Alabama Lawrence 0.054 0.055 0.001 
3a-32

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Alabama Madison 0.057 0.057 0.000 

Alabama Mobile 0.063 0.064 0.001 

Alabama Montgomery 0.054 0.054 0.000 

Alabama Morgan 0.060 0.061 0.001 

Alabama Shelby 0.061 0.063 0.002 

Alabama Sumter 0.051 0.051 0.000 

Alabama Tuscaloosa 0.052 0.052 0.000 

Arizona Cochise 0.065 0.064 -0.001 

Arizona Coconino 0.067 0.067 0.000 

Arizona Maricopa 0.069 0.068 -0.001 

Arizona Navajo 0.058 0.057 -0.001 

Arizona Pima 0.063 0.062 -0.001 

Arizona Pinal 0.064 0.063 -0.001 

Arizona Yavapai 0.064 0.064 0.000 

Arkansas Crittenden 0.068 0.068 0.000 

Arkansas Montgomery 0.051 0.051 0.000 

Arkansas Newton 0.060 0.060 0.000 

Arkansas Pulaski 0.061 0.061 0.000 

California Alameda 0.068 0.068 0.000 

California Amador 0.067 0.067 0.000 

California Butte 0.068 0.068 0.000 

California Calaveras 0.071 0.071 0.000 

California Colusa 0.058 0.058 0.000 

California Contra Costa 0.069 0.069 0.000 

California El Dorado 0.080 0.080 0.000 

California Fresno 0.091 0.091 0.000 

California Glenn 0.057 0.057 0.000 

California Imperial 0.071 0.071 0.000 

California Inyo 0.068 0.068 0.000 

California Kern 0.096 0.096 0.000 

California Kings 0.076 0.076 0.000 

California Lake 0.054 0.054 0.000 

California Los Angeles 0.104 0.104 0.000 

California Madera 0.075 0.075 0.000 

California Marin 0.041 0.040 -0.001 

California Mariposa 0.071 0.071 0.000 

California Mendocino 0.045 0.045 0.000 

California Merced 0.079 0.079 0.000 

California Monterey 0.054 0.054 0.000 

California Napa 0.050 0.050 0.000 

California Nevada 0.075 0.075 0.000 

California Orange 0.080 0.080 0.000 

California Placer 0.075 0.075 0.000 

California Riverside 0.101 0.101 0.000 

California Sacramento 0.077 0.077 0.000 

California San Benito 0.066 0.066 0.000 

California San Bernardino 0.122 0.122 0.000 

California San Diego 0.077 0.076 -0.001 

California San Francisco 0.045 0.045 0.000 

California San Joaquin 0.067 0.066 -0.001 
3a-33

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

California San Luis Obispo 0.060 0.060 0.000 

California San Mateo 0.051 0.050 -0.001 

California Santa Barbara 0.068 0.068 0.000 

California Santa Clara 0.066 0.066 0.000 

California Santa Cruz 0.054 0.054 0.000 

California Shasta 0.057 0.057 0.000 

California Solano 0.057 0.057 0.000 

California Sonoma 0.048 0.048 0.000 

California Stanislaus 0.076 0.076 0.000 

California Sutter 0.067 0.067 0.000 

California Tehama 0.065 0.065 0.000 

California Tulare 0.083 0.083 0.000 

California Tuolumne 0.072 0.072 0.000 

California Ventura 0.077 0.077 0.000 

California Yolo 0.064 0.064 0.000 

Colorado Adams 0.056 0.053 -0.003 

Colorado Arapahoe 0.069 0.064 -0.005 

Colorado Boulder 0.062 0.058 -0.004 

Colorado Denver 0.064 0.060 -0.004 

Colorado Douglas 0.072 0.067 -0.005 

Colorado El Paso 0.062 0.059 -0.003 

Colorado Jefferson 0.072 0.067 -0.005 

Colorado La Plata 0.051 0.051 0.000 

Colorado Larimer 0.066 0.061 -0.005 

Colorado Montezuma 0.062 0.062 0.000 

Colorado Weld 0.063 0.059 -0.004 

Connecticut Fairfield 0.079 0.076 -0.003 

Connecticut Hartford 0.065 0.062 -0.003 

Connecticut Litchfield 0.064 0.061 -0.003 

Connecticut Middlesex 0.073 0.070 -0.003 

Connecticut New Haven 0.076 0.073 -0.003 

Connecticut New London 0.067 0.065 -0.002 

Connecticut Tolland 0.068 0.065 -0.003 

Delaware Kent 0.069 0.067 -0.002 

Delaware New Castle 0.070 0.067 -0.003 

Delaware Sussex 0.070 0.067 -0.003 

D.C. Washington 0.068 0.065 -0.003 

Florida Alachua 0.056 0.056 0.000 

Florida Baker 0.054 0.054 0.000 

Florida Bay 0.061 0.063 0.002 

Florida Brevard 0.050 0.051 0.001 

Florida Broward 0.054 0.054 0.000 

Florida Collier 0.056 0.056 0.000 

Florida Columbia 0.052 0.052 0.000 

Florida Duval 0.052 0.052 0.000 

Florida Escambia 0.064 0.064 0.000 

Florida Highlands 0.053 0.053 0.000 

Florida Hillsborough 0.065 0.065 0.000 

Florida Holmes 0.054 0.054 0.000 

Florida Lake 0.054 0.056 0.002 
3a-34

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Florida Lee 0.055 0.056 0.001 

Florida Leon 0.054 0.054 0.000 

Florida Manatee 0.060 0.060 0.000 

Florida Marion 0.058 0.058 0.000 

Florida Miami-Dade 0.052 0.052 0.000 

Florida Orange 0.055 0.057 0.002 

Florida Osceola 0.053 0.054 0.001 

Florida Palm Beach 0.054 0.054 0.000 

Florida Pasco 0.057 0.057 0.000 

Florida Pinellas 0.060 0.060 0.000 

Florida Polk 0.057 0.058 0.001 

Florida St Lucie 0.051 0.051 0.000 

Florida Santa Rosa 0.063 0.063 0.000 

Florida Sarasota 0.060 0.060 0.000 

Florida Seminole 0.056 0.058 0.002 

Florida Volusia 0.051 0.051 0.000 

Florida Wakulla 0.059 0.059 0.000 

Georgia Bibb 0.064 0.063 -0.001 

Georgia Chatham 0.052 0.052 0.000 

Georgia Cherokee 0.053 0.051 -0.002 

Georgia Clarke 0.053 0.051 -0.002 

Georgia Cobb 0.063 0.061 -0.002 

Georgia Coweta 0.065 0.059 -0.006 

Georgia Dawson 0.056 0.054 -0.002 

Georgia De Kalb 0.066 0.064 -0.002 

Georgia Douglas 0.063 0.061 -0.002 

Georgia Fayette 0.061 0.059 -0.002 

Georgia Fulton 0.070 0.068 -0.002 

Georgia Glynn 0.054 0.053 -0.001 

Georgia Gwinnett 0.061 0.059 -0.002 

Georgia Henry 0.064 0.062 -0.002 

Georgia Murray 0.059 0.058 -0.001 

Georgia Muscogee 0.053 0.052 -0.001 

Georgia Paulding 0.060 0.058 -0.002 

Georgia Richmond 0.064 0.059 -0.005 

Georgia Rockdale 0.063 0.061 -0.002 

Georgia Sumter 0.054 0.053 -0.001 

Idaho Ada 0.069 0.069 0.000 

Idaho Butte 0.065 0.065 0.000 

Idaho Canyon 0.059 0.059 0.000 

Idaho Elmore 0.060 0.060 0.000 

Illinois Adams 0.059 0.055 -0.004 

Illinois Champaign 0.057 0.056 -0.001 

Illinois Clark 0.053 0.052 -0.001 

Illinois Cook 0.073 0.072 -0.001 

Illinois Du Page 0.060 0.059 -0.001 

Illinois Effingham 0.057 0.056 -0.001 

Illinois Hamilton 0.058 0.057 -0.001 

Illinois Jersey 0.067 0.065 -0.002 

Illinois Kane 0.062 0.060 -0.002 
3a-35

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Illinois Lake 0.070 0.069 -0.001 

Illinois McHenry 0.066 0.065 -0.001 

Illinois McLean 0.057 0.055 -0.002 

Illinois Macon 0.055 0.054 -0.001 

Illinois Macoupin 0.057 0.055 -0.002 

Illinois Madison 0.066 0.063 -0.003 

Illinois Peoria 0.062 0.061 -0.001 

Illinois Randolph 0.059 0.058 -0.001 

Illinois Rock Island 0.054 0.053 -0.001 

Illinois St Clair 0.065 0.063 -0.002 

Illinois Sangamon 0.053 0.052 -0.001 

Illinois Will 0.061 0.060 -0.001 

Illinois Winnebago 0.058 0.056 -0.002 

Indiana Allen 0.066 0.065 -0.001 

Indiana Boone 0.067 0.065 -0.002 

Indiana Carroll 0.062 0.061 -0.001 

Indiana Clark 0.068 0.066 -0.002 

Indiana Delaware 0.064 0.062 -0.002 

Indiana Elkhart 0.065 0.064 -0.001 

Indiana Floyd 0.066 0.064 -0.002 

Indiana Gibson 0.051 0.050 -0.001 

Indiana Greene 0.062 0.061 -0.001 

Indiana Hamilton 0.069 0.068 -0.001 

Indiana Hancock 0.067 0.065 -0.002 

Indiana Hendricks 0.064 0.063 -0.001 

Indiana Huntington 0.063 0.062 -0.001 

Indiana Jackson 0.062 0.060 -0.002 

Indiana Johnson 0.064 0.062 -0.002 

Indiana Lake 0.077 0.077 0.000 

Indiana La Porte 0.074 0.072 -0.002 

Indiana Madison 0.067 0.065 -0.002 

Indiana Marion 0.068 0.066 -0.002 

Indiana Morgan 0.065 0.063 -0.002 

Indiana Porter 0.075 0.074 -0.001 

Indiana Posey 0.061 0.059 -0.002 

Indiana St Joseph 0.068 0.066 -0.002 

Indiana Shelby 0.068 0.067 -0.001 

Indiana Vanderburgh 0.060 0.058 -0.002 

Indiana Vigo 0.066 0.064 -0.002 

Indiana Warrick 0.064 0.061 -0.003 

Iowa Bremer 0.058 0.058 0.000 

Iowa Clinton 0.062 0.061 -0.001 

Iowa Harrison 0.062 0.062 0.000 

Iowa Linn 0.057 0.057 0.000 

Iowa Montgomery 0.056 0.056 0.000 

Iowa Palo Alto 0.054 0.053 -0.001 

Iowa Polk 0.046 0.046 0.000 

Iowa Scott 0.061 0.060 -0.001 

Iowa Story 0.048 0.048 0.000 

Iowa Van Buren 0.059 0.057 -0.002 
3a-36

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Iowa Warren 0.049 0.048 -0.001 

Kansas Linn 0.060 0.059 -0.001 

Kansas Sedgwick 0.063 0.063 0.000 

Kansas Sumner 0.062 0.062 0.000 

Kansas Trego 0.055 0.055 0.000 

Kansas Wyandotte 0.062 0.062 0.000 

Kentucky Bell 0.056 0.055 -0.001 

Kentucky Boone 0.063 0.060 -0.003 

Kentucky Boyd 0.070 0.069 -0.001 

Kentucky Bullitt 0.061 0.059 -0.002 

Kentucky Campbell 0.070 0.067 -0.003 

Kentucky Carter 0.057 0.056 -0.001 

Kentucky Christian 0.057 0.057 0.000 

Kentucky Daviess 0.058 0.058 0.000 

Kentucky Edmonson 0.059 0.057 -0.002 

Kentucky Fayette 0.057 0.055 -0.002 

Kentucky Graves 0.059 0.058 -0.001 

Kentucky Greenup 0.064 0.063 -0.001 

Kentucky Hancock 0.063 0.064 0.001 

Kentucky Hardin 0.057 0.056 -0.001 

Kentucky Henderson 0.060 0.057 -0.003 

Kentucky Jefferson 0.064 0.063 -0.001 

Kentucky Jessamine 0.057 0.056 -0.001 

Kentucky Kenton 0.065 0.062 -0.003 

Kentucky Livingston 0.061 0.060 -0.001 

Kentucky McCracken 0.063 0.062 -0.001 

Kentucky McLean 0.059 0.058 -0.001 

Kentucky Oldham 0.063 0.061 -0.002 

Kentucky Perry 0.055 0.054 -0.001 

Kentucky Pike 0.054 0.053 -0.001 

Kentucky Pulaski 0.058 0.060 0.002 

Kentucky Scott 0.050 0.049 -0.001 

Kentucky Simpson 0.056 0.056 0.000 

Kentucky Trigg 0.052 0.052 0.000 

Kentucky Warren 0.060 0.058 -0.002 

Louisiana Ascension 0.068 0.065 -0.003 

Louisiana Beauregard 0.061 0.058 -0.003 

Louisiana Bossier 0.060 0.060 0.000 

Louisiana Caddo 0.058 0.057 -0.001 

Louisiana Calcasieu 0.066 0.063 -0.003 

Louisiana East Baton Rouge 0.076 0.073 -0.003 

Louisiana Grant 0.060 0.058 -0.002 

Louisiana Iberville 0.072 0.068 -0.004 

Louisiana Jefferson 0.069 0.066 -0.003 

Louisiana Lafayette 0.065 0.061 -0.004 

Louisiana Lafourche 0.065 0.062 -0.003 

Louisiana Livingston 0.068 0.064 -0.004 

Louisiana Orleans 0.057 0.056 -0.001 

Louisiana Ouachita 0.061 0.060 -0.001 

Louisiana Pointe Coupee 0.063 0.057 -0.006 
3a-37

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Louisiana St Bernard 0.063 0.061 -0.002 

Louisiana St Charles 0.066 0.063 -0.003 

Louisiana St James 0.064 0.061 -0.003 

Louisiana St John The Baptis 0.068 0.066 -0.002 

Louisiana St Mary 0.061 0.057 -0.004 

Louisiana West Baton Rouge 0.073 0.070 -0.003 

Maine Cumberland 0.063 0.061 -0.002 

Maine Hancock 0.071 0.068 -0.003 

Maine Kennebec 0.060 0.058 -0.002 

Maine Knox 0.063 0.061 -0.002 

Maine Oxford 0.050 0.048 -0.002 

Maine Penobscot 0.064 0.062 -0.002 

Maine Sagadahoc 0.059 0.057 -0.002 

Maine York 0.066 0.064 -0.002 

Maryland Anne Arundel 0.072 0.069 -0.003 

Maryland Baltimore 0.070 0.067 -0.003 

Maryland Carroll 0.065 0.061 -0.004 

Maryland Cecil 0.071 0.068 -0.003 

Maryland Charles 0.065 0.062 -0.003 

Maryland Frederick 0.065 0.061 -0.004 

Maryland Harford 0.076 0.073 -0.003 

Maryland Kent 0.069 0.067 -0.002 

Maryland Montgomery 0.064 0.061 -0.003 

Maryland Prince Georges 0.069 0.066 -0.003 

Maryland Washington 0.063 0.061 -0.002 

Massachusetts Barnstable 0.070 0.068 -0.002 

Massachusetts Berkshire 0.068 0.066 -0.002 

Massachusetts Bristol 0.069 0.066 -0.003 

Massachusetts Essex 0.070 0.068 -0.002 

Massachusetts Hampden 0.068 0.065 -0.003 

Massachusetts Hampshire 0.066 0.063 -0.003 

Massachusetts Middlesex 0.064 0.062 -0.002 

Massachusetts Norfolk 0.073 0.071 -0.002 

Massachusetts Suffolk 0.068 0.067 -0.001 

Massachusetts Worcester 0.065 0.062 -0.003 

Michigan Allegan 0.073 0.072 -0.001 

Michigan Benzie 0.066 0.065 -0.001 

Michigan Berrien 0.070 0.069 -0.001 

Michigan Cass 0.068 0.066 -0.002 

Michigan Clinton 0.064 0.062 -0.002 

Michigan Genesee 0.066 0.064 -0.002 

Michigan Huron 0.068 0.067 -0.001 

Michigan Ingham 0.063 0.062 -0.001 

Michigan Kalamazoo 0.062 0.061 -0.001 

Michigan Kent 0.065 0.063 -0.002 

Michigan Lenawee 0.067 0.065 -0.002 

Michigan Macomb 0.075 0.073 -0.002 

Michigan Mason 0.065 0.064 -0.001 

Michigan Missaukee 0.061 0.060 -0.001 

Michigan Muskegon 0.069 0.068 -0.001 
3a-38

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Michigan Oakland 0.072 0.071 -0.001 

Michigan Ottawa 0.066 0.064 -0.002 

Michigan St Clair 0.070 0.067 -0.003 

Michigan Schoolcraft 0.062 0.061 -0.001 

Michigan Washtenaw 0.069 0.067 -0.002 

Michigan Wayne 0.071 0.069 -0.002 

Minnesota St Louis 0.059 0.059 0.000 

Mississippi Adams 0.060 0.059 -0.001 

Mississippi Bolivar 0.057 0.057 0.000 

Mississippi De Soto 0.062 0.062 0.000 

Mississippi Hancock 0.063 0.062 -0.001 

Mississippi Harrison 0.062 0.065 0.003 

Mississippi Hinds 0.050 0.050 0.000 

Mississippi Jackson 0.067 0.067 0.000 

Mississippi Lauderdale 0.051 0.050 -0.001 

Mississippi Lee 0.056 0.058 0.002 

Mississippi Madison 0.053 0.053 0.000 

Mississippi Warren 0.052 0.052 0.000 

Missouri Cass 0.060 0.060 0.000 

Missouri Cedar 0.063 0.062 -0.001 

Missouri Clay 0.064 0.064 0.000 

Missouri Greene 0.058 0.057 -0.001 

Missouri Jefferson 0.066 0.064 -0.002 

Missouri Monroe 0.060 0.058 -0.002 

Missouri Platte 0.063 0.062 -0.001 

Missouri St Charles 0.071 0.068 -0.003 

Missouri Ste Genevieve 0.065 0.062 -0.003 

Missouri St Louis 0.070 0.067 -0.003 

Missouri St Louis City 0.070 0.068 -0.002 

Montana Flathead 0.052 0.052 0.000 

Nebraska Douglas 0.056 0.056 0.000 

Nebraska Lancaster 0.045 0.045 0.000 

Nevada Clark 0.072 0.071 -0.001 

Nevada Douglas 0.059 0.059 0.000 

Nevada Washoe 0.063 0.063 0.000 

Nevada White Pine 0.065 0.065 0.000 

Nevada Carson City 0.062 0.062 0.000 

New Hampshire Belknap 0.059 0.058 -0.001 

New Hampshire Carroll 0.055 0.054 -0.001 

New Hampshire Cheshire 0.056 0.054 -0.002 

New Hampshire Grafton 0.057 0.056 -0.001 

New Hampshire Hillsborough 0.065 0.063 -0.002 

New Hampshire Merrimack 0.057 0.056 -0.001 

New Hampshire Rockingham 0.063 0.061 -0.002 

New Hampshire Strafford 0.059 0.057 -0.002 

New Hampshire Sullivan 0.061 0.059 -0.002 

New Jersey Atlantic 0.067 0.065 -0.002 

New Jersey Bergen 0.074 0.071 -0.003 

New Jersey Camden 0.077 0.074 -0.003 

New Jersey Cumberland 0.071 0.068 -0.003 
3a-39

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

New Jersey Essex 0.052 0.051 -0.001 

New Jersey Gloucester 0.075 0.073 -0.002 

New Jersey Hudson 0.066 0.064 -0.002 

New Jersey Hunterdon 0.071 0.068 -0.003 

New Jersey Mercer 0.075 0.073 -0.002 

New Jersey Middlesex 0.073 0.070 -0.003 

New Jersey Monmouth 0.073 0.070 -0.003 

New Jersey Morris 0.071 0.068 -0.003 

New Jersey Ocean 0.079 0.076 -0.003 

New Jersey Passaic 0.067 0.064 -0.003 

New Mexico Bernalillo 0.065 0.064 -0.001 

New Mexico Dona Ana 0.069 0.068 -0.001 

New Mexico Eddy 0.063 0.063 0.000 

New Mexico Sandoval 0.063 0.063 0.000 

New Mexico San Juan 0.069 0.069 0.000 

New Mexico Valencia 0.056 0.056 0.000 

New York Albany 0.064 0.061 -0.003 

New York Bronx 0.069 0.067 -0.002 

New York Chautauqua 0.072 0.069 -0.003 

New York Chemung 0.061 0.059 -0.002 

New York Dutchess 0.068 0.065 -0.003 

New York Erie 0.075 0.072 -0.003 

New York Essex 0.069 0.067 -0.002 

New York Hamilton 0.063 0.062 -0.001 

New York Herkimer 0.059 0.057 -0.002 

New York Jefferson 0.073 0.071 -0.002 

New York Madison 0.062 0.060 -0.002 

New York Monroe 0.067 0.064 -0.003 

New York Niagara 0.075 0.073 -0.002 

New York Oneida 0.063 0.061 -0.002 

New York Onondaga 0.067 0.065 -0.002 

New York Orange 0.063 0.061 -0.002 

New York Oswego 0.053 0.052 -0.001 

New York Putnam 0.070 0.068 -0.002 

New York Queens 0.069 0.067 -0.002 

New York Rensselaer 0.066 0.063 -0.003 

New York Richmond 0.073 0.071 -0.002 

New York Saratoga 0.067 0.063 -0.004 

New York Schenectady 0.061 0.059 -0.002 

New York Suffolk 0.080 0.077 -0.003 

New York Ulster 0.063 0.061 -0.002 

New York Wayne 0.065 0.063 -0.002 

New York Westchester 0.074 0.071 -0.003 

North Carolina Alexander 0.062 0.061 -0.001 

North Carolina Avery 0.059 0.057 -0.002 

North Carolina Buncombe 0.060 0.059 -0.001 

North Carolina Caldwell 0.060 0.060 0.000 

North Carolina Caswell 0.060 0.059 -0.001 

North Carolina Chatham 0.058 0.057 -0.001 

North Carolina Cumberland 0.061 0.060 -0.001 
3a-40

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

North Carolina Davie 0.064 0.062 -0.002 

North Carolina Duplin 0.059 0.058 -0.001 

North Carolina Durham 0.061 0.060 -0.001 

North Carolina Edgecombe 0.063 0.062 -0.001 

North Carolina Forsyth 0.063 0.062 -0.001 

North Carolina Franklin 0.063 0.062 -0.001 

North Carolina Granville 0.064 0.063 -0.001 

North Carolina Guilford 0.060 0.058 -0.002 

North Carolina Haywood 0.064 0.064 0.000 

North Carolina Jackson 0.063 0.062 -0.001 

North Carolina Johnston 0.060 0.059 -0.001 

North Carolina Lenoir 0.060 0.059 -0.001 

North Carolina Lincoln 0.064 0.065 0.001 

North Carolina Martin 0.060 0.059 -0.001 

North Carolina Mecklenburg 0.071 0.070 -0.001 

North Carolina New Hanover 0.056 0.057 0.001 

North Carolina Northampton 0.062 0.060 -0.002 

North Carolina Person 0.063 0.061 -0.002 

North Carolina Pitt 0.059 0.058 -0.001 

North Carolina Randolph 0.057 0.056 -0.001 

North Carolina Rockingham 0.062 0.061 -0.001 

North Carolina Rowan 0.068 0.067 -0.001 

North Carolina Swain 0.053 0.052 -0.001 

North Carolina Union 0.062 0.061 -0.001 

North Carolina Wake 0.064 0.063 -0.001 

North Carolina Yancey 0.063 0.061 -0.002 

North Dakota Billings 0.054 0.054 0.000 

North Dakota Cass 0.055 0.055 0.000 

North Dakota Dunn 0.054 0.054 0.000 

North Dakota McKenzie 0.058 0.058 0.000 

North Dakota Mercer 0.055 0.055 0.000 

North Dakota Oliver 0.051 0.050 -0.001 

Ohio Allen 0.068 0.065 -0.003 

Ohio Ashtabula 0.075 0.073 -0.002 

Ohio Butler 0.068 0.064 -0.004 

Ohio Clark 0.066 0.062 -0.004 

Ohio Clermont 0.068 0.066 -0.002 

Ohio Clinton 0.069 0.066 -0.003 

Ohio Cuyahoga 0.067 0.065 -0.002 

Ohio Delaware 0.066 0.064 -0.002 

Ohio Franklin 0.068 0.066 -0.002 

Ohio Geauga 0.076 0.074 -0.002 

Ohio Greene 0.066 0.062 -0.004 

Ohio Hamilton 0.069 0.066 -0.003 

Ohio Jefferson 0.063 0.061 -0.002 

Ohio Knox 0.064 0.062 -0.002 

Ohio Lake 0.072 0.070 -0.002 

Ohio Lawrence 0.065 0.063 -0.002 

Ohio Licking 0.065 0.062 -0.003 

Ohio Lorain 0.067 0.065 -0.002 
3a-41

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Ohio Lucas 0.070 0.067 -0.003 

Ohio Madison 0.065 0.062 -0.003 

Ohio Mahoning 0.065 0.062 -0.003 

Ohio Medina 0.067 0.065 -0.002 

Ohio Miami 0.065 0.062 -0.003 

Ohio Montgomery 0.065 0.062 -0.003 

Ohio Portage 0.068 0.066 -0.002 

Ohio Preble 0.060 0.057 -0.003 

Ohio Stark 0.065 0.063 -0.002 

Ohio Summit 0.071 0.068 -0.003 

Ohio Trumbull 0.068 0.066 -0.002 

Ohio Warren 0.068 0.065 -0.003 

Ohio Washington 0.061 0.060 -0.001 

Ohio Wood 0.068 0.065 -0.003 

Oklahoma Canadian 0.056 0.056 0.000 

Oklahoma Cleveland 0.060 0.058 -0.002 

Oklahoma Comanche 0.061 0.059 -0.002 

Oklahoma Dewey 0.058 0.056 -0.002 

Oklahoma Kay 0.060 0.060 0.000 

Oklahoma Mc Clain 0.061 0.060 -0.001 

Oklahoma Oklahoma 0.061 0.060 -0.001 

Oklahoma Ottawa 0.062 0.062 0.000 

Oklahoma Pittsburg 0.060 0.060 0.000 

Oklahoma Tulsa 0.066 0.065 -0.001 

Oregon Clackamas 0.062 0.062 0.000 

Oregon Columbia 0.055 0.055 0.000 

Oregon Jackson 0.061 0.061 0.000 

Oregon Lane 0.059 0.059 0.000 

Oregon Marion 0.054 0.054 0.000 

Pennsylvania Adams 0.059 0.056 -0.003 

Pennsylvania Allegheny 0.072 0.069 -0.003 

Pennsylvania Armstrong 0.068 0.065 -0.003 

Pennsylvania Beaver 0.071 0.068 -0.003 

Pennsylvania Berks 0.066 0.063 -0.003 

Pennsylvania Blair 0.060 0.058 -0.002 

Pennsylvania Bucks 0.078 0.075 -0.003 

Pennsylvania Cambria 0.063 0.061 -0.002 

Pennsylvania Centre 0.062 0.059 -0.003 

Pennsylvania Chester 0.071 0.068 -0.003 

Pennsylvania Clearfield 0.065 0.062 -0.003 

Pennsylvania Dauphin 0.065 0.060 -0.005 

Pennsylvania Delaware 0.070 0.068 -0.002 

Pennsylvania Erie 0.070 0.067 -0.003 

Pennsylvania Franklin 0.067 0.064 -0.003 

Pennsylvania Greene 0.063 0.061 -0.002 

Pennsylvania Lackawanna 0.061 0.059 -0.002 

Pennsylvania Lancaster 0.067 0.062 -0.005 

Pennsylvania Lawrence 0.057 0.055 -0.002 

Pennsylvania Lehigh 0.067 0.063 -0.004 

Pennsylvania Luzerne 0.062 0.059 -0.003 
3a-42

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Pennsylvania Lycoming 0.061 0.059 -0.002 

Pennsylvania Mercer 0.068 0.065 -0.003 

Pennsylvania Montgomery 0.071 0.068 -0.003 

Pennsylvania Northampton 0.066 0.062 -0.004 

Pennsylvania Perry 0.061 0.058 -0.003 

Pennsylvania Philadelphia 0.077 0.074 -0.003 

Pennsylvania Tioga 0.064 0.062 -0.002 

Pennsylvania Washington 0.066 0.063 -0.003 

Pennsylvania Westmoreland 0.068 0.065 -0.003 

Pennsylvania York 0.067 0.062 -0.005 

Rhode Island Kent 0.069 0.067 -0.002 

Rhode Island Providence 0.069 0.066 -0.003 

Rhode Island Washington 0.070 0.068 -0.002 

South Carolina Abbeville 0.060 0.058 -0.002 

South Carolina Aiken 0.061 0.058 -0.003 

South Carolina Anderson 0.063 0.062 -0.001 

South Carolina Barnwell 0.058 0.056 -0.002 

South Carolina Berkeley 0.052 0.052 0.000 

South Carolina Charleston 0.054 0.054 0.000 

South Carolina Cherokee 0.061 0.059 -0.002 

South Carolina Chester 0.059 0.058 -0.001 

South Carolina Chesterfield 0.058 0.058 0.000 

South Carolina Colleton 0.058 0.057 -0.001 

South Carolina Darlington 0.061 0.060 -0.001 

South Carolina Edgefield 0.059 0.056 -0.003 

South Carolina Oconee 0.060 0.059 -0.001 

South Carolina Pickens 0.059 0.058 -0.001 

South Carolina Richland 0.066 0.064 -0.002 

South Carolina Spartanburg 0.062 0.061 -0.001 

South Carolina Union 0.058 0.057 -0.001 

South Carolina Williamsburg 0.052 0.051 -0.001 

South Carolina York 0.059 0.058 -0.001 

South Dakota Pennington 0.062 0.061 -0.001 

Tennessee Anderson 0.058 0.058 0.000 

Tennessee Blount 0.064 0.064 0.000 

Tennessee Davidson 0.056 0.056 0.000 

Tennessee Hamilton 0.061 0.062 0.001 

Tennessee Haywood 0.060 0.062 0.002 

Tennessee Jefferson 0.061 0.061 0.000 

Tennessee Knox 0.061 0.061 0.000 

Tennessee Lawrence 0.056 0.058 0.002 

Tennessee Meigs 0.061 0.060 -0.001 

Tennessee Putnam 0.061 0.061 0.000 

Tennessee Rutherford 0.058 0.057 -0.001 

Tennessee Sevier 0.066 0.065 -0.001 

Tennessee Shelby 0.065 0.065 0.000 

Tennessee Sullivan 0.066 0.066 0.000 

Tennessee Sumner 0.061 0.061 0.000 

Tennessee Williamson 0.060 0.060 0.000 

Tennessee Wilson 0.060 0.060 0.000 
3a-43

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Texas Bexar 0.068 0.067 -0.001 

Texas Brazoria 0.073 0.072 -0.001 

Texas Brewster 0.054 0.053 -0.001 

Texas Cameron 0.052 0.051 -0.001 

Texas Collin 0.069 0.067 -0.002 

Texas Dallas 0.068 0.066 -0.002 

Texas Denton 0.074 0.072 -0.002 

Texas Ellis 0.063 0.059 -0.004 

Texas El Paso 0.069 0.068 -0.001 

Texas Galveston 0.074 0.072 -0.002 

Texas Gregg 0.067 0.064 -0.003 

Texas Harris 0.089 0.087 -0.002 

Texas Harrison 0.061 0.058 -0.003 

Texas Hidalgo 0.062 0.061 -0.001 

Texas Hood 0.058 0.056 -0.002 

Texas Jefferson 0.074 0.071 -0.003 

Texas Johnson 0.065 0.062 -0.003 

Texas Kaufman 0.054 0.052 -0.002 

Texas Montgomery 0.073 0.072 -0.001 

Texas Nueces 0.065 0.063 -0.002 

Texas Orange 0.066 0.063 -0.003 

Texas Parker 0.063 0.061 -0.002 

Texas Rockwall 0.061 0.060 -0.001 

Texas Smith 0.064 0.061 -0.003 

Texas Tarrant 0.075 0.073 -0.002 

Texas Travis 0.063 0.062 -0.001 

Texas Victoria 0.060 0.059 -0.001 

Texas Webb 0.053 0.053 0.000 

Utah Box Elder 0.064 0.062 -0.002 

Utah Cache 0.056 0.054 -0.002 

Utah Davis 0.070 0.067 -0.003 

Utah Salt Lake 0.069 0.067 -0.002 

Utah San Juan 0.064 0.063 -0.001 

Utah Utah 0.067 0.065 -0.002 

Utah Weber 0.065 0.062 -0.003 

Vermont Bennington 0.061 0.058 -0.003 

Vermont Chittenden 0.063 0.062 -0.001 

Virginia Arlington 0.072 0.068 -0.004 

Virginia Caroline 0.059 0.057 -0.002 

Virginia Charles City 0.069 0.066 -0.003 

Virginia Chesterfield 0.066 0.064 -0.002 

Virginia Fairfax 0.071 0.067 -0.004 

Virginia Fauquier 0.058 0.056 -0.002 

Virginia Frederick 0.061 0.060 -0.001 

Virginia Hanover 0.069 0.067 -0.002 

Virginia Henrico 0.067 0.065 -0.002 

Virginia Loudoun 0.066 0.063 -0.003 

Virginia Madison 0.062 0.061 -0.001 

Virginia Page 0.058 0.056 -0.002 

Virginia Prince William 0.063 0.060 -0.003 
3a-44

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Virginia Roanoke 0.061 0.060 -0.001 

Virginia Rockbridge 0.057 0.055 -0.002 

Virginia Stafford 0.062 0.060 -0.002 

Virginia Wythe 0.060 0.059 -0.001 

Virginia Alexandria City 0.066 0.063 -0.003 

Virginia Hampton City 0.071 0.070 -0.001 

Virginia Suffolk City 0.070 0.069 -0.001 

Washington Clallam 0.041 0.041 0.000 

Washington Clark 0.061 0.061 0.000 

Washington King 0.063 0.063 0.000 

Washington Klickitat 0.061 0.059 -0.002 

Washington Mason 0.049 0.049 0.000 

Washington Pierce 0.065 0.065 0.000 

Washington Skagit 0.044 0.044 0.000 

Washington Spokane 0.060 0.060 0.000 

Washington Thurston 0.059 0.059 0.000 

Washington Whatcom 0.051 0.051 0.000 

West Virginia Berkeley 0.062 0.060 -0.002 

West Virginia Cabell 0.068 0.067 -0.001 

West Virginia Greenbrier 0.060 0.059 -0.001 

West Virginia Hancock 0.064 0.061 -0.003 

West Virginia Kanawha 0.062 0.061 -0.001 

West Virginia Monongalia 0.055 0.054 -0.001 

West Virginia Ohio 0.063 0.061 -0.002 

West Virginia Wood 0.062 0.061 -0.001 

Wisconsin Brown 0.065 0.064 -0.001 

Wisconsin Columbia 0.059 0.058 -0.001 

Wisconsin Dane 0.060 0.059 -0.001 

Wisconsin Dodge 0.063 0.061 -0.002 

Wisconsin Door 0.071 0.070 -0.001 

Wisconsin Florence 0.058 0.057 -0.001 

Wisconsin Fond Du Lac 0.061 0.060 -0.001 

Wisconsin Green 0.059 0.058 -0.001 

Wisconsin Jefferson 0.062 0.061 -0.001 

Wisconsin Kenosha 0.081 0.080 -0.001 

Wisconsin Kewaunee 0.071 0.069 -0.002 

Wisconsin Manitowoc 0.068 0.067 -0.001 

Wisconsin Marathon 0.058 0.057 -0.001 

Wisconsin Milwaukee 0.074 0.072 -0.002 

Wisconsin Oneida 0.056 0.055 -0.001 

Wisconsin Outagamie 0.060 0.059 -0.001 

Wisconsin Ozaukee 0.074 0.073 -0.001 

Wisconsin Racine 0.074 0.073 -0.001 

Wisconsin Rock 0.063 0.062 -0.001 

Wisconsin St Croix 0.059 0.059 0.000 

Wisconsin Sauk 0.057 0.056 -0.001 

Wisconsin Sheboygan 0.077 0.076 -0.001 

Wisconsin Vernon 0.060 0.059 -0.001 

Wisconsin Vilas 0.057 0.055 -0.002 

Wisconsin Walworth 0.063 0.062 -0.001 
3a-45

State County 

Baseline 8-hour 

Ozone Design Value 

(ppm) 

Control Strategy 8-

hour Ozone Design 

Value (ppm) 

Change 

(ppm) 

Wisconsin Washington 0.064 0.063 -0.001 

Wisconsin Waukesha 0.063 0.062 -0.001 

Wisconsin Winnebago 0.065 0.064 -0.001 

Wyoming Campbell 0.067 0.067 0.000 

Wyoming Teton 0.062 0.062 0.000 
4-1

Chapter 4: Approach for Estimating Reductions for Full Attainment Scenario 

Synopsis

After applying the hypothetical modeled control strategy described in Chapter 3, there were 

multiple counties that were still not projected to attain potential new ozone standards. Because it 

was impossible in some areas to meet a tighter ozone standard nationwide using only known 

controls, EPA conducted a second step in the analysis and estimated the amount of further 

emission reductions needed to attain an alternate primary ozone standard. The term “extrapolated 

tons” will be used to refer to these additionally needed emissions reductions. Sections 4.1 and 4.2 

of this chapter present the methodology EPA developed to determine the emissions reductions 

needed for full attainment of the four alternate standards analyzed in the RIA (i.e., 0.065, 0.070, 

0.075, and 0.079 ppm) and the results of that analysis. Additionally, in other areas, the known 

controls in the hypothetical strategy resulted in ozone levels lower than one or more of the four 

alternate standards. Sections 4.3 and 4.4 of this chapter discuss the methodology and present the 

results of the “overcontrolled” analyses. 

4.1 Development of Full Attainment Targets for Estimate of Extrapolated Costs 

As previewed in the draft RIA, we conducted additional supplemental air quality modeling 

analyses for the final RIA. This was intended to improve the estimates of extrapolated tons 

needed to meet various potential standards. These additional modeling scenarios were designed 

to provide more information about the response of ozone to emissions changes in terms of nonlinearities, geographic variations, the impacts of local versus upwind emissions reductions, and 

the relationship between NOx and VOC emissions changes. As a result of this additional 

information, the methodology to estimate the emissions reduction targets in the “extrapolated 

cost areas” has been improved. 

4.1.1 Design of Supplemental Modeling Scenarios 

There were 61 counties that did not meet the 0.070 ppm standard even after application of the 

controls in the hypothetical RIA modeled control scenario. There were 21 counties that did not 

meet the 0.075 ppm standard.1

 All 21 of these counties are in four broad geographic regions: 

Houston, eastern Lake Michigan,2

 the Northeast Corridor,3 and a large part of California. 

Because these four areas will require the largest emissions reductions beyond the RIA control 

1

 10 counties did not meet the 0.079 ppm standard. 166 counties did not meet the 0.065 ppm 

standard.

2

 This geographic area is an aggregate of five existing nonattainment or maintenance areas: a)

Chicago-Gary-Lake County, IL-IN; b) Milwaukee-Racine, WI; c) Sheboygan WI; d) La Porte 

IN; and e) South Bend-Elkhart IN. 

3

 This geographic area is an aggregate of six existing nonattainment or maintenance areas: a) 

Philadelphia-Wilmington-Atlantic City, PA-NJ-MD-DE; b) New York-Northern New JerseyLong Island, NY-NJ-CT; c) Greater Connecticut, CT; d) Baltimore MD; e) Kent and Queen 

Anne counties MD; and f) Poughkeepsie NY. 
4-2

scenario, and therefore likely the largest extrapolated costs, we focused on these areas within the 

supplemental modeling analyses. We will refer to these four areas as “Phase 1” areas. Later, we 

will define a second and third set of areas that also require extrapolated emissions reductions 

which we will refer to as “Phase 2” and “Phase 3”areas. The primary distinction between these 

three sets of areas is that the supplemental modeling was done only for the Phase 1 areas. 

A map of the four Phase 1 areas is shown in Figure 4.1. An approach similar to that used to 

define the geographic control areas for non-EGU point controls in the RIA control scenario 

(discussed in Chapter 3) was also used to define the supplemental modeling control zones for 

each of the four areas. 

Figure 4.1: Counties within which Across-the-Board Emissions Reductions were Applied in 

the Supplemental Modeling Analyses 

Six supplemental modeling runs were performed as part of this analysis. In the first three runs 

anthropogenic NOx emissions within the appropriate Phase 1 areas (i.e., the red, pink, and 

orange counties in Figure 4.1) were reduced across-the-board by 30, 60, and 90 percent. The 

second set of runs included 30, 60, and 90 percent across-the-board reductions to anthropogenic 

NOx and VOC emissions within the appropriate Phase 1 areas (i.e., the red, pink, and orange 

counties for NOx; only the red and pink counties for VOC). An estimate of the effects of VOC 

controls can be determined by comparing results from the NOx and VOC control run to the NOx 
4-3

only control run. In the two sets of across-the-board supplemental modeling runs the emissions 

reductions were applied on top of the controls in the hypothetical RIA control case. As in the 

modeled control strategy, NOx controls were applied to counties within a 200 km buffer and 

VOC controls were applied to counties within a 100 km buffer of the starting set of counties. 

In the draft RIA, we used the concept of “impact ratios”4 to calculate the additional tons needed 

to meet the air quality standard. The updated approach uses the supplemental modeling to 

determine what levels of ozone precursor reductions (NOx only or NOx plus VOC) are expected 

to be sufficient to bring an area into attainment of one of the various alternate ozone standards 

that were analyzed. After the development of emission targets for the 0.070 ppm alternative 

standard, we conducted a “verification” model run to assess whether our estimated emissions 

reductions actually resulted in attainment of 0.070 ppm in each area. The new estimates of 

extrapolated tons represent a considerable improvement from what was done for the draft RIA. 

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

Valley and South Coast air basins in California. The state has submitted plans to EPA for 

implementing the current ozone standard which propose that these two areas of California meet 

that standard by 2024. We have assumed for analytical purposes that the San Joaquin Valley and 

South Coast air basin would attain a new standard in 2030. There are many uncertainties 

associated with the year 2030 analysis. Between 2020 and 2030 several federal air quality rules 

are likely to further reduce emissions of NOx and VOC, such as, but not limited to National rules 

for Diesel Locomotives, Diesel Marine Vessels, and Small Nonroad Gasoline Engines. These 

emission reductions should lower ambient levels of ozone in California between 2020 and 2030. 

Complete emissions inventories as well as air quality modeling were not available for this year 

2030 analysis. Due to these limitations, it is not possible to adequately model 2030 air quality 

changes that are required to develop robust controls strategies with associated costs and benefits.

In order to provide a rough approximation of the costs and benefits of attaining 0.075 ppm and 

the alternate standards in San Joaquin and South Coast air basins, we’ve relied on the available 

data. Available data includes emission inventories, which do not include any changes in 

stationary source emissions beyond 2020, and 2020 supplemental air quality modeling. This 

data was used to develop extrapolated costs and benefits of 2030 attainment. To view the 

complete analysis for the San Joaquin Valley and South Coast air basins see Appendix 7b. 

4.1.2 Results of Supplemental Modeling for Phase 1 Areas 

Figures 4.2a through 4.2d show the projected design values for individual counties within each 

of the Phase 1 areas for seven modeling cases (i.e., the RIA control scenario and each of the six 

supplemental modeling runs). These figures are instructive in describing how the extrapolated 

control targets were determined for these areas. For each area, the three counties that need the 

most extrapolated controls were chosen for the graphs. 

Figure 4.2a indicates that the highest ozone levels in the Houston area are projected to occur in 

Harris, Galveston, and Brazoria counties with Harris being the controlling county. After 

application of the RIA scenario controls, our modeling projects that the highest 2020 8-hour 

4

 The units for impact ratios are ppb/kton. In the draft RIA we used a single, national impact ratio 

that assumed that 10,000 tons of NOx control would yield 0.001 ppm of ozone improvement. 
4-4

ozone design value in this area will be 0.087 ppm. Thus, additional precursor reductions are 

needed to reach the current standard as well as all four of the alternate standards we are 

considering. Based on the NOx plus VOC control modeling scenarios, we can see that increasing 

the level of emissions reductions beyond the RIA case yields decreasing design values. At a 30% 

NOx + VOC reduction, the projected design value is 0.084 ppm. At a 60% NOx + VOC 

reduction, the projected design value is 0.079 ppm. Finally at a 90% NOx + VOC reduction, the 

projected design value is 0.067 ppm. 

Based on these results, it is concluded that it is possible to meet the current ozone standard with 

additional NOx plus VOC emissions reductions between 0 and 30 percent. To meet an alternate 

NAAQS of 0.079 ppm, the Houston area will require additional NOx plus VOC emissions of 

approximately 60 percent. The 0.075 and 0.070 ppm standards will require between an additional 

60-90% NOx plus VOC reduction beyond the RIA control case. The supplemental modeling 

indicates that it will take more than 90% NOx plus VOC control (above and beyond the RIA 

control case) to meet a 0.065 ppm standard. Based on these figures, one can also estimate the 

levels of NOx-only controls needed to meet a particular standard. We used linear interpolation to 

determine the specific percentage reduction in cases where attainment is expected to be achieved 

Figure 4.2a: Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and 

Each of the Six Supplemental Modeling Scenarios for the Highest Three Counties within 

the Houston Area 

0.045

0.050

0.055

0.060

0.065

0.070

0.075

0.080

0.085

0.090

Harris TX Galveston TX Brazoria TX

Projected 8-hr Ozone DV (ppm)

RIA Control Scenario

30% NOx + VOC control

60% NOx + VOC control

90% NOx + VOC control

30% NOx control

60% NOx control

90% NOx control

between the supplemental scenarios of 0, 30, 60, and 90 percent.5 The specific percentage 

reductions for Phase 1 areas are shown in Table 4.1. 

Figure 4.2b shows two other aspects of the analysis. First, in some cases, the controlling county 

within an area can vary as the precursor emissions are reduced. In the eastern Lake Michigan 

area, the modeling indicates that an additional 60% NOx reduction will be sufficient to bring two 

5

 To add precision to this process, we based these calculations on projected design values that 

contained data four places to the right of the decimal (e.g., 0.0755 ppm). In the last step of the 

process however, EPA truncates all decimal places beyond the third decimal. This is consistent 

with past policy on ozone design values.
4-5

counties with high design values (Kenosha and Sheboygan WI) into attainment of an 0.070 ppm 

standard. However, another county in that area does not reach 0.070 ppm with the 60% NOx 

reduction. Lake IN is still 0.077 ppm. The full attainment, extrapolated target analysis is done on 

a county by county basis, and the final area target is based on the county that requires the most 

additional reductions. Second, it should be noted that in this area the addition of VOC controls 

can have a significant impact on the projected design value. The 0.077 ppm value in Lake IN is 

reduced to 0.073 ppm when 60% VOC controls are added to the 60% NOx controls. Figure 4.2c 

is included for completeness sake and to show the supplemental modeling results in the 

Northeast Corridor. 

Figure 4.2b: Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and 

Each of the Six Supplemental Modeling Scenarios for the Highest Counties within the 

Eastern Lake Michigan Area 

0.045

0.050

0.055

0.060

0.065

0.070

0.075

0.080

0.085

0.090

Kenosha WI Lake IN Sheboygan WI

Projected 8-hr Ozone DV (ppm)

RIA Control Scenario

30% NOx + VOC control

60% NOx + VOC control

90% NOx + VOC control

30% NOx control

60% NOx control

90% NOx control

Figure 4.2c: Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and 

Each of the Six Supplemental Modeling Scenarios for the Highest Counties within the 

Northeast Corridor 

0.045

0.050

0.055

0.060

0.065

0.070

0.075

0.080

0.085

0.090

Suffolk NY Fairfield CT Ocean NJ

Projected 8-hr Ozone DV (ppm)

RIA Control Scenario

30% NOx + VOC control

60% NOx + VOC control

90% NOx + VOC control

30% NOx control

60% NOx control

90% NOx control
4-6

As discussed in Chapter 3 and Chapter 5, there are two areas in Southern California that are not 

planning to meet the current standard by 2020 (i.e., the Los Angeles South Coast Air Basin and 

the San Joaquin Valley nonattainment areas). As a result, we have not estimated extrapolated 

targets that will be necessary to bring these two nonattainment areas into attainment of the 

alternate standards by 2020. However, due to the effects of ozone transport within California, we 

are assuming that some extrapolated controls (beyond the RIA control case) will be needed in 

these two areas to help other California nonattainment areas with earlier attainment dates meet 

the standards by 2020. These additional reductions in Los Angeles and San Joaquin Valley are 

considered to be part of the controls needed to meet the current NAAQS and are therefore not 

considered as part of the cost of any new alternate standard. Figure 4-2d shows the results of the 

supplemental modeling runs for three areas in California. 

Figure 4.2d: Projected 2020 8-hour Ozone Design Values in the RIA Control Scenario and 

Each of the Six Supplemental Modeling Scenarios for Three Specific Areas in California 

0.045

0.055

0.065

0.075

0.085

0.095

0.105

Los Angeles CA San Joaquin Valley CA Sacramento CA

Projected 8-hr Ozone DV (ppm)

RIA Control Scenario

30% NOx + VOC control

60% NOx + VOC control

90% NOx + VOC control

30% NOx control

60% NOx control

90% NOx control

Extrapolated control targets were estimated for each Phase 1 area for: a) NOx only emissions 

reductions and b) NOx plus VOC emissions reductions. The results of the analysis to estimate 

emissions reductions for attainment in the Phase 1 areas are shown in Table 4.1 and Table 4.2. 

The amount of additional emissions reductions necessary for full attainment ranges from zero to 

over 90 percent depending upon the area and the standard. 
4-7

Table 4.1: Estimated Percentage Reductions of NOx and VOC beyond the RIA Control 

Scenario Necessary to Meet Various Alternate Ozone Standards in the Phase I Areas 

Table 4.2: Estimated Percentage Reductions of NOx beyond the RIA Control Scenario 

Necessary to Meet Various Alternate Ozone Standards in the Phase I Areas 

4.1.3 Estimating Attainment of the 0.070 and 0.065 ppm Standards in Phase 2 Areas

As discussed above, there were 61 counties that did not reach attainment of the 0.070 ppm 

standard with the controls in the hypothetical RIA scenario. The majority of these counties are in 

one of the Phase 1 areas. However, there were 12 counties (9 areas) outside of the Phase 1 areas 

that were also not projected to meet the 0.070 NAAQS. (All counties outside the Phase 1 areas 

met the 0.075 and 0.079 ppm air quality standards.) For convenience, these nine areas will be 

referred to Phase 2 areas. A two-step process was used to estimate the additional emissions 

reductions necessary for full attainment in the Phase 2 areas. Based on the Phase 1 modeling 
4-8

results, targets for these areas were only generated for NOx-only control given the 

preponderance of cases where the additional VOC emissions reductions did not reduce ozone 

enough to consider from a cost perspective. 

For the Phase 2 areas, the first step in estimating attainment was to consider whether the 

emissions reductions needed to bring the Phase 1 areas into attainment of 0.070 ppm would also 

reduce ozone transport enough to bring these additional areas into attainment as well. For an 

example of how this determination was made consider two counties: Norfolk County, MA 

(Boston area) and Geauga County, OH (Cleveland area). 

In Norfolk MA, the projected design value after the RIA control scenario is 0.071 ppm. This 

county is downwind of the Northeast Corridor. The supplemental modeling showed that if the 

Phase 1 areas reduced NOx emissions by at least 30% the 2020 design value in Norfolk MA 

would be reduced to 0.069 ppm (i.e., does not exceed the 0.070 standard). As part of the Phase I 

analysis, we estimated that the Northeast Corridor region would need an additional 39% NOx 

reduction to meet the 0.070 ppm standard within this area. The supplemental modeling shows 

that the same 39% NOx reduction would enable this standard to be met in Norfolk County as 

well, without any additional local controls in the Boston area. 

In Geauga OH, the projected design value after the RIA control scenario is 0.074 ppm. Thus, 

Cleveland will need additional local emissions reductions to meet a revised ozone standard of 

0.070 ppm. However, in the supplemental modeling, which did not include emissions reductions 

in Cleveland, the Geauga design value declined by 0.001, 0.002, and 0.003 ppm, in the 30, 60, 

and 90% NOx reduction runs, respectively. Given that the Lake Michigan region is the nearest 

upwind Phase 1 area to Geauga County, we believe these ozone reductions in Geauga County are 

associated with the emissions reductions modeled in the Lake Michigan region. The Lake 

Michigan region is estimated to need 72% additional NOx control. Considering the projected 

design values with an additional digit of precision, it is estimated that a 72% reduction in the 

eastern Lake Michigan area will yield a Geauga OH design value of 0.0718 ppm.6

In the second step of the process, we estimate what level of local control is required to reach 

0.070 ppm after consideration of the impact of Phase 1 emissions reductions. For each of the 

Phase 2 areas that is still nonattainment after step 1 above, we developed a site-specific 

relationship between the ozone improvement in the RIA control case and the percent reduction in 

local NOx emissions in the RIA control case as compared to the baseline. This site-specific 

relationship was then used to determine how much additional NOx reduction was needed to meet 

the 0.070 ppm goal. Continuing with the Geauga County example helps illustrate this 

calculation. In this county there was a 0.0023 ppm reduction due to the hypothetical RIA 

controls. The RIA scenario represented a 17% reduction in NOx emissions within the 200 km 

buffer around the Cleveland area. With the existing information it is not possible to distinguish 

6

 The full step 1 calculation for the Geauga OH example is as follows. A 60 percent reduction 

yields a design value of 0.0722 ppm. A 90 percent reduction yields a design value of 0.0710 

ppm. The estimated Phase 1 target for eastern Lake Michigan is 72%, or four-tenths of the 

“distance” between 60 and 90% control. Forty percent of the 0.0012 ppm difference between the 

two runs is 0.00048 ppm. Subtracting that from 0.0722 ppm, yields the transport-considered 

design value of 0.0717 ppm which would be truncated to 0.071 ppm. 
4-9

how much of the ozone improvement is due to local controls (i.e., within 200 km) versus upwind 

controls, so we made a simplifying assumption that all local air quality improvement for such 

areas can be attributed to the controls within 200 km. Converting to units of ppb for simplicity, 

dividing 2.3 ppb improvement by a 17% NOx emissions reduction yields a Geauga-specific 

relationship of 0.135 ppb / percent NOx controlled. This ratio is applied to the 71.8 ppb value 

from step 1 and it is determined that an additional 7 % reduction (0.9 ppb) would be sufficient to 

lower the 2020 design value in Geauga County to 70.9 ppb or 0.070 ppm, thereby attaining the 

standard.

The same two step methodology described above was used to estimate the extrapolated targets 

for the 0.065 ppm standard in the Phase 2 areas. Table 4.3 shows the full set of results for each of 

the nine Phase 2 areas. The amount of additional NOx control needed to meet the 0.070 ppm 

standard in Phase 2 areas ranges from zero to 25 percent. The amount of additional NOx control 

needed to meet the 0.065 ppm standard in Phase 2 areas ranges from zero to 74 percent. 

Table 4.3: Estimated Percentage Reductions of NOx beyond the RIA Control Scenario 

Necessary to Meet the 0.070 ppm Ozone Standard in Phase 2 Areas7

4.1.4 Estimating Attainment of the 0.065 ppm Standard outside of Phase 1 and 2 Areas

The last set of reduction targets generated are for those areas that require additional ozone 

precursor controls to meet the 0.065 ppm standard but are outside Phase 1 and 2 areas. There 

were 166 counties that did not reach attainment of the 0.065 ppm standard with the emissions 

reductions in the hypothetical RIA scenario. The majority of these counties are in one of the 

Phase 1 or Phase 2 areas. However, there were 46 counties (36 areas) outside of the Phase 1 and 

Phase 2 areas that were not projected to meet the 0.065 NAAQS. For convenience, these areas 

will be referred to Phase 3 areas. 

A similar methodology as described in Section 4.1.3 was used to estimate the additional 

emissions reductions needed for the 0.065 ppm standard for the Phase 3 areas, but two 

simplifying assumptions were made to expedite the analysis. First, instead of explicitly 

accounting for the impacts of the Phase 1 and Phase 2 upwind emissions reductions on Phase 3 

areas, we assumed that the design values from the 60% NOx reduction run were the appropriate 

starting point for estimating the additional emissions reductions in the Phase 3 areas. Since the 

7

 The entry “will attain” in Tables 4.3 and 4.4 signifies that this area will come into attainment of 

the standard due to reduced ozone transport resulting from upwind controls. 
4-10

targets for the Phase 1 areas are generally greater than 60% and since we have not accounted for 

the Phase 2 reductions, these estimates should provide a conservative estimate of the percentage 

emissions reductions needed for full attainment. Secondly, we did not develop site-specific 

impact ratios for the 36 Phase 3 areas. Instead, we used a standard relationship of 0.150 ppb / 1% 

NOx reduction for calculating the emissions reductions needed to attain 0.065 ppm in these 

areas. This value was the average site-specific relationship calculated for the Phase 2 areas, as 

described above. These assumptions are reasonable given the available data and the relatively 

small role that Phase 3 areas will play in determining the full costs of meeting a 0.065 ozone 

standard. However, the estimated emissions reductions needed to attain 0.065 in the Phase 3 

areas are considered to be more uncertain than the emissions reductions calculated for attaining 

0.070, 0.075, and/or 0.079. The results of the Phase 3 analysis are shown in Table 4.4. The 

amount of additional NOx control needed to meet the 0.065 ppm standard in Phase 3 areas 

ranges from zero to 29 percent. 

Table 4.4: Estimated Percentage Reductions of NOx beyond the RIA Control Case 

Necessary to Meet the 0.065 ppm Ozone Standard in Phase 3 Areas 
4-11

4.1.5 Aggregate Results / Verification Modeling of Extrapolated Targets

The complete set of NOx targets are provided in Table 4.5a. As noted earlier, a single 2020 

target was determined for all of California. This target was based on the Sacramento area which 

had the highest 2020 design values outside the Los Angeles and San Joaquin Valley areas. The 

assumption is that if all of California reduces at that level then all areas aside from Los Angeles 

and the San Joaquin Valley air basins will attain by 2020. Areas from which reductions would be 

required include the Los Angeles and San Joaquin Valley air basins, but would not necessarily 

bring them into attainment. Additional reductions may be required. Because of their later 

attainment date, the costs and benefits of additional reductions for Los Angeles and San Joaquin 

air basins are shown in Appendix 7b.

Table 4.5a: Complete Set of Estimated Percentage Reductions of NOx beyond the RIA 

Control Scenario Necessary to Meet the Various Ozone Standards in 2020 

In total, 33 areas were determined to need additional emissions reductions for one or more of the 

alternate standards. The eastern Lake Michigan region was the only one in which NOx plus VOC 

control targets could be substantially lower than NOx only control targets. Table 4.5b shows the 

NOx + VOC targets for that area. 
4-12

Table 4.5b: Estimated Percentage Reductions of NOx + VOC beyond the RIA Control 

Scenario Necessary to Meet the Various Ozone Standards in 2020 

Figures 4.3a through 4.3d show: 1) which counties are part of the 33 extrapolated cost areas and 

2) the estimated percent reduction needed beyond the RIA control case to meet each of the four 

alternate standards within each of those areas. The conversion of these additional percentage 

reductions to actual extrapolated tons is described in Chapter 4.2. The calculation of the costs of 

these extrapolated tons is described in Chapter 5. 

Figure 4.3a: Map of Extrapolated Cost Counties for the 0.065 ppm Alternate Standard and 

the Estimated Percent NOx Controls Needed to Meet that Standard 
4-13

Figure 4.3b: Map of Extrapolated Cost Counties for the 0.070 ppm Alternate Standard and 

the Estimated Percent NOx Controls Needed to Meet that Standard 

Figure 4.3c: Map of Extrapolated Cost Counties for the 0.075 ppm Alternate Standard and 

the Estimated Percent NOx Controls Needed to Meet that Standard 
4-14

Figure 4.3d: Map of Extrapolated Cost Counties for the 0.079 ppm Alternate Standard and 

the Estimated Percent NOx Controls Needed to Meet that Standard 

As noted earlier in this section, an additional CMAQ air quality simulation, called a “verification 

run,” was completed after the extrapolated percent emissions reductions were estimated. The 

purpose of this run was to determine the ozone design values that would be expected from the 

additional extrapolated reductions shown in Table 4.5a and Table 4.5b. These are the reductions 

that were estimated to be needed for full attainment of the 0.070 ppm standard for areas outside 

of Los Angeles and San Joaquin Valley. The results of the verification modeling were 

encouraging and confirmed our approach for estimating the extrapolated reductions. For the four 

areas where we projected that no additional local controls were needed and that the additional 

upwind reductions would be sufficient for attainment of 0.070 (see Table 4.3), the verification 

modeling indicated that all four areas had ozone design values less than 0.070 ppm after the 

extrapolated reductions were applied. Of the remaining nine areas that did not reach the 0.070 

ppm standard in the RIA control case, eight of the nine were within plus or minus 0.002 ppm 

after application of the extrapolated emissions reductions. The proximity of the verification 

design values to the 0.070 ppm target provides confidence that the estimates of extrapolated tons 

are reasonable. Table 4.6 shows the results of the verification modeling for the 13 areas that were 

included in the (0.070 ppm) extrapolated cost analysis. 
4-15

Table 4.6: Summary of the Verification Modeling Results 

4.2 Conversion of Full Attainment Percentage Targets into Extrapolated Tons 

Table 4.7a provides the complete set of extrapolated tons of NOx emissions reduction needed to 

satisfy the various ozone standards. These extrapolated tons are obtained by multiplying the NOx 

targets in Table 4.5a by the remaining emissions for each area after the RIA control scenario. It 

is important to note that the extrapolated cost areas are potentially standard-specific because the 

location of counties in an extrapolated area depends on whether the particular standard is being 

violated. For example, as seen in Figures 4.3a and 4.3b, the Eastern Lake Michigan area extends 

further north into Wisconsin for the 0.065 ppm standard where areas like Green Bay attained the 

0.070 standard but not 0.065 ppm standard. 
4-16

Table 4.7a: Complete Set of Estimated Extrapolated Emissions Reductions of NOx Beyond 

the RIA Control Scenario Necessary to Meet the Various Ozone Standards in 2020 

0.065 0.070 0.075 0.079 0.084

Ada Co., ID 5,300

Atlanta, GA 21,000

Baton Rouge, LA 170,000 57,000

Boston-Lawrence-Worcester, MA 14,000

Buffalo-Niagara Falls-Jamestown, NYA

19,000 3,900

Campbell Co., WY 2,600

Charlotte-Gastonia-Rock Hill, NC-SC 62,000

Cincinnati-Hamilton, OH-KY-IN 9,400

Cleveland-Akron-Lorain, OH 83,000 13,000

Dallas-Fort Worth, TX 53,000 3,100

Denver-Boulder-Greeley-Ft Collins-Love, CO 8,600

Detroit-Ann Arbor, MI 100,000 11,000

Dona Ana CO., NM 980

El Paso Co., TX 1,700

Houston, TX 290,000 270,000 220,000 190,000 110,000

Huntington-Ashland, WV-KY 22,000

Jackson Co., MS 7,600

Jefferson Co, NY 7,300

Las Vegas, NV 5,000

Memphis, TN-AR 15,000

Norfolk-Virginia Beach-Newport News, VA 30,000

Northeast Corridor, CT-DE-MD-NJ-NY-PA 350,000 230,000 73,000

Phoenix-Mesa, AZ 4,900

Pittsburgh-Beaver Valley-Erie, PA

B

17,000

Richmond-Petersburg, VA 270

Sacramento Metro, CA 310,000 210,000 110,000 17,000

Salt Lake City, UT 4,000

San Juan Co., NM 17,000

St Louis, MO-IL 35,000

Toledo, OH 85

Additional local emissions reductions [annual tons/year] needed to meet 

various standards (ppm)

All 2020 Extrapolated Cost Areas

(NOx only)

a

Jamestown is included in the Buffalo-Niagara Falls, NY cost area because it falls within the 200km 

Buffalo-Niagara Falls buffer and has a lower design value. 

b Erie is included in the Pittsburgh-Beaver Valley, PA cost area because it falls within the 200km 

Pittsburgh-Beaver Valley buffer and has a lower design value. 

In total, additional emissions reductions are provided for 31 areas. As footnoted, Jamestown NY 

is included in the Buffalo-Niagara Falls NY area. There are three reasons for this: 1) Jamestown 

is within the 200km buffer for Buffalo-Niagara Falls, 2) as seen in Table 4-5a, the NOx target is 

greater in Buffalo-Niagara than Jamestown for each standard, and 3) Jamestown is in the same 

state. Erie is included in the Pittsburgh-Beaver Valley PA area for the same three reasons. 

As noted in Table 4.5b in Section 4.1.5, the eastern Lake Michigan area was the only one in

which NOx plus VOC additional emission reductions could be substantially lower than NOxonly emissions reductions. Table 4.7b shows the additional NOx + VOC emission reductions for 

this area. 
4-17

Table 4.7b: Estimated Extrapolated Emissions Reductions of NOx + VOC Beyond the RIA 

Control Scenario Necessary to Meet the Various Ozone Standards in 2020 

NOx VOC NOx VOC NOx VOC NOx VOC

Eastern Lake Michigan, IL-IN-WI 350,000 400,000 280,000 330,000 100,000 120,000 8,100 9,800

All 2020 Extrapolated Cost Areas

(NOx + VOC)

Additional local emissions reductions [annual tons/year] needed to meet various standards 

(ppm)

0.065 0.070 0.075 0.079

4.3 Methodology Used to Estimate the Amount of “Overcontrolled” Emissions in the 

Modeled Control Strategy 

The corollary to extrapolated tons (needed tons above and beyond the modeled control strategy) 

is “overcontrolled” tons. These are emissions reductions within the hypothetical control case that 

were subsequently determined not to be needed to meet particular alternate standards. That is, 

once we modeled the baseline and control strategy scenarios we found that we had reduced 

ozone beyond the particular alternate standard. In order to better estimate the costs and benefits 

of full attainment of the standards, EPA has estimated the “overcontrolled” emissions 

percentages within the modeled control strategy for the four alternate standards: 079, 075, 070 & 

065. These percentages are to be applied to the tons reduced between the baseline and the control 

case.

The methodology for calculating the “overcontrol” percentages is based on simple linear 

interpolation between the baseline scenario and the model control strategy. These two model 

runs were used to estimate what level of control was just needed to bring an area into attainment 

of a standard. A caveat to this approach is that it assumes that all air quality impacts are due to 

local controls; there is no consideration of the potential impacts of ozone transport. 

The details of the methodology are as follows. The first step was to identify all counties with 

ozone concentrations greater than 0.070 ppm in the base case. These 142 counties were the 

starting point for designing the modeled control strategy described in Chapter 3. Because the 

majority of the California controls are in the baseline and because several CA areas continue to 

be nonattainment of all four alternate standards in 2020 and beyond, we did not assess 

“overcontrol” in California. The remaining counties were aggregated into 32 distinct areas for an 

assessment of whether that area overcontrolled to meet an alternate standard. Each area included 

the original nonattainment county or counties, plus all counties within 200 km of that county or 

counties. The “overcontrolled” analysis was done for the county with the highest ozone levels in 

the control case modeling. These 32 areas comprised 1,199 counties. These are the same 1,199 

non-California counties over which NonEGU point and Area sources were controlled in the 

hypothetical strategy. 

A simple three-step process was used to determine the amount of overcontrol in the hypothetical 

control case for each of the 32 areas. The results are summarized in Table 4.8. 
4-18

Table 4.8: Estimated Percentages of Modeled Control Strategy Emissions Reductions not 

needed to Meet the Various Ozone Standards in 2020 

a) For each standard, we first determined if the area was below that standard in the baseline 

modeled scenario. If so, then all of the hypothetical controls should be returned from the 

control scenario. For example, the highest projected design value in the Cincinnati area 

was 0.072 ppm in the basecase and 0.070 ppm in the baseline. Thus, that area did not 

actually need any of the hypothetical controls above and beyond the baseline to meet the 

0.079, 0.075, or 0.070 standards locally. Therefore, all of the controls in that area should 

be returned for those standards. 

b) For each standard, we then determined if the area was above that standard in the modeled 

control case. If so, then none of the hypothetical controls should be given back. As an 

example, the Houston area had a projected design value of 0.087 ppm in the control case. 

Therefore, all of the emissions in the modeled control strategy (and some extrapolated 

tons) are needed in that area. 

c) For each standard, and for all other areas that were above the standard in the baseline and 

below in the control case, we used linear interpolation to estimate what percentage of the 

emissions reductions in the modeled control strategy could be returned and still allow the 

standard to be met. For example, the maximum projected design value in the Cleveland 

area was 0.0795 ppm in the basecase, 0.0765 ppm in the baseline, and 0.0742 ppm in the 
4-19

control case. Linear interpolation8

 between the baseline and the control case indicates that 

74% of the controls in the Cleveland area, including counties within a 200km buffer, 

could be given back and still just meet the 0.075 ppm target. All of the control strategy 

reductions would be given back for the less-stringent 0.079 ppm standard and none of the

reductions would be given back for the more-stringent 0.070 ppm standard. 

4.4 Conversion of Estimated Percentages of Unnecessary Emission Reductions into 

“Overcontrolled” Tons 

The percentages of modeled control strategy emissions reductions not needed to meet the various 

ozone standards in 2020 shown in Table 4.8 were applied to the control case reductions in 

Table 4.9. In areas and targets where the percentages in Table 4.8 were “ALL,” the unnecessary 

emissions reductions in Table 4.9 are equal to the baseline minus control case emissions seen in 

the same table. Similarly, in areas and targets where there was no “over-control” (“NONE” in 

Table 4.8), emission reductions not needed for alternative standards in Table 4.9 are zero; that is, 

the control scenario did not “over-control” emissions for that area and target. As seen in 

Table 4.8, ozone concentration estimates are greater than 0.0795 ppm in both Houston and 

Eastern Lake Michigan; therefore there was no over-control and no unnecessary emission 

reductions.

8

 The calculation used to determine the 74% target for the 0.075 ppm targets is as follows: 

1.0-[(0.0765-0.0759)/(0.0765-0.0742)], where 0.0759 ppm represents the highest ozone level that 

still attains a 0.075 ppm standard, due to the usual truncation of the fourth decimal place. 
4-20

Table 4.9: Estimated 2020 Control Case Emission Reductions not needed to Meet the 

Various Ozone Standards in 2020 

Area controlled within the modeled control 

Strategy

2020 Base 

Case

2020

Baseline

2020

Control 

Case

Baseline

minus

Control 

Case 0.079 0.075 0.070 0.065

Eastern Lake Michigan, IL-IN-WI-MI 600,000 500,000 460,000 36,000 0 0 0 0

Houston-Galveston-Brazoria, TX 460,000 340,000 320,000 12,000 0 0 0 0

Northeast Corridor, CT-DE-DC-NY-NJ-PA-VA 910,000 840,000 750,000 98,000 98,000 0 0 0

Jefferson Co., NY 36,000 34,000 32,000 2,000 2,000 2,000 0 0

Allegan Co., MI 20,000 18,000 15,000 3,100 3,100 3,100 0 0

Buffalo-Niagara Falls, NY 66,000 62,000 55,000 7,000 7,000 7,000 0 0

Las Vegas, NV 45,000 43,000 36,000 7,800 7,800 7,800 0 0

Boston-Lawrence-Worcester-Portsmouth, MA-NH 150,000 140,000 130,000 14,000 14,000 14,000 0 0

Cleveland-Akron-Lorain, OH 270,000 250,000 210,000 44,000 44,000 32,000 0 0

Dallas-Fort Worth, TX 210,000 200,000 160,000 43,000 43,000 43,000 0 0

Detroit-Ann Arbor, MI 260,000 240,000 190,000 50,000 50,000 50,000 0 0

Baton Rouge, LA 400,000 350,000 230,000 110,000 110,000 81,000 0 0

Richmond-Petersburg, VA 12,000 11,000 11,000 310 310 310 310 0

Muskegon Co., MI 5,100 4,400 4,000 420 420 420 420 0

Norfolk-Virginia Beach-Newport News, VA 9,600 9,100 8,300 780 780 780 520 0

Huntington-Ashland, WV-KY 5,800 5,400 4,200 1,200 1,200 1,200 1,200 0

Providence (All RI), RI 13,000 12,000 10,000 1,500 1,500 1,500 1,500 0

Toledo, OH 4,700 4,400 2,800 1,600 1,600 1,600 1,600 0

Charlotte-Gastonia-Rock Hill, NC-SC 240,000 230,000 220,000 14,000 14,000 14,000 3,200 0

Indianapolis, IN 44,000 43,000 36,000 6,600 6,600 6,600 6,600 0

Salt Lake City, UT 53,000 49,000 42,000 7,400 7,400 7,400 7,400 0

Phoenix, AZ 89,000 83,000 75,000 7,500 7,500 7,500 7,500 0

Hancock, Knox, Lincoln & Waldo Cos, ME 41,000 39,000 30,000 9,300 9,300 9,300 7,800 0

Denver, CO 110,000 110,000 81,000 25,000 25,000 25,000 16,000 0

Pittsburgh-Beaver Valley, PA 160,000 150,000 120,000 30,000 30,000 30,000 17,000 0

St Louis, MO-IL 290,000 270,000 240,000 30,000 30,000 30,000 29,000 0

Atlanta, GA 220,000 210,000 180,000 31,000 31,000 31,000 31,000 0

Cincinnati-Hamilton, OH-KY-IN 320,000 290,000 250,000 41,000 41,000 41,000 41,000 0

2020 Control Case Emission Reductions 

Annual Emissions [tons/year] not needed for alternate standards 
5-1

Chapter 5: Engineering Cost Estimates 

Synopsis

This chapter summarizes the data sources and methodology used to estimate the engineering 

costs of attaining the alternative more stringent levels for the ozone primary standard analyzed in 

this RIA. This chapter estimates the engineering costs of 0.065 ppm, 0.070 ppm, 0.075 ppm, and 

0.079 ppm. The chapter presents engineering cost estimates for the illustrative modeled control 

strategy outlined in Chapter 3 (which uses currently available known controls). The modeled 

control strategy discussion is followed by a presentation of estimates for the engineering costs of 

the additional tons of emissions that are needed to move to full attainment of the alternate 

standards analyzed, referred to as Extrapolated Costs (methodology and numbers discussed in 

Chapter 4).

As noted in Chapter 3, EPA first modeled an illustrative control strategy aimed at attaining a 

tighter standard of 0.070 ppm in 2020. EPA modeled the lower end of the proposed range to 

capture a larger number of geographic areas that may be affected by a new ozone standard. 

These known controls were insufficient to bring all areas into attainment with 0.070 ppm, and 

EPA then developed methodology to estimate additional tons of emissions needed to attain 0.079 

ppm, 0.075 ppm, 0.070 ppm, and 0.065 ppm. This chapter presents the engineering costs 

associated with each portion of the control analysis, clearly identifying the relative engineering 

costs of modeled versus extrapolated emissions reductions as well as providing an estimate of the 

total engineering cost of attainment nationwide in 2020. Nationwide attainment refers to all areas 

of the nation that are required to attain the current ozone standard by the year 2020. It does not 

reflect full attainment for the two areas of California, which have attainment dates for the current 

standard post 2020. For a complete discussion attainment for these two areas of California see 

Appendix 7b. Section 5.1 summarizes the methodology and the engineering costs associated with 

applying known and supplemental controls to partially attain a 0.070 ppm alternative standard, 

incremental to reaching the current baseline (effectively 0.084 ppm) in 2020. 

Section 5.2 describes the methodology used to estimate the engineering costs of extrapolated 

tons needed to reach attainment of the final 0.075 ppm standard as well as the three alternatives 

and provides estimates of how much additional engineering costs will be associated with moving 

from the modeled partial attainment scenario (i.e. modeled control strategy) to the nationwide 

attainment scenario (see Chapter 4 for discussion of extrapolated tons needed to attain 0.079, 

0.075, 0.070, and 0.065 ppm). 

The engineering costs described in this chapter generally include the costs of purchasing, 

installing, and operating the referenced technologies. For a variety of reasons, actual control 

costs may vary from the estimates EPA presents here. As discussed throughout this report, the 

technologies and control strategies selected for analysis are illustrative of one way in which 

nonattainment areas could meet a revised standard. There are numerous ways to construct and 

evaluate potential control programs that would bring areas into attainment with alternative 

standards, and EPA anticipates that state and local governments will consider programs that are 

best suited for local conditions. Furthermore, based on past experience, EPA believes that it is 

reasonable to anticipate that the marginal cost of control will decline over time due to 
5-2

technological improvements and more widespread adoption of previously niche control 

technologies. Also, EPA recognizes the extrapolated portion of the engineering cost estimates 

reflects substantial uncertainty about which sectors, and which technologies, might become 

available for cost-effective application in the future. This is explained in further detail in 

Section 5.3. Appendix 5a includes detailed cost and control efficiency information on different 

control measures applied as part of our modeled control strategy, and also includes summary 

results from applications of specific control measures. 

It is also important to recognize that the engineering cost estimates are limited in their scope. 

Because we is not certain of the specific actions that states will take to design State 

Implementation Plans to meet the revised standards, we do not present estimated costs that 

government agencies may incur for managing the requirement and implementation of these 

control strategies or for offering incentives that may be necessary to encourage or motivate the 

implementation of the technologies, especially for technologies that are not necessarily market 

driven. This analysis does not assume specific control measures that would be required in order 

to implement these technologies on a regional or local level. 

We use EMPAX-CGE to estimate the economic impacts and the social costs associated with the 

modeled control strategy. EMPAX uses as input the engineering costs estimated for the 

modeled control strategy to calculate its economic impacts and social costs. Economic impacts 

are estimates of changes in price and output for those industries and consumers of their output 

affected by the modeled control strategy. Social costs are costs from changes in household 

welfare due to impacts from the costs of the controls in the modeled control strategy. For more 

details on the economic impacts and social costs, please refer to Appendix 5b. 

5.1 Modeled Controls 

5.1.1 Sector Methodology 

5.1.1.1 NonEGU Point and Area Sources: AirControlNET

After designing a national hypothetical control strategy using the methodology discussed in 

Chapter 3 (see sub-section 3.2.1), EPA used AirControlNET to estimate engineering control 

costs. AirControlNET calculates engineering costs using three different methods: (1) by 

multiplying an average annualized cost per ton estimate against the total tons of a pollutant 

reduced to derive a total cost estimate; (2) by calculating cost using an equation that incorporates 

information regarding key plant information; or (3) by using both cost per ton and cost equations. 

Most control cost information within AirControlNET has been developed based on the cost per 

ton approach. This is because estimating engineering costs using an equation requires more data, 

and parameters used in other non-cost per ton methods may not be readily available or broadly 

representative across sources within the emissions inventory. The costing equations used in 

AirControlNET require either plant capacity or stack flow to determine annual, capital and/or 

operating and maintenance (O&M) costs. Capital costs are converted to annual costs, in dollars 
5-3

per ton, using the capital recovery factor.1

 Where possible cost calculations are used to calculate 

total annual control cost (TACC) which is a function of the capital (CC) and O&M costs. Capital 

costs are converted to annual costs, in dollars per ton, using the capital recovery factor (CRF). 

The capital recovery factor incorporates the interest rate and equipment life (in years) of the 

control equipment. Operating costs are calculated as a function of annual O&M and other 

variable costs. The resulting TACC equation is TACC = (CRF * CC) + O&M. 

Engineering costs will differ based upon quantity of emissions reduced, plant capacity, or stack 

flow which can vary by emissions inventory year. Engineering costs will also differ by the year 

the costs are calculated for (i.e., 1999$ versus 2006$). For capital investment, we do not assume 

early capital investment in order to attain standards by 2020. For 2020, our estimate of 

annualized costs represents a “snapshot” of the annualized costs, which include annualized 

capital and O&M costs, for those controls included in our modeled control strategy. Our 

engineering cost analysis uses the equivalent uniform annual costs (EUAC) method, in which 

annualized costs are calculated based on the equipment life for the control measure along with 

the interest rate by use of the CRF as mentioned previously in this chapter. Annualized costs are 

estimated as equal for each year the control is expected to operate. Hence, our annualized costs 

for nonEGU point and area sources estimated for 2020 are the same whether the control measure 

is installed in 2019 or in 2010. We make no presumption of additional capital investment in 

years beyond 2020. The EUAC method is discussed in detail in the EPA Air Pollution Control 

Cost Manual (found at http://epa.gov/ttn/catc/products.html#cccinfo). Applied controls and their 

respective engineering costs are provided in the Ozone NAAQS RIA docket. 

The modeled control strategy for nonEGU Point and Area sources incorporated annualized 

engineering cost per ton caps. These caps were defined as the upper cost per ton for controls of 

nonEGU point and area sources. The caps were calculated by examining the marginal cost 

curves for each pollutant for the geographic areas (approximately 1,300 counties for NOx 

controls, see Figure 3.5 and approximately 120 counties for VOC controls, see Figure 3.6) being 

analyzed for this analysis. For reductions of NOx emissions the cap (see Figure 5.1) was set at 

$23,000/ton (2006$). At this cap, ninety-eight percent of the possible reductions from known 

measures are achieved at eighty-two percent of the total annualized engineering cost. There were 

only two controls whose cost per ton were greater than this cap, and subsequently not included in 

this analysis, due to the large capital component of installing these controls. A similar process 

was followed for reductions from VOCs. The relative air quality effectiveness of reductions in 

VOC was considered, and the marginal cost curve (Figure 5.2) was analyzed. Subsequently, the 

cap was set at approximately $5,000/ton (2006$). At this cap, forty-six percent of the possible 

reductions are achieved at fifteen percent of the total engineering cost. It is important to note that

as part of the extrapolated cost analysis the VOC cap was raised to $15,000/ton (for geographic 

areas where the supplemental air quality modeling showed VOC control to be beneficial). At this 

cap (2006$) ninety-eight percent of the possible reductions could be achieved.

1

 For more information on this cost methodology and the role of AirControlNET, see Section 6 

of the 2006 PM RIA, AirControlNET 4.1 Control Measures Documentation (Pechan, 2006b), or 

the EPA Air Pollution Control Cost Manual, Section 1, Chapter 2, found at 

http://www.epa.gov/ttn/catc/products.html#cccinfo.
5-4

Figure 5.1: Marginal Cost Curve for Modeled Control Strategy Geographic Areas 

(NOX nonEGU Point and Area Source Controls Prior to Cut Points) 

$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

$160,000

0 100,000 200,000 300,000 400,000 500,000 600,000

Cumulative Emission Reductions (annual tons/year)

Annual Cost/Ton (2006$) 

Figure 5.2: Marginal Cost Curve for Modeled Control Strategy Geographic Areas 

(VOC nonEGU Point and Area Source Controls Prior to Cut Points) 

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

$35,000

- 100,000 200,000 300,000 400,000 500,000 600,000

Cumulative Emission Reductions (annual tons/year)

Annual Cost/Ton (2006$)

5.1.1.2 EGU Sources: the Integrated Planning Model

Engineering costs for the electric power sector are estimated using the Integrated Planning 

Model (IPM). The model determines the least-cost means of meeting energy and peak demand 

requirements over a specified period, while complying with specified constraints, including air 

pollution regulations, transmission bottlenecks, fuel market restrictions, and plant-specific 

operational constraints. IPM is unique in its ability to provide an assessment that integrates 

power, environmental, and fuel markets. The model accounts for key operating or regulatory 

constraints (e.g., emission limits, transmission capabilities, renewable generation requirements, 

fuel market constraints) that are placed on the power, emissions, and fuel markets. IPM is 

particularly well-suited to consider complex treatment of emission regulations involving trading 

and banking of emission allowances, as well as traditional command-and-control emission 
5-5

policies.2

 Applied controls and their respective engineering costs are provided in the docket. IPM 

is described in further detail in Appendix 3. 

5.1.1.3 Onroad and Nonroad Mobile Sources: National Mobile Inventory Model (NMIM) and 

Various Studies

Engineering cost information for mobile source controls was taken from studies conducted by 

EPA for previous rulemakings and studies conducted for development of voluntary and local 

measures that could be used by state or local programs to assist in improving air quality. Applied 

controls and their respective engineering costs are provided in the docket.3

Engineering costs, in terms of dollars per ton emissions reduced, were applied to emission 

reductions calculated for the onroad and nonroad mobile sectors that were generated using the 

NMIM. NMIM is an EPA model for estimating pollution from highway vehicles and nonroad 

mobile equipment. NMIM uses current versions of EPA’s model for onroad mobile sources, 

MOBILE6, and nonroad mobile sources, NONROAD, to calculate emission inventories 4.

5.1.2 Modeled Controls—Engineering Cost by Sector 

In this section, we provide engineering cost estimates of the control strategies identified in 

Chapter 3 that include control technologies on nonEGU stationary sources, area sources, EGUs, 

and onroad and nonroad mobile sources. Engineering costs generally refer to the capital 

equipment expense, the site preparation costs for the application, and annual operating and 

maintenance costs. 

The total annualized cost of control in each sector in the control scenario is provided in 

Table 5.1. These numbers reflect the engineering costs across sectors annualized at a discount 

rate of 7% and 3%, consistent with the guidance provided in the Office of Management and 

Budget’s (OMB) (2003) Circular A-4. However, it is important to note that it is not possible to 

estimate both 7% and 3% discount rates for each source (see section 5.1.3). In Table 5.1, an 

annualized control cost is provided to allow for comparison across sectors, and between costs 

and benefits. A 7% discount rate was used for control measures applied to nonEGU point, area, 

2

 The application of the 0.070 EGU control strategy results in annual NOx allowance price 

decreasing from $1618/ton in the baseline to $641/ton. See Technical Support Document on 

EGU Control Strategies for more details. Further detailed information on IPM is available in 

Section 6 of the 2006 PM RIA or at http://www.epa.gov/airmarkets/epa-ipm

3

 The expected emissions reductions from SCR retrofits are based on data derived from EPA 

regulations (Control of Emissions of Air Pollution from 2004 and Later Model Year Heavy-duty 

Highway Engines and Vehicles published October 2000), interviews with component 

manufacturers, and EPA’s Summary of Potential Retrofit Technologies available at 

www.epa.gov/otaq/retrofit/retropotentialtech.htm.

For more information on mobile idle reduction technologies (MIRTs) see EPA’s Idle Reduction 

Technology page at http://www.epa.gov/otaq/smartway/idlingtechnologies.htm.

4

 More information regarding the National Mobile Inventory Model (NMIM) can be found at 

http://www.epa.gov/otaq/nmim.htm 
5-6

and mobile sources. Engineering costs from EGU sources, which are calculated using the IPM 

model and variable interest rates, are captured in this table at an annualized 7% discount rate.5

5

 A different plant-specific interest rate is applied in estimating control costs within IPM. See PM 

RIA for details. 
5-7

Table 5.1: Annual Control Costs by Sector and Region, for the 

Modeled Control Strategy (2006$) a, b, e

Modeled Control Strategy 

Engineering Cost by 

Region (M 2006$) 

Source Category East West CA 

Total Cost 

(M 2006$) 

Average 

Cost/Ton 

(2006$) 

Electric Generating Units (EGU) Sector 

Controls for NOx cap and trade program and 

local measures in projected nonattainment 

areas for coal units. 

$170 $(70) c $66 $160 $1,900 f

Total $170 $(70) $66 $160 

Mobile Source Sector 

Onroad Sources (Ex: automobiles, buses, 

trucks, and motorcycles traveling on roads 

and highways) 

$360 $55 $45 $460 $2,100 

Nonroad Sources (Ex: railroad locomotives; 

marine vessels, aircraft, and farm, 

construction, industrial and lawn/garden 

equipment) 

$150 $21 $16 $190 $3,400 

Total $510 $75 $61 $650 

NonEGU Sector 

Point Sources (Ex: chemical manufacturing, 

cement manufacturing, petroleum refineries, 

and iron and steel mills) 

$1,400 $57 $4.7 $1,500 $3,800 

Area Sector 

Area Sources (Ex: residential woodstoves, 

agriculture) $480 $44 $20 $550 $1,900 

Total $2,000 

Total Annualized Costs 

(using a 7% interest rate) $2,600 $170 $160 $2,800 

Total Annualized Costs 

(using a 3% interest rate) d $2,400 $160 $160 $2,600 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. The 

modeled control strategy is that strategy applied to reach attainment of the 0.070 alternate primary 

standard, and is described in detail in Chapter 3. 

b

 All estimates provided reflect the engineering cost of the modeled control strategy, incremental to a

2020 baseline of compliance with the current standard of 0.084 ppm. 

c

 The total cost is negative in the west for the modeled control strategy due to an electricity generation 

shift. The west generates less electricity and exports from the east. 

d

 Total annualized costs were calculated using a 3% discount rate for controls which had a capital 

component and where equipment life values were available. For this modeled control strategy, data for 

calculating annualized costs at a 3% discount was only available for NonEGU point sources. Therefore, 

the total annualized cost value presented in this referenced cell is an aggregation of engineering costs at 

3% and 7% discount rate. 

e

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.
5-8

f This average cost/ton estimate is based on ozone season NOx reductions from EGUs from controls that 

operate year-round as explained in Chapter 3. By counting NOx reductions in the ozone season while 

operation of NOx controls is modeled as year-round, our cost/ton estimate may spread out reductions 

and thus affect the average cost/ton estimate. It should be noted that the resulting cost/ton of the 

controls applied within EGU control strategy is practically the same as that in 2020 for the final CAIR 

rule ($1,900 in 2006 dollars). 

Total annualized costs were calculated using a 3% discount rate for controls which had a capital 

component and where equipment life values were available. In this RIA, the nonEGU point 

source sector was the only sector with available data to perform a sensitivity analysis of our 

annualized control costs to the choice of interest rate. Sufficient information on annualized 

capital calculations was not available for area source and mobile controls to provide a reliable 3 

percent discount rate estimate. As such, the 3% value in Table 5.1 is representative of the sum of 

the nonEGU Point Source sector at a 3% discount rate, and the EGU, mobile, and Area Source 

sector at a 7% discount rate. It is expected that the 3% discount rate value is overestimated due to 

the addition of cost sectors at a higher discount rate. With the exception of the 3 % Total 

Annualized Cost estimate on Table 5.1, engineering cost estimates presented throughout this and 

subsequent chapters are based on a 7% discount rate. 

The total annualized engineering costs associated with the application of known and 

supplemental controls, incremental to the baseline, are approximately $2.8 billion using a 7% 

discount rate.

5.1.3 Limitations and Uncertainties Associated with Engineering Cost Estimates 

EPA bases its estimates of emissions control costs on the best available information from 

engineering studies of air pollution controls and has developed a reliable modeling framework 

for analyzing the cost, emissions changes, and other impacts of regulatory controls. The 

annualized cost estimates of the private compliance costs are meant to show the increase in 

production (engineering) costs to the various affected sectors in our control strategy analyses. To 

estimate these annualized costs, EPA uses conventional and widely-accepted approaches that are 

commonplace for estimating engineering costs in annual terms. However, our engineering cost 

analysis is subject to uncertainties and limitations.

One of these limitations is that we do not have sufficient information for all of our known control 

measures to calculate cost estimates that vary with an interest rate. We are able to calculate 

annualized costs at an interest rate other than 7% (e.g., 3% interest rate) where there is sufficient 

information—available capital cost data, and equipment life—to annualize the costs for 

individual control measures. For the vast majority of nonEGU point source control measures, we 

do have sufficient capital cost and equipment life data for individual control measures to prepare 

annualized capital costs using the standard capital recovery factor. Hence, we are able to provide 

annualized cost estimates at different interest rates for these point source control measures as we 

have done for the proposed ozone RIA and the PM2.5 RIA last year. 

For area source control measures, the engineering cost information is available only in 

annualized cost/ton terms. We have extremely limited capital cost and equipment life data for 

area source control measures. We know that these annualized cost/ton estimates reflect an 
5-9

interest rate of 7% because these estimates are typically products of technical memos and reports 

prepared as part of rules issued by our office (OAQPS) over the last 10 years or so, and the costs 

estimated in these reports have followed the policy provided in OMB Circular A-4 that 

recommends the use of 7% as the interest rate for annualizing regulatory costs. Capital cost

Figure 5.3: Total Annualized Costs by Emissions Sector and Region for Modeled Control 

Strategy in 2020a, b, c, d 

-$0.10

$0.10

$0.30

$0.50

$0.70

$0.90

$1.10

$1.30

$1.50

Total Costs (B 2006$)

East West CA

Area nonEGU Point EGU Onroad Nonroad

a

 Total costs presented above are for a seven percent discount rate. 

b

 All estimates provided reflect the engineering cost of the modeled control strategy, incremental to a

2020 baseline of compliance with the current standard of 0.084 ppm. 

c

 The total cost is negative in the west for the modeled control strategy due to an electricity generation 

shift. The west generates less electricity and exports from the east. 

d

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

information for these area source controls, however, is often limited since these measures are 

often not the traditional add-on controls where the capital cost is well known and convenient to 

estimate. Such area source controls can include reformulation of coatings to reduce VOC, as one 

example. The limited availability of useful capital cost data for such control measures has led to 

our use of annualized cost/ton estimates to represent the engineering costs of these controls in 

our cost tools and hence in the PM2.5 and ozone RIAs.
5-10

For mobile source measures, the situation is very much like that for our area source measures. 

We do not have sufficient capital cost information from what our mobile source office (OTAQ) 

has sent us to compute annualized costs for different interest rates other than 7%. Finally, It 

should be noted that the annualized capital costs for EGUs are prepared at an interest rate other 

than 7%. Information on the annualization of EGU control costs is presented later in this chapter.

There are some unquantified costs that are not adequately captured in this illustrative analysis. 

These costs include the costs of federal and State administration of control programs, which we 

believe are less than the alternative of States developing approvable SIPs, securing EPA approval 

of those SIPs, and Federal/State enforcement. Additionally, control measure costs referred to as 

“no cost” may require limited government agency resources for administration and oversight of 

the program not included in this analysis; those costs are generally outweighed by the saving to 

the industrial, commercial, or private sector. The Agency also did not consider transactional 

costs and/or effects on labor supply in the illustrative analysis.

The economic impacts of the cost of these modeled control strategy is included in Appendix 5b 

of this analysis. The illustrative analysis does quantify the potential for advancements in the 

capabilities of pollution control technologies as well as reductions in their engineering costs over 

time. This is discussed in Section 5.4. 

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

Valley and South Coast air basins in California. The state has submitted plans to EPA for 

implementing the current ozone standard which propose that these two areas of California meet 

that standard by 2024. We have assumed for analytical purposes that the San Joaquin Valley and 

South Coast air basin would attain a new standard in 2030. There are many uncertainties 

associated with the year 2030 analysis. Between 2020 and 2030 several federal air quality rules 

are likely to further reduce emissions of NOx and VOC, such as, but not limited to National rules 

for Diesel Locomotives, Diesel Marine Vessels, and Small Nonroad Gasoline Engines. These 

emission reductions should lower ambient levels of ozone in California between 2020 and 2030. 

Complete emissions inventories as well as air quality modeling were not available for this year 

2030 analysis. Due to these limitations, it is not possible to adequately model 2030 air quality 

changes that are required to develop robust controls strategies with associated costs and benefits.

In order to provide a rough approximation of the costs and benefits of attaining 0.075 ppm and 

the alternate standards in San Joaquin and South Coast air basins, we’ve relied on the available 

data. Available data includes emission inventories, which do not include any changes in 

stationary source emissions beyond 2020, and 2020 supplemental air quality modeling. This 

data was used to develop extrapolated costs and benefits of 2030 attainment. To view the 

complete analysis for the San Joaquin Valley and South Coast air basins see Appendix 7b.3 

5.2 Extrapolated Engineering Costs 

5.2.1 Methodology 

This section presents the methodology and results of the extrapolated engineering cost 

calculations of attainment of a new ozone standard of 0.075 ppm and analyses of three 
5-11

alternative standards, a less stringent 0.079 ppm and two more stringent options (.065 and 0.070 

ppm). 

As discussed in Chapter 3, the application of the modeled control strategy was not successful in 

reaching nationwide attainment of the alternate ozone standards. Many areas remained in 

nonattainment for all four alternate standard scenarios; therefore, the engineering costs detailed 

in Section 5.1 represent only the costs of partial attainment. 

The estimation of engineering costs for unspecified emission reductions needed to reach 

attainment many years in the future is inherently a difficult issue. As described later in this 

chapter, our experience with Clean Air Act implementation shows that technological advances 

and development of innovative strategies can make possible emissions reductions that are 

unforeseen today, and to reduce costs of emerging technologies over time. But we cannot 

quantitatively predict the amount of technology advance in the future. For areas needing 

significant additional emission reductions, much of the control must be for sources that 

historically haven’t been controlled. The relationship of the cost of such control to the cost of 

control options available today is not at all clear. Available, current known control measures 

increase in cost beyond the range of what has ever been implemented and would still not provide 

the needed additional control for full attainment in the analysis year 2020. In the absence of 

technological change, the needed control for full attainment in 2020 would not be available. 

The degree to which unknown controls are needed to achieve attainment depends significantly 

upon variables in the analysis, such as attainment date assumptions. We will better understand 

the true scope of the issue in the future as states conduct detailed area-by-area analyses to 

determine available controls and attainment dates that are appropriate under the Clean Air Act. 

We do not attempt to determine specific attainment dates in this analysis. The Clean Air Act 

provides flexibility for a nonattainment area to receive an attainment date up to 20 years after 

designation if earlier attainment is not practical based on controls that are reasonably available 

considering cost. Although we assume attainment in 2020 (except for two California areas), 

areas that face difficulty attaining could qualify under the Clean Air Act for an attainment date as 

late as 2030 (assuming designations in 2010). This would give such areas additional time to take 

advantage for national standards to reduce emissions from onroad and nonroad mobile sources 

through fleet turnover, and to take advantage of technological innovation in cleaner technologies 

after 2020.

Prior to presenting the methodology for estimating costs for unspecified emission reductions, it 

is important to provide information from EPA’s Science Advisory Board Council Advisory,6

dated June 8, 2007, on the issue of estimating costs of unidentified control measures.

812 Council Advisory, Direct Cost Report, Unidentified Measures (charge question 2.a)

“The Project Team has been unable to identify measures that yield sufficient emission 

reductions to comply with the National Ambient Air Quality Standards (NAAQS) and 

6

 U.S. Environmental Protection Agency. June 2007. Advisory Council on Clean Air Compliance 

Analysis (COUNCIL), Council Advisory on OAR’s Direct Cost Report and Uncertainty 

Analysis Plan. Washington, DC. 
5-12

relies on unidentified pollution control measures to make up the difference. Emission 

reductions attributed to unidentified measures appear to account for a large share of 

emission reductions required for a few large metropolitan areas but a relatively small 

share of emission reductions in other locations and nationwide. 

“The Council agrees with the Project Team that there is little credibility and hence 

limited value to assigning costs to these unidentified measures. It suggests taking great 

care in reporting cost estimates in cases where unidentified measures account for a 

significant share of emission reductions. At a minimum, the components of the total cost 

associated with identified and unidentified measures should be clearly distinguished. In 

some cases, it may be preferable to not quantify the costs of unidentified measures and to 

simply report the quantity and share of emissions reductions attributed to these 

measures.

“When assigning costs to unidentified measures, the Council suggests that a simple, 

transparent method that is sensitive to the degree of uncertainty about these costs is best. 

Of the three approaches outlined, assuming a fixed cost/ton appears to be the simplest 

and most straightforward. Uncertainty might be represented using alternative fixed costs 

per ton of emissions avoided.” 

EPA has considered this advice and the requirements of E.O. 12866 and OMB circular A-4, 

which provides guidance on the estimation of benefits and costs of regulations. 

To generate estimates of the costs and benefits of meeting alternative standards, EPA has 

assumed the application of unspecified future controls that make possible the emissions 

reductions needed for attainment in 2020 (excluding two California areas). By definition, there is 

no cost data in existence for unidentified future technologies or innovative strategies.

EPA used two methodologies for estimating the costs of unspecified future controls: a new 

hybrid methodology and a fixed-cost methodology. Both approaches assume that innovative 

strategies and new control options make possible the emissions reductions needed for attainment 

by 2020. The fixed cost methodology was preferred by EPA’s Science Advisory Board over two 

other options, including a marginal-cost-based approach. The hybrid approach has not yet been 

reviewed by the SAB. 

The hybrid approach creates a marginal cost curve and an average cost curve representing the 

cost of unknown future controls needed for 2020 attainment. This approach explicitly estimates 

the average per-ton cost of unspecified emissions reductions assumed for each area, with a 

higher average cost-per-ton in areas needing a higher proportion of unknown controls relative to 

known modeled controls. This requires assumptions about the average cost of the least expensive 

unspecified future controls, and the rate at which the average cost of these controls rises as more 

extrapolated tons are needed for attainment (relative to the amount of reductions from known, 

modeled controls). These factors in turn depend on implicit assumptions about future 

technological progress and innovation in emission reduction strategies. 

The fixed cost methodology utilizes a national average cost per ton of future unspecified controls 

needed for attainment, as well as two sensitivity values (presented in Appendix 5a.4.3). The 
5-13

range of estimates reflects different assumptions about the cost of additional emissions 

reductions beyond those in the modeled control strategy. The alternative estimates implicitly 

reflect different assumptions about the amount of technological progress and innovation in 

emission reduction strategies. 

The hybrid methodology has the advantage of using the information about how significant the 

needed reductions from unspecified control technology are relative to the known control 

measures and matching that with expected increasing per unit cost for going beyond the modeled 

technology. Under this approach, the relative costs of unspecified controls in different 

geographic areas reflect the expectation that average per-ton control costs are likely to be higher 

in areas needing a higher ratio of emission reductions from unspecified and known controls. 

The fixed cost methodology reflects a view that because no cost data exists for unspecified future 

strategies, it is unclear whether approaches using hypothetical cost curves will be more accurate 

or less accurate in forecasting total national costs of unspecified controls than a fixed-cost 

approach that uses a range of national cost per ton values. 

Technological change will provide new control possibilities that can be employed to provide the 

additional unspecified control needed to reach attainment. These new technologies will make 

control possible where control has not been available for estimating our known control. An 

example might be the development of a new control technology for a type of emissions that have 

never been controlled. Technological change is also expected to reduce the cost of known 

controls that currently have prohibitive costs. For example, suppose a source that was not chosen 

for control because the estimated cost was $60,000 per ton but technological change reduces the 

cost to $16,000 per ton. Finally, control technologies may change so that higher control 

efficiencies may be obtained without a significant increase in per unit costs of control.

Both approaches (the hybrid and the fixed) estimate costs using national level parameters and 

local area information about needed emission reductions. Because cost changes due to 

technological change will be available on a national level, it makes sense to use national level 

estimates of these parameters. Local areas have different levels of needed emission reductions 

and different inventories of uncontrolled emissions and estimates of needed emission reductions 

are used in both models. The hybrid model also uses information about the amount of modeled 

control estimated for the local area. 

The hybrid approach has yet to be peer reviewed and reflects a range of views about the likely 

cost of future techniques and strategies that reduce air pollutant emissions. Section 5.4 discusses 

historical experience which has shown numerous technological advances in emission reduction 

technologies, and provides a few examples of today’s emerging technologies. 

5.2.1.1 Initial Steps

The first step involved identifying supplemental known controls not included in the modeled 

control strategy. These controls include the controls discussed in Appendix 3a.1.6, as well as 

additional controls applied to select EGU sources, and VOC controls up to $15,000/ton for select 

geographic areas. For the more stringent alternative of 0.065 ppm additional geographic areas 

were included, and therefore additional known measures were available to be applied as well.
5-14

For the other three alternatives, there were geographic areas that were “over controlled” and 

controls were removed from the analysis. For a complete discussion of the supplemental and 

“over control” emission reductions and costs see Appendix 5a.4.1 and 5a4.2 respectively. After 

the supplemental controls are applied, any remaining emission reductions needed are classified 

as additional tons from unknown control measures. 

Supplemental controls were applied in addition to the known controls in this illustrative analysis 

in order to achieve the highest possible known emission reduction from NonEGU point and Area 

sources. Supplemental control measures are those controls that are 1) applied in these analyses 

but are not found in AirControlNET, and 2) are in AirControlNET but whose data have been 

modified to better approximate their applicability to source categories in 2020. The controls and 

associated data such as control cost estimates not found in AirControlNET are taken from 

technical reports prepared to support preliminary 8-hour ozone State Implementation Plans 

(SIPs) prepared by States and from various reports prepared by the staffs of various local air 

quality regulatory agencies (e.g., Bay Area Air Quality Management District). The reports that 

are the sources of additional controls data are included within footnotes in the Chapter 3 

Appendix. Modification of control data, including percent reduction levels and control cost data,

in AirControlNET occurred as a result of a review of the nonEGU point and area NOx control 

measures by technical staff. The changes EPA supplied are provided later in the Chapter 3 

Appendix.

Next. we classified the areas needing additional controls by attainment date. Because two areas 

in California require no incremental additional progress towards attainment by 2020 for a more 

stringent standard (their requirements to reach attainment of the current standard by 2024 will be 

the requirement that is binding) we separated the requirements to attain more stringent standards 

for those two areas from the analysis for the rest of the nation. A highly uncertain estimate of the 

extrapolated engineering cost in 2030 is provided in Appendix 5a.5. 

5.2.1.2 Theoretical Model for Hybrid Approach

A simple model of how marginal costs increase with increasing control requirements was 

developed. The model relies on emission estimates of unspecified emissions (E1) needed to reach 

attainment and the modeled control emission estimates. These unspecified emissions vary both 

with the area and standard being analyzed. The modeled emissions vary by area. The ratio (R) of 

unspecified emissions (E1) to controlled emissions estimates (E0) is thus unique to each area and 

standard being analyzed. The model of cost also includes two parameters developed for use that 

don’t vary across analyses of areas and standards. One is a national projected dollar per ton cost 

for the last ton controlled for the controlled emissions (N or jumping off price). The other is a 

constant multiplier (M) to determine an average cost per ton that increases as size of the needed 

unknown controls (E1) increase relative to the modeled controls (R). The following equations 

show how Average cost (AC), Total Cost (TC), and marginal (MC) are modeled in the hybrid 

approach. See the appendix for a more detailed explanation.

AC = N(1+RM) 

TC = AC(E1)
5-15

MC = N(1+ 2RM) 

For the controlled emissions estimated in the modeled control, costs increase at an increasing 

rate as more control is applied. The shape of the control cost curve for 2020 after technological 

change is unknown but would also be expected to increase at an increasing rate. With all of the 

uncertainty and as part of the trade-off between simplicity/transparency and model richness we 

chose a proportional per unit cost increase. This model assumes per unit costs increase at a 

constant rate proportional to R. 

5.2.1.3 Parameter Estimation for Hybrid Approach

The jumping off price (N) used is $15,000/ton (2006$). To determine this number we calculated 

the marginal costs for the last control applied in all geographic areas for nonEGU and Area 

known controls7 and averaged them for both the modeled control strategy and an alternate 

primary standard of 0.065 ppm, this allowed for consistency with the modeled control strategy 

marginal costs. These calculations showed a range of $14,500 to $16,000 per ton (2006$), with 

$15,000 falling in the middle. The February 2007 report, “Direct Cost Estimates for the Clean 

Air Act Second Section 812,” uses $10,000 (1999$) per ton. For simplicity and comparability we 

used the $15,000/ton. In addition the marginal cost curve for the modeled control strategy NOx 

nonEGU and Area, 90% of the controls applied are below $15,000/ton. The jumping off price 

(N) should be interpreted as the cost of the very first ton needed from the unknown control8. We 

chose the value $15,000/ton and not the $23,000/ton applied for NOx nonEGU point and Area 

source controls because the $23,000/ton was calculated as an extreme upper limit for NOx 

nonEGU controls and is not representative of the upper limit of controls applied across all 

emissions sectors. It is important to note that the cost/ton numbers calculated above are specific 

to this scenario. In an ideal world, we would have more complete information about the available 

control options in each area and we would be able to estimate what the next control to be 

employed (the “jumping off” control) would be for each area needing control beyond the 

modeled known control.

We have to estimate R and E information for each area and each standard. Figure 5.4 shows how 

for phase 1 supplemental air quality modeling areas how R varies based upon the level of the 

standard and the local geographic area emissions. 

We have no way to econometrically estimate M. The constant multiplier (M) incorporates many 

different influences on the unit costs of control such as technological change in control 

technology, change in energy technology, learning by doing, relative price changes, and 

distribution of sources with uncontrolled emissions. Using a high value for marginal cost we can 

solve for M based on this value and our parameter estimate of $15,000 for N, and our highest 

7

 NOx NonEGU point and Area controls were used for this calculation due to availability of 

detailed data across all emission sectors. 

8

 Although $15,000/ton (2006$) represents the cost of the very first ton of unknown control 

needed, marginal costs for the last ton of unknown control are assumed to be no higher than 

$46,000/ton (2006$) 
5-16

value of R9

 (2.19)for areas meeting the current standard in 2020. For the modeled control we 

used a maximum marginal cost of control of $23,000 dollars/ton. At this cost 98% of the possible 

reductions NOx from nonEGU point and area were applied. To arrive at a high value we doubled 

the maximum marginal cost value ($46,000). A number this high is rarely seen in either 

implemented controls or other RIAs (e.g. the 1997 Ozone RIA highest cost per ton was $10,000 

(1990$) which is $14,000 (2006$)). This leads to our estimate of M of 0.47. To arrive at a low 

value we used the maximum marginal cost from the modeled control strategy ($23,000). This 

leads to our estimate of M of 0.12. We calculated an M of 0.24 for the middle estimate based 

upon the higher and lower M values described above. The results reported in this chapter are for 

an M of 0.24, the estimates using the high and low value of M are reported in Appendix 5a. 

9

 The R for Eastern Lake Michigan was 2.19 for the 0.065 ppm alternative standard. The R for 

Houston was higher, yet this value was not used when calculating the highest value of M because 

Houston is the only area in our analysis for 2020 that did not meet the current standard, and 

therefore not representative of the majority of areas needing to reach a new ozone standard.
5-17

Figure 5.4: Ratio of Unspecified Emission Reductions to Known Emission Reductions 

Across Various Standards for Phase 1 Areasa, b, c 

0.00

0.40

0.80

1.20

1.60

2.00

2.40

Ratio of Unknown Emission Reductions to 

Known Emission Reductions

0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm

Eastern Lake Michigan, IL-IN-WI (NOX) Eastern Lake Michigan, IL-IN-WI (VOC)

Northeast Corridor, CT-DE-MD-NJ-NY-PA (NOX) Houston, TX (NOX)

Sacramento Metro, CA (NOX)

a

 Phase 1 Areas are defined in Chapter 4 Section 4.1.1 

b

 There are values of R for both NOx and VOC for the Eastern Lake Michigan, IL-IN-WI. This is the 

only geographic area where unknown control costs were calculated for VOC. 

c

 Houston did not meet the current standard after the modeled control strategy. 

The cost of the last ton needed for the unknown control is N(1+2RM). Thus, the per unit control 

cost for the unspecified tons in an area starts with N and linearly increases with R. The ratio of 

needed unknown control to modeled control (R) can be interpreted as a measure of “the degree 

of difficulty” (see Figure 5.4). For example, the per unit control costs would be expected to be 

higher if the unknown control needed is twice the modeled control than if it is half the modeled 

control. Table 5.2 shows how the cost of the last ton controlled for the highest R value would 

vary with different values of M. Figure 5.5 also depicts how the average cost per ton would vary. 

Table 5.2: Marginal Cost and Average Cost Values Used in Calculating Ma

Highest Annual Cost/Ton Values (2006$), Given R = 2.19 

M = 0.12 M = 0.24 M = 0.47 

Marginal Cost (MC) $23,000 $31,000 $46,000 

Average Cost (AC) $19,000 $23,000 $30,000 

a

 Marginal and average costs could be higher than the values presented above for tighter ozone standards. 

Figure 5.5 shows the range of average cost/ton values across geographic areas and standards. 

This helps graphically illustrate the interplay of all the variables to create a geographically 
5-18

specific average cost/ton that is then multiplied by the amount of unspecified emissions 

reductions needed to attain. These average cost per ton values 

Figure 5.5: Ranges of Hybrid (Mid) Average Cost/Ton Values across Geographic Areas 

and Standards 

$15,000

$16,000

$17,000

$18,000

$19,000

$20,000

$21,000

$22,000

$23,000

$24,000

0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm

Average Cost/Ton Hybrid Approach (Mid) (2006$)

Ada Co., ID Atlanta, GA Boston-Lawrence-Worcester, MA

Charlotte-Gastonia-Rock Hill, NC-SC Dallas-Fort Worth, TX Denver-Boulder-Greeley-Ft Collins-Love, CO

Detroit-Ann Arbor, MI Baton Rouge, LA Cleveland-Akron-Lorain, OH

Buffalo-Niagara Falls, NY Dona Ana CO., NM Huntington-Ashland, WV-KY

Jefferson Co, NY Las Vegas, NV Memphis, TN-AR

Norfolk-Virginia Beach-Newport News Pittsburgh-Beaver Valley, PA Sacramento Metro, CA (NOX)

Houston, TX (NOX) Northeast Corridor, CT-DE-MD-NJ-NY-PA (NOX) Eastern Lake Michigan, IL-IN-WI (NOX)

Salt Lake City, UT San Juan Co., NM St Louis, MO-IL

5.2.1.4 Fixed Cost Approach

As discussed above the Science Advisory Board advice favored a fixed cost per ton approach as 

the simplest and most straightforward. The extrapolated cost equation involves only unspecified 

emissions (E1) and Fixed Cost per ton (F). Thus the total cost (TC) equation is: 

TC= E1F

The primary estimate of F is $15,000. The $15,000 per ton amount is commensurate with that 

used in the 1997 RIA in using current dollars. It is also consistent with what an advisory 

committee to the Section 812 second prospective analysis on the Clean Air Act Amendments 

suggested.

Values of $10,000/ton and $20,000/ton are used for the sensitivity analyses found in Appendix 

5a.4.3.
5-19

5.2.2 Results 

5.2.2.1 Emission Reductions Needed to Attain Various Standards

Application of supplemental control measures (for a complete discussion see Chapter 5 

Appendix section 5a.4) mentioned above resulted in some geographic areas no longer needing 

extrapolated tons to attain various alternate primary standards. Table 5.3 shows the emission 

reductions needed by geographic area, pollutant, and standard. Eastern Lake Michigan is the only 

area with both NOx and VOC emission reductions estimates. For the other areas additional 

control of NOx only is expected to be a less expensive approach than controlling both NOx and 

VOC. As expected, more areas need extrapolated emission reductions when the alternative 

standards are more stringent. 

Table 5.3: Extrapolated Emission Reductions Needed (Post Application of Supplemental 

Controls) to Meet Various Alternate Standards in 2020 

Additional Emission Reductions Needed (annual tons/year) 

0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 2020 Extrapolated 

Cost Area 

NOX VOC NOX VOC NOX VOC NOX VOC 

Ada Co., ID 2,800 

Atlanta, GA 5,500 

Baton Rouge, LA 160,000 49,000 

Boston-LawrenceWorcester, MA 8,500 

Buffalo-Niagara 

Falls, NY 18,000 3,700 

Campbell Co., WY 50 

Charlotte-GastoniaRock Hill, NC-SC 47,000 

CincinnatiHamilton, OH-KYIN

(40)a 

Cleveland-AkronLorain, OH 78,000 11,000 

Dallas-Fort Worth, 

TX 48,000 (30) a

 

Denver-BoulderGreeley-Ft CollinsLove, CO 

1,600 

Detroit-Ann Arbor, 

MI 100,000 8,700 

Dona Ana CO., NM 410 

Eastern Lake 

Michigan, IL-IN-WI 320,000 320,000 250,000 250,000 74,000 49,000 (60) a (50) a

El Paso Co., TX - a 

Houston, TX 180,000 160,000 110,000 81,000 

HuntingtonAshland, WV-KY 800 

Jackson Co., MS (200) a 

Jefferson Co, NY 6,200 

Las Vegas, NV 3,900 

Memphis, TN-AR 1,100 
5-20

Additional Emission Reductions Needed (annual tons/year) 

0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 2020 Extrapolated 

Cost Area 

NOX VOC NOX VOC NOX VOC NOX VOC 

Norfolk-Virginia 

Beach-Newport

News

21,000 

Northeast Corridor, 

CT-DE-MD-NJNY-PA 

340,000 220,000 65,000 

Phoenix-Mesa, AZ (60) a 

Pittsburgh-Beaver 

Valley, PA 13,000 

RichmondPetersburg, VA (600) a

 

Sacramento Metro, 

CA b 130,000 89,000 44,000 1,800 

Salt Lake City, UT 430 

San Juan Co., NM 1,300 

St Louis, MO-IL 17,000 

Toledo, OH (90) a 

a

 negative or zero values indicate the supplemental measures applied yielded equal or greater emission 

reductions than were needed for the geographic area to attain the standard being analyzed. 

b

 Sacramento Metro, CA geographic area also contains the South Coast and San Joaquin Valley Areas. 

These two areas will still be reducing emissions to meet the 0.08 ozone standard, and therefore the costs 

of these emission reductions are not incurred as part of meeting a new ozone standard. The difference 

between the emission reductions needed in Table 4.7a and this table are accounted for by the tons that

South Coast and San Joaquin need to reduce to reach the current standard, and to help Sacramento 

attain a new ozone standard.

5.2.2.2 Fixed Cost Approach Extrapolated Costs

Figure 5.6 and Table 5.4 presents the extrapolated cost estimates regionally for the various 

alternative standards for a fixed cost approach of $15,000/ton. These costs are the values from 

Table 5.3 multiplied by $15,000. See the Appendix 5a.4.3 for sensitivity analyses of varying the 

fixed dollar per ton to values other than $15,000. When we evaluate the portion of costs for the 

extrapolated costs fixed approach by supplemental air quality modeling phase (as described in 

Chapter 4), 100% of the costs are allocated to phase 1 geographic areas for the 0.075 ppm and 

0.079 ppm standard. For the 0.065 ppm and 0.070 ppm standards 73% to 94% are allocated to 

phase 1 areas, 22% to 6% in phase 2 areas, and only 5% to 0% for phase 3 areas. The sensitivity 

analysis for the fixed cost approach at $10,000/ton and $20,000/ton resulted in extrapolated costs 

of $3.4 to $6.8 billion dollars for the 0.075 ppm standard. 

5.2.2.3 Hybrid Approach Extrapolated Cost Results

Table 5.5 presents the extrapolated cost estimates regionally for the various alternative standards 

for the hybrid approach (mid). See the Appendix 5a.4.4 for sensitivity analyses of values of M of 

0.47 and 0.12. A value of 0.24 is used for M because R goes up with the stringency of the 

standard, the differences in costs between cost areas increase with the stringency of the
5-21

Figure 5.6: Extrapolated Cost by Region to Meet Various Alternate Standards 

Using Fixed Cost Approach ($15,000/ton) 

0.065 ppm

0.070 ppm

0.075 ppm

0.079 ppm

CA

West

East

$0

$5

$10

$15

$20

$25

$30

$35

$40

Extrapolated Cost (B 2006$)

Table 5.4: Extrapolated Cost by Region to Meet Various Alternate Standards 

Using Fixed Cost Approach ($15,000/ton) a, b

2020 Extrapolated Cost by Fixed Cost Approach Extrapolated Cost (M 2006$) 

Region 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

East $25,000 $14,000 $4,500 $1,200 

West $160 - - - 

California $2,000 $1,300 $660 $28 

Total Extrapolated Cost $27,000 $16,000 $5,100 $1,200 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

alternative being considered. When we evaluate the portion of costs for the extrapolated costs 

fixed approach by supplemental air quality modeling phase (as described in Chapter 4), 100% of 

the costs are allocated to phase 1 geographic areas for the 0.075 ppm and 0.079 ppm standard. 

For the 0.065 ppm and 0.070 ppm standards 74% to 95% are allocated to phase 1 areas, 21% to 

5% in phase 2 areas, and only 5% to 0% for phase 3 areas. 
5-22

Figure 5.7: Extrapolated Cost by Region to Meet Various Alternate Standards Using 

Hybrid Approach (Mid) 

0.065 ppm

0.070 ppm

0.075 ppm

0.079 ppm

CA

West

East

$0

$5

$10

$15

$20

$25

$30

$35

$40

Extrapolated Cost (B 2006$)

Table 5.5: Extrapolated Cost by Region to Meet Various Alternate Standards Using 

Hybrid Approach (Mid) a, b

2020 Extrapolated Cost by Hybrid Approach Extrapolated Cost (M 2006$) 

Region 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

East $36,000 $20,000 $5,500 $1,700 

West $170 

California $2,800 $1,700 $770 $28 

Total Extrapolated Cost $39,000 $22,000 $6,300 $1,800 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.
5-23

5.3 Summary of Costs 

Table 5.6 presents a summary of the total national cost of attaining 0.079, 0.075, 0.070, and 

0.065 ppm standards in 2020. This summary includes the engineering costs presented above 

from the modeled controls and the extrapolated costs. The range presented in the extrapolated 

costs and the total costs represent the upper and lower bound cost estimates. Consistent with 

OMB Circular A-4, costs are presented at a 7% discount rate. It is more consistent to present the 

extrapolated costs at the same discount rate as the modeled control costs, for which a 7% rate 

was determined to be more representative of actual costs (see section 5.1.3). Although the 

amount of reduction assumed to occur using unknown controls increases, the uncertainty of the 

associated costs and benefits calculations increases. 

Table 5.6: Total Costs of Attainment in 2020 for Alternate Levels of the

Ozone Standard a, b, c 

Annual Engineering Costs (M 2006$) Region 

0.065 ppm 0.070 ppm 0.075 ppm d 0.079 ppm d

East $4,100 $3,100 $2,400 $960 

West $230 $14 -$4 -$5 

Known Control 

Costs ($B) 

California $160 $160 $160 $160 

Known Control Costse$4,500 $3,300 $2,500 $1,100 

 Fixed Hybrid Fixed Hybrid Fixed Hybrid Fixed Hybrid 

East $25,000 $36,000 $14,000 $20,000 $4,500 $5,500 $1,200 $1,700 

West $160 $170 $0 $0 $0 $0 $0 $0 

Extrapolated 

Costs ($B) 

California $2,000 $2,800 $1,300 $1,700 $660 $770 $28 $28 

Extrapolated Costs $27,000 $39,000 $16,000 $22,000 $5,100 $6,300 $1,200 $1,800 

Total Cost Range $32,000 $44,000 $19,000 $25,000 $7,600 $8,800 $2,400 $2,900 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for the analysis of these areas. 

c

 These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 

d Known control costs for 0.079 ppm and 0.075 ppm include the modeled EGU cap and trade strategy, and 

therefore contain greater emission reductions than are needed to attain for some geographic areas. 

Therefore these results represent an overestimate of the costs of attainment. 

e

 Known control costs consist of modeled control strategy costs presented in Table 5.1, as well as 

supplemental costs and “giveback” costs presented in Appendix 5a.4.1 and 5a.4.2. 

Our estimates of costs of attainment in 2020 assume a particular trajectory of aggressive 

technological change. This trajectory leads to a particular level of emissions reductions and 

costs which we have estimated based on two different approaches, the fixed cost and hybrid 

approaches. An alternative storyline might hypothesize a much less optimistic technological 

change path, such that emissions reductions technologies for industrial sources would be more 

expensive or would be unavailable, so that emissions reductions from many smaller sources 

might be required for 2020 attainment, at a potentially greater cost per ton. Under this 

alternative storyline, two outcomes are hypothetically possible: Under one scenario, total costs 

associated with full attainment might be substantially higher. Under the second scenario, states 

may choose to take advantage of flexibility in the Clean Air Act to adopt plan with later 
5-24

attainment dates to allow for additional technologies to be developed and for existing programs 

like EPA’s Onroad Diesel, CAIR, Nonroad Diesel, and Locomotive and Marine rules to be fully 

implemented. If states were to submit plans with attainment dates beyond our 2020 analysis 

year, benefits would clearly be lower than we have estimated under our analytical storyline.

However, in this case, state decision makers, seeking to maximize economic efficiency, would 

not impose costs, including potential opportunity costs of not meeting their attainment date, 

when they exceed the expected health benefits that states would realize from meeting their 

modeled 2020 attainment date. In this case, upper bound costs are difficult to estimate because 

we do not have an estimate of the point where marginal costs are equal to marginal benefits plus 

the costs of nonattainment. 

Figure 5.8 shows the total costs for both the fixed and hybrid approaches broken out by region.

Figure 5.8: Annual Total Costs by Regiona

$0

$5

$10

$15

$20

$25

$30

$35

$40

$45

Total Cost (B 2006$)

0.065 ppm

0.070 ppm

0.075 ppm

0.079 ppm

0.065 ppm

0.070 ppm

0.075 ppm

0.079 ppm

Total Cost - Fixed ($15,000/ton) Total Cost - Hybrid (Mid)

West CA East

a

 These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 

Figure 5.9 separates the total cost under both the fixed and extrapolated cost approaches into the 

known control costs and the extrapolated costs. This shows graphically the increasing portion of 

costs that comes from unknown controls as the standard tightens. Depending upon the standard 

and extrapolated cost methodology (fixed or hybrid) the costs from unknown control 

technologies ranges from 50% to 89% of the total costs.
5-25

Figure 5.9: National Known Control Costs and Extrapolated Costs for 

Various Standardsa, b 

$0

$5

$10

$15

$20

$25

$30

$35

$40

$45

Total Costs (B 2006$)

0.065 ppm

0.070 ppm

0.075ppm

0.079 ppm

0.065 ppm

0.070 ppm

0.075ppm

0.079 ppm

Total Cost ($15,000/ton) Total Cost (Mid)

Known Control Costs Fixed Extrapolated Cost ($15,000/ton) Hybrid Extrapolated Cost (Mid)

a

 Known control costs consist of modeled control strategy costs presented in Table 5.1, as well as 

supplemental costs and “giveback” costs presented in Appendix 5a.4.1 and 5a.4.2. 

c

 These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 

Lastly, Figure 5.10 shows the total cost range by standard. For the final standard of 0.075 ppm 

the total cost ranges from $7.6 to $8.8 billion. 
5-26

Figure 5.10: Total Cost Ranges for Various Standardsa

$0

$5

$10

$15

$20

$25

$30

$35

$40

$45

Total Annual Costs (B 2006$)

0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm

Total Cost - Fixed ($15,000/ton) Total Cost - Hybrid (Mid)

a

 These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 

5.4 Technology Innovation and Regulatory Cost Estimates 

There are many examples in which technological innovation and “learning by doing” have made 

it possible to achieve greater emissions reductions than had been feasible earlier, or have reduced 

the costs of emission control in relation to original estimates. Studies10 have suggested that costs 

of some EPA programs have been less than originally estimated due in part to inadequate 

inability to predict and account for future technological innovation in regulatory impact analyses. 

Technological change will affect baseline conditions for our analysis. This change may lead to 

potential improvements in the efficiency with which firms produce goods and services, for 

example, firms may use less energy to produce the same quantities of output. In addition, 

technological change may result in improvements in the quality of health care, which can have 

impacts on the baseline health of the population, potentially reducing the susceptibility of the 

population to the effects of air pollution. While our baseline mortality incidence rates account for 

10 Harrington et al. (2000) and previous studies cited by Harrington. 
5-27

increasing life expectancy, and thus reflect projected improvements in health care, our baseline 

incidence rates for other health endpoints such as hospital admissions do not reflect any future 

advances in health care, and thus, our estimates of avoided health impacts for these endpoints 

will potentially be overstated. For other endpoints, such as asthma, there has been an observed 

upward trend in prevalence, which we have not captured in our incidence rates. For these 

endpoints, our estimates will potentially be understated. In general, for non-mortality endpoints, 

there is increased uncertainty in our estimates due to our use of current baseline incidence and 

prevalence rates. 

Constantly increasing marginal costs are likely to induce the type of innovation that would result 

in lower costs than estimated early in this chapter. Breakthrough technologies in control 

equipment could by 2020 result in a rightward shift in the marginal cost curve for such 

equipment (Figure 5.11)11 as well as perhaps a decrease in its slope, reducing marginal costs per 

unit of abatement, and thus deviate from the assumption of one constantly increasing marginal 

cost curve. In addition, elevated abatement costs may result in significant increases in the cost of 

production and would likely induce production efficiencies, in particular those related to energy 

inputs, which would lower emissions from the production side. 

Figure 5.11: Technological Innovation Reflected by Marginal Cost Shift

MC0 MC1

Cumulative NOx Reductions

Cost/Ton

Induced Technology Shift

Slope = ȕ0

Slope = ȕ1

5.4.1 Examples of Technological Advances in Pollution Control 

There are numerous examples of low-emission technologies developed and/or commercialized 

over the past 15 or 20 years, such as: 

x Selective catalytic reduction (SCR) and ultra-low NOx burners for NOx emissions 

11 Figure 5.2 shows a linear marginal abatement cost curve. It is possible that the shape of the 

marginal abatement cost curve is non-linear. 
5-28

x Scrubbers which achieve 95% and even greater SO2 control on boilers 

x Sophisticated new valve seals and leak detection equipment for refineries and chemical 

plans

x Low or zero VOC paints, consumer products and cleaning processes 

x Chlorofluorocarbon (CFC) free air conditioners, refrigerators, and solvents 

x Water and powder-based coatings to replace petroleum-based formulations 

x Vehicles far cleaner than believed possible in the late 1980s due to improvements in 

evaporative controls, catalyst design and fuel control systems for light-duty vehicles; and 

treatment devices and retrofit technologies for heavy-duty engines 

x Idle-reduction technologies for engines, including truck stop electrification efforts 

x Market penetration of gas-electric hybrid vehicles, and clean fuels 

These technologies were not commercially available two decades ago, and some were not even 

in existence. Yet today, all of these technologies are on the market, and many are widely 

employed. Several are key components of major pollution control programs. 

What is known as “learning by doing” or “learning curve impacts” have also made it possible to 

achieve greater emissions reductions than had been feasible earlier, or have reduced the costs of 

emission control in relation to original estimates. Learning curve impacts can be defined 

generally as the extent to which variable costs (of production and/or pollution control) decline as 

firms gain experience with a specific technology. Such impacts have been identified to occur in a 

number of studies conducted for various production processes. Impacts such as these would 

manifest themselves as a lowering of expected costs for operation of technologies in the future 

below what they may have been. 

The magnitude of learning curve impacts on pollution control costs has been estimated for a 

variety of sectors as part of the cost analyses done for the Draft Direct Cost Report for the second 

EPA Section 812 Prospective Analysis of the Clean Air Act Amendments of 1990.12 In that 

report, learning curve adjustments were included for those sectors and technologies for which 

learning curve data was available. A typical learning curve adjustment example is to reduce 

either capital or O&M costs by a certain percentage given a doubling of output from that sector 

or for that technology. In other words, capital or O&M costs will be reduced by some percentage 

for every doubling of output for the given sector or technology.

12 E.H. Pechan and Associates and Industrial Economics, Direct Cost Estimates for the Clean 

Air Act Second Section 812 Prospective Analysis: Draft Report, prepared for U.S. EPA, Office 

of Air and Radiation, February 2007. Available at 

http://www.epa.gov/oar/sect812/mar07/direct_cost_draft.pdf.
5-29

T.P. Wright, in 1936, was the first to characterize the relationship between increased productivity 

and cumulative production. He analyzed man-hours required to assemble successive airplane 

bodies. He suggested the relationship is a log linear function, since he observed a constant linear 

reduction in man-hours every time the total number of airplanes assembled was doubled. The 

relationship he devised between number assembled and assembly time is called Wright’s 

Equation (Gumerman and Marnay, 2004).13 This equation, shown below, has been shown to be 

widely applicable in manufacturing: 

 Wright’s Equation: CN = Co * Nb,

where

N = cumulative production 

CN = cost to produce Nth unit of capacity 

Co = cost to produce the first unit 

B = learning parameter = ln (1-LR)/ln(2), where 

LR = learning by doing rate, or cost reduction per doubling of capacity or output.

The percentage adjustments can range from 5 to 20 percent, depending on the sector and 

technology. Learning curve adjustments were prepared in a memo by IEc (2007) supplied to US 

EPA and applied for the mobile source sector (both onroad and nonroad) and for application of 

various EGU control technologies within the Draft Direct Cost Report.14 Advice received from 

the SAB Advisory Council on Clean Air Compliance Analysis in June 2007 indicated an interest 

in expanding the treatment of learning curves to those portions of the cost analysis for which no 

learning curve impact data are currently available. Examples of these sectors are non-EGU point 

sources and area sources. The memo by IEc outlined various approaches by which learning curve 

impacts can be addressed for those sectors. The recommended learning curve impact adjustment 

for virtually every sector considered in the Draft Direct Cost Report is a 10% reduction in O&M 

costs for two doubling of cumulative output, with proxies such as cumulative fuel sales or 

cumulative emission reductions being used when output data was unavailable.

For this RIA, we do not have the necessary data for cumulative output, fuel sales, or emission 

reductions for sectors included in our analysis in order to properly generate control costs that 

reflect learning curve impacts. Clearly, the effect of including these impacts would be to lower 

13 Gumerman, Etan and Marnay, Chris. Learning and Cost Reductions for Generating 

Technologies in the National Energy Modeling System (NEMS), Ernest Orlando Lawrence 

Berkeley National Laboratory, University of California at Berkeley, Berkeley, CA. January 

2004, LBNL-52559.

14 Industrial Economics, Inc. Proposed Approach for Expanding the Treatment of Learning Curve 

Impacts for the Second Section 812 Prospective Analysis: Memorandum, prepared for U.S. EPA, 

Office of Air and Radiation, August 13, 2007.
5-30

our estimates of costs for our control strategies in 2020, but we are not able to include such an 

analysis in this RIA. 

5.4.2 Influence on Regulatory Cost Estimates 

Studies indicate that it is not uncommon for pre-regulatory cost estimates to be higher than later 

estimates, in part because of inability to predict technological advances. Over longer time 

horizons, such as the time allowed for areas with high levels of ozone pollution to meet the 

ozone NAAQS, the opportunity for technical advances is greater. 

x Multi-rule study: Harrington et al. of Resources for the Future (2000) conducted an 

analysis of the predicted and actual costs of 28 federal and state rules, including 21 issued 

by EPA and the Occupational Safety and Health Administration (OSHA), and found a 

tendency for predicted costs to overstate actual implementation costs. Costs were 

considered accurate if they fell within the analysis error bounds or if they fall within 25 

percent (greater or less than) the predicted amount. They found that predicted total costs 

were overestimated for 14 of the 28 rules, while total costs were underestimated for only 

three rules. Differences can result because of quantity differences (e.g., overestimate of 

pollution reductions) or differences in per-unit costs (e.g., cost per unit of pollution 

reduction). Per-unit costs of regulations were overestimated in 14 cases, while they were 

underestimated in six cases. In the case of EPA rules, the agency overestimated per-unit 

costs for five regulations, underestimated them for four regulations (three of these were 

relatively small pesticide rules), and accurately estimated them for four. Based on 

examination of eight economic incentive rules, “for those rules that employed economic 

incentive mechanisms, overestimation of per-unit costs seems to be the norm,” the study 

said.

 Based on the case study results and existing literature, the authors identified 

technological innovation as one of five explanations of why predicted and actual 

regulatory cost estimates differ: “Most regulatory cost estimates ignore the possibility of 

technological innovation ... Technical change is, after all, notoriously difficult to forecast 

... In numerous case studies actual compliance costs are lower than predicted because of 

unanticipated use of new technology.”15

 It should be noted that many (though not all) of the EPA rules examined by Harrington 

had compliance dates of several years, which allowed a limited period for technical 

innovation. Much longer time periods (ranging up to 20 years) are allowed by the statute 

for meeting the ozone NAAQS in areas with high ozone levels, where a substantial 

fraction of the estimated cost in this analysis is incurred. 

x Acid Rain SO2 Trading Program: Recent cost estimates of the Acid Rain SO2 trading 

program by Resources for the Future (RFF) and MIT have been as much as 83 percent 

lower than originally projected by EPA.16 Note that the original EPA cost analysis also 

relied on an optimization model like IPM to approximate the results of emissions trading. 

15 Harrington et al., 2000. 

16 Carlson et al., 2000; Ellerman, 2003. 
5-31

As noted in the RIA for the Clean Air Interstate Rule, the ex ante numbers in 1989 were 

an overestimate in part because of the limitation of economic modeling to predict 

technological improvement of pollution controls and other compliance options such as 

fuel switching. The fuel switching from high-sulfur to low-sulfur coal was spurred by a 

reduction in rail transportation costs due to deregulation of rail rates during the 

1990’sHarrington et al. report that scrubbing turned out to be more efficient (95% 

removal vs. 80-85% removal) and more reliable (95% vs. 85% reliability) than expected, 

and that unanticipated opportunities arose to blend low and high sulfur coal in older 

boilers up to a 40/60 mixture, compared with the 5/95 mixture originally estimated. 

Phase 2 Cost Estimates 

Ex ante estimates $2.7 to $6.2 billiona

Ex post estimates $1.0 to $1.4 billion 

a

 2010 Phase II cost estimate in $1995. 

x EPA Fuel Control Rules: A 2002 study by two economists with EPA’s Office of 

Transportation and Air Quality17 examined EPA vehicle and fuels rules and found a 

general pattern that “all ex ante estimates tended to exceed actual price impacts, with the 

EPA estimates exceeding actual prices by the smallest amount.” The paper notes that cost 

is not the same as price, but suggests that a comparison nonetheless can be instructive.18

An example focusing on fuel rules is provided: 

Table 5.7: Comparison of Inflation-Adjusted Estimated Costs and Actual Price Changes 

for EPA Fuel Control Rules A

 Inflation-adjusted Cost Estimates (c/gal) 

 EPA DOE API Other 

Actual Price 

Changes (c/gal) 

Gasoline 

Phase 2 RVP Control (7.8 

RVP—Summer) (1995$) 

1.1 1.8 0.5 

Reformulated Gasoline Phase 1 

(1997$) 

3.1-5.1 3.4-4.1 8.2-14.0 7.4 (CRA) 2.2 

Reformulated Gasoline Phase 2 

(Summer) (2000$) 

4.6-6.8 7.6-10.2 10.8-19.4 12 7.2 (5.1, when 

corrected to 5yr 

MTBE price) 

30 ppm sulfur gasoline (Tier 2) 1.7-1.9 2.9-3.4 2.6 5.7 (NPRA), 

3.1 (AIAM) 

N/A

Diesel 

500 ppm sulfur highway diesel 

fuel (1997$) 

1.9-2.4 3.3 

(NPRA) 

2.2 

15 ppm sulfur highway diesel 

fuel

4.5 4.2-6.0 6.2 4.2-6.1 

(NPRA) 

N/A

a Anderson et al., 2002. 

17 Anderson et al, 2002. 

18 The paper notes: “Cost is not the same as price. This simple statement reflects the fact that a lot 

happens between a producer’s determination of manufacturing cost and its decisions about what 

the market will bear in terms of price change.” 
5-32

x Chlorofluorocarbon (CFC) Phase-Out: EPA used a combination of regulatory, market 

based (i.e., a cap-and-trade system among manufacturers), and voluntary approaches to

phase out the most harmful ozone depleting substances. This was done more efficiently 

than either EPA or industry originally anticipated. The phaseout for Class I substances 

was implemented 4-6 years faster, included 13 more chemicals, and cost 30 percent less 

than was predicted at the time the 1990 Clean Air Act Amendments were enacted.19

 The Harrington study states, “When the original cost analysis was performed for the CFC 

phase-out it was not anticipated that the hydrofluorocarbon HFC-134a could be 

substituted for CFC-12 in refrigeration. However, as Hammit (1997) notes, ‘since 1991 

most new U.S. automobile air conditioners have contained HFC-134a (a compound for 

which no commercial production technology was available in 1986) instead of CFC-12” 

(p.13). He cites a similar story for HCFRC-141b and 142b, which are currently 

substituting for CFC-11 in important foam-blowing applications.” 

x Additional examples of decreasing costs of emissions controls include: SCR catalyst 

costs decreasing from $11k-$14k in 1998 to $3.5k-$5k in 2004, and improved low NOx 

burners reduced emissions by 50% from 1993-2003 while the associated capital cost 

dropped from $25-$38/kw to $15/kw (ICF, 2005). 

We can not estimate the interplay between EPA regulation and technology improvement, but it is 

clear that a priori cost estimation often results in overestimation of costs because changes in 

technology (whatever the cause) make less costly control possible.

5.5 References 

Anderson, J.F., and Sherwood, T., 2002. “Comparison of EPA and Other Estimates of Mobile 

Source Rule Costs to Actual Price Changes,” Office of Transportation and Air Quality, U.S. 

Environmental Protection Agency. Technical Paper published by the Society of Automotive 

Engineers. SAE 2002-01-1980. 

Babiker, M.H., and T.F. Rutherford. 1997. “Input Output and General Equilibrium Estimates of 

Embodied CO2: A Data Set and Static Framework for Assessment.” University of Colorado at 

Boulder, Working Paper 97 2. Available at http://debreu.colorado.edu/papers/gtaptext.html. 

Babiker, M.H., J.M. Reilly, M. Mayer, R.S. Eckaus, I.S. Wing, and R.C. Hyman. 2001. “The 

MIT Emissions Prediction and CO2 Policy Analysis (EPPA) Model: Revisions, Sensitivities, and 

Comparisons of Results.” MIT Joint Program on the Science and Policy of Global Change, 

Report No. 71. Available at http://web.mit.edu/globalchange/www/eppa.html. 

Bovenberg, L.A., and L.H. Goulder. 1996. “Optimal Environmental Taxation in the Presence of 

Other Taxes: General Equilibrium Analysis.” American Economic Review 86(4):985-1000. 

Available at <http://www.aeaweb.org/aer/>. 

19 Holmstead, 2002. 
5-33

Brooke, A., D. Kendrick, A. Meeraus, and R. Raman. 1998. GAMS: A User’s Guide. GAMS 

Development Corporation. Available at http://www.gams.com. 

Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000. “Sulfur

Dioxide Control by Electric Utilities: What Are the Gains from Trade?” Journal of Political 

Economy 108(#6):1292-1326.

Ellerman, Denny. January 2003. Ex Post Evaluation of Tradable Permits: The U.S. SO2 Capand-Trade Program. Massachusetts Institute of Technology Center for Energy and 

Environmental Policy Research. 

Feenberg, D., and E. Coutts. 1993. “An Introduction to the TAXSIM Model.” Journal of Policy 

Analysis and Management 12(1):189 194. Available at http://www.nber.org/~taxsim/. 

Fullerton, D., and D. Rogers. 1993. “Who Bears the Lifetime Tax Burden?” Washington, DC: 

The Brookings Institute. Available at http://bookstore.brookings.edu/ 

book_details.asp?product%5Fid=10403. 

Goulder, L.H., and R.C. Williams. 2003. “The Substantial Bias from Ignoring General 

Equilibrium Effects in Estimating Excess Burden, and a Practical Solution.” Journal of Political 

Economy 111:898 927. Available at http://www.journals.uchicago.edu/JPE/home.html 

Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “On the Accuracy of Regulatory Cost 

Estimates.” Journal of Policy Analysis and Management 19(2):297-322.

Hammit, J.K. (1997). “Are the costs of proposed environmental regulations overestimated? 

Evidence from the CFC phaseout.” Unpublished paper, Center for Risk Analysis, Harvard 

School of Public Health, Cambridge, MA. 

Holmstead, Jeffrey, 2002. “Testimony of Jeffrey Holmstead, Assistant Administrator, Office of 

Air and Radiation, U.S. Environmental Protection Agency, Before the Subcommittee on Energy 

and air Quality of the committee on Energy and Commerce, U.S. House of Representatives, May 

1, 2002, p. 10. 

ICF Consulting. October 2005. The Clean Air Act Amendment: Spurring Innovation and Growth 

While Cleaning the Air. Washington, DC. Available at 

http://www.icfi.com/Markets/Environment/doc_files/caaa-success.pdf. 

Minnesota IMPLAN Group. 2003. State Level Data for 2000. Available from 

http://www.implan.com/index.html. 

Nestor, D.V., and C.A. Pasurka. 1995. The U.S. Environmental Protection Industry: A Proposed 

Framework for Assessment. U.S. Environmental Protection Agency, Office of Policy, Planning, 

and Evaluation. EPA 230-R-95-001. Available at 

http://yosemite.epa.gov/ee/epa/eermfile.nsf/11f680ff78df42f585256b45007e6235/41b8b642ab93

71df852564500004b543/$FILE/EE 0217A 1.pdf.
5-34

U.S. Department of Energy, Energy Information Administration. Undated (b). State Energy Price 

and Expenditure Report. Washington DC. Available at

http://www.eia.doe.gov/emeu/states/price_multistate.html. 

U.S. Department of Energy, Energy Information Administration. 2001. Manufacturing Energy 

Consumption Survey 1998. Washington DC. Available at http://www.eia.doe.gov/emeu/mecs/. 

U.S. Department of Energy, Energy Information Administration. January 2003. Annual Energy 

Outlook 2003. DOE/EIA 0383(2003). Washington DC. Available at 

http://www.eia.doe.gov/oiaf/archive/aeo03/pdf/0383(2003).pdf.

U.S. Department of Energy, Energy Information Administration. January 2004. Annual Energy 

Outlook 2004. DOE/EIA-0383(2003). Washington, DC. Available at 

http://www.eia.doe.gov/oiaf/aeo/pdf/0383 (2001). 

U.S. Environmental Protection Agency. June 2007. Advisory Council on Clean Air Compliance 

Analysis (COUNCIL), Council Advisory on OAR’s Direct Cost Report and Uncertainty 

Analysis Plan. Washington, DC. http://www.epa.gov/sab/pdf/council-07-002.pdf.
5a-1

Appendix 5a: Additional Cost Information 

5a.1 Engineering Cost Information for NonEGU Point and Area Sources 

(Full details on controls can be found in Appendix Chapter 3) 

5a.1.1 Engineering Costs by Control Measure 

Tables 5a.1 and 5a.2 summarize the total incremental annualized engineering costs in 2020 for 

the modeled control strategy by control measure for nonEGU point and Area sources.

Table 5a.1: NOx NonEGU Point and Area Source Control Measure 

Annualized Engineering Costs 

Control Measure Source Type 

Total Cost

(M 2006$) 

Industrial Coal Combustion $11 

Industrial NG Combustion $3.3 

RACT to 25 tpy (LNB) 

Industrial Oil Combustion $0.98 

Switch to Low Sulfur Fuel Residential Home Heating $20 

Water Heater + LNB Space Commercial/Institutional—NG $7.7 

Heaters Residential NG $12 

Biosolid Injection Technology Cement Kilns $0.43 

Asphaltic Conc; Rotary Dryer; Conv Plant $0.39 

Coal Cleaning-Thrml Dryer; Fluidized Bed $0.79 

Fiberglass Mfg; Textile—Type Fbr; Recup Furn $1.1 

Fuel Fired Equip; Furnaces; Natural Gas $0.14 

In-Process Fuel Use; Natural Gas $4.3 

In-Process Fuel Use; Residual Oil $0.14 

In-Process; Process Gas; Coke Oven Gas $0.59 

Lime Kilns $4.7 

Sec Alum Prod; Smelting Furn $0.052 

Steel Foundries; Heat Treating $0.010 

LNB

Surf Coat Oper; Coating Oven Htr; Nat Gas $0.095 

Fluid Cat Cracking Units $14 

Fuel Fired Equip; Process Htrs; Process Gas $3.2 

In-Process; Process Gas; Coke Oven Gas $3.5 

Iron & Steel Mills—Galvanizing $0.030 

Iron & Steel Mills—Reheating $0.58 

Iron Prod; Blast Furn; Blast Htg Stoves $0.56 

Sand/Gravel; Dryer $0.049 

LNB + FGR 

Steel Prod; Soaking Pits $0.11 

Iron & Steel Mills—Annealing $1.6 

Process Heaters—Distillate Oil $38 

Process Heaters—Natural Gas $420 

Process Heaters—Other Fuel $110 

Process Heaters—Process Gas $61 

LNB + SCR 

Process Heaters—Residual Oil $0.29 

Rich Burn IC Engines—Gas $13 

Rich Burn IC Engines—Gas, Diesel, LPG $2.1 

NSCR

Rich Burn Internal Combustion Engines—Oil $6.6 

OXY-Firing Glass Manufacturing—Containers $5.1 
5a-2

Control Measure Source Type 

Total Cost

(M 2006$) 

Glass Manufacturing—Flat $48 

Glass Manufacturing—Pressed $22 

Ammonia—NG-Fired Reformers $10 

Cement Manufacturing—Dry $120 

Cement Manufacturing—Wet $93 

IC Engines—Gas $220 

ICI Boilers—Coal/Cyclone $2.3 

ICI Boilers—Coal/Wall $34 

ICI Boilers—Coke $0.89 

ICI Boilers—Distillate Oil $12 

ICI Boilers—Liquid Waste $1.6 

ICI Boilers—LPG $1.1 

ICI Boilers—Natural Gas $110 

ICI Boilers—Process Gas $25 

ICI Boilers—Residual Oil $31 

Natural Gas Prod; Compressors $3.3 

Space Heaters—Distillate Oil $0.088 

Space Heaters—Natural Gas $2.1

SCR

Sulfate Pulping—Recovery Furnaces $24 

SCR + Steam Injection Combustion Turbines—Natural Gas $55 

SCR + Water Injection Combustion Turbines—Oil $0.69 

By-Product Coke Mfg; Oven Underfiring $10 

Comm./Inst. Incinerators $2.3 

ICI Boilers—Coal/Stoker $10 

Indust. Incinerators $0.42 

In-Process Fuel Use; Bituminous Coal $0.058 

Municipal Waste Combustors $7.2 

Nitric Acid Manufacturing $2.5 

SNCR

Solid Waste Disp; Gov; Other Inc $0.16 

SNCR—Urea ICI Boilers—MSW/Stoker $0.29 

ICI Boilers—Coal/FBC $0.13 

ICI Boilers—Wood/Bark/Stoker—Large $8.4 

In-Process; Bituminous Coal; Cement Kilns $0.33 

SNCR—Urea Based 

In-Process; Bituminous Coal; Lime Kilns $0.034 

Table 5a.2: VOC NonEGU Point and Area Source Control Measure Annualized 

Engineering Costs 

Control Measure Source 

Total Cost

(M 2006$) 

CARB Long-Term Limits Consumer Solvents $320 

Catalytic Oxidizer Conveyorized Charbroilers $240 

Equipment and Maintenance Oil and Natural Gas Production $210 

Gas Collection (SCAQMD/BAAQMD) Municipal Solid Waste Landfill $1.1 

Incineration >100,000 lbs bread Bakery Products $5.8 

Low Pressure/Vacuum Relief Valve Stage II Service Stations $16 

Stage II Service Stations—Underground Tanks $15 

OTC Mobile Equipment Repair and Aircraft Surface Coating $2 

Refinishing Rule Machn, Electric, Railroad Ctng $12 

OTC Solvent Cleaning Rule Cold Cleaning $16 

SCAQMD—Low VOC Rubber and Plastics Mfg $2.6 
5a-3

Control Measure Source 

Total Cost

(M 2006$) 

SCAQMD Limits Metal Furniture, Appliances, Parts $19 

SCAQMD Rule 1168 Adhesives—Industrial $69 

Large Appliances $4.1 

Metal Furniture $0.90 

Solvent Utilization 

Paper SIC 26 $3.5 

Switch to Emulsified Asphalts Cutback Asphalt $0 

Permanent Total Enclosure (PTE) Fabric Printing, Coating and Dyeing $0.069 

Paper and Other Web Coating $0.85 

Petroleum and Solvent Evaporation Printing and Publishing $4.4 

Surface Coating $0.42 

5a.1.2 Engineering Costs of Supplemental Controls 

5a.1.1.1 Low Emission Combustion (LEC)

The average cost effectiveness for large IC engines using LEC technology was estimated to be 

$760/ton (ozone season, 2006 dollars).1 The EC/R report on IC engines (Ec/R, September 1, 

2000) estimates the average cost effectiveness for IC engines using LEC technology to range 

from $600–1,200/ton (ozone season) for engines in the 2,000–8,000 bhp range. The key 

variables in determining average cost effectiveness for LEC technology are the average 

uncontrolled emissions at the existing source, the projected level of controlled emissions, 

annualized costs of the controls, and number of hours of operation in the ozone season. The ACT 

document uses an average uncontrolled level of 16.8 g/bhp-hr, a controlled level of 2.0 g/bhp-hr 

(87% decrease), and nearly continuous operation in the ozone season. The EPA believes the 

ACT document provides a reasonable approach to calculating cost effectiveness for LEC 

technology.

5a.1.1.2 Leak Detection and Repair (LDAR) for Fugitive Leaks

The control efficiency is 80 percent reduction of VOC at an annualized engineering cost of 

$6,900 per ton.

5a.1.1.3 Enhanced LDAR for Fugitive Leaks

The control efficiency of this measure is estimated at 50 percent at a engineering cost of 

$4,360/ton of VOC reduced.2

1

 “NOx Emissions Control Costs for Stationary Reciprocating Internal Combustion Engines in 

the NOx SIP Call States,” E.H. Pechan and Associates, Inc., Springfield, VA, August 11, 2000. 

Available on the Internet at http://www.epa.gov/ttn/ecas/regdata/cost/pechan8-11.pdf
5a-4

5a.1.1.4 Flare Gas Recovery

The control efficiency of this measure is 98 percent reduction of VOC emissions at an 

annualized engineering cost of $3,860/ton. Costs may become negligible as the size of the flare 

increases due to recovery credit.3

5a.1.1.5 Cooling Towers

There is not a general estimate of control efficiency for this measure; one is to apply a 

continuous flow monitor until VOC emissions have reached a level of 1.7 tons/year for a given 

cooling tower.4

 The annualized engineering cost for a continuous flow monitor is $90,000– this 

is constant over a variety of cooling tower sizes.

5a.1.1.6 Wastewater Drains and Separators

The control efficiency is 65 percent reduction of VOC emissions at an annualized engineering 

cost of $4,360/ton. This is based on actual sampling and cost data for 5 refineries in the Bay Area 

Air Quality Management District (BAAQMD).5

5a.1.1.7 Work Practices and Use of Low VOC Coatings in Solvent Utilization and Other 

Processes

The control efficiency is 90 percent reduction of VOC emissions at an engineering cost of 

$1,200/ton (2006 dollars). This is based on analyzes applied to the 2002 National Emissions 

Inventory (NEI) and summarized in the proposed CTG for paper, film and foil coatings, metal 

furniture, and large appliances published by US EPA in July 2007.6

5a.2 Engineering Cost Information for EGU Sources 

(Full details on controls can be found in Appendix Chapter 3) 

3

 MARAMA Multipollutant Rule Basis for Flares, part of “Assessment of Control Technology 

Options for Petroleum Refineries in the mid-Atlantic Region.” February 19, 2007. Found on the 

Internet at http://www.marama.org/reports/021907_Refinery_Control_Options_TSD_Final.pdf.

4

 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of Regulation 8, 

Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004. 

5

 Bay Area Air Quality Management District (BAAQMD). Proposed Revision of Regulation 8, 

Rule 8: Wastewater Collection Systems. Staff Report, March 17, 2004. 

6

 U.S. Environmental Protection Agency. Consumer and Commercial Products: Control 

Techniques Guidelines in Lieu of Regulations for Paper, Film, and Foil Coatings; Metal 

Furniture Coatings; and Large Appliance Coatings. 40 CFR 59. July 10, 2007. Available on the 

Internet at http://www.epa.gov/ttncaaa1/t1/fr_notices/ctg_ccp092807.pdf. It should be noted that 

this CTG became final in October 2007. 
5a-5

5a.2.1 Cost of Controls as a Result of Lower Nested Caps within the MWRPO, OTC, and East 

Texas and other Local Controls Outside of these Regions Nationwide

As previously discussed, the power sector will achieve significant emission reductions under the 

Clean Air Interstate Rule (CAIR) over the next 10 to 15 years. When fully implemented, CAIR 

(in conjunction with NOx SIP Call) will reduce ozone season NOx emissions by over 60 percent 

from 2003 levels within the CAIR states. These reductions will greatly improve air quality and 

will lessen the challenges that some areas face when solving nonattainment issues significantly. 

Power sector impacts analyzed in detail in the Final PM NAAQS RIA 15/35 and in the Proposed 

Ozone NAAQS RIA (http://www.epa.gov/ttn/ecas/ria.html) provides the baseline for this RIA. 

The analysis and projections in this section attempt to show the potential impacts of the 

additional controls applied (see section 3.3.3 of this RIA) to facilitate attainment of the more 

stringent 8-hr ozone standard. Generally, the incremental impacts of these controls on the power 

sector are marginal. 

Projected Costs. EPA projects that the annualized incremental cost of the new ozone standard 

approach is $0.15 billion in 2020 ($2004)7

. The additional annualized costs reflect additional 

retrofits (SCR and SNCR) and generation shifts. Annualized cost of CAIR is projected to be 

$6.17 billion in 2020 ($2004). The approach applied in this RIA would add $0.15 billion 

incremental to this cost. Annualized cost of the EGU controls (in $2004) for the entire country 

for fossil units > 25MW is about $5,500. Table 5.a3 below summarizes increase in NOx control 

(SCR and SNCR) capacity. 

Table 5a.3: NOx Control (SCR and SNCR) Capacity (GWs) 

Baseline 

CAIR/CAMR/CAVR 

Modeled Control 

Strategy 

Retrofits (GWs) 

SCR 57.0 66.4 

SNCR 2.1 4.5 

Total Controls (GWs) (Existing + Retrofits + New Units) 

SCR 219.6 229.9 

SNCR 11.8 15.0 

Projected Generation Mix. Coal-fired generation and natural gas/oil-fired generation are 

projected to remain almost unchanged. Installation of approximately 9.4 GWs of SCR and 2.4 

GWs of SNCR incremental to the base case are projected as a result of the lower sub-regional 

caps. There are very small changes in the generation mix. Coal-fired generation decreases about 

6,000 GWh (a decrease of approximately 0.1% of the total generation) and gas-fired generation 

increases a similar amount. Hydro, nuclear, other, and renewable based generation projected to 

remain the same. Projected retirements of both coal and oil/gas units remained same compared to 

the base case approach. 

7

 IPM calculates costs in 2004$. All costs presented in Chapter 5 are in 2006$. The costs 

presented here were converted to 2006$ prior to being compared or added to other control 

measure costs. 
5a-6

Projected Nationwide Retail Electricity Prices. Retail electricity prices are projected to decrease 

marginally, about 1%. The extension of the cap-and-trade approach in the form of lower subregional caps allows industry to meet the requirements of CAIR in the most cost-effective 

manner, thereby minimizing the costs passed on to consumers. Retail electricity prices are 

projected to increase less than 1% within the MWRPO, OTC, and East Texas, and decrease

elsewhere.

5a.3 Engineering Cost Information for Onroad and Nonroad Mobile Sources 

(Full details on controls can be found in Appendix Chapter 3) 

Table 5a.4 and 5a.5 summarize the total incremental engineering costs for the modeled control 

strategy by mobile source control measure. 

Table 5a.4: NOx Mobile Modeled Control Strategy Incremental Annualized Engineering 

Costs by Control Measure 

Sector Control Measure Total Cost (M$) 

Eliminate Long Duration Idling $— 

Low RVP $—

Onroad Retrofit $280 

Continuous Inspection and Maintenance $— 

Onroad 

Commuter Programs $79 

Nonroad Nonroad Retrofit $150 

Table 5a.5: VOC Mobile Modeled Control Strategy Incremental Annualized Engineering 

Costs by Control Measure 

Sector Control Measure Total Cost (M$) 

Low RVP $95 

Onroad Retrofits $— 

Continuous Inspection and Maintenance $— 

Onroad 

Commuter Programs $— 

Low RVP $36 

Nonroad Retrofits & Engine Rebuilds $— 

Nonroad 

International Aircraft NOx Standard $— 

5a.3.1 Diesel Retrofits and Engine Rebuilds 

To calculate engineering costs for the use of selective catalytic reduction as a retrofit technology, 

the assumption was made that all relevant vehicles would be affected by the control. Therefore, 

all on-road heavy duty diesel vehicles that received a retrofit were assumed to employ selective 

catalytic reduction as a retrofit technology. The average cost of a selective catalytic reduction 

system ranges from $10,000 to $20,000 per vehicle depending on the size of the engine, the sales 

volume, and other factors. One study calculated the average estimated cost of this system to be 

$15,000 per heavy duty diesel vehicle. (Source: AirControlNET Documentation, III-160). OTAQ 

conducted an additional assessment of current SCR costs and calculated that for the year 2020, 

the cost of SCRs will be approximately $13,000 per unit. This estimate reflects an economy of 
5a-7

scale cost reduction of 33%, which is consistent with trends in other mobile source control 

technologies that enter large scale production8.

The rebuild/upgrade kit is applied to nonroad equipment. OTAQ estimates the engineering cost 

of this kit to be $2,000 to $4,000 per vehicle. For this analysis, the average estimated cost is 

$3,000 per vehicle.

The cost effectiveness numbers are presented in Tables 5a.6, 5a.7, and 5a.8. 

Table 5a.6: Summary of Cost Effectiveness for Rebuild/Upgrade Kit for Various Nonroad 

Vehicles

Nonroad Vehicle 

Retrofit 

Technology 

Range of $/ton NOx 

Emission Reduced 

Range of $/ton HC 

Emission Reduced 

Tractors/Loaders/Backhoes $1,300 $2,200 $9,600 $18,900 

Excavators $1,100 $4,200 $8,100 $43,400 

Crawler Tractor/Dozers $1,100 $4,200 $8,300 $43,500

Skid Steer Loaders $1,000 $1,600 $7,400 $14,800 

Agricultural Tractors 

Rebuild/ 

Upgrade kit 

$1,200 $4,900 $9,300 $34,300 

Table 5a.7: Summary of Cost Effectiveness for SCR for Various Nonroad Vehicles 

Nonroad Vehicle Retrofit Technology 

Range of $/ton NOx 

Emission Reduced 

Range of $/ton HC 

Emission Reduced 

Tractors/Loaders/Backhoes $2,900 $5,300 $32,200 $63,700 

Excavators $2,700 $10,400 $27,400 $146,200 

Crawler Tractor/Dozers $2,800 $10,400 $27,900 $146,700 

Skid Steer Loaders $2,600 $4,000 $24,900 $52,100 

Agricultural Tractors 

SCR

$3,000 $7,600 $31,200 $115,500 

Table 5a.8: Summary of Cost Effectiveness for SCR for Various Highway Vehicles 

Highway Vehicle Retrofit Technology 

Range of $/ton NOx 

Emission Reduced 

Range of $/ton HC 

Emission Reduced 

Class 6&7 Truck $5,600 $14,100 $46,900 $126,200 

Class 8b Truck 

SCR

$1,100 $2,500 $14,900 $44,600 

5a.3.2 Implement Continuous Inspection and Maintenance Using Remote Onboard Diagnostics 

(OBD) 

Continuous I/M can significantly lower test costs and “convenience” costs of I/M programs. 

Using the radio-frequency approach as an example, the costs of periodic testing to Remote OBD 

can be compared. Note that this is just an example to illustrate the difference in cost of traditional 

8

 The expected emissions reductions from SCR retrofits are based on data derived from EPA 

regulations (Control of Emissions of Air Pollution from 2004 and Later Model Year Heavy-duty 

Highway Engines and Vehicles published October 2000), interviews with component 

manufacturers, and EPA’s Summary of Potential Retrofit Technologies available at 

www.epa.gov/otaq/retrofit/retropotentialtech.htm.
5a-8

periodic I/M and Remote OBD. In this scenario, the assumption is that all 1996 and newer 

vehicles currently subject to I/M will participate in a mandatory Remote OBD program. The 

national fleet of vehicles subject to I/M are considered over a 10 year period a static set of 

vehicles. The estimated cost of setting up and maintaining a data processing and reporting 

system is shown in Table 5a.9 and ranges from 50¢ to $3.00 per vehicle in the program per year.9

For the purposes of this example, we will assume $1 to $3 per vehicle per year. These estimates 

assume one record per vehicle per month is actually stored (although additional readings will 

usually be taken since vehicles will routinely pass receivers many times a month). This cost does 

not include installing Remote OBD on the vehicle or the network of receivers to pick up signals 

from equipped vehicles, which is covered by the $50 fee discussed above. If we assume an 

average vehicle life span of 14 years,10 with the first test at 4 years of age, the typical vehicle will 

get 5 inspections in a biennial program and 10 in an annual program (not including additional 

change of ownership inspections, which are required in some areas). Thus, in a Remote OBD 

program, an additional cost of $10–$30 will be incurred for each vehicle over its life to cover 

data processing and reporting. 

Table 5a.9: Remote OBD VID Service Cost Estimate Per Vehicle Per Year 

Number of Vehicles 

in Remote OBD 

Program

Level 1 

Database Design, Installation, 

Maintenance, and Communications 

Level 2 

Add Reporting 

Level 3 

Add Auditing 

250,000 $1.50 $2.00 $3.00 

250,001–500,000 $1.00 $1.50 $2.75 

500,001–1,500,000 $0.75 $1.00 $2.50 

>1,500,000 $0.50 $0.75 $2.00 

In addition to test costs, Remote OBD avoids most of the consumer convenience and indirect 

costs associated with I/M—the time and fuel it takes to drive to the station, get a test, and return 

home. The one-time installation of the transmitter requires a visit to the test station, but no 

further visits are required. Hard data are not available on the actual average time motorists spend 

driving to a test station, getting a test, and returning to their point of origin or to their next stop in 

a trip chain. In some centralized programs, wait times can be very long. In decentralized 

programs, motorists often drop off their vehicle (requiring two trips to the test station). For the 

sake of illustrating the convenience costs associated with I/M, a reasonable range for the typical 

test cycle is one to two hours. If we assign a cost of $20 per hour11 and a half-gallon of gas (10 

miles round trip with an average fuel economy of 20 mpg) at $3 per gallon, the total cost of the 

typical cycle is $21.50 to $41.50. Over the life of the vehicle, this would amount to $104 to $208 

in a biennial program or $208 to $415 in an annual program. Compare this to the one time 

installation trip for Remote OBD at a cost of $21.5 to $41.50, it is clear that substantial savings 

are realized. 

9

 Table provided by Systech International, Inc. and Gordon-Darby, Inc. It should be noted that 

careful design of the data management system is necessary to achieve these cost levels. 

10 Greenspan, A. & D. Cohen, Motor Vehicle Stocks, Scrappage, and Sales; October 1996 

11 This is the same dollar amount assumed in EPA’s original Technical Support Document

published along with the 1992 Enhanced I/M Rule. 
5a-9

For the purposes of illustrating the nationwide costs and benefits of doing remote OBD, the 

following analysis assumes 100% participation. It is likely, however, that in the short run states 

will gradually introduce remote OBD initially on a voluntary basis (except possibly for fleets), 

and that participation rates will build over time as motorists recognize the cost and convenience 

advantages. Another caveat is that those states that require motorists to get safety checks, the 

convenience costs may not be fully realized (see Discussion of Issues, below). Table 5a.10 

shows the lifetime inspection and convenience costs of a mandatory, nationwide remote OBD 

program versus a periodic OBD program (assuming the current nationwide mix of annual and 

biennial testing and current test costs; see Appendix 3) for a static fleet of about 80 million 

vehicles. Note that in reality, fleet size generally grows over time and vehicles come and go. 

Thus, this is a simplifying assumption for the purposes of illustrating the comparative costs. The 

“low” and “high” refer to the range of convenience costs (1 to 2 hours) and oversight costs in the 

case of Remote OBD ($1–$3). Current periodic OBD testing costs about $12 billion12 over a 10-

year lifecycle with an additional $9 to $17 billion in convenience costs for a total of $21 to $29 

billion. By contrast, Remote OBD has a test and install cost of $4 to $5 billion over the same 10 

year period, and a convenience cost of $1 to $2 billion for a total of about $5 to $7 billion. Thus, 

nationwide installation of Remote OBD would save the nation’s motorists about $16 to $22 

billion in inspection and convenience costs over a 10 year period. 

Table 5a.10: Range of Lifetime Inspection and Convenience Costs of I/M 

Periodic OBD 

($B 2006) 

Remote OBD 

($B 2006) 

Savings

($B 2006) 

Test/Install Cost Low $12 $4 $8 

High $12 $5 $7 

Convenience Cost Low $9 $1 $8 

High $17 $2 $15 

Total Low $21 $5 $16 

High $29 $7 $22 

Given that Continuous I/M will actually reduce the cost of I/M, implementation of this measure 

is highly cost-effective. More information on I/M can be found at 

http://www.epa.gov/otaq/regs/im/im-tsd.pdf and www.epa.gov/obd/regtech/inspection.htm. 

Cost-Effectiveness of Measure: $0/ton NOx

5a.3.3 Eliminating Long Duration Truck Idling 

For purposes of this RIA, we identified this measure as a no cost strategy i.e., $0/ton NOx. Both 

TSEs and MIRTs have upfront capital costs, but these costs can be fully recovered by the fuel 

savings. The examples below illustrate the potential rate of return on investments in idle 

reduction strategies. 

12 Test volumes and costs were derived from Sierra Research’s annual I/M summary for 2005 

and updated in some cases by members of the workgroup. 
5a-10

Truck Stop Electrification

The average price of TSE technology is $11,500 per parking space. The average service life of 

this technology is 15 years. Truck engines at idle consume approximately 1 gallon per hour of 

idle. Current TSE projects are operating in environments where trucks are idling, on average, for 

8 hours per day per space for 365 days per year (or about 2,920 hours per year). Since TSE 

technology can completely eliminate long duration idling at truck spaces (i.e., a 100% fuel 

savings), this translates into 2,920 gallons of fuel saved per year per space. At current diesel 

prices ($2.90/gallon), this fuel savings translates into $8,468. Therefore, an $11,500 capital 

investment should be recovered within about 17 months. In this scenario, TSE investments offer 

over a 70% annual rate of return over the life of the technology.

While it is technically feasible to electrify all parking spaces that support long duration idling 

trucks, we should note that TSE technology is generally deployed at a minimum of 25-50 

parking spaces per location to maximize economies of scale. The financial attractiveness of 

installing TSE technology will depend on the demonstrated truck idling behavior—the greater 

the rates of idling, the greater the potential emissions reductions and associated fuel and cost 

savings.

Mobile Idle Reduction Technologies

The price of MIRT technologies ranges from $1,000-$10,000. The most popular of these 

technologies is the auxiliary power unit (APU) because it provides air conditioning, heat, and 

electrical power to operate appliances. The average price of an APU is $7,000. The average 

service life of an APU is 10 years. An APU consumes two-tenths of a gallon per hour, so the net 

fuel savings is 0.80 gallons per hour. EPA estimates that trucks idle for 7 hours per rest period, 

on average, and about 300 days per year (or 2,100 hours per year). Since idling trucks consume 1 

gallon of fuel per hour of idle, APUs can reduce fuel consumption for truck drivers/owners by 

approximately 1,680 gallons per year. At current diesel prices ($2.90/gallon), truck 

drivers/owners would save $4,872 on fuel if they used an APU. Therefore, a $7,000 capital 

investment should be recovered within about 18 months. In this scenario, APU investments offer 

almost a 70% annual rate of return over the life of the technology. 

Cost-Effectiveness of Measure: $0/ton NOx

5a.3.4 Commuter Programs 

We used the Transportation Research Board’s (TRB) cost-effectiveness analysis of Congestion 

Mitigation and Air Quality Improvement Program (CMAQ) projects to estimate the costeffectiveness of this measure.13 TRB conducted an extensive literature review and then 

synthesized the data to develop comparable estimates of cost-effectiveness of a wide range of 

CMAQ-funded measures. We took the average of the median cost-effectiveness of a sampling of 

13 Transportation Research Board, National Research Council, 2002. The Congestion Mitigation 

and Air Quality Improvement Program: assessing 10 years of experience, Committee for the 

Evaluation of the Congestion Mitigation and Air Quality Improvement Program. 
5a-11

CMAQ-funded measures and then applied this number to the overarching commuter reduction 

measure. The CMAQ-funded measures we selected were: 

x regional rideshares

x vanpool programs 

x park-and-ride lots 

x regional transportation demand management 

x employer trip reduction programs 

We felt that these measures were a representative sampling of commuter reduction incentive 

programs. There is a great deal of variability, however, in the type of programs and the level of 

incentives that employers offer which can impact both the amount of emissions reductions and 

the cost of commuter reduction incentive programs. 

We chose to apply the resulting average cost-effectiveness estimate to one pollutant—NOx—in

order to be able to compare commuter reduction programs to other NOx reduction strategies. 

TRB reported the cost-effectiveness of each measure, however, as a $/ton reduction of both VOC 

and NOx by applying the total cost of the program to a 1:4 weighted sum of VOC and NOx 

[[total emissions reduction = (VOC * 1) + (NOx * 4)). There was not enough information in the 

TRB study to isolate the $/ton cost-effectiveness for just NOx reductions, so we used the 

combined NOx and VOC estimate. The results are presented in Table 5a.11. 

Table 5a.11: Cost-Effectiveness of Best Workplaces for Commuters Type Measures from 

the 2002 TRB Study 

 $/ton (2000$) 1:4 VOC:NOx (reported in the RIA as $/ton NOx) 

 Low High Median 

Regional Rideshare $1,200 $16,000 $7,400 

Vanpool Programs $5,200 $89,000 $10,500 

Park-and-ride lots $8,600 $70,700 $43,000 

Regional TDM $2,300 $33,200 $12,500 

Employer trip reduction programs $5,800 $175,500 $22,700 

Average of All Measures $4,620 $76,900 $19,200 

Cost-Effectiveness of Measure: $19,200/ton NOx 

5a.3.5 Reduce Gasoline RVP from 7.8 to 7.0 

Michigan has conducted the most recent study on the cost of reducing RVP to 7.0. The analysis 

was undertaken as part of their proposed revision to Michigan’s SIP for their 7.0 low vapor 

pressure request for Southeast Michigan. According to their analysis, the costs of the program 

are:
5a-12

x 0.6–3.0¢ per gallon 

x $1–$11 per vehicle per year 

x Total annual cost =$6.9–$48.1 million 

Cost-Effectiveness of Measure: Cost per ton will be $5,700 to $36,000 / ton VOC 

For more information on RVP: 

x Michigan Department of Environmental Quality and Southeast Michigan Council of 

Governments. Proposed Revision to State of Michigan State Implementation Plan for 7.0 

Low Vapor Pressure Gasoline Vapor Request for Southeast Michigan. May 24, 2006. 

x U.S. EPA. Guide on Federal and State Summer RVP Standards for Conventional 

Gasoline Only. EPA420-B-05-012. November 2005 

5a.3.6 Aircraft Engine NOx Standard 

The Committee on Aviation Environmental Protection (CAEP) is a committee within the 

International Civil Aviation Organization (ICAO) that makes recommendations to the ICAO for 

environmental standards for aircraft. ICAO is a United Nations body that sets voluntary 

international standards for aircraft. Manufacturers in the U.S. and other countries generally 

comply with these standards. A few years ago, ICAO set a new standard (CAEP/6) for NOx 

emissions from commercial aircraft to reduce emissions 12% compared to the existing standard. 

Compliance with this standard is reflected in the analysis. No costs are attributed to EPA 

rulemaking. 

5a.4 Characterization of Unknown Controls 

5a.4.1 Supplemental Control Information 

Supplemental emission controls came from a variety of sources. The 0.065 ppm standard 

geographic areas were broader than those for the modeled control strategy; therefore additional 

local known controls were available for mobile sources as well as nonEGU point and Area. In 

addition, supplemental controls were achieved through controls applied to select natural gas and 

oil fired electric generating units. Other supplemental controls applied to nonEGU point and 

Area sources are described in the appendix to Chapter 3 (3a.1.6 Supplemental Controls). Lastly, 

for the Eastern Lake Michigan area, the cut point for applying VOC controls was raised from 

$5,000/ton (2006$) to $15,000/ton (2006$). Table 5a.12 summarizes the emission reductions 

achieved through the application of supplemental control measures. The total annualized cost of 

these measures is broken down by extrapolated cost area in Table 5a.13 and is presented at a 

seven percent discount rate.
5a-13

Table 5a.12: Supplemental Local Control Measure Emission Reductions [annual tons/year] 

Applied for Various Standardsa

2020 Extrapolated Cost Area 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

NOX VOC NOX VOC NOX VOC NOX VOC 

Ada Co., ID 2,600 340 

Atlanta, GA 16,000 3,500 

Baton Rouge, LA 8,300 23 7,200 

Boston-Lawrence-Worcester, MA 5,200 3,600 

Buffalo-Niagara Falls, NY 630 140 190 

Campbell Co., WY 2,600 69 

Charlotte-Gastonia-Rock Hill, NCSC15,000 3,300 

Cincinnati-Hamilton, OH-KY-IN 9,400 3,700 

Cleveland-Akron-Lorain, OH 5,100 390 2,400 

Dallas-Fort Worth, TX 5,100 3,100 

Denver-Boulder-Greeley-Ft 

Collins-Love, CO 

7,000 4,300 

Detroit-Ann Arbor, MI 2,100 2,100 

Dona Ana CO., NM 560 200 

Eastern Lake Michigan, IL-IN-WI 33,000 82,000 29,000 75,000 29,000 74,000 8,200 9,800 

El Paso Co., TX 1,700 

Houston, TX 49 53 

Huntington-Ashland, WV-KY 21,000 1,200 

Jackson Co., MS 7,800 410 

Jefferson Co, NY 1,100 710 

Las Vegas, NV 1,000 1,300 

Memphis, TN-AR 14,000 1,100 

Norfolk-Virginia Beach-Newport 

News 

9,100 2,400 

Northeast Corridor, CT-DE-MDNJ-NY-PA 9,500 750 8,100 7,600 

Phoenix-Mesa, AZ 5,000 3,300 

Pittsburgh-Beaver Valley, PA 4,500 1,400 

Richmond-Petersburg, VA 820 530 

Sacramento Metro, CA 5,600 5,600 5,600 5,600 

Salt Lake City, UT 3,600 2,200 

San Juan Co., NM 16,000 190 

St Louis, MO-IL 18,000 3,400 

Toledo, OH 180 50 

TOTAL by Pollutant 230,000 120,000 58,000 75,000 42,000 74,000 14,000 9,800 

a These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.
5a-14

Table 5a.13: Supplemental Local Control Measure Total Annualized Costs [M 2006$] 

Applied for Various Standards (ppm) a

2020 Extrapolated Cost Area 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

NOX VOC NOX VOC NOX VOC NOX VOC 

Ada Co., ID $6.0 $0.8 

Atlanta, GA $44 $5.8 

Baton Rouge, LA $52 $0.1 $48 

Boston-Lawrence-Worcester, MA $13 $1.7 

Buffalo-Niagara Falls, NY $2.6 $0.3 $0.9 

Campbell Co., WY $10 $0.2 

Charlotte-Gastonia-Rock Hill, NC-SC $50 $7.6 

Cincinnati-Hamilton, OH-KY-IN $30 $7.1 

Cleveland-Akron-Lorain, OH $27 $1.0 $13 

Dallas-Fort Worth, TX $16 $15 

Denver-Boulder-Greeley-Ft Collins-Love, CO $20 $4.9 

Detroit-Ann Arbor, MI $10 $10 

Dona Ana CO., NM $1.9 $0.7 

Eastern Lake Michigan, IL-IN-WI $130 $750 $120 $690 $120 $680 $33 $100 

El Paso Co., TX $8.1 

Houston, TX $0.7 $0.6 

Huntington-Ashland, WV-KY $81 $3.40 

Jackson Co., MS $37 $1.50 

Jefferson Co, NY $3.9 $1.20 

Las Vegas, NV $3.6 $4.50 

Memphis, TN-AR $46 $2.40 

Norfolk-Virginia Beach-Newport News $23 $3.50 

Northeast Corridor, CT-DE-MD-NJ-NY-PA $60 $0.99 $55 $52 

Phoenix-Mesa, AZ $7.9 $6.80 

Pittsburgh-Beaver Valley, PA $19 $3.10 

Richmond-Petersburg, VA $2.0 $1.20 

Sacramento Metro, CA $13 $13 $13 $13 

Salt Lake City, UT $11 $1.70 

San Juan Co., NM $54 $0.52 

St Louis, MO-IL $72 $4.80 

Toledo, OH $0.6 $0.17 

TOTAL by Pollutant $860 $820 $280 $690 $190 $680 $46 $100 

TOTAL COSTS $1,680 $970 $870 $146 a

These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

5a.4.2 Modeled Control Strategy Costs Not Needed 

As presented in Chapter 4, there were areas in our Modeled control strategy that were “over 

controlled.” Table 4.8 provides the amount of emissions that were not needed to meet the various 

ozone standards in 2020. Given these targets, the modeled control strategy emission reductions 

were analyzed to asses what measures could be removed. Table 5a.14 and 5a.15 respectively, 

show the amount of emission reductions and costs that were removed from the analysis. It was 

not possible in all extrapolated cost areas to remove all the emissions presented in Table 4.8. 

This was due to the nature of the EGU trading program, as well as the application of measures 

statewide for mobile sources. The emission reductions that were not able to be removed from the 

analysis of attainment for these standards is presented in Table 5a.16. it is important to note that 

since there was “over control” for 0.070ppm, 0.075 ppm, and 0.079ppm, the full costs of 

attainment of these levels of the standard will be an overestimate. 
5a-15

Table 5a.14: Modeled Control Strategy Control Measure Emissions Reductions [annual 

tons/year] removed from Extrapolated Analysis for Various Standards 

2020 Extrapolated Cost Area 0.070 ppm 0.075 ppm 0.079 ppm 

NOX VOC NOX VOC NOX VOC 

Allegan Co., MI 2,600 240 2,600 240 

Atlanta, GA 22,000 3,400 22,000 3,400 22,000 3,400 

Baton Rouge, LA 81,000 110,000 1,300 

Boston-Lawrence-Worcester-Portsmouth, MA-NH 12,000 3,800 12,000 3,800 

Buffalo-Niagara Falls, NY 6,000 1,300 7,000 1,400

Charlotte-Gastonia-Rock Hill, NC-SC 3,200 14,000 14,000 

Cincinnati-Hamilton, OH-KY-IN 29,000 4,000 29,000 4,000 31,000 4,100 

Cleveland-Akron-Lorain, OH 24,000 4,100 30,000 4,600 

Dallas-Fort Worth, TX 25,000 1,800 25,000 1,800 

Denver, CO 12,000 3,600 15,000 4,100 15,000 4,100 

Detroit-Ann Arbor, MI 30,000 3,600 30,000 3,600 

Eastern Lake Michigan, IL-IN-WI-MI 83 8 83 8 

Hancock, Knox, Lincoln & Waldo Cos, ME 7,800 9,300 460 9,300 460 

Houston-Galveston-Brazoria, TX 

Huntington-Ashland, WV-KY 1,200 84 1,200 84 1,200 84 

Indianapolis, IN 760 190 760 190 760 190 

Jefferson Co., NY 1,200 630 1,700 660 

Las Vegas, NV 1,500 1,300 1,800 1,300 

Muskegon Co., MI 290 90 420 100 420 100 

Norfolk-Virginia Beach-Newport News, VA 530 640 85 780 93 

Northeast Corridor, CT-DE-DC-NY-NJ-PA-VA 87,000 19,000 

Phoenix, AZ 7,600 3,200 7,600 3,200 7,600 3,200 

Pittsburgh-Beaver Valley, PA 17,000 23,000 1,500 25,000 1,700 

Providence (All RI), RI 1,500 690 1,500 690 1,500 690 

Richmond-Petersburg, VA 310 310 58 300 64 

Salt Lake City, UT 7,400 2,100 7,400 2,100 7,400 2,100 

St Louis, MO-IL 29,000 3,300 29,000 3,300 29,000 3,300 

Toledo, OH 1,500 42 1,500 42 1,600 49 

Rest of VA 910 50 

Rest of OH 46 4 

Rest of MI 420 35 420 35 

Rest of NY 110 9 

Rest of KY 1,100 82 1,100 82 1,100 82 

Rest of PA 180 14 

TOTALS 140,000 21,000 350,000 40,000 470,000 62,000 
5a-16

Table 5a.15: Modeled Control Strategy Control Measure Annualized Total Costs [M 

2006$] Removed from Extrapolated Analysis for Various Standards 

2020 Extrapolated Cost Area 0.070 ppm 0.075 ppm 0.079 ppm 

NOX VOC NOX VOC NOX VOC 

Allegan Co., MI $10 $0.9 $10 $0.9 

Atlanta, GA $66 $5.7 $66 $5.7 $66 $5.7 

Baton Rouge, LA $180 $490 $4.1 

Boston-Lawrence-Worcester-Portsmouth, MA-NH $32 $2.8 $32 $2.8 

Buffalo-Niagara Falls, NY $17 $2.3 $20 $2.3 

Charlotte-Gastonia-Rock Hill, NC-SC $3.8 $33 $33 

Cincinnati-Hamilton, OH-KY-IN $99 $9.0 $99 $9.0 $110 $9.0 

Cleveland-Akron-Lorain, OH $110 $12 $130 $12 

Dallas-Fort Worth, TX $80 $2.1 $80 $2.1 

Denver, CO $41 $4.8 $49 $4.8 $49 $4.8 

Detroit-Ann Arbor, MI $130 $12 $130 $12 

Eastern Lake Michigan, IL-IN-WI-MI $0.2 $0.2 

Hancock, Knox, Lincoln & Waldo Cos, ME $19 $24 $0.9 $24 $0.9 

Houston-Galveston-Brazoria, TX 

Huntington-Ashland, WV-KY $4.8 $0.2 $4.8 $0.2 $4.8 $0.2 

Indianapolis, IN $3.4 $0.8 $3.4 $0.8 $3.4 $0.8 

Jefferson Co., NY $4.5 $1.2 $5.8 $1.2 

Las Vegas, NV $4.7 $4.4 $5.8 $4.4 

Muskegon Co., MI $0.9 $0.4 $1.2 $0.4 $1.2 $0.4 

Norfolk-Virginia Beach-Newport News, VA $1.4 $2.1 $0.3 $2.6 $0.3 

Northeast Corridor, CT-DE-DC-NY-NJ-PA-VA $300 $21 

Phoenix, AZ $20 $6.7 $20 $6.7 $20 $6.7 

Pittsburgh-Beaver Valley, PA $48 $82 $3.9 $89 $3.9

Providence (All RI), RI $3.0 $0.3 $3.0 $0.3 $3.0 $0.3 

Richmond-Petersburg, VA $0.6 $0.6 $0.3 $0.8 $0.3 

Salt Lake City, UT $18 $1.7 $18 $1.7 $18 $1.7 

St Louis, MO-IL $130 $4.9 $130 $4.9 $130 $4.9 

Toledo, OH $6.0 $0.2 $6.0 $0.2 $6.3 $0.2 

Rest of VA $2.7 

Rest of OH $0.2 

Rest of MI $1.2 $1.2 

Rest of NY $0.3 

Rest of KY $3.1 $3.1 $3.1 

Rest of PA $0.5 

TOTAL by Pollutant $460 $35 $1,100 $78 $1,800 $100 

TOTAL $500 $1,200 $1,900 
5a-17

Table 5a.16: Emission Reductions Not Needed [annual tons/year] Remaining After 

Removing Control Measures Not Needed to Meet Various Ozone Standards a

2020 Extrapolated Cost Area 0.070 ppm 0.075 ppm 0.079 ppm 

NOX NOX NOX 

Allegan Co., MI 460 460 

Atlanta, GA 8,700 8,700 8,700 

Baton Rouge, LA (1) 7,606 

Boston-Lawrence-Worcester-Portsmouth, MA-NH 1,800 1,800 

Buffalo-Niagara Falls, NY 1,000 

Charlotte-Gastonia-Rock Hill, NC-SC (10) (40) (40) 

Cincinnati-Hamilton, OH-KY-IN 12,000 12,000 9,000 

Cleveland-Akron-Lorain, OH 8,900 14,100 

Dallas-Fort Worth, TX 18,000 18,000 

Denver, CO 4,300 11,000 11,000 

Detroit-Ann Arbor, MI 20,000 20,000 

Eastern Lake Michigan, IL-IN-WI-MI 

Hancock, Knox, Lincoln & Waldo Cos, ME 2 6 6 

Houston-Galveston-Brazoria, TX 

Huntington-Ashland, WV-KY 10 10 10 

Indianapolis, IN 5,800 5,800 5,800 

Jefferson Co., NY 700 250 

Las Vegas, NV 6,400 6,100 

Muskegon Co., MI 130 0 0 

Norfolk-Virginia Beach-Newport News, VA (8) 140 

Northeast Corridor, CT-DE-DC-NY-NJ-PA-VA 11,242 

Phoenix, AZ (90) (90) (90) 

Pittsburgh-Beaver Valley, PA (6) 6,700 4,400 

Providence (All RI), RI (4) (4) (4) 

Richmond-Petersburg, VA (5) (5) 8 

Salt Lake City, UT 

St Louis, MO-IL 2 1,200 1,200 

Toledo, OH 110 110 

TOTALS 30,000 100,000 120,000 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

5a.4.3 Fixed Cost Approach Detailed Results and Sensitivities 

The range of values from the fixed cost ($10,000/ton) to the fixed cost ($20,000/ton) is presented 

in Figure 5a.1. You can see that as the amount of unknown emissions increases for the alternate 

primary standards, the range of total extrapolated cost values becomes larger. The detailed costs 

by geographic area and alternate primary standard are presented in Tables 5a.17 through 5a.20.
5a-18

Figure 5a.1: Fixed Cost Approach Sensitivity Analysis Results Ranges 

$-

$5

$10

$15

$20

$25

$30

$35

$40

$45

$50

$55

0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm

Extrapolated Costs (B 2006$)

Fixed Cost ($15,000/ton) Fixed Cost ($10,000/ton) Fixed Cost ($20,000/ton)

Table 5a.17: Extrapolated Cost by Geographic Area to Meet 0.065 ppm Alternate Standard 

Fixed Cost Approach a, b

2020 Extrapolated Cost Area Fixed Cost Approach Extrapolated Costs (M 2006$) 

($10,000/ton) ($15,000/ton) ($20,000/ton) 

Ada Co., ID $28 $42 $55 

Atlanta, GA $55 $83 $110 

Baton Rouge, LA $1,600 $2,500 $3,300 

Boston-Lawrence-Worcester, MA $85 $130 $170 

Buffalo-Niagara Falls, NY $180 $270 $360 

Campbell Co., WY $0.5 $0.8 $1.0 

Charlotte-Gastonia-Rock Hill, NC-SC $470 $710 $940 

Cleveland-Akron-Lorain, OH $780 $1,200 $1,600 

Dallas-Fort Worth, TX $480 $720 $960 

Denver-Boulder-Greeley-Ft Collins-Love, CO $16 $25 $33 

Detroit-Ann Arbor, MI $1,000 $1,500 $2,000 

Dona Ana CO., NM $4.1 $6.2 $8.2 

Eastern Lake Michigan, IL-IN-WI $6,400 $9,600 $13,000 

Houston, TX $1,800 $2,700 $3,600 

Huntington-Ashland, WV-KY $8.0 $12 $16 

Jefferson Co, NY $62 $93 $120 

Las Vegas, NV $39 $59 $78 

Memphis, TN-AR $11 $16 $21 

Norfolk-Virginia Beach-Newport News $210 $310 $410 

Northeast Corridor, CT-DE-MD-NJ-NY-PA $3,400 $5,100 $6,800 
5a-19

2020 Extrapolated Cost Area Fixed Cost Approach Extrapolated Costs (M 2006$) 

($10,000/ton) ($15,000/ton) ($20,000/ton) 

Pittsburgh-Beaver Valley, PA $130 $190 $250 

Sacramento Metro, CA $1,300 $2,000 $2,600 

Salt Lake City, UT $4.3 $6.5 $8.6 

San Juan Co., NM $13 $19 $25 

St Louis, MO-IL $170 $250 $330 

Total Extrapolated Cost $18,000 $27,000 $36,000 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

Table 5a.18: Extrapolated Cost by Geographic Area to Meet 0.070 ppm Alternate Standard 

Fixed Cost Approacha, b

2020 Extrapolated Cost Area Extrapolated Costs (M 2006$) 

($10,000/ton) ($15,000/ton) ($20,000/ton) 

Baton Rouge, LA $490 $740 $990 

Buffalo-Niagara Falls, NY $37 $56 $75 

Cleveland-Akron-Lorain, OH $110 $170 $220 

Detroit-Ann Arbor, MI $87 $130 $170 

Eastern Lake Michigan, IL-IN-WI $7,000 $7,500 $10,000 

Houston, TX $1,600 $2,300 $3,100 

Northeast Corridor, CT-DE-MD-NJ-NY-PA $2,200 $3,300 $4,400 

Sacramento Metro, CA $890 $1,300 $1,800 

Total Extrapolated Cost $10,000 $16,000 $21,000 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

Table 5a.19: Extrapolated Cost by Geographic Area to Meet 0.075 ppm Alternate Standard 

Fixed Cost Approacha, b

2020 Extrapolated Cost Area Extrapolated Costs (M 2006$) 

($10,000/ton) ($15,000/ton) ($20,000/ton) 

Eastern Lake Michigan, IL-IN-WI $740 $1,800 $1,500 

Houston, TX $1,200 $1,600 $2,500 

Northeast Corridor, CT-DE-MD-NJ-NY-PA $650 $980 $1,300 

Sacramento Metro, CA $440 $660 

Total Extrapolated Cost $3,400 $5,100 $6,800 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.
5a-20

Table 5a.20: Extrapolated Cost by Geographic Area to Meet 0.079 ppm Alternate Standard 

Fixed Cost Approacha, b

2020 Extrapolated Cost Area Extrapolated Costs (Thousands 2006$) 

($10,000/ton) ($15,000/ton) ($20,000/ton) 

Houston, TX $810 $1,200 $1,600 

Sacramento Metro, CA $18 $28 $37 

Total Extrapolated Costs (NOX + VOC) $830 $1,200 $1,700 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

5a.4.4 Hybrid Approach 

5a.4.4.1 Hybrid Approach Equations

We begin with a linear increasing marginal cost (MC) curve represented here as 

MC b  2cQ

Where (b+2cQ) is a nonnegative function, and b is the intercept and 2c represents the slope, and 

Q is the quantity of emissions reduced from unknown controls.

For geographic areas that have reached the baseline in the modeled control strategy the total cost 

(TC) is calculated by taking the integral of the marginal cost function from 0 of emission 

reductions from unknown controls to all emissions reductions needed from unknown controls 

(Q).

Figure 5a.2: Example Extrapolated Marginal Cost for Geographic Areas Meeting the 

Baseline in the Modeled Control Strategy 

b

0 Q

Area =   b cQ dx

Q

3



0

2

$

Linear MC Curve 

Emission reductions from unknown controls

Cost/Ton of unknown controls 
5a-21

 Evaluate   b cQ dx

Q

3



0

2 bQ  cQ  a b  c  a

2 2

0 0

Where MC is nonnegative for 0 d   b  2cQ d Q the definite integral of MC equals the area of the 

shaded region, which is the total cost (TC)

TC = bQ + cQ2

To calculate average cost (AC) divide TC by Q

Q

bQ cQ

Q

TC 2

 

AC = b +cQ

Replace the intercept b with the national cost/ton jumping off point (N), and the slope (c) of the 

average cost curve with 

E0

NM where M is the multiplier, and E0

 represents the known emission 

reductions from the modeled control strategy. This slope represents; control technology changes, 

energy technology changes, relative price changes, technological innovation, and geographic 

distribution of sources with uncontrolled emissions, and emission reductions from known 

controls. Lastly, Q is represented by E1

(the total unknown emission reductions) 

AC N  1

0

E

E

NM

 ̧

 ̧

1

·

 ̈

 ̈

©

§

If we replace 

0

1

E

E

 with R, and pull out N the equation becomes 

AC = N(1+RM)

For geographic areas that have not reached the baseline in the modeled control strategy (Houston 

and parts of California), the total cost is calculated between Q0 and Q, where Q0 represents the 

quantity of emission reductions from unknown controls to reach the current ozone standard. 

Therefore the quantity of emissions that are extrapolated is 

Q - Q0.
5a-22

Figure 5a.3: Example Extrapolated Marginal Cost for Geographic Areas Not Meeting the 

Baseline in the Modeled Control Strategy 

Evaluate   b cQ dx

Q

3Q



0

2 bQ  cQ  a bQ  cQ  a

2

0 0

2

Where MC is nonnegative for Q0 d   b  2cQ d Q the definite integral of MC equals the area of 

the shaded region, which is the total cost (TC)

2

0

2

0 bQ  bQ  cQ  cQ

TC = ( ) ( )

2

0

2

b Q  Q0  c Q  Q

To calculate average cost (AC) divide TC by (Q - Q0)

( )

( ) ( )

0

2

0

2

0

Q Q

b Q Q c Q Q

Q

TC



  

 

AC = b +c(Q + Q0)

Replace the intercept b with the national cost/ton jumping off point (N), and the slope (c) with 

E0

NM where M is the multiplier, and E0

 represents the known emission reductions from the 

modeled control strategy. This slope represents; control technology changes, energy technology 

changes, relative price changes, technological innovation, and geographic distribution of sources 

with uncontrolled emissions, and emission reductions from known controls. Lastly, Q is 

represented by E1

(the total unknown emission reductions), and Q0 is represented by 

E084 (unknown emission reductions to reach the current standard) 

b

0 Q

Area =   b cQ dx

Q

3Q



0

2

$

Linear MC Curve 

Q0

Emission reductions from unknown controls

Cost/Ton of unknown controls 
5a-23

AC N  ( ) 1 084

0

E E

E

NM

  ̧

 ̧

1

·

 ̈

 ̈

©

§

If we replace 

0

1

E

E

 with R, replace

0

084

E

E

 with Rs and pull out N the equation becomes 

AC = N (1+RM+ RsM)

Figure 5a.4 shows a graphic al example that in the hybrid approach the total cost will be identical 

if calculated using the marginal cost framework or average cost framework. The total cost using 

the marginal cost framework is the grey area plus the blue area. The total cost using the average 

cost framework is the grey area plus the green area. By the nature of geometry, the blue area and 

the green area are equal. Therefore the total cost under either framework is equal. 

Figure 5a.4: Example Marginal Cost versus Average Cost for the Hybrid Approach 

$-

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

$35,000

$40,000

$45,000

0 200 400 600 800 1000 1200

Unknown Emission Reductions (Tons)

Cost/Ton 

Marginal Cost Average Cost

$-

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

$35,000

$40,000

$45,000

0 200 400 600 800 1000 1200

Unknown Emission Reductions (Tons)

Cost/Ton 

Marginal Cost Average Cost

$-

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

$35,000

$40,000

$45,000

0 200 400 600 800 1000 1200

Unknown Emission Reductions (Tons)

Cost/Ton 

Marginal Cost Average Cost

5a.4.4.3 Hybrid Approach Detailed Results by Geographic Area

Tables 5a.21 through 5a.24 present the detailed results by geographic area and standard for the 

hybrid approach (mid). 
5a-24

Table 5a.21: Extrapolated Cost by Geographic Area to Meet 0.065 ppm Alternate Primary 

Standard Using Hybrid Approach (Mid) a, b, c

2020 Extrapolated Cost Area Ratio of Unknown 

to Known Emission 

Reductions 

Average 

Cost/Ton 

(2006$) 

Hybrid Approach 

Extrapolated Cost 

(M 2006$) 

Ada Co., ID 0.81 $18,000 $49 

Atlanta, GA 0.10 $15,000 $85 

Baton Rouge, LA 0.95 $18,000 $3,000 

Boston-Lawrence-Worcester, MA 0.36 $16,000 $140 

Buffalo-Niagara Falls, NY 1.60 $21,000 $370 

Campbell Co., WY 0.01 $15,000 $0.75 

Charlotte-Gastonia-Rock Hill, NC-SC 1.20 $19,000 $910 

Cleveland-Akron-Lorain, OH 1.11 $19,000 $1,500 

Dallas-Fort Worth, TX 0.85 $18,000 $860 

Denver-Boulder-Greeley-Ft Collins-Love, CO 0.04 $15,000 $25 

Detroit-Ann Arbor, MI 1.65 $21,000 $2,100 

Dona Ana CO., NM 0.27 $16,000 $6.6 

NOX 2.00 $22,000 Eastern Lake Michigan, IL-IN-WI VOC 2.19 $23,000 $14,000 

El Paso Co., TX 0.00 $15,000 

Houston, TXd1.78 $24,000 $4,200 

Huntington-Ashland, WV-KY 0.02 $15,000 $12 

Jefferson Co, NY 1.18 $19,000 $120 

Las Vegas, NV 0.37 $16,000 $64 

Memphis, TN-AR 0.04 $15,000 $16 

Norfolk-Virginia Beach-Newport News 0.64 $17,000 $360 

Northeast Corridor, CT-DE-MD-NJ-NY-PA 2.15 $23,000 $7,700 

Pittsburgh-Beaver Valley, PA 0.35 $16,000 $210 

Sacramento Metro, CA 1.90 $22,000 $2,800 

Salt Lake City, UT 0.03 $15,000 $6.5 

San Juan Co., NM 0.07 $15,000 $19 

St Louis, MO-IL 0.23 $16,000 $260 

Total Extrapolated Cost $39,000 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

c

 Houston did not reach the baseline, and therefore has an additional R to reach the current standard of 

0.62.

d

 Houston did not reach the baseline, and therefore has an additional R to reach the current standard of 

0.62.
5a-25

Table 5a.22: Extrapolated Cost by Geographic Area to Meet 0.070 ppm Alternate Primary 

Standard Using Hybrid Approach (Mid) a, b

2020 Extrapolated Cost Area Ratio of Unknown 

to Known Emission 

Reductions 

Average 

Cost/Ton 

(2006$) 

Hybrid Approach 

Extrapolated Cost 

(M 2006$) 

Baton Rouge, LA 0.31 $16,000 $800 

Buffalo-Niagara Falls, NY 0.39 $16,000 $61 

Cleveland-Akron-Lorain, OH 0.18 $16,000 $170 

Detroit-Ann Arbor, MI 0.14 $16,000 $130 

Eastern Lake Michigan, IL-IN-WI NOX 1.65 $21,000 

VOC 1.86 $22,000 

$11,000 

Houston, TXc1.63 $23,000 $3,600 

Northeast Corridor, CT-DE-MD-NJ-NY-PA 1.47 $20,000 $4,400 

Sacramento Metro, CA 1.30 $20,000 $1,700 

Total Extrapolated Cost $22,000 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas. 

c

 Houston did not reach the baseline, and therefore has an additional R to reach the current standard of 

0.62.

Table 5a.23: Extrapolated Cost by Geographic Area to Meet 0.075 ppm Alternate Primary 

Standard Using Hybrid Approach (Mid) a, b

2020 Extrapolated Cost Area Ratio of Unknown 

to Known Emission 

Reductions 

Average 

Cost/Ton 

(2006$) 

Hybrid Approach 

Extrapolated Cost 

(M 2006$) 

NOX 0.50 $17,000 Eastern Lake Michigan, IL-IN-WI VOC 0.36 $16,000 $2,000 

Houston, TXc1.36 $22,000 $2,400 

Northeast Corridor, CT-DE-MD-NJ-NY-PA 0.46 $17,000 $1,100 

Sacramento Metro, CA 0.67 $17,000 $770 

Total Extrapolated Cost $6,300 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

c

 Houston did not reach the baseline, and therefore has an additional R to reach the current standard of 

0.62.

Table 5a.24: Extrapolated Cost by Geographic Area to Meet 0.075 ppm Alternate Primary 

Standard Using Hybrid Approach (Mid) a, b, c

2020 Extrapolated Cost Area Ratio of Unknown 

to Known Emission 

Reductions 

Average 

Cost/Ton 

(2006$) 

Hybrid Approach 

Extrapolated Cost 

(M 2006$) 

Houston, TXd1.17 $21,000 $1,700 

Sacramento Metro, CA 0.07 $15,000 $28 

Total Extrapolated Cost $1,800 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

c These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 
5a-26

d

 Houston did not reach the baseline, and therefore has an additional R to reach the current standard of 

0.62.

5a.4.4.3 Hybrid Approach Sensitivity Analysis Results

Sensitivity analysis was performed on the variable M to explore the degree that this variable 

effects total costs of attainment across alternate primary standards. The lowest value of M (0.12), 

as well as the highest (0.47) was used. The detailed results of these sensitivity analyses are 

presented in Tables 5a.25 through 5a.29. Figure 5a.5 shows graphically the range of values for 

national extrapolated costs for the four levels of the alternate primary standard analyzed. 

Figure 5a.5: Hybrid Approach Sensitivity Analysis Results Ranges 

$-

$5

$10

$15

$20

$25

$30

$35

$40

$45

$50

$55

0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm

Extrapolated Costs (B 2006$)

Hybrid Mid Range Hybrid Lower Bound Hybrid Upper Bound
5a-27

Table 5a.25: Extrapolated Cost by Geographic Area to Meet 0.065 ppm Alternate Standard 

Hybrid Approach Sensitivities a, b, c

Hybrid Approach (Low) Hybrid Approach (High) 

2020 Extrapolated Cost Area Average 

Cost/Ton 

(2006$) 

Hybrid 

Approach 

Extrapolated 

Cost 

(M 2006$) 

Average 

Cost/Ton 

(2006$) 

Hybrid 

Approach 

Extrapolated 

Cost 

(M 2006$) 

Ada Co., ID $16,000 $46 $21,000 $57 

Atlanta, GA $15,000 $84 $16,000 $86 

Baton Rouge, LA $17,000 $2,700 $22,000 $3,600 

Boston-Lawrence-Worcester, MA $16,000 $130 $18,000 $150 

Buffalo-Niagara Falls, NY $18,000 $320 $26,000 $480

Campbell Co., WY $15,000 $0.75 $15,000 $0.75 

Charlotte-Gastonia-Rock Hill, NC-SC $17,000 $810 $23,000 $1,100 

Cleveland-Akron-Lorain, OH $17,000 $1,300 $23,000 $1,800 

Dallas-Fort Worth, TX $17,000 $790 $21,000 $1,000 

Denver-Boulder-Greeley-Ft Collins-Love, CO $15,000 $25 $15,000 $25 

Detroit-Ann Arbor, MI $18,000 $1,800 $27,000 $2,700

Dona Ana CO., NM $15,000 $6.4 $17,000 $6.9 

NOX $19,000 $29,000 Eastern Lake Michigan, IL-IN-WI VOC $19,000 $12,000 $31,000 $19,000 

Houston, TX $19,000 $3,400 $32,000 $5,700 

Huntington-Ashland, WV-KY $15,000 $12 $15,000 $12 

Jefferson Co, NY $17,000 $110 $23,000 $140 

Las Vegas, NV $16,000 $61 $18,000 $69 

Memphis, TN-AR $15,000 $16 $15,000 $16 

Norfolk-Virginia Beach-Newport News $16,000 $330 $20,000 $400 

Northeast Corridor, CT-DE-MD-NJ-NY-PA $19,000 $6,400 $30,000 $10,000 

Pittsburgh-Beaver Valley, PA $16,000 $200 $17,000 $220 

Sacramento Metro, CA $18,000 $2,400 $28,000 $3,700 

Salt Lake City, UT $15,000 $6.5 $15,000 $6.6 

San Juan Co., NM $15,000 $19 $16,000 $19 

St Louis, MO-IL $15,000 $250 $17,000 $280 

Total Extrapolated Cost $33,000 $51,000 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

c These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 
5a-28

Table 5a.26: Extrapolated Cost by Geographic Area to Meet 0.070 ppm Alternate Standard 

Hybrid Approach Sensitivities a, b, c

2020 Extrapolated Cost Area Hybrid Approach (Low) Hybrid Approach (High) 

Average 

Cost/Ton 

(2006$)

Hybrid 

Approach 

Extrapolated 

Cost (M 

2006$)

Average 

Cost/Ton 

(2006$)

Hybrid 

Approach 

Extrapolated 

Cost (M 

2006$)

Baton Rouge, LA $16,000 $770 $17,000 $850 

Buffalo-Niagara Falls, NY $16,000 $59 $18,000 $67 

Cleveland-Akron-Lorain, OH $15,000 $170 $16,000 $180 

Detroit-Ann Arbor, MI $15,000 $130 $16,000 $140 

NOX $18,000 $27,000 Eastern Lake Michigan, IL-IN-WI VOC $18,000 $9,000 $28,000 $14,000 

Houston, TX $19,000 $3,000 $31,000 $4,800 

Northeast Corridor, CT-DE-MD-NJ-NY-PA $18,000 $3,800 $25,000 $5,500 

Sacramento Metro, CA $17,000 $1,500 $24,000 $2,100 

Total Extrapolated Cost $19,000 $27,000 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

c These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 

Table 5a.27: Extrapolated Cost by Geographic Area to Meet 0.075 ppm Alternate Standard 

Hybrid Approach Sensitivities a, b, c

2020 Extrapolated Cost Area Hybrid Approach (Low) Hybrid Approach (High) 

Average 

Cost/Ton 

(2006$)

Hybrid 

Approach 

Extrapolated 

Cost (M 

2006$)

Average 

Cost/Ton 

(2006$)

Hybrid 

Approach 

Extrapolated 

Cost (M 

2006$)

NOX $16,000 $19,000 Eastern Lake Michigan, IL-IN-WI VOC $16,000 $2,000 $18,000 $2,300 

Houston, TX $19,000 $2,000 $29,000 $3,100 

Northeast Corridor, CT-DE-MD-NJ-NY-PA $16,000 $1,000 $18,000 $1,200 

Sacramento Metro, CA $16,000 $710 $20,000 $870 

Total Extrapolated Cost $5,700 $7,500 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

c These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 
5a-29
5a-30

Table 5a.28: Extrapolated Cost by Geographic Area to Meet 0.079 ppm Alternate Standard 

Hybrid Approach Sensitivities a, b, c

2020 Extrapolated Cost Area Hybrid Approach (Low) Hybrid Approach (High) 

Average 

Cost/Ton 

(2006$)

Hybrid 

Approach 

Extrapolated 

Cost (M 

2006$)

Average 

Cost/Ton 

(2006$)

Hybrid 

Approach 

Extrapolated 

Cost (M 

2006$)

Houston, TX $18,000 $1,500 $28,000 $2,200 

Sacramento Metro, CA $15,000 $28 $15,000 $29 

Total Extrapolated Cost $1,500 $2,300 

a All estimates rounded to two significant figures. As such, totals will not sum down columns. 

b

 These estimates do not reflect benefits or costs for the San Joaquin Valley or South Coast Air Basins. 

Please see Appendix 7b for analysis of these areas.

c These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 
5b-1

Appendix 5b: Economic Impact of Modeled Controls

5b.1 Synopsis 

This appendix presents the economic impact results of the illustrative modeled control strategy. 

Given the possible impacts of ozone precursor control measures on manufacturing industries, the 

transportation sector, electricity generators, consumers, and U.S. Gross Domestic Product (GDP) 

as a whole, we believe it is important to gauge the extent to which other parts of the economy 

might also be affected by implementing an alternate primary ozone standard. Therefore, an 

analysis of the economy-wide effects of implementing the alternate standard is conducted by 

inputting estimated direct engineering costs to EPA’s computable general equilibrium model 

(Economic Model for Policy Analysis, or EMPAX-CGE). 

Before the appendix commences with a background and description of EMPAX-CGE followed 

by a presentation of the results, three points are highlighted below that will assist the reader in 

interpreting the economic impacts and relating these impacts to the modeled control strategy 

engineering costs presented in Chapter 5. 

(a) The selection criteria for the modeled control strategy, and its related compliance costs, is 

designed to select the least cost controls, from an engineering cost standpoint, that 

generate the greatest ozone reductions, but not necessarily the lowest economic impact. 

Therefore, although the control strategy is selected to reduce ozone at the lowest 

engineering cost, it does not necessarily represent the lowest impact strategy from an 

economic impact standpoint. Thus, while this economic impact analysis presents results 

for the modeled control strategy approach detailed in Chapter 3 of the RIA, it should not 

be viewed as reflecting the approach with the smallest economic impact. Instead, the 

results should be viewed as guidance or useful information for states preparing their 

implementation plans. It is likely that states will design implementation plans that apply 

alternative control strategies and in some cases design plans that take into account 

secondary impacts to industries and consumers within their boundaries. In such a case, 

the end result would be a set of State Implementation Plans (SIPs) that could be more 

economically optimal and may have lower impacts than those described below. 

(b) The costs analyzed in this economic impact appendix include only the modeled 

engineering costs detailed in Chapter 5 for the alternate primary ozone standard. Thus, the 

economic impacts presented in this appendix reflect only the modeled engineering costs. 

Not included in estimating these economic impacts are the extrapolated cost estimates 

detailed in Chapter 5. This is because the extrapolated cost estimates are not available by 

industry, a necessary input to the operation of EMPAX. Therefore, the engineering costs 

for the illustrative modeled control strategy that are input to EMPAX in this analysis are 

those that reflect the $2.8 billion (2006 dollars) in 2020 for the application of known 

controls.

(c) In the interest of learning how possible changes in manufactured-goods prices might 

affect businesses and households, along with how changes in electricity/energy prices 

might affect industry groups that are large energy users, EPA employed the “EMPAXCGE” computable general equilibrium (CGE) model, which has been peer reviewed and 
5b-2

used in recent analyses of the Clean Air Interstate Rule (CAIR), the Clean Air Visibility 

Rule (CAVR), and the PM2.5 NAAQS. As with similar models, EMPAX-CGE focuses on 

the cost-side of spillover effects on the economy. This implies its estimated industrysector impacts may be overstated because EMPAX-CGE is not configured to capture the 

beneficial economic consequences of the increased labor availability and productivity 

expected to result from air quality improvements. EPA continues to investigate the 

feasibility of incorporating labor productivity gains and other beneficial effects of air 

quality improvements in CGE models and will incorporate labor productivity gains and 

other effects of air quality model improvements within future versions of EMPAX-CGE 

as is feasible. 

EMPAX-CGE may also be used to generate the social costs associated with a regulation. The 

social costs associated with a regulation are those costs that result from the reaction of 

consumers and producers to the direct engineering costs of a regulation. The welfare of 

consumers and producers may be affected positively or negatively depending on the nature of the 

regulation, and this welfare change is a measure of the social costs. Such a welfare change could 

result from higher prices on output, which may lead to less demand by consumers and less output 

by producers. These changes due to the higher output prices are estimated as part of social costs. 

We apply the equivalent variation (EV) approach to estimate social costs using EMPAX-CGE in 

this RIA. This the first application of EV to estimate social costs as part of analysis using a CGE 

model in an RIA of this type. We explain how the EV approach can be used to estimate social 

costs, and why it is a better approach to estimating social cost than one using GDP in section 

5b.4.4 of this appendix . Given a substantial number of caveats on results generated by EMPAXCGE, we include social cost and do not compare these costs to the monetized benefits estimates 

provided later in this RIA. We also intend to solicit review and the advice of the SAB before we 

use this approach to estimate social costs before conducting any future economic impact analyses 

using CGE models.

5b.2 Background 

To complement the analysis of effects on specific manufacturing sectors from AirControlNET 

4.1, implications for mobile sources from MOBILE 6.2, NMIM, and NONROAD, and changes 

in electricity generation from IPM, the macroeconomic implications of the modeled control 

strategy have been estimated using EPA’s EMPAX-CGE model. The focus of this component of 

the Ozone RIA is on examining the sectoral and regional distribution of economic effects across 

the U.S. economy. This section briefly discusses the EMPAX model and the approach used to 

incorporate findings from other models in EMPAX-CGE. 

5b.2.1 Background and Summary of EMPAX-CGE Model 

EMPAX was first developed in 2000 to support economic analysis of EPA’s maximum 

achievable control technology (MACT) rules for combustion sources (reciprocating internal 

combustion engines, boilers, and turbines). The initial framework consisted of a national 

multimarket partial-equilibrium model with linkages only between manufacturing industries and 

the energy sector. Modified versions of EMPAX were subsequently used to analyze economic 
5b-3

impacts of strategies for improving air quality in the Southern Appalachian mountain region as 

part of efforts associated with the Southern Appalachian Mountain Initiative (SAMI). 

Recent work on EMPAX has extended its scope to cover all aspects of the U.S. economy at a 

regional level in either static or dynamic modes. Although major regulations directly affect a 

large number of industries, substantial indirect impacts may also result from changes in 

production, input use, income, and household consumption patterns. Consequently, EMPAX 

now includes economic linkages among all industrial and energy sectors as well as households 

that supply factors of production such as labor and purchase goods (i.e., a CGE framework). This 

gives the version of EMPAX called EMPAX-CGE the ability to trace economic impacts as they 

are transmitted throughout the economy and allows it to provide critical insights to policy makers 

evaluating the magnitude and distribution of costs associated with environmental policies. The 

dynamic version of EMPAX-CGE employed n this analysis, and its data sources, are described 

later on in Section 5b.3. EMPAX-CGE underwent peer review in 2006, and the results of that 

peer review can be found on the EPA Web site.1 We have incorporated a number of 

recommendations offered in the peer review, including updating the energy production and 

consumption data (from DOE) to allow for more up to date characterization of energy markets 

and revising the uncompensated labor supply elasticities used in the model. 

5b.2.2 EMPAX Modeling Methodology for the Modeled Control Strategy 

EMPAX-CGE can be used to analyze a wide array of policy issues and is capable of estimating 

how a change in a single part (or multiple parts) of the economy will influence producers and 

consumers across the United States. However, some types of policies, including the Ozone 

National Ambient Air Quality Standard, are difficult to capture adequately within a CGE 

structure because of the boiler- and firm-specific nature of emission reduction costs. 

Consequently, an interface has been developed that allows linkages between EMPAX-CGE and 

the detailed technology models discussed in Chapter 5 (AirControlNET 4.1, MOBILE6.2, 

NMIM, and IPM 3.0). These linkages give the combined modeling system the advantages of 

technology detail and broad macroeconomic coverage, thereby permitting EMPAX-CGE to 

investigate economy-wide policy implications. 

The technology models mentioned above estimate engineering cost changes by industry and 

region of the United States for the sectors of the economy affected by the alternate primary 

ozone standard. In order for EMPAX-CGE to effectively incorporate these additional costs, they 

have to be expressed in terms of the productive inputs used in CGE models (i.e., capital, labor, 

and material inputs produced by other industries). Rather than assume the costs represent a 

proportional scaling up of all inputs, Nestor and Pasurka (1995) data on purchases made by 

industries for environmental-protection reasons are used to allocate these additional expenditures 

across inputs within EMPAX-CGE. Once these expenditures are specified, the incremental 

engineering costs from the technology models can be used to adjust the production technologies 

in the CGE model. Also, for the modeled control strategy, linkages are made between EMPAX1 http://www.epa.gov/ttn/ecas/models/empax_peer_review_comments_responses.pdf.
5b-4

CGE and IPM to handle specific IPM findings related to resource costs and fuel consumption in 

electricity generation.2

5b.3 EMPAX-CGE Model Description: General Model Structure 

This section provides additional details on the EMPAX-CGE model structure, data sources, and 

assumptions. The version of EMPAX-CGE used in this analysis is a dynamic, intertemporally 

optimizing model that solves in five year intervals from 2005 to 2050. It uses the classical 

Arrow- Debreu general equilibrium framework wherein households maximize utility subject to 

budget constraints, and firms maximize profits subject to technology constraints. The model 

structure, in which agents are assumed to have perfect foresight and maximize utility across all 

time periods, allows agents to modify behavior in anticipation of future policy changes, unlike 

dynamic recursive models that assume agents do not react until a policy has been implemented. 

Nested CES functions are used to portray substitution possibilities available to producers and 

consumers. Figure 5b.1 illustrates this general framework and gives a broad characterization of 

the model.3 Along with the underlying data, these nesting structures and associated substitution 

elasticities determine the effects that will be estimated for policies. These nesting structures and 

elasticities used in EMPAX-CGE are generally based on the Emissions Prediction and Policy 

Analysis (EPPA) Model developed at the Massachusetts Institute of Technology (Paltsev et al., 

2005). This updated version of the EPPA model incorporates some extensions over the EPPA 

version documented in Babiker et al. (2001) such as specification of transportation purchases by 

households. These updates to transportation choices have been incorporated in this version of 

EMPAX-CGE as shown on the left-hand side of Figure 5b.1. Although the two models continue 

to have different focuses (EPPA is a model focused on analysis of national-level climate change 

policies while EMPAX is a model focused on regional-level analysis of pollution control 

policies), both are intended to simulate how agents will respond to environmental policies and as 

such EPPA provides a strong basis to develop the theoretical structure of EMPAX-CGE. 

Given this basic similarity, EMPAX-CGE has adopted a comparable structure. EMPAX-CGE is 

programmed in the GAMS4

 language (Generalized Algebraic Modeling System) and solved as a 

mixed complementarity problem (MCP)5 using MPSGE software (Mathematical Programming 

2

 See Appendix E in the RIA for the Final CAIR rule for additional discussion of these IPMEMPAX linkages (http://www.epa.gov/interstateairquality/technical.html).

3

 Although it is not illustrated in Figure 6.1, some differences across industries exist in their 

handling of energy inputs. In addition, the agriculture and fossil-fuel sectors in EMPAX-CGE 

contain equations that account for the presence of fixed inputs to production (land and fossil-fuel 

resources, respectively). 

4

 See Brooke, Kendrick, and Meeraus (1996) for a description of GAMS (http://www.gams.com/). 

5

 Solving EMPAX-CGE as a MCP problem implies that complementary slackness is a feature of 

the equilibrium solution. In other words, any firm in operation will earn zero economic profits 

and any unprofitable firms will cease operations. Similarly, for any commodity with a positive 

price, supply will equal demand, or conversely any good in excess supply will have a zero price. 
5b-5

Subsystem for General Equilibrium).6

 The PATH solver from GAMS is used to solve the MCP 

equations generated by MPSGE. 

6

 See Rutherford (1999) for MPSGE documentation (http://www.mpsge.org/). 
5b-6

Figure 5b.1: General Production and Consumption Nesting Structure in EMPAX-CGE 

Consumption Leisure

Consumption is a CobbDouglas composite of 

transportation plus goods. 

Domestic Foreign

Domestic goods are a CES 

composite of local goods and 

goods from other U.S. regions. 

Local

Output 

Regional 

Output 

KLE Intermediates 

Most producer goods use 

fixed proportions of 

intermediate inputs and KLE. 

Energy Value Added 

Capital Labor

Energy

(5 Types) 

KLE is a CES 

composite of 

energy (E) and 

value-added (KL). 

Value added is a CobbDouglas composite of 

capital and labor (KL). 

 Utility

Intermediate inputs are the 

30 types of non-energy 

goods, in fixed proportion 

for each industry. 

Energy

Consumption of 

energy is a CES 

composite of 

energy and other 

consumption 

goods. 

Energy (E) is a 

CES composite of 5 

energy types. The 

structure of this 

function varies across 

industries. 

Imports are a CES 

composite across 

foreign supply sources. 

Purchased 

Transport

Personal 

Transport

Transportation Consumption Goods 

Petroleum 

Services Manufactured

Goods 

Goods and 

Services

Transportation is a 

CES composite of 

personal vehicle 

transport and 

purchased transport. 

Personal vehicles 

use fuel and 

goods/services.

Each consumption good 

is a CES composite of 

foreign and domestically 

produced goods. 

(30 types) 

(5 types) 

Household utility is a CES function 

of consumption and leisure. 
5b-7

5b.3.1 Data Sources 

The economic data come from state level information provided by the Minnesota IMPLAN 

Group7 and energy data come from EIA.8 Forecasts for economic growth are taken from EIA’s

Annual Energy Outlook 2007 (AEO) and Global Insight.9 Although IMPLAN data contain 

information on the value of energy production and consumption in dollars, these data are 

replaced with EIA data since the policies being investigated by EMPAX-CGE typically focus on 

energy markets, making it essential to include the best possible characterization of these markets 

in the model. Although the IMPLAN data are developed from a variety of government data 

sources at the U.S. Bureau of Economic Analysis and U.S. Bureau of Labor Statistics, these data 

do not always agree with energy information collected by EIA directly from manufacturers and 

electric utilities.

EMPAX-CGE combines these economic and energy data to create a balanced social accounting 

matrix (SAM) that provides a baseline characterization of the economy. The SAM contains data 

on the value of output in each sector, payments for factors of production and intermediate inputs 

by each sector, household income and consumption, government purchases, investment, and 

trade flows. A balanced SAM for the year 2005 consistent with the desired sectoral and regional 

aggregation is produced using procedures developed by Babiker and Rutherford (1997) and 

described in Rutherford and Paltsev (2000). This methodology relies on optimization techniques 

to maintain the calculated energy statistics (in both quantity and value terms) while minimizing 

any changes needed in the other economic data to create a new balanced SAM based on 

EIA/IMPLAN data for the baseline model year (in essence, industry production functions are 

adjusted, if necessary, to account for discrepancies between EIA energy data and IMPLAN 

economic data by matching the energy data and adjusting the use of non-energy inputs so that the 

industry is in balance, i.e., the value of inputs to production equals the value of output). 

These data are used to define economic conditions in 50 states within the United States (plus the 

District of Columbia), each of which contains 80 industries. Prior to solving EMPAX-CGE, the 

states and industries are aggregated up to the categories to be included in the analysis. 

Aggregated regions have been selected to capture important differences across the country in 

electricity generation technologies, while industry aggregations are controlled by available 

energy consumption data. 

7

 See http://www.implan.com/index.html for a description of the Minnesota IMPLAN Group and 

its data. 

8

 These EIA sources include AEO 2007, the Manufacturing Energy Consumption Survey, State

Energy Data Report, State Energy Price and Expenditure Report, and various annual industry 

profiles.

9

 See http://www.globalinsight.com/ProductsServices/ProductDetail1100.htm for a description of 

the Global Insight U.S. State Forecasting Service. 
5b-8

Table 5b.1 presents the 35 industry categories included in EMPAX-CGE for policy analysis. 

Their focus is on maintaining as much detail in the energy intensive and manufacturing sectors10

as is

Table 5b.1: Industries in Dynamic EMPAX-CGE 

EMPAX Industry NAICS Classifications 

Energy

Coal 2121 

Crude Oila211111, 4861 

Electricity (fossil and nonfossil) 2211 

Natural Gas 211112, 2212, 4862 

Petroleum Refiningc324, 48691 

General

Agriculture 11 

Mining (w/o coal, crude, gas) 21 

Construction 23 

Manufacturing 

Food Products 311 

Textiles and Apparel 313, 314, 315, 316 

Lumber 321 

Paper and Allied 322 

Printing 323 

Chemicals 325 

Plastic & Rubber 326 

Glass 3272 

Cement 3273 

Other Minerals 3271, 3274, 3279 

Iron and Steel 3311, 3312 

Aluminum 3313 

Other Primary Metals 3314, 3316 

Fabricated Metal Products 332 

Manufacturing Equipment 333 

Computers & Communication Equipment 334 

Electronic Equipment 335 

Transportation Equipment 336 

Miscellaneous remaining 312, 337, 339 

Services

Wholesale & Retail Trade 42, 44, 45 

Transportationb

481-488 

Information 51 

Finance & Real Estate 52, 54 

Business/Professional 53, 55, 56 

Education (w/public) 61 

Health Care (w/public) 62 

Other Services 71, 72, 81, 92 

a Although NAICS 211111 covers crude oil and gas extraction, the gas component of this sector is moved 

to the natural gas industry. 

b The petroleum refining industry provided oil in delivered terms, which includes pipeline transport. 

c

 Transportation does not include NAICS 4862 (natural gas distribution), which is part of the natural gas 

industry. 

10 Energy-intensive industry categories are based on EIA definitions of energy-intensive 

manufacturers in the Assumptions for the Annual Energy Outlook 2007.
5b-9

allowed by available energy consumption data and computational limits of dynamic CGE 

models. In addition, the electricity industry is separated into fossil fuel generation and nonfossil 

generation, which is necessary because many electricity policies affect only fossil fired 

electricity.

Figure 5b.2 shows the five regions included in EMPAX-CGE in this analysis, which have been 

defined based on the expected regional distribution of policy impacts, availability of economic 

and energy data, and computational limits on model size. These regions have been constructed 

from the underlying state-level database designed to follow, as closely as possible, the electricity 

market regions defined by the North American Electric Reliability Council (NERC).11

Figure 5b.2: Regions Defined in Dynamic EMPAX-CGE 

5b.3.2 Production Functions 

All productive markets are assumed to be perfectly competitive and have production 

technologies that exhibit constant returns to scale, except for the agriculture and natural resource 

extracting sectors, which have decreasing returns to scale because they use factors in fixed 

supply (land and fossil fuels, respectively). The electricity industry is separated into two distinct 

sectors: fossil fuel generation and nonfossil generation. This allows tracking of variables such as 

heat rates for fossil fired utilities (Btus of energy input per kilowatt hour of electricity output). 

11 Economic data and information on nonelectricity energy markets are generally available only 

at the state level, which necessitates an approximation of the NERC regions that follows state 

boundaries.
5b-10

All markets, must clear (i.e., supply must equal demand in every sector) in every period, and the 

income of each agent in the model must equal their factor endowments plus any net transfers. 

Markets in EMPAX clear in the 5 regions included in the dynamic model. Along with the 

underlying data, the nesting structures shown in Figure 5b-1 and associated substitution 

elasticities define current production technologies and possible alternatives. 

5b.3.3 Utility Functions 

Each region in the dynamic version of EMPAX-CGE contains four representative households, 

classified by income, that maximize intertemporal utility over all time periods in the model 

subject to budget constraints, where the income groups are: 

x $0 to $14,999,

x $15,000 to $29,999,

x $30,000 to $49,999, and

x $50,000 and above.12

The percentage of U.S. households in each of these household classes is: 13% - $0 to $14,999; 

18% - $15,000 to $29,999, 20% - $30,000 to $49,999, and 49% - $50,000 and above.13 These 

representative households are endowed with factors of production including labor, capital, 

natural resources, and land inputs to agricultural production. Factor prices are equal to the 

marginal revenue received by firms from employing an additional unit of labor or capital. The 

value of factors owned by each representative household depends on factor use implied by 

production within each region. Income from sales of these productive factors is allocated to 

purchases of consumption goods to maximize welfare.

Within each time period, intratemporal utility received by a household is formed from 

consumption of goods and leisure. All consumption goods are combined using a Cobb Douglas 

structure to form an aggregate consumption good. This composite good is then combined with 

leisure time to produce household utility. The elasticity of substitution between consumption 

goods and leisure depends on empirical estimates of labor supply elasticities and indicates how 

willing households are to trade off leisure time for consumption. Over time, households consider 

the discounted present value of utility received from all periods’ consumption of goods and 

leisure. 

12 Computational limitations on EMPAX-CGE limit the number of household classes to four, and 

this is due to the complex modeling needed for the dynamic version of the model. We intend to 

review and potentially increase the number of household classes in future version of EMPAXCGE, and will better reflect higher income household classes as part of that effort. 

13 U.S. Census Bureau, Current Population Survey, 2007 Annual Social and 

Economic Supplement. HINC-01. Selected Characteristics of Households, by 

Total Money Income. Found on the Internet at 

(http://pubdb3.census.gov/macro/032007/hhinc/new01_001.htm)
5b-11

Following standard conventions of CGE models, factors of production are assumed to be 

intersectorally mobile within regions, but migration of productive factors is not allowed across 

regions. This assumption is necessary to calculate welfare changes for the representative 

household located in each region in EMPAX-CGE. EMPAX-CGE also assumes that ownership 

of natural resources and capital embodied in nonfossil electricity generation is spread across the 

United States through capital markets. 

5b.3.4 Trade 

In EMPAX-CGE, all goods and services are assumed to be composite, differentiated 

“Armington” goods made up of locally manufactured commodities and imported goods. Output 

of local industries is initially separated into output destined for local consumption by producers 

or households and output destined for export. This local output is then combined with goods 

from other regions in the United States using Armington trade elasticities that indicate agents 

make relatively little distinction between output from firms located within their region and 

output from firms in other regions within the United States. Finally, the domestic composite 

goods are aggregated with imports from foreign sources using lower trade elasticities to capture 

the fact that foreign imports are more differentiated from domestic output than are imports from 

other regional suppliers in the United States.

5b.3.5 Tax Rates and Distortions 

Taxes and associated distortions in economic behavior have been included in EMPAX-CGE 

because theoretical and empirical literature found that taxes can substantially alter estimated 

policy costs (e.g., Bovenberg and Goulder [1996]; Goulder and Williams [2003]). For example, 

existing labor taxes distort economic choices because they encourage people to work below the 

levels they would choose in an economy without labor taxes and reduces economic efficiency14.

When environmental policies raise production costs for firms and the price of goods and 

services, people may choose to work even less; the additional economic costs from this decision 

has been described as the “tax interaction” effect.

EMPAX-CGE considers these interaction effects by utilizing tax data from several sources and 

by explicitly modeling household labor supply decisions. The IMPLAN economic database 

provides information on taxes such as indirect business taxes (all sales and excise taxes) and 

social security taxes. However, since IMPLAN reports factor payments for labor and capital at 

their gross of tax values, we use additional data sources to determine personal income and capital 

tax rates. Information from the TAXSIM model at the National Bureau of Economic Research 

(Feenberg and Coutts, 1993), along with user cost of capital calculations from Fullerton and 

Rogers (1993), are used to establish tax rates. Elasticity parameters describing labor supply 

choice ultimately determine how distortionary existing taxes are in the CGE model. EMPAXCGE currently uses elasticities based on the relevant literature (i.e., 0.4 for the compensated 

labor supply elasticity and 0.15 for the uncompensated labor supply elasticity). These elasticity 

14 These efficiency losses are often expressed in terms of overall marginal excess burden; the cost 

associated with raising an additional dollar of tax revenue. Estimates range from $0.10 to $0.35 

per dollar (Ballard et al., 1985). 
5b-12

values give an overall marginal excess burden associated with the existing tax structure of 

approximately 0.3. 

5b.3.6 Intertemporal Dynamics and Economic Growth 

There are four sources of economic growth in EMPAX-CGE: technological change from 

improvements in energy efficiency, growth in the available labor supply (from both population 

growth and changes in labor productivity), increases in stocks of natural resources, and capital 

accumulation. Energy consumption per unit of output tends to decline over time because of 

improvements in production technologies and energy conservation. These changes in energy use 

per unit of output are modeled as AEEIs (Autonomous Energy Efficiency Improvements), which 

are used to replicate energy consumption forecasts by industry and fuel from EIA.15 The AEEI 

values provide the means for matching expected trends in energy consumption that have been 

taken from the AEO forecasts. They alter the amount of energy needed to produce a given 

quantity of output by incorporating improvements in energy efficiency and conservation. Labor 

force and regional economic growth, electricity generation, changes in available natural 

resources, and resource prices are also based on the AEO forecasts. 

Savings provide the basis for capital formation and are motivated through people’s expectations 

about future needs for capital. Savings and investment decisions made by households determine 

aggregate capital stocks in EMPAX-CGE. The IMPLAN dataset provides details on the types of 

goods and services used to produce the investment goods underlying each region’s capital stocks. 

Adjustment dynamics associated with formation of capital are controlled by using quadratic 

adjustment costs experienced when installing new capital, which imply that real costs are 

experienced to build and install new capital equipment. 

Prior to investigating policy scenarios, it is necessary to establish a baseline path for the 

economy that incorporates economic growth and technology changes that are expected to occur 

in the absence of the policy actions. Beginning from the initial balanced SAM dataset, the model 

is calibrated to replicate forecasts from the AEO 2007. Upon incorporating these forecasts, 

EMPAX-CGE is solved to generate a baseline based on them through 2030. Once this baseline is 

established, it is possible to run the “counterfactual” policy experiments discussed below. 

5b.3.7 Caveats Regarding EMPAX Modeling and the Results of this Analysis 

The results generated by EMPAX-CGE that are provided in this RIA appendix, which include 

estimates of price and output changes by industry and energy impacts, have a number of caveats 

and limitations associated with them that one should be cognizant of. They are as follows: 

As mentioned above, the current EMPAX-CGE model only considers the costs of policies and 

ignores the beneficial economic consequences of air quality improvements such as increased 

labor availability and productivity. If these health-related improvements were included in the 

model, any production decreases estimated by the model might be partially offset. 

15 See Babiker et al. (2001) for a discussion of how this methodology was used in the EPPA 

model (EPPA assumes that AEEI parameters are the same across all industries in a country, 

while AEEI values in EMPAX-CGE are industry specific).
5b-13

The extent of these potential benefits, along with current estimates of GDP impacts, depend on 

the labor supply elasticities in the model that have been chosen from the CGE literature on labor 

markets and tax distortions as discussed above. More flexible labor supply elasticities would 

allow additional response in labor markets to policy impacts, potentially with both positive and 

negative effects. Other critical assumptions in EMPAX-CGE largely revolve around the 

production technologies and input substitution options, which are based on the MIT EPPA 

model.

It is also highly uncertain as to which industries will be affected in the future when moving 

beyond where known engineering controls can currently apply. This mix of industries affected 

may be different than those current controls apply to, and tighter ozone standards may lead to 

consideration of controls to industries previously unaffected by measures related to ozone 

implementation. Ozone SIPs sometimes provide a “black box” (as per Section 182(e)(5) of the 

Clean Air Act) for additional controls to be supplied by unknown measures, and individual 

sectors where these controls may apply are never specified.16

EMPAX requires identification of costs by industry (by NAICS or SIC code) in order to operate. 

The capability of EMPAX to generate impacts is thus dependent on the extent to which the input 

costs by industry are defined. With a lack of knowledge of affected industries, there is also a lack 

of knowledge of affected consumers or households (thus, no way to estimate completely 

household welfare impacts). 

Results from EMPAX are strongly influenced by elements in its baseline data set such as energy 

production and consumption data taken from the Energy Information Administration’s Annual 

Energy Outlook (AEO). The current EMPAX version uses such energy data taken from the latest 

AEO version available (2007). This version of the AEO does not incorporate effects on energy 

production and consumption data associated with provision of the Energy Independence and 

Security Act of 2007 (or EISA) signed by the President on December 19, 2007.17 Such effects 

include increased biofuels production, increased vehicle fuel efficiency, and new minimum 

energy efficiency standards for many electric appliances and products. The effects of EISA will 

be incorporated in a revised version of the AEO that will be released to the public in March, 

2008.18

EMPAX keeps the location of labor constant in response to a supply shock. Hence, labor is not 

allowed to migrate between regions based on changes in wage rates. By not allowing labor 

migration, some inaccuracies in estimated changes in labor and wage rates may take place. 

These inaccuracies in estimated labor and wage rate changes may offset the inclusion of other 

16 Section 182 (e) (5) of the Clean Air Act allows estimation of reductions (or so-called “black 

box” measures) in ozone SIPs that are not allocated by source category or sectors. An example of 

this is on pp. 6-12 and 6-13 in the 2003 California Air Quality Management Plan for Ozone and 

PM found at http://www.aqmd.gov/aqmp/docs/2003AQMPChap6.pdf.

17 The entire text of this legislation can be read at http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=110_cong_bills&docid=f:h6enr.txt.pdf. This is found at the Government 

Printing Office’s official web site.

18 This is noted on the Energy Information Administration web site at 

http://www.eia.doe.gov/oiaf/aeo/index.html.
5b-14

effects into EMPAX that would lead to reduced economic impact estimates such as how 

improvements in air quality lead to increased labor productivity.

Other caveats that can typically be applied to CGE analyses, including this one, cover issues 

such as transitional dynamics in the economy. CGE models such as EMPAX, which assume 

foresight on the part of businesses and households, will allow agents to adapt to anticipated 

policy impacts coming in the future. These adaptations may occur more quickly than if agents 

adopted a wait-and-see approach to new regulations. The alternative, recursive-dynamic structure 

used in CGE models such as MIT EPPA imply that no anticipation or adjustments will occur 

until the policy is in place, which tends to overstate the costs of policies. 

Finally, in addition to transition dynamics, while CGE models are ideally suited for analyzing 

broad, economy-wide impacts of policies, they are not able to examine firm-specific impacts on 

profits/losses or estimate how particular types of disadvantaged households may be affected by 

policies. Similarly, environmental justice concerns may not be fully addressed. 

5b.4 EMPAX-CGE Results for the Modeled Control Strategy 

This section compares the modeled control strategy to a baseline for the economy that includes 

the current ozone standard (effectively, 0.084 ppm), along with other rules used to form the basis 

of the AEO 2007 forecasts by EIA such as the Clean Air Interstate Rule (CAIR) and the Clean 

Air Mercury Rule (CAMR). Impacts are measured assuming a 2020 implementation year and are 

the result of engineering costs described in Section 5.1. Thus, the following graphs compare the 

modeled control strategy to a baseline economic growth path in EMPAX-CGE that includes the 

current ozone standard and currently implemented legislation in the AEO 2007 forecasts. 

5b.4.1 Projected Energy Impacts and Impacts on U.S. Industries of Incremental Costs From 

Modeled Control Strategy 

Impacts of the modeled control strategy on manufacturing costs can affect output and prices of 

all industries in the EMPAX-CGE model. These effects may increase or decrease output and/or 

revenue, depending on their implications for production costs and technologies and shifts in 

household demands. In general, the impacts on energy producers and other industries will be 

dependent on the control strategy and follow a pattern similar to the stringency of the ozone 

standard.

As shown in Figure 5b.3, impacts on energy and industrial output quantities are generally small 

across all industries for modeled control strategy. Outside of the energy-intensive sectors,19

estimated changes in output of most manufactured goods are less than five one-hundredths of 

one percent (0.05%). Effects on coal output are somewhat higher, but impacts on other types of 

energy producers are low and can be positive or negative, which limits any spillover effects to 

other businesses and households. These changes in output quantities are different than any 

19 Energy-intensive sectors include food processing, pulp and paper, chemicals, glass, cement, 

iron and steel, and aluminum manufacturing. The definition of energy-intensive sectors applied 

in EMPAX-CGE is identical to that used by the U.S. Energy Information Administration (EIA) 

for their AEO modeling. 
5b-15

changes in gross output revenues, which include effects of changes in both quantity and output 

prices (which reflect changes in production costs) and may be either positive or negative, 

regardless of changes in output quantities. Also, across the economy as a whole, although there is 

almost no change in the quantity of services produced, these changes in output can potentially be 

larger in absolute terms than any changes in energy-related industries, which are much smaller 

than service industries in the U.S. economy. For more information on energy impacts at a 

nationwide level, please refer to Chapter 8 where we provide energy impact results in response to 

Executive Order 13211. 
5b-16

Figure 5b.3: Modeled Control Strategy Impacts on U.S. Domestic Output Quantity, 2020 

-2.00% -1.50% -1.00% -0.50% 0.00% 0.50% 1.00%

Coal

Crude Oil

Electricity

Natural Gas

Petroleum

Agriculture

Mining (other)

Construction

Food

Textiles & Apparel

Lumber

Pulp & Paper

Printing

Chemicals

Plastics & Rubber

Glass

Cement

Other Minerals

Iron & Steel

Aluminum

Other Primary Metals

Fabricated Metal Products

Machinery & Equipment

Computer Equipment

Electronic Equipment

Transportation Equipment

Miscellaneous Manufacturing

Wholesale & Retail Trade

Transportation Services

Information Services

Finance & Real Estate

Business Services

Education

Health Services

Other Services

Percent Change from Baseline

Source: EMPAX-CGE. 
5b-17

As described in Chapter 3, selected control options for the modeled control strategy involve 

additional actions by electric utilities, which tend to slightly decrease coal consumption 

(influencing U.S. coal production) and increase natural gas use. EMPAX-CGE uses these 

findings on coal and gas use directly from the IPM model (as described in Appendix E in the 

RIA for the Final CAIR rule).20 As part of its economy-wide estimation, EMPAX-CGE then 

considers how these changes in electricity markets affect other consumers of energy. Outside of 

electricity, other energy-producing industries also engage in additional measures, which can 

affect energy users such as energy-intensive manufacturers. Cement, chemicals and glass 

production are influenced by direct control costs on their respective industries and any changes 

in energy markets. Note, however, that across energy-intensive industries as a group, output 

quantities decline on average by less than a two-tenths of a percent (<0.2%). 

5b.4.2 Projected Regional Impacts 

Regional effects will tend to show variation that does not appear at the national level. To 

examine how such variations might occur in response to the modeled control strategy, this 

section presents findings for selected industries and groups for the five regions in EMPAX-CGE. 

These divergences between average national impacts and regional effects arise from several 

sources such as: 

x differences in control measures from the AirControlNET, IPM, and MOBILE models; 

x differences in regional mixes of generation technologies (coal, gas, oil, and nonfossil 

use), which may be averaged out at a national level; 

x differences in regional production and consumption patterns for electricity and 

nonelectricity energy goods; 

x differences in industrial composition of regional economies; 

x differences in household consumption patterns; and 

x differences in regional growth forecasts. 

Figure 5b.4 first presents regional impacts on industrial output from the modeled control 

strategy. Except for energy producers (shown in Figure 5b.5), this graph summarizes results for 

all the industries shown in Figure 5b.3, where similar industries are grouped together to facilitate 

the presentation. Aside from energy-intensive manufacturing (illustrated in more detail in 

Figure 5b.6), the adjustments in output are on the order of a few one-hundredths of one percent.

20 See http://www.epa.gov/interstateairquality/technical.html for additional discussion of these 

linkages.
5b-18

Figure 5b.4: Modeled Control Strategy Impacts on Regional Energy-Intensive Output 

Quantities, 2020 

-2.0% -1.0% 0.0% 1.0%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Agriculture Energy-Intensive

Manufacturing

Other Manufacturing Services Transportation

Percent Change from Baseline

Source: EMPAX-CGE. 
5b-19

Figure 5b.5: Modeled Control Strategy Impacts on Regional Industry Output Quantities, 2020

-2.0% -1.0% 0.0% 1.0%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Coal Crude Oil Electricity Natural Gas Petroleum

Percent Change from Baseline

Source: EMPAX-CGE. 
5b-20

Figure 5b.6: Modeled Control Strategy Impacts on Regional Energy Output Quantities, 2020 

-2.0% -1.0% 0.0% 1.0%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Food and Kindred Paper and Allied Chemicals Glass Cement Iron and Steel Aluminum

Percent Change from Baseline
5b-21

Unlike the broader industries, energy production that is more directly affected by the standards shows more regional variation than

seen in the U.S. results in Figure 5b.3. However, all impacts are still less than one percent (<1.0%) across the regions with mostadjustments smaller than that. Under the modeled control strategy, coal consumption by electric utilities tends to decrease slightly in 

2020, except in the South. Natural gas use in electricity rises, but is offset by declines in other parts of the economy. Such results

reflect the impacts from applying the EGU control strategy discussed in Chapter 3 of the RIA. This control strategy is applying

primarily to EGUs in the Northeast and is applied only to coal-fired units. This leads to the costs of power generation becomingrelatively cheaper in the South relative to the Northeast. Also, there are few controls applied to coal-fired EGUs in the South. The net 

impact from these effects is that EMPAX estimates that coal-fired power generation in the South decreases while it increases in the 

Northeast. These EMPAX results are shown in percentage and physical terms in Table 5b-2. The crude oil and petroleum refining

industries react to the alternative standard by minor changes in output, although refining in some regions rises in cases where they may have a small comparative advantage as fewer refiners need to install additional controls.Table 5b.2 Results from EMPAX in 2020 for Changes in Fuel Use and Generation by EGUs in Northeast and South Regions Under 

Modeled Control Strategy Region Baseline 

Use of Coal(trillionBTU) by EGUs

Use of CoalUnderModeledControlStrategy(trillionBTU)

PercentDifference in Coal Use (%) 

BaselineUse of NaturalGas(trillionBTU) by EGUs

Use of NaturalGasUnderModeledControlStrategy(trillionBTU)

PercentDifference in Natural Gas Use (%)

Electricity Generationin Baseline (millions kWh) 

ElectricityGenerationUnderModeledControlStrategy(millions kWh) 

PercentDifference inElectricity Generation(%)

Northeast 2,622 2,598 -0.9 871 870 -0.1 681,046 687,175 0.9 South 7,689 7,728 0.5 1,497 1,506 0.1 1,392,374 1,339,336 -0.5 BTU = British Thermal Unit kWh = kilowatt-hour
5b-22

Figure 5b.6 illustrates how changes in energy markets may affect those industries particularly 

reliant on energy inputs to their production processes. As with the U.S. average results from 

Figure 5b.3, even though the energy-intensive sectors show more regional variation, based on 

differences in production methods and changes in manufacturing costs, the majority of the 

impacts are on the order of a few tenths of one percent. However, there are measurable impacts 

in the output of specific industries. Under the modeled control strategy, energy-intensive output 

tends to be redistributed slightly from eastern to western regions as decreases in industries such 

as glass manufacturing in some regions are partially offset by increases in other regions.21

When examining such findings, however, it is important to note that these impacts and 

redistributions are directly related to the specific control strategy assumed in this illustrative 

analysis. As previously stated, these results represent the impact of the modeled control strategy 

presented by EPA. It is expected that States will evaluate the best strategies for achieving 

compliance and may choose options that could significantly alter these regional effects. 

Therefore, SIPs will likely be different than the strategy developed in this RIA and could be 

designed to alleviate any disproportionate impacts on sensitive industries. For example, given the 

impact on glass and cement production, assumed with this scenario, affected States may design 

SIP strategies that mitigate the impact on these particular industries, perhaps distributing costs 

more uniformly among all sectors. 

5b.4.3 Projected Macroeconomic Impact: GDP 

The combination of economic interactions affecting business and household behavior will be 

reflected in the changes in GDP estimated by a CGE model. The impacts on GDP are provided 

here only for illustration of the macroeconomic impacts of this standard. They are not meant to 

illustrate the social costs associated with the modeled control strategy applied to attain the 0.070 

alternate Ozone standard 

Figure 5b.7 illustrates GDP in the EMPAX-CGE model’s baseline forecast and the modeled 

control strategy. As shown, the estimated GDP impact is negligible and, in fact, it is not possible 

to adjust the scale of the graph to the point where the two lines do not overlap. Projected 

decrease in GDP for the modeled control strategy is roughly 0.02 percent (0.02%), respectively, 

for the year 2020. This is equivalent to a $3.6 billion decrease in GDP during the implementation 

year. In absolute terms, these estimated changes in U.S. GDP are extremely small relative to the 

total size of the economy. Even these small costs could be reduced if the CGE analyses were 

extended to include benefits associated with any alternate primary ozone standard such as 

improvements in labor productivity from environmental improvements. 

21 Redistribution of production will also tend to occur among states in each region, with some 

states’ increasing output to offset any declines in neighboring states. 
5b-23

Figure 5b.7: Change in U.S. GDP Compared to EMPAX-CGE Baseline 

Source: Department of Energy, Energy Information Administration; EMPAX-CGE 

5b.4.4 Social Cost Approaches and Estimates 

To provide an estimate of the social costs associated with the modeled control strategy, EMPAXCGE monetizes welfare changes from the general equilibrium simulation using Hicksian 

equivalent variation (EV), which is related in concept to the producer/consumer surplus 

measures used in partial-equilibrium models. EV is a long-recognized technique to estimate 

welfare gains and losses in economic theory, having been developed by Sir John Hicks in 1939.22

EV provides an estimate of the change in income that would provide an equivalent change in 

household welfare as the policy being considered and includes changes in utility households 

receive from both consumption and leisure time.23 It is a technique that is widely used by 

economists to measure welfare change. For example, Chipman and Moore (1980) showed that 

22 Hicks introduced this concept into economic theory in his book “Value and Capital: An inquiry 

into some fundamental principles of economic theory,” published in 1939. 

23 Including leisure time in the model and household decisions allows the labor supply to expand 

or contract in response to changes in wage rates, etc. It is also essential when modeling 

interactions between tax interactions and the economy. 

$15,041

$17,354

$20,214

$23,330

$15,041 (Ozone 084)

$17,355 (Ozone 084)

$20,218 (Ozone 084)

$23,333 (Ozone 084)

$26,629 (Ozone 084) $26,625

$12,500

$15,000

$17,500

$20,000

$22,500

$25,000

$27,500

2010 2015 2020 2025 2030

GDP 

(Billion 2006$) 

EMPAX Baseline

Modeled Control Strategy
5b-24

EV is appropriate for welfare comparisons.24 However, as calculated using EMPAX-CGE 

currently, it excludes measures of the standard’s environmental benefits (e.g., environment, 

public health, and labor productivity). In addition, these social cost estimates from EMPAXCGE do not incorporate extrapolated costs since these costs do not have a clear link to specific 

industries. The general equilibrium model estimates that the relative change in infinite-horizon25

and average annual welfare losses are extremely small (approximately 0.025%). Over the 2005-

2020 time horizon used in EMPAX-CGE for this analysis, the social costs are 93 percent of the 

engineering costs for the illustrative modeled control strategy when estimated in present value 

terms (2006 dollars).26,27 We estimate social costs using a 5 percent real interest rate to discount 

future production and consumption as per EPA guidance from the SAB provided in 2003. 

We use EV to provide an estimate of social costs in this analysis instead of a metric such as GDP 

since changes in GDP are a poor measure of impacts on consumer welfare. Although GDP is a 

common metric among policymakers for expressing “costs to society,” it is a poor measure of 

“social costs.” GDP as a measure of welfare has been criticized for many years by different 

economists. Much of that criticism is well summarized in a response to the 2004 Draft 

Thompson Report to Congress prepared by Arik Levinson and quoted as follows: “... GDP 

growth is a poor measure of welfare. It measures the flow of economic activity rather than the 

flow of assets. If there is over-fishing, regulations that reduce fish catch will reduce GDP in the 

short run, but increase long-run economic prosperity... Finally, GDP excludes non-traded 

benefits: environmental quality, health, workplace safety...” 28 Changes in household 

consumption are much closer to changes in the welfare of households (ignoring leisure) than 

changes in GDP. For example, since consumption is around two-thirds of GDP, a ballpark 

estimate might be that any changes in consumption will only be around two-thirds as large in 

dollar terms as changes in GDP. GDP also does not account for the value of leisure, which is 

accounted for directly in estimates of welfare impacts using an EV approach as mentioned 

above. Regarding exports and imports, GDP does account directly for the effect of export and 

24 Chipman, John S., and James C. Moore. 1980. Compensating Variation, Consumer’s Surplus, 

and Welfare. American Economic Review 70 (5): 933-49. 

25 By infinite horizon, what is meant is an infinite number of time horizons. Since it is not 

computationally feasible for EMPAX-CGE to provide estimates to this many time horizons, the 

model approximates an infinite horizon. Turn to p. 6-9 of the EMPAX-CGE documentation at 

http://www.epa.gov/ttnecas1/models/empax_model_documentation.pdf for details.

26 It should be noted that we will not compare this social cost estimate with the benefits estimates 

for alternate primary standards presented later in this RIA. We do not make this comparison for 

two key reasons: 1) the lack of linkage between air quality changes and effect categories such as 

labor productivity and health care costs among households; and 2) our inability to provide 

extrapolated costs by industry to serve as input to EMPAX. 

27 As mentioned in Chapter 5, the engineering cost estimate for the modeled control strategy of 

$2.8 billion (2006$) is calculated using the Equivalent Uniform Annual Cost (EUAC) method. 

The EUAC method does not generate the present value of the annual costs of controls on a yearby-year basis from 2005 to 2020. 

28 Levinson, Arik. Response to 2004 Draft Report to Congress on the Costs and Benefits of 

Federal Regulation and Unfunded Mandates on State, Local, and Tribal Entities (or “Thompson 

Report”). Submitted to the U.S. Office of Management and Budget. June 2, 2004. Found on the 

Internet at http://www.whitehouse.gov/omb/inforeg/2004_cb/c.pdf.
5b-25

imports upon U.S. expenditure on goods and services. The effect of purchasing imports upon 

household welfare as measured by the EV approach is accounted for indirectly through changes 

in household consumption and does not account for changes due to exports. We conclude 

thatThus, GDP is a poor metric for estimating welfare impacts in comparison to the EV 

approach, and therefore social costs. 29

As part of being a dynamic, forward-looking model, EMPAX uses an interest rate to place a 

value on the future (including both the benefits of consumption and costs of production). We 

have been using a 5% real interest rate, based on the MIT EPPA model referred to earlier in this 

chapter and SAB guidance as discussed in U.S. EPA (2003). This interest rate will form the basis 

for how the model reacts to any engineering costs it sees coming in the future. Following the 

guidance provided in OMB’s Circular A-4, we also provide social cost estimates in this appendix 

over the same 2005-2020 time horizon that reflect a 3% real interest rate, and a 7% real interest 

rate. These social cost estimates are 90 and 91 percent, respectively, of the engineering costs 

when costs are calculated in present value terms. 

This is the first application of EV to estimate social costs as part of analysis using a CGE model 

in an RIA of this type. We intend to solicit review and advice from the SAB before its use in 

future economic impact analyses using CGE models. 

5b.5 References 

Babiker, M.H., and T.F. Rutherford. 1997. “Input Output and General Equilibrium Estimates of 

Embodied CO2: A Data Set and Static Framework for Assessment.” University of Colorado at 

Boulder, Working Paper 97 2. Available at http://www.mpsge.org/mainpage/mpsge.htm. 

Babiker, M.H., J.M. Reilly, M. Mayer, R.S. Eckaus, I.S. Wing, and R.C. Hyman. 2001. “The 

MIT Emissions Prediction and CO2 Policy Analysis (EPPA) Model: Revisions, Sensitivities, and 

Comparisons of Results.” MIT Joint Program on the Science and Policy of Global Change, 

Report No. 71. Available at <http://web.mit.edu/globalchange/www/eppa.html>. 

Ballard, C. J. Shoven, and J. Whalley. 1985. “General Equilibrium Computations of the Marginal 

Welfare Costs of Taxation in the United States” American Economic Review 75(1): 128-138. 

Bovenberg, L.A., and L.H. Goulder. 1996. “Optimal Environmental Taxation in the Presence of 

Other Taxes: General Equilibrium Analysis.” American Economic Review 86(4):985-1000. 

Available at <http://www.aeaweb.org/aer/>. 

Brooke, A., D. Kendrick, A. Meeraus, and R. Raman. 1998. GAMS: A User’s Guide. GAMS 

Development Corporation. Available at http://www.gams.com. 

29 We provide estimates of the changes in GDP in 2020 from implementation of the modeled 

control strategy in Chapter 6, but only to provide information on this commonly known 

macroeconomic metric. 
5b-26

Feenberg, D., and E. Coutts. 1993. “An Introduction to the TAXSIM Model.” Journal of Policy 

Analysis and Management 12(1):189 194. Available at http://www.nber.org/~taxsim/. 

Fullerton, D., and D. Rogers. 1993. “Who Bears the Lifetime Tax Burden?” Washington, DC: 

The Brookings Institute. Available at http://bookstore.brookings.edu/ 

book_details.asp?product%5Fid=10403. 

Global Insight. 2007. “U.S. State, Metropolitan Area, and County-level Forecasting Services.” 

<http://www.globalinsight.com/ProductsServices/ProductDetail1100.htm> 

Goulder, L.H., and R.C. Williams. 2003. “The Substantial Bias from Ignoring General 

Equilibrium Effects in Estimating Excess Burden, and a Practical Solution.” Journal of Political 

Economy 111:898 927. Available at <http://www.journals.uchicago.edu/JPE/home.html> 

Minnesota IMPLAN Group. 2006. State Level Data for 2004. Available from 

http://www.implan.com/index.html. 

Nestor, D.V., and C.A. Pasurka. 1995. The U.S. Environmental Protection Industry: A Proposed 

Framework for Assessment. U.S. Environmental Protection Agency, Office of Policy, Planning, 

and Evaluation. EPA 230-R-95-001. Available at 

http://yosemite.epa.gov/ee/epa/eermfile.nsf/11f680ff78df42f585256b45007e6235/41b8b642ab93

71df852564500004b543/$FILE/EE 0217A 1.pdf.

Paltsev, S., J.M. Reilly, H.D. Jacoby, R.S. Eckaus, J. McFarland, M. Sarofim, M. Asadoorian, 

and M. Babiker. 2005. “The MIT Emissions Prediction and Policy Analysis (EPPA) Model: 

Version 4.” MIT Joint Program on the Science and Policy of Global Change, Report No. 125. 

Cambridge, MA. <http://web.mit.edu/globalchange/www/eppa.html>. 

Rutherford, T.F. 1999. “Applied General Equilibrium Modeling with MPSGE as a GAMS 

Subsystem: An Overview of the Modeling Framework and Syntax.” Computational Economics 

14(1):1 46. Available at http://www.gams.com/solvers/mpsge/syntax.htm. 

Rutherford, T.F., and S.V. Paltsev. 2000. “GTAP Energy in GAMS: The Dataset and Static 

Model.” University of Colorado at Boulder, Working Paper 00 2. Available at 

http://www.mpsge.org/mainpage/mpsge.htm. 

U.S. Department of Energy, Energy Information Administration. Undated (a). State Energy Data 

Report. Washington DC. Available at http://www.eia.doe.gov/emeu/states/ _use_multistate.html. 

U.S. Department of Energy, Energy Information Administration. Undated (b). State Energy Price 

and Expenditure Report. Washington DC. Available at

http://www.eia.doe.gov/emeu/states/price_multistate.html. 

U.S. Department of Energy, Energy Information Administration. 2003. Manufacturing Energy 

Consumption Survey 2002. Washington DC. Available at http://www.eia.doe.gov/emeu/mecs/. 
5b-27

U.S. Department of Energy, Energy Information Administration. January 2007. Annual Energy 

Outlook 2007. DOE/EIA-0383(2007). Washington, DC. Available at 

http://www.eia.doe.gov/oiaf/aeo/index.html. 

U.S. Environmental Protection Agency (EPA), Office of Policy Analysis and Review. 2003. 

“Benefits and Costs of the Clean Air Act 1990–2020: Revised Analytical Plan For EPA’s Second 

Prospective Analysis.” 
6-1

Chapter 6: Incremental Benefits of Attaining Alternative Ozone Standards Relative to the 

Current 8-hour Standard (0.08 ppm)

Synopsis

Based on projected emissions and air quality modeling, in 2020, 28 counties in the U.S. with 

ozone monitors are anticipated to fail to meet an alternative ozone standard of 0.075 ppm for the 

4

th highest maximum 8-hour ozone concentration. This number falls to 11 for an ozone standard 

of 0.079 ppm, and increases to 89 for a standard of 0.070 ppm, and increases to 231 for an 

alternative standard of 0.065 ppm (see Figure 3.4 in Chapter 3). We estimated the health benefits 

of attaining these alternative ozone standards across the nation using the EPA Environmental 

Benefits Modeling and Analysis Program (BenMAP) using a two-stage analysis. 

In the first stage, we estimated the benefits associated with improving modeled air quality using 

known control technologies. These control strategies were sufficient to bring some, but not all, 

areas into attainment with the alternative standards. Thus, for some areas, the benefits computed 

during this first stage only represented partial attainment. In the second stage, we estimated the 

benefits of fully attaining the standards in all areas by using a “rollback” method. This method 

reduced ozone concentrations at nonattaining monitors to a level that would just meet the 

standards. To estimate the benefits for the 0.075 ppm and 0.079 standards, we deviated from this 

two-stage approach. Instead, we used an interpolation technique (please see Appendix 6a for 

more details on this technique). Benefits for the South Coast and San Joaquin areas of California 

(which are not expected to reach attainment of the current standard until after 2020) are 

estimated separately and can be found in Appendix 7b.1

 For all alternative standards, we used 

health impact functions based on published epidemiological studies and valuation functions 

derived from the economics literature to calculate the monetary value of the adverse health 

outcomes potentially avoided due to these reductions in ambient ozone levels.2 Key health 

endpoints included premature mortality, hospital and emergency room visits, school absences, 

and minor restricted activity days. 

There is considerable uncertainty in the magnitude of the association between ozone and 

premature mortality. This analysis presents four independent estimates of this association based 

upon different functions reported in the scientific literature. We also note that this range of 

estimates do not fully capture the uncertainties within each study. Recognizing that additional 

research is necessary to clarify the underlying mechanisms causing these effects, we also 

consider the possibility that the observed associations between ozone and mortality may not be 

causal in nature. Using the National Morbidity, Mortality and Air Pollution Study (NMMAPS), 

which was used as the primary basis for the risk analysis presented in our Staff Paper and 

reviewed by Clean Air Science Advisory Committee (CASAC), we estimated 250 avoided 

premature deaths annually in 2020 from reducing ozone levels to meet a standard of 0.070 ppm. 

When added to the other projected benefits from reduced ozone, including 3,000 hospital and 

1

 All subsequent estimates of full attainment ozone benefits and PM2.5 co-benefits found in this 

chapter exclude these two areas of California. 

2

 Health impact functions measure the change in a health endpoint of interest, such as hospital 

admissions, for a given change in ambient ozone or PM concentration 
6-2

emergency room admissions, 640,000 school absences, and over 1.7 million minor restricted 

activity days, we estimated a total ozone-related benefit of $2.2 billion/yr (2006$). Using three 

studies that synthesize data across a large number of individual studies, we estimate between 810 

and 1,100 avoided premature deaths annually in 2020 from reducing ozone to 0.070 ppm, 

leading to total monetized ozone-related benefits of between $6.5 and $9 billion/yr. 

Alternatively, if there is no causal relationship between ozone and mortality, avoided premature 

deaths associated with reduced ozone exposure would be zero and total monetized ozone-related 

morbidity benefits would be $230 million/yr.

For the selected standard of 0.075 ppm, using the NMMAPS ozone mortality study resulted in 71 

premature deaths avoided and total monetized benefits of $620 million/yr, incremental to 

attainment of the 0.08 ppm standard. Using the three synthesis studies, estimated premature 

deaths avoided for the less stringent standard are between 230 and 320 with total monetized 

ozone benefits between $1.9 and $2.6 billion/yr. Alternatively, if there is no causal relationship 

between ozone and mortality, avoided premature deaths associated with reduced ozone exposure 

would be zero and total monetized ozone-related morbidity benefits would be $73 million/yr. 

For a less stringent standard of 0.079 ppm, using the NMMAPS ozone mortality study resulted in 

24 premature deaths avoided and total monetized benefits of $220 million/yr, incremental to 

attainment of the 0.08 ppm standard. Using the three synthesis studies, estimated premature 

deaths avoided for the less stringent standard are between 80 and 110, with total monetized 

ozone benefits between $640 and $890 million/yr. Alternatively, if there is no causal relationship 

between ozone and mortality, avoided premature deaths associated with reduced ozone exposure 

would be zero and total monetized ozone-related morbidity benefits would be $28 million/yr. 

For a more stringent standard of 0.065 ppm, using the NMMAPS ozone mortality study resulted 

in 450 premature deaths avoided and total monetized benefits of $3.9 billion/yr, incremental to 

attainment of the 0.08 ppm standard. Using the three synthesis studies, estimated premature 

deaths avoided for the more stringent standard are between 1,500 and 2,100, with total 

monetized ozone benefits between $12 and $16 billion/yr. Alternatively, if there is no causal 

relationship between ozone and mortality, avoided premature deaths associated with reduced 

ozone exposure would be zero and total monetized ozone-related morbidity benefits would be 

$420 million/yr. 

These estimates reflect EPA’s interim approach to characterizing the benefits of reducing 

premature mortality associated with ozone exposure. EPA has requested advice from the 

National Academy of Sciences on how best to quantify uncertainty in the relationship between 

ozone exposure and premature mortality in the context of quantifying benefits associated with 

alternative ozone control strategies. We expect to receive this advice later this spring. 

The monetary benefits of visibility improvements from PM2.5 reductions associated with from the 

0.070 modeled attainment strategy in selected federal Class I Areas in 2020 is $160 million/yr. 

In addition to the direct benefits from reducing ozone, attainment of the standards would likely 

result in additional health and welfare benefits because reducing the ozone precursors NOx and 

VOC will also reduce PM2.5. Using both modeled and extrapolated reductions in these precursor 

emissions, we estimated PM-related co-benefits for the four alternative standards. For each 
6-3

alternative standard, we provide a range of estimated benefits based on several different PM 

mortality effect estimates. These effect estimates were derived from two different sources: the 

published epidemiology literature and an expert elicitation study conducted by EPA in 2006. 

For the 2020 attainment of the 0.075 ppm alternative, incremental to attainment of the 0.08 ppm 

standard, we estimate total ozone and PM2.5-related co-benefits to be between $3.6 and $16 

billion/yr; this range encompasses the expert functions and the ozone mortality functions as well 

as the possibility that there is no causal relationship between ozone and mortality. 

For the 2020 attainment of the 0.079 ppm alternative, incremental to attainment of the 0.08 ppm 

standard, we estimate total ozone and PM2.5-related co-benefits to be between $2 and $11 

billion/yr; this range encompasses the expert functions and the ozone mortality functions as well 

as the possibility that there is no causal relationship between ozone and mortality. 

For the 2020 attainment of the 0.070 ppm alternative, incremental to attainment of the 0.08 ppm 

standard, we estimate total ozone and PM2.5-related co-benefits to be between $6.5 and $27 

billion/yr (3% and 7% discount rates, 2006$); this range encompasses the expert functions and 

the ozone mortality functions as well as the possibility that there is no causal relationship 

between ozone and mortality. 

For the 2020 attainment of the 0.065 ppm alternative, incremental to attainment of the 0.08 ppm 

standard, we estimate total ozone and PM2.5-related co-benefits of between $11 and $42 

billion/yr; this range encompasses the expert functions and the ozone mortality functions as well 

as the possibility that there is no causal relationship between ozone and mortality. 

6.1 Background 

The purpose of this analysis is to assess the human health benefits of attaining the selected 8-

hour ozone standard of 0.075 ppm as well as alternative standards, including 0.079 ppm, 0.070 

ppm, and 0.065 ppm, incremental to attainment of the current 8-hour ozone standard of 0.08 

ppm.3

 We applied a damage function approach similar to those used in several recent U.S. EPA 

regulatory impact analyses, including those for the 2006 Particulate Matter (PM) NAAQS (U.S. 

EPA, 2006) and the Clean Air Interstate Rule (U.S. EPA, 2005). This approach estimates 

changes in individual health and welfare endpoints (specific effects that can be associated with 

changes in air quality) and assigns values to those changes assuming independence of the 

individual values. We calculated total benefits simply by summing the values for all nonoverlapping health and welfare endpoints. This analysis largely builds on both the analytical 

approach used in the 2006 PM NAAQS RIA and the analysis of ozone health impacts reported in 

Hubbell et al. (2005) and the Clean Air Interstate Rule RIA (2005). For a more detailed 

discussion of the principles of benefits analysis used here, please see those documents, as well as 

the EPA Guidelines for Economic Analysis (2000).4,5,6

3

 This is effectively 0.084 ppm due to current rounding conventions. When calculating benefits in 

this chapter we followed the rounding convention and rounded to 0.084 ppm. 

4

 U.S. EPA. 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards 

for Particle Pollution, Chapter 5. Available at http://www.epa.gov/ttn/ecas/ria.html.
6-4

We applied a two-stage approach to estimate the benefits of fully attaining each alternative 

standard. In the first stage, we estimated the benefits associated with improving modeled air 

quality using known and available control technologies. These control strategies were sufficient 

to bring some, but not all, areas into attainment with the various alternative standards. Thus, for 

some areas, the benefits computed during this first stage only represented partial attainment (see 

Chapter 3 for details on these control technologies and the results of the air quality modeling). In 

the second stage, we estimated the benefits of fully attaining the standards in all areas by using a 

“rollback” method. This method reduced ozone concentrations at residually nonattaining 

monitors to a level that would just meet the standards (see Appendix 6a for details on this 

methodology). We tested the sensitivity of our results to different assumptions, including the 

choice of health effect estimates from epidemiological studies and economic valuation 

parameters for those health effects. A quantitative assessment of non-health benefits (e.g., 

benefits from reduced ozone-related crop damage) was beyond the scope of this analysis due to 

data and resource limitations. 

For this assessment, we estimated the benefits of reducing ozone and PM concentrations by 

applying illustrative control strategies on ozone precursor emissions to attain alternative ozone 

NAAQS. With the exception of ozone-related premature mortality, we used methods consistent 

with previous PM and ozone benefits assessments. Specifically, we used the same approach to 

analyze PM co-benefits as the 2006 PM NAAQS RIA (U.S. EPA, 2006). In addition, we used a 

nearly identical approach to analyze the ozone benefits as the 2007 Ozone RIA (U.S. EPA, 

2007).

All estimates of ozone benefits and PM2.5 co-benefits in this chapter are incremental to a baseline 

of national full attainment with 0.08 ppm.7

 This baseline incorporates emission reductions 

projected to be achieved through an array of federal rules such as the Clean Air Interstate and 

Non-Road Diesel Rules, as well as ozone and PM2.5 state implementation plans. Moreover, the 

PM2.5 co-benefits are incremental to an assumption of full attainment of the 2006 PM2.5 NAAQS. 

See Chapter 3 for a complete discussion of the baseline. The PM co-benefits presented in this 

chapter are incremental to the PM benefits estimated in the 2006 PM NAAQS RIA and reflect 

the PM benefits from NOx reductions associated with each ozone control strategy. 

Furthermore, none of the estimates of incidence or monetary benefits provided in this chapter 

include South Coast and San Joaquin Valley Air Basins. Attainment dates will be determined in 

the future through the SIP process based on criteria in the CAA, future air quality data, and 

future rulemakings and are not knowable at this time. For analytical simplicity, and in keeping 

5

 Hubbell, B., A. Hallberg, D.R. McCubbin, and E. Post. 2005. Health-Related Benefits of 

Attaining the 8-Hr Ozone Standard. Environmental Health Perspectives 113:73–82. 

U.S. EPA. 2000. Guidelines for Preparing Economic Analyses. 

http://yosemite1.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/Guidelines.pdf

6

 U.S. EPA. 2000. Guidelines for Preparing Economic Analyses. 

http://yosemite1.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/Guidelines.pdf

7

 The PM2.5 benefits presented below reflect the NOx emission reductions from the ozone control 

strategy. Reductions from Ocean-Going Vessels burning residual diesel fuel were included both 

East and West in the baseline PM co-benefits, but not included in the ozone baseline for the 

west. See chapter 3 for more details of this rule and its application. 
6-5

with the proposal analysis, we have chosen to use an analysis year of 2020 and generally assume 

attainment in that year. The exception is the San Joaquin and South Coast California areas where 

SIP submittals for the current standard show that they would have current standard attainment 

dates later than 2020. For these two areas in California, we are assuming a new standard 

attainment date of 2030. Estimates of the costs and benefits of attaining the 0.075 ppm standard 

and the alternate air quality standards for these two areas in 2030 not included in the primary 

benefit analysis and are provided in Appendix 7b. 

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

Valley and South Coast air basins in California. The state has submitted plans to EPA for 

implementing the current ozone standard which propose that these two areas of California meet 

that standard by 2024. We have assumed for analytical purposes that the San Joaquin Valley and 

South Coast air basin would attain a new standard in 2030. There are many uncertainties 

associated with the year 2030 analysis. Between 2020 and 2030 several federal air quality rules 

are likely to further reduce emissions of NOx and VOC, such as, but not limited to National rules 

for Diesel Locomotives, Diesel Marine Vessels, and Small Nonroad Gasoline Engines. These 

emission reductions should lower ambient levels of ozone in California between 2020 and 2030. 

Complete emissions inventories as well as air quality modeling were not available for this year 

2030 analysis. Due to these limitations, it is not possible to adequately model 2030 air quality 

changes that are required to develop robust controls strategies with associated costs and benefits.

In order to provide a rough approximation of the costs and benefits of attaining 0.075 ppm and 

the alternate standards in San Joaquin and South Coast air basins, we have relied on the available 

data. Available data includes emission inventories, which do not include any changes in 

stationary source emissions beyond 2020, and 2020 supplemental air quality modeling. This 

data was used to develop extrapolated costs and benefits of 2030 attainment. These results 

indicate that benefits would be between $0.13 billion and $2.0 billion for the selected ozone 

standard of 0.075 ppm in 2030. To view the complete analysis for the San Joaquin Valley and 

South Coast air basins, see Appendix 7b.3 

The remainder of this chapter describes the data and methods used in this analysis, along with 

the results. Appendix 6a of this RIA provides additional details of the analysis. Section 6.2 

discusses the probabilistic framework for the benefits analysis and how key uncertainties are 

addressed in the analysis. Section 6.3 discusses the literature on ozone- and PM-related health 

effects and describes the specific set of health impact functions we used in the benefits analysis. 

Section 6.4 describes the economic values selected to estimate the dollar value of ozone- and 

PM- related health impacts. Finally, Section 6.5 presents the results and implications of the 

analysis.

6.2 Characterizing Uncertainty: Moving Toward a Probabilistic Framework for 

Benefits Assessment 

The National Research Council (NRC) (2002) highlighted the need for EPA to conduct rigorous 

quantitative analysis of uncertainty in its benefits estimates and to present these estimates to 

decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. In 

response to these comments, EPA’s Office of Air and Radiation (OAR) is developing a 

comprehensive strategy for characterizing the aggregate impact of uncertainty in key modeling 
6-6

elements on both health incidence and benefits estimates. Components of that strategy include 

emissions modeling, air quality modeling, health effects incidence estimation, and valuation.

Two aspects of OAR’s strategy have been used in several recent RIAs and are also employed in 

this analysis.8,9,10 First, we used Monte Carlo methods for estimating characterizing random 

sampling error associated with the concentration response functions from epidemiological 

studies and economic valuation functions. Monte Carlo simulation uses random sampling from 

distributions of parameters to characterize the effects of uncertainty on output variables, such as 

incidence of premature mortality. Specifically, we used Monte Carlo methods to generate 

confidence intervals around the estimated health impact and dollar benefits. The reported 

standard errors in the epidemiological studies determined the distributions for individual effect 

estimates. Table 6.4 describes the distributions for unit values. 

Second, because characterization of random statistical error omits important sources of 

uncertainty (e.g., in the functional form of the model—e.g., whether or not a threshold may exist) 

we used a recently completed expert elicitation of the concentration response function describing 

the relationship between premature mortality and ambient PM2.5 concentration.11 Use of the 

expert elicitation and incorporation of the standard errors approaches provide insights into the 

likelihood of different outcomes and about the state of knowledge regarding the benefits 

estimates. Both approaches have different strengths and weaknesses, which are fully described in 

Chapter 5 of the PM NAAQS RIA. 

In benefit analyses of air pollution regulations conducted to date, the estimated impact of

reductions in premature mortality has accounted for 85% to 95% of total benefits. Therefore, it is 

particularly important to attempt to characterize the uncertainties associated with reductions in 

premature mortality. The health impact functions used to estimate avoided premature deaths 

associated with reductions in ozone have associated standard errors that represent the statistical 

errors around the effect estimates in the underlying epidemiological studies.12 In our results, we 

report credible intervals based on these standard errors, reflecting the uncertainty in the estimated 

change in incidence of avoided premature deaths. We also provide multiple estimates, to reflect 

model uncertainty between alternative study designs. In addition, we characterize the uncertainty 

introduced by the inability of existing empirical studies to discern whether the relationship 

8

 U.S. Environmental Protection Agency, 2004a. Final Regulatory Analysis: Control of 

Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Prepared by Office of Air and 

Radiation. Available at http://www.epa.gov/nonroad-diesel/2004fr/420r04007.pdf

9

 U.S. Environmental Protection Agency, 2005. Regulatory Impact Analysis for the Clean Air 

Interstate Rule. EPA 452/-03-001. Prepared by Office of Air and Radiation. Available at: 

http://www.epa.gov/interstateairquality/tsd0175.pdf

10 U.S. Environmental Protection Agency, 2006. Regulatory Impact Analysis for the PM 

NAAQS. EPA Prepared by Office of Air and Radiation. Available at: 

http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf

11 Expert elicitation is a formal, highly structured and well documented process whereby expert 

judgments, usually of multiple experts, are obtained (Ayyb, 2002). 

12 Health impact functions measure the change in a health endpoint of interest, such as hospital 

admissions, for a given change in ambient ozone or PM concentration. 
6-7

between ozone and pre-mature mortality is causal by providing an effect estimate preconditioned 

on an assumption that the effect estimate for pre-mature mortality from ozone is zero. 

For premature mortality associated with exposure to PM, we follow the same approach used in 

the RIA for 2006 PM NAAQS (U.S. EPA, 2006), presenting several empirical estimates of 

premature deaths avoided, and a set of twelve estimates based on results of the expert elicitation 

study.13 Even these multiple characterizations, including confidence intervals, omit the 

contribution to overall uncertainty of uncertainty in air quality changes, baseline incidence rates, 

populations exposed and transferability of the effect estimate to diverse locations. Furthermore, 

the approach presented here does not yet include methods for addressing correlation between 

input parameters and the identification of reasonable upper and lower bounds for input 

distributions characterizing uncertainty in additional model elements. As a result, the reported 

confidence intervals and range of estimates give an incomplete picture about the overall 

uncertainty in the estimates. This information should be interpreted within the context of the 

larger uncertainty surrounding the entire analysis. 

6.3 Health Impact Functions 

Health impact functions measure the change in a health endpoint of interest, such as hospital 

admissions, for a given change in ambient ozone or PM concentration. Health impact functions 

are derived from primary epidemiology studies, meta-analyses of multiple epidemiology studies, 

or expert elicitations. A standard health impact function has four components: 1) an effect 

estimate from a particular study; 2) a baseline incidence rate for the health effect (obtained from 

either the epidemiology study or a source of public health statistics such as the Centers for 

Disease Control); 3) the size of the potentially affected population; and 4) the estimated change 

in the relevant ozone or PM summary measures. 

A typical health impact function might look like: 

 1 0

 

 x

y y e

E ' '

where y0 is the baseline incidence (the product of the baseline incidence rate times the potentially 

affected population), ȕ is the effect estimate, and 'x is the estimated change in the summary 

ozone measure. There are other functional forms, but the basic elements remain the same. 

Chapter 3 described the ozone and PM air quality inputs to the health impact functions. The 

following subsections describe the sources for each of the other elements: size of potentially 

affected populations; effect estimates; and baseline incidence rates. 

 

13 Industrial Economics, Inc. 2006. Expanded Expert Judgment Assessment of the ConcentrationResponse Relationship Between PM2.5 Exposure and Mortality. Prepared for EPA Office of Air 

Quality Planning and Standards, September. Available at: 

http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf
6-8

6.3.1 Potentially Affected Populations 

The starting point for estimating the size of potentially affected populations is the 2000 U.S. 

Census block level dataset (Geolytics 2002). Benefits Modeling and Analysis Program 

(BenMAP) incorporates 250 age/gender/race categories to match specific populations potentially 

affected by ozone and other air pollutants. The software constructs specific populations matching 

the populations in each epidemiological study by accessing the appropriate age-specific 

populations from the overall population database. BenMAP projects populations to 2020 using 

growth factors based on economic projections (Woods and Poole Inc. 2001).

6.3.2 Effect Estimate Sources 

The most significant monetized benefits of reducing ambient concentrations of ozone and PM 

are attributable to reductions in human health risks. EPA’s Ozone and PM Criteria Documents 

outline numerous health effects known or suspected to be linked to exposure to ambient ozone 

and PM (US EPA, 2006; US EPA, 2005; Anderson et al., 2004). EPA recently evaluated the PM 

literature for use in the benefits analysis for the 2006 PM NAAQS RIA. Because we use the 

same literature for the PM co-benefits analysis in this RIA, we do not provide a detailed 

discussion of individual effect estimates for PM in this section. Instead, we refer the reader to the 

2006 PM NAAQS RIA for details.14

More than one thousand new ozone health and welfare studies have been published since EPA 

issued the 8-hour ozone standard in 1997. Many of these studies investigated the impact of ozone 

exposure on health effects such as changes in lung structure and biochemistry; lung 

inflammation; asthma exacerbation and causation; respiratory illness-related school absence; 

hospital and emergency room visits for asthma and other respiratory causes; and premature 

death.

We were not able to separately quantify all of the PM and ozone health effects that have been 

reported in the ozone and PM criteria documents in this analysis for four reasons: (1) the 

possibility of double counting (such as hospital admissions for specific respiratory diseases); 

(2) uncertainties in applying effect relationships that are based on clinical studies to the 

potentially affected population; (3) the lack of an established concentration-response 

relationship; or (4) the inability to appropriately value the effect (for example, changes in forced 

expiratory volume) in economic terms. Table 6.1 lists the human health and welfare effects of 

pollutants affected by the alternative standards. Table 6.2 lists the health endpoints included in 

this analysis. 

In order to select appropriate epidemiological studies to use for our effect estimates, we applied 

several criteria to determine the set of studies that is likely to provide the best estimates of effects 

in the U.S. To account for the potential effects of different health care systems or underlying 

health status of populations, we gave preference to U.S. studies over non-U.S. studies. In 

addition, due to the potential for confounding by co-pollutants, we gave preference to effect 

 

14 U.S. Environmental Protection Agency, 2005. Regulatory Impact Analysis for the PM 

NAAQS. EPA Prepared by Office of Air and Radiation. Available at: 

http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf pp. 5-29. 
6-9

estimates from models that included both ozone and PM over effect estimates from singlepollutant models.15,16

A number of endpoints that are not health-related may also contribute significant monetized 

benefits. Potential welfare benefits associated with ozone exposure include increased outdoor 

worker productivity; increased yields for commercial and non-commercial crops; increased 

commercial forest productivity; reduced damage to urban ornamental plants; increased 

recreational demand for undamaged forest aesthetics; and reduced damage to ecosystem 

functions (U.S. EPA 1999, 2006). Although we estimate the value of increased outdoor worker 

productivity, estimation of other welfare effects is beyond the scope of this analysis. 

 

15 U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the 

Analytical Plan for EPA’s Second Prospective Analysis –Benefits and Costs of the Clean Air 

Act, 1990—2020. EPA-SAB-COUNCIL-ADV-04-004. 

16 National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed 

Air Pollution Regulations. Washington, DC: The National Academies Press. 
6-10

Table 6.1: Human Health and Welfare Effects of Ozone and PM2.5

Pollutant/Effect 

Quantified and Monetized in Base 

Estimatesa Unquantified Effectsh—Changes in: 

PM/Healthb Premature mortality based on both cohort 

study estimates and on expert elicitationc,d

Bronchitis: chronic and acute 

Hospital admissions: respiratory and 

cardiovascular 

Emergency room visits for asthma 

Nonfatal heart attacks (myocardial infarction) 

Lower and upper respiratory illness 

Minor restricted-activity days 

Work loss days 

Asthma exacerbations (asthmatic population) 

Respiratory symptoms (asthmatic population) 

Infant mortality 

Subchronic bronchitis cases 

Low birth weight 

Pulmonary function 

Chronic respiratory diseases other than 

chronic bronchitis 

Non-asthma respiratory emergency room 

visits 

UVb exposure (+/-)e

PM/Welfare Visibility in Southeastern, southwestern and 

California Class I areas 

Visibility in northeastern and Midwestern 

Class I areas 

Household soiling 

Visibility in residential and non-Class I 

areas

UVb exposure (+/-)e

Ozone/Healthf Premature mortality: short-term exposures 

Hospital admissions: respiratory 

Emergency room visits for asthma 

Minor restricted-activity days 

School loss days 

Asthma attacks 

Acute respiratory symptoms 

Cardiovascular emergency room visits 

Chronic respiratory damage 

Premature aging of the lungs 

Non-asthma respiratory emergency room 

visits 

UVb exposure (+/-)e

Ozone/Welfare Decreased outdoor worker productivity 

Yields for commercial crops 

Yields for commercial forests and 

noncommercial crops 

Damage to urban ornamental plants 

Recreational demand from damaged forest 

aesthetics 

Ecosystem functions 

UVb exposure (+/-)e

a

 Primary quantified and monetized effects are those included when determining the primary estimate of total monetized benefits 

of the alternative standards. 

b

 In addition to primary economic endpoints, there are a number of biological responses that have been associated with PM health

effects including morphological changes and altered host defense mechanisms. The public health impact of these biological 

responses may be partly represented by our quantified endpoints. 

c

 Cohort estimates are designed to examine the effects of long-term exposures to ambient pollution, but relative risk estimates 

may also incorporate some effects due to shorter term exposures (see Kunzli, 2001 for a discussion of this issue). 

d

 While some of the effects of short-term exposure are likely to be captured by the cohort estimates, there may be additional 

premature mortality from short-term PM exposure not captured in the cohort estimates included in the primary analysis. 

e

 May result in benefits or disbenefits. Appendix 6d includes a sensitivity analysis that partially quantifies this endpoint. This 

analysis was performed for the purposes of this RIA only. 

f

 In addition to primary economic endpoints, there are a number of biological responses that have been associated with ozone 

health including increased airway responsiveness to stimuli, inflammation in the lung, acute inflammation and respiratory cell 

damage, and increased susceptibility to respiratory infection. The public health impact of these biological responses may be 

partly represented by our quantified endpoints. 

g

 The categorization of unquantified toxic health and welfare effects is not exhaustive. 

h

 Health endpoints in the unquantified benefits column include both a) those for which there is not consensus on causality and b)

those for which causality has been determined but empirical data are not available to allow calculation of benefits. 
6-11

Table 6.2: Ozone and PM Related Health Endpoints Basis for the Concentration-Response 

Function Associated with that Endpoint, and Sub-Populations for which They Were 

Computed

Endpoint Pollutant Study Study Population 

Premature Mortality 

Premature 

mortality—daily 

time series, nonaccidental

O3 (8-hour max) 

O3 (8-hour max) 

O3 (8-hour max) 

O3 (8-hour max) 

Bell et al (2004) (NMMAPS study) 

Meta-analyses: 

Bell et al (2005) 

Ito et al (2005) 

Levy et al (2005) 

All ages 

Premature 

mortality—cohort 

study, all-cause 

PM2.5 (annual 

avg)

Pope et al. (2002) 

Laden et al. (2006) 

>29 years 

>25 years 

Premature 

mortality, total 

exposures 

PM2.5 (annual 

avg)

Expert Elicitation (IEc, 2006) >24 years 

Premature 

mortality—allcause

PM2.5 (annual 

avg)

Woodruff et al. (1997) Infant (<1 year) 

Chronic Illness 

Chronic bronchitis PM2.5 (annual 

avg)

Abbey et al. (1995) >26 years 

Nonfatal heart 

attacks

PM2.5 (24-hour 

avg)

Peters et al. (2001) Adults (>18 years) 

Hospital Admissions 

Pooled estimate: 

Schwartz (1995)—ICD 460–519 (all resp) 

Schwartz (1994a; 1994b)—ICD 480–486 

(pneumonia) 

Moolgavkar et al. (1997)—ICD 480–487 

(pneumonia) 

Schwartz (1994b)—ICD 491–492, 494–496 

(COPD) 

Moolgavkar et al. (1997)—ICD 490–496 

(COPD) 

>64 years 

O3 (24-hour avg) 

Burnett et al. (2001) <2 years 

PM2.5 (24-hour 

avg)

Pooled estimate: 

Moolgavkar (2003)—ICD 490–496 (COPD) 

Ito (2003)—ICD 490–496 (COPD) 

>64 years 

PM2.5 (24-hour 

avg)

Moolgavkar (2000)—ICD 490–496 (COPD) 20–64 years 

PM2.5 (24-hour 

avg)

Ito (2003)—ICD 480–486 (pneumonia) >64 years 

Respiratory 

PM2.5 (24-hour 

avg)

Sheppard (2003)—ICD 493 (asthma) <65 years 

PM2.5 (24-hour 

avg)

Pooled estimate: 

Moolgavkar (2003)—ICD 390–429 (all 

cardiovascular) 

Ito (2003)—ICD 410–414, 427–428 (ischemic 

heart disease, dysrhythmia, heart failure) 

Cardiovascular >64 years 

PM2.5 (24-hour 

avg)

Moolgavkar (2000)—ICD 390–429 (all 

cardiovascular) 

20–64 years 

Asthma-related 

ER visits 

O3 (8-hour max) Pooled estimate: 

Jaffe et al (2003) 5–34 years 
6-12

Endpoint Pollutant Study Study Population 

Peel et al (2005) 

Wilson et al (2005) 

All ages 

All ages 

Asthma-related 

ER visits (con’t) 

PM2.5 (24-hour 

avg)

Norris et al. (1999) 0–18 years 

Other Health Endpoints 

Acute bronchitis PM2.5 (annual 

avg)

Dockery et al. (1996) 8–12 years 

Upper respiratory 

symptoms 

PM10 (24-hour 

avg)

Pope et al. (1991) Asthmatics, 9–11 

years

Lower respiratory 

symptoms 

PM2.5 (24-hour 

avg)

Schwartz and Neas (2000) 7–14 years 

Asthma 

exacerbations

PM2.5 (24-hour 

avg)

Pooled estimate: 

Ostro et al. (2001) (cough, wheeze and 

shortness of breath) 

Vedal et al. (1998) (cough) 

6–18 yearsa

Work loss days PM2.5 (24-hour 

avg)

Ostro (1987) 18–65 years 

School absence 

days O3 (8-hour avg) 

O3 (1-hour max) 

Pooled estimate: 

Gilliland et al. (2001) 

Chen et al. (2000) 

5–17 yearsb

Minor Restricted O3 (24-hour avg) Ostro and Rothschild (1989) 18–65 years 

Activity Days 

(MRADs) 

PM2.5 (24-hour 

avg)

Ostro and Rothschild (1989) 18–65 years 

a

 The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et 

al. (1998) study. Based on advice from the Science Advisory Board Health Effects Subcommittee 

(SAB-HES), we extended the applied population to 6 to 18, reflecting the common biological basis for 

the effect in children in the broader age group. See: U.S. Science Advisory Board. 2004. Advisory 

Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis –

Benefits and Costs of the Clean Air Act, 1990—2020. EPA-SAB-COUNCIL-ADV-04-004. See also 

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air 

Pollution Regulations. Washington, DC: The National Academies Press. 

b

 Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based 

on recent advice from the National Research Council and the EPA SAB-HES, we have calculated 

reductions in school absences for all school-aged children based on the biological similarity between 

children aged 5 to 17.
6-13

6.3.2.1 Premature Mortality Effects Estimates

While particulate matter is the criteria pollutant most clearly associated with premature 

mortality, recent research suggests that short-term repeated ozone exposure also likely 

contributes to premature death. The 2006 Ozone Criteria Document states, “Consistent with 

observed ozone-related increases in respiratory- and cardiovascular-related morbidity, several 

newer multi-city studies, single-city studies, and several meta-analyses of these studies have 

provided relatively strong epidemiologic evidence for associations between short-term ozone 

exposure and all-cause mortality, even after adjustment for the influence of season and PM” 

(EPA, 2006: E-17). The epidemiologic data are also supported by recent experimental data from 

both animal and human studies, which provide evidence suggestive of plausible pathways by 

which risk of respiratory or cardiovascular morbidity and mortality could be increased by 

ambient ozone. With respect to short-term exposure, the Ozone Criteria Document concludes, 

“This overall body of evidence is highly suggestive that ozone directly or indirectly contributes 

to non-accidental and cardiopulmonary-related mortality, but additional research is needed to 

more fully establish underlying mechanisms by which such effects occur” (pg. E-18). 

With respect to the time-series studies, the conclusion regarding the relationship between shortterm exposure and premature mortality is based, in part, upon recent city-specific time-series 

studies such as the Schwartz (2004) analysis in Houston and the Huang et al. (2004) analysis in 

Los Angeles.17 This conclusion is also based on recent meta-analyses by Bell et al. (2005), Ito et 

al. (2005), and Levy et al. (2005), and a new analysis of the National Morbidity, Mortality, and 

Air Pollution Study (NMMAPS) data set by Bell et al. (2004), which specifically sought to 

disentangle the roles of ozone, PM, weather-related variables, and seasonality. The 2006 Criteria 

Document states that “the results from these meta-analyses, as well as several single- and 

multiple-city studies, indicate that co-pollutants generally do not appear to substantially 

confound the association between ozone and mortality” (p. 7-103). However, CASAC raised 

questions about the implications of these time-series results in a policy context. Specifically, 

CASAC emphasized that “...while the time-series study design is a powerful tool to detect very 

small effects that could not be detected using other designs, it is also a blunt tool” (Henderson, 

2006: 3). They point to findings (e.g., Stieb et al., 2002, 2003) that indicated associations 

between premature mortality and all of the criteria pollutants, indicating that “findings of timeseries studies do not seem to allow us to confidently attribute observed effects to individual 

pollutants” (id.). They note that “not only is the interpretation of these associations complicated 

by the fact that the day-to-day variation in concentrations of these pollutants is, to a varying 

degree, determined by meteorology, the pollutants are often part of a large and highly correlated 

mix of pollutants, only a very few of which are measured” (id.). Even with these uncertainties, 

the CASAC Ozone Panel, in its review of EPA’s Staff Paper, found “...premature total nonaccidental and cardiorespiratory mortality for inclusion in the quantitative risk assessment to be 

appropriate.” 

 

17 For an exhaustive review of the city-specific time-series studies considered in the ozone staff 

paper, see: U.S. Environmental Protection Agency, 2007. Review of the National Ambient Air 

Quality Standards for Ozone: Policy Assessment of Scientific and Technical Information. 

Prepared by the Office of Air and Radiation. Available at 

http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_01_ozone_staff_paper.pdf. pp. 5-36. 
6-14

Consistent with the methodology used in the ozone risk assessment found in the Characterization 

of Health Risks found in the Review of the National Ambient Air Quality Standards for Ozone: 

Policy Assessment of Scientific and Technical Information, we included ozone mortality in the 

primary health effects analysis, with the recognition that the exact magnitude of the effects 

estimate is subject to continuing uncertainty. We used estimates from the Bell et al. (2004) 

NMMAPS analysis, as well as effect estimates from the three meta-analyses. In addition, we

include the possibility that there is not a causal association between ozone and mortality, i.e., that

the effect estimate for premature mortality could be zero. EPA expects to receive advice from the 

National Academy of Sciences on how best to quantify uncertainty in the relationship between 

ozone exposure and premature mortality in the context of quantifying benefits associated with 

alternative ozone control strategies later this spring. 

We estimate the change in mortality incidence and estimated credible interval18 resulting from 

application of the effect estimate from each study and present them separately to reflect 

differences in the study designs and assumptions about causality. However, it is important to 

note that this procedure only captures the uncertainty in the underlying epidemiological work, 

and does not capture other sources of uncertainty, such as uncertainty in the estimation of 

changes in air pollution exposure (Levy et al., 2000). 

Ozone Exposure Metric. Both the NMMAPS analysis and the individual time series studies 

upon which the meta analyses were based use the 24-hour average or 1-hour maximum ozone 

levels as exposure metrics. The 24-hour average is not the most relevant ozone exposure metric 

to characterize population-level exposure. Given that the majority of the people tend to be 

outdoors during the daylight hours and concentrations are highest during the daylight hours, the 

24-hour average metric is not appropriate. Moreover, the 1-hour maximum metric uses an 

exposure window different than that that used for the current ozone NAAQS. Together, this 

means that the most biologically relevant metric, and the one used in the ozone NAAQS since 

1997 is the 8-hour maximum standard. Thus, although our analysis at proposal calculated impact 

functions based on either the 24 hour average or 1-hour maximum ozone levels originally 

reported in the epidemiogical studies, for the final rule analysis, we have converted ozone 

mortality health impact functions that use a 24-hour average or 1-hour maximum ozone metric to 

maximum 8-hour average ozone concentration using standard conversion functions.

This practice is consistent both with the available exposure modeling and with the form of the 

current ozone standard. This conversion also does not affect the relative magnitude of the health 

impact function. An equivalent change in the 24-hour average, 1-hour maximum and 8-hour 

maximum will provide the same overall change in incidence of a health effect. The conversion 

ratios are based on observed relationships between the 24-hour average and 8-hour maximum 

ozone values. For example, in the Bell et al., 2004 analysis of ozone-related premature mortality, 

the authors found that the relationship between the 24-hour average, the 8-hour maximum, and 

the 1-hour maximum was 2:1.5:1, so that the derived health impact effect estimate based on the 

1-hour maximum should be half that of the effect estimate based on the 24-hour values (and the 

8-hour maximum three-quarters of the 24-hour effect estimate). 

 

18 A credible interval is a posterior probability interval used in Bayesian statistics, which is 

similar to a confidence interval used in frequentist statistics. 
6-15

In EPA’s risk analysis for the ozone NAAQS rule, mortality risks were estimated for 8 urban 

areas based on application of city-specific effect estimates derived from single city studies and 

from the Bell et al (2004) and Huang et al (2005) multi-city studies. These effect estimates were 

based on 24-hour average daily ozone concentrations. While it may have been preferable to use 

shorter averaging times, conversions from daily averages to shorter averaging times was not 

appropriate due to the lack of city-specific conversion factors. In our benefits analysis for the 

ozone NAAQS, we applied national effect estimates based on the pooled multi-city results 

reported in Bell et al (2004) and the three meta-analysis studies. Bell et al (2004), Bell et al 

(2005), Levy et al (2005), and Ito et al (2005) all provide national conversion ratios between 

daily average and 8-hour and 1-hour maxima, based on national data. However, these 

conversions were not specific to the ozone “warm” season which was the period used in the 

health risk assessment. As such we were able to convert the national C-R function parameters 

from daily average to 8-hour average, albeit with the introduction of additional uncertainty due 

to the use of effect estimates based on a mixture of warm season and all year data in the 

epidemiological studies. Given the heterogeneity in ratios of daily average to 8-hour and 1-hour 

maxima that exists between cities, it would be inappropriate to use national conversion ratios to 

adjust C-R functions for individual cities. 

6.3.2.2 Respiratory Hospital Admissions Effect Estimates

Detailed hospital admission and discharge records provide data for an extensive body of 

literature examining the relationship between hospital admissions and air pollution. This is 

especially true for the portion of the population aged 65 and older, because of the availability of 

detailed Medicare records. In addition, there is one study (Burnett et al., 2001) providing an 

effect estimate for respiratory hospital admissions in children under two. 

Because the number of hospital admission studies we considered is so large, we used results 

from a number of studies to pool some hospital admission endpoints. Pooling is the process by 

which multiple study results may be combined in order to produce better estimates of the effect 

estimate, or ȕ. For a complete discussion of the pooling process, see Abt (2005).19 To estimate 

total respiratory hospital admissions associated with changes in ambient ozone concentrations for 

adults over 65, we first estimated the change in hospital admissions for each of the different 

effects categories that each study provided for each city. These cities included Minneapolis, 

Detroit, Tacoma and New Haven. To estimate total respiratory hospital admissions for Detroit, 

we added the pneumonia and COPD estimates, based on the effect estimates in the Schwartz 

study (1994b). Similarly, we summed the estimated hospital admissions based on the effect 

estimates the Moolgavkar study reported for Minneapolis (Moolgavkar et al., 1997). To estimate 

total respiratory hospital admissions for Minneapolis using the Schwartz study (1994a), we 

simply estimated pneumonia hospital admissions based on the effect estimate. Making this 

assumption that pneumonia admissions represent the total impact of ozone on hospital 

admissions in this city will give some weight to the possibility that there is no relationship 

between ozone and COPD, reflecting the equivocal evidence represented by the different studies. 

We then used a fixed-effects pooling procedure to combine the two total respiratory hospital 

admission estimates for Minneapolis. Finally, we used random effects pooling to combine the 

 

19 Abt Associates, Incorporated. Environmental Benefits Mapping and Analysis Program, 

Technical Appendices. May 2005. pp. I-3 
6-16

results for Minneapolis and Detroit with results from studies in Tacoma and New Haven from 

Schwartz (1995). As noted above, this pooling approach incorporates both the precision of the 

individual effect estimates and between-study variability characterizing differences across study 

locations.

6.3.2.3 Asthma-Related Emergency Room Visits Effect Estimates

We used three studies as the source of the concentration-response functions we used to estimate 

the effects of ozone exposure on asthma-related emergency room (ER) visits: Peel et al. (2005); 

Wilson et al. (2005); and Jaffe et al. (2003). We estimated the change in ER visits using the 

effect estimate(s) from each study and then pooled the results using the random effects pooling 

technique (see Abt, 2005). The study by Jaffe et al. (2003) examined the relationship between 

ER visits and air pollution for populations aged five to 34 in the Ohio cities of Cleveland, 

Columbus and Cincinnati from 1991 through 1996. In single-pollutant Poisson regression 

models, ozone was linked to asthma visits. We use the pooled estimate across all three cities as 

reported in the study. The Peel et al. study (2005) estimated asthma-related ER visits for all ages 

in Atlanta, using air quality data from 1993 to 2000. Using Poisson generalized estimating 

equations, the authors found a marginal association between the maximum daily 8-hour average 

ozone level and ER visits for asthma over a 3-day moving average (lags of 0, 1, and 2 days) in a 

single pollutant model. Wilson et al. (2005) examined the relationship between ER visits for 

respiratory illnesses and asthma and air pollution for all people residing in Portland, Maine from 

1998–2000 and Manchester, New Hampshire from 1996–2000. For all models used in the 

analysis, the authors restricted the ozone data incorporated into the model to the months ozone 

levels are usually measured, the spring-summer months (April through September). Using the 

generalized additive model, Wilson et al. (2005) found a significant association between the 

maximum daily 8-hour average ozone level and ER visits for asthma in Portland, but found no 

significant association for Manchester. Similar to the approach used to generate effect estimates 

for hospital admissions, we used random effects pooling to combine the results across the 

individual study estimates for ER visits for asthma. The Peel et al. (2005) and Wilson et al. 

(2005) Manchester estimates were not significant at the 95 percent level, and thus, the 

confidence interval for the pooled incidence estimate based on these studies includes negative 

values. This is an artifact of the statistical power of the studies, and the negative values in the 

tails of the estimated effect distributions do not represent improvements in health as ozone 

concentrations are increased. Instead, these should be viewed as a measure of uncertainty due to 

limitations in the statistical power of the study. We included both hospital admissions and ER 

visits as separate endpoints associated with ozone exposure because our estimates of hospital 

admission costs do not include the costs of ER visits and most asthma ER visits do not result in a 

hospital admission. 

6.3.2.4 Minor Restricted Activity Days Effects Estimate

Minor restricted activity days (MRADs) occur when individuals reduce most usual daily 

activities and replace them with less-strenuous activities or rest, but do not miss work or school. 

We estimated the effect of ozone exposure on MRADs using a concentration-response function 

derived from Ostro and Rothschild (1989). These researchers estimated the impact of ozone and 

PM2.5 on MRAD incidence in a national sample of the adult working population (ages 18 to 65) 

living in metropolitan areas. We developed separate coefficients for each year of the Ostro and 
6-17

Rothschild analysis (1976–1981), which we then combined for use in EPA’s analysis. The effect 

estimate used in the impact function is a weighted average of the coefficients in Ostro and 

Rothschild (1989, Table 4), using the inverse of the variance as the weight. 

6.3.2.5 School Absences Effect Estimate

Children may be absent from school due to respiratory or other acute diseases caused, or 

aggravated by, exposure to air pollution. Several studies have found a significant association 

between ozone levels and school absence rates. We use two studies (Gilliland et al., 2001; Chen 

et al., 2000) to estimate changes in school absences resulting from changes in ozone levels. The 

Gilliland et al. study estimated the incidence of new periods of absence, while the Chen et al. 

study examined daily absence rates. We converted the Gilliland et al. estimate to days of absence 

by multiplying the absence periods by the average duration of an absence. We estimated 1.6 days 

as the average duration of a school absence, the result of dividing the average daily school 

absence rate from Chen et al. (2000) and Ransom and Pope (1992) by the episodic absence 

duration from Gilliland et al. (2001). Thus, each Gilliland et al. period of absence is converted 

into 1.6 absence days. 

Following recent advice from the National Research Council (2002), we calculated reductions in 

school absences for the full population of school age children, ages five to 17. This is consistent 

with recent peer-reviewed literature on estimating the impact of ozone exposure on school 

absences (Hall et al. 2003). We estimated the change in school absences using both Chen et al. 

(2000) and Gilliland et al. (2001) and then, similar to hospital admissions and ER visits, pooled 

the results using the random effects pooling procedure. 

6.3.2.6 Outdoor Worker Productivity

To monetize benefits associated with increased worker productivity resulting from improved 

ozone air quality, we used information reported in Crocker and Horst (1981). Crocker and Horst 

examined the impacts of ozone exposure on the productivity of outdoor citrus workers. The 

study measured productivity impacts. Worker productivity is measuring the value of the loss in 

productivity for a worker who is at work on a particular day, but due to ozone, cannot work as 

hard. It only applies to outdoor workers, like fruit and vegetable pickers, or construction workers. 

Here, productivity impacts are measured as the change in income associated with a change in 

ozone exposure, given as the elasticity of income with respect to ozone concentration. The 

reported elasticity translates a ten percent reduction in ozone to a 1.4 percent increase in income. 

Given the national median daily income for outdoor workers engaged in strenuous activity 

reported by the U.S. Census Bureau (2002), $68 per day (2000$), a ten percent reduction in 

ozone yields about $0.97 in increased daily wages. We adjust the national median daily income 

estimate to reflect regional variations in income using a factor based on the ratio of county 

median household income to national median household income. No information was available 

for quantifying the uncertainty associated with the central valuation estimate. Therefore, no 

uncertainty analysis was conducted for this endpoint. 
6-18

6.3.2.7 Visibility Benefits

Changes in the level of ambient PM2.5 caused by the reduction in emissions associated with the 

alternative standards will change the level of visibility throughout the United States. Increases in 

PM concentrations cause increases in light extinction, a measure of how much the components of 

the atmosphere absorb light. This chapter contains an estimate of the monetized benefits of 

improved visibility associated with the simulated emission control strategy to attain the 0.070 

ppm ozone standard. The methodology we followed to estimate changes in visibility benefits is 

consistent with the PM2.5 RIA (EPA, 2006), which is described on page 5-60 of that document. 

6.3.2.8 Other Unquantified Effects

Direct Ozone Effects on Vegetation. The Ozone Criteria Document notes that “current ambient 

concentrations in many areas of the country are sufficient to impair growth of numerous common 

and economically valuable plant and tree species” (U.S. EPA, 2006, page 9-1). Changes in 

ground-level ozone resulting from the implementation of alternative ozone standards may affect 

crop and forest yields throughout the affected area. Recent scientific studies have also found that 

at sufficient concentrations ozone negatively affects the quality or nutritive value of some 

sensitive crops (U.S. EPA, 2006, page 9-16). 

Well-developed techniques exist to provide monetary estimates of these benefits to agricultural 

producers and to consumers. These techniques use models of planting decisions, yield response 

functions, and the supply of and demand for agricultural products. The resulting welfare 

measures are based on predicted changes in market prices and production costs. Models also 

exist to measure benefits to silvicultural producers and consumers. There is considerable 

uncertainty, however, in such estimates, including the fact that the extensive management of 

agricultural crops may mitigate the potential O3-related effects. For this reason, the estimates of 

economic crop loss developed using the updated AGSIM model were not relied on for this 

analysis of alternative O3 standards. In addition, these models have not been adapted for use in 

analyzing ozone-related forest impacts. Again, because there commercial activities are highly 

managed the potential benefits of alternative O3 standards are uncertain. Because of these 

uncertainties and resource limitations, we are unable to provide benefits estimates for the 

commercial production of agricultural and silvaculture commodities. 

An additional welfare benefit of reducing ambient ozone concentrations is the economic value of 

reduced aesthetic injury to forests. There is sufficient scientific information available to reliably 

establish that ambient ozone causes visible injury to foliage and impair the growth of some 

sensitive plant species (U.S. EPA, 2006, page 9-19). However, present analytic tools and 

resources preclude us from quantifying the benefits of improved forest aesthetics. 

Urban ornamentals (floriculture and nursery crops) are an additional vegetation category that 

may experience negative effects from exposure to ambient ozone and may affect large economic 

sectors. However, the absence of adequate exposure-response functions and economic damage 

functions for the potential range of effects relevant to these types of vegetation precludes us from 

quantifying these direct economic benefits. The farm production value of ornamental crops was 

estimated at over $14 billion in 2003 (USDA, 2004). This is therefore a potentially important 

welfare effects category, but information and valuation methods are not available to allow for 
6-19

plausible estimates of the percentage of these expenditures that may be related to impacts 

associated with ozone exposure. 

Nitrogen Deposition. Deposition to Estuarine and Coastal Waters. Excess nutrient loads, 

especially of nitrogen, cause a variety of adverse consequences to the health of estuarine and 

coastal waters. These effects include toxic and/or noxious algal blooms such as brown and red 

tides, low (hypoxic) or zero (anoxic) concentrations of dissolved oxygen in bottom waters, the 

loss of submerged aquatic vegetation due to the light-filtering effect of thick algal mats, and 

fundamental shifts in phytoplankton community structure (Bricker et al., 1999). A recent study 

found that for the period 1990–2002, atmospheric deposition accounted for 17 percent of nitrate 

loadings in the Gulf of Mexico, where severe hypoxic zones have been existed over the last two 

decades (Booth and Campbell, 2007).20

Reductions in atmospheric deposition of NOx are expected to reduce the adverse impacts 

associated with nitrogen deposition to estuarine and coastal waters. However, direct functions 

relating changes in nitrogen loadings to changes in estuarine benefits are not available. The 

preferred WTP-based measure of benefits depends on the availability of these functions and on 

estimates of the value of environmental responses. Because neither appropriate functions nor 

sufficient information to estimate the marginal value of changes in water quality exist at present, 

calculation of a WTP measure is not possible.

Deposition to Agricultural and Forested Land. Implementation strategies for alternative 

standards that reduce NOX emissions will also reduce nitrogen deposition on agricultural land 

and forests. There is some evidence that nitrogen deposition may have positive effects on 

agricultural output through passive fertilization. Holding all other factors constant, farmers’ use 

of purchased fertilizers or manure may increase as deposited nitrogen is reduced. Estimates of 

the potential value of this possible increase in the use of purchased fertilizers are not available, 

but it is likely that the overall value is very small relative to other health and welfare effects. The 

share of nitrogen requirements provided by this deposition is small, and the marginal cost of 

providing this nitrogen from alternative sources is quite low. In some areas, agricultural lands 

suffer from nitrogen over-saturation due to an abundance of on-farm nitrogen production, 

primarily from animal manure. In these areas, reductions in atmospheric deposition of nitrogen 

from PM represent additional agricultural benefits.

Information on the effects of changes in passive nitrogen deposition on forests and other 

terrestrial ecosystems is very limited. The multiplicity of factors affecting forests, including other

potential stressors such as ozone, and limiting factors such as moisture and other nutrients, 

confound assessments of marginal changes in any one stressor or nutrient in forest ecosystems. 

However, reductions in deposition of nitrogen could have negative effects on forest and 

vegetation growth in ecosystems where nitrogen is a limiting factor (US EPA, 1993). Moreover, 

 

20 Booth, M.S., and C. Campbell. 2007. Spring Nitrate Flux in the Mississippi River Basin: A 

Landscape Model with Conservation Applications. Environ. Sci. Technol.; 2007; ASAP Web 

Release Date: 20-Jun-2007; (Article) DOI: 10.1021/es070179e 
6-20

any positive effect that nitrogen deposition has on forest productivity would enhance the level of 

carbon dioxide sequestration as well.21,22,23

On the other hand, there is evidence that forest ecosystems in some areas of the United States 

(such as the western U.S.) are nitrogen saturated (US EPA, 1993). Once saturation is reached, 

adverse effects of additional nitrogen begin to occur such as soil acidification, which can lead to 

leaching of nutrients needed for plant growth and mobilization of harmful elements such as 

aluminum. Increased soil acidification is also linked to higher amounts of acidic runoff to 

streams and lakes and leaching of harmful elements into aquatic ecosystems. 

Ultraviolet Radiation. Atmospheric ozone absorbs a harmful band of ultraviolet radiation from 

the sun called UV-B, thus providing a protective shield to the Earth’s surface. The majority of 

this protection occurs in the stratosphere where 90% of atmospheric ozone is located. The 

remaining 10% of the Earth’s ozone is present at ground level (referred to as tropospheric ozone) 

(NAS, 1991; NASA). Only a portion of the tropospheric fraction of UV-B shielding is from 

anthropogenic sources (e.g., power plants, byproducts of combustion). The portion of ground 

level ozone associated with anthropogenic sources varies by locality and over time. Even so, it is 

reasonable to assume that reductions in ground level ozone would lead to increases in the same 

health effects linked to in UV-B exposures. These effects include fatal and nonfatal melanoma 

and non-melanoma skin cancers and cataracts. The values of $15,000 per case for non-fatal 

melanoma skin cancer, $5,000 per case for non-fatal non-melanoma skin cancer, and $15,000 per 

case of cataracts have been used in analyses of stratospheric ozone depletion (U.S. EPA, 1999). 

Fatal cancers are valued using the standard VSL estimate, which for 2020 is $6.6 million 

(2006$). UV-B has also been linked to ecological effects including damage to crops and forest. 

For a more complete listing of quantified and unquantified UV-B radiation effects, see Table G-4 

and G-7 in the Benefits and Costs of the Clean Air Act, 1990–2010 (U.S. EPA, 1999). UV-B 

related health effects are also discussed in the context of stratospheric ozone in a 2006 report by 

ICF Consulting, prepared for the U.S. EPA. 

There are many factors that influence UV-B radiation penetration to the earth’s surface, 

including latitude, altitude, cloud cover, surface albedo, PM concentration and composition, and 

gas phase pollution. Of these, only latitude and altitude can be defined with small uncertainty in 

any effort to assess the changes in UV-B flux that may be attributable to any changes in 

tropospheric ozone as a result of any revision to the Ozone NAAQS. Such an assessment of UVB related health effects would also need to take into account human habits, such as outdoor 

activities (including age- and occupation-related exposure patterns), dress and skin care to 

adequately estimate UV-B exposure levels. However, little is known about the impact of these 

factors on individual exposure to UV-B. 

 

21 Peter M. Vitousek et. al., “Human Alteration of the Global Nitrogen Cycle: Causes and 

Consequences” Issues in Ecology No. 1 (Spring) 1997. 

22 Knute J. Nadelhoffer et. al., “Nitrogen deposition makes a minor contribution to carbon 

sequestration in temperate forests” Nature 398, 145-148 (11 March 1999). 

23 Martin Köchy and Scott D. Wilson, “Nitrogen deposition and forest expansion in the northern 

Great Plains Journal of Ecology Journal of Ecology 89 (5), 807–817. 
6-21

Moreover, detailed information does not exist regarding other factors that are relevant to 

assessing changes in disease incidence, including: type (e.g., peak or cumulative) and time 

period (e.g., childhood, lifetime, current) of exposures related to various adverse health outcomes 

(e.g., damage to the skin, including skin cancer; damage to the eye, such as cataracts; and 

immune system suppression); wavelength dependency of biological responses; and 

interindividual variability in UV-B resistance to such health outcomes. Beyond these wellrecognized adverse health effects associated with various wavelengths of UV radiation, the 

Criteria Document (Section 10.2.3.6) also discusses protective effects of UV-B radiation. Recent 

reports indicate the necessity of UV-B in producing vitamin D, and that vitamin D deficiency can 

cause metabolic bone disease among children and adults, and may also increase the risk of many 

common chronic diseases (e.g., type I diabetes and rheumatoid arthritis) as well as the risk of 

various types of cancers. Thus, the Criteria Document concludes that any assessment that

attempts to quantify the consequences of increased UV-B exposure on humans due to reduced 

ground-level O3 must include consideration of both negative and positive effects. However, as 

with other impacts of UVB on human health, this beneficial effect of UVB radiation has not 

previously been studied in sufficient detail. EPA has conducted a screening level analysis of the 

effects of reduced ozone concentrations on UVB exposures. This analysis is based on the air 

quality modeling conducted for the proposed Ozone NAAQS RIA, and is described in Appendix 

6d to the this RIA. The screening analysis has been peer-reviewed and a summary of the peerreview comments and responses are provided with the report. 

Climate Implications of Tropospheric Ozone. Although climate and air quality are generally 

treated as separate issues, they are closely coupled through atmospheric processes. Ozone, itself, 

is a major greenhouse gas and climate directly influences ambient concentrations of ozone. 

The concentration of tropospheric ozone has increased substantially since the pre-industrial era 

and has contributed to warming. Tropospheric ozone is (after carbon dioxide and methane) the 

third most important contributor to greenhouse gas warming. The National Academy of Sciences 

recently stated24 that regulations targeting ozone precursors would have combined benefits for 

public health and climate. As noted in the OAQPS Staff Paper, the overall body of scientific 

evidence suggests that high concentrations of ozone on a regional scale could have a discernible 

influence on climate. However, the Staff Paper concludes that insufficient information is 

available at this time to quantitatively inform the secondary NAAQS process with regard to this 

aspect of the ozone-climate interaction 

 

24 National Academy of Sciences, “Radiative Forcing of Climate Change: Expanding the 

Concept and Addressing Uncertainties,” October 2005. 
6-22

Climate change can affect tropospheric ozone by modifying emissions of precursors, chemistry, 

transport and removal.25 Climate change affects the sources of ozone precursors through physical 

response (lightning), biological response (soils, vegetation, and biomass burning) and human 

response (energy generation, land use, and agriculture). Increases in regional ozone pollution are 

expected due to higher temperatures and weaker circulation. Simulations with global climate 

models for the 21st century indicate a decrease in the lifetime of tropospheric ozone due to 

increasing water vapor, which could decrease global background ozone concentrations. 

The Intergovernmental Panel on Climate Change (IPCC) recently released a report26 that 

projects, with “virtual certainty,” declining air quality in cities due to warmer and fewer cold 

days and nights and/or warmer/more frequent hot days and nights over most land areas. The 

report states that projected climate change-related exposures are likely to affect the health status 

of millions of people, in part, due to higher concentrations of ground level ozone related to 

climate change. 

The IPCC also reports27 that the current generation of tropospheric ozone models is generally 

successful in describing the principal features of the present-day global ozone distribution. 

However, there is much less confidence in the ability to reproduce the changes in ozone 

associated with perturbations of emissions or climate. There are major discrepancies with 

observed long-term trends in ozone concentrations over the 20th century, including after 1970 

when the reliability of observed ozone trends is high. Resolving these discrepancies is needed to 

establish confidence in the models. 

The EPA is currently leading a research effort with the goal of identifying changes in regional 

US air quality that may occur in a future (2050) climate, focusing on fine particles and ozone. 

The research builds first on an assessment of changes in US air quality due to climate change, 

which includes direct meteorological impacts on atmospheric chemistry and transport and the 

effect of temperature changes on air pollution emissions. Further research will result in an 

assessment that adds the emission impacts from technology, land use, demographic changes, and 

air quality regulations to construct plausible scenarios of US air quality 50 years into the future. 

As noted in the Staff Paper, results from these efforts are expected to be available for 

consideration in the next review of the ozone NAAQS.

 

25Denman, K.L., G. Brasseur, A. Chidthaisong, P. Ciais, P.M. Cox, R.E. Dickinson, D. 

Hauglustaine, C. Heinze, E. Holland, D. Jacob, U. Lohmann, S Ramachandran, P.L. da Silva 

Dias, S.C. Wofsy and X. Zhang, 2007: Couplings Between Changes in the Climate System and 

Biogeochemistry. In: Climate Change 2007: The Physical Science Basis. Contribution of 

Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate 

Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and 

H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, 

NY, USA. 

26 IPCC, Climate Change 2007: Climate Change Impacts, Adaptation and Vulnerability, 

Summary for Policymakers. 

27 Denman, et al, 2007: Couplings Between Changes in the Climate System and 

Biogeochemistry. In: Climate Change 2007: The Physical Science Basis. 
6-23

6.3.3 Baseline Incidence Rates 

Epidemiological studies of the association between pollution levels and adverse health effects 

generally provide a direct estimate of the relationship of air quality changes to the relative risk of

a health effect, rather than estimating the absolute number of avoided cases. For example, a 

typical result might be that a 10 ppb decrease in daily ozone levels might, in turn, decrease 

hospital admissions by 3 percent. The baseline incidence of the health effect is necessary to 

convert this relative change into a number of cases. A baseline incidence rate is the estimate of 

the number of cases of the health effect per year in the assessment location, as it corresponds to 

baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate

must be multiplied by the corresponding population number. For example, if the baseline 

incidence rate is the number of cases per year per million people, that number must be multiplied 

by the millions of people in the total population. 

Table 6.3 summarizes the sources of baseline incidence rates and provides average incidence 

rates for the endpoints included in the analysis. For both baseline incidence and prevalence data, 

we used age-specific rates where available. We applied concentration-response functions to 

individual age groups and then summed over the relevant age range to provide an estimate of 

total population benefits. In most cases, we used a single national incidence rate, due to a lack of 

more spatially disaggregated data. Whenever possible, the national rates used are national 

averages, because these data are most applicable to a national assessment of benefits. For some 

studies, however, the only available incidence information comes from the studies themselves; in 

these cases, incidence in the study population is assumed to represent typical incidence at the 

national level. Regional incidence rates are available for hospital admissions, and county-level 

data are available for premature mortality. We have projected mortality rates such that future 

mortality rates are consistent with our projections of population growth (Abt Associates, 2005). 

6.4 Economic Values for Health Outcomes 

Reductions in ambient concentrations of air pollution generally lower the risk of future adverse 

health effects for a large population. Therefore, the appropriate economic measure is willingnessto-pay (WTP) for changes in risk of a health effect rather than WTP for a health effect that would 

occur with certainty (Freeman, 1993). Epidemiological studies generally provide estimates of the 

relative risks of a particular health effect that is avoided because of a reduction in air pollution. 

We converted those to units of avoided statistical incidence for ease of presentation. We 

calculated the value of avoided statistical incidences by dividing individual WTP for a risk 

reduction by the related observed change in risk. For example, suppose a pollution-reduction 

regulation is able to reduce the risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a 

reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the WTP for an 

avoided statistical premature death is $1 million ($100/0.0001 change in risk). 
6-24

Table 6.3: National Average Baseline Incidence Rates 

Rate per 100 people per yeard by Age Group 

Endpoint Source Notes <18

18–

24

25–

34

35–

44

45–

54

55–

64 65+ 

Mortality CDC Compressed 

Mortality File, accessed 

through CDC Wonder 

(1996–1998) 

nonaccidental0.025 0.022 0.057 0.150 0.383 1.006 4.937 

Respiratory 

Hospital 

Admissions 

1999 NHDS public use 

data filesb

incidence 0.043 0.084 0.206 0.678 1.926 4.389 11.629 

Asthma ER 

visits 

2000 NHAMCS public 

use data filesc; 1999 

NHDS public use data 

filesb

incidence 1.011 1.087 0.751 0.438 0.352 0.425 0.232

Minor 

Restricted

Activity Days 

(MRADs) 

Ostro and Rothschild 

(1989, p. 243) 

incidence — 780 780 780 780 780 — 

School Loss 

Days 

National Center for 

Education Statistics 

(1996) and 1996 HIS 

(Adams et al., 1999, 

Table 47); estimate of 

180 school days per year 

all-cause 990.0 — — — — — — 

 

Endpoint Source Notes

Rate per 100 People 

per Year

Ostro et al. 

(2001) 

Incidence (and prevalence) 

among asthmatic AfricanAmerican children 

Daily wheeze 

Daily cough 

Daily dyspnea 

0.076 (0.173) 

0.067 (0.145) 

0.037 (0.074) 

Asthma 

Exacerbations 

Vedal et al. 

(1998) 

Incidence (and prevalence) 

among asthmatic children 

Daily wheeze 

Daily cough 

Daily dyspnea 

0.038 

0.086 

0.045 

a

 The following abbreviations are used to describe the national surveys conducted by the National Center 

for Health Statistics: HIS refers to the National Health Interview Survey; NHDS—National Hospital 

Discharge Survey; NHAMCS—National Hospital Ambulatory Medical Care Survey. 

b

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/

c

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/

d

 All of the rates reported here are population-weighted incidence rates per 100 people per year. 

Additional details on the incidence and prevalence rates, as well as the sources for these rates are 

available upon request. 
6-25

WTP estimates generally are not available for some health effects, such as hospital admissions. 

In these cases, we used the cost of treating or mitigating the effect as a primary estimate. These 

cost-of-illness (COI) estimates generally understate the true value of reducing the risk of a health 

effect, because they reflect the direct expenditures related to treatment, but not the value of 

avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987). We provide unit 

values for health endpoints (along with information on the distribution of the unit value) in 

Table 6.4. All values are in constant year 2006 dollars, adjusted for growth in real income out to 

2020 using projections provided by Standard and Poor’s. Economic theory argues that WTP for 

most goods (such as environmental protection) will increase if real income increases. Many of 

the valuation studies used in this analysis were conducted in the late 1980s and early 1990s. 

Because real income has grown since the studies were conducted, people’s willingness to pay for 

reductions in the risk of premature death and disease likely has grown as well. We did not adjust 

cost of illness-based values because they are based on current costs. Similarly, we did not adjust 

the value of school absences, because that value is based on current wage rates. Table 6.4 

presents the values for individual endpoints adjusted to year 2020 income levels. The discussion 

below provides additional details on ozone related endpoints. For details on valuation estimates 

for PM related endpoints, see the 2006 PM NAAQS RIA. 

6.4.1 Mortality Valuation 

To estimate the monetary benefit of reducing the risk of premature death, we used the “value of 

statistical lives” saved (VSL) approach, which is a summary measure for the value of small 

changes in mortality risk for a large number of people. The VSL approach applies information 

from several published value-of-life studies to determine a reasonable monetary value of 

preventing premature mortality. The mean value of avoiding one statistical death is estimated to 

be roughly $6.6 million at 1990 income levels (2006$), and $7.9 million (2006$) at 2020 income 

levels. This represents an intermediate value from a variety of estimates in the economics 

literature (see the 2006 PM NAAQS RIA for more details on the calculation of VSL). 

6.4.2 Hospital Admissions Valuation 

In the absence of estimates of societal WTP to avoid hospital visits/admissions for specific 

illnesses, estimates of total cost of illness (total medical costs plus the value of lost productivity) 

typically are used as conservative, or lower bound, estimates. These estimates are biased 

downward, because they do not include the willingness-to-pay value of avoiding pain and 

suffering. 

The International Classification of Diseases (ICD-9, 1979) code-specific COI estimates used in 

this analysis consist of estimated hospital charges and the estimated opportunity cost of time 

spent in the hospital (based on the average length of a hospital stay for the illness). We based all 

estimates of hospital charges and length of stays on statistics provided by the Agency for 

Healthcare Research and Quality (AHRQ 2000). We estimated the opportunity cost of a day 

spent in the hospital as the value of the lost daily wage, regardless of whether the hospitalized 

individual is in the workforce. To estimate the lost daily wage, we divided the 1990 median 

weekly wage by five and inflated the result to year 2000$ using the CPI-U “all items.” The 

resulting estimate is $109.35. The total cost-of-illness estimate for an ICD code-specific hospital 

stay lasting n days, then, was the mean hospital charge plus $109 multiplied by n.
6-26

Table 6.4: Unit Values for Economic Valuation of Health Endpoints (2006$) 

Central Estimate of Value 

Per Statistical Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Level Derivation of Distributions of Estimates 

Premature Mortality (Value of a Statistical Life)

$6,600,000 $7,900,000 Point estimate is the mean of a normal distribution with a 95% confidence interval between $1 and 

$10 million. Confidence interval is based on two meta-analyses of the wage-risk VSL literature: $1 

million represents the lower end of the interquartile range from the Mrozek and Taylor (2002) metaanalysis and $10 million represents the upper end of the interquartile range from the Viscusi and Aldy (2003) meta-analysis. The mean of the distribution is consistent with the mean estimate from a 

third meta-analysis (Kochi et al 2006). The VSL represents the value of a small change in mortality risk aggregated over the affected population. 

Chronic Bronchitis (CB)

$410,000 $500,000 The WTP to avoid a case of pollution-related CB is calculated as WTP WTP e x x   13 13 * *( ) E , where x is 

the severity of an average CB case, WTP13 is the WTP for a severe case of CB, and Қ is the 

parameter relating WTP to severity, based on the regression results reported in Krupnick and Cropper (1992). The distribution of WTP for an average severity-level case of CB was generated by Monte Carlo methods, drawing from each of three distributions: (1) WTP to avoid a severe case of CB is assigned a 1/9 probability of being each of the first nine deciles of the distribution of WTP responses in Viscusi et al. (1991); (2) the severity of a pollution-related case of CB (relative to the case described in the Viscusi study) is assumed to have a triangular distribution, with the most likely value at severity level 6.5 and endpoints at 1.0 and 12.0; and (3) the constant in the elasticity 

of WTP with respect to severity is normally distributed with mean = 0.18 and standard deviation = 0.0669 (from Krupnick and Cropper [1992]). This process and the rationale for choosing it is described in detail in the Costs and Benefits of the Clean Air Act, 1990 to 2010 (EPA, 1999). 

Nonfatal Myocardial Infarction (heart attack) 3% discount rate Age 0–24  Age 25–44  Age 45–54  Age 55–65  Age 66 and over  7% discount rate Age 0–24  Age 25–44  Age 45–54  Age 55–65  Age 66 and over 

$79,685 $88,975 $93,897 

$167,532 

$79,685 $77,769 $87,126 $91,559 

$157,477 

$77,769 

$79,685 $88,975 $93,897 

$167,532 

$79,685 $77,769 $87,126 $91,559 

$157,477 

$77,769 

No distributional information available. Age-specific cost-of-illness values reflect lost earnings and

direct medical costs over a 5-year period following a nonfatal MI. Lost earnings estimates are based on Cropper and Krupnick (1990). Direct medical costs are based on simple average of estimates from Russell et al. (1998) and Wittels et al. (1990). Lost earnings: Cropper and Krupnick (1990). Present discounted value of 5 years of lost earnings: 

age of onset: at 3% at 7%  25–44 $8,774 $7,855  45–54 $12,932 $11,578  55–65 $74,746 $66,920 Direct medical expenses: An average of: 

1. Wittels et al. (1990) ($102,658—no discounting) 2. Russell et al. (1998), 5-year period ($22,331 at 3% discount rate; $21,113 at 7% discount rate) 
6-27

Central Estimate of Value 

Per Statistical Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Level Derivation of Distributions of Estimates 

Hospital Admissions 

Chronic Obstructive Pulmonary Disease (COPD) 

$16,606 $16,606

No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD-9 code-level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total COPD category illnesses) reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov). 

Asthma Admissions $8,900 $8,900

No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD-9 code-level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total asthma category illnesses) reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov). 

All Cardiovascular 

$24,668 $24,668

No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD-9 code-level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total cardiovascular category illnesses) reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov). 

All respiratory (ages 65+) $24,622 $24,622

No distributions available. The COI point estimates (lost earnings plus direct medical costs) are based on ICD-9 code level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total COPD category illnesses) reported in Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov). 

All respiratory (ages 0–2) $10,385 $10,385

No distributions available. The COI point estimates (lost earnings plus direct medical costs) are based on ICD-9 code level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total COPD category illnesses) reported in Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov). 

Emergency Room Visits for Asthma $384 $384

No distributional information available. Simple average of two unit COI values: (1) $311.55, from Smith et al. (1997) and (2) $260.67, from Stanford et al. (1999). 

Respiratory Ailments Not Requiring Hospitalization 

Upper Respiratory Symptoms (URS) 

$30 $30 Combinations of the three symptoms for which WTP estimates are available that closely match 

those listed by Pope et al. result in seven different “symptom clusters,” each describing a “type” of 

URS. A dollar value was derived for each type of URS, using mid-range estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assuming additivity of WTPs. In the absence of 

information surrounding the frequency with which each of the seven types of URS occurs within the URS symptom complex, we assumed a uniform distribution between $9.2 and $43.1. 
6-28

Central Estimate of Value 

Per Statistical Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Level Derivation of Distributions of Estimates 

Lower Respiratory Symptoms (LRS) 

$19 $21

Combinations of the four symptoms for which WTP estimates are available that closely match those listed by Schwartz et al. result in 11 different “symptom clusters,” each describing a “type” of LRS. A dollar value was derived for each type of LRS, using mid-range estimates of WTP (IEc, 1994) to 

avoid each symptom in the cluster and assuming additivity of WTPs. The dollar value for LRS is the average of the dollar values for the 11 different types of LRS. In the absence of information 

surrounding the frequency with which each of the 11 types of LRS occurs within the LRS symptom 

complex, we assumed a uniform distribution between $6.9 and $24.46. 

Asthma Exacerbations 

$50 $54

Asthma exacerbations are valued at $45 per incidence, based on the mean of average WTP estimates for the four severity definitions of a “bad asthma day,” described in Rowe and Chestnut (1986). This study surveyed asthmatics to estimate WTP for avoidance of a “bad asthma day,” as defined by the subjects. For purposes of valuation, an asthma exacerbation is assumed to be equivalent to a day in which asthma is moderate or worse as reported in the Rowe and Chestnut (1986) study. The value is assumed have a uniform distribution between $15.6 and $70.8. 

Acute Bronchitis 

$429 $453

Assumes a 6-day episode, with the distribution of the daily value specified as uniform with the low and high values based on those recommended for related respiratory symptoms in Neumann et al. (1994). The low daily estimate of $10 is the sum of the mid-range values recommended by IEc 

(1994) for two symptoms believed to be associated with acute bronchitis: coughing and chest tightness. The high daily estimate was taken to be twice the value of a minor respiratory restrictedactivity day, or $110. 

Work Loss Days (WLDs) 

Variable 

(U.S. median 

= $130) 

 No distribution available. Point estimate is based on county-specific median annual wages divided 

by 50 (assuming 2 weeks of vacation) and then by 5—to get median daily wage. U.S. Year 2000 Census, compiled by Geolytics, Inc. 

Minor Restricted Activity Days (MRADs) 

$61 $64 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). Distribution is assumed to be

triangular with a minimum of $22 and a maximum of $83, with a most likely value of $52. Range is 

based on assumption that value should exceed WTP for a single mild symptom (the highest estimate for a single symptom—for eye irritation—is $16.00) and be less than that for a WLD. The triangular 

distribution acknowledges that the actual value is likely to be closer to the point estimate than either 

extreme. 

School Absence Days 

$89 $89 No distribution available 
6-29

6.4.3 Asthma-Related Emergency Room Visits Valuation 

To value asthma emergency room visits, we used a simple average of two estimates from the 

health economics literature. The first estimate comes from Smith et al. (1997), who reported 

approximately 1.2 million asthma-related emergency room visits in 1987, at a total cost of 

$186.5 million (1987$). The average cost per visit that year was $155; in 2000$, that cost was 

$311.55 (using the CPI-U for medical care to adjust to 2000$). The second estimate comes from 

Stanford et al. (1999), who reported the cost of an average asthma-related emergency room visit 

at $260.67, based on 1996–1997 data. A simple average of the two estimates yields a (rounded) 

unit value of $286. 

6.4.4 Minor Restricted Activity Days Valuation 

No studies are reported to have estimated WTP to avoid a minor restricted activity day. 

However, one of EPA’s contractors, IEc (1993) has derived an estimate of willingness to pay to 

avoid a minor respiratory restricted activity day, using estimates from Tolley et al. (1986) of 

WTP for avoiding a combination of coughing, throat congestion and sinusitis. The IEc estimate 

of WTP to avoid a minor respiratory restricted activity day is $38.37 (1990$), or about $52 

($2000).

Although Ostro and Rothschild (1989) statistically linked ozone and minor restricted activity 

days, it is likely that most MRADs associated with ozone exposure are, in fact, minor respiratory

restricted activity days. For the purpose of valuing this health endpoint, we used the estimate of 

mean WTP to avoid a minor respiratory restricted activity day. 

6.4.5 School Absences 

To value a school absence, we: (1) estimated the probability that if a school child stays home 

from school, a parent will have to stay home from work to care for the child; and (2) valued the 

lost productivity at the parent’s wage. To do this, we estimated the number of families with 

school-age children in which both parents work, and we valued a school-loss day as the 

probability that such a day also would result in a work-loss day. We calculated this value by 

multiplying the proportion of households with school-age children by a measure of lost wages. 

We used this method in the absence of a preferable WTP method. However, this approach 

suffers from several uncertainties. First, it omits willingness to pay to avoid the symptoms/illness 

that resulted in the school absence; second, it effectively gives zero value to school absences that 

do not result in work-loss days; and third, it uses conservative assumptions about the wages of 

the parent staying home with the child. Finally, this method assumes that parents are unable to 

work from home. If this is not a valid assumption, then there would be no lost wages. 

For this valuation approach, we assumed that in a household with two working parents, the 

female parent will stay home with a sick child. From the Statistical Abstract of the United States 

(U.S. Census Bureau, 2001), we obtained: (1) the numbers of single, married and “other” 

(widowed, divorced or separated) working women with children; and (2) the rates of 

participation in the workforce of single, married and “other” women with children. From these 

two sets of statistics, we calculated a weighted average participation rate of 72.85 percent. 
6-30

Our estimate of daily lost wage (wages lost if a mother must stay at home with a sick child) is 

based on the year 2000 median weekly wage among women ages 25 and older (U.S. Census 

Bureau, 2001). This median weekly wage is $551. Dividing by five gives an estimated median 

daily wage of $103. To estimate the expected lost wages on a day when a mother has to stay 

home with a school-age child, we first estimated the probability that the mother is in the 

workforce then multiplied that estimate by the daily wage she would lose by missing a workday: 

72.85 percent times $103, for a total loss of $75. This valuation approach is similar to that used 

by Hall et al. (2003). 

6.5 Results and Implications 

6.5.1 Ozone Benefit Estimates 

Figure 6.1 summarizes the valuation of ozone benefits. Tables 6.6 through 6.21 summarize the 

reduction in incidence for ozone- and PM-related health endpoints for each of the alternative 

ozone standards evaluated. Tables 6.22 through 6.37 summarize the ozone-related economic 

benefits for each of the alternative standards.28 Note that incidence and valuation estimates for 

each standard alternative are presented in separate tables In addition to the mean incidence 

estimates, we have included 5th and 95th percentile estimates when available, based on the Monte 

Carlo simulations described above. In the tables for the 0.065 ppm and 0.070 ppm alternative 

standards, the change in ozone-related incidence from attaining the alternative standards is 

presented for both the partial attainment scenario and the full attainment scenario (i.e., sum of the 

change in incidence associated with achieving the partial attainment increment plus the residual 

attainment increment). As described in Appendix 6a, to calculate the additional change in ozone 

concentrations to get from partial attainment to full attainment, we rolled back the ozone monitor 

data so that the 4th highest daily maximum 8-hour average just met the level required to attain the 

alternative standard. This approach will likely understate the benefits that would occur due to 

implementation of actual controls to reduce ozone precursor emissions because controls 

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

reductions in ozone concentrations at attaining monitors down-wind (i.e., the controls would lead 

to concentrations below the standard in down-wind locations); estimating benefits that occur at 

these downwind monitors as a result of air quality improvements below the standard would be 

appropriate because ozone is a non-threshold pollutant. Therefore, air quality improvements and 

resulting health benefits from full attainment would be more widespread than we have estimated 

in our rollback analyses. The incidence and valuation results for attainment of the 0.075 ppm and 

0.079 ppm alternatives are derived through an interpolation technique described in Appendix 6a. 

As such, these estimates are presented as full attainment only. 

We model all ozone-related premature mortality and morbidity to occur in the same year as the 

change in exposure rather than assuming a ‘lag’ in the change in health state, as we do for PM. 

Therefore, we do not discount ozone estimates. 

 

28 Note that the valuation estimates for ozone benefits are not discounted due to the fact that there 

is no lag between changes in exposure and premature mortality, as is calculated for PM2.5

benefits.
6-31

 Figure 6.1: Valuation of Ozone Morbidity and Mortality Benefits Results by Standard 

Alternative*

* This figure reflects full attainment in all locations of the U.S. except two areas of California. These two 

areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. 

The estimates in the figure do not reflect benefits for the San Joaquin and South Coast Air Basins. 

6.5.2 PM2.5 Co-Benefit Estimation Methodology 

Figure 6.2 summarizes the valuation of PM benefits at a 3% and 7% discounted rate, 

respectively. A series of tables below present the PM2.5 co-benefits associated with full 

attainment of the 0.065 ppm, 0.070 ppm, 0.075 ppm and 0.079 ppm alternatives. To derive 

estimates of incidence and valuation for the PM2.5 related co-benefits of full attainment of each 

ozone standard alternative, we applied a scaling technique described below. To estimate total 

valuation estimates, we applied benefit per-ton metrics; this procedure is detailed further below. 

Valuation estimates of the PM2.5-related full attainment benefits are presented at a 3% discount 

rate and at a 7% discount rate. All PM2.5 co-benefit estimates are incremental to the 2006 PM 

NAAQS RIA. 
6-32

Figure 6.2: Valuation of PM Co-Benefits by Standard Alternative at 3% and 7%* 

* This figure reflects full attainment in all locations of the U.S. except two areas of California. These 

two areas, which have high levels of ozone, are not planning to meet the current standard until after 

2020. The estimates in the figure do not reflect benefits for the San Joaquin and South Coast Air 

Basins.
6-33

Estimating PM2.5 Co-Benefits Resulting from Full Attainment of the Selected Standard and Each 

Standard Alternative 

The modeled PM2.5 air quality scenario reflects the PM2.5 changes associated with partially 

attaining 0.070 ppm incremental to a partial attainment of 0.08 ppm; due to analytical limitations 

it was not possible to model a full-attainment PM2.5 scenario for the selected standard or each 

standard alternative. Thus, using this projected air quality change to estimate PM2.5 co-benefits 

would under or overstate the benefits of attaining each standard alternative; this is due in part to 

the fact that the model run projects the air quality changes from NOx reductions needed to attain 

a baseline of 0.08 ppm. Of greater analytical value would be an estimate of the PM2.5 co-benefits 

associated with fully attaining 0.070 ppm incremental to full attainment of the 0.08 ppm 

standard.

To generate such an estimate, we calculated a new PM2.5 baseline that established the PM2.5 air 

quality associated with full attainment of 0.08 ppm. To create such a baseline, EPA utilized 

benefit PM2.5 per-ton estimates. These PM2.5 benefit per-ton estimates provide the total 

monetized human health benefits (the sum of premature mortality and premature morbidity) of 

reducing one ton of PM2.5 from a specified source. EPA has used a similar technique in previous 

Regulatory Impact Analyses.29 These estimates are based on the sum of the valuation of the Pope 

(2002) estimates of mortality (3% discount rate, 2006$) and valuation of the morbidity 

incidence. Readers interested in reviewing the complete methodology for creating the benefit 

per-ton estimates used in this analysis can consult the Technical Support Document 

accompanying this RIA. 

Estimating the PM2.5 benefits that represented the full attainment of both 0.070 ppm incremental 

to full attainment of 0.08 ppm entailed the following four steps:

1. Estimate the number of tons of NOx necessary to attain a baseline of 0.08 ppm. Chapter 4 

described the method used to estimate the extrapolated NOx emissions reductions 

necessary to attain a baseline of 0.08 ppm full attainment. 

2. Calculate the benefits of attaining 0.08 ppm incremental to partial attainment of 0.08 

ppm. To estimate the benefits of fully attaining 0.08 ppm incremental to partial 

attainment of 0.08 ppm, the relevant benefit per ton is simply multiplied by the total 

number of extrapolated NOx tons abated. Note that this calculation step allows us to net 

out the benefits of attaining the current standard, so that all subsequent benefits are 

incremental to the full attainment of 0.080 ppm.

3. Calculate the benefits of partially attaining 0.070 ppm incremental to full attainment of 

0.08 ppm. Subtract the benefits of fully attaining 0.080 ppm incremental to the partial 

 

29 Final Regulatory Impact Analysis: Industrial Boilers and Process Heaters. Prepared by Office 

of Air and Radiation. Available: http://www.epa.gov/ttn/ecas/regdata/EIAs/chapter10.pdf

[accessed 18 May 2007]. 
6-34

attainment of 0.08 ppm to create a new estimate of incremental 0.070 ppm partial 

attainment.

4. Calculate the PM2.5 benefits of fully attaining 0.070 ppm. Multiplying the estimate of the 

extrapolated NOx tons necessary to attain 0.070 ppm fully (Table 5.3) produces an 

estimate of the incremental benefits of fully attaining 0.070 ppm incremental to partial 

attainment of 0.070 ppm. By adding this incremental benefit estimate to the benefits 

generated in step 3, we derived a total benefit estimate of attaining 0.070 ppm 

incremental to 0.08 ppm. 

5. Repeat step 4 to estimate the benefits of 0.075 ppm, 0.079 ppm and 0.065 ppm. Step 4 

may be repeated by substituting the NOx tons necessary to attain the selected alternative 

of 0.075 ppm and the remaining alternatives of 0.079 ppm and 0.065 ppm to produce an 

estimate of total PM2.5 co-benefits. 

The process for estimating the PM2.5 co-benefits of fully attaining 0.065 ppm, 0.075 ppm, and 

0.079 ppm is identical to the steps above, with the following exception; in step four we 

substituted the number of extrapolated tons necessary to attain 0.065 ppm, 0.075 ppm, and 0.079 

ppm respectively. Table 7-5 below provides the inputs to the calculation steps described above. 

In the example below, we calculate total benefits using the Pope et al. (2002) mortality estimate. 

However, in subsequent tables we present benefits using Laden et al. (2006) as well as the twelve 

expert functions described previously in this document. Note that while our benefit per ton 

estimates are associated with broad source categories (in this case, NOx emitting Electrical 

Generating Units, Other NOx emitting point sources and NOx emitting Mobile sources) the 

extrapolated tons were not. For this reason we simply assumed that the total number of 

extrapolated NOx tons were evenly distributed between these three source types. 

The PM2.5 benefits of attaining 0.065 ppm, 0.075 ppm and 0.079 ppm incremental to partial 

attainment of 0.070 ppm are $7.5 billion, $0.6 billion and -$1 billion respectively. Simulated 

attainment of the 0.79 ppm alternative required fewer emission reductions than were modeled in 

the emissions control strategy to simulate attainment with 0.070 ppm. For this reason, we “netted 

out” the benefits of the incremental NOx emission reductions that were present in the 0.070 ppm 

control case but not necessary to attain 0.079 ppm.

The benefit per-ton estimates produce estimates of total valuation but not incidence. To estimate 

total incidence, we applied a simple scaling factor. To estimate PM2.5-related incidence 

associated with the attainment of each ozone alternative, we calculated a separate scaling factor 

as follows: (1) we calculated the ratio of the full attainment PM2.5 valuation estimate (calculated 

using the benefit per ton metrics described below) to the partial attainment to the partial 

attainment PM2.5 valuation estimate; (2) multiply this scaling ratio against each of the PM2.5

partial attainment mortality and morbidity endpoints to generate a scaled estimate of mortality 

and morbidity. While there are clearly substantial uncertainties inherent in this technique, it does 

produce useful screening-level estimates of PM2.5-related incidence.

The total PM2.5 benefits of attaining 0.065 ppm, 0.075 ppm and 0.079 ppm are $11 billion, $3.6 

billion and $2 billion respectively. The full attainment PM2.5 benefits do not include confidence 

intervals. Because this full attainment estimate was derived by summing the modeled PM2.5
6-35

benefits and the benefits derived using the benefit per-ton metrics—and these benefit per ton 

metrics do not include confidence intervals—the resulting sum of total PM2.5 benefits do not 

include confidence intervals. 

Table 6.5: Estimated PM2.5 Co-Benefits Associated with Full Attainment of 0.070 ppm 

Incremental to 0.08 ppma

Calculation 

Extrapolated NOx 

Tons 

Benefit per Ton 

Estimate 

Valuation of PM2.5

Benefits

(Billions 2006$)b

Benefits of attaining 0.08 ppm partially and 

0.070 ppm partially (i.e. the benefits of the 

modeled scenario): 

— — $3.4 

NOx EGU: 37,400 $3,200 

NOx Point: 37,400 $3,000 

Benefits of attaining 0.08 ppm from a 

baseline of 0.08 ppm partial attainment: 

NOx Mobile: 37,400 $4,800 

$0.4 

Benefits of attaining 0.070 ppm partially, 

incremental to attainment of 0.08 ppm — — $3 

NOx EGU: 310,000 $3,200 

NOx Point: 310,000 $3,000 

NOx Mobile: 310,000 $4,800 

Benefits of attaining 0.070 ppm in 2020 

incremental to partial attainment of 0.070 

ppm 

VOC: 310,000 $430 

$3.5 

Benefits of attaining 0.070 ppm incremental 

to attainment of 0.08 ppm $6.5 

a Numbers have been rounded to two significant figures and therefore summation may not match table 

estimates. PM2.5 benefit estimates do not include confidence intervals because they are derived using 

benefit per-ton estimates. 

b All estimates derived using the Pope et al. (2002) mortality estimate at a 3% and 7% discount rate, in

2006$. This table reflects full attainment in all locations of the U.S. except two areas of California. 

These two areas, which have high levels of ozone, are not planning to meet the current standard until 

after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air

Basins.

Estimated reductions in ozone mortality incidence provided in Tables 6.6, 6.10, 6.14, and 6.18 

represent the number of premature deaths potentially avoided due to reductions in ozone 

exposure in 2020 using warm season functions from the recent ozone-mortality NMMAPS 

analysis of 95 U.S. communities (Bell et al., 2004) and three meta-analyses of the available 

published literature on ozone-mortality effects (Bell et al., 2005; Ito et al., 2005; Levy et al., 

2005). These same tables also include the possibility that there is not a causal association 

between ozone and mortality, i.e., that the estimate for premature mortality avoided could be 

zero. Model uncertainty, including whether or not the relationship is assumed to be causal, is a 

key source of uncertainty. Although multiple estimates are presented in these tables, no attempt 

was made to quantify the likelihood of a causal relationship between short-term ozone exposure 

and increased mortality or to weigh the results of the various models.

The estimate of central tendency for premature mortality is expressed as the arithmetic mean, 

with the assumption of a normal distribution, and represents the central estimate of the number of 

premature deaths avoided in association with the alternative standards based on each study. 
6-36

Statistical uncertainty associated with the model estimate for each study is characterized by the 

95% credible interval30 around the mean estimate (i.e., 2.5th and 97.5th percent interval). Of the 

four available studies, the NMMAPS study by Bell et al. (2004) is considered to be the most 

representative for evaluating potential mortality-related benefits associated with the alternative 

standards due to its extensive coverage (examination of 95 large communities across the United 

States over an extended period of time, from 1987 to 2000) and its specific focus on the ozonemortality relationship. Annual estimates of lives saved from this study are lower than those from 

the three meta-analyses, possibly due to more stringent adjustment for meteorological factors (Ito 

et al., 2005; Ostro et al., 2006), publication bias in the meta-analyses (Bell et al., 2005; Ito et al., 

2005) or other factors. Clearly, the ozone-mortality reduction estimates are conditional on a 

causal relationship.

The Ozone Criteria Document (U.S. EPA, 2006) and Staff Paper (U.S. EPA, 2007) concluded 

that the overall body of evidence is highly suggestive that (short-term exposure to) ozone directly 

or indirectly contributes to non-accidental cardiopulmonary-related mortality. However, various 

sources of uncertainty remain, including the possibility that there is no causal relationship 

between ozone and mortality (i.e., zero effect). For instance, because results of time-series 

studies implicate all of the criteria air pollutants, and those who would be expected to be 

potentially more susceptible to ozone exposure are likely to have lower exposure to ozone due to 

the amount of time that they spend indoors, CASAC31 stated that it seems unlikely that the 

observed associations between short-term ozone concentrations and daily mortality are due 

solely to ozone itself (i.e., ozone may be serving as a marker for other agents that are 

contributing to the short-term exposure effects on mortality). Even so, CASAC concluded that 

the evidence was strong enough to support a quantitative risk assessment of the relationship 

between short-term exposure to ozone and premature mortality as part of the Staff Paper. EPA 

has asked the National Academy of Sciences32 for their advice on how best to quantify the 

uncertainty about the relationship between ambient ozone exposure and premature mortality 

within the context of quantifying projected benefits of alternative control strategies. We expect to 

receive this advice later this spring. 

Using the NMMAPS study that was used as the basis for the risk analysis presented in our Staff 

Paper, we estimate 71 avoided premature deaths annually in 2020 from reducing ozone levels to 

meet the selected standard of 0.075 ppm, which, when added to the other projected ozone related 

benefits, leads to an estimated total benefit of $620 million/yr. Using three studies that synthesize 

data across a large number of individual studies, we estimate between 230 and 320, with total 

monetized ozone benefits to be between $1.9 and $2.6 billion/yr. Alternatively, if there is no 

causal relationship between ozone and mortality, avoided premature deaths would be zero. For a 

 

30 A credible interval is a posterior probability interval used in Bayesian statistics, which is 

similar to a confidence interval used in frequentist statistics. 

31 Clean Air Scientific Advisory Committee’s Peer Review of the Agency’s 2nd Draft Ozone 

Staff Paper, October 24, 2006. EPA-CASAC-07-001. Available at http://www.epa.gov/sab/pdf/casac07-001.pdf

32 National Academy of Sciences (2007) Project Scope. Estimating Mortality Risk Reduction 

Benefits from Decreasing Tropospheric Ozone Exposure. Division on Earth and Life Studies, 

Board on Environmental Studies and Toxicology. Available at 

http://www8.nationalacademies.org/cp/projectview.aspx?key=48768
6-37

standard of 0.079 ppm, using the NMMAPS ozone mortality study, we estimate 24 premature 

deaths avoided and total monetized benefits of $220 million/yr. Using the three synthesis studies, 

we estimate premature deaths avoided for the less stringent standard to be between 80 and 110, 

with total monetized ozone benefits to be between $640 and $890 million/yr. For a standard of 

0.070 ppm, using the NMMAPS ozone mortality study, we estimate 250 premature deaths 

avoided and total monetized benefits of $2.2 billion/yr. Using the three synthesis studies, we 

estimate premature deaths avoided for the less stringent standard to be between 810 and 1,100 

avoided premature deaths annually in 2020, leading to total monetized benefits of between $6.5 

and $9 billion/yr. For a standard of 0.065 ppm, using the NMMAPS ozone mortality study, we 

estimated to result in 450 premature deaths avoided and total monetized benefits of $3.9 

billion/yr. Using the three synthesis studies, estimated premature deaths avoided for the more 

stringent standard are between 1,500 and 2,100, with total monetized ozone benefits between $12 

and $16 billion/yr. Including premature mortality in our estimates had the largest impact on the 

overall magnitude of benefits: Premature mortality benefits account for more than 95 percent of 

the total benefits we can monetize. We note that these estimates reflect EPA’s interim approach 

to characterizing the benefits of reducing premature mortality associated with ozone exposure. 

As mentioned above, EPA has requested advice from the NAS on this issue. 

6.5.3 Estimate of Full Attainment Benefits 

Tables 6.38 through 6.41 below summarize the estimates of full attainment and PM2.5 co-benefit 

estimate for each standard alternative. The presentation of ozone benefits and PM2.5 co-benefits 

for each standard alternative is broken into two tables. The first table presents the national ozone 

benefits and PM2.5 co-benefits. Tables 6.42 through 6.49 summarize the combined ozone and 

PM2.5 co-benefits. 
6-38

Table 7-6: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the 

Incidence of Premature Mortality Associated with Ozone Exposure in 2020 (Incremental 

to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals in 

Parentheses) B, C, D, E

Model or AssumptionA Reference National Modeled Partial 

Attainment 

National Rolled-Back Full 

Attainment 

120 450 NMMAPS Bell et al. 2004 (43--210) (170--730) 

400 1500 Bell et al. 2005 (200--610) (760--2,200) 

550 2000 Ito et al. 2005 (340--760) (1,300--2,700) 

560 2100 

Meta-Analysis 

Levy et al. 2005 (390--730) (1,500--2,600) 

Assumption that association is not causal 0 0 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text 

in section 6.3.2.1). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval 

around the mean estimates of the annual number of lives saved are based on an assumption of a normal 

distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence 

interval used in frequentist statistics. 

D

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and 

totals will not sum across columns. 

E

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-39

Table 7-7: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Reductions in the 

Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current Ozone 

Standard, 95% Confidence Intervals in Parentheses)A,B

 

Morbidity Endpoint 

National Modeled Partial 

Attainment 

National Rolled Back Full 

Attainment 

700 2,700 Hospital Admissions (ages 0-1) (310--1,100) (1,300--4,000) 

420 3,200 Hospital Admissions (ages 65-99) (-190--1,100) (74--6,200) 

Emergency Department Visits, 550 1900 

Asthma-RelatedC

(-57--1,500) (-130--5,500) 

300,000 1,100,000 School Absences (77,000--560,000) (320,000--1,800,000) 

 810,000 2,900,000 Minor Restricted Activity Days (350,000--1,300,000) (1,300,000--4,400,000) 

 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and 

totals will not sum across columns. 

B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 

C

 The negative 5th percentile incidence estimates for this health endpoint are a result of the weak statistical power 

of the study and should not be inferred to indicate that decreased ozone exposure may cause an increase in 

asthma-related emergency department visits.
6-40

Table 7-8: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of PM Premature Mortality associated with PM co-benefitC

Mortality Endpoint National 2020 Benefits 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyA1,000 

Harvard Six-City StudyB 2,300 

Woodruff et al 1997 (infant mortality) 2.9 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A 4,000 

Expert B 3,100 

Expert C 3,100 

Expert D 2,100 

Expert E 5,000 

Expert F 2,800 

Expert G 1,800 

Expert H 2,300 

Expert I 3,000 

Expert J 2,400 

Expert K 490 

Expert L 2,100 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs. 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for 

advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

C All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not include confidence intervals because they were derived through a scaling technique described above. This 

table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the 

table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-41

Table 7-9: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with PM Co-benefitA, B

Morbidity Endpoint National 2020 Benefits 

Chronic Bronchitis (age >25 and over) 970 

Nonfatal myocardial infarction (age >17) 940 

Hospital admissions--respiratory (all ages) 660,000 

Hospital admissions-- cardiovascular (age >17) 17,000 

Emergency room visits for asthma (age <19) 13,000 

Acute bronchitis (age 8-12) 110,000 

Lower respiratory symptoms (age 7-14) 2,600 

Upper respiratory symptoms (asthmatic children age 9-18) 16,000 

Asthma exacerbation (asthmatic children age 6-18) 270 

Work loss days (age 18-65) 550 

Minor restricted activity days (age 18-65) 2,300 

A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. 

Estimates do not include confidence intervals because they were derived through a scaling technique 

described above. This table reflects full attainment in all locations of the U.S. except two areas of California. 

These two areas, which have high levels of ozone, are not planning to meet the current standard until after 

2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 

B Morbidity Impact Functions Derived from Epidemiology Literature 
6-42

Table 7-10: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in 

the Incidence of Premature Mortality Associated with Ozone Exposure in 2020 

(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals 

in Parentheses) B, C, D, E

 

Model or AssumptionA Reference National Modeled Partial 

Attainment 

National Rolled Back Full 

Attainment 

120 250 NMMAPS Bell et al. 2004 

(43--210) (92--410) 

400 810 Bell et al. 2005 (200--610) (410--1,200) 

550 1100 Ito et al. 2005 (340--760) (690--1,500) 

560 1100 

Meta-Analysis 

Levy et al. 2005 (390--730) (800--1,500) 

Assumption that association is not causal 0 0 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text 

in section 6.3.2.1). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval 

around the mean estimates of the annual number of lives saved are based on an assumption of a normal 

distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence 

interval used in frequentist statistics. 

D

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and 

totals will not sum across columns. 

E

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-43

Table 7-11: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Reductions in 

the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current 

Ozone Standard, 95% Confidence Intervals in Parentheses)A,B

 

Morbidity Endpoint 

National Modeled Partial 

Attainment 

National Rolled Back Full 

Attainment 

700 1,500 Hospital Admissions (ages 0-1) (310--1,100) (720--2,400) 

420 1,400 Hospital Admissions (ages 65-99) (-190--1,100) (-110--3,000) 

Emergency Department Visits, 550 1000 

Asthma-RelatedC

(-57--1,500) (-82--3,000) 

300,000 640,000 School Absences (77,000--560,000) (180,000--1,000,000) 

 810,000 1,700,000 Minor Restricted Activity Days (350,000--1,300,000) (740,000--2,600,000) 

 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and 

totals will not sum across columns. 

B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 

C

 The negative 5th percentile incidence estimates for this health endpoint are a result of the weak statistical power 

of the study and should not be inferred to indicate that decreased ozone exposure may cause an increase in 

asthma-related emergency department visits.
6-44

Table 7-12: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of PM Premature Mortality associated with PM co-benefitC

Mortality Endpoint National 2020 Benefits 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyA650 

Harvard Six-City StudyB 1,500 

Woodruff et al 1997 (infant mortality) 1.9 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A 2,600 

Expert B 2,000 

Expert C 2,000 

Expert D 1,400 

Expert E 3,200 

Expert F 1,800 

Expert G 1,100 

Expert H 1,500 

Expert I 1,900 

Expert J 1,600 

Expert K 310 

Expert L 1,400 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs. 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for 

advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

C All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not include confidence intervals because they were derived through a scaling technique described above. This 

table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the 

table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-45

Table 7-13: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with PM Co-benefitA, B

Morbidity Endpoint National 2020 Benefits 

Chronic Bronchitis (age >25 and over) 630 

Nonfatal myocardial infarction (age >17) 610 

Hospital admissions--respiratory (all ages) 430,000 

Hospital admissions-- cardiovascular (age >17) 11,000 

Emergency room visits for asthma (age <19) 8,100 

Acute bronchitis (age 8-12) 72,000 

Lower respiratory symptoms (age 7-14) 1,700 

Upper respiratory symptoms (asthmatic children age 9-18) 10,000 

Asthma exacerbation (asthmatic children age 6-18) 180 

Work loss days (age 18-65) 350 

Minor restricted activity days (age 18-65) 1,500 

A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. 

Estimates do not include confidence intervals because they were derived through a scaling technique 

described above. This table reflects full attainment in all locations of the U.S. except two areas of California. 

These two areas, which have high levels of ozone, are not planning to meet the current standard until after 

2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 

B Morbidity Impact Functions Derived from Epidemiology Literature 
6-46

Table 7-14: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in 

the Incidence of Premature Mortality Associated with Ozone Exposure in 2020 

(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals 

in Parentheses) B, C, D, E

 

Model or AssumptionA Reference National Full Attainment 

71 NMMAPS Bell et al. 2004 

(27--110) 

230 Bell et al. 2005 (120--340) 

310 Ito et al. 2005 (200--430) 

320 

Meta-Analysis 

Levy et al. 2005 (230--420) 

Assumption that association is not causal 0 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text 

in section 6.3.2.1). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval 

around the mean estimates of the annual number of lives saved are based on an assumption of a normal 

distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence 

interval used in frequentist statistics. 

D

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical. 

E

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-47

Table 7-15: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Reductions in 

the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current 

Ozone Standard, 95% Confidence Intervals in Parentheses)A,B

 

Morbidity Endpoint National Full Attainment 

480 Hospital Admissions (ages 0-1) (230--730) 

470 Hospital Admissions (ages 65-99) (-5.1--930) 

Emergency Department Visits, 280 

Asthma-RelatedC

(-18--830) 

200,000 School Absences (58,000--320,000) 

500,000 Minor Restricted Activity Days (230,000--760,000) 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical. 

B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 

C

 The negative 5th percentile incidence estimates for this health endpoint are a result of the weak statistical power 

of the study and should not be inferred to indicate that decreased ozone exposure may cause an increase in 

asthma-related emergency department visits.
6-48

Table 7-16: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of PM Premature Mortality associated with PM co-benefitC

Mortality Endpoint National 2020 Benefits 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyA390 

Harvard Six-City StudyB 880 

Woodruff et al 1997 (infant mortality) 1.1 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A 1,600 

Expert B 1,200 

Expert C 1,200 

Expert D 820 

Expert E 2,000 

Expert F 1,100 

Expert G 690 

Expert H 880 

Expert I 1,200 

Expert J 950 

Expert K 190 

Expert L 820 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs. 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for 

advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

C All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not include confidence intervals because they were derived through a scaling technique described above. This 

table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the 

table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-49

Table 7-17: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of Morbidity Associated with PM Co-benefitA, B

Morbidity Endpoint National 2020 Benefits 

Chronic Bronchitis (age >25 and over) 380 

Nonfatal myocardial infarction (age >17) 370 

Hospital admissions--respiratory (all ages) 260,000 

Hospital admissions-- cardiovascular (age >17) 6,700 

Emergency room visits for asthma (age <19) 4,900 

Acute bronchitis (age 8-12) 43,000 

Lower respiratory symptoms (age 7-14) 1,000 

Upper respiratory symptoms (asthmatic children age 9-18) 6,100 

Asthma exacerbation (asthmatic children age 6-18) 110 

Work loss days (age 18-65) 210 

Minor restricted activity days (age 18-65) 890 

A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not include confidence intervals because they were derived through a scaling technique described above. This 

table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the 

table do not reflect benefits for the San Joaquin and South Coast Air Basins. 

B Morbidity Impact Functions Derived from Epidemiology Literature 
6-50

Table 7-18: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in 

the Incidence of Premature Mortality Associated with Ozone Exposure in 2020 

(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals 

in Parentheses) B, C, D, E

 

Model or AssumptionA Reference National Full Attainment 

24 NMMAPS Bell et al. 2004 

(10--39) 

80 Bell et al. 2005 (42--120) 

110 Ito et al. 2005 (69--150) 

110 

Meta-Analysis 

Levy et al. 2005 (80--140) 

Assumption that association is not causal 0 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text 

in section 6.3.2.1). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval 

around the mean estimates of the annual number of lives saved are based on an assumption of a normal 

distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence 

interval used in frequentist statistics. 

D

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical. 

E

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-51

Table 7-19: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Reductions in 

the Incidence of Morbidity Associated with Ozone Exposure (Incremental to Current 

Ozone Standard, 95% Confidence Intervals in Parentheses)A,B

 

Morbidity Endpoint National Full Attainment 

190 Hospital Admissions (ages 0-1) (9.0--350) 

190 Hospital Admissions (ages 65-99) (90--280) 

Emergency Department Visits, 87

Asthma-RelatedC

(-5.2--250) 

72,000 School Absences (21,000--110,000) 

180,000 Minor Restricted Activity Days (83,000--270,000) 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical. 

B

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 

C

 The negative 5th percentile incidence estimates for this health endpoint are a result of the weak statistical power 

of the study and should not be inferred to indicate that decreased ozone exposure may cause an increase in 

asthma-related emergency department visits.
6-52

Table 7-20: Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual 

Reductions in the Incidence of PM Premature Mortality associated with PM co-benefitC

Mortality Endpoint National 2020 Benefits 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyA250 

Harvard Six-City StudyB 560 

Woodruff et al 1997 (infant mortality) 0.71 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A 1,000 

Expert B 760 

Expert C 750 

Expert D 530 

Expert E 1,200 

Expert F 690 

Expert G 440 

Expert H 560 

Expert I 750 

Expert J 600 

Expert K 120 

Expert L 530 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs. 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for 

advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

C All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not include confidence intervals because they were derived through a scaling technique described above. This 

table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the 

table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-53

Table 7-21: Illustrative 0.079 ppm Full Attainment Scenario: 

Estimated Annual Reductions in the Incidence of Morbidity Associated 

with PM Co-benefitA, B, C

Morbidity Endpoint National 2020 Benefits 

Chronic Bronchitis (age >25 and over) 240 

Nonfatal myocardial infarction (age >17) 230 

Hospital admissions--respiratory (all ages) 160,000

Hospital admissions-- cardiovascular (age >17) 4,200 

Emergency room visits for asthma (age <19) 3,100 

Acute bronchitis (age 8-12) 28,000 

Lower respiratory symptoms (age 7-14) 640 

Upper respiratory symptoms (asthmatic children age 9-18) 3,900 

Asthma exacerbation (asthmatic children age 6-18) 67 

Work loss days (age 18-65) 140 

Minor restricted activity days (age 18-65) 570 

A All estimates rounded to two significant figures. All estimates incremental to 2006 PM 

NAAQS RIA. Estimates do not include confidence intervals because they were derived 

through a scaling technique described above. 

B Morbidity Impact Functions Derived from Epidemiology Literature 

C

 This table reflects full attainment in all locations of the U.S. except two areas of 

California. These two areas, which have high levels of ozone, are not planning to meet the 

current standard until after 2020. The estimates in the table do not reflect benefits for the 

San Joaquin and South Coast Air Basins. 
6-54

Table 7-22: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure 

(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence 

Intervals in Parentheses, Millions of 2006$)B,C,D,E

Model or 

AssumptionA Reference National Modeled Partial Attainment National Rolled Back Full Attainment 

 $960 $3,500 NMMAPS Bell et al. 

2004 ($140--$2,200) ($510--$7,800) 

Bell et al. $3,100 $11,000 

2005 ($490--6,600) ($1,800--24,000) 

 $4,200 $15,000 Ito et al. 2005 (730--$8,600) (2,700--$31,000) 

 $4,400 $16,000 

Meta-Analysis 

Levy et al. 

2005 ($770--$8,500) ($2,800--$31,000) 

Assumption that association is not 

causal 0 0 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see 

text in section 6.3.2.1). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible 

interval around the mean estimates of the annual number of lives saved are based on an assumption of a

normal distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a 

confidence interval used in frequentist statistics.

D

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and 

totals will not sum across columns. 

E

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two 

areas, which have high levels of ozone, are not planning to meet the current standard until after 2020. The 

estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-55

Table 7-23: Illustrative Strategy to Attain 0.065 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to 

Current Ozone Standard, 95% Confidence Intervals in Parentheses, Millions of 2006$) A,B

 

Morbidity Endpoint 

National Modeled Partial 

Attainment 

National Rolled Back Full 

Attainment 

$6.9 $26 Hospital Admissions (ages 0-1) ($3.4--10) ($14.0--39) 

Hospital Admissions (ages 65- $9.9 $74 

99)C

(-$3.3--$24) ($8.40--$140) 

Emergency Department Visits, $0.20 $0.69 

Asthma-RelatedC

($0.0--$0.56) ($0.0--$2.0) 

$27 $99 School Absences ($8.4--$48) ($34--$150) 

 $48 $170 Minor Restricted Activity Days ($18--$89) ($67--$310) 

Worker Productivity $6.8 $49 

 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and 

totals will not sum across columns. This table reflects full attainment in all locations of the U.S. except two areas 

of California. These two areas, which have high levels of ozone, are not planning to meet the current standard 

until after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 

B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 

C

 The negative 5th percentile incidence estimates for this health endpoint are a result of the weak statistical power 

of the study and should not be inferred to indicate that decreased ozone exposure may cause an increase in 

asthma-related emergency department visits.
6-56

Table 7-24: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual 

Valuation of Reductions in the Incidence of PM Premature Mortality associated with PM 

co-benefit (Millions of 2006$)C

Mortality Endpoint 

National 2020 Benefits 

(3% discount rate) 

National 2020 Benefits 

(7% discount rate) 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyA$9,700 $8,800 

Harvard Six-City StudyB $22,000 $20,000 

Woodruff et al 1997 (infant mortality) $20 $16 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A $33,000 $30,000 

Expert B $25,000 $23,000 

Expert C $25,000 $22,000 

Expert D $17,000 $16,000 

Expert E $41,000 $37,000 

Expert F $23,000 $20,000 

Expert G $15,000 $13,000 

Expert H $19,000 $17,000 

Expert I $25,000 $22,000 

Expert J $20,000 $18,000 

Expert K $4,300 $3,900 

Expert L $18,000 $16,000 

 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs. 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for 

advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

C All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not include confidence intervals because they were derived through a scaling technique described above. This 

table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the 

table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-57

Table 7-25: Illustrative 0.065 ppm Full Attainment Scenario: Estimated Annual 

Valuation of Reductions in the Incidence of Morbidity Associated with PM Co-benefit 

(Millions of 2006$)A, B, C

Morbidity Endpoint National 2020 Benefits 

Chronic Bronchitis (age >25 and over) $480 

Nonfatal myocardial infarction (age >17) 

3% discount rate $250 

7% discount rate $240 

Hospital admissions--respiratory (all ages) $5.8 

Hospital admissions-- cardiovascular (age >17) $15 

Emergency room visits for asthma (age <19) $0.35 

Acute bronchitis (age 8-12) $1.3 

Lower respiratory symptoms (age 7-14) $0.33 

Upper respiratory symptoms (asthmatic children age 9-18) $0.39 

Asthma exacerbation (asthmatic children age 6-18) $0.84 

Work loss days (age 18-65) $14 

Minor restricted activity days (age 18-65) $19 

A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. 

Estimates do not include confidence intervals because they were derived through a scaling technique described 

above.

B Morbidity Impact Functions Derived from Epidemiology Literature 

C

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-58

Table 7-26: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure 

(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals 

in Parentheses, Millions of 2006$)B,C,D,E

Model or 

AssumptionA Reference National Modeled Partial Attainment National Rolled Back Full Attainment 

 $960 $1,900 NMMAPS Bell et al. 

2004 ($140--$2,200) ($280--$4,300) 

Bell et al. $3,100 $6,200 

2005 ($490--6,600) ($1,000--13,000) 

 $4,200 $8,500 Ito et al. 2005 (730--$8,600) (1,500--$17,000) 

 $4,400 $8,800 

Meta-Analysis 

Levy et al. 

2005 ($770--$8,500) ($1,600--$17,000) 

Assumption that association is not 

causal 0 0 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text 

in section 6.3.2.1). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval 

around the mean estimates of the annual number of lives saved are based on an assumption of a normal 

distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence 

interval used in frequentist statistics. 

D

 All estimates rounded to two significant figures. 

E

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-59

Table 7-27: Illustrative Strategy to Attain 0.070 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to 

Current Ozone Standard, 95% Confidence Intervals in Parentheses, Millions of 2006$) A,B

 

Morbidity Endpoint 

National Modeled Partial 

Attainment 

National Rolled Back Full 

Attainment 

$6.9 $15 Hospital Admissions (ages 0-1) ($3.4--10) ($7.8--23) 

$9.9 $34 Hospital Admissions (ages 65-99) (-$3.3--$24) ($0.59--$67) 

Emergency Department Visits, $0.20 $0.37 

Asthma-Related ($0.0--$0.56) ($0.0--$1.1) 

$27 $57 School Absences ($8.4--$48) ($19--$88) 

 $48 $98 Minor Restricted Activity Days ($18--$89) ($38--$180) 

Worker Productivity $6.8 $27 

 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and 

totals will not sum across columns. This table reflects full attainment in all locations of the U.S. except two areas 

of California. These two areas, which have high levels of ozone, are not planning to meet the current standard 

until after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 

B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-60

Table 7-28: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual 

Valuation of Reductions in the Incidence of PM Premature Mortality associated with PM 

co-benefit (Millions of 2006$)C

Mortality Endpoint 

National 2020 Benefits 

(3% discount rate) 

National 2020 Benefits (7% 

discount rate) 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyA$6,000 $5,400 

Harvard Six-City StudyB $13,000 $12,000 

Woodruff et al 1997 (infant mortality) $13 $11 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A $21,000 $19,000 

Expert B $16,000 $15,000 

Expert C $16,000 $15,000 

Expert D $11,000 $10,000 

Expert E $27,000 $24,000 

Expert F $15,000 $13,000 

Expert G $9,500 $8,600 

Expert H $12,000 $11,000 

Expert I $16,000 $14,000 

Expert J $13,000 $12,000 

Expert K $2,700 $2,500 

Expert L $12,000 $10,000 

 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs. 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for 

advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

C All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not include confidence intervals because they were derived through a scaling technique described above. This 

table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the 

table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-61

Table 7-298: Illustrative 0.070 ppm Full Attainment Scenario: Estimated Annual 

Valuation of Reductions in the Incidence of Morbidity Associated with PM Co-benefit 

(Millions of 2006$)A, B, C

Morbidity Endpoint National 2020 Benefits 

Chronic Bronchitis (age >25 and over) $310 

Nonfatal myocardial infarction (age >17) 

3% discount rate $160 

7% discount rate $160 

Hospital admissions--respiratory (all ages) $3.7 

Hospital admissions-- cardiovascular (age >17) $9.8 

Emergency room visits for asthma (age <19) $0.22 

Acute bronchitis (age 8-12) $0.85 

Lower respiratory symptoms (age 7-14) $ 0.22 

Upper respiratory symptoms (asthmatic children age 9-18) $0.25 

Asthma exacerbation (asthmatic children age 6-18) $0.54 

Work loss days (age 18-65) $8.9 

Minor restricted activity days (age 18-65) $12 

A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not include confidence intervals because they were derived through a scaling technique described above. 

B Morbidity Impact Functions Derived from Epidemiology Literature 

C

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-62

Table 7-30: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure 

(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals 

in Parentheses, Millions of 2006$)B,C,D,E

Model or 

AssumptionA Reference National Full Attainment 

$550 NMMAPS Bell et al. 2004 ($81--$1,200) 

 $1,800 Bell et al. 2005 ($290--3,800) 

 $2,400 Ito et al. 2005 (420--$4,900) 

 $2,500 

Meta-Analysis 

Levy et al. 

2005 ($450--$4,900) 

Assumption that association is not 

causal 0

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text 

in section 6.3.2.1). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval 

around the mean estimates of the annual number of lives saved are based on an assumption of a normal 

distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence 

interval used in frequentist statistics. 

D

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical. 

E

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-63

Table 7-31: Illustrative Strategy to Attain 0.075 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to 

Current Ozone Standard, 95% Confidence Intervals in Parentheses, Millions of 2006$) A,B

 

Morbidity Endpoint National Full Attainment 

$4.8 Hospital Admissions (ages 0-1) ($2.5--$7.1) 

$11 Hospital Admissions (ages 65-99) ($0.89--21) 

Emergency Department Visits, $0.10 

Asthma-Related ($0.00--$0.3) 

$18 School Absences ($6.1--$27) 

$29 Minor Restricted Activity Days ($12--$54) 

Worker Productivity $10 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical. 

B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-64

Table 7-32: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual 

Valuation of Reductions in the Incidence of PM Premature Mortality associated with PM 

co-benefit (Millions of 2006$)C

Mortality Endpoint 

National 2020 Benefits 

(3% discount rate) 

National 2020 Benefits (7% 

discount rate) 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyA$3,300 $3,000 

Harvard Six-City StudyB $7,400 $6,600 

Woodruff et al 1997 (infant mortality) $8 $6 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A $13,000 $12,000 

Expert B $9,900 $8,900 

Expert C $9,800 $8,900 

Expert D $6,900 $6,200 

Expert E $16,000 $15,000 

Expert F $9,000 $8,100 

Expert G $5,800 $5,200 

Expert H $7,300 $6,600 

Expert I $9,700 $8,800 

Expert J $7,900 $7,100 

Expert K $1,600 $1,500 

Expert L $6,900 $6,200 

 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs. 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for 

advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

C All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical and 

totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA. Estimates do not include 

confidence intervals because they were derived through a scaling technique described above. This table reflects 

full attainment in all locations of the U.S. except two areas of California. These two areas, which have high levels 

of ozone, are not planning to meet the current standard until after 2020. The estimates in the table do not reflect 

benefits for the San Joaquin and South Coast Air Basins. 
6-65

Table 7-33: Illustrative 0.075 ppm Full Attainment Scenario: Estimated Annual 

Valuation of Reductions in the Incidence of Morbidity Associated with PM Co-benefit 

(Millions of 2006$)A, B, C

Morbidity Endpoint National 2020 Benefits 

Chronic Bronchitis (age >25 and over) $180 

Nonfatal myocardial infarction (age >17) 

3% discount rate $97 

7% discount rate $94 

Hospital admissions--respiratory (all ages) $2.3 

Hospital admissions-- cardiovascular (age >17) $5.9 

Emergency room visits for asthma (age <19) $0.13 

Acute bronchitis (age 8-12) $0.51 

Lower respiratory symptoms (age 7-14) $0.13 

Upper respiratory symptoms (asthmatic children age 9-18) $0.15 

Asthma exacerbation (asthmatic children age 6-18) $0.33 

Work loss days (age 18-65) $5.3 

Minor restricted activity days (age 18-65) $7.2 

A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. 

Estimates do not include confidence intervals because they were derived through a scaling technique described 

above.

B Morbidity Impact Functions Derived from Epidemiology Literature 

C

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates 

in the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-66

Table 7-34: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Premature Mortality Associated with Ozone Exposure 

(Incremental to Current Ozone Standard, Arithmetic Mean, 95% Confidence Intervals 

in Parentheses, Millions of 2006$)B,C,D,E

Model or 

AssumptionA Reference National Full Attainment 

$190 NMMAPS Bell et al. 2004 ($28--$420) 

 $620 Bell et al. 2005 ($100--1,300) 

 $830 Ito et al. 2005 (140--$1,700) 

 $860 

Meta-Analysis 

Levy et al. 

2005 ($160--$1,700) 

Assumption that association is not 

causal 0

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text 

in section 6.3.2.1). 

B

 With the exception of the assumption of no causal relationship, the arithmetic mean and 95% credible interval 

around the mean estimates of the annual number of lives saved are based on an assumption of a normal 

distribution. 

C

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence 

interval used in frequentist statistics. 

D

 All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical. 

E

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-67

Table 7-35: Illustrative Strategy to Attain 0.079 ppm: Estimated Annual Valuation of 

Reductions in the Incidence of Morbidity Associated with Ozone Exposure (Incremental to 

Current Ozone Standard, 95% Confidence Intervals in Parentheses, Millions of 2006$) A,B

 

Morbidity Endpoint National Full Attainment 

$4.4 Hospital Admissions (ages 0-1) ($0.60--$7.9) 

$1.9 Hospital Admissions (ages 65-99) ($0.98--2.7) 

Emergency Department Visits, $0.03 

Asthma-Related ($0.00--$0.09) 

$6.4 School Absences ($2.2--$9.5) 

$11 Minor Restricted Activity Days ($4.2--$19) 

Worker Productivity $4.7 

A All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical. 

B This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-68

Table 7-36: Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual 

Valuation of Reductions in the Incidence of PM Premature Mortality associated with PM 

co-benefit (Millions of 2006$)C

Mortality Endpoint 

National 2020 Benefits 

(3% discount rate) 

National 2020 Benefits (7% 

discount rate) 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyA$1,800 $1,600 

Harvard Six-City StudyB $4,100 $3,700 

Woodruff et al 1997 (infant mortality) $5.0 $4.0 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A $8,400 $7,600 

Expert B $6,400 $5,700 

Expert C $6,400 $5,700 

Expert D $4,400 $4,000 

Expert E $11,000 $9,500 

Expert F $5,800 $5,200 

Expert G $3,700 $3,400 

Expert H $4,700 $4,300 

Expert I $6,300 $5,700 

Expert J $5,100 $4,600 

Expert K $1,000 $910 

Expert L $4,400 $3,900 

 

A

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs. 

B

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for 

advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

C All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical. All 

estimates incremental to 2006 PM NAAQS RIA. Estimates do not include confidence intervals because they were 

derived through a scaling technique described above. This table reflects full attainment in all locations of the U.S. 

except two areas of California. These two areas, which have high levels of ozone, are not planning to meet the 

current standard until after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South 

Coast Air Basins. 
6-69

Table 7-37: Illustrative 0.079 ppm Full Attainment Scenario: Estimated Annual 

Valuation of Reductions in the Incidence of Morbidity Associated with PM Co-benefit 

(Millions of 2006$)A, B, C

Morbidity Endpoint National 2020 Benefits 

Chronic Bronchitis (age >25 and over) $120 

Nonfatal myocardial infarction (age >17) 

3% discount rate $62 

7% discount rate $60 

Hospital admissions--respiratory (all ages) $1.4 

Hospital admissions-- cardiovascular (age >17) $3.8 

Emergency room visits for asthma (age <19) $0.086 

Acute bronchitis (age 8-12) $0.33 

Lower respiratory symptoms (age 7-14) $0.083 

Upper respiratory symptoms (asthmatic children age 9-18) $0.10 

Asthma exacerbation (asthmatic children age 6-18) $0.21 

Work loss days (age 18-65) $3.4 

Minor restricted activity days (age 18-65) $4.6 

A All estimates rounded to two significant figures. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not include confidence intervals because they were derived through a scaling technique described above. 

B Morbidity Impact Functions Derived from Epidemiology Literature 

C

 This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-70

Table 7-38: Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality 

(Millions of 2006$) for the 0.065 ppm Full Attainment 

 

Ozone Mortality and Morbidity Benefits of Attaining 0.065 ppm Total 

NMMAPS Bell (2004) $3,900 

Bell (2005) $12,000 

Meta-Analysis Ito (2005) $16,000 

Levy (2005) $16,000 

No Causality $420 

 

PM2.5 Mortality and Morbidity Benefits of Attaining 0.065 ppm 

Total (3% 

Discount Rate) 

Total (7% 

Discount Rate) 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyC$11,000 $9,600 

Harvard Six-City StudyD$23,000 $20,000 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A $34,000 $31,000 

Expert B $26,000 $24,000 

Expert C $26,000 $23,000 

Expert D $18,000 $17,000 

Expert E $42,000 $38,000 

Expert F $24,000 $21,000 

Expert G $15,000 $14,000 

Expert H $19,000 $18,000 

Expert I $25,000 $23,000 

Expert J $21,000 $19,000 

Expert K $5,100 $4,700 

Expert L $19,000 $17,000 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text 

in section 6.3.2.1). 

B

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence 

interval used in frequentist statistics. Credible intervals for ozone estimates and confidence intervals for PM2.5

estimates not provided because the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. 

C

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs. 

D

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for 

advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

E

 All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates 

discounted at 3% and 7%. This table reflects full attainment in all locations of the U.S. except two areas of 

California. These two areas, which have high levels of ozone, are not planning to meet the current standard until 

after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-71

Table 7-39: Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality 

(Millions of 2006$) for the 0.070 ppm Full Attainment 

 

Ozone Mortality and Morbidity Benefits of Attaining 0.070 ppm Total 

NMMAPS Bell (2004) $2,200 

Bell (2005) $6,500 

Meta-Analysis Ito (2005) $8,800 

Levy (2005) $9,000 

No Causality $230 

 

PM2.5 Mortality and Morbidity Benefits of Attaining 0.070 ppm 

Total (3% 

Discount Rate) 

Total (7% 

Discount Rate) 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyC $6,500 $5,900 

Harvard Six-City StudyD $14,000 $13,000 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A $22,000 $20,000 

Expert B $17,000 $15,000 

Expert C $17,000 $15,000 

Expert D $12,000 $11,000 

Expert E $27,000 $24,000 

Expert F $15,000 $14,000 

Expert G $10,000 $9,100 

Expert H $13,000 $11,000 

Expert I $17,000 $15,000 

Expert J $13,000 $12,000 

Expert K $3,200 $3,000 

Expert L $12,000 $11,000 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text 

in section 6.3.2.1). 

B

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence 

interval used in frequentist statistics. Credible intervals for ozone estimates and confidence intervals for PM2.5

estimates not provided because the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. 

C

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs. 

D

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for 

advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

E

 All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates 

discounted at 3% and 7%. This table reflects full attainment in all locations of the U.S. except two areas of 

California. These two areas, which have high levels of ozone, are not planning to meet the current standard until 

after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-72

Table 7-40: Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality 

(Millions of 2006$) for the 0.075 ppm Full Attainment 

 

Ozone Mortality and Morbidity Benefits of Attaining 0.075 ppm Total 

NMMAPS Bell (2004) $620 

Bell (2005) $1,900 

Meta-Analysis Ito (2005) $2,500 

Levy (2005) $2,600 

No Causality $73 

 

PM2.5 Mortality and Morbidity Benefits of Attaining 0.075 ppm 

Total (3% 

Discount Rate) 

Total (7% 

Discount Rate) 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyC $3,600 $3,300 

Harvard Six-City StudyD $7,700 $7,000 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A $13,000 $12,000 

Expert B $10,000 $9,200 

Expert C $10,000 $9,200 

Expert D $7,200 $6,500 

Expert E $16,000 $15,000 

Expert F $9,300 $8,400 

Expert G $6,100 $5,500 

Expert H $7,600 $6,900 

Expert I $10,000 $9,100 

Expert J $8,200 $7,400 

Expert K $1,900 $1,800 

Expert L $7,200 $6,500 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text 

in section 6.3.2.1). 

B

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence 

interval used in frequentist statistics. Credible intervals for ozone estimates and confidence intervals for PM2.5

estimates not provided because the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. 

C

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs. 

D

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for 

advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

E

 All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates 

discounted at 3% and 7%. This table reflects full attainment in all locations of the U.S. except two areas of 

California. These two areas, which have high levels of ozone, are not planning to meet the current standard until 

after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-73

Table 7-41: Estimate of Annual Ozone and PM2.5 Combined Morbidity and Mortality 

(Millions of 2006$) for the 0.079 ppm Full Attainment 

 

Ozone Mortality and Morbidity Benefits of Attaining 0.079 ppm Total 

NMMAPS Bell (2004) $220 

Bell (2005) $640 

Meta-Analysis Ito (2005) $860 

Levy (2005) $890 

No Causality $28 

 

PM2.5 Mortality and Morbidity Benefits of Attaining 0.079 ppm 

Total (3% 

Discount Rate) 

Total (7% 

Discount Rate) 

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyC$2,000 $1,800 

Harvard Six-City StudyD$4,300 $3,900 

Mortality Impact Functions Derived from Expert Elicitation 

Expert A $8,600 $7,800 

Expert B $6,600 $5,900 

Expert C $6,600 $5,900 

Expert D $4,600 $4,200 

Expert E $11,000 $9,700 

Expert F $6,000 $5,400 

Expert G $3,900 $3,600 

Expert H $4,900 $4,500 

Expert I $6,500 $5,900 

Expert J $5,300 $4,800 

Expert K $1,200 $1,100 

Expert L $4,600 $4,100 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text 

in section 6.3.2.1). 

B

 A credible interval is a posterior probability interval used in Bayesian statistics, which is similar to a confidence 

interval used in frequentist statistics. Credible intervals for ozone estimates and confidence intervals for PM2.5

estimates not provided because the valuation estimates were derived through a scaling technique (see above) that 

precluded us from generating such estimates. 

C

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs. 

D

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for 

advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

E

 All estimates incremental to 2006 PM NAAQS RIA. Estimates derived using benefit per ton estimates 

discounted at 3% and 7%. This table reflects full attainment in all locations of the U.S. except two areas of 

California. These two areas, which have high levels of ozone, are not planning to meet the current standard until 

after 2020. The estimates in the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-74

Table 7-42: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

3% Discount Rate) for the 0.065 ppm Alternative Standard 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $14,000 $22,000 $26,000 $27,000 $11,000 

Harvard Six-City StudyC $26,000 $34,000 $38,000 $39,000 $23,000 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $34,000 $38,000 $46,000 $50,000 $50,000 

Expert B $26,000 $30,000 $38,000 $42,000 $42,000 

Expert C $26,000 $30,000 $38,000 $42,000 $42,000 

Expert D $19,000 $22,000 $30,000 $34,000 $35,000 

Expert E $42,000 $46,000 $54,000 $58,000 $58,000 

Expert F $24,000 $27,000 $35,000 $39,000 $40,000 

Expert G $16,000 $19,000 $27,000 $31,000 $32,000 

Expert H $20,000 $23,000 $31,000 $35,000 $36,000 

Expert I $26,000 $29,000 $37,000 $41,000 $42,000 

Expert J $21,000 $25,000 $33,000 $37,000 $37,000 

Expert K $5,500 $9,000 $17,000 $21,000 $21,000 

Expert L $19,000 $23,000 $30,000 $35,000 $35,000 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in 

section 6.3.2.1). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate in 

recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice 

on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to 

the fact that the valuation estimates were derived through a scaling technique (see above) that precluded us from 

generating such estimates. Estimates derived using a combination of modeling data and benefit per ton estimates. 

This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table 

do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-75

Table 7-43: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

7% Discount Rate) for the 0.065 ppm Alternative Standard 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $13,000 $21,000 $25,000 $26,000 $10,000 

Harvard Six-City StudyC $24,000 $32,000 $36,000 $37,000 $21,000 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $34,000 $42,000 $46,000 $47,000 $31,000 

Expert B $27,000 $35,000 $39,000 $40,000 $24,000 

Expert C $27,000 $35,000 $39,000 $40,000 $24,000 

Expert D $20,000 $28,000 $32,000 $33,000 $17,000 

Expert E $42,000 $50,000 $54,000 $54,000 $38,000 

Expert F $25,000 $33,000 $37,000 $38,000 $22,000 

Expert G $18,000 $26,000 $30,000 $30,000 $14,000 

Expert H $21,000 $29,000 $33,000 $34,000 $18,000 

Expert I $27,000 $35,000 $39,000 $39,000 $23,000 

Expert J $23,000 $31,000 $35,000 $35,000 $19,000 

Expert K $8,600 $16,000 $21,000 $21,000 $5,100 

Expert L $21,000 $29,000 $33,000 $33,000 $17,000 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in 

section 6.3.2.1). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate in 

recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice 

on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to 

the fact that the valuation estimates were derived through a scaling technique (see above) that precluded us from 

generating such estimates. Estimates derived using a combination of modeling data and benefit per ton estimates. 

This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table 

do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-76

Table 7-44: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

3% Discount Rate) for the 0.070 ppm Alternative Standard 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $8,700 $13,000 $15,000 $16,000 $6,700 

Harvard Six-City StudyC $16,000 $20,000 $23,000 $23,000 $14,000 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $24,000 $28,000 $31,000 $31,000 $22,000 

Expert B $19,000 $23,000 $26,000 $26,000 $17,000 

Expert C $19,000 $23,000 $25,000 $26,000 $17,000 

Expert D $14,000 $18,000 $21,000 $21,000 $12,000 

Expert E $29,000 $34,000 $36,000 $36,000 $27,000 

Expert F $17,000 $22,000 $24,000 $24,000 $15,000 

Expert G $12,000 $16,000 $19,000 $19,000 $10,000 

Expert H $15,000 $19,000 $21,000 $22,000 $13,000 

Expert I $19,000 $23,000 $25,000 $26,000 $17,000 

Expert J $16,000 $20,000 $22,000 $22,000 $14,000 

Expert K $5,400 $9,700 $12,000 $12,000 $3,500 

Expert L $14,000 $19,000 $21,000 $21,000 $12,000 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in 

section 6.3.2.1). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate in 

recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice 

on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to 

the fact that the valuation estimates were derived through a scaling technique (see above) that precluded us from 

generating such estimates. Estimates derived using a combination of modeling data and benefit per ton estimates. 

This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table 

do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-77

Table 7-45: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

7% Discount Rate) for the 0.070 ppm Alternative Standard 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $8,100 $12,000 $15,000 $15,000 $6,100 

Harvard Six-City StudyC $15,000 $19,000 $21,000 $22,000 $13,000 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $22,000 $26,000 $29,000 $29,000 $20,000 

Expert B $17,000 $22,000 $24,000 $24,000 $15,000 

Expert C $17,000 $22,000 $24,000 $24,000 $15,000 

Expert D $13,000 $17,000 $19,000 $20,000 $11,000 

Expert E $27,000 $31,000 $33,000 $33,000 $25,000 

Expert F $16,000 $20,000 $23,000 $23,000 $14,000 

Expert G $11,000 $16,000 $18,000 $18,000 $9,300 

Expert H $14,000 $18,000 $20,000 $20,000 $12,000 

Expert I $17,000 $21,000 $24,000 $24,000 $15,000 

Expert J $14,000 $19,000 $21,000 $21,000 $12,000 

Expert K $5,100 $9,500 $12,000 $12,000 $3,200 

Expert L $13,000 $17,000 $20,000 $20,000 $11,000 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in 

section 6.3.2.1). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate in 

recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice 

on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to 

the fact that the valuation estimates were derived through a scaling technique (see above) that precluded us from 

generating such estimates. Estimates derived using a combination of modeling data and benefit per ton estimates. 

This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table 

do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-78

Table 7-46: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

3% Discount Rate) for the 0.075 ppm Alternative Standard 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $4,200 $5,500 $6,100 $6,200 $3,700 

Harvard Six-City StudyC $8,300 $9,500 $10,000 $10,000 $7,800 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $14,000 $15,000 $16,000 $16,000 $13,000 

Expert B $11,000 $12,000 $13,000 $13,000 $10,000 

Expert C $11,000 $12,000 $13,000 $13,000 $10,000 

Expert D $7,800 $9,000 $9,700 $9,800 $7,300 

Expert E $17,000 $18,000 $19,000 $19,000 $17,000 

Expert F $9,900 $11,000 $12,000 $12,000 $9,300 

Expert G $6,700 $7,900 $8,600 $8,700 $6,100 

Expert H $8,300 $9,500 $10,000 $10,000 $7,700 

Expert I $11,000 $12,000 $13,000 $13,000 $10,000 

Expert J $8,800 $10,000 $11,000 $11,000 $8,300 

Expert K $2,600 $3,800 $4,400 $4,500 $2,000 

Expert L $7,800 $9,000 $9,700 $9,800 $7,300 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in 

section 6.3.2.1). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate in 

recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice 

on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to 

the fact that the valuation estimates were derived through a scaling technique (see above) that precluded us from 

generating such estimates. Estimates derived using a combination of modeling data and benefit per ton estimates. 

This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table 

do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-79

Table 7-47: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

7% Discount Rate) for the 0.075 ppm Alternative Standard 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $3,900 $5,100 $5,800 $5,900 $3,400 

Harvard Six-City StudyC $7,600 $8,800 $9,500 $9,500 $7,000 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $13,000 $14,000 $15,000 $15,000 $12,000 

Expert B $9,800 $11,000 $12,000 $12,000 $9,300 

Expert C $9,800 $11,000 $12,000 $12,000 $9,200 

Expert D $7,100 $8,400 $9,000 $9,100 $6,600 

Expert E $16,000 $17,000 $17,000 $17,000 $15,000 

Expert F $9,000 $10,000 $11,000 $11,000 $8,500 

Expert G $6,100 $7,400 $8,000 $8,100 $5,600 

Expert H $7,500 $8,800 $9,400 $9,500 $7,000 

Expert I $9,700 $11,000 $12,000 $12,000 $9,100 

Expert J $8,000 $9,300 $9,900 $10,000 $7,500 

Expert K $2,400 $3,600 $4,300 $4,300 $1,800 

Expert L $7,100 $8,400 $9,000 $9,100 $6,600 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in 

section 6.3.2.1). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate in 

recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice 

on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to 

the fact that the valuation estimates were derived through a scaling technique (see above) that precluded us from 

generating such estimates. Estimates derived using a combination of modeling data and benefit per ton estimates. 

This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table 

do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-80

Table 7-48: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

3% Discount Rate) for the 0.079 ppm Alternative Standard 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $2,200 $2,700 $2,900 $2,900 $2,100 

Harvard Six-City StudyC $4,500 $4,900 $5,200 $5,200 $4,300 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $8,900 $9,300 $9,500 $9,500 $8,700 

Expert B $6,800 $7,200 $7,400 $7,400 $6,600 

Expert C $6,800 $7,200 $7,400 $7,500 $6,600 

Expert D $4,900 $5,300 $5,500 $5,500 $4,700 

Expert E $11,000 $11,000 $12,000 $12,000 $11,000 

Expert F $6,200 $6,700 $6,900 $6,900 $6,000 

Expert G $4,100 $4,600 $4,800 $4,800 $4,000 

Expert H $5,200 $5,600 $5,800 $5,800 $5,000 

Expert I $6,700 $7,200 $7,400 $7,400 $6,500 

Expert J $5,500 $5,900 $6,200 $6,200 $5,300 

Expert K $1,400 $1,900 $2,100 $2,100 $1,200 

Expert L $4,800 $5,200 $5,400 $5,400 $4,600 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in 

section 6.3.2.1). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate in 

recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice 

on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to 

the fact that the valuation estimates were derived through a scaling technique (see above) that precluded us from 

generating such estimates. Estimates derived using a combination of modeling data and benefit per ton estimates. 

This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, which 

have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in the table 

do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-81

Table 7-49: Combined Estimate of Annual Ozone and PM2.5 Benefits (Millions of $2006, 

7% Discount Rate) for the 0.079 ppm Alternative Standard 

Alternative Standard and Model or AssumptionA

Bell (2004) Bell (2005) Ito (2005) Levy (2005) No Causality

Mortality Impact Functions Derived from Epidemiology Literature 

ACS StudyB $2,100 $2,500 $2,700 $2,700 $1,900 

Harvard Six-City StudyC $4,100 $4,500 $4,800 $4,800 $3,900 

Mortality Impact Functions Derived from Expert Elicitation

Expert A $8,000 $8,400 $8,700 $8,700 $7,800 

Expert B $6,100 $6,600 $6,800 $6,800 $5,900 

Expert C $6,200 $6,600 $6,800 $6,800 $6,000 

Expert D $4,400 $4,800 $5,100 $5,100 $4,200 

Expert E $9,900 $10,000 $11,000 $11,000 $9,700 

Expert F $5,600 $6,100 $6,300 $6,300 $5,500 

Expert G $3,800 $4,200 $4,400 $4,400 $3,600 

Expert H $4,700 $5,100 $5,300 $5,400 $4,500 

Expert I $6,100 $6,500 $6,700 $6,800 $5,900 

Expert J $5,000 $5,400 $5,700 $5,700 $4,800 

Expert K $1,300 $1,800 $2,000 $2,000 $1,100 

Expert L $4,300 $4,800 $5,000 $5,000 $4,100 

 

A

 Does not represent equal weighting among models or between assumption of causality vs. no causality (see text in 

section 6.3.2.1). 

B

 The estimate is based on the concentration-response (C-R) function developed from the study of the American 

Cancer Society cohort reported in Pope et al (2002), which has previously been reported as the primary estimate 

in recent RIAs 

C

 Based on Laden et al (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB for advice 

on the appropriate method for incorporating what has previously been a sensitivity estimate. 

D

 All estimates incremental to 2006 PM NAAQS RIA. Confidence intervals for PM2.5 estimates not provided due to 

the fact that the valuation estimates were derived through a scaling technique (see above) that precluded us from 

generating such estimates. Estimates derived using a combination of modeling data and benefit per ton estimates. 

This table reflects full attainment in all locations of the U.S. except two areas of California. These two areas, 

which have high levels of ozone, are not planning to meet the current standard until after 2020. The estimates in 

the table do not reflect benefits for the San Joaquin and South Coast Air Basins. 
6-82

Figure 6.3: Ozone and PM2.5 Benefits by Standard Alternative (3% and 7% Discount 

Rates)

Figure 7.3 graphically shows the breakdown between ozone and PM morbidity and mortality 

monetized benefits for one example combination with PM benefits discounted at 3% and 7%, 

respectively. This example combination of Bell 2004 and Pope have been used in previous 

RIAs and Risk Assessments. 
6-83

Figure 6.4: Example Combined Ozone and PM2.5 Monetized Benefits Estimates by 

Standard Alternative (3% and 7% Discount Rates)* 

Figure 7.4 graphically shows four combinations of ozone and PM benefits estimates. These 

intermediate combinations represent reference points: 

x Bell 2004 is the epidemiological study that underlies the ozone NAAQS risk assessment and Pope 

is the PM mortality function that was in several EPA RIAs, and 

x Bell 2005 is one of three ozone meta-analyses and Laden is a more recent PM epidemiological 

study that was used as an alternative in the PM NAAQS RIA 
6-84

Figure 6.5 and Figure 6.6 show the complete range of combinations of ozone and PM mortality 

functions at 3 and 7 percent, respectively. These graphs display all possible combinations of 

benefits, utilizing the five different ozone functions and the fourteen different PM functions, for 

each standard alternative. Each of the 70 bars represents an independent and equally probably 

point estimate of benefits under a certain combination of ozone and PM functions. Thus it is not 

possible to infer the likelihood of any single benefit estimate. 

Figure 6.5:*

079

075

070 

0 065

10000

20000

30000

40000

50000

60000

Millions of 2006$

Ozone and PM Total Benefits including all combinations of mortality 

estimates 

(3% Discount Rate)

No Causality & Expert K

Bell 2004 & Pope

Bell 2005 & Laden

Levy & Expert E

* These figures reflect full attainment in all locations of the U.S. except two areas of California. 

These two areas, which have high levels of ozone, are not planning to meet the current 

standard until after 2020. The estimates in the table do not reflect benefits for the San Joaquin 

and South Coast Air Basins. No causality, Bell, and Levy represent ozone estimates. Expert 

K, Pope, Laden, and Expert E represent PM estimates. 
6-85

Figure 6.6*:

079

075 

070 

065

0

10000

20000

30000

40000

50000

60000

Millions of 2006$

Ozone and PM Total Benefits including all combinations of mortality 

estimates

 (7% Discount Rate)

No Causality & Expert K

Bell 2004 & Pope

Bell 2005 & Laden

Levy & Expert E

* These figures reflect full attainment in all locations of the U.S. except two areas of California. 

These two areas, which have high levels of ozone, are not planning to meet the current 

standard until after 2020. The estimates in the table do not reflect benefits for the San Joaquin 

and South Coast Air Basins. No causality, Bell, and Levy represent ozone estimates. Expert K, 

Pope, Laden, and Expert E represent PM estimates. 
6-86

6.5.4 Estimates of Visibility Benefits 

Table 7-50 below summarizes the regional distribution of visibility benefits in Class I areas in 

2020. Note that these estimates represent the monetized visibility benefits associated with the 

modeled ozone emission control strategy, and do not reflect the visibility benefits of fully 

attaining the 0.075 ppm selected alternative. For this reason, they are not added to the human 

health-based benefits estimates. The methodology we followed to generate these estimates may 

be found in the PM2.5 RIA (EPA, 2006) 

Table 7-50: Monetary Benefits Associated with Visibility Improvements from the 

0.070 Simulated Ozone Attainment Strategy in Selected Federal Class I Areas in 

2020 (in millions of 2006$)A

 

California Southwest Southeast Total 

$5 $95 $56 $160 

 

A

 All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded 

estimates. As such, totals will not sum across columns. 

6.5.5 Discussion of Results and Uncertainties 

The results of this analysis suggest there will be significant additional health and welfare benefits 

arising from reducing emissions from a variety of sources in and around projected nonattaining 

counties in 2020. While 2020 is the expected date that states would need to demonstrate 

attainment with the revised standard, it is expected that benefits (and costs) will begin occurring 

much earlier, as states begin implementing control measures to show reasonable progress 

towards attainment. Using the full range of benefits (including the results of the expert 

elicitation), we estimate that total ozone benefits and PM2.5 co-benefits would be between $2.0 

and $19 billion annually for the 0.075 ppm selected alternative when the emissions reductions 

from implementing the new standard are fully realized. The magnitude of these estimated 

benefits provide additional evidence of the important role that implementation of the standards 

plays in reducing the health risks associated with exceeding the standard. 

There are several important factors to consider when evaluating the relative benefits of the 

attainment strategies for each of the alternative ozone standards: 

1. California (outside of San Joaquin Valley and South Coast) accounts for a substantial 

share of the total benefits for each of the evaluated standards. Benefits are most 

uncertain for California due to the unique challenge of modeling attainment with the 

standards in this state. These challenges include high levels of ozone, difficulties in 

modeling the impacts of emissions controls on air quality, and the very large proportion 

of California benefits that were derived through extrapolation. On the one hand, these 

California benefits are likely to understate the actual benefits of attainment strategies, 

because we applied an estimation approach that reduced concentrations only at the 

specific violating monitors and not surrounding monitors that did not violate the 
6-87

standards. The magnitude of this underestimate is unknown. On the other hand, it is 

possible that new technologies might not meet the specifications, development timelines, 

or cost estimates provided in this analysis, thereby increasing the uncertainty in when and 

if such benefits would be truly achieved. 

2. The extrapolation and interpolation techniques used to estimate the full attainment 

benefits of the selected and three alternate standards contributed some uncertainty to the 

analysis. The great majority of benefits estimated for the 0.065 ppm standard alternative 

were derived through extrapolation. As noted previously in this chapter, these benefits are 

likely to be more uncertain than the modeled benefits. The 0.075 ppm and 0.079 ppm 

benefits were derived by interpolating the full attainment benefits of the 0.070 ppm 

alternative (a process which is described in Appendix 6a). This approach may under- or 

over-estimate benefits if the actual geographic distribution of air quality changes is 

different than that assumed in the interpolation. 

3. There are a variety of uncertainties associated with the health impact functions used in 

this modeling effort. These include: within study variability, which is the precision with 

which a given study estimates the relationship between air quality changes and health 

effects; across study variation, which refers to the fact that different published studies of 

the same pollutant/health effect relationship typically do not report identical findings and 

in some instances the differences are substantial; the application of C-R functions 

nationwide, which does not account for any relationship between region and health effect, 

to the extent that such a relationship exists; extrapolation of impact functions across 

population, in which we assumed that certain health impact functions applied to age 

ranges broader than that considered in the original epidemiological study; and, finally, 

there are various uncertainties in the C-R function, including causality, the correlation 

among multiple pollutants, the shape of the C-R function and the relative toxicity of PM 

component species, and the lag between exposure and the onset of the health effect. 

4. There are a variety of uncertainties associated with the economic valuation of the health 

endpoints estimated in this analysis. Uncertainties specific to the valuation of premature 

mortality include across study variation; the assumption that WTP for mortality risk 

reduction is linear; assuming that voluntary and involuntary mortality risk will be valued 

equally; assuming that premature mortality from air pollution risk, which tend to involve 

longer periods of time, will be valued the same as short catastrophic events; the 

possibility for self-selection in avoiding risk, which may bias WTP estimates upward. 

5. This analysis includes estimates of PM2.5 co-benefits that were derived through benefit 

per-ton estimates. These benefit per-ton estimates represent regional averages. As such, 

they do not reflect any local variability in the incremental PM2.5 benefits per ton of NOx 

abated. As discussed in the PM2.5 NAAQS RIA (Table 5.5), there are a variety of 

uncertainties associated with these PM benefits. 

6. PM2.5 co-benefits represent a substantial proportion of total benefits. For the 0.075 ppm 

selected standard, we estimate co-benefits from PM to be between 42% and 99% of total 

benefits, depending on the PM2.5 and ozone mortality functions used. When calculating 

PM2.5 co-benefits we assume that states will pursue an ozone strategy that reduces NOx 
6-88

emissions. As such, these estimates are strongly influenced by the assumption that all PM 

components are equally toxic. We also acknowledge that when implementing any new 

standard, states may elect to pursue a different ozone strategy, which would in turn affect 

the level of PM2.5 co-benefits.

7. Projecting key variables introduces uncertainty. Inherent in any analysis of future 

regulatory programs are uncertainties in projecting atmospheric conditions and sourcelevel emissions, as well as population, health baselines, incomes, technology, and other 

factors. In addition, data limitations prevent an overall quantitative estimate of the 

uncertainty associated with estimates of total economic benefits. If one is mindful of 

these limitations, the magnitude of the benefits estimates presented here can be useful 

information in expanding the understanding of the public health impacts of reducing 

ozone precursor emissions. 

8. This analysis omits certain unquantified effects due to lack of data, time and resources.

These unquantified endpoints include the direct effects of ozone on vegetation, the 

deposition of nitrogen to estuarine and coastal waters and agricultural and forested land, 

and the changes in the level of exposure to ultraviolet radiation from ground level ozone. 

EPA will continue to evaluate new methods and models and select those most appropriate 

for estimating the health benefits of reductions in air pollution. It is important to continue 

improving benefits transfer methods in terms of transferring economic values and 

transferring estimated impact functions. The development of both better models of 

current health outcomes and new models for additional health effects such as asthma, 

high blood pressure, and adverse birth outcomes (such as low birth weight) will be 

essential to future improvements in the accuracy and reliability of benefits analyses (Guo 

et al., 1999; Ibald-Mulli et al., 2001). Enhanced collaboration between air quality 

modelers, epidemiologists, toxicologists, and economists should result in a more tightly 

integrated analytical framework for measuring health benefits of air pollution policies. 

Readers interested in a more extensive discussion of the sources of uncertainty in human 

health benefits analyses should consult the PM NAAQS RIA. 

6.5.6 Summary of Total Benefits 

Table 6.51 presents the total number of estimated ozone and PM2.5-related premature mortalities 

and morbidities avoided nationwide in 2020. Ranges within the mortality section reflect 

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

derived. The lower end of the range reflects the Expert K derived mortality functions, and the 

upper end of the range reflects the Expert E derived mortality functions. Figure 6.7 graphically 

presents the total number of estimated ozone and PM2.5-related premature mortalities avoided in 

2020 by standard. Tables 6.52 through 6.56 show the overall ozone, PM, and combined results 

with regional breakdowns. 
6-89

Table 6.51: Summary of Total Number of Annual Ozone and PM2.5 -Related Premature Mortalities and Premature Morbidity Avoided in 

2020A

Combined Estimate of Mortality

 Combined Range of Ozone Benefits and PM2.5 Co-benefits by Standard AlternativeD

Model or Assumption 0.079 ppm 0.075 ppm 0.070 ppm 0.65 ppm 

NMAPS Bell (2004) 140 to 1,300 260 to 2,000 560 to 3,500 940 to 5,500 

Bell (2005) 200 to 1,300 420 to 2,200 1,100 to 4,100 2,000 to 6,500 

Meta-analysis Ito 230 to 1,400 500 to 2,300 1,400 to 4,300 2,500 to 7,000 

Levy 230 to 1,400 510 to 2,300 1,400 to 4,400 2,500 to 7,100 

No Causality 120 to 1,200 190 to 2,000 310 to 3,200 490 to 5,000 

Combined Estimate of Morbidity Combined Ozone Benefits and PM2.5 Co-benefits by Standard Alternative 

Acute Myocardial InfarctionB 570 890 1,500 2,300 

Upper Respiratory SymptomsB 3,100 4,900 8,100 13,000 Lower Respiratory SymptomsB 4,200 6,700 11,000 17,000 Chronic BronchitisB 240 380 630 970 Acute BronchitisB 640 1,000 1,700 2,600 Asthma ExacerbationB 3,900 6,100 10,000 16,000 Work Loss DaysB 28,000 43,000 72,000 110,000 School Loss DaysC 72,000 200,000 640,000 1,100,000 Hospital and ER Visits 890 1,900 5,100 9,400 Minor Restricted Activity Days 340,000 750,000 2,100,000 3,500,000 A Does not reflect estimates for the San Joaquin and South Coast Air Basins 

B PM-related benefits only C Ozone-related benefits only 

D Includes ozone benefits, and PM2.5 co-benefits. Range was developed by adding the estimate from the ozone premature mortality function to both the 

lower and upper ends of the range of the PM2.5 premature mortality functions characterized in the expert elicitation. 
6-90

Figure 6.7: Total Annual Ozone and PM2.5-Related Premature Mortalities Avoided in 2020 by Standard Alternative 
6-91

Table 6.52: Regional Breakdown of Annual Ozone Benefit Results by Health Endpoint in 2020 (thousands of 2006$)* 

Endpoint Group Author Year 079 Valuation 079 Incidence 075 Valuation 075 Incidence 070 Valuation 070 Incidence 065 Valuation 065 Incidence

Hospital Admissions, Respiratory (0-1) 470 $ 47 2,200 $ 220 8,800 $ 880 15,000 $ 1,500

Hospital Admissions, Respiratory (65+) 1,300 $ 54 5,200 $ 220 20,000 $ 870 50,000 $ 2,100

Emergency Room Visits, Respiratory 13 $ 35 68 $ 190 290 $ 770 530 $ 1,400

School Loss Days 1,700 $ 19,000 8,500 $ 95,000 35,000 $ 390,000 61,000 $ 690,000

Worker Productivity 430 $ 370,000 2,100 $ 1,800,000 8,700 $ 7,500,000 16,000 $ 14,000,000

Acute Respiratory Symptoms 2,800 $ 47,000 15,000 $ 250,000 61,000 $ 1,000,000 110,000 $ 1,800,000

Mortality Bell et al. 2004 50,000 $ 7 290,000 $ 38 1,300,000 $ 170 2,400,000 $ 300

Mortality Bell et al. 2005 160,000 $ 21 940,000 $ 120 4,100,000 $ 530 7,600,000 $ 980

Mortality Ito et al. 220,000 $ 29 1,300,000 $ 170 5,600,000 $ 730 10,000,000 $ 1,300

Mortality Levy et al. 230,000 $ 30 1,300,000 $ 170 5,800,000 $ 750 11,000,000 $ 1,400

Hospital Admissions, Respiratory (0-1) 10 $ 1.0 18 $ 1.8 820 $ 83 2,000 $ 200

Emergency Room Visits, Respiratory 0.14 $ 0.39 0.27 $ 0.74 9.4 $ 26 27 $ 74

School Loss Days 33 $ 370 60 $ 670 2,600 $ 29,000 6,500 $ 72,000

Worker Productivity 6.3 $ 5,500 11 $ 9,900 360 $ 310,000 1,900 $ 1,600,000

Acute Respiratory Symptoms 61 $ 1,000 110 $ 1,800 4,500 $ 76,000 11,000 $ 180,000

Hospital Admissions, Respiratory (65+) 30 $ 1.3 58 $ 2.5 (39) $ (1.6) 3,200 $ 140

Mortality Bell et al. 2004 1,500 $ 0.20 2,700 $ 0.35 69,000 $ 9.0 200,000 $ 26

Mortality Bell et al. 2005 5,100 $ 0.65 8,900 $ 1.2 230,000 $ 30 670,000 $ 87

Mortality Ito et al. 6,800 $ 0.88 12,000 $ 1.6 310,000 $ 40 900,000 $ 120

Mortality Levy et al. 7,100 $ 0.92 13,000 $ 1.6 320,000 $ 42 950,000 $ 120

Hospital Admissions, Respiratory (0-1) 1,400 $ 140 2,600 $ 260 5,800 $ 580 9,100 $ 910

Emergency Room Visits, Respiratory 19 $ 51 36 $ 97 79 $ 220 130 $ 340

School Loss Days 4,700 $ 53,000 9,000 $ 100,000 20,000 $ 220,000 31,000 $ 350,000

Worker Productivity 4,300 $ 3,800,000 8,000 $ 7,100,000 18,000 $ 16,000,000 31,000 $ 26,000,000

Acute Respiratory Symptoms 7,800 $ 130,000 15,000 $ 250,000 33,000 $ 550,000 52,000 $ 880,000

Hospital Admissions, Respiratory (65+) 3,100 $ 130 5,800 $ 240 13,000 $ 560 22,000 $ 910

Mortality Bell et al. 2004 140,000 $ 18 260,000 $ 33 580,000 $ 75 940,000 $ 120

Mortality Bell et al. 2005 450,000 $ 58 840,000 $ 110 1,900,000 $ 250 3,100,000 $ 400

Mortality Ito et al. 610,000 $ 78 1,100,000 $ 150 2,600,000 $ 330 4,200,000 $ 540

Mortality Levy et al. 630,000 $ 81 1,200,000 $ 150 2,700,000 $ 340 4,300,000 $ 560

Hospital Admissions, Respiratory (0-1) 1,900 $ 190 4,800 $ 480 15,000 $ 1,500 26,000 $ 2,700

Hospital Admissions, Respiratory (65+) 4,400 $ 190 11,000 $ 470 34,000 $ 1,400 74,000 $ 3,200

Emergency Room Visits, Respiratory 32 $ 87 100 $ 280 370 $ 1,000 690 $ 1,900

School Loss Days 6,400 $ 72,000 18,000 $ 200,000 57,000 $ 640,000 99,000 $ 1,100,000

Worker Productivity 4,700 $ 4,200,000 10,000 $ 9,000,000 27,000 $ 23,000,000 49,000 $ 42,000,000

Acute Respiratory Symptoms 11,000 $ 180,000 29,000 $ 500,000 98,000 $ 1,700,000 170,000 $ 2,900,000

Mortality Bell et al. 2004 190,000 $ 24 550,000 $ 71 1,900,000 $ 250 3,500,000 $ 450

Mortality Bell et al. 2005 620,000 $ 80 1,800,000 $ 230 6,200,000 $ 810 11,000,000 $ 1,500

Mortality Ito et al. 830,000 $ 110 2,400,000 $ 310 8,500,000 $ 1,100 15,000,000 $ 2,000

Mortality Levy et al. 860,000 $ 110 2,500,000 $ 320 8,800,000 $ 1,100 16,000,000 $ 2,100

East Rest of West California National Total

* National Total does not reflect benefits for the South Coast and San Joaquin Air Basins. Confidence intervals not available for PM estimates. All estimates rounded to 

two significant figures. Valuation results for mortality and nonfatal myocardial infarctions are shown at a 3% discount rate. Does not include visibility benefits. 
6-92

Table 6.53: Regional Breakdown of Annual PM Benefit Results by Health Endpoint in 2020 

(thousands of 2006$) at 3%* 

Endpoint Group Author 079 Valuation 079 

Incidence 075 Valuation 075Incidence 070 Valuation 070Incidence 065 Valuation 065 Incidence

Chronic Bronchitis 31,000 $ 64 91,000 $ 190 190,000 $ 390 300,000 $ 620 

Emergency Room Visits, Respiratory 23 $ 62 66 $ 180 140 $ 380 220 $ 600 

Acute Respiratory Symptoms 1,200 $ 43,000 3,600 $ 130,000 7,500 $ 260,000 12,000 $ 420,000 

Upper+Lower Respiratory Symptoms 47 $ 1,900 140 $ 5,700 290 $ 12,000 460 $ 19,000 

Work Loss Days $ 7,300 910 2,600 $ 21,000 5,500 $ 45,000 8,800 $ 71,000 

Acute Bronchitis $ 170 87 250 $ 490 530 $ 1,000 840 $ 1,600 

Asthma Exacerbation 55 $ 1,000 160 $ 3,000 340 $ 6,300 530 $ 10,000 

Hospital Admissions 1,400 $ 54 4,000 $ 160 8,400 $ 330 13,000 $ 520 

Non-fatal myocardial infarction 16,000 $ 150 48,000 $ 440 100,000 $ 920 160,000 $ 1,500 

Infant Mortality Woodruff 1,300 $ 0.19 3,900 $ 0.55 8,100 $ 1.20 13,000 $ 1.80 

Mortality Pope 480,000 $ 66 1,600,000 $ 190 3,700,000 $ 400 6,200,000 $ 640 

Mortality Laden 1,100,000 $ 150 3,600,000 $ 430 8,300,000 $ 900 14,000,000 $ 1,400 

Mortality Expert E 2,800,000 $ 330 8,000,000 $ 960 16,000,000 $ 2,000 26,000,000 $ 3,200 

Mortality Expert K 270,000 $ 32 800,000 $ 93 1,700,000 $ 190 2,700,000 $ 310 

Chronic Bronchitis 740 $ 1.5 740 $ 1.5 10,000 $ 21 27,000 $ 55 

Emergency Room Visits, Respiratory 0.54 $ 1.5 0.54 $ 1.5 7.4 $ 20 20 $ 53 

Acute Respiratory Symptoms 29 $ 1,000 29 $ 1,000 400 $ 14,000 1,100 $ 37,000 

Upper+Lower Respiratory Symptoms 1.1 $ 46 1.1 $ 46 15 $ 630 40 $ 1,700 

Work Loss Days $ 170 21 21 $ 170 290 $ 2,400 780 $ 6,300 

Acute Bronchitis $ 4.0 2.0 2.0 $ 4.0 28 $ 55 74 $ 140 

Asthma Exacerbation 1.3 $ 24 1.3 $ 24 18 $ 330 47 $ 890 

Hospital Admissions 32 $ 1.3 32 $ 1.3 450 $ 17 1,200 $ 46 

Non-fatal myocardial infarction 390 $ 3.5 390 $ 3.5 5,300 $ 49 14,000 $ 130 

Infant Mortality Woodruff 31 $ 0.00 31 $ 0.00 430 $ 0.06 1,100 $ 0.16 

Mortality Pope 11,000 $ 1.5 13,000 $ 1.5 200,000 $ 21 550,000 $ 56 

Mortality Laden 26,000 $ 3.5 29,000 $ 3.5 440,000 $ 48 1,200,000 $ 130 

Mortality Expert E 66,000 $ 7.8 65,000 $ 7.8 880,000 $ 110 2,300,000 $ 280 

Mortality Expert K 6,300 $ 0.8 6,500 $ 0.8 90,000 $ 10 240,000 $ 27 

Chronic Bronchitis 86,000 $ 180 93,000 $ 190 110,000 $ 220 150,000 $ 300 

Emergency Room Visits, Respiratory 63 $ 170 68 $ 180 78 $ 210 110 $ 290 

Acute Respiratory Symptoms 3,400 $ 120,000 3,600 $ 130,000 4,200 $ 150,000 5,800 $ 200,000 

Upper+Lower Respiratory Symptoms 130 $ 5,300 140 $ 5,800 160 $ 6,700 220 $ 9,200 

Work Loss Days 2,500 $ 20,000 2,700 $ 22,000 3,100 $ 25,000 4,300 $ 35,000 

Acute Bronchitis $ 460 240 260 $ 500 300 $ 580 410 $ 800 

Asthma Exacerbation 150 $ 2,800 160 $ 3,100 190 $ 3,500 260 $ 4,900 

Hospital Admissions 3,800 $ 150 4,100 $ 160 4,700 $ 180 6,500 $ 250 

Non-fatal myocardial infarction 45,000 $ 410 49,000 $ 450 56,000 $ 510 77,000 $ 710 

Infant Mortality Woodruff 3,600 $ 0.52 3,900 $ 0.56 4,500 $ 0.65 6,200 $ 0.89 

Mortality Pope 1,300,000 $ 180 1,700,000 $ 200 2,100,000 $ 220 3,000,000 $ 310 

Mortality Laden 3,000,000 $ 410 3,700,000 $ 440 4,700,000 $ 510 6,700,000 $ 700 

Mortality Expert E 7,600,000 $ 910 8,100,000 $ 980 9,200,000 $ 1,100 13,000,000 $ 1,600 

Mortality Expert K 740,000 $ 88 810,000 $ 95 950,000 $ 110 1,300,000 $ 150 

Chronic Bronchitis 120,000 $ 240 180,000 $ 380 310,000 $ 630 480,000 $ 970 

Emergency Room Visits, Respiratory 86 $ 230 130 $ 370 220 $ 610 350 $ 940 

Acute Respiratory Symptoms 4,600 $ 160,000 7,200 $ 260,000 12,000 $ 430,000 19,000 $ 660,000 

Upper+Lower Respiratory Symptoms 180 $ 7,300 280 $ 12,000 460 $ 19,000 720 $ 30,000 

Work Loss Days 3,400 $ 28,000 5,300 $ 43,000 8,900 $ 72,000 14,000 $ 110,000 

Acute Bronchitis $ 640 330 510 $ 1,000 850 $ 1,700 1,300 $ 2,600 

Asthma Exacerbation 210 $ 3,900 330 $ 6,100 540 $ 10,000 840 $ 16,000 

Hospital Admissions 5,200 $ 200 8,100 $ 320 14,000 $ 530 21,000 $ 820 

Non-fatal myocardial infarction 62,000 $ 570 97,000 $ 890 160,000 $ 1,500 250,000 $ 2,300 

Infant Mortality Woodruff 5,000 $ 0.71 7,800 $ 1.10 13,000 $ 1.90 20,000 $ 2.90 

Mortality Pope 1,800,000 $ 250 3,300,000 $ 390 6,000,000 $ 650 9,700,000 $ 1,000 

Mortality Laden 4,100,000 $ 560 7,400,000 $ 880 13,000,000 $ 1,500 22,000,000 $ 2,300 

Mortality Expert E 11,000,000 $ 1,200 16,000,000 $ 2,000 27,000,000 $ 3,200 41,000,000 $ 5,000 

Mortality Expert K 1,000,000 $ 120 1,600,000 $ 190 2,700,000 $ 310 4,300,000 $ 490 

National Total East Rest of West California

* National Total does not reflect benefits for the South Coast and San Joaquin Air Basins. Confidence intervals not available 

for PM estimates. All estimates rounded to two significant figures. Valuation results for mortality and nonfatal myocardial 

infarctions are shown at a 3% discount rate. PM incidence and other PM morbidity incidence and valuation estimates are 

identical to Table 6.54 because these are not discounted. Does not include visibility benefits. 
6-93

Table 6.54: Regional Breakdown of Annual PM Benefit Results by Health Endpoint in 2020 

(thousands of 2006$) at 7%* 

Endpoint Group Author 079 Valuation 079

Incidence 075 Valuation 075 

Incidence 070 Valuation 070

Incidence 065 Valuation 065

Incidence

Chronic Bronchitis 31,000 $ 64 91,000 $ 190 190,000 $ 390 300,000 $ 620 

Emergency Room Visits, Respiratory 23 $ 62 66 $ 180 140 $ 380 220 $ 600 

Acute Respiratory Symptoms 1,200 $ 43,000 3,600 $ 130,000 7,500 $ 260,000 12,000 $ 420,000 

Upper+Lower Respiratory Symptoms 47 $ 1,900 140 $ 5,700 290 $ 12,000 460 $ 19,000 

Work Loss Days 910 $ 7,300 2,600 $ 21,000 5,500 $ 45,000 8,800 $ 71,000 

Acute Bronchitis $ 170 87 250 $ 490 530 $ 1,000 840 $ 1,600 

Asthma Exacerbation 55 $ 1,000 160 $ 3,000 340 $ 6,300 530 $ 10,000 

Hospital Admissions 1,400 $ 54 4,000 $ 160 8,400 $ 330 13,000 $ 520 

Non-fatal myocardial infarction 16,000 $ 150 46,000 $ 440 97,000 $ 920 150,000 $ 1,500 

Infant Mortality Woodruff 1,100 $ 0.17 3,100 $ 0.50 6,500 $ 1.00 10,000 $ 1.60 

Mortality Pope 440,000 $ 66 1,500,000 $ 190 3,300,000 $ 400 5,600,000 $ 640 

Mortality Laden 980,000 $ 150 3,300,000 $ 430 7,500,000 $ 900 12,000,000 $ 1,400 

Mortality Expert E 2,500,000 $ 330 7,200,000 $ 960 15,000,000 $ 2,000 23,000,000 $ 3,200 

Mortality Expert K 240,000 $ 32 720,000 $ 93 1,500,000 $ 190 2,500,000 $ 310 

Chronic Bronchitis 740 $ 1.5 740 $ 1.5 10,000 $ 21 27,000 $ 55 

Emergency Room Visits, Respiratory 0.54 $ 1.5 0.54 $ 1.5 7.4 $ 20 20 $ 53 

Acute Respiratory Symptoms 29 $ 1,000 29 $ 1,000 400 $ 14,000 1,100 $ 37,000 

Upper+Lower Respiratory Symptoms 1.1 $ 46 1.1 $ 46 15 $ 630 40 $ 1,700 

Work Loss Days $ 170 21 21 $ 170 290 $ 2,400 780 $ 6,300 

Acute Bronchitis $ 4.0 2.0 2.0 $ 4.0 28 $ 55 74 $ 140 

Asthma Exacerbation 1.3 $ 24 1.3 $ 24 18 $ 330 47 $ 890 

Hospital Admissions 32 $ 1.3 32 $ 1.3 450 $ 17 1,200 $ 46 

Non-fatal myocardial infarction 370 $ 3.5 370 $ 3.5 5,100 $ 49 14,000 $ 130 

Infant Mortality Woodruff 25 $ 0.00 25 $ 0.00 350 $ 0.06 920 $ 0.15 

Mortality Pope 10,000 $ 1.5 12,000 $ 1.5 180,000 $ 21 490,000 $ 56 

Mortality Laden 23,000 $ 3.5 27,000 $ 3.5 400,000 $ 48 1,100,000 $ 130 

Mortality Expert E 59,000 $ 7.8 58,000 $ 7.8 790,000 $ 110 2,100,000 $ 280 

Mortality Expert K 5,700 $ 0.8 5,800 $ 0.8 81,000 $ 10 220,000 $ 27 

Chronic Bronchitis 86,000 $ 180 93,000 $ 190 110,000 $ 220 150,000 $ 300 

Emergency Room Visits, Respiratory 63 $ 170 68 $ 180 78 $ 210 110 $ 290 

Acute Respiratory Symptoms 3,400 $ 120,000 3,600 $ 130,000 4,200 $ 150,000 5,800 $ 200,000 

Upper+Lower Respiratory Symptoms 130 $ 5,300 140 $ 5,800 160 $ 6,700 220 $ 9,200 

Work Loss Days 2,500 $ 20,000 2,700 $ 22,000 3,100 $ 25,000 4,300 $ 35,000 

Acute Bronchitis 240 $ 460 260 $ 500 300 $ 580 410 $ 800 

Asthma Exacerbation 150 $ 2,800 160 $ 3,100 190 $ 3,500 260 $ 4,900 

Hospital Admissions 3,800 $ 150 4,100 $ 160 4,700 $ 180 6,500 $ 250 

Non-fatal myocardial infarction 44,000 $ 410 47,000 $ 450 54,000 $ 510 75,000 $ 710 

Infant Mortality Woodruff 2,900 $ 0.47 3,200 $ 0.51 3,700 $ 0.58 5,100 $ 0.80 

Mortality Pope 1,200,000 $ 180 1,500,000 $ 200 1,900,000 $ 220 2,700,000 $ 310 

Mortality Laden 2,700,000 $ 410 3,300,000 $ 440 4,200,000 $ 510 6,000,000 $ 700 

Mortality Expert E 6,900,000 $ 910 7,300,000 $ 980 8,300,000 $ 1,100 11,000,000 $ 1,600 

Mortality Expert K 670,000 $ 88 740,000 $ 95 860,000 $ 110 1,200,000 $ 150 

Chronic Bronchitis 120,000 $ 240 180,000 $ 380 310,000 $ 630 480,000 $ 970 

Emergency Room Visits, Respiratory 86 $ 230 130 $ 370 220 $ 610 350 $ 940 

Acute Respiratory Symptoms 4,600 $ 160,000 7,200 $ 260,000 12,000 $ 430,000 19,000 $ 660,000 

Upper+Lower Respiratory Symptoms 180 $ 7,300 280 $ 12,000 460 $ 19,000 720 $ 30,000 

Work Loss Days 3,400 $ 28,000 5,300 $ 43,000 8,900 $ 72,000 14,000 $ 110,000 

Acute Bronchitis 330 $ 640 510 $ 1,000 850 $ 1,700 1,300 $ 2,600 

Asthma Exacerbation 210 $ 3,900 330 $ 6,100 540 $ 10,000 840 $ 16,000 

Hospital Admissions 5,200 $ 200 8,100 $ 320 14,000 $ 530 21,000 $ 820 

Non-fatal myocardial infarction 60,000 $ 570 94,000 $ 890 160,000 $ 1,500 240,000 $ 2,300 

Infant Mortality Woodruff 4,000 $ 0.64 6,300 $ 1.00 11,000 $ 1.70 16,000 $ 2.60 

Mortality Pope 1,600,000 $ 250 3,000,000 $ 390 5,400,000 $ 650 8,800,000 $ 1,000 

Mortality Laden 3,700,000 $ 560 6,600,000 $ 880 12,000,000 $ 1,500 20,000,000 $ 2,300 

Mortality Expert E 9,500,000 $ 1,200 15,000,000 $ 2,000 24,000,000 $ 3,200 37,000,000 $ 5,000 

Mortality Expert K 910,000 $ 120 1,500,000 $ 190 2,500,000 $ 310 3,900,000 $ 490 

National Total East Rest of West California

* National Total does not reflect benefits for the South Coast and San Joaquin Air Basins. Confidence intervals not available 

for PM estimates. All estimates rounded to two significant figures. Valuation results for mortality and nonfatal myocardial 

infarctions are shown at a 7% discount rate. PM incidence and other PM morbidity incidence and valuation estimates are 

identical to Table 6.53 because these are not discounted. Does not include visibility benefits. 
6-94

Table 6.55: Regional Breakdown of Annual Ozone and PM Benefit Results by Health 

Endpoint in 2020 (3% discount rate, thousands of 2006$)* 

Endpoint Group Author Year 079 

Valuation 

075 

Valuation 

070 

Valuation 

065 

Valuation 

Ozone Morbidity (non-causal) $6,600 $33,000 $130,000 $250,000 

Ozone Mortality Bell 2004 $50,000 $290,000 $1,300,000 $2,400,000 

Ozone Mortality Bell 2005 $160,000 $940,000 $4,100,000 $7,600,000 

Ozone Mortality Ito 2005 $220,000 $1,300,000 $5,600,000 $10,000,000 

Ozone Mortality Levy 2005 $230,000 $1,300,000 $5,800,000 $11,000,000 

PM Infant Mortality Woodruff $1,300 $3,900 $8,100 $13,000 

PM Morbidity $51,000 $150,000 $310,000 $500,000 

PM Mortality Pope $480,000 $1,600,000 $3,700,000 $6,200,000 

PM Mortality Laden $1,100,000 $3,600,000 $8,300,000 $14,000,000 

PM Mortality Expert E $2,800,000 $8,000,000 $16,000,000 $26,000,000 

East 

PM Mortality Expert K $270,000 $800,000 $1,700,000 $2,700,000 

Ozone Morbidity (non-causal) $140 $260 $8,200 $24,000 

Ozone Mortality Bell 2004 $1,500 $2,700 $69,000 $200,000 

Ozone Mortality Bell 2005 $5,100 $8,900 $230,000 $670,000 

Ozone Mortality Ito 2005 $6,800 $12,000 $310,000 $900,000 

Ozone Mortality Levy 2005 $7,100 $13,000 $320,000 $950,000 

PM Infant Mortality Woodruff $31 $31 $430 $1,100

PM Morbidity $1,200 $1,200 $17,000 $44,000 

PM Mortality Pope $11,000 $13,000 $200,000 $550,000 

PM Mortality Laden $26,000 $29,000 $440,000 $1,200,000 

PM Mortality Expert E $66,000 $65,000 $880,000 $2,300,000 

Rest of West 

PM Mortality Expert K $6,300 $6,500 $90,000 $240,000 

Ozone Morbidity (non-causal) $21,000 $40,000 $90,000 $140,000 

Ozone Mortality Bell 2004 $140,000 $260,000 $580,000 $940,000 

Ozone Mortality Bell 2005 $450,000 $840,000 $1,900,000 $3,100,000 

Ozone Mortality Ito 2005 $610,000 $1,100,000 $2,600,000 $4,200,000 

Ozone Mortality Levy 2005 $630,000 $1,200,000 $2,700,000 $4,300,000 

PM Infant Mortality Woodruff $3,600 $3,900 $4,500 $6,200 

PM Morbidity $140,000 $150,000 $180,000 $240,000

PM Mortality Pope $1,300,000 $1,700,000 $2,100,000 $3,000,000 

PM Mortality Laden $3,000,000 $3,700,000 $4,700,000 $6,700,000 

PM Mortality Expert E $7,600,000 $8,100,000 $9,200,000 $13,000,000 

California 

PM Mortality Expert K $740,000 $810,000 $950,000 $1,300,000 

Ozone Morbidity (non-causal) $28,000 $73,000 $230,000 $420,000 

Ozone Mortality Bell 2004 $190,000 $550,000 $1,900,000 $3,500,000 

Ozone Mortality Bell 2005 $620,000 $1,800,000 $6,200,000 $11,000,000 

Ozone Mortality Ito 2005 $830,000 $2,400,000 $8,500,000 $15,000,000 

Ozone Mortality Levy 2005 $860,000 $2,500,000 $8,800,000 $16,000,000 

PM Infant Mortality Woodruff $5,000 $7,800 $13,000 $20,000 

PM Morbidity $190,000 $300,000 $500,000 $780,000

PM Mortality Pope $1,800,000 $3,300,000 $6,000,000 $9,700,000 

PM Mortality Laden $4,100,000 $7,400,000 $13,000,000 $22,000,000 

PM Mortality Expert E $11,000,000 $16,000,000 $27,000,000 $41,000,000 

National Total 

PM Mortality Expert K $1,000,000 $1,600,000 $2,700,000 $4,300,000 

* Totals do not reflect benefits for the South Coast and San Joaquin Air Basins. Confidence intervals not 

available for PM estimates. All estimates rounded to two significant figures. Valuation results for mortality and 

nonfatal myocardial infarctions are shown at a 3% discount rate. Does not include visibility benefits.
6-95

Table 6.56: Regional Breakdown of Annual Ozone and PM Benefit Results by Health 

Endpoint in 2020 (7% discount rate, thousands of 2006$)* 

Endpoint Group Author Year 079 

Valuation 

075 

Valuation 

070 

Valuation 

065 

Valuation 

Ozone Morbidity (non-causal) $6,600 $33,000 $130,000 $250,000 

Ozone Mortality Bell 2004 $50,000 $290,000 $1,300,000 $2,400,000 

Ozone Mortality Bell 2005 $160,000 $940,000 $4,100,000 $7,600,000 

Ozone Mortality Ito 2005 $220,000 $1,300,000 $5,600,000 $10,000,000 

Ozone Mortality Levy 2005 $230,000 $1,300,000 $5,800,000 $11,000,000 

PM Infant Mortality Woodruff $1,100 $3,100 $6,500 $10,000 

PM Morbidity $51,000 $150,000 $310,000 $490,000 

PM Mortality Pope $440,000 $1,500,000 $3,300,000 $5,600,000 

PM Mortality Laden $980,000 $3,300,000 $7,500,000 $12,000,000 

PM Mortality Expert E $2,500,000 $7,200,000 $15,000,000 $23,000,000 

East 

PM Mortality Expert K $240,000 $720,000 $1,500,000 $2,500,000 

Ozone Morbidity (non-causal) $140 $260 $8,200 $24,000 

Ozone Mortality Bell 2004 $1,500 $2,700 $69,000 $200,000 

Ozone Mortality Bell 2005 $5,100 $8,900 $230,000 $670,000 

Ozone Mortality Ito 2005 $6,800 $12,000 $310,000 $900,000 

Ozone Mortality Levy 2005 $7,100 $13,000 $320,000 $950,000 

PM Infant Mortality Woodruff $25 $25 $350 $920 

PM Morbidity $1,200 $1,200 $16,000 $44,000 

PM Mortality Pope $10,000 $12,000 $180,000 $490,000 

PM Mortality Laden $23,000 $27,000 $400,000 $1,100,000 

PM Mortality Expert E $59,000 $58,000 $790,000 $2,100,000 

Rest of West 

PM Mortality Expert K $5,700 $5,800 $81,000 $220,000 

Ozone Morbidity (noncausal) $21,000 $40,000 $90,000 $140,000 

Ozone Mortality Bell 2004 $140,000 $260,000 $580,000 $940,000 

Ozone Mortality Bell 2005 $450,000 $840,000 $1,900,000 $3,100,000 

Ozone Mortality Ito 2005 $610,000 $1,100,000 $2,600,000 $4,200,000 

Ozone Mortality Levy 2005 $630,000 $1,200,000 $2,700,000 $4,300,000 

PM Infant Mortality Woodruff $2,900 $3,200 $3,700 $5,100 

PM Morbidity $140,000 $150,000 $180,000 $240,000

PM Mortality Pope $1,200,000 $1,500,000 $1,900,000 $2,700,000 

PM Mortality Laden $2,700,000 $3,300,000 $4,200,000 $6,000,000 

PM Mortality Expert E $6,900,000 $7,300,000 $8,300,000 $11,000,000 

California 

PM Mortality Expert K $670,000 $740,000 $860,000 $1,200,000 

Ozone Morbidity (non-causal) $28,000 $73,000 $230,000 $420,000 

Ozone Mortality Bell 2004 $190,000 $550,000 $1,900,000 $3,500,000 

Ozone Mortality Bell 2005 $620,000 $1,800,000 $6,200,000 $11,000,000 

Ozone Mortality Ito 2005 $830,000 $2,400,000 $8,500,000 $15,000,000 

Ozone Mortality Levy 2005 $860,000 $2,500,000 $8,800,000 $16,000,000 

PM Infant Mortality Woodruff $4,000 $6,300 $11,000 $16,000 

PM Morbidity $190,000 $300,000 $500,000 $780,000

PM Mortality Pope $1,600,000 $3,000,000 $5,400,000 $8,800,000 

PM Mortality Laden $3,700,000 $6,600,000 $12,000,000 $20,000,000 

PM Mortality Expert E $9,500,000 $15,000,000 $24,000,000 $37,000,000 

National Total 

PM Mortality Expert K $910,000 $1,500,000 $2,500,000 $3,900,000 

* Totals do not reflect benefits for the South Coast and San Joaquin Air Basins. Confidence intervals not 

available for PM estimates. All estimates rounded to two significant figures. Valuation results for mortality and 

nonfatal myocardial infarctions are shown at a 7% discount rate. Does not include visibility benefits.
6-96

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6a-1

Appendix 6a: Additional Benefits Information 

Summary

This appendix provides additional information regarding the benefits analysis, including 

(1) methods for developing estimate of full attainment air quality; (2) the process for 

interpolating the 0.075 ppm and 0.079 ppm benefits estimates; (3) the partial attainment PM2.5 

incidence and valuation estimates. 

6a.1 Developing an Air Quality Estimate of Full Attainment with the Alternative Ozone 

Standards

As discussed in chapter 3, the modeled attainment scenarios were not sufficient to simulate full 

attainment with each of the three alternative ozone standards analyzed. To meet our analytical 

goal of estimating the human health benefits of full simulated attainment with each of these 

standard alternatives, it became necessary to derive an estimate of the full attainment air quality 

increment through a simple monitor rollback approach. 

We rolled back the values at each monitor such that no monitor in the U.S. exceeded the 

alternative standard in question. This approach makes the bounding assumption that ozone 

concentrations can be reduced only at monitors projected to exceed the alternative standards. 

From a benefits perspective, this approach leads to a downward bias in the estimates because 

populations are assumed to be exposed at a distance weighted average of surrounding monitors. 

Thus, any individual’s reduction in exposure from a change at a given monitor will be weighted 

less if there are other attaining monitors in close proximity. 

We determined projected attainment status of each monitor by calculating design values. 

However, to estimate changes in ozone-related health effects resulting from improvement in air 

quality, the BenMAP model requires a series of metrics. When performing a benefits assessment 

with air quality modeling data, BenMAP calculates these metrics based on the distribution of 

CMAQ-modeled hourly ozone concentrations for the ozone season. However, because we were 

performing a benefits assessment based on monitor values that have been rolled-back, it was 

necessary to derive each of these metrics outside of the BenMAP model. Thus, we first 

developed a scaling ratio that related the calculated design value to each of the ozone metrics. 

A summary of this procedure is as follows: 

1. Import partial attainment 0.08 ppm calculated design values into the BenMAP model 

2. Perform a spatial interpolation of these design values using the Voronoi Neighborhood 

Averaging algorithm. Design values are then interpolated to the CMAQ grid cell. 

3. Import distribution of air quality modeled daily and hourly ozone concentrations into 

BenMAP. Create air quality grid in BenMAP using spatial and temporal scaling 
6a-2

technique.1

 This procedure creates grid cell level summer season ozone metrics (1 hour 

maximum, 5 hour average, 8 hour maximum, 8 hour average and 24 hour average). 

4. Calculate grid cell-level ratio of each ozone metric to calculated design value. The result 

of this calculation is a grid cell-level ratio of metric to design value that can then be 

subsequently used to scale the calculated design value and thus derive each of the 

metrics. 

After having calculated these scaling ratios we then performed the monitor rollback as follows: 

1. Roll back the calculated 0.08 ppm partial attainment design value to just equal the 0.08 

ppm standard. This process creates a new baseline design value grid. 

2. Scale the design value grid cell values to ozone metric grid cell values by using ratios 

described above.

3. Create new 0.084 ppm baseline air quality grid from grid cell-level ozone metrics. 

4. Roll back the calculated 0.070 ppm and 0.065 ppm partial attainment design values at 

each monitor to just reach the 0.070 ppm and 0.065 ppm standards, respectively. 

5. Scale the calculated full attainment design value to grid cell-level ozone metric using 

ratios described above. 

6. Create new 0.070 ppm and 0.065 ppm air quality grids from grid cell-level ozone metrics. 

7. Perform benefits analysis with baseline and control grids. 

To develop the full attainment air quality grids for 0.075 ppm and 0.079 ppm, we performed an 

interpolation of the 0.070 ppm full attainment air quality grid, rather than a monitor rollback. We 

used this technique because air quality modeling incorporating control strategies was only 

available for 0.070 ppm. This interpolation for 0.075 ppm entailed the following steps: 

1. We identified any monitors that were projected to not attain 0.075 ppm alternative in the 

0.084 ppm base case air quality grid. 

2. For these monitors we calculated an adjustment factor that would scale down the air 

quality improvement at that monitor. The purpose of this adjustment was to ensure that 

the improvement in air quality at that monitor reflected the attainment of the 0.075 ppm 

standard. This ratio was calculated by dividing the improvement in the design value 

necessary to attain 0.075 ppm by the improvement in the design value necessary to attain 

0.070 ppm. For example, a monitor whose baseline is 0.084 would receive 2/3 of the air 

quality improvement from attaining 0.075 ppm than they would from attaining 0.070 

ppm. 

1

 BenMAP Technical Appendices, Abt Associates: May 2005. Page C-12. 
6a-3

3. We then interpolated these monitor-specific ratios to the grid cell-level in BenMAP, 

constraining the interpolation to within 200 km of the control buffer. 

4. Finally, we used these grid cell-level ratios as the basis for scaling down the grid celllevel estimates of incidence and valuation from the 0.070 ppm analysis. 

5. Next, we followed the same process for the 0.079 ppm interpolation. 

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

Tables 6a.1 through 6a.5 below summarize the estimates of PM2.5 incidence and valuation 

resulting from the 0.070 ppm partial attainment scenario. These estimates provided the basis for 

the full attainment PM2.5 co-benefit estimates found in Chapter 6 of this RIA. Details about the 

methodology for this approach can also be found in Chapter 6. 
6a-4

Table 6a.1: Illustrative 0.070 ppm Partial Attainment Scenario: Estimated Reductions in 

PM Premature Mortality associate with PM Co-Benefit (95

th percentile confidence 

intervals provided in parentheses)c

 Eastern U.S. 

Western U.S. 

Excluding 

California California 

National PM CoBenefits

Mortality Impact Functions Derived from Epidemiology Literature

420 6.3 5.4 430 ACS Studya

(110--730) (2--10) (2--9) (110--750) 

950 14 12 980 Harvard Six-City Studyb

(420--1,500) (7--21) (6--18) (440--1,500) 

Woodruff et al. 1997 1.1 0.15 0.02 1.3 

(infant mortality) (0.34--1.8) (0.07--0.23) (0.01--0.04) (0.42--2.1) 

Mortality Impact Functions Derived from Expert Elicitation

1,600 150 32 1,800 Expert A (-92--3,200) (0.90--310) (3.4--60) (-87--3,600) 

1,200 110 24 1,300 Expert B (-100--2,900) (-4.3--270) (2.3--53) (-100--3,200) 

1,200 120 24 1,300 Expert C (-100--2,900) (-0.89--280) (2.7--54) (-99--3,200) 

830 81 17 920 Expert D (42--1,500) (5.7--140) (1.7--28) (49--1,700) 

2,000 190 39 2,200 Expert E (690--3,300) (76--310) (18--62) (790--3,600) 

1,100 110 22 1,200 Expert F (660--1,700) (66--160) (15--32) (740--1,900) 

690 68 14 770 Expert G (0.00--1,400) (0.00--130) (0.00--27) (0.00--1,500) 

880 86 18 990 Expert H (-250--2,300) (-17--220) (-0.93--43) (-270--2,600) 

1,200 120 24 1,300 Expert I (-14--2,400) (1.5--220) (1.2--44) (-11--2,600) 

950 93 19 1,100 Expert J (44--2,400) (11--230) (5--45) (60--2,700) 

190 18 3.8 210 Expert K (0.00--1,000) (0.00--98) (0.00--20) (0.00--1,100) 

840 70 16 920 Expert L (25--1,800) (1.5--170) (1.2--33) (28--2,000) 

a The estimate is based on the concentration-response (C-R) function developed from the study of the 

American Cancer Society cohort reported in Pope et al. (2002), which has previously been reported as 

the primary estimate in recent RIAs. 

bBased on Laden et al. (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB

for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

c All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical

and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not reflect benefits for the San Joaquin Valley or South Coast Air Basins. Negative values indicate

that an increase in incidence could occur. 
6a-5

Table 6a.2: Illustrative 0.070 ppm Partial Attainment Scenario: Estimated Reductions in 

Morbidity Associated with PM Co-Benefit (95th percentile confidence intervals provided in 

parentheses)a

 Eastern U.S. 

Western U.S. 

Excluding California California 

National PM CoBenefits

Morbidity Impact Functions Derived from Epidemiology Literature

Chronic Bronchitis (age >25 380 38 8.7 420 

and over) (-11--760) (4--72) (1--17) (-6--850) 

Nonfatal myocardial 970 12 11 1,000 

infarction (age >17) (440--1,500) (6--18) (5--16) (450--1,500) 

Hospital admissions— 120 1.3 1.1 120 

respiratory (all ages) (46--184) (1--2) (1--2) (46--186) 

Hospital admissions— 230 2.8 2.3 240 

cardiovascular (age >17) (127--340) (2--4) (1--3) (130--340) 

Emergency room visits for 400 3.6 2.4 410 

asthma (age <19) (200--610) (2--5) (1--4) (200--620) 

980 120 23 1,100 Acute bronchitis (age 8–12) (-310--2,300) (-16--250) (-3--50) (-320--2,600) 

Lower respiratory symptoms 7,100 150 130 7,400 

(age 7–14) (2,600--12,000) (63--230) (57--210) (2,800--12,000) 

Upper respiratory symptoms 5,200 110 95 5,400 

(asthmatic children age 9–18) (880--9,500) (27--190) (24--170) (930--9,900) 

Asthma exacerbation 6,500 130 120 6,800 

(asthmatic children age 6–18) (-78--21,000) (10--420) (9--380) (-60--22,000) 

47,000 830 800 48,000 Work loss days (age 18–65) (39,000--54,000) (710--950) (680--910) (41,000--56,000) 

Minor restricted activity days 280,000 4,800 4,700 290,000 

(age 18–65) (220,000--330,000) (4,000--5,700) (3,900--5,500) (230,000--340,000) 

a All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical

and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not reflect benefits for the San Joaquin Valley or South Coast Air Basins. Negative values indicate

that an increase in incidence could occur. 
6a-6

Table 6a.3: Illustrative Strategy to Partially Attain 0.070 ppm: Estimated Partial 

Attainment Value of Reductions in PM2.5-Related Premature Mortality Associated with 

PM Co-Benefit (3 percent discount rate, in millions of 2006$) 95th Percentile Confidence 

Intervals Provided in Parenthesesc

Eastern U.S. 

Western U.S. 

Excluding 

California California 

National PM CoBenefits

Mortality Impact Functions Derived from Epidemiology Literature

ACS Study $3,000 $44 $38 $3,000 a

($380--$7,000) ($6.8--$110) ($5.8--$95) ($440--$7,200) 

Harvard Six-City Study $6,700 $99 $85 $6,900 b

($1,000--$14,000) ($16--$210) ($14--$180) ($1,000--$15,000) 

Woodruff et al., 1997 $7.5 $1.0 $0.17 $8.8 

(infant mortality) ($1.0--$17) ($0.16--$2.3) ($0.03--$0.36) ($1.2--$20) 

Mortality Impact Functions Derived from Expert Elicitation

$11,000 $1,100 $220 $12,000 Expert A ($200--$30,000) ($55--$2,800) ($20--$560) ($280--$33,000) 

$8,400 $790 $170 $9,300 Expert B (-$600--$28,000) (-$23--$2,700) ($9.0--$520) (-$620--$31,000) 

$8,300 $810 $170 $9,300 Expert C (-$33--$27,000) ($32--$2,600) ($15--$500) ($13--$30,000) 

$5,800 $570 $120 $6,500 Expert D ($480--$15,000) ($53--$1,400) ($13--$280) ($540--$16,000) 

$14,000 $1,300 $280 $15,000 Expert E ($2,000--$32,000) ($200--$3,000) ($43--$600) ($2,300--$35,000) 

$7,600 $740 $150 $8,500 Expert F ($1,400--$17,000) ($130--$1,600) ($27--$330) ($1,400--$19,000) 

$4,900 $480 $98 $5,400 Expert G ($0.00--$13,000) ($0.00--$1,300) ($0.00--$260) ($0.00--$14,000) 

$6,200 $610 $120 $6,900 

Expert H (-$1,700--

$21,000) (-$100--$2,000) ($0.26--$390) (-$1,700--$23,000) 

$8,200 $810 $170 $9,200 Expert I ($430--$22,000) ($53--$2,100) ($14--$420) ($500--$24,000) 

$6,700 $650 $130 $7,400 Expert J ($430--$22,000) ($61--$2,100) ($17--$410) ($520--$24,000) 

$1,300 $130 $27 $1,500 Expert K ($0.00--$8,200) ($0.00--$800) ($0.00--$160) ($0.00--$9,200) 

$5,900 $490 $110 $6,500 Expert L ($240-$17,000) ($7.2--$1,600) ($5.7--$330) ($260--$19,000) 

a The estimate is based on the concentration-response (C-R) function developed from the study of the 

American Cancer Society cohort reported in Pope et al. (2002), which has previously been reported as 

the primary estimate in recent RIAs. 

bBased on Laden et al. (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB

for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

c All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical

and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not reflect benefits for the San Joaquin Valley or South Coast Air Basins. Negative values indicate

that an increase in incidence could occur. 
6a-7

Table 6a.4: Illustrative Strategy to Partially Attain 0.070 ppm: Estimated Partial 

Attainment Value of Reductions in PM2.5-Related Premature Mortality Associated with 

PM Co-Benefit (7 percent discount rate, in millions of 2006$) 95th Percentile Confidence 

Intervals Provided in Parenthesesc

Eastern U.S. 

Western U.S. 

Excluding 

California California 

National PM CoBenefits

Mortality Impact Functions Derived from Epidemiology Literature

ACS Study $2,700 $40 $34 $2,700 a

($340--$6,300) ($6.8--$110) ($5.8--$95) ($360--$6,400) 

Harvard Six-City $6,000 $89 $77 $6,200 

Studyb ($920--$13,000) ($14--$190) ($12--$160) ($940--$13,000) 

Woodruff et al., 1997 $6.8 $0.94 $0.15 $7.9 

(infant mortality) ($0.90--$16) ($0.14--$2.0) ($0.02--$0.33) ($1.1--$18) 

Mortality Impact Functions Derived from Expert Elicitation

$9,900 $970 $200 $11,000 Expert A ($180--$27,000) ($50--$2,500) ($18--$510) ($250--$30,000) 

$7,500 $720 $150 $8,400 Expert B (-$550--$25,000) (-$20--$2,400) ($8.1--$470) (-$560--$28,000) 

$7,500 $730 $150 $8,400 Expert C (-$30--$24,000) ($28--$2,300) ($14--$450) ($12--$27,000) 

$5,200 $510 $110 $5,800 Expert D ($430--$13,000) ($47--$1,300) ($11--$250) ($490--$15,000) 

$12,000 $1,200 $250 $14,000 Expert E ($1,800--$29,000) ($180--$2,700) ($39--$540) ($2,000--$32,000) 

$6,800 $660 $140 $7,600 Expert F ($1,200--$16,000) ($110--$1,500) ($24--$300) ($1,300--$17,000) 

$4,400 $430 $88 $4,900 Expert G ($0.00--$12,000) ($0.00--$1,200) ($0.00--$240) ($0.00--$13,000) 

$5,600 $550 $110 $6,200 Expert H (-$1,500--$19,000) (-$90--$1,800) ($0.24--$350) (-$1,600--$21,000) 

$7,400 $730 $150 $8,300 Expert I ($380--$20,000) ($48--$1,900) ($12--$380) ($450--$22,000) 

$6,000 $590 $120 $6,700 Expert J ($390--$19,000) ($55--$1,900) ($16--$370) ($470--$22,000) 

$1,200 $110 $24 $1,300 Expert K ($0.00--$7,400) ($0.00--$720) ($0.00--$150) ($0.00--$8,200) 

$5,300 $440 $99 $5,800 Expert L ($220--$16,000) ($6.5--$1,500) ($5.1--$300) ($230--$17,000) 

a The estimate is based on the concentration-response (C-R) function developed from the study of the 

American Cancer Society cohort reported in Pope et al. (2002), which has previously been reported as 

the primary estimate in recent RIAs. 

bBased on Laden et al. (2006) reporting of the extended Six-cities study; to be reviewed by the EPA-SAB

for advice on the appropriate method for incorporating what has previously been a sensitivity estimate. 

c All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical

and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS RIA. Estimates 

do not reflect benefits for the San Joaquin Valley or South Coast Air Basins. Negative values indicate

that an increase in incidence could occur. 
6a-8

Table 6a.5: Illustrative Strategy to Partially Attain 0.070 ppm: Estimated Partial 

Attainment Monetary Value of Reductions in Risk of PM2.5-Related Morbidity Reductions 

Associated with PM Co-Benefit (in millions of 2006$) 95th Percentile Confidence Intervals 

Provided in Parenthesesa

 Eastern U.S. 

Western U.S. 

Excluding 

California 

California National PM CoBenefits

Morbidity Impact Functions Derived from Epidemiology Literature

Chronic Bronchitis (age $180 $19 $4.3 $210 

>25 and over) ($4.0--$870) ($1.0--$86) ($0.24--$20) ($5.2--$980) 

Nonfatal myocardial $210 $2.6 $2.3 $210 

infarction (age >17) ($50--$480) ($0.65--$5.8) ($0.61--$5.2) ($50--$490) 

Hospital admissions— $2.5 $0.03 $0.02 $ 2.5 

respiratory (all ages) ($1.10--$3.80) ($0.01--$0.04) ($0.01--$0.04) ($1.1--$3.8) 

Hospital admissions— $6.5 $0.08 $0.06 $6.6 

cardiovascular (age >17) ($3.80--$9.10) ($0.05--$0.11) ($0.04--$0.09) ($3.9--$9.3) 

Emergency room visits $0.15 $0.00 $0.00 $0.15 

for asthma (age <19) ($0.07--$0.25) ($0.00--$0.00) ($0.00--$0.00) ($0.07--$0.25) 

Acute bronchitis (age 8– $0.50 $0.06 $0.01 $0.57 

12) (-$0.14--$1.50) ($0.00--$0.17) ($0.00--$0.03) (-$0.14--$1.7) 

Lower respiratory $ 0.14 $0.00 $0.00 $0.14 

symptoms (age 7–14) ($0.04--$0.29) ($0.00--$0.01) ($0.00--$0.01) ($0.04--$0.30) 

Upper respiratory $0.16 $0.00 $0.00 $0.17 

symptoms (asthmatic 

children age 9–18) 

($0.03--$0.41) ($0.00--$0.01) ($0.00--$0.01) ($0.03--$0.42) 

Asthma exacerbation $0.35 $0.01 $0.01 $0.36 

(asthmatic children age 

6–18) 

($0.01--$1.30) ($0.00--$0.03) ($0.00--$0.02) ($0.01--$1.4) 

Work loss days (age 18– $5.7 $0.10 $0.12 $6.0 

65) ($4.9--$6.6) ($0.09--$0.11) ($0.10--$0.13) ($5.1--$6.8) 

Minor restricted activity $7.8 $0.14 $0.13 $8.1 

days (age 18–65) ($0.39--$16) ($0.01--$0) ($0.01--$0) ($0.40--$17) 

a All estimates rounded to two significant figures. As such, confidence intervals may not be symmetrical

and totals will not sum across columns. All estimates incremental to 2006 PM NAAQS. Estimates do 

not reflect benefits for the San Joaquin Valley or South Coast Air Basins. 
Health-Based Cost-Effectiveness of Reductions in Ambient O3 and PM2.5

Associated with Illustrative O3 NAAQS Attainment Strategies 

Draft Report 

Submitted by: 

Ellen Post

Don McCubbin 

Nathan Frey 

Hardee Mahoney 

Abt Associates Inc. 

4800 Montgomery Lane 

Bethesda, MD 20814 

(301) 913-0500 

Submitted to: 

Ronn Dexter, Work Assignment Manager 

U.S. Environmental Protection Agency 

National Center for Environmental Economics 

1200 Pennsylvania Ave., NW 

Washington, D.C. 20460 

March 11, 2008 
7b-1

Appendix Chapter 7b: Health-Based Cost-Effectiveness of Reductions in Ambient 

O3 and PM2.5 Associated with Illustrative O3 NAAQS Attainment Strategies 

7b.1 Summary 

Health-based cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) have been used 

to analyze numerous health interventions but have not been widely adopted as tools to analyze 

environmental policies. Analyses of environmental regulations have typically used benefit-cost 

analysis to characterize impacts on social welfare. Benefit-cost analyses allow for aggregation of 

the benefits of reducing mortality risks with other monetized benefits of reducing air pollution, 

including reduced risk of acute and chronic morbidity, and non-health benefits. One of the great 

advantages of the benefit-cost paradigm is that a wide range of quantifiable benefits can be 

compared to costs to evaluate the economic efficiency of particular actions. However, alternative 

paradigms such as CEA and CUA analyses may also provide useful insights. CEA involves 

estimation of the costs per unit of benefit (e.g., lives or life years saved). CUA is a special type 

of CEA using preference-based measures of effectiveness, such as quality-adjusted life years 

(QALYs). QALYs were developed to evaluate the effectiveness of individual medical 

treatments, and EPA is still evaluating the appropriate methods for CEA for environmental 

regulations.

In this CEA, we estimated statistical lives saved, statistical life years saved, and QALYs gained.

In addition, where relevant, we used an alternative aggregate effectiveness metric, Morbidity 

Inclusive Life Years (MILYs), to address some of the concerns about aggregation of life 

extension and quality-of-life impacts. MILYs represent the sum of life years gained due to 

reductions in premature mortality and the QALYs gained due to reductions in chronic morbidity. 

This measure may be preferred to existing QALY aggregation approaches because it does not 

devalue life extensions in individuals with preexisting illnesses that reduce quality of life. 

However, the MILY measure is still based on life years and thus still inherently gives more 

weight to interventions that reduce mortality and morbidity impacts for younger populations with 

higher remaining life expectancy. 

Following the methodology used in the CEA for the PM NAAQS RIA, we did not assign QALY 

weights to the life years saved – i.e., we calculated life years saved, rather than QALYs gained 

from mortality avoided. Put another way, we assumed weights of 1.0 for all life years saved.

Life years saved in the future, however, were discounted to reflect people’s time preference (i.e., 

a benefit received now is worth more than the same benefit received in the future). We used 

discount rates of 3 percent and 7 percent. 

For each illustrative O3 NAAQS attainment strategy, we present several metrics: lives saved, life 

years saved, and cost of the regulation per life saved and per life year saved. Where possible, 

benefits that could not be quantified in the denominator of our cost-effectiveness ratios were 

monetized and subtracted from the cost of the regulation in the numerator. 

Although there are indirect PM2.5-related co-benefits associated with all the illustrative O3

NAAQS attainment strategies considered, we were able to model the changes in PM2.5 occurring 
7b-2

as a result of only one illustrative O3 NAAQS attainment strategy1

. Therefore PM2.5-related cobenefits are included in the cost effectiveness metrics presented only for that one strategy. The 

cost effectiveness metrics presented for all of the other illustrative O3 NAAQS attainment 

strategies omit the PM2.5-related co-benefits and are therefore likely to understate the cost 

effectiveness of those strategies. 

For the illustrative O3 NAAQS attainment strategy for which we were able to include both direct 

O3-related health benefits and indirect PM2.5-related co-benefits, in addition to the cost 

effectiveness metrics listed above we also calculated MILYs and the cost of the regulation (net 

of the monetized benefits not included in the denominator) per MILY gained. 

The results of the analysis are summarized as follows: 

x Estimates of O3-related lives saved were substantially affected by the underlying O3-

mortality study used and, to a greater extent, by the attainment scenario considered. 

Because all O3-related mortality was assumed to occur in 2020, we did not discount O3-

related lives saved. Non-zero estimates of O3-related lives saved based on Bell et al. 

(2004) ranged from 36 (95% CI: 12 – 60), under full attainment of an alternative 

standard of 0.079 ppm, to 520 (95% CI: 170 – 880), under full attainment of an 

alternative standard of 0.065 ppm. Estimates of O3-related lives saved based on Levy et 

al. (2005) ranged from 160 (95% CI: 110 – 210) to 2,400 (95% CI: 1,600 – 3,100), 

under full attainment of the 0.079 ppm and 0.065 ppm alternative standards, respectively. 

x Non-zero estimates of O3-related life years saved also depended substantially on the 

underlying mortality study used and the attainment scenario considered. In addition, we 

hypothesized several alternative possible sets of life expectancies associated with agespecific O3-related deaths avoided, and the choice of life expectancies had a large impact 

on the estimates of O3-related life years saved. Using a 3 percent discount rate, the 

smallest non-zero estimate of O3-related life years saved was 160 (95% CI: 54 – 270), 

under full attainment of the alternative standard of 0.079 ppm, based on Bell et al. (2004), 

and assuming that O3-related mortality occurs only in the subpopulation with severe 

preexisting conditions (and thus the shortest life expectancies). The largest estimate of 

O3-related life years saved was 26,000 (95% CI: 18,000 – 34,000), under full attainment 

of the alternative standard of 0.065 ppm, based on Levy et al. (2004), and assuming that 

O3-related mortality occurs in the general population. 

x Using a 7 percent discount rate, the smallest non-zero estimate of O3-related life years 

saved was 140 (95% CI: 46 – 230), under full attainment of the alternative standard of 

0.079 ppm, based on Bell et al. (2004), and assuming that O3-related mortality occurs 

only in the subpopulation with severe preexisting conditions (and thus the shortest life 

expectancies). The largest estimate of O3-related life years saved was 19,000 (95% CI:

13,000 – 25,000), under full attainment of the alternative standard of 0.065 ppm, based 

1

 This illustrative attainment strategy has a baseline of partial attainment of the current standard 

of 0.084 ppm and a control scenario of partial attainment of an alternative standard of 0.070 

ppm. 
7b-3

on Levy et al. (2004), and assuming that O3-related mortality occurs in the general 

population.

x The estimate of PM2.5-related lives saved under the single illustrative attainment strategy 

for which we were able to model the indirect changes in PM2.5 concentrations and thus 

include PM2.5 co-benefits, was 440 (95% CI: 170 – 700), based on Pope et al. (2002), 

and 2,400 (95% CI: 540 – 1,400), based on Laden et al. (2006). Unlike O3-related

mortality, PM2.5-related mortality was not all assumed to occur in the year of exposure. 

Estimates of PM2.5-related life years saved were thus discounted twice – first life years 

saved were discounted back to the year of avoided death, and then were further 

discounted back to 2020. Using a 3 percent discount rate, PM2.5-related life years saved 

was estimated to be 4,400 (95% CI: 1,700 – 7000), based on Pope et al. (2002), and 

9,900 (95% CI: 5,400 – 14,000), based on Laden et al. (2006). Using a 7 percent 

discount rate, the corresponding estimates using Pope et al. (2002) and Laden et al. 

(2006) were 3,000 (95% CI: 1,200 – 4,800) and 6,700 (95% CI: 3,700 – 9,800), 

respectively.

x Under the single scenario for which we were able to model the indirect changes in PM2.5

concentrations and thus include PM2.5 co-benefits, we estimated PM2.5-related reductions 

in chronic bronchitis (CB) and non-fatal acute myocardial infarction (AMI) and the 

corresponding improvements in quality of life as QALYs gained. QALYs gained from 

PM2.5-related reductions in CB were estimated to be 1,970 (95% CI: 270 – 4,700), using 

a 3 percent discount rate, and 1,300 (95% CI: 180 – 3,000) using a 7 percent discount 

rate. QALYs gained from PM2.5-related reductions in AMI were estimated to be 870

(95% CI: 220 – 1,800) and 680 (95% CI: 180 – 1,400), using 3 percent and 7 percent 

discount rates, respectively. 

x Because both costs (in the numerator) and benefits (in the denominator) increased with 

the stringency of the alternative regulations considered, the cost effectiveness ratios 

would not necessarily be expected to show a monotonic pattern across the regulations.

Net cost per O3-related life saved (in 2006 $) (in those illustrative attainment strategies 

for which we incorporated only O3-related benefits) were greatest in the illustrative 

attainment strategy of full attainment of a 0.075 ppm standard. Even under this one 

strategy, however, cost effectiveness estimates varied substantially, depending on the 

underlying mortality study used and the discount rate (for cost) assumed – from a low 

estimate of $18 million per life saved (95% CI: $13 million – $25 million), based on 

Levy et al. (2005) and using a lower bound estimate of the 7 percent discounted cost, to a 

high estimate of $110 million (95% CI: $55 million – $280 million), based on Bell et al. 

(2004) and using an upper bound estimate of the 7 percent discounted cost. Note, 

however, that all of the cost effectiveness ratios for illustrative attainment strategies for 

which we incorporated only O3-related benefits would tend to overstate the cost per life 

saved – i.e., understate cost effectiveness – because PM2.5 co-benefits were not included 

in the denominator. 

x Net cost per life saved tended to be substantially lower for the single scenario for which 

both O3-related and PM2.5-related lives saved were included, ranging from $1.8 million 

(95% CI: $1.3 million – $2.6 million), using Levy et al. (2005) and Laden et al. (2006), 
7b-4

to $5.4 million (95% CI: $3.2 million – $9.9 million), using Bell et al. (2004) and Pope 

et al. (2002). 

x The pattern seen for cost per life year saved was similar to that seen for cost per life 

saved. Net costs per O3-related life year saved were greatest in the illustrative attainment 

strategy of full attainment of a 0.075 ppm standard. However, there was substantial 

variability in cost effectiveness estimates across these illustrative attainment strategies. 

The lowest cost per life year saved was estimated to be $1.6 million (95% CI: $1.2 

million – $2.3 million), under full attainment of a 0.079 ppm standard, using Levy et al. 

(2005) and a 3 percent discount rate, and assuming life expectancies of the general 

population. The highest cost per life year saved was estimated to be $29 million (95% 

CI: $15 million – $75 million), under full attainment of a 0.075 ppm standard, using Bell 

et al. (2004) and a 7 percent discount rate, and assuming life expectancies of a 

subpopulation with severe preexisting conditions. 

x Net costs per life year saved in the single illustrative strategy for which we included both 

O3-related and PM2.5-related benefits were substantially smaller than for the other 

scenarios. This is not surprising, since the cost effectiveness of those other scenarios was 

understated – and thus the cost per life year saved was overstated – because of the 

omission of PM2.5-related live years saved. The lowest estimate of net cost per life year 

saved for this illustrative strategy was $0.14 million (95% CI: $0.1 million – $0.2 

million), based on Levy et al. (2005) and Laden et al. (2006), and, for O3-related

mortality avoided, assuming life expectancies of the general population, and using a 3 

percent discount rate. The highest estimate was $0.79 million (95% CI: $0.44 million – 

$1.6 million), based on Bell et al. (2004) and Pope et al. (2002), and, for O3-related

mortality avoided, assuming life expectancies of a subpopulation with severe preexisting 

conditions, and using a 7 percent discount rate.

x Finally, under the single illustrative strategy for which we included both O3-related and 

PM2.5-related benefits, the lowest estimate of net costs per MILY gained, using a 3 

percent discount rate, was $0.12 million (95% CI: $0.09 million – $0.17 million), based 

on Levy et al. (2005) and Laden et al. (2006) and, for O3-related mortality avoided, 

assuming life expectancies of the general population; the highest estimate was $0.30 

million (95% CI: $0.19 million – $0.53 million), based on Bell et al. (2004) and Pope et 

al. (2002) and, for O3-related mortality avoided, assuming life expectancies of a

subpopulation with severe preexisting conditions. 

x Using a 7 percent discount rate, the lowest estimate of net costs per MILY gained was 

$0.18 million (95% CI: $0.14 million – $0.26 million), based on Levy et al. (2005) and 

Laden et al. (2006) and, for O3-related mortality avoided, assuming life expectancies of 

the general population; the highest estimate was $0.48 million (95% CI: $0.29 million – 

$0.86 million), based on Bell et al. (2004) and Pope et al. (2002) and, for O3-related

mortality avoided, assuming life expectancies of a subpopulation with severe preexisting 

conditions.
7b-5

7b.2 Introduction 

Health-based cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) have been used 

to analyze numerous health interventions but have not been widely adopted as tools to analyze 

environmental policies. Analyses of environmental regulations have typically used benefit-cost 

analysis to characterize impacts on social welfare. Benefit-cost analyses allow for aggregation of 

the benefits of reducing mortality risks with other monetized benefits of reducing air pollution, 

including reduced risk of acute and chronic morbidity, and non-health benefits. One of the great 

advantages of the benefit-cost paradigm is that a wide range of quantifiable benefits can be 

compared to costs to evaluate the economic efficiency of particular actions. However, alternative 

paradigms such as CEA and CUA analyses may also provide useful insights. CEA involves 

estimation of the costs per unit of benefit (e.g., lives or life years saved). CUA is a special type 

of CEA using preference-based measures of effectiveness, such as quality-adjusted life years 

(QALYs).

QALYs were developed to evaluate the effectiveness of individual medical treatments, and EPA 

is still evaluating the appropriate methods for CEA for environmental regulations. Agency 

concerns with the standard QALY methodology include the treatment of people with fewer years 

to live (the elderly); fairness to people with preexisting conditions that may lead to reduced life 

expectancy and reduced quality of life; and how the analysis should best account for non-health 

benefits.

The Office of Management and Budget (OMB) recently issued Circular A-4 guidance on 

regulatory analyses, requiring federal agencies to “prepare a CEA for all major rulemakings for 

which the primary benefits are improved public health and safety to the extent that a valid 

effectiveness measure can be developed to represent expected health and safety outcomes.” 

Environmental quality improvements may have multiple health and ecological benefits, however, 

making application of CEA more difficult and less straightforward.

The Institute of Medicine (a member institution of the National Academies of Science) 

established the Committee to Evaluate Measures of Health Benefits for Environmental, Health, 

and Safety Regulation to assess the scientific validity, ethical implications, and practical utility 

of a wide range of effectiveness measures used or proposed in CEA. This committee prepared a 

report titled “Valuing Health for Regulatory Cost-Effectiveness Analysis” which concluded that 

CEA is a useful tool for assessing regulatory interventions to promote human health and safety, 

although not sufficient for informed regulatory decisions (Miller, Robinson, and Lawrence, 

2006). They emphasized the need for additional data and methodological improvements for CEA 

analyses, and urged greater consistency in the reporting of assumptions, data elements, and 

analytic methods. They also provided a number of recommendations for the conduct of 

regulatory CEA analyses. EPA is evaluating these recommendations and will determine a 

response for upcoming analyses. 

CEA and CUA are most useful for comparing programs that have similar goals, for example, 

alternative medical interventions or treatments that can save a life or cure a disease. They are less 

readily applicable to programs with multiple categories of benefits, such as those reducing 

ambient air pollution, because the cost-effectiveness calculation is based on the quantity of a 

single benefit category. In other words, we cannot readily convert non-health benefits, such as 
7b-6

visibility improvements associated with reductions in PM2.5 or increases in worker productivity 

associated with reductions in O3, to a health metric such as life years saved. For these reasons, 

environmental economists prefer to present results in terms of monetary benefits and net 

benefits.

However, QALY-based CUA has been widely adopted within the health economics literature 

(Neumann, 2003; Gold et al., 1996) and in the analysis of public health interventions (US FDA, 

2004). QALY-based analyses have not been as accepted in the environmental economics 

literature because of concerns about the theoretical consistency of QALYs with individual 

preferences (Hammitt, 2002), treatment of nonhuman health benefits, and a number of other 

factors (Freeman, Hammitt, and De Civita, 2002). For environmental regulations, benefit-cost 

analysis has been the preferred method of choosing among regulatory alternatives in terms of 

economic efficiency. Recently several academic analyses have proposed the use of life yearsbased benefit-cost or CEAs of air pollution regulations (Cohen, Hammitt, and Levy, 2003; Coyle 

et al., 2003; Rabl, 2003; Carrothers, Evans, and Graham, 2002). In addition, the World Health 

Organization has adopted the use of disability-adjusted life years, a variant on QALYs, to assess 

the global burden of disease due to different causes, including environmental pollution (Murray 

et al., 2002; de Hollander et al., 1999). 

One of the ongoing controversies in health impact assessment regards whether reductions in 

mortality risk should be reported and valued in terms of statistical lives saved or in terms of 

statistical life years saved. Life years saved measures differentiate among premature mortalities 

based on the remaining life expectancy of affected individuals. In general, under the life years 

approach, older individuals will gain fewer life years than younger individuals for the same 

reduction in mortality risk during a given time period, making interventions that benefit older 

individuals seem less beneficial relative to similar interventions benefiting younger individuals. 

A further complication in the debate is whether to apply quality adjustments to life years lost. 

Under this approach, individuals with preexisting health conditions would have fewer QALYs 

lost relative to healthy individuals for the same loss in life expectancy, making interventions that 

primarily benefit individuals with poor health seem less beneficial than similar interventions 

affecting primarily healthy individuals. 

In this CEA, we calculated both life years saved and statistical lives saved. Following the 

methodology used in the CEA for the PM NAAQS RIA, we did not assign QALY weights to the 

life years saved – i.e., we calculated life years saved, rather than QALYs gained from mortality 

avoided. Put another way, we assumed weights of 1.0 for all life years saved. Life years saved 

in the future, however, were discounted to reflect people’s time preference (i.e., a benefit 

received now is worth more than the same benefit received in the future). We used discount 

rates of 3 percent and 7 percent. 

Where possible, benefits that could not be quantified in the denominator of our cost-effectiveness 

ratios were monetized and subtracted from the cost of the regulation in the numerator. For 

example, developing QALYs for acute health effects is problematic (Bala and Zarkin, 2000).

Therefore, rather than try to derive QALYs for the acute morbidity endpoints, we instead applied 

valuation estimates and subtracted the total monetized value of all avoided acute morbidity 

effects from the cost of the regulation, in the numerator of the cost-effectiveness ratios. The 
7b-7

monetized benefits of non-health improvements, where they were estimated, were similarly 

subtracted from the cost of the regulation. Finally, although QALY estimates were derived for 

the (PM2.5-related) chronic morbidity endpoints, the medical and opportunity costs associated 

with these chronic illnesses were also subtracted from the cost of the regulation. 

Although there are indirect PM2.5-related co-benefits associated with all the illustrative O3

NAAQS attainment strategies, we were able to model the changes in PM2.5 occurring as a result 

of only one illustrative O3 NAAQS attainment strategy (see Chapter 7 for a full discussion of this 

issue). Therefore PM2.5-related co-benefits are included in the cost effectiveness metrics 

presented only for that one strategy. The cost effectiveness metrics presented for all of the other 

illustrative O3 NAAQS attainment strategies omit the PM2.5-related co-benefits and are therefore 

likely to understate the cost effectiveness of those strategies.

The indirect PM2.5-related co-benefits derive not only from avoided cases of premature mortality 

and acute morbidity, but from avoided cases of chronic morbidity (chronic bronchitis and nonfatal myocardial infarction) as well. In the CEA for the PM NAAQS RIA, EPA derived QALYs 

for these two chronic morbidity endpoints (see Appendix G of the PM NAAQS RIA, 

http://www.epa.gov/ttn/ecas/regdata/RIAs/Appendix%20G--

Health%20Based%20Cost%20Effectiveness%20Analysis.pdf) and used an alternative aggregate 

effectiveness metric, Morbidity Inclusive Life Years (MILYs), to address some of the concerns 

about aggregation of life extension and quality-of-life impacts. MILYs represent the sum of life 

years gained due to reductions in premature mortality and the QALYs gained due to reductions 

in chronic morbidity. This measure may be preferred to existing QALY aggregation approaches 

because it does not devalue life extensions in individuals with preexisting illnesses that reduce 

quality of life. However, the MILY measure is still based on life years and thus still inherently 

gives more weight to interventions that reduce mortality and morbidity impacts for younger 

populations with higher remaining life expectancy. 

For each illustrative O3 NAAQS attainment strategy, we present several metrics: lives saved, life 

years saved, and cost of the regulation (net of the monetized benefits not included in the 

denominator) per life saved and per life year saved. 

For the illustrative O3 NAAQS attainment strategy for which we were able to include both direct 

O3-related health benefits and indirect PM2.5-related co-benefits, in addition to the cost 

effectiveness metrics listed above we also calculated MILYs and the cost of the regulation (net 

of the monetized benefits not included in the denominator) per MILY gained. 

Note that, like future life years saved, future QALYs gained from avoided cases of chronic 

bronchitis and myocardial infarction are discounted. All costs and monetized benefits are in 

2006 dollars.

Monte Carlo simulation methods as implemented in the Crystal BallTM software program were 

used to propagate uncertainty in several of the model parameters throughout the analysis. In 

particular, we incorporated uncertainty surrounding the coefficients in the concentrationresponse (C-R) functions, the unit values for the various morbidity endpoints included in the 
7b-8

analysis, and the quality of life weights for the two chronic morbidity endpoints for which we 

developed QALYs. 

We characterized overall uncertainty in the results with 95 percent credible or confidence 

intervals based on the Monte Carlo simulations. In addition, we examined the impacts on the cost 

effectiveness metrics of changing key parameters and/or assumptions, including 

x the discount rate (for the cost of the regulation in the numerator and future lives or life 

years saved and QALYs gained in the denominator); 

x the C-R functions for O3-related and PM2.5-related mortality ; and 

x the life expectancies (and therefore years of potential life lost) of individuals who die as a 

result of exposure to O3 (as explained in Section 7b.5 below).

The methodology presented in this appendix is not intended to stand as precedent either for 

future air pollution regulations or for other EPA regulations where it may be inappropriate. It is 

intended solely to demonstrate one particular approach to estimating the cost-effectiveness of 

direct reductions in ambient O3 (and indirect reductions in PM2.5, where possible) in achieving 

improvements in public health. Reductions in ambient O3 and PM2.5 are estimated to have other 

health and environmental benefits that will not be reflected in this CEA. Other EPA regulations 

affecting other aspects of environmental quality and public health may require additional data 

and models that may preclude the development of similar health-based CEAs. A number of 

additional methodological issues must be considered when conducting CEAs for environmental 

policies, including treatment of non-health effects, aggregation of acute and long-term health 

impacts, and aggregation of life extensions and quality-of-life improvements in different 

populations. The appropriateness of health-based CEA should be evaluated on a case-by-case 

basis subject to the availability of appropriate data and models, among other factors. 

The remainder of this appendix provides an overview of the methods used to derive the cost 

effectiveness metrics developed for this CEA and presents the resulting metrics. Section 7b.3 

provides an overview of effectiveness measures. Section 7b.4 discusses general issues in 

constructing cost-effectiveness ratios. Section 7b.5 presents the methods and results for those 

illustrative O3 NAAQS attainment strategies for which we were able to incorporate only the O3-

related benefits; and Section 7b.6 presents the methods and results for the single illustrative O3

NAAQS attainment strategy for which we were able to include both the O3-related benefits and 

PM2.5-related co-benefits. Finally, Section 7b.7 presents concluding remarks.

7b.3 Effectiveness Measures 

For the purposes of CEA, we focus the effectiveness measures on the quantifiable health impacts 

of the reductions in O3 and, where possible, PM2.5, estimated to result from each illustrative O3

NAAQS attainment strategy considered. If the main impact of interest is reductions in mortality 

risk from air pollution, the effectiveness measures are relatively straightforward to develop.

Mortality impacts can be characterized similar to the benefits analysis, by counting the number 

of premature deaths avoided, or can be characterized in terms of increases in life expectancy or 
7b-9

life years.2

 Estimates of premature mortality have the benefit of being relatively simple to 

calculate, are consistent with the benefit-cost analysis, and do not impose additional assumptions 

on the degree of life shortening. However, some have argued that counts of premature deaths 

avoided are problematic because a gain in life of only a few months would be considered 

equivalent to a gain of many life years, and the true effectiveness of an intervention is the gain in 

life expectancy or life years (Rabl, 2003; Miller and Hurley, 2003). 

Calculations of changes in life years and life expectancy can be accomplished using standard life 

table methods (Miller and Hurley, 2003). However, the calculations require assumptions about 

the baseline mortality risks for each age cohort affected by air pollution. A general assumption 

may be that air pollution mortality risks affect the general mortality risk of the population in a 

proportional manner. However, some concerns have been raised that air pollution affects mainly 

those individuals with preexisting cardiovascular and respiratory disease, who may have reduced 

life expectancy relative to the general population. This issue is explored in more detail below. 

Air pollution is also associated with a number of significant chronic and acute morbidity 

endpoints. Failure to consider these morbidity effects may understate the cost-effectiveness of 

air pollution regulations or give too little weight to reductions in particular pollutants that have 

large morbidity impacts but no effect on life expectancy. The QALY approach explicitly 

incorporates morbidity impacts into measures of life years gained and is often used in health 

economics to assess the cost-effectiveness of medical spending programs (Gold et al., 1996). 

Using a QALY rating system, health quality ranges from 0 to 1, where 1 may represent full 

health, 0 death, and some number in between (e.g., 0.8) an impaired condition. QALYs thus 

measure morbidity as a reduction in quality of life over a period of life. QALYs assume that 

duration and quality of life are equivalent, so that 1 year spent in perfect health is equivalent to 2 

years spent with quality of life half that of perfect health. QALYs can be used to evaluate 

environmental rules under certain circumstances, although some very strong assumptions 

(detailed below) are associated with QALYs. The U.S. Public Health Service Panel on Cost 

Effectiveness in Health and Medicine recommended using QALYs when evaluating medical and 

public health programs that primarily reduce both mortality and morbidity (Gold et al., 1996).

Although there are significant non-health benefits associated with air pollution regulations, over 

90 percent of quantifiable monetized benefits are health-related. Thus, it can be argued that 

QALYs are more applicable for these types of regulations than for other environmental policies. 

However, the value of non-health benefits should not be ignored. As discussed below, we have 

chosen to subtract the value of non-health benefits from the costs in the numerator of the costeffectiveness ratio. 

2

 Life expectancy is an ex ante concept, indicating the impact on an entire population’s 

expectation of the number of life years they have remaining, before knowing which individuals 

will be affected. Life expectancy thus incorporates both the probability of an effect and the 

impact of the effect if realized. Life years is an ex post concept, indicating the impact on 

individuals who actually die from exposure to air pollution. Changes in population life 

expectancy will always be substantially smaller than changes in life years per premature 

mortality avoided, although the total life years gained in the population will be the same. This is 

because life expectancy gains average expected life years gained over the entire population, 

while life years gained measures life years gained only for those experiencing the life extension. 
7b-10

The use of QALYs is predicated on the assumptions embedded in the QALY analytical 

framework. As noted in the QALY literature, QALYs are consistent with the utility theory that 

underlies most of economics only if one imposes several restrictive assumptions, including 

independence between longevity and quality of life in the utility function, risk neutrality with 

respect to years of life (which implies that the utility function is linear), and constant 

proportionality in trade-offs between quality and quantity of life (Pliskin, Shepard, and 

Weinstein, 1980; Bleichrodt, Wakker, and Johannesson, 1996). To the extent that these 

assumptions do not represent actual preferences, the QALY approach will not provide results 

that are consistent with a benefit-cost analysis based on the Kaldor-Hicks criterion.3

 Even if the 

assumptions are reasonably consistent with reality, because QALYs represent an average 

valuation of health states rather than the sum of societal WTP, there are no guarantees that the 

option with the highest QALY per dollar of cost will satisfy the Kaldor-Hicks criterion (i.e., 

generate a potential Pareto improvement [Garber and Phelps, 1997]). 

Benefit-cost analysis based on WTP is not without potentially troubling underlying structures as 

well, incorporating ability to pay (and thus the potential for equity concerns) and the notion of 

consumer sovereignty (which emphasizes wealth effects). Table 7b-1 compares the two 

approaches across a number of parameters. For the most part, WTP allows parameters to be 

determined empirically, while the QALY approach imposes some conditions a priori.

Table 7b-1. Comparison of QALY and WTP Approaches 

Parameter QALY WTP 

Risk aversion Risk neutral Empirically determined 

Relation of duration and quality Independent Empirically determined 

Proportionality of duration/ quality trade-off Constant Variable 

Treatment of time/age in utility function Utility linear in time Empirically determined 

Preferences Community/Individual Individual 

Source of preference data Stated Revealed and stated 

Treatment of income and prices Not explicitly considered Constrains choices 

7b.4 Construction of Cost-Effectiveness Ratios: General Issues 

7b.4.1 Dealing with Morbidity Health Effects and Non-health Effects 

Health effects from exposure to O3 and PM2.5 air pollution encompass a wide array of chronic 

and acute conditions in addition to premature mortality. EPA’s Ozone and PM Criteria 

Documents outline numerous health effects known or suspected to be linked to exposure to 

ambient ozone and PM (US EPA, 2006; US EPA, 2005; Anderson et al., 2004). Although 

chronic conditions and premature mortality generally account for the majority of monetized 

3

 The Kaldor-Hicks efficiency criterion requires that the “winners” in a particular case be 

potentially able to compensate the “losers” such that total societal welfare improves. In this 

case, it is sufficient that total benefits exceed total costs of the regulation. This is also known as 

a potential Pareto improvement, because gains could be allocated such that at least one person in 

society would be better off while no one would be worse off. 
7b-11

benefits, acute symptoms can affect a broad population or sensitive populations (e.g., asthmarelated emergency room visits among asthmatics). In addition, reductions in air pollution may 

result in a broad set of non-health environmental benefits, including improved worker 

productivity, improved visibility in national parks, increased agricultural and forestry yields, 

reduced acid damage to buildings, and a host of other impacts. Lives saved, life years saved, and 

QALYs gained address only health impacts, and the OMB guidance notes that “where regulation 

may yield several different beneficial outcomes, a cost-effectiveness comparison becomes more 

difficult to interpret because there is more than one measure of effectiveness to incorporate in the 

analysis.”

With regard to acute health impacts, Bala and Zarkin (2000) suggest that QALYs are not 

appropriate for valuing acute symptoms, because of problems with both measuring utility for 

acute health states and applying QALYs in a linear fashion to very short duration health states.

Johnson and Lievense (2000) suggest using conjoint analysis to get healthy-utility time 

equivalences that can be compared across acute effects, but it is not clear how these can be 

combined with QALYs for chronic effects and loss of life expectancy. There is also a class of 

effects that EPA has traditionally treated as acute, such as hospital admissions, which may also 

result in a loss of quality of life for a period of time following the effect. For example, life after 

asthma hospitalization has been estimated with a utility weight of 0.93 (Bell et al., 2001; 

Kerridge, Glasziou, and Hillman, 1995). 

How should these effects be combined with QALYs for chronic and mortality effects? One 

method would be to convert the acute effects to QALYs; however, as noted above, there are 

problems with the linearity assumption (i.e., if a year with asthma symptoms is equivalent to 0.7 

year without asthma symptoms, then 1 day without asthma symptoms is equivalent to 0.0019 

QALY gained). This is troubling from both a conceptual basis and a presentation basis. An 

alternative approach is simply to treat acute health effects like non-health benefits and subtract 

the dollar value (based on WTP or COI) from compliance costs in the CEA. 

To address the issues of incorporating acute morbidity and non-health benefits, OMB suggests 

that agencies “subtract the monetary estimate of the ancillary benefits from the gross cost 

estimate to yield an estimated net cost.” As with benefit-cost analysis, any unquantified benefits 

and/or costs should be noted and an indication of how they might affect the cost-effectiveness 

ratio should be described. We followed this recommended “net cost” approach, specifically in 

netting out the benefits of health improvements other than reduced mortality and improved 

quality of life from avoided chronic illness – in particular, the monetized benefits of acute 

morbidity avoided, the medical and opportunity costs (“cost of illness”) of avoided chronic 

illness, and the benefits of non-health improvements, including increases in worker productivity 

associated with reductions in O3 and visibility improvements at national parks associated with 

reductions in PM2.5 (see Chapter 7 for more details on these benefit categories). 

7b.4.2 Should Life Years Gained Be Adjusted for Initial Health Status? 

The methods outlined below in Sections 7b.5 and 7b.6 provide estimates of the total number of 

life years gained in a population, regardless of the quality of those life years, or equivalently, 

assuming that all life years gained are in perfect health. In some CEAs (Cohen, Hammitt, and 

Levy, 2003; Coyle et al., 2003), analysts have adjusted the number of life years gained to reflect 
7b-12

the fact that 1) the general public is not in perfect health and thus “healthy” life years are less 

than total life years gained and 2) those affected by air pollution may be in a worse health state 

than the general population and therefore will not gain as many “healthy” life years adjusted for 

quality, from an air pollution reduction. This adjustment, which converts life years gained into 

QALYs, raises a number of serious ethical issues. Proponents of QALYs have promoted the 

nondiscriminatory nature of QALYs in evaluating improvements in quality of life (e.g., an 

improvement from a score of 0.2 to 0.4 is equivalent to an improvement from 0.8 to 1.0), so the 

starting health status does not affect the evaluation of interventions that improve quality of life.

However, for life-extending interventions, the gains in QALYs will be directly proportional to 

the baseline health state (e.g., an individual with a 30-year life expectancy and a starting health 

status of 0.5 will gain exactly half the QALYs of an individual with the same life expectancy and 

a starting health status of 1.0 for a similar life-extending intervention). This is troubling because

it imposes an additional penalty for those already suffering from disabling conditions. Brock 

(2002) notes that “the problem of disability discrimination represents a deep and unresolved 

problem for resource prioritization.” 

OMB (2003) has recognized this issue in their Circular A-4 guidance, which includes the 

following statement: 

 When CEA is performed in specific rulemaking contexts, you should be prepared to 

make appropriate adjustments to ensure fair treatment of all segments of the 

population. Fairness is important in the choice and execution of effectiveness 

measures. For example, if QALYs are used to evaluate a lifesaving rule aimed at a 

population that happens to experience a high rate of disability (i.e., where the rule is 

not designed to affect the disability), the number of life years saved should not 

necessarily be diminished simply because the rule saves the lives of people with lifeshortening disabilities. Both analytic simplicity and fairness suggest that the 

estimated number of life years saved for the disabled population should be based on 

average life expectancy information for the relevant age cohorts. More generally, 

when numeric adjustments are made for life expectancy or quality of life, analysts 

should prefer use of population averages rather than information derived from 

subgroups dominated by a particular demographic or income group. (p. 13) 

This suggests two adjustments to the standard QALY methodology: one adjusting the relevant 

life expectancy of the affected population, and the other affecting the baseline quality of life for 

the affected population. 

In addition to the issue of fairness, potential measurement issues are specific to the air pollution 

context that might argue for caution in applying quality-of-life adjustments to life years gained 

due to air pollution reductions. A number of epidemiological and toxicological studies link 

exposure to air pollution with chronic diseases, such as CB and atherosclerosis (Abbey et al., 

1995; Schwartz, 1993; Suwa et al., 2002). If these same individuals with chronic disease caused 

by exposure to air pollution are then at increased risk of premature death from air pollution, there 

is an important dimension of “double jeopardy” involved in determining the correct baseline for 

assessing QALYs lost to air pollution (see Singer et al. [1995] for a broader discussion of the 

double-jeopardy argument). 
7b-13

Analyses estimating mortality from acute exposures that ignore the effects of long-term exposure 

on morbidity may understate the health impacts of reducing air pollution. Individuals exposed to 

chronically elevated levels of air pollution may realize an increased risk of death and chronic 

disease throughout life. If at some age they contract heart (or some other chronic) disease as a 

result of the exposure to air pollution, they will from that point forward have both reduced life 

expectancy and reduced quality of life. The benefit to that individual from reducing lifetime 

exposure to air pollution would be the increase in life expectancy plus the increase in quality of 

life over the full period of increased life expectancy. If the QALY loss is determined based on 

the underlying chronic condition and life expectancy without regard to the fact that the person 

would never have been in that state without long-term exposure to elevated air pollution, then the 

person is placed in double jeopardy. In other words, air pollution has placed more people in the

susceptible pool, but then we penalize those people in evaluating policies by treating their 

subsequent deaths as less valuable, adding insult to injury, and potentially downplaying the 

importance of life expectancy losses due to air pollution. If the risk of chronic disease and risk 

of death are considered together, then there is no conceptual problem with measuring QALYs, 

but this has not been the case in recent applications of QALYs to air pollution (Carrothers, 

Evans, and Graham, 2002; Coyle et al., 2003). The use of QALYs thus highlights the need for a 

better understanding of the relationship between chronic disease and long-term exposure and 

suggests that analyses need to consider morbidity and mortality jointly, rather than treating each 

as a separate endpoint (this is an issue for current benefit-cost approaches as well). 

Because of the fairness and measurement concerns discussed above, for the purposes of this 

analysis, we do not reduce the number of life years gained to reflect any differences in 

underlying health status that might reduce quality of life in remaining years. Thus, we maintain 

the assumption that all direct gains in life years resulting from mortality risk reductions will be 

assigned a weight of 1.0. The U.S. Public Health Service Panel on Cost Effectiveness in Health 

and Medicine recommends that “since lives saved or extended by an intervention will not be in 

perfect health, a saved life year will count as less than 1 full QALY” (Gold et al., 1996). 

However, for the purposes of this analysis, we propose an alternative to the traditional aggregate 

QALY metric that keeps separate quality adjustments to life expectancy and gains in life 

expectancy. As such, we do not make any adjustments to life years gained to reflect the less than 

perfect health of the general population. Gains in quality of life will be addressed as they accrue 

because of reductions in the incidence of chronic diseases. This is an explicit equity choice in 

the treatment of issues associated with quality-of-life adjustments for increases in life expectancy 

that still capitalizes on the ability of QALYs to capture both morbidity and mortality impacts in a 

single effectiveness measure. 

7b.4.3 Constructing Cost-Effectiveness Ratios 

Construction of cost-effectiveness ratios requires estimates of effectiveness (in this case 

measured by lives saved, life years gained, or MILYs gained) in the denominator and estimates 

of costs in the numerator. The estimate of costs in the numerator should include both the direct 

costs of the controls necessary to achieve the reduction in ambient concentrations of the air 

pollutant and the avoided costs (cost savings) associated with the reductions in morbidity (Gold 

et al., 1996). In general, because reductions in air pollution do not require direct actions by the 

affected populations, there are no specific costs to affected individuals (aside from the overall 

increases in prices that might be expected to occur as control costs are passed on by affected 
7b-14

industries). Likewise, because individuals do not engage in any specific actions to realize the 

health benefit of the pollution reduction, there are no decreases in utility (as might occur from a 

medical intervention) that need to be adjusted for in the denominator. Thus, the elements of the 

numerator are direct costs of controls minus the avoided costs of illness (COI) associated with 

chronic illnesses. In addition, as noted above, to account for the value of reductions in acute 

health impacts and non-health benefits, we netted out the monetized value of these benefits from 

the numerator to yield a “net cost” estimate. 

The denominators of the cost-effectiveness ratios we calculated are either lives saved, life years 

saved, or, for the single scenario in which we were able to include both O3-related and PM2.5-

related benefits, MILYs gained. For the MILY aggregate effectiveness measure, the denominator 

is simply the sum of life years gained from increased life expectancy and QALYs gained from 

the reductions in incidence of chronic illnesses associated with PM2.5 – chronic bronchitis (CB) 

and nonfatal acute myocardial infarction (AMI). 

7b.5 Cost Effectiveness Metrics Incorporating Only O3-Related Benefits 

In this section we describe the development of cost effectiveness metrics for those illustrative O3

NAAQS attainment strategies for which we were able to incorporate only O3-related benefits.

This includes the scenarios in which the baseline is full attainment of the current O3 standard of 

0.084 ppm and the control scenarios are full attainment of the following four alternative 

standards: 0.079 ppm, 0.075 ppm, 0.070 ppm, and 0.065 ppm. 

To generate health outcomes, we used the same framework as for the benefit-cost analysis 

described in Chapter 8. For convenience, we summarize the basic methodologies here. For 

more details, see Chapter 8 and the Environmental Benefits Mapping and Analysis Program 

(BenMAP) user’s manual (http://www.epa.gov/ttn/ecas/benmodels.html).

BenMAP uses health impact functions to generate changes in the incidence of health effects.

Health impact functions are derived from the C-R functions reported in the epidemiology 

literature. A standard health impact function has four components: an effect estimate from a 

particular epidemiological study, a baseline incidence rate for the health effect (obtained from 

either the epidemiology study or a source of public health statistics, such as CDC), the affected 

population, and the estimated change in the relevant pollutant summary measure. 

A typical health impact function might look like this: 

'

'

y y e

x

  



0

( ) , 1

E

where y0 is the baseline incidence, equal to the baseline incidence rate times the potentially 

affected population;  is the effect estimate; x is the estimated change in the pollutant (e.g., O3

or PM2.5) and y is the estimated change in incidence of the health effect (e.g., the number of 

deaths avoided) associated with the change in the pollutant, x. There are other functional 

forms, but the basic elements remain the same. 
7b-15

7b.5.1 Reductions in O3-Related Premature Deaths 

To calculate O3-related life years saved under a given illustrative O3 NAAQS attainment 

strategy, we first calculated the numbers of O3-related statistical lives saved within 5-year age 

groups, using BenMAP. (For more details on the calculation of statistical lives saved using 

BenMAP, see Chapter 8 or the BenMAP user’s manual 

(http://www.epa.gov/ttn/ecas/benmodels.html). We used two studies used in the benefit analysis

for the O3 NAAQS RIA – Bell et al. (2004) and Levy et al. (2005). Both studies report estimated 

C-R functions of the association between premature mortality and short-term exposures to 

ambient O3. Bell et al. (2004) is a multi-city study of 95 cities, and as such may avoid the 

potential for publication bias that may be inherent in single-city studies or meta-analyses of 

single-city studies. This study provides the lowest estimate of O3-related premature deaths 

among the mortality studies included in the O3 NAAQS RIA benefit analysis. An upper bound 

estimate of O3-related premature deaths in the O3 NAAQS RIA benefit analysis was provided by 

Levy et al. (2005). More extensive discussions of these studies are given in Chapter 8. 

We checked to confirm that, for each O3 NAAQS attainment strategy, the total number of O3-

related statistical lives saved, summed across all age groups, equals the corresponding number 

calculated in the benefit analysis. Age group-specific O3-related premature deaths avoided under 

the illustrative O3 NAAQS attainment strategies for which we considered only O3-related 

benefits are given in Table 7b-2. 
7b-16

Table 7b-2. Estimated Reduction in Incidence of O3-Related Premature Mortality Associated with 

Illustrative O3 NAAQS Attainment Strategies in 2020

Baseline of Full Attainment of Current (0.084 ppm) Standard to Control Scenario of Full Attainment of:

0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm

0 0 1 2 1 2 7 12

(0 - 0) (0 - 1) (0 - 2) (1 - 3) (1 - 1) (2 - 3) (5 - 9) (8 - 16)

0 0 0 1 0 1 3 5

(0 - 0) (0 - 0) (0 - 0) (0 - 1) (0 - 1) (1 - 1) (2 - 4) (4 - 7)

0 0 0 1 0 1 3 5

(0 - 0) (0 - 0) (0 - 0) (0 - 1) (0 - 1) (1 - 1) (2 - 4) (4 - 7)

0 0 0 1 1 2 5 9

(0 - 0) (0 - 0) (0 - 1) (0 - 1) (1 - 1) (1 - 2) (4 - 7) (6 - 12)

0 0 0 1 1 3 9 15

(0 - 0) (0 - 0) (0 - 1) (0 - 1) (1 - 2) (2 - 4) (6 - 11) (10 - 20)

0 0 1 3 2 4 12 22

(0 - 0) (0 - 1) (0 - 2) (1 - 4) (1 - 2) (3 - 6) (9 - 16) (15 - 29)

0 0 1 2 2 4 12 21

(0 - 0) (0 - 1) (0 - 2) (1 - 4) (1 - 2) (3 - 5) (8 - 15) (14 - 27)

0 1 3 5 3 6 18 32

(0 - 1) (0 - 2) (1 - 5) (2 - 9) (2 - 3) (4 - 8) (12 - 24) (22 - 42)

0 1 3 5 2 6 17 29

(0 - 1) (0 - 2) (1 - 5) (2 - 8) (2 - 3) (4 - 8) (11 - 22) (20 - 38)

1 2 7 12 5 12 34 60

(0 - 2) (1 - 4) (2 - 12) (4 - 20) (3 - 6) (8 - 15) (23 - 45) (41 - 79)

1 2 7 13 5 12 35 62

(0 - 2) (1 - 4) (2 - 12) (4 - 21) (3 - 6) (8 - 15) (24 - 47) (43 - 82)

3 7 20 35 12 30 91 160

(1 - 4) (2 - 11) (6 - 34) (12 - 59) (8 - 15) (21 - 39) (63 - 120) (110 - 210)

2 6 19 33 11 27 85 150

(1 - 4) (2 - 10) (6 - 32) (11 - 55) (7 - 14) (19 - 36) (58 - 110) (100 - 190)

4 11 36 63 19 50 160 280

(1 - 7) (4 - 19) (11 - 61) (21 - 110) (13 - 25) (34 - 65) (110 - 210) (190 - 360)

3 9 28 49 15 38 120 220

(1 - 6) (3 - 15) (9 - 47) (16 - 83) (10 - 19) (27 - 50) (84 - 160) (150 - 290)

5 14 45 80 23 61 200 350

(2 - 9) (5 - 23) (14 - 75) (26 - 130) (16 - 30) (42 - 80) (130 - 260) (240 - 460)

3 9 29 51 15 39 130 220

(1 - 6) (3 - 15) (9 - 49) (17 - 85) (10 - 19) (27 - 51) (86 - 170) (150 - 290)

11 30 94 170 49 130 140 240

(4 - 19) (10 - 50) (30 - 160) (54 - 280) (34 - 64) (90 - 170) (280 - 540) (500 - 960)

36 94 300 520 160 430 1,300 2,400

(12 - 60) (31 - 160) (93 - 500) (170 - 880) (110 - 210) (300 - 560) (920 - 1,800) (1,600 - 3,100)

*95 percent confidence or credible intervals (CIs) are based on the uncertainty about the coefficient in the mortality C-R functions. 

All estimates rounded to two significant figures. 

Bell et al. (2004) Levy et al. (2005)

Reduction in O3-Related Premature Mortality 

(95% CI)*

Age 

Interval

0 - 4

5 - 9

10 - 14

15 - 19

20 - 24

25 - 29

30 - 34

35 - 39

40 - 44

45 - 49

50 - 54

55 - 59

Total:

60 - 64

65 - 69

70 - 74

75 - 79

80 - 84

85+
7b-17

7b.5.2 Life Years Saved as a Result of Reductions in O3-Related Mortality Risk

The number of life years saved depends not only on the number of statistical lives saved, but also 

on the life expectancies associated with those statistical lives. As was pointed out in the CEA for 

the PM NAAQS RIA, age-specific life expectancies for the general population are calculated 

from mortality rates for the general population, and these reflect the prevalence of chronic 

disease, which shortens life expectancies. The only reason one might use lower life expectancies 

than those for the general population in the CEA for the O3 NAAQS RIA is if the population at 

risk from exposure to O3 was limited solely or disproportionately to individuals with preexisting 

chronic illness, whose life expectancies were, on average, shorter than those of the general 

population (unless all of those individuals had preexisting chronic illness because of long-term 

exposure to O3).

It is reasonable to assume that someone who dies from exposure to an air pollutant is already in a 

compromised state. However, there are both acute and chronic compromised states. If an 

individual has an acute illness (e.g., pneumonia) that puts him at risk of mortality when exposed 

to a high concentration of an air pollutant, then in the absence of that high concentration he could 

be expected to recover from the illness and go on to live the expected number of years for 

someone his age – i.e., he would have the age-specific life expectancy of the general population.

If an individual has a chronic illness that makes him vulnerable to a high concentration of an air 

pollutant, then an important question is whether or not he would have had that chronic illness if 

he had not been exposed over the long term to high levels of the air pollutant.

We can categorize individuals who are at risk of dying because of exposure to an air pollutant 

into three groups:

x those who are vulnerable because of a preexisting acute condition; 

x those who are vulnerable because of a preexisting chronic condition that they would not

have had, had they not been exposed over the long term to high levels of the air pollutant; 

and

x those who are vulnerable because of a preexisting chronic condition that they would have 

had even in the absence of long term exposure to high levels of the air pollutant.

The age-specific life expectancies of the general population should apply to the first two groups, 

and the age-specific life expectancies of the subpopulation with the relevant chronic condition(s) 

should apply to the third group. If we knew the proportions of people who die from exposure to 

O3 who are in each group, and the life expectancies of people in the third group, we could 

calculate the number of life years saved as follows:

(* * * * )

*

1i i 2i i 3i i

i

Total life years saved ¦ Mi p LE  p LE  p LE

 where

Mi

 denotes the number of O3-related deaths of individuals age i,

LEi denotes the general population life expectancy for age i,
7b-18

LEi

*

 denotes the life expectancy for age i of the subpopulation with the relevant chronic 

condition(s) – i.e., the third group; 

p1i denotes the proportion of the Mi O3-related deaths that are in the first group; 

p2i denotes the proportion of the Mi O3-related deaths that are in the second group; and 

p3i denotes the proportion of the Mi O3-related deaths that are in the third group. 

Unlike for PM2.5 (discussed below in Section 7b.6), we currently lack information that would 

allow us to estimate the relevant proportions necessary to estimate the set of life expectancies 

that would be appropriate to apply to O3-related deaths. Although there is substantial evidence 

linking premature mortality to short-term exposures to O3, there is currently not similar evidence 

for long-term exposures. We therefore do not know if the second group above is relevant in the 

case of O3-related mortality. Nor do we know what proportion of O3-related deaths can be 

attributed to preexisting acute conditions (the first group) versus preexisting chronic conditions 

that these individuals would have had even in the absence of long term exposure to O3 (the third 

group).

Because we currently lack the necessary information to determine the appropriate set of life 

expectancies to use in calculating life years saved associated with O3-related premature mortality 

avoided, we calculated life years saved based on four different underlying assumptions: 

x A lower bound assumption of zero life years saved, based on the hypothesis that the 

observed statistical association between premature mortality and short-term exposures to 

O3 is not actually a causal relationship; 

x An upper bound assumption that an O3-related premature death of an individual of a 

given age will result in a loss of life years equal to the life expectancy in the general 

population of that age; 

x Two intermediate assumptions: That the proportions of O3-related premature deaths in 

the three groups delineated above (p1i, p2i, and p3i) are such that, on average, the agespecific life expectancies among people who die O3-related premature deaths are those 

of

o people with severe preexisting chronic conditions, whose life expectancies are 

substantially shorter than those of the general population; and 

o people with preexisting chronic conditions of a range of severities, whose life 

expectancies are somewhat shorter than those of the general population. 

Life years saved based on the upper bound assumption were calculated from age-specific 

mortality probabilities for the general population taken from the Centers for Disease Control 

(CDC) National Vital Statistics Reports, Vol. 56, No. 9, December 28, 2007, Table 1. Life table 

for the total population: United States, 2004.4 We used a simplified method of calculating life 

expectancies from these age-specific mortality probabilities that yielded life expectancies that 

were close to the life expectancies derived using the more complicated method employed by the 

4

http://www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr56_09.pdf
7b-19

CDC.5

 In particular, starting with a cohort of size 1,000,000 at birth, we calculated the life-years 

lived between ages x and (x+1), for x = 0, 1, 2, ..., 99, using the age-specific mortality 

probabilities taken from the CDC Vital Statistics Report (see above) and assuming that all deaths 

that occurred between ages x and (x+1) occurred midway through the year (i.e., we assigned 0.5 

life-year to each year of death). The life expectancy at age n was then calculated as the sum of 

the life-years lived from age n through age 100 divided by the cohort size at age n. The life 

expectancy at age n is the number of life years lost due to an O3-related premature mortality of 

an individual age n.

To estimate life years saved under the two intermediate assumptions about the life years lost as a 

result of O3-related premature mortality, we turned to the epidemiological evidence of a 

statistically significant association between short-term exposures to O3 and respiratory hospital 

admissions. This evidence suggests that these short-term exposures may exacerbate respiratory 

conditions that were preexisting. It is reasonable to suppose that some of these hospitalizations 

for respiratory illnesses on days of relatively high O3 concentrations might result in death. It 

may also be the case that some individuals who did not go to the hospital might also die. We 

therefore looked for information on life expectancies of people with chronic respiratory 

conditions.

While there is information readily available in vital statistics sources on rates of death from

chronic respiratory diseases, there is not similarly available information on rates of death among

that subpopulation who suffer from those diseases. It is the latter rate – the rate of death among 

that subpopulation who suffers from those diseases – that is of interest. 

A recent study of people with and without chronic obstructive pulmonary disease (COPD) 

provided data from which we were able to construct estimates of the mortality rates of interest. 

Mannino et al. (2006) followed a cohort of 15,440 subjects ages 43 to 66 for up to 11 years. The 

cohort subjects were selected from the larger cohort of the Atherosclerosis Risk in Communities 

(ARIC) study, which selected its subjects from the population of four U.S. communities by 

probability sampling.6 The subjects in the Mannino study were limited to the ARIC participants 

who provided baseline information on respiratory symptoms and diagnoses, who underwent 

pulmonary function testing, and for whom follow-up data were available. 

Using a modification of the criteria developed by the Global Initiative on Obstructive Lung 

Disease (GOLD), Mannino et al. (2006) classified the study subjects into COPD severity groups 

(or stages), with GOLD stage 0 (presence of respiratory symptoms in the absence of any lung 

function abnormality) being the least severe COPD group, and GOLD stages 3 and 4 being the 

most severe. The unadjusted death rates of the study participants (taken from Table 1 of 

Mannino et al., 2006), ratios of (unadjusted) death rates, and hazard ratios, based on Cox 

5

 We calculated life expectancies from the mortality probabilities rather than using the life 

expectancies given in the CDC table because we were going to also calculate life expectancies 

for the subpopulations with severe COPD and with “average” COPD by adjusting the agespecific mortality probabilities and then calculating life expectancies using these adjusted 

probabilities.

6

 In one of the four communities probability sampling was used to select African-Americans 

only.
7b-20

proportional hazard regressions, which took into account several covariates (including, among 

others, age, sex, race, smoking status, and education level) are shown in the table below. In 

addition, the right-most column of the table below shows the proportion of COPD subjects in the 

study in each GOLD category. 

Table 7b-3. Death Rates and Hazard Ratios for Subjects with Varying Degrees of Severity of 

COPD (from Mannino et al., 2006) 

GOLD 3 or 4 271 92 33.9% 2,143 42.9 7.97 5.7 4.77%

GOLD 2 1,484 232 15.6% 12,852 18.1 3.35 2.4 26.14%

GOLD 1 1,679 137 8.2% 15,031 9.1 1.69 1.4 29.57%

GOLD 0 2,244 204 9.1% 20,191 10.1 1.88 1.5 39.52%

Restricted 1,101 150 13.6% 9,644 15.6 2.89 2.3

Normal 8,661 427 4.9% 79,317 5.4 1.00 1.0

Total 15,440 1,242 8.0% 139,178 8.9

*Global Inititative on Obstructive Lung Disease (GOLD) guidelines for the staging of COPD severity.

**See Mannino et al. (2006), p. 117.

PersonYears

Death Rate 

per 1,000 

Person-Years

Hazard 

Ratio**

Proportion of COPD 

Subjects in GOLD 

Category

Ratio of Death Rate 

to Death Rate for 

Normal Population

GOLD* Category N Deaths (%)

The ratios of unadjusted death rates are somewhat larger than the corresponding hazard ratios 

because these ratios were not adjusted for age. COPD is a progressive disease, so it would be 

expected that the proportion of older individuals would increase as the stages (and severity) 

increased, and this was indeed the case in the Mannino study. The hazard ratios, being based on 

regressions that took age into account, avoid this problem. We therefore used the hazard ratios 

to derive age-specific mortality rates for individuals with (1) severe COPD and (2) COPD of 

“average” severity. In particular, to derive age-specific mortality probabilities for the 

subpopulation with severe COPD, we multiplied each age-specific mortality probability for the 

general population by 5.7 (the hazard ratio for GOLD 3 or 4); to derive age-specific mortality 

probabilities for the subpopulation with “average” COPD, we multiplied each age-specific 

mortality probability for the general population by a weighted average of the GOLD categoryspecific hazard ratios, where the weight for a GOLD category was the proportion of COPD 

subjects in that GOLD category (given in the right-most column of Table 1 above). The 

weighted average hazard ratio was 1.906. Age-specific life expectancies were then derived for 

the severe COPD and “average” COPD subpopulations using these adjusted mortality 

probabilities and the method for calculating life expectancies described above.

Once an appropriate set of life expectancies has been determined (e.g., life expectancies for the 

general population or life expectancies for a subpopulation with severe COPD), these then 

provide the number of life years lost for an individual who dies at a given age. This information 

can then be combined with the estimated number of O3-related premature deaths at each age 

calculated with BenMAP (see previous subsection). Because BenMAP calculates numbers of 

premature deaths avoided within age intervals, we can either allocate the premature deaths 

avoided within an age interval uniformly to the ages within the interval or, alternatively, we can 

calculate average life expectancies for the age intervals. We illustrate the first approach in 
7b-21

calculating O3-related life years saved and the second approach in calculating PM2.5-related life 

years saved (see Section 7b.6).

Total O3-related life years gained was calculated as the sum of life years gained at each age: 

i

N

i

Total life years gained ¦LEi u M

 0

where LEi is the remaining life expectancy for age i, Mi is the number of premature deaths 

avoided among individuals age i, and N is the oldest age considered. 

For the purposes of determining cost effectiveness, it is also necessary to consider the timedependent nature of the gains in life years. Standard economic theory suggests that benefits 

occurring in future years should be discounted relative to benefits occurring in the present. OMB 

and EPA guidance suggest discount rates of three and seven percent. Selection of a 3 percent 

discount rate is also consistent with recommendations from the U.S. Public Health Service Panel 

on Cost Effectiveness in Health and Medicine (Gold et al., 1996). 

Discounted total life years gained is calculated as follows: 

Discounted LY e dt rt

LE

 



3

,

0

where r is the discount rate, t indicates time, and LE is the life expectancy at the time when the 

premature death would have occurred. Because O3-related premature mortality is associated 

only with short-term exposures, all O3-related premature deaths are assumed to occur in the year 

of exposure. We therefore did not discount O3-related premature deaths avoided. 

Undiscounted age-specific life expectancies, and age-specific life expectancies using discount 

rates of 3 percent and 7 percent are given for the general population, the subpopulation of 

individuals with severe COPD, and the subpopulation of individuals with COPD of average 

severity in Tables 7b-4, 7b-5, and 7b-6, respectively. The O3-related (discounted) life years 

saved, based on each of the two O3-mortality studies and each of the assumptions about relevant 

life expectancies, are given, using 3 percent and 7 percent discount rates, in Tables 7b-7 and 7b8, respectively. The O3-related (discounted) life years saved, under the first assumption – that 

the observed statistical association between premature mortality and short-term exposures to O3

is not actually a causal relationship – is zero in all cases (i.e., regardless of the mortality study 

used and the scenario considered), and is therefore not shown in these Tables. 
7b-22

Table 7b-4. Undiscounted and Discounted Age-Specific Life Expectancies for the General 

Population

Age at 

Beginning 

of Year

Mortality 

Probability* Cohort Size

Deaths in 

Year

Life-Years 

in Year

Age-Specific 

Life 

Expectancy

3% Discounted 

Remaining Life 

Expectancy

7% Discounted 

Remaining Life 

Expectancy

0 0.006799 1,000,000 6,799 996,600 77.8 30.9 15.2

1 0.000483 993,201 480 992,961 77.3 30.8 15.2

2 0.000297 992,721 295 992,574 76.4 30.7 15.2

3 0.000224 992,427 222 992,315 75.4 30.6 15.2

4 0.000188 992,204 187 992,111 74.4 30.5 15.2

5 0.000171 992,017 170 991,932 73.4 30.4 15.2

6 0.000161 991,847 159 991,768 72.4 30.3 15.2

7 0.000151 991,688 149 991,613 71.4 30.2 15.2

8 0.000136 991,538 135 991,471 70.4 30.1 15.2

9 0.000119 991,403 118 991,345 69.5 29.9 15.1

10 0.000106 991,286 105 991,233 68.5 29.8 15.1

11 0.000112 991,180 111 991,125 67.5 29.7 15.1

12 0.000149 991,070 148 990,996 66.5 29.5 15.1

13 0.000227 990,922 225 990,809 65.5 29.4 15.1

14 0.000337 990,697 333 990,530 64.5 29.2 15.1

15 0.000460 990,363 456 990,135 63.5 29.1 15.1

16 0.000579 989,907 573 989,621 62.5 28.9 15.1

17 0.000684 989,334 677 988,996 61.6 28.8 15.0

18 0.000763 988,657 755 988,280 60.6 28.6 15.0

19 0.000819 987,902 809 987,498 59.7 28.4 15.0

20 0.000873 987,093 862 986,662 58.7 28.3 15.0

21 0.000926 986,231 913 985,775 57.8 28.1 15.0

22 0.000960 985,318 946 984,845 56.8 27.9 15.0

23 0.000972 984,372 957 983,893 55.9 27.8 14.9

24 0.000969 983,415 953 982,939 54.9 27.6 14.9

25 0.000960 982,462 943 981,991 54.0 27.4 14.9

26 0.000954 981,519 936 981,051 53.0 27.2 14.9

27 0.000952 980,583 933 980,117 52.1 27.0 14.8

28 0.000958 979,650 939 979,181 51.1 26.8 14.8

29 0.000973 978,712 952 978,235 50.2 26.5 14.8

30 0.000994 977,759 972 977,273 49.2 26.3 14.7

31 0.001023 976,787 999 976,287 48.3 26.1 14.7

32 0.001063 975,788 1,038 975,269 47.3 25.9 14.7

33 0.001119 974,750 1,091 974,205 46.4 25.6 14.6

34 0.001192 973,659 1,160 973,079 45.4 25.4 14.6

35 0.001275 972,499 1,240 971,879 44.5 25.1 14.5

36 0.001373 971,259 1,334 970,592 43.5 24.9 14.5

37 0.001493 969,925 1,448 969,201 42.6 24.6 14.4

38 0.001634 968,477 1,582 967,686 41.7 24.3 14.4

39 0.001788 966,895 1,729 966,031 40.7 24.0 14.3

40 0.001945 965,166 1,877 964,228 39.8 23.7 14.3

41 0.002107 963,290 2,029 962,275 38.9 23.5 14.2

42 0.002287 961,260 2,198 960,161 38.0 23.2 14.1

43 0.002494 959,062 2,392 957,866 37.0 22.8 14.0

44 0.002727 956,670 2,609 955,366 36.1 22.5 14.0

45 0.002982 954,061 2,845 952,639 35.2 22.2 13.9

46 0.003246 951,216 3,088 949,672 34.3 21.9 13.8
7b-23

Table 7b-4. Undiscounted and Discounted Age-Specific Life Expectancies for the General 

Population (cont’d) 

Age at 

Beginning 

of Year

Mortality 

Probability* Cohort Size

Deaths in 

Year

Life-Years 

in Year

Age-Specific 

Life 

Expectancy

3% Discounted 

Remaining Life 

Expectancy

7% Discounted 

Remaining Life 

Expectancy

47 0.003520 948,129 3,337 946,460 33.5 21.6 13.7

48 0.003799 944,792 3,589 942,997 32.6 21.2 13.6

49 0.004088 941,203 3,848 939,279 31.7 20.9 13.5

50 0.004404 937,355 4,128 935,291 30.8 20.5 13.4

51 0.004750 933,227 4,433 931,010 30.0 20.2 13.3

52 0.005113 928,794 4,749 926,419 29.1 19.8 13.2

53 0.005488 924,045 5,071 921,510 28.2 19.4 13.0

54 0.005879 918,974 5,403 916,273 27.4 19.1 12.9

55 0.006295 913,571 5,751 910,696 26.6 18.7 12.7

56 0.006754 907,820 6,131 904,755 25.7 18.3 12.6

57 0.007280 901,689 6,564 898,407 24.9 17.9 12.4

58 0.007903 895,125 7,074 891,588 24.1 17.5 12.3

59 0.008633 888,051 7,667 884,217 23.3 17.1 12.1

60 0.009493 880,384 8,357 876,205 22.5 16.7 11.9

61 0.010449 872,027 9,112 867,471 21.7 16.2 11.8

62 0.011447 862,915 9,878 857,976 20.9 15.8 11.6

63 0.012428 853,037 10,601 847,736 20.1 15.4 11.4

64 0.013408 842,435 11,295 836,788 19.4 15.0 11.2

65 0.014473 831,140 12,029 825,126 18.6 14.5 11.0

66 0.015703 819,111 12,863 812,680 17.9 14.1 10.7

67 0.017081 806,249 13,771 799,363 17.2 13.7 10.5

68 0.018623 792,477 14,758 785,098 16.5 13.2 10.3

69 0.020322 777,719 15,805 769,817 15.8 12.8 10.0

70 0.022104 761,915 16,841 753,494 15.1 12.3 9.8

71 0.024023 745,073 17,899 736,124 14.4 11.9 9.5

72 0.026216 727,174 19,064 717,642 13.7 11.5 9.3

73 0.028745 708,110 20,355 697,933 13.1 11.0 9.0

74 0.031561 687,756 21,706 676,903 12.5 10.6 8.7

75 0.034427 666,050 22,930 654,585 11.9 10.2 8.4

76 0.037379 643,120 24,039 631,100 11.3 9.7 8.2

77 0.040756 619,080 25,231 606,465 10.7 9.3 7.9

78 0.044764 593,849 26,583 580,558 10.1 8.9 7.6

79 0.049395 567,266 28,020 553,256 9.6 8.5 7.3

80 0.054471 539,246 29,373 524,560 9.0 8.1 7.0

81 0.059772 509,873 30,476 494,635 8.5 7.7 6.7

82 0.065438 479,397 31,371 463,712 8.1 7.3 6.4

83 0.071598 448,026 32,078 431,987 7.6 6.9 6.1

84 0.078516 415,949 32,659 399,619 7.1 6.5 5.8

85 0.085898 383,290 32,924 366,828 6.7 6.2 5.6

86 0.093895 350,366 32,897 333,917 6.3 5.8 5.3

87 0.102542 317,468 32,554 301,192 5.9 5.5 5.0

88 0.111875 284,915 31,875 268,977 5.5 5.1 4.7

89 0.121928 253,040 30,853 237,613 5.1 4.8 4.5

90 0.132733 222,187 29,492 207,441 4.8 4.5 4.2

91 0.144318 192,695 27,809 178,791 4.4 4.2 3.9

92 0.156707 164,886 25,839 151,967 4.1 3.9 3.7

93 0.169922 139,047 23,627 127,234 3.7 3.6 3.4

94 0.183975 115,420 21,234 104,803 3.4 3.3 3.1

95 0.198875 94,186 18,731 84,820 3.0 3.0 2.8

96 0.214620 75,454 16,194 67,357 2.7 2.6 2.5

97 0.231201 59,260 13,701 52,410 2.3 2.2 2.2

98 0.248600 45,559 11,326 39,896 1.8 1.8 1.8

99 0.266786 34,233 9,133 29,667 1.2 1.2 1.2

100 1.000000 25,100 25,100 12,550 0.5 0.5 0.5

*Mortality probabilities for the general population taken from Table 1. Life table for the total population: 

United States, 2004. CDC National Vital Statistics Reports, Vol. 56, No. 9, December 28, 2007

http://www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr56_09.pdf
7b-24

Table 7b-5. Undiscounted and Discounted Age-Specific Life Expectancies for the Subpopulation 

with Severe COPD 

Age at 

Beginning 

of Year

Mortality 

Probability* Cohort Size

Deaths in 

Year

Life-Years 

in Year

Age-Specific 

Life 

Expectancy

3% Discounted 

Remaining Life 

Expectancy

7% Discounted 

Remaining Life 

Expectancy

0 0.038755 1,000,000 38,755 980,622 54.5 27.5 14.9

1 0.002752 961,245 2,646 959,922 55.7 27.7 14.9

2 0.001692 958,599 1,622 957,788 54.9 27.5 14.9

3 0.001277 956,977 1,222 956,366 53.9 27.4 14.9

4 0.001074 955,755 1,026 955,242 53.0 27.2 14.9

5 0.000978 954,729 933 954,263 52.1 27.0 14.8

6 0.000916 953,796 873 953,359 51.1 26.8 14.8

7 0.000859 952,923 819 952,513 50.2 26.5 14.8

8 0.000777 952,104 739 951,734 49.2 26.3 14.7

9 0.000677 951,365 644 951,043 48.2 26.1 14.7

10 0.000606 950,721 576 950,433 47.3 25.8 14.7

11 0.000636 950,145 605 949,842 46.3 25.6 14.6

12 0.000850 949,540 807 949,137 45.3 25.3 14.6

13 0.001295 948,733 1,229 948,119 44.4 25.1 14.5

14 0.001918 947,505 1,818 946,596 43.4 24.8 14.5

15 0.002625 945,687 2,482 944,446 42.5 24.6 14.4

16 0.003301 943,205 3,113 941,648 41.6 24.3 14.4

17 0.003901 940,092 3,667 938,258 40.8 24.0 14.3

18 0.004351 936,424 4,075 934,387 39.9 23.8 14.3

19 0.004671 932,350 4,355 930,172 39.1 23.5 14.2

20 0.004976 927,995 4,618 925,686 38.3 23.3 14.1

21 0.005278 923,377 4,873 920,941 37.5 23.0 14.1

22 0.005472 918,504 5,026 915,991 36.7 22.7 14.0

23 0.005542 913,478 5,063 910,947 35.9 22.4 13.9

24 0.005522 908,415 5,016 905,907 35.1 22.2 13.9

25 0.005470 903,399 4,942 900,928 34.2 21.9 13.8

26 0.005436 898,458 4,884 896,016 33.4 21.6 13.7

27 0.005425 893,573 4,847 891,150 32.6 21.2 13.6

28 0.005461 888,726 4,853 886,300 31.8 20.9 13.5

29 0.005547 883,873 4,903 881,422 31.0 20.6 13.4

30 0.005668 878,970 4,982 876,479 30.1 20.2 13.3

31 0.005830 873,988 5,095 871,440 29.3 19.9 13.2

32 0.006061 868,893 5,266 866,260 28.5 19.5 13.1

33 0.006380 863,626 5,510 860,872 27.6 19.2 12.9

34 0.006792 858,117 5,828 855,203 26.8 18.8 12.8

35 0.007269 852,289 6,195 849,191 26.0 18.4 12.7

36 0.007827 846,094 6,622 842,783 25.2 18.0 12.5

37 0.008510 839,472 7,144 835,900 24.4 17.6 12.3

38 0.009312 832,328 7,750 828,452 23.6 17.2 12.2

39 0.010191 824,577 8,403 820,376 22.8 16.8 12.0

40 0.011084 816,174 9,047 811,651 22.0 16.4 11.8

41 0.012008 807,128 9,692 802,282 21.3 16.0 11.7

42 0.013035 797,436 10,395 792,238 20.5 15.6 11.5

43 0.014215 787,041 11,187 781,447 19.8 15.2 11.3

44 0.015546 775,854 12,061 769,823 19.1 14.8 11.1

45 0.016996 763,792 12,981 757,301 18.4 14.4 10.9

46 0.018503 750,811 13,892 743,865 17.7 14.0 10.7
7b-25

Table 7b-5. Undiscounted and Discounted Age-Specific Life Expectancies for the Subpopulation 

with Severe COPD (cont’d) 

Age at 

Beginning 

of Year

Mortality 

Probability* Cohort Size

Deaths in 

Year

Life-Years 

in Year

Age-Specific 

Life 

Expectancy

3% Discounted 

Remaining Life 

Expectancy

7% Discounted 

Remaining Life 

Expectancy

47 0.020061 736,919 14,784 729,527 17.0 13.6 10.4

48 0.021652 722,135 15,636 714,317 16.3 13.1 10.2

49 0.023303 706,500 16,464 698,268 15.7 12.7 10.0

50 0.025103 690,036 17,322 681,375 15.0 12.3 9.8

51 0.027075 672,714 18,214 663,607 14.4 11.9 9.5

52 0.029144 654,500 19,075 644,963 13.8 11.5 9.3

53 0.031280 635,425 19,876 625,487 13.2 11.1 9.0

54 0.033512 615,549 20,628 605,235 12.6 10.7 8.8

55 0.035880 594,921 21,346 584,248 12.0 10.3 8.5

56 0.038497 573,575 22,081 562,535 11.5 9.9 8.2

57 0.041497 551,494 22,885 540,052 10.9 9.5 8.0

58 0.045046 528,609 23,812 516,703 10.3 9.0 7.7

59 0.049211 504,797 24,842 492,376 9.8 8.6 7.4

60 0.054108 479,956 25,969 466,971 9.3 8.2 7.1

61 0.059560 453,986 27,040 440,467 8.8 7.9 6.9

62 0.065249 426,947 27,858 413,018 8.3 7.5 6.6

63 0.070839 399,089 28,271 384,953 7.9 7.1 6.3

64 0.076425 370,818 28,340 356,648 7.4 6.8 6.0

65 0.082495 342,478 28,253 328,352 7.0 6.4 5.8

66 0.089507 314,225 28,125 300,163 6.6 6.1 5.5

67 0.097361 286,100 27,855 272,173 6.2 5.7 5.2

68 0.106149 258,245 27,413 244,539 5.8 5.4 5.0

69 0.115833 230,833 26,738 217,463 5.4 5.1 4.7

70 0.125993 204,094 25,714 191,237 5.1 4.8 4.4

71 0.136933 178,380 24,426 166,167 4.7 4.5 4.2

72 0.149433 153,954 23,006 142,451 4.4 4.2 3.9

73 0.163847 130,948 21,455 120,220 4.1 3.9 3.7

74 0.179896 109,493 19,697 99,644 3.8 3.6 3.5

75 0.196231 89,795 17,621 80,985 3.5 3.4 3.2

76 0.213062 72,175 15,378 64,486 3.2 3.1 3.0

77 0.232309 56,797 13,194 50,200 3.0 2.9 2.8

78 0.255152 43,603 11,125 38,040 2.7 2.7 2.6

79 0.281552 32,477 9,144 27,905 2.5 2.4 2.4

80 0.310486 23,333 7,245 19,711 2.3 2.2 2.2

81 0.340699 16,089 5,481 13,348 2.1 2.0 2.0

82 0.372994 10,607 3,956 8,629 1.9 1.9 1.8

83 0.408108 6,651 2,714 5,294 1.7 1.7 1.7

84 0.447543 3,937 1,762 3,056 1.5 1.5 1.5

85 0.489619 2,175 1,065 1,642 1.4 1.4 1.4

86 0.535199 1,110 594 813 1.3 1.3 1.2

87 0.584489 516 302 365 1.1 1.1 1.1

88 0.637689 214 137 146 1.0 1.0 1.0

89 0.694992 78 54 51 0.9 0.9 0.9

90 0.756579 24 18 15 0.8 0.8 0.8

91 0.822612 6 5 3 0.6 0.6 0.6

92 0.893232 1 0 0 0.0 0.0 0.0

*Mortality probabilities derived from mortality probabilities for the general population by multiplying

by the hazard ratio (5.7) for GOLD 3 or 4, from Mannino et al. (2006).
7b-26

Table 7b-6. Undiscounted and Discounted Age-Specific Life Expectancies for the Subpopulation 

with COPD of Average Severity 

Age at 

Beginning 

of Year

Mortality 

Probability* Cohort Size

Deaths in 

Year

Life-Years 

in Year

Age-Specific 

Life 

Expectancy

3% Discounted 

Remaining Life 

Expectancy

7% Discounted 

Remaining Life 

Expectancy

0 0.012960 1,000,000 12,960 993,520 69.6 29.9 15.1

1 0.000920 987,040 908 986,586 69.5 29.9 15.1

2 0.000566 986,132 558 985,853 68.6 29.8 15.1

3 0.000427 985,574 421 985,363 67.6 29.7 15.1

4 0.000359 985,153 354 984,976 66.7 29.5 15.1

5 0.000327 984,799 322 984,638 65.7 29.4 15.1

6 0.000306 984,477 301 984,326 64.7 29.3 15.1

7 0.000287 984,176 283 984,034 63.7 29.1 15.1

8 0.000260 983,893 256 983,765 62.7 29.0 15.1

9 0.000226 983,638 223 983,526 61.8 28.8 15.1

10 0.000203 983,415 199 983,315 60.8 28.6 15.0

11 0.000213 983,216 209 983,111 59.8 28.5 15.0

12 0.000284 983,006 279 982,867 58.8 28.3 15.0

13 0.000433 982,727 426 982,514 57.8 28.1 15.0

14 0.000642 982,302 630 981,986 56.8 27.9 15.0

15 0.000878 981,671 862 981,241 55.9 27.8 14.9

16 0.001104 980,810 1,083 980,268 54.9 27.6 14.9

17 0.001304 979,727 1,278 979,088 54.0 27.4 14.9

18 0.001455 978,449 1,424 977,737 53.1 27.2 14.9

19 0.001562 977,025 1,526 976,262 52.1 27.0 14.8

20 0.001664 975,499 1,623 974,688 51.2 26.8 14.8

21 0.001765 973,876 1,719 973,017 50.3 26.6 14.8

22 0.001830 972,157 1,779 971,268 49.4 26.4 14.7

23 0.001853 970,378 1,798 969,479 48.5 26.1 14.7

24 0.001846 968,580 1,788 967,686 47.6 25.9 14.7

25 0.001829 966,792 1,769 965,907 46.7 25.7 14.6

26 0.001818 965,023 1,754 964,146 45.7 25.5 14.6

27 0.001814 963,269 1,747 962,395 44.8 25.2 14.5

28 0.001826 961,521 1,756 960,643 43.9 25.0 14.5

29 0.001855 959,766 1,780 958,875 43.0 24.7 14.5

30 0.001896 957,985 1,816 957,077 42.1 24.4 14.4

31 0.001949 956,169 1,864 955,237 41.1 24.2 14.3

32 0.002027 954,305 1,934 953,338 40.2 23.9 14.3

33 0.002133 952,371 2,032 951,355 39.3 23.6 14.2

34 0.002271 950,339 2,158 949,260 38.4 23.3 14.1

35 0.002431 948,181 2,305 947,028 37.5 23.0 14.1

36 0.002617 945,876 2,476 944,638 36.6 22.7 14.0

37 0.002846 943,400 2,685 942,058 35.7 22.4 13.9

38 0.003114 940,716 2,929 939,251 34.8 22.0 13.8

39 0.003408 937,786 3,196 936,189 33.9 21.7 13.7

40 0.003707 934,591 3,464 932,859 33.0 21.4 13.6

41 0.004016 931,127 3,739 929,257 32.1 21.0 13.5

42 0.004359 927,388 4,042 925,366 31.2 20.7 13.4

43 0.004753 923,345 4,389 921,151 30.4 20.3 13.3

44 0.005199 918,956 4,777 916,567 29.5 20.0 13.2

45 0.005683 914,179 5,196 911,581 28.7 19.6 13.1

46 0.006187 908,983 5,624 906,171 27.8 19.2 13.0
7b-27

Table 7b-6. Undiscounted and Discounted Age-Specific Life Expectancies for the Subpopulation 

with COPD of Average Severity (cont’d) 

Age at 

Beginning 

of Year

Mortality 

Probability* Cohort Size

Deaths in 

Year

Life-Years 

in Year

Age-Specific 

Life 

Expectancy

3% Discounted 

Remaining Life 

Expectancy

7% Discounted 

Remaining Life 

Expectancy

47 0.006709 903,359 6,060 900,329 27.0 18.9 12.8

48 0.007241 897,298 6,497 894,050 26.2 18.5 12.7

49 0.007793 890,801 6,942 887,331 25.3 18.1 12.5

50 0.008395 883,860 7,420 880,150 24.5 17.7 12.4

51 0.009054 876,440 7,935 872,472 23.7 17.3 12.2

52 0.009746 868,505 8,464 864,273 23.0 16.9 12.1

53 0.010460 860,040 8,996 855,542 22.2 16.5 11.9

54 0.011207 851,044 9,537 846,276 21.4 16.1 11.7

55 0.011999 841,507 10,097 836,458 20.6 15.7 11.5

56 0.012874 831,410 10,703 826,058 19.9 15.3 11.3

57 0.013877 820,707 11,389 815,012 19.1 14.8 11.1

58 0.015064 809,318 12,191 803,222 18.4 14.4 10.9

59 0.016456 797,127 13,118 790,568 17.7 14.0 10.7

60 0.018094 784,009 14,186 776,916 17.0 13.5 10.4

61 0.019917 769,823 15,333 762,157 16.3 13.1 10.2

62 0.021820 754,490 16,463 746,259 15.6 12.7 10.0

63 0.023689 738,028 17,483 729,286 14.9 12.3 9.7

64 0.025557 720,545 18,415 711,337 14.3 11.8 9.5

65 0.027587 702,130 19,370 692,445 13.6 11.4 9.2

66 0.029932 682,760 20,436 672,542 13.0 11.0 8.9

67 0.032558 662,324 21,564 651,542 12.4 10.5 8.7

68 0.035497 640,760 22,745 629,388 11.8 10.1 8.4

69 0.038735 618,015 23,939 606,046 11.2 9.7 8.1

70 0.042133 594,076 25,030 581,561 10.6 9.3 7.8

71 0.045791 569,046 26,057 556,017 10.1 8.9 7.6

72 0.049971 542,989 27,134 529,422 9.6 8.4 7.3

73 0.054791 515,855 28,264 501,723 9.0 8.0 7.0

74 0.060158 487,591 29,333 472,924 8.5 7.6 6.7

75 0.065621 458,258 30,071 443,223 8.0 7.3 6.4

76 0.071249 428,187 30,508 412,933 7.6 6.9 6.1

77 0.077685 397,679 30,894 382,232 7.1 6.5 5.8

78 0.085324 366,785 31,296 351,137 6.7 6.1 5.6

79 0.094152 335,489 31,587 319,696 6.2 5.8 5.3

80 0.103828 303,902 31,554 288,125 5.8 5.4 5.0

81 0.113932 272,349 31,029 256,834 5.5 5.1 4.7

82 0.124731 241,319 30,100 226,269 5.1 4.8 4.5

83 0.136473 211,219 28,826 196,806 4.8 4.5 4.2

84 0.149661 182,394 27,297 168,745 4.4 4.2 4.0

85 0.163731 155,096 25,394 142,399 4.1 3.9 3.7

86 0.178974 129,702 23,213 118,096 3.8 3.7 3.5

87 0.195456 106,489 20,814 96,082 3.5 3.4 3.3

88 0.213247 85,675 18,270 76,540 3.3 3.2 3.1

89 0.232409 67,405 15,666 59,572 3.0 3.0 2.8

90 0.253004 51,740 13,090 45,194 2.8 2.7 2.7

91 0.275086 38,649 10,632 33,333 2.6 2.5 2.5

92 0.298702 28,017 8,369 23,833 2.4 2.4 2.3

93 0.323890 19,649 6,364 16,467 2.2 2.2 2.1

94 0.350677 13,285 4,659 10,955 2.0 2.0 2.0

95 0.379078 8,626 3,270 6,991 1.9 1.8 1.8

96 0.409089 5,356 2,191 4,261 1.7 1.7 1.6

97 0.440695 3,165 1,395 2,468 1.5 1.5 1.5

98 0.473858 1,770 839 1,351 1.3 1.3 1.3

99 0.508523 931 474 695 1.0 1.0 1.0

100 1.000000 458 458 229 0.5 0.5 0.5

*Mortality probabilities derived from mortality probabilities for the general population (see Table 2) by multiplying

by the weighted average of hazard ratios for the GOLD severity categories (1.906) from Mannino et al. (2006).
7b-28

Table 7b-7. Estimated Discounted O3-Related Life Years Saved Under Alternative Illustrative O3 NAAQS Attainment Strategies in 2020, 

Using a 3 Percent Discount Rate 

0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm

380 980 3,000 5,400 1,800 4,700 15,000 26,000

(130 - 630) (320 - 1,600) (960 - 5,100) (1,700 - 9,000) (1,300 - 2,400) (3,300 - 6,200) (10,000 - 19,000) (18,000 - 34,000)

290 750 2,300 4,100 1,400 3,700 11,000 20,000

(97 - 480) (250 - 1,300) (740 - 3,900) (1,300 - 6,900) (1,000 - 1,900) (2,500 - 4,800) (7,800 - 15,000) (14,000 - 26,000)

160 420 1,300 2,300 840 2,100 6,500 11,000

(54 - 270) (140 - 700) (400 - 2,200) (730 - 3,800) (580 - 1,100) (1,500 - 2,800) (4,500 - 8,600) (7,900 - 15,000)

**95 percent confidence or credible intervals (CIs) are based on the uncertainty about the coefficient in the mortality C-R functions. All estimates rounded to 

two significant figures. 

Bell et al. (2004) Levy et al. (2005)

Assuming Life Expectancies of the General Population

Assuming Life Expectancies of the Sub-Population with COPD of Average Severity

Assuming Life Expectancies of the Sub-Population with Severe COPD

*The O3-related (discounted) life years saved, under the first assumption – that the observed statistical association between premature mortality and shortterm exposures to O3 is not actually a causal relationship – is zero in all cases (i.e., regardless of the mortality study used and the scenario considered, and 

is therefore not shown.

Baseline: Full Attainment of Current (0.084 ppm) Standard; Control Scenario: Full Attainment of

Alternative Standard of:

Estimated O3-Related Life Years Saved*

(95% CI)**
7b-29

Table 7b-8. Estimated Discounted O3-Related Life Years Saved Under Alternative Illustrative O3 NAAQS Attainment Strategies in 2020, 

Using a 7 Percent Discount Rate 

0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm

290 750 2,300 4,100 1,400 3,500 11,000 19,000

(96 - 480) (250 - 1,200) (740 - 3,900) (1,300 - 6,900) (940 - 1,800) (2,400 - 4,600) (7,500 - 14,000) (13,000 - 25,000)

230 600 1,900 3,300 1,100 2,900 8,900 16,000

(77 - 390) (200 - 1,000) (590 - 3,200) (1,100 - 5,500) (770 - 1,500) (2,000 - 3,800) (6,100 - 12,000) (11,000 - 21,000)

140 350 1,100 1,900 690 1,800 5,400 9,500

(46 - 230) (120 - 590) (340 - 1,800) (620 - 3,200) (480 - 900) (1,200 - 2,300) (3,700 - 7,100) (6,500 - 12,000)

Estimated O3-Related Life Years Saved*

(95% CI)**

Assuming Life Expectancies of the General Population

Assuming Life Expectancies of the Sub-Population with COPD of Average Severity

Baseline: Full Attainment of Current (0.084 ppm) Standard; Control Scenario: Full Attainment of

Alternative Standard of:

Bell et al. (2004) Levy et al. (2005)

Assuming Life Expectancies of the Sub-Population with Severe COPD 

**95 percent confidence or credible intervals (CIs) are based on the uncertainty about the coefficient in the mortality C-R functions. All estimates rounded to 

*The Otwo significant figures. 

3-related (discounted) life years saved, under the first assumption – that the observed statistical association between premature mortality and shortterm exposures to O3 is not actually a causal relationship – is zero in all cases (i.e., regardless of the mortality study used and the scenario considered, and 

is therefore not shown.
7b-30

7b.5.3 Cost-Effectiveness Ratios 

For each illustrative O3 NAAQS attainment strategy for which we considered only O3-related

benefits, we calculated one set of cost-effectiveness ratios using total lives saved, based on the 

Bell study and the Levy study, as the denominator, and another set using total life years saved as 

the denominator. As discussed above in Section 7b.4, we netted out the monetized benefits of 

avoided cases of O3-related acute morbidity (respiratory hospital admissions, asthma-related ER 

visits, school absence days, and minor restricted activity days) as well as avoided O3-related

worker productivity losses from the direct costs of the controls necessary to achieve the 

reductions in ambient concentrations of O3 in the numerator. Incidences of avoided acute 

morbidity are given in Chapter 8. 

We used Monte Carlo procedures to incorporate the uncertainty surrounding the O3 coefficient in 

each of the C-R functions (including C-R functions for each of the acute morbidity endpoints as 

well as the C-R function for mortality) as well as the uncertainty surrounding the unit value 

(monetized benefit of an avoided case) of each acute morbidity endpoint. This procedure was 

repeated separately for each of the two mortality C-R functions used, and, for cost-effectiveness 

ratios using life years saved, for each combination of mortality C-R function and assumption 

about relevant life expectancies. The results are shown in Table 7b-9 for cost-effectiveness 

ratios using lives saved. As noted above, O3-related premature mortality avoided (lives saved) 

are assumed to be related only to short-term exposures and are not discounted. The cost of the 

regulation, however, which occurs over a period of time, is discounted (using discount rates of 3 

percent and 7 percent). Tables 7b-10 and 7b-11 show cost-effectiveness ratios using life years 

saved, using discount rates of 3 and 7 percent, respectively. Both the costs of the regulation and 

the lives saved are discounted.

As noted in Section 1, these cost-effectiveness ratios omit the PM2.5-related co-benefits of these 

illustrative O3 NAAQS strategies and are therefore likely to understate the cost effectiveness of 

these strategies. As can be seen in Tables 7b-9 through 7b-11, the direct costs of the controls 

necessary to achieve the reductions in ambient concentrations of O3, in the numerators of the 

cost-effectiveness ratios, increase with the stringency of the alternative standards. The lives and 

life years saved, in the denominators of the cost-effectiveness ratios, similarly increase with the 

stringency of the alternative standards. It is therefore not surprising that we do not see a 

monotonic trend in these ratios across the increasingly more stringent alternative standards.
7b-31

Table 7b-9. Estimated Net Cost (2006$) per O3-Related Life Saved Under Alternative Illustrative O3 NAAQS Attainment Strategies in 2020 

0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm

Estimated 3% discounted cost of the regulation (in Billion $):*** 

$2.9 $8.8 $25 $44

$93 $110 $98 $96

($48 - $240) ($55 - $280) ($50 - $260) ($50 - $260)

$18 $21 $19 $19

($13 - $25) ($15 - $29) ($14 - $27) ($14 - $27)

Using lower bound estimate of 7% discounted cost of the regulation (in Billion $): 

$2.4 $7.6 $19 $32

$76 $92 $74 $70

($40 - $200) ($48 - $240) ($38 - $200) ($36 - $190)

$15 $18 $14 $14

($11 - $21) ($13 - $25) ($11 - $20) ($10 - $19)

Using upper bound estimate of 7% discounted cost of the regulation (in Billion $): 

$2.9 $8.8 $25 $44

$93 $110 $98 $96

($48 - $240) ($55 - $280) ($50 - $260) ($50 - $260)

$18 $21 $19 $19

($13 - $25) ($15 - $29) ($14 - $27) ($14 - $27)

***Uses the upper bound estimates of the 7% discounted costs of the regulations as proxies for the 3% discounted costs.

Cost Effectiveness Ratio: Net Cost (in Million $) per Life Saved*

*Because PM2.5-related benefits are not incorporated in these cost effectiveness ratios, the cost effectiveness of full attainment of 

each alternative O3 standard shown in this table will tend to be understated.

(95% CI)**

Change From Full Attainment of the Current (0.084 ppm) Std. To Full Attainment of Alternative Std. of: Mortality Study

**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 coefficients in the mortality and 

morbidity endpoints as well as the uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two Bell et al. (2004)Levy et al. (2005)Bell et al. (2004)Levy et al. (2005)Bell et al. (2004)Levy et al. (2005)significant figures. 
7b-32

Table 7b-10. Estimated Net Cost (2006$) per O3-Related Life Year Saved Under Alternative Illustrative O3 NAAQS Attainment 

Strategies in 2020, Using a 3 Percent Discount Rate 

0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm

Estimated 3% discounted cost 

of the regulation (in Billion $):*** $2.9 $8.8 $25 $44

$8.7 $10 $9.5 $9.6

($4.6 - $23) ($5.3 - $27) ($4.8 - $26) ($4.8 - $25)

$11 $13 $12 $12

($5.9 - $29) ($6.9 - $35) ($6.3 - $34) ($6.3 - $33)

$20 $24 $22 $22

($11 - $53) ($13 - $63) ($11 - $61) ($12 - $59)

$1.6 $1.9 $1.7 $1.7

($1.2 - $2.3) ($1.4 - $2.7) ($1.3 - $2.5) ($1.3 - $2.5)

$2.0 $2.4 $2.2 $2.2

($1.5 - $2.9) ($1.8 - $3.4) ($1.7 - $3.2) ($1.7 - $3.2)

$3.5 $4.2 $3.9 $3.9

($2.6 - $4.9) ($3.1 - $5.9) ($2.9 - $5.5) ($2.9 - $5.5)

***Uses the upper bound estimates of the 7% discounted costs of the regulations as proxies for the 3% discounted costs.

*Because PM2.5-related benefits are not incorporated in these cost effectiveness ratios, the cost effectiveness of full attainment of each alternative O3 standard shown in 

this table will tend to be understated.

Cost Effectiveness Ratio: Net Cost (in Million $) per Life Year Saved*

(95% CI)**

Change From Full Attainment of the Current (0.084 ppm) Std. To Full Attainment of Alternative Std. of: Life Expectancy Assumption

Levy et al. (2005) Subpopulation with Average COPDLevy et al. (2005) Subpopulation with Severe COPD

Mortality Study

**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 coefficients in the mortality and morbidity C-R functions as well as the 

uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures. 

Bell et al. (2004) General PopulationBell et al. (2004) Subpopulation with Average COPDBell et al. (2004) Subpopulation with Severe COPDLevy et al. (2005) General Population
7b-33

Table 7b-11. Estimated Net Cost (2006$) per O3-Related Life Year Saved Under Alternative Illustrative O3 NAAQS Attainment Strategies 

in 2020, Using a 7 Percent Discount Rate 

0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm

Using lower bound estimate of 7% discounted cost 

of the regulation (in Billion $): $2.4 $7.6 $19 $32

$9.4 $12 $9.5 $8.8

($4.9 - $25) ($6 - $30) ($4.8 - $25) ($4.6 - $24)

$12 $14 $12 $11

($6.1 - $31) ($7.5 - $38) ($5.9 - $32) ($5.7 - $29)

$20 $25 $20 $19

($10 - $52) ($13 - $64) ($10 - $55) ($9.8 - $50)

$1.8 $2.2 $1.7 $1.7

($1.3 - $2.5) ($1.6 - $3.1) ($1.3 - $2.5) ($1.2 - $2.4)

$2.2 $2.7 $2.2 $2.1

($1.6 - $3.1) ($2 - $3.8) ($1.6 - $3.1) ($1.5 - $2.9)

$3.5 $4.4 $3.6 $3.4

($2.6 - $5) ($3.3 - $6.2) ($2.6 - $5.1) ($2.5 - $4.8)

Using upper bound estimate of 7% discounted cost 

of the regulation (in Billion $): $2.9 $8.8 $25 $44

$11 $14 $13 $12

($6 - $30) ($7 - $35) ($6.3 - $34) ($6.3 - $32)

$14 $17 $16 $15

($7.4 - $37) ($8.7 - $44) ($7.8 - $42) ($7.9 - $41)

$24 $29 $27 $26

($13 - $63) ($15 - $75) ($13 - $72) ($14 - $70)

$2.2 $2.5 $2.3 $2.3

($1.6 - $3) ($1.9 - $3.6) ($1.7 - $3.3) ($1.7 - $3.3)

$2.6 $3.1 $2.9 $2.8

($2 - $3.7) ($2.3 - $4.4) ($2.1 - $4.1) ($2.1 - $4)

$4.3 $5.1 $4.7 $4.7

($3.2 - $6) ($3.8 - $7.2) ($3.5 - $6.7) ($3.5 - $6.7)

**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 coefficients in the mortality and morbidity C-R functions as well as the 

uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures. 

Cost Effectiveness Ratio: Net Cost (in Million $) per Life Year Saved*

(95% CI)**

Change From Full Attainment of the Current (0.084 ppm) Std. To Full Attainment of Alternative Std. of: Life Expectancy Assumption

Levy et al. (2005) Subpopulation with Average COPDLevy et al. (2005) Subpopulation with Severe COPDBell et al. (2004) General PopulationBell et al. (2004) Subpopulation with Average COPDBell et al. (2004) Subpopulation with Severe COPDLevy et al. (2005) General PopulationLevy et al. (2005) Subpopulation with Average COPDLevy et al. (2005) Subpopulation with Severe COPD

Mortality Study

*Because PM2.5-related benefits are not incorporated in these cost effectiveness ratios, the cost effectiveness of full attainment of each alternative O3 standard shown in 

Bell et al. (2004) General PopulationBell et al. (2004) Subpopulation with Average COPDBell et al. (2004) Subpopulation with Severe COPDLevy et al. (2005) General Populationthis table will tend to be understated.
7b-34

7b.6 Cost-Effectiveness Metrics Incorporating Both O3-Related and PM2.5-Related

Benefits

In this section we describe the development of cost-effectiveness metrics for the single 

illustrative O3 NAAQS attainment strategy for which we were able to incorporate both O3-

related benefits and PM2.5-related co-benefits, in which the baseline is partial attainment of the 

current O3 standard of 0.084 ppm and the control scenario is partial attainment of an alternative 

standard of 0.070 ppm. 

7b.6.1 O3-related Lives Saved and Life Years Saved 

The methods used to calculate O3-related lives saved and O3-related life years saved under this 

scenario are the same as those described above in Section 7b.5. Estimated numbers of O3-related

premature deaths avoided are shown in Table 7b-12. The corresponding O3-related life years 

saved, discounted using 3 percent and 7 percent discount rates, are shown in Tables 7b-13 and 

7b-14, respectively. 
7b-35

Table 7b-12. Estimated Reduction in Incidence of O3-Related Premature Mortality Associated with 

Illustrative O3 NAAQS Attainment Strategy in 2020: Changing from Partial Attainment 

of the Current O3 NAAQS to Partial Attainment of an Alternative O3 NAAQS of 0.07 

ppm

Bell et al. (2004) Levy et al. (2005)

0 3

(0 - 1) (2 - 3)

0 1

(0 - 0) (1 - 2)

0 1

(0 - 0) (1 - 1)

0 2

(0 - 0) (1 - 3)

0 3

(0 - 0) (2 - 4)

1 4

(0 - 1) (3 - 6)

0 4

(0 - 1) (3 - 6)

1 7

(0 - 2) (4 - 9)

1 6

(0 - 2) (4 - 8)

3 13

(1 - 5) (9 - 17)

3 14

(1 - 5) (9 - 18)

8 37

(2 - 14) (25 - 50)

8 36

(2 - 14) (24 - 48)

16 70

(5 - 27) (47 - 94)

12 55

(4 - 21) (37 - 73)

20 86

(6 - 33) (58 - 110)

12 55

(4 - 21) (37 - 73)

40 170

(12 - 68) (120 - 230)

130 570

(36 - 220) (380 - 760) Total:

*95 percent confidence or credible intervals (CIs) are based on the uncertainty about 

the coefficient in the mortality C-R functions. All estimates rounded to two significant 

figures. 

Reduction in O3-Related Premature Mortality 

(95% CI)*

Baseline of Partial Attainment of Current (0.084 ppm) Standard to Control 

Scenario of Partial Attainment of 0.07 ppm

0 - 4

5 - 9

10 - 14

Age

Interval

15 - 19

20 - 24

25 - 29

30 - 34

35 - 39

40 - 44

45 - 49

50 - 54

55 - 59

80 - 84

85+

60 - 64

65 - 69

70 - 74

75 - 79
7b-36

Table 7b-13. Estimated O3-Related Life Years Saved Associated with Illustrative O3 NAAQS 

Attainment Strategy in 2020: Changing from Partial Attainment of the Current O3

NAAQS to Partial Attainment of an Alternative O3 NAAQS of 0.07 ppm, Using a 3 

Percent Discount Rate 

Mortality Study: Bell et al (2004) Levy et al. (2005)

1,300 6,100

(370 - 2,200) (4,100 - 8,100)

980 4,700

(280 - 1,700) (3,200 - 6,300)

530 2,700

(150 - 910) (1,800 - 3,500)

Baseline: Partial Attainment of Current (0.084 ppm) Standard; Control Scenario: Partial 

Attainment of Alternative Standard of 0.070 ppm

Estimated O3-Related Life Years Saved

(95% CI)*

*95 percent confidence or credible intervals are based on the uncertainty about the coefficient in 

the mortality C-R functions. All estimates rounded to two significant figures. 

Assuming Life Expectancies of the General Population

Assuming Life Expectancies of the Sub-Population with 

COPD of Average Severity

Assuming Life Expectancies of the Sub-Population with 

Severe COPD

Table 7b-14. Estimated O3-Related Life Years Saved Associated with Illustrative O3 NAAQS 

Attainment Strategy in 2020: Changing from Partial Attainment of the Current O3

NAAQS to Partial Attainment of an Alternative O3 NAAQS of 0.07 ppm, Using a 7 

Percent Discount Rate 

Mortality Study: Bell et al (2004) Levy et al. (2005)

990 4,600

(280 - 1,700) (3,100 - 6,100)

790 3,700

(230 - 1,400) (2,500 - 4,900)

450 2,200

(130 - 780) (1,500 - 2,900)

Baseline: Partial Attainment of Current (0.084 ppm) Standard; Control Scenario: Partial 

Attainment of Alternative Standard of 0.070 ppm

Estimated O3-Related Life Years Saved

(95% CI)*

*95 percent confidence or credible intervals are based on the uncertainty about the coefficient in 

the mortality C-R functions. All estimates rounded to two significant figures. 

Assuming Life Expectancies of the General Population

Assuming Life Expectancies of the Sub-Population with 

COPD of Average Severity

Assuming Life Expectancies of the Sub-Population with 

Severe COPD

7b.6.2 Reductions in PM2.5-Related Premature Deaths 

To generate PM2.5-related health outcomes, we used the same framework as for the benefit-cost 

analysis described in Chapter 8 and briefly summarized above in the introductory portion of 

Section 8.4.
7b-37

As in several recent air pollution health impact assessments (e.g., Kunzli et al., 2000; EPA, 

2004), we focused on the prospective cohort long-term exposure studies in deriving the health 

impact function for the estimate of premature mortality. Cohort analyses are better able to 

capture the full public health impact of exposure to air pollution over time (Kunzli et al., 2001; 

NRC, 2002). We selected an effect estimate from the extended analysis of the ACS cohort (Pope 

et al., 2002) as well as from the Harvard Six City Study (Laden et al., 2006). Given the focus in 

this analysis on developing a broader expression of uncertainties in the benefits estimates, and 

the weight that was placed on both the ACS and Harvard Six-city studies by experts participating 

in the PM2.5 mortality expert elicitation, we elected to provide estimates derived from both Pope 

et al. (2002) and Laden et al. (2006). 

This latest re-analysis of the ACS cohort data (Pope et al, 2002) provides additional refinements 

to the analysis of PM-related mortality by (a) extending the follow-up period for the ACS study 

subjects to 16 years, which triples the size of the mortality data set; (b) substantially increasing 

exposure data, including consideration for cohort exposure to PM2.5 following implementation of 

PM2.5 standard in 1999; (c) controlling for a variety of personal risk factors including 

occupational exposure and diet; and (d) using advanced statistical methods to evaluate specific 

issues that can adversely affect risk estimates, including the possibility of spatial autocorrelation 

of survival times in communities located near each other. The effect estimate from Pope et al. 

(2002) quantifies the relationship between annual mean PM2.5 levels and all-cause mortality in 

adults 30 and older. We selected the effect estimate estimated using the measure of PM 

representing average exposure over the follow-up period, calculated as the average of 1979–1984 

and 1999–2000 PM2.5 levels. The effect estimate from this study is 0.0058, which is equivalent 

to a relative risk of 1.06 for a 10 g change in PM2.5.

A recent follow up to the Harvard 6-city study (Laden et al., 2006) both confirmed the effect size 

from the first study and provided additional confirmation that reductions in PM2.5 directly result 

in reductions in the risk of premature death. This additional evidence stems from the observed 

reductions in PM2.5 in each city during the extended follow-up period. Laden et al. (2006) found 

that mortality rates consistently went down at a rate proportionate to the observed reductions in 

PM2.5. The effect estimate obtained from Laden et al. (2006) is 0.0148, which is equivalent to a 

relative risk of 1.16 for a 10 ȝg/m3 change in PM2.5.

Age, cause, and county-specific mortality rates were obtained from CDC for the years 1996 

through 1998. CDC maintains an online data repository of health statistics, CDC Wonder, 

accessible at http://wonder.cdc.gov/. The mortality rates provided are derived from U.S. death 

records and U.S. Census Bureau postcensal population estimates. Mortality rates were averaged 

across 3 years (1996 through 1998) to provide more stable estimates. When estimating rates for 

age groups that differed from the CDC Wonder groupings, we assumed that rates were uniform 

across all ages in the reported age group. For example, to estimate mortality rates for individuals 

ages 30 and up, we scaled the 25- to 34-year old death count and population by one-half and then 

generated a population-weighted mortality rate using data for the older age groups. 

The reductions in incidence of PM2.5-related premature mortality within each age group 

associated with the illustrative 0.07 ppm partial attainment strategy in 2020 are summarized in 

Table 7b-15. 
7b-38

Table 7b-15: Estimated Reduction in Incidence of PM2.5-Related All-Cause Premature Mortality 

Under an Illustrative Strategy of Changing from Partial Attainment of the Current 

(0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS 

in 2020 

 Reduction in All-Cause Premature Mortality 

(95% CI)*

Age Interval Pope (2002) Laden (2006) 

30 – 34 4 

(1 – 6) 

8

(5 – 12) 

35 – 44 11 

(4 – 18) 

25

(13 – 36) 

45 – 54 23 

(9 – 36) 

51

(28 – 75) 

55 – 64 56 

(22 – 90) 

130

(69 – 180) 

65 – 74 93 

(37 – 150) 

210

(120 – 310) 

75 – 84 110 

(43 – 180) 

250

(130 – 360) 

85+ 140 

(56 – 230) 

320

(180 – 470) 

Total 440 

(170 – 700) 

990

(540 – 1,400) 

*95% confidence intervals are based on the uncertainty surrounding the effect estimate (coefficient) in the 

mortality C-R function. All estimates rounded to two significant figures. 

7b.6.3 Life Years Saved as a Result of Reductions in PM2.5-Related Mortality Risk 

To calculate life years saved associated with a given change in air pollution, we used a life table 

approach coupled with age-specific estimates of reductions in premature mortality. We began 

with the complete unabridged life table for the United States in 2000, obtained from CDC (CDC, 

2002). For each 1-year age interval (e.g., zero to one, one to two) the life table provides 

estimates of the baseline probability of dying during the interval, person years lived in the 

interval, and remaining life expectancy. From this unabridged life table, we constructed an 

abridged life table to match the age intervals for which we have predictions of changes in 

incidence of premature mortality. We used the abridgement method described in CDC (2002). 

Table 7b-16 presents the abridged life table for 10-year age intervals for adults over 30 (to match 

the Pope et al. [2002] study population). Note that the abridgement actually includes one 5-year 

interval, covering adults 30 to 34, with the remaining age intervals covering 10 years each. This 

is to provide conformity with the age intervals available for mortality rates. 

From the abridged life table (Table 7b-16), we obtained the remaining life expectancy for each 

age cohort, conditional on surviving to that age. This is then the number of life years lost for an 

individual in the general population dying during that age interval. This information can then be 
7b-39

combined with the estimated number of premature deaths in each age interval calculated with 

BenMAP (see previous subsection). Total life years gained will then be the sum of life years 

gained in each age interval: 

TotalLife Years LEi i M

i

N

 u

 

¦

1

,

where LEi is the remaining life expectancy for age interval i, Mi is the change in incidence of 

mortality in age interval i, and N is the number of age intervals. 

As noted above, for the purposes of determining cost-effectiveness, it is also necessary to 

consider the time-dependent nature of the gains in life years. Standard economic theory suggests 

that benefits occurring in future years should be discounted relative to benefits occurring in the 

present. OMB and EPA guidance suggest discount rates of three and seven percent. Selection of 

a 3 percent discount rate is also consistent with recommendations from the U.S. Public Health 

Service Panel on Cost Effectiveness in Health and Medicine (Gold et al., 1996). 

Table 7b-16. Abridged Life Table for the Total Population, United States, 2000 

Age Interval 

Probability

of Dying 

Between

Ages x to 

x+1 

Number 

Surviving to 

Age x 

Number 

Dying 

Between

Ages x to 

x+1 

Person

Years

Lived 

Between

Ages x to 

x+1 

Total

Number of 

Person

Years

Lived 

Above Age 

x

Expectation 

of Life at 

Age x 

Start

Age

End

Age qx Ix dx Lx Tx ex

30 35 0.00577 97,696 564 487,130 4,723,539 48.3 

35 45 0.01979 97,132 1,922 962,882 4,236,409 43.6 

45 55 0.04303 95,210 4,097 934,026 3,273,527 34.4 

55 65 0.09858 91,113 8,982 872,003 2,339,501 25.7 

65 75 0.21779 82,131 17,887 740,927 1,467,498 17.9 

75 85 0.45584 64,244 29,285 505,278 726,571 11.3 

85 95 0.79256 34,959 27,707 196,269 221,293 6.3 

95 100 0.75441 7,252 5,471 20,388 25,024 3.5 

100+ 1.00000 1,781 1,781 4,636 4,636 2.6 

Unlike O3-related premature deaths, PM2.5-related premature deaths are associated with longterm exposures. We therefore did not assume that these deaths all occur in 2020. The PM2.5-

related premature deaths avoided and associated life years saved are thus further discounted to 

account for the lag between the reduction in ambient PM2.5 and the corresponding reduction in 

mortality risk. We used the same 20-year segmented lag structure that is used in the benefit-cost 

analysis (see Chapter 8). 
7b-40

The most complete estimate of the impacts of PM2.5 on life years is calculated using the Pope et 

al. (2002) C-R function relating all-cause mortality in adults 30 and over with ambient PM2.5

concentrations averaged over the periods 1979–1983 and 1999–2000. Use of all-cause mortality 

is appropriate if there are no differences in the life expectancy of individuals dying from air 

pollution-related causes and those dying from other causes. The argument that long-term 

exposure to PM2.5 may affect mainly individuals with serious preexisting illnesses is not 

supported by current empirical studies. For example, the Krewski et al. (2000) ACS reanalysis 

suggests that the mortality risk is no greater for those with preexisting illness at time of 

enrollment in the study. Life expectancy for the general population in fact includes individuals 

with serious chronic illness. Mortality rates for the general population then reflect prevalence of 

chronic disease, and as populations age the prevalence of chronic disease increases. 

The only reason one might use a lower life expectancy is if the population at risk from air 

pollution was limited solely to those with preexisting disease. Also, note that the OMB Circular 

A-4 notes that “if QALYs are used to evaluate a lifesaving rule aimed at a population that 

happens to experience a high rate of disability (i.e., where the rule is not designed to affect the 

disability), the number of life years saved should not necessarily be diminished simply because 

the rule saves lives of people with life-shortening disabilities. Both analytic simplicity and 

fairness suggest that the estimate number of life years saved for the disabled population should 

be based on average life expectancy information for the relevant age cohorts.” As such, use of a 

general population life expectancy is preferred over disability-specific life expectancies. Our 

primary life years calculations are thus consistent with the concept of not penalizing individuals 

with disabling chronic health conditions by assessing them reduced benefits of mortality risk 

reductions. PM2.5-Related life years saved associated with the illustrative 0.07 ppm partial 

attainment strategy in 2020 are given in Table 7b-17. 

Table 7b-17. Estimated PM2.5-Related Life Years Saved Associated with Illustrative O3 NAAQS 

Attainment Strategy in 2020: Changing from Partial Attainment of the Current O3

NAAQS to Partial Attainment of an Alternative O3 NAAQS of 0.07 ppm 

Pope et al (2002) Laden et al. (2006)

4,400 9,900

(1,700 - 7,000) (5,400 - 14,000)

3,000 6,700

(1,200 - 4,800) (3,700 - 9,800)

Discounted back to 2020, using a 7 percent discount rate:

*95 percent confidence or credible intervals (CIs) are based on the uncertainty about the coefficient in 

the mortality C-R functions. All estimates rounded to two significant figures. 

Estimated PM2.5-Related Life Years Saved

(95% CI)*

Discounted back to 2020, using a 3 percent discount rate:

For this analysis, direct impacts on life expectancy are measured only through the estimated 

change in mortality risk based on the Pope et al. (2002) C-R function. The SAB-HES has 

advised against including additional gains in life expectancy due to reductions in incidence of 

chronic disease or nonfatal heart attacks (EPA-SAB-COUNCIL-ADV-04-002). Although 

reductions in these endpoints are likely to result in increased life expectancy, the HES has 

suggested that the cohort design and relatively long follow-up period in the Pope et al. study 
7b-41

should capture any life-prolonging impacts associated with those endpoints. Impacts of CB and 

nonfatal heart attacks on quality of life will be captured separately in the QALY calculation as 

years lived with improved quality of life. The methods for calculating this benefit are discussed 

below.

7b.6.4 Calculating Changes in the Quality of Life Years (PM2.5-Related Chronic Morbidity) 

In addition to directly measuring the quantity of life gained, measured by life years, it may also 

be informative to measure gains in the quality of life. The indirect reductions in levels of PM2.5

also lead to reductions in serious illnesses that affect quality of life. These include chronic 

bronchitis (CB) and cardiovascular disease, for which we are able to quantify changes in the 

incidence of nonfatal heart attacks. To capture these important benefits in the measure of 

effectiveness, they must first be converted into a life-year equivalent so that they can be 

combined with the direct gains in life expectancy. 

For the cost effectiveness analysis for the PM NAAQS RIA, we developed estimates of the 

QALYs gained from reductions in the incidence of CB and nonfatal heart attacks associated with 

reductions in ambient PM2.5. In general, QALY calculations require four elements: 

1. the estimated change in incidence of the health condition, 

2. the duration of the health condition, 

3. the quality-of-life weight with the health condition, and 

4. the quality-of-life weight without the health condition (i.e., the baseline health state). 

The first element is derived using the health impact function approach. The second element is 

based on the medical literature for each health condition. The third and fourth elements are 

derived from the medical cost-effectiveness and cost-utility literature. In the following two 

subsections, we discuss the choices of elements for CB and nonfatal heart attacks. 

The preferred source of quality-of-life weights are those based on community preferences, rather 

than patient or clinician ratings (Gold et al., 1996). Several methods are used to estimate qualityof-life weights. These include rating scale, standard gamble, time trade-off, and person trade-off 

approaches (Gold, Stevenson, and Fryback, 2002). Only the standard gamble approach is 

completely consistent with utility theory. However, the time trade-off method has also been 

widely applied in eliciting community preferences (Gold, Stevenson, and Fryback, 2002). 

Quality-of-life weights can be directly elicited for individual specific health states or for a more 

general set of activity restrictions and health states that can then be used to construct QALY 

weights for specific conditions (Horsman et al., 2003; Kind, 1996). For this analysis, we used 

weights based on community-based preferences, using time trade-off or standard gamble when 

available. In some cases, we used patient or clinician ratings when no community preferencebased weights were available. Sources for weights are discussed in more detail below. 

Table 7b-18 summarizes the key inputs for calculating QALYs associated with chronic health 

endpoints.
7b-42

Table 7b-18. Summary of Key Parameters Used in QALY Calculations for Chronic Disease 

Endpoints

Parameter Value(s) Source(s) 

Discount rate 0.03 (0.07 

sensitivity

analysis) 

Gold et al. (1996), U.S. EPA (2000), U.S. OMB (2003) 

Quality of life preference 

score for chronic 

bronchitis 

0.5 – 0.7 Triangular distribution centered at 0.7 with upper bound at 

0.9 (Vos, 1999a) (slightly better than a mild/moderate case) 

and a lower bound at 0.5 (average weight for a severe case 

based on Vos [1999a] and Smith and Peske [1994]) 

Duration of acute phase 

of acute myocardial 

infarction (AMI) 

5.5 days – 22 

days

Uniform distribution with lower bound based on average 

length of stay for an AMI (AHRQ, 2000) and upper bound 

based on Vos (1999b). 

Probability of CHF post 

AMI

0.2 Vos, 1999a (WHO Burden of Disease Study, based on 

Cowie et al., 1997) 

Probability of angina post 

AMI

0.51 American Heart Association, 2003 

(Calculated as the population with angina divided by the 

total population with heart disease) 

Quality-of-life preference 

score for post-AMI with 

CHF (no angina) 

0.80 – 0.89 Uniform distribution with lower bound at 0.80 (Stinnett et 

al., 1996) and upper bound at 0.89 (Kuntz et al., 1996). 

Both studies used the time trade-off elicitation method. 

Quality-of-life preference 

score for post-AMI with 

CHF and angina 

0.76 – 0.85 Uniform distribution with lower bound at 0.76 (Stinnett et 

al., 1996, adjusted for severity) and upper bound at 0.85 

(Kuntz et al., 1996). Both studies used the time trade-off 

elicitation method. 

Quality-of-life preference 

score for post-AMI with 

angina (no CHF) 

0.7 – 0.89 Uniform distribution with lower bound at 0.7, based on the 

standard gamble elicitation method (Pliskin, Stason, and 

Weinstein, 1981) and upper bound at 0.89, based on the 

time trade-off method (Kuntz et al., 1996). 

Quality-of-life preference 

score for post-AMI (no 

angina, no CHF) 

0.93 Only one value available from the literature. Thus, no 

distribution is specified. Source of value is Kuntz et al. 

(1996). 

7b.6.4.1 Calculating QALYs Associated with Reductions in the Incidence of Chronic Bronchitis 

CB is characterized by mucus in the lungs and a persistent wet cough for at least 3 months a year 

for several years in a row. CB affects an estimated 5 percent of the U.S. population (American 

Lung Association, 1999). For gains in quality of life resulting from reduced incidences of PMinduced CB, discounted QALYs are calculated as 

DISCOUNTED QALYGAINED CBi i i i D w w  

CB

i

 u u  ¦'

*

where CBi is the number of incidences of CB avoided in age interval i, wi is the average QALY 

weight for the ith age interval, CB wi

 is the QALY weight associated with CB in the ith age 
7b-43

interval, and * Di

 is the discounted duration of life with CB for individuals with onset of disease in 

the ith age interval, equal to 3



Di

rt

e dt

0

 , where Di is the duration of life with CB for individuals 

with onset of disease the ith age interval. 

A limited number of studies have estimated the impact of air pollution on new incidences of CB.

Schwartz (1993) and Abbey et al. (1995) provide evidence that long-term PM exposure gives 

rise to the development of CB in the United States. Only the Abbey et al. (1995) study was used, 

because it is the only study focusing on the relationship between PM2.5 and new incidences of 

CB. The number of cases of CB in each age interval was derived by applying the impact 

function from Abbey et al. (1995) to the population in each age interval with the appropriate 

baseline incidence rate.7

 The effect estimate from the Abbey et al. (1995) study is 0.0137, 

which, based on the logistic specification of the model, is equivalent to a relative risk of 1.15 for 

a 10 g change in PM2.5. Table 7b-19 presents the estimated reduction in new incidences of CB 

associated with the 0.070 ppm partial attainment strategy. 

CB is assumed to persist for the remainder of an affected individual’s lifespan. Duration of CB 

will thus equal life expectancy conditioned on having CB. CDC has estimated that COPD (of 

which CB is one element) results in an average loss of life years equal to 4.26 per COPD death, 

relative to a reference life expectancy of 75 years (CDC, 2003). Thus, we subtracted 4.26 from 

the remaining life expectancy for each age group, up to age 75. For age groups over 75, we 

applied the ratio of 4.26 to the life expectancy for the 65 to 74 year group (0.237) to the life 

expectancy for the 75 to 84 and 85 and up age groups to estimate potential life years lost and 

then subtracted that value from the base life expectancy. 

7

 Prevalence rates for CB were obtained from the 1999 National Health Interview Survey 

(American Lung Association, 2002). Prevalence rates were available for three age groups: 18–

44, 45–64, and 65 and older. Prevalence rates per person for these groups were 0.0367 for 18–

44, 0.0505 for 45–64, and 0.0587 for 65 and older. The incidence rate for new cases of CB 

(0.00378 per person) was taken directly from Abbey et al. (1995). 
7b-44

Table 7b-19. Estimated Reduction in Incidence of Chronic Bronchitis Under an Illustrative 

Strategy of Changing from Partial Attainment of the Current (0.084 ppm) O3 NAAQS 

to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 2020 

Age Interval Reduction in Incidence 

(95% Confidence Interval)* 

25 – 34 75

(14 – 140) 

35 – 44 85

(16 – 150) 

45 – 54 80

(15 – 150) 

55 – 64 85

(16 – 160) 

65 – 74 60

(11 – 110) 

75 – 84 30

(6 – 54) 

85+ 13

(2 – 24) 

Total 430

(78 – 770) 

*95% confidence intervals are based on the uncertainty surrounding the effect estimate (coefficient) in 

the CB C-R function. All estimates rounded to two significant figures.

Quality of life with chronic lung diseases has been examined in several studies. In an analysis of 

the impacts of environmental exposures to contaminants, de Hollander et al. (1999) assigned a 

weight of 0.69 to years lived with CB. This weight was based on physicians’ evaluations of 

health states similar to CB. Salomon and Murray (2003) estimated a pooled weight of 0.77 

based on visual analogue scale, time trade-off, standard gamble, and person trade-off techniques 

applied to a convenience sample of health professionals. The Harvard Center for Risk Analysis 

catalog of preference scores reports a weight of 0.40 for severe COPD, with a range from 0.2 to 

0.8, based on the judgments of the study’s authors (Bell et al., 2001). The Victoria Burden of 

Disease (BoD) study used a weight of 0.47 for severe COPD and 0.83 for mild to moderate 

COPD, based on an analysis by Stouthard et al. (1997) of chronic diseases in Dutch populations

(Vos, 1999a). Based on the recommendations of Gold et al. (1996), quality-of-life weights based 

on community preferences are preferred for CEA of interventions affecting broad populations.

Use of weights based on health professionals is not recommended. It is not clear from the

Victoria BoD study whether the weights used for COPD are based on community preferences or 

judgments of health professionals. The Harvard catalog score is clearly identified as based on 

author judgment. Given the lack of a clear preferred weight, we selected a triangular distribution 

centered at 0.7 with an upper bound at 0.9 (slightly better than a mild/moderate case defined by 

the Victoria BoD study) and a lower bound at 0.5 based on the Victoria BoD study. We will 

need additional empirical data on quality of life with chronic respiratory diseases based on 

community preferences to improve our estimates. 
7b-45

Selection of a reference weight for the general population without CB is somewhat uncertain. It 

is clear that the general population is not in perfect health; however, there is some uncertainty as 

to whether individuals’ ratings of health states are in reference to a perfect health state or to a 

generally achievable “normal” health state given age and general health status. The U.S. Public 

Health Service Panel on Cost Effectiveness in Health and Medicine recommends that “since 

lives saved or extended by an intervention will not be in perfect health, a saved life year will 

count as less than 1 full QALY” (Gold et al., 1996). Following Carrothers, Evans, and Graham 

(2002), we assumed that the reference weight for the general population without CB is 0.95. To 

allow for uncertainty in this parameter, we assigned a triangular distribution around this weight, 

bounded by 0.9 and 1.0. Note that the reference weight for the general population is used solely 

to determine the incremental quality-of-life improvement applied to the duration of life that 

would have been lived with the chronic disease. For example, if CB has a quality-of-life weight 

of 0.7 relative to a reference quality-of-life weight of 0.9, then the incremental quality-of-life 

improvement in 0.2. If the reference quality-of-life weight is 0.95, then the incremental qualityof-life improvement is 0.25. As noted above, the population is assumed to have a reference 

weight of 1.0 for all life years gained due to mortality risk reductions. 

We present discounted QALYs over the duration of the lifespan with CB using a 3 percent 

discount rate. Based on the assumptions defined above, we used Monte Carlo simulation 

methods as implemented in the Crystal BallTM software program to develop the distribution of 

QALYs gained per incidence of CB for each age interval.8 Based on the assumptions defined 

above, the mean 3 percent discounted QALY gained per incidence of CB for each age interval 

along with the 95 percent confidence interval resulting from the Monte Carlo simulation is 

presented in Table 7b-20. Table 7b-20 presents both the undiscounted and discounted QALYs 

gained per incidence, using a 3 percent discount rate. 

8

 Monte Carlo simulation uses random sampling from distributions of parameters to characterize 

the effects of uncertainty on output variables. For more details, see Gentile (1998). 
7b-46

Table 7b-20. QALYs Gained per Avoided Incidence of CB 

Age Interval QALYs Gained per Incidence 

Start Age End Age Undiscounted Discounted (3%) 

25 34 12.15

(4.40-19.95) 

6.52

(2.36-10.71) 

35 44 9.91

(3.54-16.10) 

5.94

(2.12-9.66) 

45 54 7.49

(2.71-12.34) 

5.03

(1.82-8.29) 

55 64 5.36

(1.95-8.80) 

4.03

(1.47-6.61) 

65 74 3.40

(1.22-5.64) 

2.84

(1.02-4.71) 

75 84 2.15

(0.77-3.49) 

1.92

(0.69-3.13) 

85+ 0.79

(0.27-1.29) 

0.77

(0.26-1.25) 

7b.6.4.2 Calculating QALYs Associated with Reductions in the Incidence of Nonfatal Myocardial 

Infarctions 

Nonfatal heart attacks, or acute myocardial infarctions, require more complicated calculations to 

derive estimates of QALY impacts. The actual heart attack, which results when an area of the 

heart muscle dies or is permanently damaged because of oxygen deprivation, and subsequent 

emergency care are of relatively short duration. Many heart attacks result in sudden death.

However, for survivors, the long-term impacts of advanced coronary heart disease (CHD) are 

potentially of long duration and can result in significant losses in quality of life and life 

expectancy. 

In this phase of the analysis, we did not independently estimate the gains in life expectancy 

associated with reductions in nonfatal heart attacks. Based on recommendations from the SABHES, we assumed that all gains in life expectancy are captured in the estimates of reduced 

mortality risk provided by the Pope et al. (2002) analysis. We estimated only the change in 

quality of life over the period of life affected by the occurrence of a heart attack. This may 

understate the QALY impacts of nonfatal heart attacks but ensures that the overall QALY impact 

estimates across endpoints do not double-count potential life-year gains. 

Our approach adapts a CHD model developed for the Victoria Burden of Disease study (Vos, 

1999b). This model accounts for the lost quality of life during the heart attack and the possible 

health states following the heart attack. Figure 7b-1 shows the heart attack QALY model in 

diagrammatic form. 

The total gain in QALYs is calculated as: 
7b-47

   

DISCOUNTED AMI QALY GAINED

AMI D w w AMI p D w w i i

AMI

i i

AMI

i j

i j ij

PostAMI

i ij

postAMI

i

 

¦ ¦ u u   u u  ¦ 

' '

* *

1

4

where AMIi is the number of nonfatal acute myocardial infarctions avoided in age interval i,

wi

AMI is the QALY weight associated with the acute phase of the AMI, pj is the probability of 

being in the jth post-AMI status, wij

postAMI is the QALY weight associated with post-AMI health 

status j, wi is the average QALY weight for age interval i, D e dt i

AMI rt

t

Di

AMI

*

 



 

3 1

 , the discounted 

value of Di

AMI , the duration of the acute phase of the AMI, and D e dt i

postAMI rt

t

Di

postAMI

*

 



 

3 1

, is the 

discounted value of Dij

PostAMI , the duration of post-AMI health status j.
7b-48

Figure 7b-1. Decision Tree Used in Modeling Gains in QALYs from Reduced Incidence of 

Nonfatal Acute Myocardial Infarctions 

Nonfatal heart attacks have been linked with short-term exposures to PM2.5 in the United States 

(Peters et al., 2001) and other countries (Poloniecki et al., 1997). We used a recent study by 

Peters et al. (2001) as the basis for the impact function estimating the relationship between PM2.5

and nonfatal heart attacks. Peters et al. is the only available U.S. study to provide a specific 

estimate for heart attacks. Other studies, such as Samet et al. (2000) and Moolgavkar (2000), 

show a consistent relationship between all cardiovascular hospital admissions, including for 

nonfatal heart attacks, and PM. Given the lasting impact of a heart attack on longer-term health 

costs and earnings, we chose to provide a separate estimate for nonfatal heart attacks based on 

the single available U.S. effect estimate. The finding of a specific impact on heart attacks is 

consistent with hospital admission and other studies showing relationships between fine particles 

and cardiovascular effects both within and outside the United States. These studies provide a 

weight of evidence for this type of effect. Several epidemiologic studies (Liao et al., 1999; Gold 

et al., 2000; Magari et al., 2001) have shown that heart rate variability (an indicator of how much 

Nonfatal AMI

Congestive Heart Failure

Yes

No

Angina

Angina

Yes

No

No

Yes

Post AMI QALY with Angina and CHF

Post AMI QALY with CHF without Angina

Post AMI QALY with Angina without CHF

Post AMI QALY without Angina or CHF

Acute Treatment Stage Chronic Post-AMI Follow up Stage
7b-49

the heart is able to speed up or slow down in response to momentary stresses) is negatively 

related to PM levels. Heart rate variability is a risk factor for heart attacks and other CHDs 

(Carthenon et al., 2002; Dekker et al., 2000; Liao et al., 1997, Tsuji et al., 1996). As such, 

significant impacts of PM on heart rate variability are consistent with an increased risk of heart 

attacks. 

The number of avoided nonfatal AMI in each age interval was derived by applying the impact 

function from Peters et al. (2001) to the population in each age interval with the appropriate 

baseline incidence rate.9 The effect estimate from the Peters et al. (2001) study is 0.0241, which, 

based on the logistic specification of the model, is equivalent to a relative risk of 1.27 for a 10 g

change in PM2.5. Table 7b-21 presents the estimated reduction in nonfatal AMI associated with 

the illustrative Ozone NAAQS attainment strategies. 

Table 7b-21. Estimated Reduction in Nonfatal Acute Myocardial Infarctions Under an Illustrative 

Strategy of Changing from Partial Attainment of the Current (0.084 ppm) O3 NAAQS 

to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 2020 

Age Interval

Reduction in Incidence 

(95% Confidence Interval)* 

18 – 24 1

(0 – 1) 

25 – 34 5

(2 – 7) 

35 – 44 32

(17 – 46) 

45 – 54 97

(52 – 140) 

55 – 64 240

(130 – 350) 

65 – 74 290

(150 – 420) 

75 – 84 210

(120 – 310) 

85+ 130

(71 – 190) 

Total 1,000

(540 – 1,500) 

*95% confidence intervals are based on the uncertainty surrounding the effect estimate (coefficient) in 

the AMI C-R function.

9

 Daily nonfatal myocardial infarction incidence rates per person were obtained from the 1999 

National Hospital Discharge Survey (assuming all diagnosed nonfatal AMI visit the hospital).

Age-specific rates for four regions are used in the analysis. Regional averages for populations 18 

and older are 0.0000159 for the Northeast, 0.0000135 for the Midwest, 0.0000111 for the South, 

and 0.0000100 for the West. 
7b-50

Acute myocardial infarction results in significant loss of quality of life for a relatively short 

duration. The WHO Global Burden of Disease study, as reported in Vos (1999b), assumes that 

the acute phase of an acute myocardial infarction lasts for 0.06 years, or around 22 days. An 

alternative assumption is the acute phase is characterized by the average length of hospital stay 

for an AMI in the United States, which is 5.5 days, based on data from the Agency for 

Healthcare Research and Quality’s Healthcare Cost and Utilization Project (HCUP).10 We 

assumed a distribution of acute phase duration characterized by a uniform distribution between 

5.5 and 22 days, noting that due to earlier discharges and in-home therapy available in the United 

States, duration of reduced quality of life may continue after discharge from the hospital. In the 

period during and directly following an AMI (the acute phase), we assigned a quality of life 

weight equal to 0.605, consistent with the weight for the period in treatment during and 

immediately after an attack (Vos, 1999b). 

During the post-AMI period, a number of different health states can determine the loss in quality 

of life. We chose to classify post-AMI health status into four states defined by the presence or 

absence of angina and congestive heart failure (CHF). This makes a very explicit assumption 

that without the occurrence of an AMI, individuals would not experience either angina or CHF.

If in fact individuals already have CHF or angina, then the quality of life gained will be 

overstated. We do not have information about the percentage of the population have been 

diagnosed with angina or CHF with no occurrence of an AMI. Nor do we have information on 

what proportion of the heart attacks occurring due to PM exposure are first heart attacks versus 

repeat attacks. Probabilities for the four post-AMI health states sum to one. 

Given the occurrence of a nonfatal AMI, the probability of congestive heart failure is set at 0.2, 

following the heart disease model developed by Vos (1999b). The probability is based on a 

study by Cowie et al. (1997), which estimated that 20 percent of those surviving AMI develop 

heart failure, based on an analysis of the results of the Framingham Heart Study. 

The probability of angina is based on the prevalence rate of angina in the U.S. population. Using 

data from the American Heart Association, we calculated the prevalence rate for angina by 

dividing the estimated number of people with angina (6.6 million) by the estimated number of 

people with CHD of all types (12.9 million). We then assumed that the prevalence of angina in 

the population surviving an AMI is similar to the prevalence of angina in the total population 

with CHD. The estimated prevalence rate is 51 percent, so the probability of angina is 0.51. 

Combining these factors leads to the probabilities for each of the four health states as follows: 

I. Post AMI with CHF and angina = 0.102 

II. Post AMI with CHF without angina = 0.098 

III. Post AMI with angina without CHF = 0.408 

IV. Post AMI without angina or CHF = 0.392 

10 Average length of stay estimated from the HCUP data includes all discharges, including those 

due to death. As such, the 5.5-day average length of stay is likely an underestimate of the 

average length of stay for AMI admissions where the patient is discharged alive. 
7b-51

Duration of post-AMI health states varies, based in part on assumptions regarding life 

expectancy with post-AMI complicating health conditions. Based on the model used for 

established market economies (EME) in the WHO Global Burden of Disease study, as reported 

in Vos (1999b), we assumed that individuals with CHF have a relatively short remaining life 

expectancy and thus a relatively short period with reduced quality of life (recall that gains in life 

expectancy are assumed to be captured by the cohort estimates of reduced mortality risk). 

Table 7b-22 provides the duration (both discounted and undiscounted) of CHF assumed for postAMI cases by age interval. 

Table 7b-22. Assumed Duration of Congestive Heart Failure 

Age Interval Duration of Heart Failure (years) 

Start Age End Age Undiscounted Discounted (3%) 

18 24 7.11 6.51 

25 34 6.98 6.40 

35 44 6.49 6.00 

45 54 5.31 4.99 

55 64 1.96 1.93 

65 74 1.71 1.69 

75 84 1.52 1.50 

85+ 1.52 1.50 

Duration of health states without CHF is assumed to be equal to the life expectancy of 

individuals conditional on surviving an AMI. Ganz et al. (2000) note that “Because patients with 

a history of myocardial infarction have a higher chance of dying of CHD that is unrelated to 

recurrent myocardial infarction (for example, arrhythmia), this cohort has a higher risk for death 

from causes other than myocardial infarction or stroke than does an unselected population.”

They go on to specify a mortality risk ratio of 1.52 for mortality from other causes for the cohort 

of individuals with a previous (nonfatal) AMI. The risk ratio is relative to all-cause mortality for 

an age-matched unselected population (i.e., general population). We adopted the same ratios and 

applied them to each age-specific all-cause mortality rate to derive life expectancies (both 

discounted and undiscounted) for each age group after an AMI, presented in Table 7b-23. These 

life expectancies were then used to represent the duration of non-CHF post-AMI health states (III 

and IV). 
7b-52

Table 7b-23. Assumed Duration of Non-CHF Post-AMI Health States 

Age Interval Post-AMI Years of Life Expectancy (non-CHF) 

Start Age End Age Undiscounted Discounted (3%) 

18 24 55.5 27.68 

25 34 46.1 25.54 

35 44 36.8 22.76 

45 54 27.9 19.28 

55 64 19.8 15.21 

65 74 12.8 10.82 

75 84 7.4 6.75 

85+ 3.6 3.47 

For the four post-AMI health states, we used QALY weights based on preferences for the 

combined conditions characterizing each health state. A number of estimates of QALY weights 

are available for post-AMI health conditions. 

The first two health states are characterized by the presence of CHF, with or without angina.

The Harvard Center for Risk Analysis catalog of preference scores provides several specific 

weights for CHF with and without mild or severe angina and one set specific to post-AMI CHF.

Following the Victoria Burden of Disease model, we assumed that most cases of angina will be 

treated and thus kept at a mild to moderate state. We thus focused our selection on QALY 

weights for mild to moderate angina. The Harvard database includes two sets of community 

preference-based scores for CHF (Stinnett et al., 1996; Kuntz et al., 1996). The scores for CHF 

with angina range from 0.736 to 0.85. The lower of the two scores is based on angina in general 

with no delineation by severity. Based on the range of the scores for mild to severe cases of 

angina in the second study, one can infer that an average case of angina has a score around 0.96 

of the score for a mild case. Applying this adjustment raises the lower end of the range of 

preference scores for a mild case of angina to 0.76. We selected a uniform distribution over the 

range 0.76 to 0.85 for CHF with mild angina, with a midpoint of 0.81. The same two studies in 

the Harvard catalog also provide weights for CHF without angina. These scores range from 

0.801 to 0.89. We selected a uniform distribution over this range, with a midpoint of 0.85. 

The third health state is characterized by angina, without the presence of CHF. The Harvard 

catalog includes five sets of community preference-based scores for angina, one that specifies 

scores for both mild and severe angina (Kuntz et al., 1996), one that specifies mild angina only 

(Pliskin, Stason, and Weinstein, 1981), one that specifies severe angina only (Cohen, Breall, and 

Ho, 1994), and two that specify angina with no severity classification (Salkeld, Phongsavan, and 

Oldenburg, 1997; Stinnett et al., 1996). With the exception of the Pliskin, Stason, and Weinstein 

score, all of the angina scores are based on the time trade-off method of elicitation. The Pliskin, 

Stason, and Weinstein score is based on the standard gamble elicitation method. The scores for 

the nonspecific severity angina fall within the range of the two scores for mild angina 

specifically. Thus, we used the range of mild angina scores as the endpoints of a uniform 

distribution. The range of mild angina scores is from 0.7 to 0.89, with a midpoint of 0.80. 
7b-53

For the fourth health state, characterized by the absence of CHF and/or angina, there is only one 

relevant community preference score available from the Harvard catalog. This score is 0.93, 

derived from a time trade-off elicitation (Kuntz et al., 1996). Insufficient information is 

available to provide a distribution for this weight; therefore, it is treated as a fixed value. 

Similar to CB, we assumed that the reference weight for the general population without AMI is 

0.95. To allow for uncertainty in this parameter, we assigned a triangular distribution around this 

weight, bounded by 0.9 and 1.0. 

Based on the assumptions defined above, we used Monte Carlo simulation methods as 

implemented in the Crystal BallTM software program to develop the distribution of QALYs 

gained per incidence of nonfatal AMI for each age interval. For the Monte Carlo simulation, all 

distributions were assumed to be independent. The mean QALYs gained per incidence of 

nonfatal AMI for each age interval is presented in Table 7b-24, along with the 95 percent 

confidence interval resulting from the Monte Carlo simulation. Table 7b-24 presents both the 

undiscounted and discounted QALYs gained per incidence. 

Table 7b-24. QALYs Gained per Avoided Nonfatal Myocardial Infarction 

Age Interval QALYs Gained per Incidencea

Start Age End Age Undiscounted Discounted (3%) 

18 24 4.18

(1.24-7.09) 

2.17

(0.70-3.62) 

25 34 3.48

(1.09-5.87) 

2.00

(0.68-3.33) 

35 44 2.81

(0.88-4.74) 

1.79

(0.60-2.99) 

45 54 2.14

(0.67-3.61) 

1.52

(0.51-2.53) 

55 64 1.49

(0.42-2.52) 

1.16

(0.34-1.95) 

65 74 0.97

(0.30-1.64) 

0.83

(0.26-1.39) 

75 84 0.59

(0.20-0.97) 

0.54

(0.19-0.89) 

85+ 0.32

(0.13-0.50) 

0.31

(0.13-0.49) 

a

 Mean of Monte Carlo generated distribution; 95% confidence interval presented in parentheses. 

7b.6.5 Aggregating Life Expectancy and Quality-of-Life Gains 

Given the estimates of changes in life expectancy and quality of life, the next step is to aggregate 

life expectancy and quality-of-life gains to form an effectiveness measure that can be compared 

to costs to develop cost-effectiveness ratios. This section discusses the proper characterization of 

the combined effectiveness measure for the denominator of the cost-effectiveness ratio. 
7b-54

To develop an integrated measure of changes in health, we simply sum together the gains in life 

years from reduced mortality risk in each age interval with the gains in QALYs from reductions 

in incidence of chronic morbidity endpoints (CB and acute myocardial infarctions). The 

resulting measure of effectiveness then forms the denominator in the cost-effectiveness ratio. 

This combined measure of effectiveness is not a QALY measure in a strict sense, because we 

have not adjusted life-expectancy gains for preexisting health status (quality of life). It is 

however, an effectiveness measure that adds a scaled morbidity equivalent to the standard life 

years calculation. Thus, we term the aggregate measure morbidity inclusive life years, or 

MILYs. Alternatively, the combined measure could be considered as QALYs with an 

assumption that the community preference weight for all life-expectancy gains is 1.0. If one 

considers that this weight might be considered to be a “fair” treatment of those with preexisting 

disabilities, the effectiveness measure might be termed “fair QALY” gained. However, this 

implies that all aspects of fairness have been addressed, and there are clearly other issues with 

the fairness of QALYs (or other effectiveness measures) that are not addressed in this simple 

adjustment. The MILY measure violates some of the properties used in deriving QALY weights, 

such as linear substitution between quality of life and quantity of life. However, in aggregating 

life expectancy and quality-of-life gains, it merely represents an alternative social weighting that 

is consistent with the spirit of the recent OMB guidance on CEA. The guidance notes that 

“fairness is important in the choice and execution of effectiveness measures” (OMB, 2003). The 

resulting aggregate measure of effectiveness will not be consistent with a strict utility 

interpretation of QALYs; however, it may still be a useful index of effectiveness. 

Applying the life expectancies and distributions of QALYs per incidence for CB and AMI to 

estimated distributions of incidences yields distributions of life expectancy and QALYs gained 

under the illustrative attainment strategy with a baseline of partial attainment of the current 

(0.084 ppm) O3 NAAQS and a control scenario of partial attainment of an alternative 0.070 ppm 

O3 NAAQS. These distributions reflect both the quantified uncertainty in estimates of avoided 

incidence and the quantified uncertainty in QALYs gained per incidence avoided. 

Tables 7b-25 and 7b-26 present the discounted life years, QALYs, and MILYs gained, based on 

each combination of O3-mortality study, PM2.5-mortality study, and life expectancy assumption 

for O3-related life years saved used for the analysis of this attainment strategy, using a 3 percent 

and 7 percent discount rate, respectively.
7b-55

Table 7b-25. Estimated Gains in Discounted MILYs, Using a 3 Percent Discount Rate, Under an Illustrative Strategy of Changing from 

Partial Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 

2020

(95% CI) (95% CI) (95% CI) (95% CI) (95% CI)

1,300 8,500

(400 - 2,200) (4,700 - 12,000)

1,000 8,200

(300 - 1,700) (4,500 - 12,000)

500 7,700

(200 - 900) (4,100 - 12,000)

6,100 13,000

(4,100 - 8,100) (9,100 - 18,000)

4,700 12,000

(3,200 - 6,300) (7,900 - 16,000)

2,700 9,900

(1,800 - 3,500) (6,200 - 14,000)

1,300 14,000

(400 - 2,200) (8,500 - 20,000)

1,000 14,000

(300 - 1,700) (8,200 - 19,000)

500 13,000

(200 - 900) (7,800 - 19,000)

6,100 19,000

(4,100 - 8,100) (13,000 - 25,000)

4,700 17,000

(3,200 - 6,300) (12,000 - 23,000)

2,700 15,000

(1,800 - 3,500) (9,900 - 21,000)

Total MILYs 

Gained

1,970

(270 - 4,700)

870

(220 - 1,800)

4,400

(1,700 - 7,000)

9,900

(5,400 - 14,000)

O3-Related Life Years 

Gained from Mortality 

Risk 

Reductions

PM2.5-Related Life 

Years Gained from 

Mortality Risk 

Reductions

QALYs Gained from 

Reductions in PM2.5- Related Chronic 

Bronchitis

QALYs Gained from 

Reductions in PM2.5- Related Non-Fatal 

Myocardial Infarction

Levy et al. (2005) Laden et al. (2006) Subpopulation with Average COPDLevy et al. (2005) Laden et al. (2006) Subpopulation with Severe COPDLevy et al. (2005) Laden et al. (2006) General Population*Life years, QALYs, and MILYs are discounted back to 2020. 95% confidence or credible intervals (CIs) around the point estimates are based on the uncertainty surrounding the effect 

estimates (coefficients) in the C-R functions and, for QALYs and MILYs, the uncertainty surrounding the quality of life weights. All estimates rounded to two significant figures. Bell et al. (2004) Laden et al. (2006) Subpopulation with Average COPDBell et al. (2004) Laden et al. (2006) Subpopulation with Severe COPDLevy et al. (2005) Pope et al. (2002) Subpopulation with Severe COPDBell et al. (2004) Laden et al. (2006) General PopulationLevy et al. (2005) Pope et al. (2002) General PopulationLevy et al. (2005) Pope et al. (2002) Subpopulation with Average COPDBell et al. (2004) Pope et al. (2002) Subpopulation with Average COPDBell et al. (2004) Pope et al. (2002) Subpopulation with Severe COPD

O3 Mortality Study PM2.5 Mortality Study Life Expectancy Assumption for O3- Related Mortality

Bell et al. (2004) Pope et al. (2002) General Population
7b-56

Table 7b-26. Estimated Gains in Discounted MILYs, Using a 7 Percent Discount Rate, Under an Illustrative Strategy of Changing from 

Partial Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 

2020

(95% CI) (95% CI) (95% CI) (95% CI) (95% CI)

990 5,900

(280 - 1,700) (3,300 - 8,700)

790 5,700

(230 - 1,400) (3,100 - 8,500)

450 5,400

(130 - 780) (2,800 - 8,100)

4,600 9,500

(3,100 - 6,100) (6,600 - 13,000)

3,700 8,600

(2,500 - 4,900) (5,800 - 12,000)

2,200 7,100

(1,500 - 2,900) (4,400 - 10,000)

990 9,700

(280 - 1,700) (5,900 - 13,000)

790 9,500

(230 - 1,400) (5,700 - 13,000)

450 9,200

(130 - 780) (5,400 - 13,000)

4,600 13,000

(3,100 - 6,100) (9,400 - 17,000)

3,700 12,000

(2,500 - 4,900) (8,600 - 16,000)

2,200 11,000

(1,500 - 2,900) (7,200 - 15,000)

1,300

(180 - 3,000)

680

(180 - 1,400)

3,000

(1,200 - 4,800)

6,700

(3,700 - 9,800)

*Life years, QALYs, and MILYs are discounted back to 2020. 95% confidence or credible intervals (CIs) around the point estimates are based on the uncertainty surrounding the effect 

estimates (coefficients) in the C-R functions and, for QALYs and MILYs, the uncertainty surrounding the quality of life weights. All estimates rounded to two significant figures. 

Levy et al. (2005) Laden et al. (2006) Subpopulation with Average COPDLevy et al. (2005) Laden et al. (2006) Subpopulation with Severe COPDBell et al. (2004) Laden et al. (2006) Subpopulation with Severe COPDLevy et al. (2005) Laden et al. (2006) General PopulationBell et al. (2004) Laden et al. (2006) General PopulationBell et al. (2004) Laden et al. (2006) Subpopulation with Average COPDLevy et al. (2005) Pope et al. (2002) Subpopulation with Average COPDLevy et al. (2005) Pope et al. (2002) Subpopulation with Severe COPDBell et al. (2004) Pope et al. (2002) Subpopulation with Severe COPDBell et al. (2004) Pope et al. (2002) General PopulatiLevy et al. (2005) Pope et al. (2002) General Population

on

Bell et al. (2004) Pope et al. (2002) Subpopulation with Average COPD

Total MILYs 

Gained O3 Mortality Study PM2.5 Mortality Study Life Expectancy Assumption for O3- Related Mortality

O3-Related Life Years 

Gained from Mortality 

Risk 

Reductions

PM2.5-Related Life 

Years Gained from 

Mortality Risk 

Reductions

QALYs Gained from 

Reductions in PM2.5- Related Chronic 

Bronchitis

QALYs Gained from 

Reductions in PM2.5- Related Non-Fatal 

Myocardial Infarction
7b-57

7b.6.6 Estimating the Avoided Costs of Chronic Illness 

Construction of cost-effectiveness ratios requires estimates of effectiveness (in this case 

measured by lives saved, life years gained, or MILYs gained) in the denominator and estimates 

of costs in the numerator. As noted above (see Section 7b.4.1), our estimate of costs in the 

numerator is net of the avoided costs (cost savings) associated with the reductions in morbidity 

(Gold et al., 1996). Among the morbidity costs subtracted from the direct costs of controls in the 

numerator are the avoided costs of illness (COI) associated with PM2.5-related CB and nonfatal 

AMI.

Avoided costs for CB and nonfatal AMI are based on estimates of lost earnings and medical 

costs.11 Using age-specific annual lost earnings and medical costs estimated by Cropper and 

Krupnick (1990) and a 3 percent discount rate, we estimated a lifetime present discounted value 

(in 2006$) due to CB of $185,774 for someone between the ages of 27 and 44; $121,177 for 

someone between the ages of 45 and 64; and $14,293 for someone over 65. The corresponding 

age-specific estimates of lifetime present discounted value (in 2006$) using a 7 percent discount 

rate are $105,974, $89,506, and $11,641, respectively. These estimates assumed that 1) lost 

earnings continue only until age 65, 2) medical expenditures are incurred until death, and 3) life 

expectancy is unchanged by CB. 

Because the costs associated with a myocardial infarction extend beyond the initial event itself, 

we consider costs incurred over several years. Using age-specific annual lost earnings estimated 

by Cropper and Krupnick (1990) and a 3 percent discount rate, we estimated a present 

discounted value in lost earnings (in 2006$) over 5 years due to a myocardial infarction of 

$10,758 for someone between the ages of 25 and 44, $15,856 for someone between the ages of 

45 and 54, and $91,647 for someone between the ages of 55 and 65. The corresponding agespecific estimates of lost earnings (in 2006$) using a 7 percent discount rate are $9,631, $14,195, 

and $82,051, respectively. Cropper and Krupnick (1990) do not provide lost earnings estimates 

for populations under 25 or over 65. Thus, we do not include lost earnings in the cost estimates 

for these age groups. 

Two estimates of the direct medical costs of myocardial infarction are used. The first estimate is 

from Wittels, Hay, and Gotto (1990), which estimated expected total medical costs of MI over 5 

years to be $51,211 (in 1986$) for people who were admitted to the hospital and survived 

hospitalization (there does not appear to be any discounting used). Using the CPI-U for medical 

care, the Wittels estimate is $141,124 in year 2006$. This estimated cost is based on a medical 

cost model, which incorporated therapeutic options, projected outcomes, and prices (using 

“knowledgeable cardiologists” as consultants). The model used medical data and medical 

11 Gold et al. (1996) recommend not including lost earnings in the cost-of-illness estimates, 

suggesting that in some cases, they may be already be counted in the effectiveness measures.

However, this requires that individuals fully incorporate the value of lost earnings and reduced 

labor force participation opportunities into their responses to time-tradeoff or standard-gamble 

questions. For the purposes of this analysis and for consistency with the way costs-of-illness are 

calculated for the benefit-cost analysis, we have assumed that individuals do not incorporate lost 

earnings in responses to these questions. This assumption can be relaxed in future analyses with 

improved understanding of how lost earnings are treated in preference elicitations. 
7b-58

decision algorithms to estimate the probabilities of certain events and/or medical procedures 

being used. The second estimate is from Russell et al. (1998), which estimated first-year direct 

medical costs of treating nonfatal myocardial infarction of $15,540 (in 1995$), and $1,051 

annually thereafter. Converting to year 2006$, that would be $28,787 for a 5-year period 

(without discounting). 

The two estimates from these studies are substantially different, and we have not adequately 

resolved the sources of differences in the estimates. Because the wage-related opportunity cost 

estimates from Cropper and Krupnick (1990) cover a 5-year period, we used estimates for 

medical costs that similarly cover a 5-year period. We used a simple average of the two 5-year 

estimates, or $84,956, and add it to the 5-year opportunity cost estimate. The resulting estimates 

are given in Table 7b-27. 

Table 7b-27. Estimated Costs Over a 5-Year Period (in 2006$) of a Nonfatal Myocardial Infarction 

Age Group Opportunity Cost Medical Costa Total Cost 

0 – 24 $0 $84,956 $84,956 

25-44 $10,757b $84,956 $95,714 

45 – 54 $15,856b $84,956 $100,812 

55 – 65 $91,647b $84,956 $176,603 

>65 $0 $84,956 $84,956 

a

 An average of the 5-year costs estimated by Wittels, Hay, and Gotto (1990) and Russell et al. (1998).

b

 From Cropper and Krupnick (1990), using a 3 percent discount rate. 

The total avoided COI by age group associated with the reductions in CB and nonfatal acute 

myocardial infarctions (using a 3 percent discount rate) is provided in Table 7b-28. The total 

avoided COI associated with this illustrative attainment strategy (using a 3 percent discount rate) 

is about $172 million. Note that these estimates do not include any direct avoided medical costs 

associated with premature mortality. Nor do they include any medical costs that occur more than 

5 years from the onset of a nonfatal AMI. Therefore, they are likely underestimates of the true 

avoided COI associated with this illustrative attainment strategy. 
7b-59

Table 7b-28. Avoided Costs of Illness Associated with Reductions in Chronic Bronchitis and 

Nonfatal Acute Myocardial Infarctions Under an Illustrative Strategy of Changing 

from Partial Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of 

an Alternative 0.070 ppm O3 NAAQS in 2020 

Avoided Cost of Illness 

(in millions of 2006$)

Age

Range Chronic Bronchitis Nonfatal Acute Myocardial Infarction 

18-24 — $0.07 

25-34 $17 $0.4 

35-44 $19 $3 

45-54 $12 $9.8 

55-64 $13 $42 

65-74 $1.1 $24 

75-84 $0.5 $18 

85+ $0.2 $11 

Total $63 $110 

7b.6.7 Cost-Effectiveness Ratios 

Construction of cost-effectiveness ratios requires estimates of effectiveness (in this case 

measured by lives saved, life years gained, or MILYs gained) in the denominator and estimates 

of costs in the numerator. As noted above (see Section 7b.4.1), the estimate of costs in the 

numerator should include both the direct costs of the controls necessary to achieve the reduction 

in ambient O3 (and, indirectly, PM2.5) and the avoided costs (cost savings) associated with the 

reductions in morbidity (Gold et al., 1996). In general, because reductions in air pollution do not 

require direct actions by the affected populations, there are no specific costs to affected 

individuals (aside from the overall increases in prices that might be expected to occur as control 

costs are passed on by affected industries). Likewise, because individuals do not engage in any 

specific actions to realize the health benefit of the pollution reduction, there are no decreases in 

utility (as might occur from a medical intervention) that need to be adjusted for in the 

denominator. Thus, the elements of the numerator are direct costs of controls minus the avoided 

COI associated with CB and nonfatal AMI. In addition, to account for the value of reductions in 

O3- and PM2.5-related acute health impacts and non-health benefits, we netted out the monetized 

value of these benefits from the numerator to yield a “net cost” estimate. For the MILY 

aggregate effectiveness measure, the denominator is simply the sum of (O3- and PM2.5-related) 

life years gained from increased life expectancy and QALYs gained from the (PM2.5-related)

reductions in CB and nonfatal AMI. The separate O3- and PM2.5-related inputs to the 

denominators of the cost-effectiveness ratios are summarized above in Tables 7b-25 through 7b26. The cost-effectiveness ratios and 95 percent confidence (credible) intervals resulting from all 

of the sources of uncertainty considered, using Monte Carlo procedures as implemented in the 

Crystal BallTM software program and incorporating both the O3- and PM2.5-related benefits are 

shown in the tables below. Tables 7b-29 and 7b-30 show cost per life saved, using a 3 percent 
7b-60

and 7 percent discount rate, respectively. Tables 7b-31 and 7b-32 show cost per life year saved 

at the two discount rates; and Tables 7b-33 and 7b-34 show cost per MILY gained. 

Table 7b-29. Estimated Net Cost (2006$) per O3- and PM2.5-Related Life Saved Under an Illustrative 

Strategy of Changing from Partial Attainment of the Current (0.084 ppm) O3 NAAQS 

to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 2020, Using a 3 

Percent Discount Rate 

Cost Effectiveness Ratio: 

Net Cost (in Million $) per Life Saved*

(95% CI)**

$4.5

($2.7 - $8.7)

$2.3

($1.5 - $3.8)

$2.3

($1.7 - $3.4)

$1.5

($1.1 - $2.2)

O3 Mortality Study PM2.5 Mortality Study

*The 3 percent discounted cost of the regulation is estimated to be $2.6 billion. PM2.5-related avoided deaths 

are discounted back to 2020. O3-related deaths are assumed to occur in 2020. 

**95 percent confidence or credible intervals incorporate uncertainty surrounding the O3 and PM2.5 coefficients 

in the mortality and morbidity C-R functions as well as the uncertainty surrounding unit values of morbidity 

endpoints. All estimates rounded to two significant figures. 

Laden et al. (2006)

Pope et al. (2002)

Laden et al. (2006)

Levy et al. (2005) Pope et al. (2002)

Levy et al. (2005)

Bell et al. (2004)

Bell et al. (2004)

Table 7b-30. Estimated Net Cost (2006$) per O3- and PM2.5-Related Life Saved Under an Illustrative 

Strategy of Changing from Partial Attainment of the Current (0.084 ppm) O3 NAAQS 

to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 2020, Using a 7 

Percent Discount Rate 

Cost Effectiveness Ratio: 

Net Cost (in Million $) per Life Saved*

(95% CI)**

$5.4

($3.2 - $9.9)

$2.7

($1.8 - $4.5)

$2.6

($1.9 - $3.8)

$1.8

($1.3 - $2.6)

**95 percent confidence or credible intervals incorporate uncertainty surrounding the O3 and PM2.5 coefficients 

in the mortality and morbidity C-R functions as well as the uncertainty surrounding unit values of morbidity 

endpoints. All estimates rounded to two significant figures. 

Laden et al. (2006)

Pope et al. (2002)

Laden et al. (2006)

Levy et al. (2005) Pope et al. (2002)

Levy et al. (2005)

Bell et al. (2004)

Bell et al. (2004)

O3 Mortality Study PM2.5 Mortality Study

*The 7 percent discounted cost of the regulation is estimated to be $2.8 billion. PM2.5-related avoided deaths 

are discounted back to 2020. O3-related deaths are assumed to occur in 2020. 
7b-61

Table 7b-31. Estimated Net Cost (2006$) per O3- and PM2.5-Related Life Year Saved Under an Illustrative Strategy of Changing from 

Partial Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 

2020, Using a 3 Percent Discount Rate 

Cost Effectiveness Ratio: 

Net Cost (in Million $) per Life Year Saved*

(95% CI)**

$0.42

($0.25 - $0.81)

$0.45

($0.26 - $0.89)

$0.50

($0.28 - $1)

$0.22

($0.16 - $0.32)

$0.25

($0.18 - $0.38)

$0.33

($0.22 - $0.54)

$0.21

($0.13 - $0.35)

$0.21

($0.14 - $0.36)

$0.22

($0.14 - $0.38)

$0.14

($0.1 - $0.2)

$0.16

($0.11 - $0.23)

$0.18

($0.12 - $0.29)

Levy et al. (2005)Levy et al. (2005)

*The 3 percent discounted cost of the regulation is estimated to be $2.6 billion. All life years are discounted back to the year of death. PM2.5-related avoided 

deaths are discounted back to 2020. O3-related deaths are assumed to occur in 2020.

**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 and PM2.5 coefficients in the mortality and morbidity C-R functions 

as well as the uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures. 

Laden et al. (2006) Subpopulation with Average COPDLaden et al. (2006) Subpopulation with Severe COPD

Bell et al. (2004)Levy et al. (2005)Levy et al. (2005)Levy et al. (2005)Bell et al. (2004)Bell et al. (2004)Bell et al. (2004)Levy et al. (2005)

Pope et al. (2002) General PopulationPope et al. (2002) Subpopulation with Average COPDPope et al. (2002) Subpopulation with Severe COPDPope et al. (2002) General PopulationPope et al. (2002) Subpopulation with Average COPDPope et al. (2002) Subpopulation with Severe COPDLaden et al. (2006) General PopulationLaden et al. (2006) Subpopulation with Average COPDLaden et al. (2006) Subpopulation with Severe COPDLaden et al. (2006) General Population

Life Expectancy Assumption for O3-Related 

Mortality

PM2.5 Mortality Study O3 Mortality Study

Bell et al. (2004)Bell et al. (2004)
7b-62

Table 7b-32. Estimated Net Cost (2006$) per O3- and PM2.5-Related Life Year Saved Under an Illustrative Strategy of Changing from 

Partial Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 

2020, Using a 7 Percent Discount Rate 

Cost Effectiveness Ratio: 

Net Cost (in Million $) per Life Year Saved*

(95% CI)**

$0.67

($0.39 - $1.2)

$0.71

($0.41 - $1.4)

$0.79

($0.44 - $1.6)

$0.33

($0.24 - $0.47)

$0.37

($0.26 - $0.55)

$0.49

($0.33 - $0.78)

$0.33

($0.21 - $0.55)

$0.34

($0.22 - $0.56)

$0.35

($0.23 - $0.6)

$0.22

($0.16 - $0.31)

$0.24

($0.17 - $0.34)

$0.28

($0.19 - $0.42)

Pope et al. (2002) General PopulationPope et al. (2002) Subpopulation with Average COPD

Life Expectancy Assumption for O3-Related 

Mortality

PM2.5 Mortality Study O3 Mortality Study

Bell et al. (2004)Bell et al. (2004)

Pope et al. (2002) Subpopulation with Severe COPDPope et al. (2002) General PopulationPope et al. (2002) Subpopulation with Average COPDPope et al. (2002) Subpopulation with Severe COPDLaden et al. (2006) General PopulationLaden et al. (2006) Subpopulation with Average COPDLaden et al. (2006) Subpopulation with Severe COPDLaden et al. (2006) General Population

Bell et al. (2004)Levy et al. (2005)Levy et al. (2005)Levy et al. (2005)Bell et al. (2004)Bell et al. (2004)Bell et al. (2004)Levy et al. (2005)Levy et al. (2005)Levy et al. (2005)

*The 7 percent discounted cost of the regulation is estimated to be $2.8 billion. All life years are discounted back to the year of death. PM2.5-related avoided 

deaths are discounted back to 2020. O3-related deaths are assumed to occur in 2020.

**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 and PM2.5 coefficients in the mortality and morbidity C-R functions 

as well as the uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures. 

Laden et al. (2006) Subpopulation with Average COPDLaden et al. (2006) Subpopulation with Severe COPD
7b-63

Table 7b-33. Estimated Net Cost (2006$) per O3- and PM2.5-Related MILY Gained Under an Illustrative Strategy of Changing from Partial 

Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 2020, 

Using a 3 Percent Discount Rate 

Cost Effectiveness Ratio: 

Net Cost (in Million $) per MILY Gained*

(95% CI)**

$0.27

($0.17 - $0.46)

$0.28

($0.18 - $0.49)

$0.30

($0.19 - $0.53)

$0.17

($0.12 - $0.24)

$0.19

($0.14 - $0.28)

$0.23

($0.16 - $0.35)

$0.16

($0.11 - $0.26)

$0.17

($0.11 - $0.27)

$0.17

($0.12 - $0.28)

$0.12

($0.09 - $0.17)

$0.13

($0.09 - $0.18)

$0.15

($0.1 - $0.22)

General PopulationSubpopulation with Average COPDSubpopulation with Severe COPDGeneral PopulationSubpopulation with Average COPDSubpopulation with Severe COPDGeneral PopulationSubpopulation with Average COPDSubpopulation with Severe COPDGeneral PopulationSubpopulation with Average COPDSubpopulation with Severe COPD

Bell et al. (2004)Bell et al. (2004)Bell et al. (2004)Levy et al. (2005)Levy et al. (2005)Levy et al. (2005)Bell et al. (2004)Bell et al. (2004)Bell et al. (2004)Levy et al. (2005)Levy et al. (2005)Levy et al. (2005)*The 3 percent discounted cost of the regulation is estimated to be $2.6 billion. All life years are discounted back to the year of death. PM2.5- related avoided deaths are discounted back to 2020. All QALYs are discounted back to 2020. O3-related deaths are assumed to occur in 2020.

**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 and PM2.5 coefficients in the mortality and morbidity 

C-R functions as well as the uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures. 

Pope et al. (2002)Pope et al. (2002)Pope et al. (2002)Pope et al. (2002)Pope et al. (2002)Pope et al. (2002)Laden et al. (2006)Laden et al. (2006)Laden et al. (2006)Laden et al. (2006)Laden et al. (2006)Laden et al. (2006)

Life Expectancy Assumption for O3-Related 

Mortality

PM2.5 Mortality Study O3 Mortality Study
7b-64

Table 7b-34. Estimated Net Cost (2006$) per O3- and PM2.5-Related MILY Gained Under an Illustrative Strategy of Changing from Partial 

Attainment of the Current (0.084 ppm) O3 NAAQS to Partial Attainment of an Alternative 0.070 ppm O3 NAAQS in 2020, 

Using a 7 Percent Discount Rate 

Cost Effectiveness Ratio: 

Net Cost (in Million $) per MILY Gained*

(95% CI)**

$0.43

($0.27 - $0.73)

$0.45

($0.28 - $0.77)

$0.48

($0.29 - $0.86)

$0.26

($0.19 - $0.37)

$0.29

($0.2 - $0.41)

$0.35

($0.24 - $0.54)

$0.26

($0.17 - $0.41)

$0.26

($0.18 - $0.42)

$0.27

($0.18 - $0.44)

$0.18

($0.14 - $0.26)

$0.20

($0.14 - $0.28)

$0.23

($0.16 - $0.33)

Life Expectancy Assumption for O3-Related 

Mortality

PM2.5 Mortality Study O3 Mortality Study

Laden et al. (2006)Laden et al. (2006)Laden et al. (2006)Laden et al. (2006)

*The 7 percent discounted cost of the regulation is estimated to be $2.8 billion. All life years are discounted back to the year of death. PM2.5- related avoided deaths are discounted back to 2020. All QALYs are discounted back to 2020. O3-related death are assumed to occur in 2020.

**95 percent confidence or credible intervals (CIs) incorporate uncertainty surrounding the O3 and PM2.5 coefficients in the mortality and morbidity 

C-R functions as well as the uncertainty surrounding unit values of morbidity endpoints. All estimates rounded to two significant figures. 

Pope et al. (2002)Pope et al. (2002)Pope et al. (2002)Pope et al. (2002)Pope et al. (2002)Pope et al. (2002)Laden et al. (2006)Laden et al. (2006)

Bell et al. (2004)Bell et al. (2004)Bell et al. (2004)Levy et al. (2005)Levy et al. (2005)Levy et al. (2005)Bell et al. (2004)Bell et al. (2004)Bell et al. (2004)Levy et al. (2005)Levy et al. (2005)Levy et al. (2005)

General PopulationSubpopulation with Average COPDSubpopulation with Severe COPDGeneral PopulationSubpopulation with Average COPDSubpopulation with Severe COPDGeneral PopulationSubpopulation with Average COPDSubpopulation with Severe COPDGeneral PopulationSubpopulation with Average COPDSubpopulation with Severe COPD
7b-65

7b.7 Conclusions 

We estimated the effectiveness of several illustrative O3 NAAQS attainment strategies based on 

reductions in premature deaths and, in the case of the one strategy for which we were able to 

estimate both direct O3-related benefits and indirect PM2.5-related co-benefits, incidence of 

chronic disease. We measured effectiveness using several different metrics, including lives 

saved, life years saved, and QALYs gained (for improvements in quality of life due to reductions 

in incidence of chronic disease). We suggested a new metric for aggregating life years saved and 

improvements in quality of life, morbidity inclusive life years (MILY) which assumes that 

society assigns a weight of one to years of life extended regardless of preexisting disabilities or 

chronic health conditions. As noted above, however, the cost effectiveness metrics presented for 

all but one of the illustrative O3 NAAQS attainment strategies omit the PM2.5-related co-benefits 

and are therefore likely to understate the cost effectiveness of those strategies 

CEA of environmental regulations that have substantial public health impacts may be 

informative in identifying programs that have achieved cost-effective reductions in health 

impacts and can suggest areas where additional controls may be justified. However, the overall 

efficiency of a regulatory action can only be judged through a complete benefit-cost analysis that 

takes into account all benefits and costs, including both health and non-health effects. The 

benefit-cost analysis for the O3 NAAQS attainment strategies, provided in Chapter 9, shows that 

the attainment strategies we modeled have potentially large net benefits, indicating that 

implementation of the revised O3 NAAQS will likely result in improvements in overall public 

welfare.
7b-66

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6c-1

Appendix 6c: Additional Sensitivity Analyses Related To the Benefits Analysis 

The analysis presented in Chapter 6 is based on our current interpretation of the scientific and 

economic literature. That interpretation requires judgments regarding the best available data, 

models, and modeling methodologies and the assumptions that are most appropriate to adopt in 

the face of important uncertainties. The majority of the analytical assumptions used to develop 

the primary estimates of benefits have been reviewed and approved by EPA’s SAB. Both EPA 

and the SAB recognize that data and modeling limitations as well as simplifying assumptions can 

introduce significant uncertainty into the benefit results and that alternative choices exist for 

some inputs to the analysis, such as the mortality C-R functions. 

This appendix supplements our primary analysis of benefits with three additional sensitivity 

calculations. These supplemental estimates examine sensitivity to both valuation issues (e.g., the 

appropriate income elasticity) and for physical effects issues (e.g., the structure of the cessation 

lag and the sensitivity of the premature mortality estimate to the presence of a presumed 

threshold). These supplemental estimates are not meant to be comprehensive. Rather, they reflect 

some of the key issues identified by EPA or commentors as likely to have a significant impact on 

total benefits. The individual adjustments in the tables should not simply be added together 

because 1) there may be overlap among the alternative assumptions and 2) the joint probability 

among certain sets of alternative assumptions may be low. 

6c.1 Premature Mortality Cessation Lag Structure 

Over the last ten years, there has been a continuing discussion and evolving advice regarding the 

timing of changes in health effects following changes in ambient air pollution. It has been 

hypothesized that some reductions in premature mortality from exposure to ambient PM2.5 will 

occur over short periods of time in individuals with compromised health status, but other effects 

are likely to occur among individuals who, at baseline, have reasonably good health that will 

deteriorate because of continued exposure. No animal models have yet been developed to 

quantify these cumulative effects, nor are there epidemiologic studies bearing on this question. 

The SAB-HES has recognized this lack of direct evidence. However, in early advice, they also 

note that “although there is substantial evidence that a portion of the mortality effect of PM is 

manifest within a short period of time, i.e., less than one year, it can be argued that, if no lag 

assumption is made, the entire mortality excess observed in the cohort studies will be analyzed as 

immediate effects, and this will result in an overestimate of the health benefits of improved air 

quality. Thus some time lag is appropriate for distributing the cumulative mortality effect of PM 

in the population” (EPA-SAB-COUNCIL-ADV-00-001, 1999, p. 9). In recent advice, the SABHES suggests that appropriate lag structures may be developed based on the distribution of 

cause-specific deaths within the overall all-cause estimate (EPA-SAB-COUNCIL-ADV-04-002, 

2004). They suggest that diseases with longer progressions should be characterized by longerterm lag structures, while air pollution impacts occurring in populations with existing disease 

may be characterized by shorter-term lags. 
6c-2

A key question is the distribution of causes of death within the relatively broad categories 

analyzed in the long-term cohort studies. Although it may be reasonable to assume the cessation 

lag for lung cancer deaths mirrors the long latency of the disease, it is not at all clear what the 

appropriate lag structure should be for cardiopulmonary deaths, which include both respiratory 

and cardiovascular causes. Some respiratory diseases may have a long period of progression, 

while others, such as pneumonia, have a very short duration. In the case of cardiovascular 

disease, there is an important question of whether air pollution is causing the disease, which 

would imply a relatively long cessation lag, or whether air pollution is causing premature death 

in individuals with preexisting heart disease, which would imply very short cessation lags. The 

SAB-HES provides several recommendations for future research that could support the 

development of defensible lag structures, including using disease-specific lag models and 

constructing a segmented lag distribution to combine differential lags across causes of death 

(EPA-SAB-COUNCIL-ADV-04-002, 2004). The SAB-HES indicated support for using “a 

Weibull distribution or a simpler distributional form made up of several segments to cover the 

response mechanisms outlined above, given our lack of knowledge on the specific form of the 

distributions” (EPA-SAB-COUNCIL-ADV-04-002, 2004, p. 24). However, they noted that “an 

important question to be resolved is what the relative magnitudes of these segments should be, 

and how many of the acute effects are assumed to be included in the cohort effect estimate” 

(EPA-SAB-COUNCIL-ADV-04-002, 2004, p. 24-25). Since the publication of that report in 

March 2004, EPA has sought additional clarification from this committee. In its followup advice 

provided in December 2004, this SAB suggested that until additional research has been 

completed, EPA should assume a segmented lag structure characterized by 30 percent of 

mortality reductions occurring in the first year, 50 percent occurring evenly over years 2 to 5 

after the reduction in PM2.5, and 20 percent occurring evenly over the years 6 to 20 after the 

reduction in PM2.5 (EPA-COUNCIL-LTR-05-001, 2004). The distribution of deaths over the 

latency period is intended to reflect the contribution of short-term exposures in the first year, 

cardiopulmonary deaths in the 2- to 5-year period, and long-term lung disease and lung cancer in 

the 6- to 20-year period. Furthermore, in their advisory letter, the SAB-HES recommended that 

EPA include sensitivity analyses on other possible lag structures. In this appendix, we investigate 

the sensitivity of premature mortality-reduction related benefits to alternative cessation lag 

structures, noting that ongoing and future research may result in changes to the lag structure used 

for the primary analysis. 

In previous advice from the SAB-HES, they recommended an analysis of 0-, 8-, and 15-year 

lags, as well as variations on the proportions of mortality allocated to each segment in the 

segmented lag structure (EPA-SAB-COUNCIL-ADV-00-001, 1999, (EPA-COUNCIL-LTR-05-

001, 2004). The 0-year lag is representative of EPA’s assumption in previous RIAs. The 8- and 

15-year lags are based on the study periods from the Pope et al. (1995) and Dockery et al. (1993) 

studies, respectively.1

 However, neither the Pope et al. nor Dockery et al. studies assumed any 

lag structure when estimating the relative risks from PM exposure. In fact, the Pope et al. and 

Dockery et al. analyses do not supporting or refute the existence of a lag. Therefore, any lag 

structure applied to the avoided incidences estimated from either of these studies will be an 

1Although these studies were conducted for 8 and 15 years, respectively, the choice of the 

duration of the study by the authors was not likely due to observations of a lag in effects but is 

more likely due to the expense of conducting long-term exposure studies or the amount of 

satisfactory data that could be collected during this time period. 
6c-3

assumed structure. The 8- and 15-year lags implicitly assume that all premature mortalities occur 

at the end of the study periods (i.e., at 8 and 15 years).

In addition to the simple 8- and 15-year lags, we have added three additional sensitivity analyses 

examining the impact of assuming different allocations of mortality to the segmented lag of the 

type suggested by the SAB-HES. The first sensitivity analysis assumes that more of the mortality 

impact is associated with chronic lung diseases or lung cancer and less with acute 

cardiopulmonary causes. This illustrative lag structure is characterized by 20 percent of mortality 

reductions occurring in the first year, 50 percent occurring evenly over years 2 to 5 after the 

reduction in PM2.5, and 30 percent occurring evenly over the years 6 to 20 after the reduction in 

PM2.5. The second sensitivity analysis assumes the 5-year distributed lag structure used in 

previous analyses, which is equivalent to a three-segment lag structure with 50 percent in the 

first 2-year segment, 50 percent in the second 3-year segment, and 0 percent in the 6- to 20-year 

segment. The third sensitivity analysis assumes a negative exponential relationship between 

reduction in exposure and reduction in mortality risk. This structure is based on an analysis by 

Röösli et al. (2004), which estimates the percentage of total mortality impact in each period t as 

(C.1) 

The Röösli et al. (2004) analysis derives the lag structure by calculating the rate constant

(–0.5) for the exponential lag structure that is consistent with both the relative risk from the 

cohort studies and the change in mortality observed in intervention type studies (e.g., Pope et al. 

[1992] and Clancy et al. [2002]). This is the only lag structure examined that is based on 

empirical data on the relationship between changes in exposure and changes in mortality. 

The estimated impacts of alternative lag structures on the monetary benefits associated with 

reductions in PM-related premature mortality (estimated with the Pope et al. ACS impact 

function) are presented in Table J-1. These estimates are based on the value of statistical lives 

saved approach (i.e., $6.6 million per incidence in 2006$) and are presented for both a 3 and 7 

percent discount rate over the lag period.

The results of this sensitivity analyses demonstrate that because of discounting of delayed 

benefits, the lag structure may also have a large impact on monetized benefits, reducing benefits 

by 30 percent if an extreme assumption that no effects occur until after 15 years is applied. 

However, for most reasonable distributed lag structures, differences in the specific shape of the 

lag function have relatively small impacts on overall benefits. For example, the overall impact of 

moving from the previous 5-year distributed lag to the segmented lag recommended by the SABHES in 2004 in the primary estimate is relatively modest, reducing benefits by approximately 5 

percent when a 3 percent discount rate is used and 17 percent when a 7 percent discount rate is 

used. If no lag is assumed, benefits are increased by approximately 10 percent relative to the 

segmented lag with a 3 percent discount rate and 22 percent with a 7 percent discount rate.

>  @

> @  

%

.

.

Mortality Reduction(t) 

  

  





 

f

¦

RR e

RR e

t

t

t

1 1 1

1 1 1

0 5

0 5

1
6c-4

Table 6c-1: Sensitivity of Benefits of Premature Mortality Reductions to Alternative 

Cessation Lag Structures, Using Pope et al (2002) Effect Estimate

Alternative Lag Structures for PM-Related Premature

Mortality

Value

(billions of 2006$)a

Percent

Difference from 

Base Estimate 

Incidences all occur in the first year 

None 3% discount rate $3.4 10.4% 

7% discount rate $3.4 22.5% 

Incidences all occur in the 8th year 

8-year 3% discount rate $2.8 -10.3% 

7% discount rate $2.1 -23.7% 

Incidences all occur in the 15th year 

15-year 3% discount rate $2.2 -27.0% 

7% discount rate $1.3 -52.5% 

20 percent of incidences occur in 1st year, 50 

percent in years 2 to 5, and 30 percent in years 

6 to 20 

 

3% discount rate $3.0 -3.2% 

Alternative

Segmented 

7% discount rate $2.6 -6.6% 

50 percent of incidences occur in years 1 and 2 

and 50 percent in years 2 to 5 

 

3% discount rate $3.2 4.9% 

5-Year

Distributed

7% discount rate $3.0 9.4% 

Incidences occur at an exponentially declining 

rate following year of change in exposure 

 

3% discount rate $3.2 5.6% Exponential

7% discount rate $3.1 11.3% 

a

 All valuations rounded to two significant figures. This table reflects full attainment in all locations of the 

U.S. except two areas of California. These two areas, which have high levels of ozone, are not planning 

to meet the current standard until after 2020. The estimates in the table do not reflect benefits for the 

San Joaquin and South Coast Air Basins. 

6c.2 Threshold Sensitivity Analysis 

Chapter 6 presents the results of the PM2.5 premature mortality benefits analysis based on an 

assumed cutpoint in the long-term mortality concentration-response function at 10 μg/m3, and an 

assumed cutpoint in the short-term morbidity concentration-response functions at 10 μg/m3.

There is ongoing debate as to whether there exists a threshold below which there would be no 

benefit to further reductions in PM2.5. Some researchers have hypothesized the presence of a 

threshold relationship. The nature of the hypothesized relationship is the possibility that there 

exists a PM concentration level below which further reductions no longer yield premature 

mortality reduction benefits. EPA’s most recent PM2.5 Criteria Document concludes that “the 

available evidence does not either support or refute the existence of thresholds for the effects of 

PM on mortality across the range of concentrations in the studies” (U.S. EPA, 2004b, p. 9-44). 
6c-5

EPA’s Science Advisory Board (SAB) that provides advice on benefits analysis methods2

 has 

been to model premature mortality associated with PM exposure as a non-threshold effect, that 

is, with harmful effects to exposed populations regardless of the absolute level of ambient PM 

concentrations. 

For these reasons we provide the results of a sensitivity analysis in which we estimate the change 

in reduction in incidence of PM2.5-related premature mortality resulting from changes in the 

presumed threshold. We also provide a corresponding estimate of the valuation of these changes 

in incidence. 

Table 6c-2: Mortality Threshold Sensitivity Analysis for 0.070 ppm Ozone Scenario (Using 

Pope et al., 2002 Effect Estimate with Slope Adjustment for Thresholds Above 7.5 ug) 95th 

Percentile Confidence Intervals Provided in Parentheses a

 East 

Western U.S. 

Excluding CA California Total 

Less Certain That Benefits 580 56 12 640 

Are at Least as Large No Threshold (120--1,000) (15--98) (3.9--19) (140--1,100) 

570 49 11 630 Threshold at 7.5 μg (130--1,000) (16--81) (3.6--18) (150--1,100) 

420 6.3 5.4 430 Threshold at 10 μg (110--730) (2.1--10) (2--9) (110--750) 

46 0.00 3.7 50 Threshold at 12 μg (14--79) (0.00--0.00) (1.2--6.2) (15--85) 

More Certain That Benefits 1.0 0.00 2.9 4.0 

are at Least as Large Threshold at 14 μg 

(0.35--1.7) (0.00--0.00) (1.0--4.9) (1.3--6.6) 

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded 

estimates. As such, totals will not sum across columns. Estimates do not include South Coast and San 

Joaquin Air Basins. 

2

 The advice from the 2004 SAB-HES (U.S. EPA-SAB, 2004b) is characterized by the 

following: “For the studies of long-term exposure, the HES notes that Krewski et al. (2000) have 

conducted the most careful work on this issue. They report that the associations between PM2.5

and both all-cause and cardiopulmonary mortality were near linear within the relevant ranges, 

with no apparent threshold. Graphical analyses of these studies (Dockery et al., 1993, Figure 3, 

and Krewski et al., 2000, page 162) also suggest a continuum of effects down to lower levels. 

Therefore, it is reasonable for EPA to assume a no threshold model down to, at least, the low end 

of the concentrations reported in the studies.” 
6c-6

Table 6c-3: Sensitivity of Monetized Benefits of Reductions in Mortality Risk to Assumed 

Thresholds for 0.070 ppm Partial Attainment Scenario (Using Pope et al., 2002 Effect 

Estimate with Slope Adjustment for Thresholds Above 7.5 ug, 95th Percentile Confidence 

Intervals Provided in Parentheses, in billions of 2006$)a

 Eastern U.S. 

Western U.S. 

Excluding CA California 

Total 

Nationwide 

Attainment 

4.0 0.40 0.08 4.5 3% ($0.49--$10) ($0.05--$0.94) ($0.01--$0.19) ($0.55--$11) 

3.6 0.36 0.02 4.1 

Less Certain 

that Benefits 

Are at Least 

as Large 

No Threshold 

7% ($0.44--$8.8) ($0.05--$0.84) ($0.01--$0.17) ($0.49--$10) 

4.0 0.34 0.08 4.4 3% ($0.49--$10) ($0.05--$0.78) ($0.01--$0.17) ($0.55--$11) 

3.6 0.31 0.07 4.0 

Threshold at 7.5 μg 

7% ($0.44--$8.6) ($0.04--$0.70) ($0.01--$0.16) ($0.49--$9.5) 

3.0 0.04 0.04 3.0 3% ($0.38--$7.0) ($0.01--$0.10) ($0.01--$0.09) ($0.39--$7.2) 

2.7 0.04 0.03 2.7 

Threshold at 10 μg 

7% ($0.35--$6.3) ($0.01--$0.09) ($0.00--$0.08) ($0.36--$6.5) 

0.33 0.00 0.03 0.35 3% ($0.04--$0.76) ($0.00--$0.0) ($0.00--$0.06) ($0.05--$0.82) 

0.29 0.00 0.02 0.32 

Threshold at 12 μg 

7% ($0.04--$0.68) ($0.00--$0.00) ($0.00--$0.05) ($0.04--$0.73) 

0.01 0.00 0.02 0.03 

3%

($0.00--$0.02) ($0.00--$0.0) ($0.00--$0.05) ($0.00--$0.06) 

0.01 0.00 0.02 0.03 

More Certain 

that Benefits 

Are at Least 

as Large 

Threshold at 14 μg 

7%

($0.00--$0.01) ($0.00--$0.00) ($0.00--$0.04) ($0.00--$0.06) 

a All estimates are rounded to 2 significant digits. All rounding occurs after final summing of unrounded 

estimates. As such, totals will not sum across columns. Estimates do not include South Coast and San 

Joaquin Air Basins. 

6c.3 Income Elasticity of Willingness to Pay 

As discussed in Chapter 6, our estimates of monetized benefits account for growth in real GDP 

per capita by adjusting the WTP for individual endpoints based on the central estimate of the 

adjustment factor for each of the categories (minor health effects, severe and chronic health 

effects, premature mortality, and visibility). We examined how sensitive the estimate of total 

benefits is to alternative estimates of the income elasticities. Table 6c.3 lists the ranges of 

elasticity values used to calculate the income adjustment factors, while Table 6c.4 lists the ranges 

of corresponding adjustment factors. The results of this sensitivity analysis, giving the monetized

benefit subtotals for the four benefit categories, are presented in Table 6c.5. 
6c-7

Table 6c-4. Ranges of Elasticity Values Used to Account for Projected Real Income 

Growtha

Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound

Minor Health Effect 0.04 0.30 

Severe and Chronic Health Effects 0.25 0.60 

Premature Mortality 0.08 1.00 

Visibilityb — — 

a Derivation of these ranges can be found in Kleckner and Neumann (1999) and Chestnut (1997). COI 

estimates are assigned an adjustment factor of 1.0. 

b

 No range was applied for visibility because no ranges were available in the current published literature. 

Table 6c-5. Ranges of Adjustment Factors Used to Account for Projected Real Income 

Growtha

Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound

Minor Health Effect 1.018 1.147 

Severe and Chronic Health Effects 1.121 1.317 

Premature Mortality 1.037 1.591 

Visibilityb — — 

aBased on elasticity values reported in Table C-4, U.S. Census population projections, and projections of 

real GDP per capita. 

b No range was applied for visibility because no ranges were available in the current published literature. 

Table 6c-6. Sensitivity of Monetized Benefits to Alternative Income Elasticitiesa

Ozone Analysis PM Analysis 

Benefit Category Lower Sensitivity 

Bound

Upper Sensitivity 

Bound

Lower Sensitivity 

Bound

Upper Sensitivity 

Bound

Minor Health Effect $48 $48 $8.3 $8.5 

Severe and Chronic Health 

Effects -- -- $170 $200 

Premature Mortalityb

 $340 $520 $2,600 $4,000 

Total Benefitsb $380 $560 $2,800 $4,200 

a All estimates rounded to two significant digits. All Benefits Incremental to 080 ppm Partial Attainment 

Strategy (Millions of 2006$). This table reflects full attainment in all locations of the U.S. except two 

areas of California. These two areas, which have high levels of ozone, are not planning to meet the 

current standard until after 2020. The estimates in the table do not reflect benefits for the San Joaquin 

and South Coast Air Basins. 

b Using mortality effect estimate from Bell (2004) and mortality effect estimate from Pope et al (2002) to 

estimate PM2.5 mortality at a 3% discount rate. 

c No range was applied for visibility because no ranges were available in the current published literature. 

Consistent with the impact of mortality on total benefits, the adjustment factor for mortality has 

the largest impact on total benefits. The value of mortality in 2020 ranges from 90 percent to 130 

percent of the primary estimate based on the lower and upper sensitivity bounds on the income 

adjustment factor. The effect on the value of minor and chronic health effects is much less 

pronounced, ranging from 98 percent to 105 percent of the primary estimate for minor effects 

and from 93 percent to 106 percent for chronic effects. 
6c-8

6c.4 References 

Chestnut, L.G. 1997. “Draft Memorandum: Methodology for Estimating Values for Changes in 

Visibility at National Parks.” April 15. 

Chestnut, L.G., and R.D. Rowe. 1990a. Preservation Values for Visibility Protection at the 

National Parks: Draft Final Report. Prepared for Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency, Research Triangle Park, NC and Air Quality 

Management Division, National Park Service, Denver, CO.

Chestnut, L.G., and R.D. Rowe. 1990b. “A New National Park Visibility Value Estimates.” In 

Visibility and Fine Particles, Transactions of an AWMA/EPA International Specialty

Conference, C.V. Mathai, ed. Air and Waste Management Association, Pittsburgh. 

Clancy, L., P. Goodman, H. Sinclair, and D.W. Dockery. 2002. “Effect of Air-pollution Control 

on Death Rates in Dublin, Ireland: An Intervention Study.” Lancet Oct 19;360(9341):1210-4. 

Desvousges, W.H., F.R. Johnson, and H.S. Banzhaf. 1998. Environmental Policy Analysis With 

Limited Information: Principles and Applications of the Transfer Method (New Horizons in 

Environmental Economics.) Edward Elgar Pub: London. 

EPA-SAB-COUNCIL-ADV-00-001. October 1999. The Clean Air Act Amendments (CAAA) 

Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Health and 

Ecological Effects Subcommittee on Initial Assessments of Health and Ecological Effects. Part 2.

EPA-SAB-COUNCIL-ADV-99-012. July 1999. The Clean Air Act Amendments (CAAA) Section 

812 Prospective Study of Costs and Benefits (1999): Advisory by the Health and Ecological 

Effects Subcommittee on Initial Assessments of Health and Ecological Effects. Part 1.

EPA-SAB-COUNCIL-ADV-01-004. September 2001. Review of the Draft Analytical Plan for 

EPA’s Second Prospective Analysis—Benefits and Costs of the Clean Air Act 1990-2020: An 

Advisory by a Special Panel of the Advisory Council on Clean Air Compliance Analysis.

EPA-SAB-COUNCIL-ADV-04-002. March 2004. Advisory on Plans for Health Effects Analysis 

in the Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of the Clean 

Air Act, 1990-2020: Advisory by the Health Effects Subcommittee of the Advisory Council on 

Clean Air Compliance Analysis.

Kleckner, N., and J. Neumann. June 3, 1999. “Recommended Approach to Adjusting WTP 

Estimates to Reflect Changes in Real Income.” Memorandum to Jim Democker, US EPA/OPAR. 

Roosli M, Kunzli N, Braun-Fahrlander C, Egger M. 2005. “Years of life lost attributable to air 

pollution in Switzerland: dynamic exposure-response model.” International Journal of 

Epidemiology 34(5):1029-35. 

U.S. Environmental Protection Agency (EPA). 2004. Air Quality Criteria for Particulate Matter, 

Volume II. Office of Research and Development. EPA/600/P-99/002bF, October 2004. 
6d-1 

Appendix Chapter 6d: Exploring the Effects of Changes in Tropospheric Ozone on UVB 

Atmospheric ozone filters harmful solar ultraviolet radiation (UV-B), thereby reducing the 

amount of UV-B reaching the Earth’s surface. The majority of ozone—about 90%—is located in 

the stratosphere, and the stratospheric ozone layer provides most of this protective filtration. 

Tropospheric ozone, located at ground level, accounts for the remaining 10% of atmospheric 

ozone. Although only a portion of ground level ozone can be attributed to anthropogenic sources, 

it is reasonable to assume that reducing ground level ozone would reduce the UV-B filtration 

provided, and thus would lead to increases in health effects normally associated with reductions 

in stratospheric ozone. UV radiation-induced health effects are primarily related to the skin (e.g., 

melanoma and nonmelanoma skin cancer), eyes (e.g., cataracts), and immune system. 

The attached preliminary report entitled “Analysis of the Impact of Emissions Changes on 

Tropospheric Ozone” represents the EPA’s first attempt to develop a methodology for capturing 

the changes in skin cancers and their economic value that might be associated with changes in 

tropospheric ozone. This initial effort was designed as a scoping analysis to determine the 

potential magnitude of impacts, and is not intended to serve as a standard methodology for 

assessing UVB impacts in future RIAs. This scoping analysis focuses on a scenario reflecting 

the likely distribution of ground level ozone in the Eastern United States domain under an 

illustrative set of controls intended to reduce ozone concentrations towards attainment of an 

ozone standard of 70 parts per billion (ppb), as compared to the current ozone National Ambient 

Air Quality Standard (NAAQS) for 2020.1 The report only examines the effects of this reduced 

UV filtration on incidence of and mortality associated with skin cancers – specifically, basal cell 

carcinoma (BCC), squamous cell carcinoma (SCC) and cutaneous malignant melanoma (CMM). 

The general methodology developed for this draft scoping analysis was applied in four steps.

First, changes in ground level UV radiation (for geographical extent) were estimated using the 

Community Multiscale Air Quality model results as an input to the Tropospheric Ultraviolet –

Visible radiation model (TUV). The CMAQ model runs provided data for each of 14 altitude 

layers for each location on a 12x12 km grid at hourly intervals for 24 hours of each day from 

June 1, 2020 to August 31, 2020. Using these data, the TUV model produced estimates of the 

daily integrated dose of UV exposure. Second, population-weighted exposure estimates were 

derived using county based population projections developed using a cohort-component 

methodology. Third, the resulting estimates were used in the Atmospheric Health Effects 

Framework model to quantify expected changes in incidence in and mortality from basal cell 

carcinoma (BCC), squamous cell carcinoma (SCC) and cutaneous malignant melanoma (CMM) 

associated with the given change in ground level ozone. Fourth, the resulting health effects were 

monetized using a combination of estimates of the value of statistical life and willingness to pay 

to avoid a case of skin cancer.

This research makes use of results from the CMAQ, TUV and AHEF models. These models 

have all been applied extensively in other contexts but this is their first application to estimate 

1

 This scenario was developed for the Ozone NAAQS Proposal and does not match runs produced for the Ozone 

NAAQS Final. 
6d-2 

skin cancer effects associated with changes in tropospheric ozone.2 While all of these models 

have been extensively peer reviewed and validated in different contexts, the reviews were 

focused on different model applications and did not extend necessarily to the current problem. 

We subjected this scoping analysis to peer review by five experts external to the Agency, 

including Dr. Edward DeFabo, George Washington University; Dr. Hugh Ellis, Johns Hopkins 

University; Dr. Scott Farrow, University of Maryland – Baltimore County; Dr. Randy Kawa, 

National Atmospheric Sciences Administration; and Dr. Helen Suh, Harvard School of Public 

Health.3 Unfortunately, due to time constraints, we were unable to incorporate the 

recommendations from the reviewers in time for this rule. However, the Agency plans to 

respond to peer reviewer remarks in the near future as we continue our efforts on exploring this 

topic.

Although the draft report addresses a number of sources of uncertainty, we recognize that others 

may remain including, but not limited to, the applicability of epidemiologic studies of long-term 

UV-B exposures over broad geographic regions to scenarios involving impacts of smaller, more 

variable, localized changes in ground level ozone; the variation in activity patterns and other 

factors that determine population exposures and sensitivities to UV-B radiation; as well as the 

effects of aerosols. These uncertainties have been recognized by the Agency and discussed in 

Chapter 10 of the most recent Ozone Criteria Document (U.S. EPA, 2006). The Agency will 

consider whether to conduct additional exploratory analyses related to UVB screening as we 

continue our efforts to quantify health effects associated with reduced tropospheric ozone in a 

rigorous and defensible manner. 

Because the CMAQ modeling runs used for this scoping analysis do not match those used for the 

Ozone NAAQS Final Regulatory Impact Assessment (RIA), direct comparisons of the monetized 

skin cancer effects associated with reduced UV-B filtration presented in this report cannot be 

made with health benefit results presented in the RIA for the final rule. Still, comparing the 

results of this scoping analysis with the estimates of benefits presented in the proposal RIA, 

provides a general sense of the order of magnitude of the resulting effects. The estimates of 

monetized disbenefits resulting from increased UVB levels due to reduced tropospheric ozone as 

captured by this scoping analysis amount to approximately 0.3 to 0.6 percent of the monetized 

health benefits associated with the modeled set of ozone precursor control strategies reported in 

the proposal RIA.

2

 TUV and AHEF were developed to estimate health effects associated with changes in stratospheric ozone. 

3

 The individual reports from each of the peer reviewers are contained in the docket for this rule. 
6d-3 

Analysis of the Impact of Emissions Changes on 

Tropospheric Ozone 

DRAFT Report 

February 18, 2008 

Prepared by: 

ICF International 
Health Impacts from Ozone Changes

Draft report 6d-4

Table of Contents 

1. Introduction........................................................................................................................... 5

2. Methodology .......................................................................................................................... 5

2.1. CMAQ Modeling............................................................................................................ 5

2.2. TUV Modeling................................................................................................................ 5 

2.2.1. Tropospheric Ozone Scenarios ............................................................................... 5 

2.2.2. TUV Model Calculations........................................................................................ 9 

2.2.3. TUV Results.......................................................................................................... 12 

2.3. Population Adjustments................................................................................................ 13 

2.3.1. Cohort-Component Methodology Overview ........................................................ 13 

2.3.2. Component Data and Methods.............................................................................. 15 

2.3.3. Use of these Projections........................................................................................ 16 

2.4. AHEF Modeling............................................................................................................ 17

2.4.1. Overview of Methodology to Estimate Changes in Health Effects...................... 17 

2.4.2. Selected Action Spectrum and Derived Dose-Response Relationships ............... 18 

2.5. Valuation of Human Health Effects.............................................................................. 19 

3. Results .................................................................................................................................. 20

3.1. Changes in Ground Level SCUP-h UV ........................................................................ 20 

3.2. Changes in Human Health Effects................................................................................ 24 

4. Uncertainty .......................................................................................................................... 25

4.1. Uncertainty in estimated impacts.................................................................................. 25 

4.2. Uncertainty in CMAQ Modeling.................................................................................. 26 

4.3. Uncertainty in TUV Modeling...................................................................................... 26 

4.3.1. Uncertainty Analysis of TUV Calculations .......................................................... 26 

4.3.2. Comparison with UV Changes Due to Other Factors........................................... 29 

4.4. Uncertainty in Population Adjustments........................................................................ 30 

4.5. Uncertainty in AHEF Modeling.................................................................................... 31 

4.5.1. Uncertainties in Selected Derived Dose-Response Relationships........................ 31 

4.5.2. Behavioral Uncertainties....................................................................................... 32 

4.5.3. Latency.................................................................................................................. 32 

4.6. Uncertainty in Valuation of Human Health Effects...................................................... 33 

4.7. Unquantified Sources of Uncertainty............................................................................ 33 

4.8. Summary of Quantified and Unquantified Sources of Uncertainty.............................. 36 

References.................................................................................................................................... 37

Appendix A: Ground Level SCUP-h UV with 70 and 84 ppb by Day ................................... 41

Appendix B: Overview of Evaluation Methodology................................................................ 49

Glossary ....................................................................................................................................... 50
Health Impacts from Ozone Changes

Draft report 6d-5

Introduction 

Atmospheric ozone filters harmful solar ultraviolet radiation (UV-B), thereby reducing the 

amount of the UV-B reaching the Earth’s surface. The majority of ozone—about 90%—is 

located in the stratosphere, and the stratospheric ozone layer provides most of this protective 

filtration. Tropospheric ozone, located at ground level, accounts for the remaining 10% of 

atmospheric ozone. Although only a portion of ground level ozone can be attributed to 

anthropogenic sources, it is reasonable to assume that reducing ground level ozone would reduce 

the UV-B filtration provided, and thus would lead to increases in health effects normally 

associated with reductions in stratospheric ozone. UV radiation-induced health effects are 

primarily related to the skin (e.g., melanoma and nonmelanoma skin cancer), eyes (e.g., 

cataracts), and immune system. 

The purpose of this report is to assess these human health effects of reduced UV filtration 

associated with the reduction of ground level ozone under an ozone standard of 70 parts per 

billion (ppb) compared to the current ozone National Ambient Air Quality Standard (NAAQS) 

for 2020. 

The remainder of this paper is organized as follows: 

x Section 2 describes the methodology used to carry out this assessment, including 

modeling using the Tropospheric Ultraviolet-Visible radiation model (TUV) and the 

U.S. EPA’s Atmospheric and Health Effects Framework (AHEF); 

x Section 3 presents the results of the analysis, including changes in ground level UV 

and health effects; and 

x Section 4 addresses the uncertainties associated with modeling undertaken for this 

analysis. 

Methodology

1.1. CMAQ Modeling 

The inputs for this analysis were generated through Community Multiscale Air Quality (CMAQ) 

ozone modeling runs. The CMAQ model produced spatial fields of gridded ozone 

concentrations on an hourly basis for the Eastern United States domain with 12 km horizontal 

resolution and 14 vertical layers topping out at 16,200 meters. 

1.2. TUV Modeling 

1.2.1. Tropospheric Ozone Scenarios 

The CMAQ model provided ozone concentrations in parts per billion (ppb) for each of the 14 

altitude layers given in Table 1. These values are specified for each location (latitude, longitude) 

on a 12 × 12 km grid (66920 locations) at hourly intervals for 24 hours (UT) of each day from 1 
Health Impacts from Ozone Changes

Draft report 6d-6

Jun to 31 Aug 2020. Two scenarios are considered with identifiers:

 2020bk_v4.5_084_12km.o3_hr_shift_LST, and 

2020bk_v4.5_070b_12km.o3_hr_shift_LST.

For brevity, these scenarios will be called 084 and 070, respectively. 

In order to model a hypothetical control strategy incremental to attainment of the current 

standard (84 ppb), EPA approached the analysis in stages. First, EPA identified controls to be 

included in the baseline. These included current state and federal programs plus controls to 

attain the current ozone standard and PM2.5 PM standards (see 

http://www.epa.gov/ttnecas1/ria.html for a complete list of controls). Then, EPA applied 

additional known controls within geographic areas designed to bring areas predicted to exceed 

70 ppb in 2020 into attainment (U.S. EPA, 2008). 

Table 1 gives the vertical structure of the model. The 14 layers are bounded by 15 levels defined 

on unequally spaced modified normalized pressure coordinates (sigma = 1 at the surface, 0 at the 

top of the model). The actual atmospheric pressures, and corresponding geometric altitudes, are 

determined by the meteorological input to CMAQ and vary in time and space. Approximate 

values are given in the table. For the purposes of the radiative transfer calculations, the 

approximate heights given in Table 1 were used, and sensitivity calculations were made to 

bracket the effect of this approximation. The last column of Table 1 gives the number of air 

molecules, per square centimeter, in a vertical column within each layer, and their calculation is 

described in the following section.

Table 1: Vertical Structure for 14 Layer CMAQ (heights are the top of layer). 

Layer

Number Sigma Approximate 

Height (m) 

Approximate 

Pressure (mb)

Air column between 

levels (molecules cmí2)

0 1.000 0 1000 —

1 0.995 38 995 9.67 × 1022

2 0.990 77 991 9.89 × 1022

3 0.980 154 982 1.94 × 1023

4 0.960 310 964 3.89 × 1023

5 0.940 469 946 3.90 × 1023

6 0.910 712 919 5.85 × 1023

7 0.860 1,130 874 9.73 × 1023

8 0.800 1,657 820 1.17 × 1024

9 0.740 2,212 766 1.17 × 1024

10 0.650 3,108 685 1.75 × 1024

11 0.550 4,212 595 1.95 × 1024
Health Impacts from Ozone Changes

Draft report 6d-7

Layer

Number Sigma Approximate Height (m) 

Approximate 

Pressure (mb)

Air column between 

levels (molecules cmí2

)

12 0.400 6,153 460 2.91 × 1024

13 0.200 9,625 280 3.85 × 1024

14 0.000 15,674 100 3.58 × 1024

The detailed CMAQ ozone values were used in the calculations of the UV radiation. However,

to illustrate the magnitude of the changes in ozone, Figure 1 shows the concentration changes 

from the 084 to the 070 CMAQ scenarios, averaged over the entire geographic domain and over 

hours of all days of each month. Also shown are the values for 15 July (the mid-time of the 

simulation), since this date will be used in some sensitivity studies in Section 4.3. The largest 

changes are seen to occur between ca. 500 and 1000 m above the surface (layers 5–7, see 

Table 1) and are non-negligible even in the highest layers. 

Figure 1: Domain-averaged ozone concentration changes (ppb) in each CMAQ layer.

Vertically averaged changes (ppb) are given in the legend. 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 5 10 15

Layer number

ppb

15 JUL/0.479

JUN/0.336

JUL/0.405

AUG/0.401

The contribution of each layer to the ozone column change is given in Figure 2. This was 

obtained by multiplying the concentration changes (ppb) by 10-9 times the air column in each 
Health Impacts from Ozone Changes

Draft report 6d-8

corresponding layer (from Table 1), and converting to Dobson Units (DU) by dividing by 

2.687 × 1016 molecules cm-3 DU-1. The total ozone column change is the sum of the 

contributions in each layer, and is shown in the figure legend. The small contribution from the 

lowest levels is due mainly to their small vertical thickness, while the decreasing contribution of 

the uppermost layers is due to the exponential decrease in air density with altitude. Notably, the 

highest contributions are from layers 7–10 (ca. 1–3 km altitudes), with non-negligible 

contributions from the upper troposphere as already noted above.

Figure 2: Domain-averaged ozone column changes (Dobson Units) in each CMAQ 

layer. The sum of the ozone changes (Dobson Units) is given in the legend, 

and is the total ozone column change. 

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0 5 10 15

Layer number

Dobson Units

15 JUL/0.196

JUN/0.150

JUL/0.193

AUG/0.191

The ozone changes shown in Figures 1 and 2 cannot be translated directly into changes of 

surface UV radiation, because they are averaged over different locations and times. For 

example, they include night-time values when the UV radiation is non-existent. This can be seen 

in Table 2, where the domain-averaged ozone changes for 15 July were divided according to 

whether they occur for solar zenith angels (sza) smaller than 45 degrees (high sun) and lower 

than 45 degrees (low sun). The table shows that the changes in surface and column values are 

largest for high sun, consistent with photochemical formation near sources, and coincident with 

times of highest surface biologically active irradiances. Mid-tropospheric values have a weaker 

dependence on sza, consistent with long-range transport and a relatively long ozone lifetime. 
Health Impacts from Ozone Changes

Draft report 6d-9

Table 2: Ozone change statistics for 15 July 2020, CMAQ scenarios 084-070 

 Number of 

points

Average

ozone

change

(ppb) at 

surface

Average

ozone change 

(ppb) at level 

12 (~ 500 mb)

Average

ozone column 

change,

Dobson Units 

All sza 1,605,600 0.58 0.10 0.20

sza < 45o high sun 426,139 0.69 0.11 0.22

45o < sza < 90o low sun 538,529 0.60 0.09 0.19

sza > 90o night 640,932 0.50 0.11 0.19

An independent albeit rough estimate of the ozone column (DU) change can be obtained from 

the concentrations given in Table 2. Considering only the values for sza < 45o (c.f., the simple 

rule that UV exposure should be avoided when a person’s shadow is shorter than a person’s 

height), even a simple linear average of the ozone changes at the surface and 500 mb yields 

~ 0.4 ppb, and can be taken as applicable to the lower half of the atmosphere (below 500 mb). 

The total atmospheric column of air is about 2.0 × 1025 molecules cm-2, so taking half of this and 

0.4 ppb ozone yields 4 × 1015 molecules cm-2 of ozone (1.0 × 1025 × 0.4 × 10-9), or 

§ 0.15 Dobson Units. This is reasonably close to the column value in the table, 0.22 DU, which 

was calculated within TUV from the full vertical variation of ozone and air concentrations, and 

included changes above 500 mb as well as an exponential profile of air density which of course 

gives more weight to the lower altitudes. 

1.2.2. TUV Model Calculations 

The surface ultraviolet radiation was calculated with the Tropospheric Ultraviolet-Visible (TUV) 

model developed by Madronich and co-workers at the National Center for Atmospheric Research 

(NCAR). The TUV model is widely used for the calculation of atmospheric and surface UV 

radiation including international assessments of the environmental effects of stratospheric ozone 

depletion (e.g., Madronich et al., 1998), and has been evaluated in numerous modelmeasurement intercomparison studies (e.g., Koepke et al., 1998; Bais et al., 2003). An early 

version of TUV, of similar accuracy but lesser flexibility, is used within the CMAQ atmospheric 

chemistry module to compute photolysis frequencies. The model has been described in the 

literature (e.g., Madronich and Flocke, 1999) and the latest version (version 4.5, used here) is 

freely available to the scientific community through NCAR Community Data Portal 

(http://cdp.ucar.edu).

Several modifications to the TUV model were made for the present purposes, specifically to (i) 

interface the model with the CMAQ ozone concentrations, and (ii) to speed up the computational 

time in view of the large number of locations reported by the CMAQ model. 

The altitude grid was modified to match the values given in Table 1, then continuing to 16 km 

and increasing by 2 km to 40 km, and by 5 km to 80 km. These represent altitude levels, while 

layers (to which the ozone concentrations are applied) are the volume between these levels. The 

TUV model used the U.S. Standard Atmosphere (USSA, 1976) vertical profiles of temperature 
Health Impacts from Ozone Changes

Draft report 6d-10

(K), air density and ozone (both molecules cm-3), specified from sea level to 80 km in 1 km 

increments, and then interpolated to the altitude grid described above. Because the CMAQ 

model has layers that are both smaller and larger than the standard USSA 1 km grid, some 

attention was given to proper vertical interpolation of air density. Specifically, the logarithm of 

the USSA air number density (molecules cmí3) was interpolated linearly to obtain the logarithm 

of the air density at the CMAQ levels. Then, the vertical air column (molecules cmí2) of each 

layer was obtained by logarithmic integration: 

Air column in layer k = dz [ y(k+1) – y(k) ] / ln[ y(k+1) / y(k) ] 

where dz = z(k+1) – z(k) = vertical thickness of the layer. The air column of each layer was then 

multiplied by the CMAQ ozone concentrations (ppb × 10í9) to yield the ozone column in each 

layer (molecules cmí2

), so overwriting the USSA ozone values for these altitudes. For altitudes 

above the highest level of Table 1, the interpolated USSA ozone values were used. 

For each wavelength interval (see below), the radiative transfer solution was expressed 

analytically using the delta-Eddington approximation (Joseph et al., 1976) formulated in 

generalized 2-stream equations (Toon et al., 1989) corrected for atmospheric curvature using a 

pseudo-spherical approximation (Petropavlovskih, 1995). The resulting set of coupled 2N 

equations (N = number of layers) was solved by tridiagonal matrix inversion to obtain the 

spectral irradiance, I(O) in W m-2 nm-1 for a given wavelength, time, and location. This 

calculation was repeated for the center of each wavelength interval, for each location, for each 

hour (on the half-hour) of each day of June, July, and August for each of the two given CMAQ 

scenarios. The spectral irradiance was multiplied by a biological sensitivity function (action 

spectrum) B(O), then integrated numerically all wavelengths with non-zero contributions, to 

obtain the surface biological exposure (biologically effective irradiance) Ibio (W m-2). Two 

different action spectra were considered: (1) the CIE standard erythemal (skin-reddening) 

spectrum (McKinlay and Diffey, 1987) which forms the basis of the WMO/WHO-recognized 

UV Index computed operationally in the United States by NOAA and highlighted by the EPA, 

and (2) the spectrum for the induction of non-melanoma skin cancer in mice, corrected for 

human skin transmission (deGruijl and van der Leun, 1994). The latter spectrum has been used 

extensively in the assessments of ozone depletion, and is named SCUP-h (Skin Cancer UtrechtPhiladelphia, reflecting the location of the research groups that originated it), and its sensitivity 

to ozone changes is quite similar to that of the erythemal spectrum (as shown by Madronich et 

al., 1998). For brevity, biologically effective irradiances computed from these two spectra are 

hereafter called IERY and ISCUP. Values of ISCUP are used in ICF’s AHEF model as measures of 

human exposure to UV radiation. 

The TUV wavelength (nm) grid extended from 294 to 330 by 2 nm, to 350 by 5 nm, and to 400 

by 10 nm. The higher resolution at the shorter wavelengths is required to represent accurately 

the absorption by ozone which is strongly dependent on wavelength, while the coarser resolution 

provides computational efficiency. A resolution of 2 nm in the ozone-dependent region has been 

show to be sufficiently accurate for photolysis calculations, including O3 + hQ Æ O2 + O(1D)

which has a spectral dependence similar or steeper (and therefore more sensitive to spectral 

resolution) than the action spectra used here (Madronich and Weller, 1990). 
Health Impacts from Ozone Changes

Draft report 6d-11

For each location on the 12 × 12 km grid, the values of IERY and ISCUP were integrated over 24 

hours to provide daily integrated doses, and over each month (June, July, and August) to provide 

monthly integrated values. Otherwise identical calculations were performed for the 070 and 084 

scenarios, and the difference between scenarios was computed for each location. 

Tropospheric ozone causes a larger change, on a per molecule basis, than stratospheric ozone 

(Bruhl and Crutzen, 1989), at least for high sun. This is because of coupling between molecular 

(Rayleigh) scattering and ozone absorption: Scattering increases the tropospheric photon path 

lengths and therefore increases the probability of absorption by tropospheric absorbers including 

ozone. Figure 3 shows the normalized sensitivity of SCUP-h weighted UV as a function of the 

altitude where the ozone perturbation occurs. The normalized sensitivity (also called the 

Radiation Amplification Factor, RAF) is the % increase in radiation for each % decrease in 

ozone column. For this plot, a 1 DU of ozone was inserted in a 1 km layer at various altitudes 

(the altitudes of ozone perturbation in the figure), and the resultant surface UV-SCUP values 

compared to the reference calculation (without the 1 DU). The RAF is then: 

RAF = í ln(UV2/UV1)/ln(DU2/DU1)

where the subscripts 2 and 1 refer to the perturbed and reference calculations (Micheletti et al., 

2003).

Figure 3: Normalized sensitivity (% for %) of UV-SCUP changes to the altitude at 

which ozone perturbations are made. 

RAF for SCUP-h Action Spectrum

1.0

1.2

1.4

1.6

1.8

2.0

0 5 10 15 20 25 30

Altitude of O3 perturbation, km

Normalized sensitivit

y

15

30

60
Health Impacts from Ozone Changes

Draft report 6d-12

For example, a 0.22 DU increment (from Table 1, 15 July, high sun) represents about 0.06% 

change in the ozone column (349 DU for the USSA, but actually somewhat different and 

variable when using the CMAQ values up to 16 km). From Figure 3, a RAF of 1.6–1.7 is 

reasonable for ozone perturbations in the low-mid troposphere and relatively high sun when UV 

matters. Multiplying (0.06 % × 1.65), the surface UV-SCUP radiation is expected to change by 

about 0.1 %. This is the approximated magnitude of the UV-SCUP changes expected between 

the two CMAQ scenarios. 

Of course, the full TUV calculations were done with high spectral resolution (not simple scaling 

with RAFs), time integration over actual sza values, full vertical distributions of tropospheric 

ozone given by the CMAQ, and fully coupled scattering-absorption multi-layer radiative 

transfer. Therefore they are expected to be more accurate, and more firmly anchored in the stateof-the-science. 

1.2.3. TUV Results 

Detailed maps of UV-SCUP distributions and percent changes are given in Appendix A. Here, 

the results are summarized in Figure 4 as domain-averaged UV-SCUP percent changes for each 

day. They range from 0.05 % to 0.16 %, with most values near 0.1 % or slightly higher (note that 

% changes of the monthly UV increments are not strictly equal to the monthly averages of daily 

% changes, though they happen to be quite similar). Figure 5 shows the frequency distribution 

of the monthly increments expressed as percent. The most common value is near zero, and few 

values above 0.3%. A few negative values were noted. Finally, it should be clear that the data in 

these figures are not yet weighted by the affected populations, and therefore should be viewed as 

changes in the physical state of the atmosphere, not as measures of population exposure. 

Figure 4: Domain-averaged percent changes in SCUP-weighted daily doses changes 

between 070 and 084 scenarios 

070-084 scenarios

Domain-averaged changes (%) in daily UV-SCUP

0.05

0.10

0.15

1 8 15 22 29

Day of the month

Percent

JUN

JUL

AUG
Health Impacts from Ozone Changes

Draft report 6d-13

Figure 5: Frequency distribution of percent changes in erythemal surface UV radiation 

Distrbution of percent changes in erythemal (CIE) surface UV

CMAQ 12x12 km grid, scenarios 084-070b

0

2000

4000

6000

8000

10000

12000

14000

-0.1 0 0.1 0.2 0.3 0.4 0.5

Percent

Number

JUN

JUL

AUG

1.3. Population Adjustments 

This analysis required county-based population projections for two purposes: to calculate the 

population-weighted changes in ground level UV for each latitude band modeled in the AHEF 

model for the year 2020 and to provide future population projections for the years 2005–2050.

Although the U.S. Census Bureau provides population projections, they could not be used for 

this purpose because the publicly available datasets lack the level of detail needed by the AHEF 

model: population by county, race, gender, and five-year age cohorts. Existing population 

projections traditionally used by the AHEF model also could not be used because they cover the 

entire United States, while the area analyzed by CMAQ model covers all or part of 42 states. To 

meet the data needs of this analysis, county-based population projections were developed using a 

simple cohort-component methodology. 

1.3.1. Cohort-Component Methodology Overview 

The cohort-component methodology is a common technique for projecting population changes 

over time. In this case, three independent components of population change were used: fertility, 

mortality, and net international migration (i.e., migrations between U.S. counties and foreign 

countries). Domestic migration (i.e., migrations between U.S. counties) was not included in this 

projection exercise for reasons discussed below. To project population changes over time, the 
Health Impacts from Ozone Changes

Draft report 6d-14

population was divided into cohorts that were age-, gender-, and race-specific. Changes due to 

these three components of change were estimated over time as each cohort was tracked 

separately, hence the term “cohort-component.” 

The population of a county in any year t as estimated by the model is determined using the 

following equation: 

where:

Pt = Population in year t

Ptí1 = Population in the previous year 

Bt

 = Births in year t

Dt

 = Deaths in year t

NIMt = Net International Migration in year t

Beginning with an initial set of populations, annual components of change were applied in the 

following process, which were repeated annually until the desired end year was reached: 

1. Add births by cohort

2. Deduct deaths by cohort 

3. Add net international migration 

4. Age population one year and repeat for the next year 

This methodology is illustrated in Figure 6 below. The cycle begins with an initial Year 2000 

population and is repeated until reaching Year 2100. 

Figure 6: Demographic model flow 

Pt = Ptí1 + Bt í Dt + NIMt

Equation 1 
Health Impacts from Ozone Changes

Draft report 6d-15

In the following sections, the methods and data used for the initial population and each 

component are discussed in greater detail. 

1.3.2. Component Data and Methods 

Initial Population 

In order to use the rates for components of change provided by the U.S. Census Bureau 

(discussed below), it was necessary to begin with an initial Year 2005 population dataset that 

was disaggregated using the same cohorts. These cohorts in the rates data are divided into two 

genders (male and female), 100 age groups (0–99 in one year increments), and four racial groups 

(White, Black, American Indian or Alaskan Native, and Asian or Pacific Islander). This 

represents 800 distinct population cohorts (2 genders × 100 ages × 4 bridged race groups). 

County populations using bridged race and one-year age cohorts were most readily accessible 

using the Vintage 2006 July 1, 2005 dataset provided by the National Center for Health Statistics 

(NCHS; 2007). 

Fertility and Mortality 

For fertility, the number of children born equals the number of women in a given cohort times 

the average number of children born annually to every 1000 women in that cohort divided by 

1000. Because virtually all births occur to women between the ages of 10 and 49, only those 

cohorts were considered in this model. These births are summed by race and used to create a 

new age zero cohort. To allocate births between males and females, a historic ratio of 1046 

males born for every 1000 females born was calculated and it was assumed that this ratio holds 

steady. Data from the CDC’s “Trend Analysis the Sex Ratio at Birth in the United States” were 

used to complete this calculation (Matthews and Hamilton 2005). 

Similarly, mortality was estimated by multiplying the number of people in a given cohort by the 

cohort-specific mortality rates. The resulting number of deaths was then subtracted from the 

cohort. Unlike fertility, all cohorts are subject to mortality. Therefore, mortality rates were 

applied to each cohort. Although an increasing number of Americans are living to the age of 100 

or more, the model assumes 100% mortality after age 99 for the sake of computational 

efficiency. Even with continued rates of survivorship past this age, the 100-plus age group will 

remain a miniscule portion of the population (0.02% of national population on July 1, 2005).

For fertility and mortality rates, the U.S. Census Bureau’s “Component Assumptions of the 

Resident Population by Age, Sex, Race, and Hispanic Origin” were used (U.S. Census Bureau 

2000). These are the same data used in Census projections. These components of change are 

associated with the 1990 National Projections and were used in both the 1990 State Projections 

and the 2000 National Projections. While it would be preferable to use more recent data, at this 

time components of change based on the 2000 Census have not yet been released.

For both fertility and mortality, the so-called Middle Series of component information was used. 

Fertility rates were provided in a single file; mortality rates for each component were provided in 

three different tables, for the years 1999–2010, 2015–2055, and 2060–2100. Projected fertility 

rates were provided for each year to 2100, but beginning with 2010, mortality rates were 
Health Impacts from Ozone Changes

Draft report 6d-16

provided in five year increments only. We assumed that 2010 mortality rates held steady from 

2010–2014, 2015 mortality rates held steady from 2015–2019, and so on. 

Net International Migration 

The projections for net international migration utilized a simple method based on the Census 

Bureau’s international migration projections for the entire country. These files contain the 

projected net international migration for each gender, age, and race cohort for the years 2000–

2100. Like the fertility and morality rates, these data are part of the Census Bureau’s 

“Component Assumptions of the Resident Population by Age, Sex, Race, and Hispanic Origin” 

(U.S. Census Bureau 2000). Since the tables “Foreign-born Net Migration to the United States” 

contain only national level data, it was necessary to allocate the national migrants to the counties.

Using 2000 Census data (Summary File 3, Table P22), we determined each county’s share of the 

total population of recent immigrants (i.e., those who entered within the last five years). These 

county shares were then used to allocate each cohort of immigrants among the nation’s counties. 

The estimated number of immigrants in each cohort was then added to the existing county 

population of each cohort. This method assumes a constant distribution of recent immigrants 

based on Year 2000 immigration patterns. While it is likely that new settlement patterns for 

immigrants will develop in the future, this is the same method the Census Bureau uses for 

assigning immigrants to states in its state projections (U.S. Census Bureau 2005). The Census 

Bureau provides a low, medium, and high series for net international immigration. In the base 

case, the middle series was used. 

Domestic Migration 

Although domestic migration is also a major component of local population change, it could not 

be accurately modeled here. The Census Bureau’s methodology for state estimates does contain 

data about state-to-state migration rates based on the observed trend from 1975–2000, but that 

method does not consider county-to-county migration patterns. The commonly used Woods and 

Poole projections do consider domestic migration, but are only available to 2030. Developing a 

method for estimating future migrations was beyond the requirements of this analysis, and likely 

to introduce more error. The potential impacts of excluding domestic migration from this 

analysis are discussed in the Section 4.4 which addresses uncertainty in the population 

adjustments. 

1.3.3. Use of these Projections 

The population projections developed using the above methodology were used for two purposes 

in this analysis. First, they were used to calculate the population-weighted change in UV 

exposure based on the CMAQ and TUV modeling discussed above. These models provided the 

percent change in ground-level UV exposure for each 12 × 12 km cell in a grid that roughly 

covers the eastern two-thirds of the United States. To link the change in UV exposure to the 

population in each county, the average percent change in UV exposure was calculated for each 

county. In calculating the average for any given county, each cell was given a weighting equal 

to the percentage of its area of that is located in that county. These county averages were then 

used to calculate the population-weighted average change in UV exposure for each sex, age 

group, and latitude band. The modeled population for 2020 was aggregated into male and 

female, 18 age cohorts (0–4 years, 5–9 years, 10–14 years, and...85-plus years), and three 
Health Impacts from Ozone Changes

Draft report 6d-17

latitude bands (20–30°, 30–40°, and 40–50°), or 108 population groups (2 sexes × 18 age groups 

× 3 latitude bands = 108 population groups). For each population group, the populationweighted average exposure was calculated by summing the product of the population in each 

county multiplied by the change in UV exposure in each county divided by the total population 

of that population group across all counties.

These projections were also used in the AHEF model runs. Model outputs for each five-year 

increment from 2005 to 2050 were aggregated for the 108 population groups. Because the 

CMAQ model did not cover the entire United States, those counties that were not included in the 

CMAQ modeling area were not included in the aggregated populations.

1.4. AHEF Modeling 

The projections of population-weighted percentage change in UV exposure and future 

populations, as described in Section 2.3.3 above, were inputted into the AHEF model to estimate 

associated changes in health effects—specifically basal cell carcinoma (BCC), squamous cell 

carcinoma (SCC), and cutaneous malignant melanoma (CMM) incidences and mortalities. 

1.4.1. Overview of Methodology to Estimate Changes in Health Effects 

To yield health effects estimates, the AHEF first projected future baseline skin cancer incidence 

and mortality; this calculation was based on the future population estimates derived in Section 

2.3 and baseline incidence and mortality rates for each health effect (based on a scenario of 

compliance with the Montreal Adjustments to the Montreal Protocol). Then the AHEF 

multiplied the population-weighted percentage changes in UV exposure in a future year by the 

appropriate dose-response relationship (described in Section 4.2.2 below) to yield the percentage 

change in future skin cancer incidence/mortality attributable to the proposed change in the 

NAAQS ozone standard (from 84 ppb to 70 ppb). These percentages were then multiplied by the 

baseline incidence and/or mortality for that health effect to compute the absolute number of 

additional future cases or deaths attributable to the tropospheric ozone standard change.4

 These 

calculations are shown in Equation 2 below using BCC as an example health effect. 

These calculations were performed for each health effect and for each future population group5

to produce predictions of the incremental health effects in each future year through 2100 

associated with a one-pulse change in the NAAQS ozone standard from 84 ppb to 70 ppb in 

4

 This method of multiplying the changes in UV exposure by the biological amplification factor (BAF) and the 

underlying baseline incidence or mortality is the same as that used by other researchers to estimate changes in health 

effects based on changes in ozone concentrations (e.g., Madronich and de Gruijl 1994, Pitcher and Longstreth 1991). 

5

 The future population group is a subset of the total U.S. population, calculated specifically for this analysis, as 

described in Section Error! Reference source not found. above. 

(Cumulative Percentage Increase in UV Exposure) × (Biological Amplification Factor for 

BCC) × (Baseline Incidence of BCC for the Population Group) = Absolute Increase in BCC 

Incidence 

Equation 2 
Health Impacts from Ozone Changes

Draft report 6d-18

2020. It is important to note that because the percentage increase in UV exposure associated 

with the tightening of the NAAQS standard from 84 ppb to 70 ppb is being used as the 

environmental input in the AHEF, only the incremental number of health effects associated with 

the standard change were modeled. The absolute number of health effects associated with the 

current NAAQS standard was not expressly calculated.

1.4.2. Selected Action Spectrum and Derived Dose-Response 

Relationships

The calculation of incremental health effects in Equation 2 above involves the use of a derived 

dose-response relationship, or biological amplification factor (BAF). Determining the health 

effects caused by UV exposure first requires information on the relative weights to be placed on 

each discrete UV wavelength to reflect the degree to which each wavelength causes biologic 

damage. Such a weighting function is called an action spectrum—an experimentally derived 

function that describes the relative effectiveness of each UV wavelength in the induction of skin 

cancers. The AHEF relies on action spectra for each health effect because action spectra provide 

information regarding which wavelengths of the total UV spectrum are most effective at causing 

the particular health effect. Based on the available action spectra, the Skin Cancer UtrechtPhiladelphia-human (SCUP-h) action spectrum (derived based on the induction of SCC in 

hairless mice and corrected for human skin transmission) was selected for modeling SCC, BCC, 

and CMM in the AHEF.6

Based on the action spectrum selected for each health effect, the relationship between those 

health effects and the intensity of UV exposure can then be explored. These dose-response 

relationships are derived by correlating measurements or estimates of UV exposure received for 

a specific action spectrum and given health effect at various locations, and the level of incidence 

or mortality for that health effect at those same locations. In the AHEF, statistical regression 

analyses were used to estimate the dose-response relationship, known in technical terms as the 

BAF, for each health effect. The BAF measures the degree to which changes in UV exposure 

weighted by the appropriate action spectrum (as measured in Watts/m2

) cause incremental 

changes in health effects (incidence or mortality), and is estimated after accounting for the 

influence of birth year and age, as necessary.

BAFs are defined as the percent change in a health effect resulting from a one-percent change in 

the intensity of UV radiation (weighted by the chosen action spectrum). For example, for BCC 

incidence in white males, a one-percent change in the intensity of UV radiation results in a 1.5 

percent change in BCC incidence. For each health effect, the AHEF applies the BAF to predict 

future incidence and mortality as shown in Equation 2 above. 

Table 3 presents a summary of calculated BAFs and selected action spectra for each health 

effect.

6

 Since a mammalian action spectrum for CMM still remains to be determined, the SCUP-h is also used to model 

CMM.
Health Impacts from Ozone Changes

Draft report 6d-19

Table 3: Summary of Calculated BAFs, Selected Action Spectra, and Key Inputs 

BAF: Used in AHEF 

(Annual Exposures) 

Health Effect Data Sources Selected Action 

Spectrum 

Males Females 

CMM

Incidence/ 

Mortality 

Incidence: Ratios from SEER data set 

Mortality: EPA/NCI data set 

BAF: Developed using econometric analysis 

SCUP-h (1993) 0.5846 0.5047 

BCC Incidence Incidence: Based on methods used in U.S. EPA (1987) 

and Fears and Scotto (1983) 

BAF: de Gruijl and Forbes (1995) 

SCUP-h (1993) 1.5 1.3 

SCC Incidence Incidence: Based on methods used in U.S. EPA (1987) 

and Fears and Scotto (1983) 

BAF: de Gruijl and Forbes (1995) 

SCUP-h (1993) 2.6 2.6 

Nonmelanoma 

Mortality 

Mortality: EPA/NCI data set 

BAF: Developed using econometric analysis 

SCUP-h (1993) 0.7094 0.4574 

1.5. Valuation of Human Health Effects 

The monetary value of incremental cases of basal cell carcinoma (BCC), squamous cell 

carcinoma (SCC), and cutaneous malignant melanoma (CMM) was calculated as the number of 

additional cases multiplied by the medical and productivity loss cost per case. Cost per case is 

for cancer care only and excludes the costs of unrelated care, such as increased costs for treating 

other medical conditions later in life that might have occurred after the projected skin cancer 

mortality. For a change in the NAAQS ozone standard in one year (2020) only, the AHEF 

output gave the associated increase in skin cancer incidence and mortality, by health effect type, 

in each year through 2150. Total incremental costs were calculated over 2020–2150 and 

discounted to 2020 using discount rates of 3 percent and 7 percent, consistent with the guidance 

provided in the Office of Management and Budget’s (OMB) (2003) Circular A-4.

The medical costs and productivity loss per case are shown in Table 4. These monetary values 

(in 2005$) were employed in a peer-reviewed publication (Kyle et al. forthcoming). 

Table 4: Total Cost per Case of Non-fatal Skin Cancer and Mortality (2005 $) 

Medical Cost Productivity Loss Cost Total Cost per 

Case/Mortality

Non-fatal Skin Cancer Case

Basal Cell Carcinoma $1,066* $1,161† $2,228

Squamous Cell Carcinoma $1,066* $4,477† $5,543

Cutaneous Malignant Melanoma -- -- $37,220‡

Skin Cancer Mortality $6.6 million§

* Chen et al. (2001), adjusted to 2005 $ using the medical care component of the Consumer Price Index (CPI-U).
Health Impacts from Ozone Changes

Draft report 6d-20

† Calculated by ICF, based on U.S. EPA (1988) and U.S. BLS (2007). 

‡ U.S. EPA (1988), adjusted to 2005 $ using the CPI-U for medical care.

§ Adjusted from $5.5 million at 1990 income levels (2000 $) to $6,600,000 at 2020 income levels. $5.5 million is 

the mean of a normal distribution with a 95% confidence interval between $1 million (Mrozek and Taylor 2002) and 

$10 million (Viscusi and Aldy 2003).

Medical costs per case of BCC and SCC were based on Chen et al. (2001); this study used data 

from the Medicare Current Beneficiary Survey (1999–2000) to estimate medical treatment costs 

associated with BCC and SCC in different practice settings. To determine an average medical 

treatment cost per case, weighted averages were calculated based on the percentage of episodes 

managed in each setting. 

Productivity loss costs were based on a U.S. EPA analysis supporting the Regulatory Impact 

Analysis: Protection of Stratospheric Ozone (U.S. EPA 1988). The cost per case was calculated 

by multiplying EPA’s estimates of the loss of work due to illness and care giving performed by 

others for the patient for BCC and SCC by the national mean annual wage for 2005 (U.S. BLS 

2007). For CMM, EPA’s estimate of the total medical cost and productivity loss per case was 

used and adjusted to 2005 $ using the CPI-U for medical care (U.S. EPA 1988). 

The value of a statistical life (VSL) is estimated to be $5.5 million at 1990 income levels and 

$6.6 million at 2020 income levels. The estimate of $5.5 million is the mean of a normal 

distribution with a 95 % confidence interval between $1 and $10 million. The confidence 

interval is based on two meta-analyses of the wage-risk VSL literature: $1 million represents the 

lower end of the interquartile range from the Mrozek and Taylor (2002) meta-analysis; and $10 

million represents the upper end of the interquartile range from the Viscusi and Aldy (2003) 

meta-analysis. The VSL represents the value of a small change in mortality risk aggregated over 

the affected population. 

Results

This section provides an overview of the results of this analysis, including changes in ground 

level SCUP-h UV, changes in health effects (i.e., incremental skin cancer incidence and 

mortalities), and the resulting monetized disbenefits.

1.6. Changes in Ground Level SCUP-h UV 

Using the methodology described in Section 2 above, the percent change in ground-level 

SCUP-h UV was calculated for each day and averaged across each month. The figures below 

represent average changes in SCUP-h UV associated with achieving an ozone standard of 70 ppb 

(down from 84 ppb) in the summer months of June, July, and August. 
Health Impacts from Ozone Changes

Draft report 6d-21Figure 7: Ground Level UV Percent Change between 70 and 84 ppb, June 
Health Impacts from Ozone Changes

Draft report 6d-22Figure 8: Ground Level SCUP-h UV Percent Change between 70 and 84 ppb, July 
Health Impacts from Ozone Changes

Draft report 6d-23Figure 9: Ground Level SCUP-h UV Percent Change between 70 and 84 ppb, August 
Health Impacts from Ozone Changes

Draft report 6d-24

1.7. Changes in Human Health Effects 

This section presents results in terms of the changes in skin cancer incidence and mortality 

associated with a one-year change in the ozone standard in 2020. Table 5 below gives the 

cumulative change in incidence and premature mortality associated with a one-time pulse (i.e., a 

change in the ozone standard from 84 ppb to 70 ppb in one year, 2020). As shown, 3,538 

additional cases of skin cancer and about 16 additional mortalities are expected. For the age 

cohorts relevant to this analysis (those populations potentially alive in 2020 and thereafter; i.e., 

those born from 1930 to 2100) and for the population subset analyzed, baseline skin cancer 

modeled in the AHEF through 2150 totals more than 188 million cases and about 2.6 million 

mortalities. Thus, the additional cases and mortalities associated with changing the ozone 

standard represent less than 0.002% and less than 0.001% of baseline, respectively. 

This section also provides the monetary value of those future health effects, discounted back to 

2020, in Table 6 and Table 7. 

Table 5: Additional Skin Cancer Incidence and Mortality Associated with a Change 

in the Ozone Standard in 2020 

Incidence Mortality

 Skin Cancer Type Central Estimate* 

(Uncertainty Range†)

Nonmelanoma Skin Cancer 3,454

(2,348–4,560) 

5.7

(3.9–7.5) 

Cutaneous Malignant Melanoma 84

(57–110) 

10.5

(7.1–13.8) 

Total 3,538

(2,405–4,671) 

16.2

(11.0–21.3)

* From the AHEF model. 

† The uncertainty range is derived by applying the quantified uncertainty (approximately 32%), as calculated in 

Section 1.15, to the central estimate. 

Table 6: Monetized Summary Table (3% discount rate, discounted to 2020 with prices 

in 2005 $) 

Incidence Mortality

 Skin Cancer Type Central Estimate* 

(Uncertainty Range†)

Nonmelanoma Skin Cancer $4,717,452

($3,207,130–$6,227,773)

$13,334,430

($9,065,329–$17,603,530) 

Cutaneous Malignant Melanoma $1,399,631

($951,531–$1,847,732) 

$28,630,454

($19,464,236–$37,796,672) 

Total $6,117,083

($4,158,661–$8,075,505)

$41,964,884

($28,529,566–$55,400,202) 
Health Impacts from Ozone Changes

Draft report 6d-25

* Based on incidence and mortality projected by the AHEF model. 

† The uncertainty range is derived by applying the quantified uncertainty (approximately 32%), as calculated in 

Section 1.15, to the central estimate. 

Table 7: Monetized Summary Table (7% discount rate, discounted to 2020 with prices 

in 2005 $) 

Incidence Mortality

 Skin Cancer Type Central Estimate* 

(Uncertainty Range†)

Nonmelanoma Skin Cancer $2,234,469

($1,519,090–$2,949,848)

$5,819,781

($3,956,542–$7,683,021)

Cutaneous Malignant Melanoma $720,816

($490,042–$951,590) 

$14,068,218

($9,564,191–$18,572,246) 

Total $2,955,285

($2,009,132–$3,901,438)

$19,888,000

($13,520,733–$26,255,267) 

* Based on incidence and mortality projected by the AHEF model.. 

† The uncertainty range is derived by applying the quantified uncertainty (approximately 32%), as calculated in 

Section 1.15, to the central estimate.

Uncertainty

1.8. Uncertainty in estimated impacts 

Uncertainty in the estimation of human health impacts arising from a tightening of the NAAQS 

standards from 84 ppb to 70 ppb arise from various sources. These uncertainties are addressed in 

the following sections: 

 CMAQ Modeling—uncertainty in the prediction of precise tropospheric ozone column 

changes under the NAAQS scenarios 

 TUV Modeling—uncertainty in the calculation of consequent changes in surface UVSCUP

 Population Adjustments—uncertainty in the determination of county based population 

projections

 AHEF Modeling—uncertainty in the estimate of associated changes in health effects 

including latency 

 Valuation Of Human Health Effects—uncertainty in the monetary value of incremental 

skin cancer incidence/mortality 

 Unquantified Sources of Uncertainty—other qualitative sources of uncertainty 

 Summary of Quantified and Unquantified Sources of Uncertainty 

The sources and magnitudes of the uncertainties associated with each step of the analysis were 

identified and are discussed in the relevant sections below. 
Health Impacts from Ozone Changes

Draft report 6d-26

1.9. Uncertainty in CMAQ Modeling 

Output from CMAQ modeling runs from U.S. EPA (CMAQ version 4.5) were provided for the 

two NAAQS scenarios with identifiers: 

 2020bk_v4.5_084_12km.o3_hr_shift_LST, and 

 2020bk_v4.5_070b_12km.o3_hr_shift_LST

The CMAQ model did not cover the entire United States, the area analyzed covers all or part of 

42 states in the eastern two-thirds of the country 

Eder and Yu (2006) have conducted performance evaluations comparing annual simulations 

(2001) of CMAQ (version 4.4) covering the contiguous United States against monitoring data for 

four nationwide networks. This effort represents one of the most spatially and temporally 

comprehensive performance evaluations of the model. Simulations of the peak 1- and 8-h ozone 

concentrations during the summer (April–September) were “relatively good” (correlation 

(r)=0.68, 0.69; normalized mean bias = 4.0 %, 8.1 % and normalized mean error = 18.3 % and 

19.6 % respectively). No performance evaluation could be assessed for the provided scenarios; 

however, analysis for the CMAQ review process (see http://www.cmascenter.org/index.cfm) 

typically returns normalized mean errors for ozone § 20%. 

As described in Section 2.2, the CMAQ ozone concentrations are accommodated into the TUV 

model to determine overall column ozone values. 

1.10. Uncertainty in TUV Modeling 

1.10.1. Uncertainty Analysis of TUV Calculations 

The uncertainties in the TUV calculations can be divided into two types: 

1) Uncertainties inherent in the TUV numerical model, primarily from the approximate 2-

stream (delta-Eddington) solution of the radiative transfer equation, and the discretization 

of altitudes and wavelength and related interpolations. These uncertainties have been 

shown in many earlier studies to be negligible, on the order of 5% or less, when 

compared to higher stream models and higher vertical and spectral resolution.

2) Uncertainties in the input parameters that describe atmospheric composition (vertical 

profiles of air, ozone, other absorbing gases, aerosols, and clouds) and the earth’s surface 

reflectivity.

If the input parameters are well known (e.g., cloud-free and pollution-free conditions with 

measured total ozone column as inputs), the TUV results are accurate to a few percent, which is 

also the accuracy of the best instruments for measuring atmospheric UV radiation. For the 

present purposes, the inherent TUV uncertainty (item 1) is taken, conservatively, as 5%. 

The atmospheric input parameters (item 2 above) are generally not well known in any specific 

situation, and are highly variable spatially and temporally, with long-term trends also a 

possibility. For the purposes of these calculations, we adopt the principle that UV changes 
Health Impacts from Ozone Changes

Draft report 6d-27

stemming from CMAQ scenario changes in tropospheric ozone can be calculated under the 

premise that all other atmospheric conditions remain exactly the same between the two 

scenarios, including clouds, aerosols, and surface reflectivity. This is consistent with the 

approach used in calculations relating stratospheric ozone changes to surface UV increases. 

Table 8 shows the predicted changes in UV-SCUP calculated by the TUV model, between the 

two CMAQ scenarios (084 to 070), for 15 July. The changes are expressed as percent changes in 

daily UV-SCUP doses at each location, then domain-averaged to give the values in the third 

column of the table. The reference model (test number 0) would be used in the AHEF estimates 

of skin cancer changes. The other entries in the table (tests number 1–6), show the UV-SCUP % 

change between scenarios, if other atmospheric conditions are changed individually and equally 

for both scenarios, as described in the second column. The last column gives the % effect of 

changing the atmospheric conditions. For example, the reference calculation (test 0) gives a UVSCUP increase of 0.118% in going from scenario 084 to 070. If aerosols are removed from the 

model (test 1), the UV-SCUP increase between scenarios is only 0.112, which is a 5.1 % 

reduction relative to the reference case. A brief explanation of the effects from each factor is 

given below. 

Table 8: UV-SCUP changes between CMAQ scenarios 084 and 070 on 15 July, for 

different values of other factors (aerosol, surface albedo, clouds, and 

stratospheric ozone). 

Test

number Description

Domainaveraged

change in 

UV-SCUP, 

%

Effect of 

other factors, 

%

0

Reference (Elterman* aerosols, 10% surface 

albedo, no clouds, sea level, USSA 

stratospheric O3)

0.118 Ł 0 

1 No aerosols 0.112 -5.1 

2 0% surface albedo 0.112 -5.1 

3

High thin cloud, at 9-10 km, optical depth 

=2 0.132 11.9 

4

Low moderately heavy cloud, at 1-2 km, 

optical depth = 16 0.169 43 

5 850 mb surface pressure 0.098 -17 

6

20 DU reduction in stratospheric O3 (above 

16 km) 0.123 4.2 

(*) Elterman continental aerosol vertical profile, with total optical depth (at 550 nm) = 0.235, 

Angstrom alpha = 1.0, single scattering albedo = 0.99, asymmetry factor = 0.61. 

1. Aerosols increase the photons’ pathlengths, and therefore increase the probability of 

absorption by tropospheric ozone. By removing aerosols from the reference run, the UV 

increase from changing ozone scenarios is somewhat smaller. 
Health Impacts from Ozone Changes

Draft report 6d-28

2. Surface albedo reflects light back to the atmosphere, and a fraction of this can be scattered 

back toward the surface, effectively increasing the photons’ path-lengths for absorption by 

tropospheric ozone. If the surface is not reflecting (albedo = 0%), these photon reflections do not 

occur and the interaction with tropospheric ozone is smaller. 

3. High clouds (e.g., cirrus) make the incident (down-welling) light more diffuse and therefore 

more slanted as it passes the troposphere. They also reflect a fraction of the up-welling radiation 

(up-scattered by tropospheric molecules), back to the lower troposphere (much like surface 

albedo, but in the opposite direction). Both effects increase tropospheric photon pathlengths and 

therefore the probability of absorption from any additional tropospheric ozone. 

4. Low thick clouds (e.g., stratocumulus, marine stratus) have a larger effect because they are at 

altitudes closer to where the ozone changes are largest. In-cloud increases of ozone are 

particularly significant because of the long in-cloud photon pathlengths, as has been observed 

and modeled (e.g., Mayer et al., 1997). Broken clouds (e.g., fair-weather cumulus) are expected 

to be intermediate between fully overcast and fully clear (Nack and Green, 1974). 

5. Decreases in atmospheric pressure reduce, in direct proportion according to the ideal gas law, 

the conversion factor between ozone molar mixing ratios (ppb, specified by CMAQ) and the 

ozone number density (molecules cmí3

, which is integrated to obtain the ozone column in 

Dobson Units) used for atmospheric transmission. Also, lower pressures decrease the Rayleigh 

optical depth and therefore the photon path coupling between scattering and absorption. These 

factors combine to yield a smaller SCUP-UV change. The pressure reduction chosen here, 850 

mb, is roughly representative of cities at high elevation. Thus, this case can also be considered a 

surrogate test for the effect of surface elevation (varying the surface elevation directly is possible 

within the TUV code, but would have created some ambiguity between the nominal CMAQ 

altitudes and the TUV geometric grid). 

6. Reductions in stratospheric ozone imply that any tropospheric ozone changes are a larger 

fraction of the total column ozone. Therefore the sensitivity to CMAQ scenario changes is 

greater if the stratospheric ozone is smaller. This is consistent with the power law first proposed 

by Madronich (1993): 

UVbio v (DU)

íRAF

for which the theoretical basis is described by Micheletti et al. (2003). 

The sensitivity studies (cases 1-6) show that how the baseline environmental conditions, under 

which the difference between the two tropospheric ozone scenarios was assessed, could 

contribute to the uncertainties of the TUV-calculated changes in surface SCUP-UV radiation. 

The worst case is that of low clouds: If the entire domain were actually covered by low clouds 

for the entire period of interest (June–August), the TUV calculations made under cloud-free 

assumption would underestimate the UV increases stemming from the changes in tropospheric 

ozone, by about 43%. This extreme case is patently unrealistic. Conservatively, if it is assumed 
Health Impacts from Ozone Changes

Draft report 6d-29

that low clouds are present no more than 1/4 of the time, their error is reduced to about 11%.

Thus, the uncertainty budget can be summarized as follows: 

 Inherent TUV uncertainties 5 % 

 Aerosols 5.1 % 

 Surface albedo 5.1 % 

 High clouds 11.9 % 

 Low clouds (1/4 of the time) 11 % 

 Surface pressure 17 % 

 Stratospheric ozone 4.2 % 

 _____________________________________

 TOTAL (quadrature) 25 % 

For example, for the 15 July case, the reference UV-SCUP change of 0.118 % is estimated to be, 

with high certainty, in the range 0.088–0.148 %. 

Finally, it should be noted that these estimates are generally overly conservative. For example, 

high clouds are likely to be present only a fraction of the time, and the 850 mb pressure may 

apply to only a few locations. Therefore the 25% uncertainty estimated here should be viewed as 

a very conservative upper limit. 

The TUV model also has the option of calculating radiation incidence on a sphere or on a 

horizontal plane. Incidence on a sphere is presently considered a better metric for UV exposure 

and was therefore used in this analysis. A small uncertainty is introduced over incidence on a 

horizontal plane, the previous standard. The percent change in UV is reduced by about 8 % by 

taking the spherical output in preference to the planar output (i.e., for the 15 July domainaverage, from 0.126 % to 0.118 %). This is a small effect and it should be noted that the average 

SCUP-UV changes are still near 0.1 % using either output. 

1.10.2. Comparison with UV Changes Due to Other Factors 

In Section 2.2.3, the UV-SCUP change resulting from tropospheric ozone change between the 

two CMAQ scenarios was calculated and shown to be of order ~ 0.1 %, if all other 

environmental factors are kept constant between the two scenarios. Below, we consider, for 

comparison only, the UV changes that would result if these other factors are allowed to vary 

between two scenarios. To illustrate this, Table 9 shows the UV changes, calculated for the 

CMAQ 084 tropospheric ozone scenario, when other environmental conditions, rather than 

tropospheric ozone, are changed relative to the reference conditions. The magnitude of changes 

in the conditions is the same as used for Table 8. It should be emphasized that the % UV 

changes shown in Table 9 are NOT those associated with changes in tropospheric ozone, 

but rather with direct changes in the other environmental conditions. 
Health Impacts from Ozone Changes

Draft report 6d-30

Table 9: Effect on surface SCUP-UV radiation of varying environmental conditions 

other than tropospheric O3.

Test

number Description

Domainaveraged

% change in 

UV-SCUP 

0

Reference (Elterman aerosols, 10% surface 

albedo, no clouds, sea level, USSA 

stratospheric O3), tropospheric O3 scenario 

084

Ł 0 

1a No aerosols 7.3 

2a 0% surface albedo -3.8 

3a High thin cloud, at 9-10 km, optical depth 

=2 -12.1

4a Low moderately heavy cloud, at 1-2 km, 

optical depth = 16 -50.

5a 850 mb surface pressure 10.4 

6a 20 DU reduction in stratospheric O3 (above 

16 km) 7.6

Should the baseline environmental conditions actually change between the two CMAQ 

tropospheric ozone scenarios (084 and 070), the SCUP-UV changes could be far larger. Of 

course, there is no solid scientific basis for expecting such environmental changes in response to 

relatively small changes in tropospheric ozone. Some interactions are known, (e.g. oxidant 

photochemistry leading to the formation of sulfate and secondary organic aerosols, which can 

affect radiation directly as well as change cloud nucleation and lifetimes) but these effects are 

still poorly quantified, and although subjects of active current research, are not expected to be as 

large as the variations used in this sensitivity analysis. 

1.11. Uncertainty in Population Adjustments 

The Cohort-Component Methodology (see Section 2.3.1) for population adjustment used in the 

analysis gave a 2020 total population of 336.1 million in very close agreement with the U.S. 

Census Bureau projection for 2020 of 335.8 million—a difference of less than 0.1 %. However, 

as discussed above, the model did not consider domestic migration between counties due to the 

lack of suitable alternative estimates. It is assumed that migration between neighboring counties 

within the same metropolitan area is not likely to have an impact on the results because the 

change in ozone concentration is similar in adjacent areas. When aggregated across broad 

latitude bands with hundreds of counties, small differences from one county to the next due to 

migration are likely to cancel each other out. 

Interregional migration—such as the observed historic migrations from the Northeast and upper 

Midwest to the Sun Belt states—is a potential source of uncertainty in this analysis. Since the 

model estimated that all local populations change only through births, deaths, and the arrival of 

international immigrants, it is possible that populations of regions that are losing migrants to 
Health Impacts from Ozone Changes

Draft report 6d-31

other parts of the country are overrepresented in this analysis, while the populations of fastgrowing regions attracting these migrants are underrepresented. Because the populationweighted change in UV exposure is higher in the southern latitude band than in the northern 

latitude band, this analysis may be underestimating the change in exposure if the historic northto-south migration pattern holds. However, this effect is not uniform—Florida, for example, 

exhibits much lower changes in UV than other areas of the South, but has traditionally received a 

large portion of migrants from the North. 

Ultimately, it was decided that the uncertainty associated with predicting migration patterns 

outweighed the uncertainty introduced by excluding domestic migration from this model. 

Because migration between regions is a matter of percentage points rather than degrees of 

magnitude, it is assumed that the overall uncertainty associated with the population projections is 

relatively small. 

The CMAQ model area also has population implications. The area analyzed covers all or part of 

42 states in the eastern two-thirds of the country. As a result, those counties that were not 

included in the CMAQ modeling area were not included in the aggregated populations (26.2 % 

of the total population). It would be reasonable to assume, given this truncation of population 

(e.g., 13.5 % of the population reside in California) and the historically high proportion of cases 

of skin cancer and/or mortality on the West Coast (e.g., California counties, especially Los 

Angeles), that this input alone would introduce a disproportional large, unquantifiable 

uncertainty if the estimated health effects from the analysis were extrapolated to the rest of the 

population. Therefore, the results of this analysis must be viewed in this context when drawing 

comparisons with other studies which consider the continuous United States (e.g., Lutter and 

Wolz, 1997). 

1.12. Uncertainty in AHEF Modeling 

AHEF modeling contributes uncertainties to the estimates of human health effects—resulting 

from a change in NAAQS standards—in two major areas: 

1) the dose-response relationships (expressed as a BAF) for the three endpoints of concern 

(i.e., BCC, SCC, and CMM), and 

2) the future size, behavior, and distribution of the populations that will be affected (see 

Section 4.4. Uncertainty in Population Adjustments).

It should be noted that for this analysis, only estimated uncertainty in the BAF parameter is 

quantifiable. 

1.12.1. Uncertainties in Selected Derived Dose-Response Relationships 

The AHEF model (described in Section 2.4) incorporates information on the dose-response 

relationships for BCC, SCC, and CMM through the use of a BAF (i.e., the slope of the doseresponse relationship). . The estimate of BAF and associated standard error generated for CMM 

incidence/mortality using the SCUP-h action spectrum is 0.5846 ± 0.02 for males, 0.5047 ± 0.02 

for females which yields an uncertainty range of approximately 3 % for changes in these health 
Health Impacts from Ozone Changes

Draft report 6d-32

effects estimates; the BAF and associated standard errors generated for NMSC mortality 

0.7094 ± 0.03 for males, 0.4574 ± 0.03 for females which yields an uncertainty range of 

approximately 4 and 7 % respectively; and BAFs and associated standard errors generated for 

BCC and SCC are 1.5 ± 0.5 for males, 1.3 ± 0.4 for females and 2.6 ± 0.7 for males, 2.6 ± 0.8, 

respectively (deGruijl and Forbes, 1995) which yields an uncertainty range of approximately 

30% for changes in these health effects estimates. 

1.12.2. Behavioral Uncertainties 

While the AHEF assumes that human exposure behavior remains constant over time, changes in 

human behavior affect the amount of UV radiation exposure received. For example, changes in 

(1) the amount of time spent outdoors, (2) in socioeconomic profiles that impact travel to areas 

where high UV exposure can be expected (i.e., the beach), or (3) in the use and/or efficacy of sun 

protection technologies such as sunglasses and sunscreens can impact the extent of UV exposure 

received.

A number of recent studies have examined UV exposure behaviors in the U.S. Godar et al. 

(2003) found that Americans get about 23 % of their lifetime UV dose by the age of 18, 46 % by 

the age of 40, and about 74 % by the age of 59, assuming that individuals live up to the age of 

78. Among U.S. youth ages 11–18, Cokinnides et al. (2001) found that about 10 % reported 

practicing three or more sun protection behaviors regularly and nearly 60 % practiced one or two 

routinely; however, about one-third of the youth overall did not practice any recommended sun 

protection behaviors.

1.12.3. Latency

Another source of uncertainty in the AHEF health effects estimate is associated with the 

exposure period over a person’s lifetime that is most likely to be the cause of UV-related health 

effects. This is especially relevant for CMM, since it has been hypothesized that CMM is largely 

the product of intense exposures early in life (e.g., through age 20) rather than cumulative 

lifetime exposure. The AHEF uses whole life exposure for all skin cancer types as the default 

assumption. Using early life exposure for CMM is not the same as evaluating a latency effect, 

but can be used as a proxy for latency in this health end point. Figure 10 shows the effect of this 

proxy measure for latency on CMM mortality changes by § 10 percent when the exposure 

assumptions (early life versus whole life) are changed (U.S. EPA, 2003), with uncertainty 

concerning the appropriate exposure dose manifesting itself less in the total incremental risks 

predicted, than in when those incremental effects are predicted to occur, and who will bear them 

(i.e., shifting the risk to future generations). Modeling this lag time further is difficult given the 

current state of knowledge about latency and its mechanisms (Madronich, 1999). 
Health Impacts from Ozone Changes

Draft report 6d-33

Figure 10: Excess CMM mortality for the Montreal Adjustment scenario for equal-age 

exposure weighting and weighting for exposures only for ages 1–20: 

cumulative annual exposure (U.S. EPA/NASA, 2001). 

1.13. Uncertainty in Valuation of Human Health Effects 

An extensive literature review was conducted to determine the best medical cost estimates for 

NMSC for the Economic Evaluation of the U.S. Environmental Protection Agency’s SunWise 

Program: Sun Protection Education for Young Children (Kyle et al., forthcoming). Values were 

taken from Chen et al. (2001), considered to be the best available source of data for these health 

endpoints; however, the authors did not include uncertainty bands around their central estimates. 

The national mean annual wage for 2005 (U.S. BLS, 2007) is $37, 870 (mean annual wage for 

all occupations) which has a mean relative standard error of 0.1%. 

1.14. Unquantified Sources of Uncertainty 

There are a number of other sources of uncertainty in the analysis’ health effects predictions. 

Some of these sources of uncertainty are possible to quantify, but are not central to the structure 

of the analysis. Others cannot be quantified because any assumptions or estimates would be 

simply speculative. These other sources of uncertainty include: 

 Composition of the future atmosphere; 

 Future conditions of the ozone column; 

 Effect of climate change; 

 Compliance with modeled policy scenarios; 

 Laboratory techniques and instrumentation for deriving action spectra; 

 Improvements in medical care/increased longevity; and 

 Baseline information. 
Health Impacts from Ozone Changes

Draft report 6d-34

These uncertainties are described qualitatively in more detail below. 

Composition of the Future Atmosphere 

The exact composition of the future atmosphere as a result of compliance with different policies 

(i.e., ODS phaseout under the Montreal Adjustments to the Montreal Protocol) is unknown. As 

levels of atmospheric chlorine are reduced, the impact of ozone depletion from chlorine and 

bromine radical species generated from ODS would change. In addition, long-term systematic 

changes in atmospheric opacity (e.g., clouds, aerosols, other pollutants) will also impact the 

ability to model changes in ozone. Likewise, future changes in climate could result in changes in 

the atmospheric circulation patterns and therefore could change cloud cover. The impacts of such 

changes on the predicted recovery of the ozone layer and subsequently tropospheric ozone are 

unknown. All of these uncertainties could influence the ability to model atmospheric processes 

accurately.

Future Conditions of the Ozone Column 

Uncertainties also can be contributed by assumptions regarding the future conditions of the 

ozone column in response to the phaseout of ODS. Some computer models predict that the 

phaseout of ODS will slow and eventually stop the rate of ozone depletion, and suggest that 

natural ozone-making processes will enable stratospheric ozone to return to 1979–1980 ozone 

conditions. These models also predict that the recovery will eventually result in increased 

concentrations beyond 1979–1980 levels7 (see Chapter 12 in WMO 

1999 for more detail). Because there is incomplete knowledge about the behavior of ozone prior 

to the satellite measurements taken in 1979–1980, the AHEF imposes a limit on future ozone 

recovery to the conditions observed in 1979–1980. 

Effect of Climate Change 

The effects of global climate variations on stratospheric temperature and, in turn, on ozone 

depletion, are not well understood, and have therefore not been assessed in the analysis. While 

this effect is not typically incorporated into models used to assess future ozone depletion, it does 

represent a modeling constraint that should be noted. 

Compliance with Modeled Policy Scenarios 

This analysis assumes compliance with each of the modeled NAAQS policy scenarios. To the 

extent that these limitations are not adhered to, future ozone column conditions could be 

different.

Laboratory Techniques and Instrumentation 

Additional uncertainty can be contributed by the laboratory techniques and instrumentation used 

for deriving the action spectra used to weight UV exposure. Discrepancies between the 

wavelengths of UV radiation intended to be administered and the wavelengths actually received 

by the test organism can result in orders of magnitude differences in the measured response. In 

7

 Whether this recovery scenario, called “ozone superabundance,” is likely to occur is open to debate, particularly 

because of the potential for complex interactions between global climate change and stratospheric ozone dynamics. 

Model computations have predicted both higher and lower amounts of ozone in the future. 
Health Impacts from Ozone Changes

Draft report 6d-35

addition, many action spectra are derived using monochromatic light sources that do not fully 

simulate the polychromatic light received directly from the sun. 

Improvements in medical care/increased longevity 

Improvements in medical care and predictions of increased longevity for many population 

subgroups could affect estimates of future skin cancer incidence and mortality significantly. 

Changes in socioeconomic factors 

Changes in socioeconomic factors (e.g., demographics and human behavioral changes) that could 

affect the accuracy of the analysis include: 

 Changes in human UV exposure behavior: This evaluation assumes that human exposure 

behavior remains constant through time, and does not take into account innovations in 

sun protection technology (e.g., improved sunglasses and sunscreens), increased public 

awareness of the effects of overexposure to UV, and increased sensitization to the need 

for early treatment of suspicious lesions. 

 Changes in socioeconomic profiles: Socioeconomic profiles can impact a variety of 

factors, ranging from demand for air travel to areas where high UV exposure is expected 

(i.e., the beach), to the types of skin cancer most commonly observed. 

 Changes in population composition and size: Population composition changes such as the 

expected increase in Hispanic populations, whose more pigmented skin is thought to 

decrease skin cancer risk, could have significant effects on future U.S. skin cancer rates. 

The above factors are either not easily quantified (e.g., human behavior; see Section 4.5.2.

Behavioral Uncertainties), or they are not central to the analysis (e.g., improvements in medical 

care), and are therefore not addressed further in this evaluation. 

Baseline Information 

It is possible that error is introduced to the AHEF’s results through misreporting of skin cancer 

incidence and mortality data (i.e., the AHEF’s baseline estimates). With disease data, under-, 

over-, and misreporting are not uncommon. For example, a studies have revealed that the 

incidence of CMM has been systematically under-reported in the SEER data (Clegg et al. 2002).8

The original SEER data indicated that CMM rates in white males were relatively flat or even 

falling (ranging from -11.1 percent to 3.3 percent annually after 1996). However, after adjusting 

for underreporting, CMM rates were actually found to have increased between 3.8 to 4.4 percent 

annually since 1981 (Clegg et al. 2002). Underreporting of CMM incidence is largely 

attributable to diagnosis in doctors’ offices, as opposed to hospitals and other treatment centers 

with better reporting accuracy. However, the AHEF results are not significantly affected by this 

underreporting because CMM incidence estimates in the AHEF are not based directly on SEER 

incidence data. Rather, because the AHEF estimates CMM incidence based on the ratio of SEER 

8

 There is little reason to believe that the SEER CMM incidence under-reporting extends to the NCI-based CMM 

mortality input information. 
Health Impacts from Ozone Changes

Draft report 6d-36

incidence data to projected annual mortality estimates, and because underreporting would affect 

both baseline and scenario estimates, the effects on incremental changes in CMM incidence 

would be second order. 

1.15. Summary of Quantified and Unquantified Sources of 

Uncertainty 

Of the major sources of uncertainty associated with the analysis, the total quantified uncertainty 

is roughly 32 percent, as summarized in Table 10. 

Table 10: Major Sources of Quantified Uncertainty 

Source of Uncertainty Quantified Uncertainty

Translating column ozone to ground-level UV 

TUV Model § 5 % 

Translating UV exposure to human health effects 

Uncertainty in BAFs 

 CMM mortality (3 %) 

 NMSC mortality (4–7 %) 

 NMSC incidence (30 %) 

Early life exposure versus whole life exposure 

 30 % 

§ 10 % 

Total ¥(52 + 302 + 102) § 32 % 

There are a variety of other unquantified sources of uncertainty that may contribute to overall 

analytical uncertainty associated with modeled ozone changes, changes in UV radiation, and 

changes in health effects. Table 11 summarizes the parameters that relate to these unquantified 

uncertainties. 
Health Impacts from Ozone Changes

Draft report 6d-37

Table 11: Factors with unknown contributions to uncertainty 

Factor Parameter 

Composition of future atmosphere 

Ability to model atmospheric processes accurately 

Response of tropospheric ozone to ozone layer recovery 

Effect of climate change 

Changes in ozone estimates 

Compliance with modeled NAAQS policy scenarios 

Change in UV radiation 

estimates

Long-term systematic changes in atmospheric opacity 

(e.g., clouds, aerosols, other pollutants) 

Changes in human UV exposure behavior 

Laboratory techniques and instrumentation for deriving 

an action spectrum 

Uncertainty with choice of action spectra 

Improvements in medical care/increased longevity 

Changes in socioeconomic factors (e.g., demographics 

and human behavioral changes) 

Baseline information (e.g., misreporting of skin cancer 

incidence and mortality data) 

Change in health effect 

estimates

Changes in population composition and size (including 

truncation of CMAQ model analysis area) 

Accurate prediction of future changes in human health effects would require consideration of the 

net effect of all the factors described above. This challenge is beyond the ability of the current 

state of atmospheric and epidemiological science. In addition, direct measurements (e.g., of 

future UV levels or skin cancer incidence) cannot attribute explicitly observed changes to any 

specific factor, unless that factor is far more important than all the others combined. However, 

the principle of superposition can be used to examine the NAAQS impact (i.e., one effect in 

isolation) under the assumption that the other factors remain constant at current conditions. The 

validity of this principle is based on the assumption that the NAAQS impacts are independent of 

the other factors (e.g., behavioral changes will occur regardless of whether a new NAAQS 

standard is in place). 

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McKinlay, A. F. and Diffey, B. L., A reference action spectrum for ultraviolet induced erythema in 

human skin. In Human Exposure to Ultraviolet Radiation: Risks and Regulations (Edited by W. R. 

Passchier and B. F. M. Bosnjakovic), pp. 83-87. Elsevier, Amsterdam, 1987. 

Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A Meta-Analysis.” 

Journal of Policy Analysis and Management 21(2):253-270. 

Nack, M. L. and A. E. S. Green, Influence of clouds, haze, and smog on the middle ultraviolet 

reaching the ground., 12, 2405-2415, 1974. 

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Estimates for July 1, 2000–July 1, 2006. Available online at: 

http://www.cdc.gov/nchs/about/major/dvs/popbridge/datadoc.htm 

Petropavlovskikh, I. Evaluation of Photodissociation Coefficient Calculations for Use in Atmospheric 

Chemical Models, Ph.D. thesis, U. of Brussels/NCAR Cooperative Thesis No. 159, National Center 

for Atmospheric Research, Boulder, 1995. 

Pitcher, H.M., and J.D. Longstreth (1991), "Melanoma mortality and exposure to ultraviolet radiation: 

An empirical relationship," Environment International 17:7-21. 

Toon, O. B., C. P. McKay, T. P. Ackerman, and K. Santhanam, J. Geophys. Res., 94, 16287, 1989. 

U.S. Bureau of Labor Statistics, Department of Labor. Occupational Employment Statistics (OES) 

Survey: May 2005 National OES Estimates. [cited 2007 Dec 26]. Available from: 

http://www.bls.gov/oes/.

U.S. Census Bureau (2000). Component Assumptions of the Resident Population by Age, Sex, Race, and 

Hispanic Origin. Available online at: http://www.census.gov/population/www/projections/natdetD5.html

U.S. Census Bureau (2005). State Interim Population Projections by Age and Sex: 2004–2030.

Available online at: http://www.census.gov/population/www/projections/projectionsagesex.html 

U.S. Environmental Protection Agency. EPA Guidelines for Preparing Economic Analyses. 

Washington, DC: Office of the Administrator, U.S. Environmental Protection Agency; 2001. Report 

No. 240-R-00-003. 

U.S. EPA (2003). Human Health Benefits of Stratospheric Ozone Protection. Global Programs Division, 

Office of Air and Radiation, U.S. EPA, Washington, DC. 

U.S. EPA and NASA (2001). Human Health Effects of Ozone Depletion From Stratospheric Aircraft. 

U.S. EPA, Washington, DC 

U.S. EPA (2008). Personal communication. 
Health Impacts from Ozone Changes

Draft report 6d-40

U.S. Environmental Protection Agency. Regulatory Impact Analysis: Protection of Stratospheric Ozone, 

E. Final Report. 1988. 

USSA, US Standard Atmosphere National Oceanic and Atmospheric Administration, National 

Aeronautics and Space Administration, United States Air Force, Washington D.C., 1976. 

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Estimates Throughout the World.” Journal of Risk and Uncertainty 27(1):5-76. 
Health Impacts from Ozone Changes

Draft report 6d-41Appendix A: Ground Level SCUP-h UV with 70 and 84 ppb by Day This Appendix will provide a series of maps showing ground level SCUP-h UV levels under 70 and 84 ppb NAAQS for ozone for several

specific days in the summer months – June 1, June 20, July 1, and August 1. Figure A-1: Ground Level SCUP UV, June 1; 70 ppb Scenario 
Health Impacts from Ozone Changes

Draft report 6d-42Figure A-2: Ground Level SCUP UV, June 1; 84 ppb Scenario 
Health Impacts from Ozone Changes

Draft report 6d-43Figure A-3: Ground Level SCUP UV, June 20; 70 ppb Scenario 
Health Impacts from Ozone Changes

Draft report 6d-44Figure A-4: Ground Level SCUP UV, June 20; 84 ppb Scenario 
Health Impacts from Ozone Changes

Draft report 6d-45Figure A-5: Ground Level SCUP UV, July 1; 70 ppb Scenario 
Health Impacts from Ozone Changes

Draft report 6d-46Figure A-6: Ground Level SCUP UV, July 1; 85 ppb Scenario 
Health Impacts from Ozone Changes

Draft report 6d-47Figure A-7: Ground Level SCUP UV, August 1; 70 ppb Scenario 
Health Impacts from Ozone Changes

Draft report 6d-48Figure A-8: Ground Level SCUP UV, August 1; 84 ppb Scenario 
Health Impacts from Ozone Changes

6d-47

Appendix B: Overview of Evaluation Methodology 

The schematic presented below provides a graphical summary of the method used in this 

evaluation. Atmospheric inputs to the process are listed along the left-hand side and the various 

process stages are described along the bottom. 
6d-47

Glossary

AHEF Atmospheric and Health Effects Framework 

BAF Biological Amplification Factor 

BAU Business as Usual 

BCC Basal Cell Carcinoma 

BLS Bureau of Labor Statistics 

CDC Centers for Disease Control and Prevention 

CMAQ Community Multiscale Air Quality 

CMM Cutaneous Malignant Melanoma 

DU Dobson Units 

EPA United States Environmental Protection Agency 

NAAQS National Ambient Air Quality Standard 

NCEE National Center for Environmental Economics 

NCI National Cancer Institute 

OAR Office of Air and Radiation 

SCC Squamous Cell Carcinoma 

SCUP-h Skin Cancer Utrecht-Philadelphia-human 

sza solar zenith angle 

TOCOR Task Order Contracting Representative 

USSA United States Standard Atmosphere 
7-1

Chapter 7: Conclusions and Implications of the Illustrative Benefit-Cost Analysis 

7.1 Synopsis 

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

nationally attaining alternative 0.075 ppm ozone standard. We have also considered 3 alternative 

standards incremental to attaining the current ozone standard: 0.079 ppm, 0.070 ppm, and 0.065 

ppm. This chapter summarizes these results and discusses the implications of the analysis. This 

analysis serves both to satisfy the requirements of E.O. 12866 and to provide the public with an 

estimate of the potential costs and benefits of attaining alternative ozone standards. The benefit 

and cost estimates below are calculated incremental to a 2020 baseline that incorporates air 

quality improvements achieved through the projected implementation of existing regulations and 

full attainment of the current standards for ozone and PM NAAQS (including the hypothetical 

control strategy developed in the RIA for full attainment of the PM NAAQS 15/35 promulgated 

in September, 2006). This RIA presents the costs and benefits of full attainment in all locations 

except two areas of California, which would not be required to meet an alternate primary 

standard until 2024. Estimates for these two areas are presented in Appendix 7b. This chapter 

provides additional context for the RIA analysis and a discussion of limitations and uncertainties. 

7.2 Results 

7.2.1 Presentation of Results 

For analytical purposes explained previously, we assume that almost all areas of the country will 

meet each alternate primary standard in 2020 through the development of technologies at least as 

effective as the hypothetical strategies used in this illustration. It is expected that benefits and 

costs will begin occurring earlier, as states begin implementing control measures to attain earlier 

or to show progress towards attainment. Some areas with very high levels of ozone do not plan to 

meet even the current standard until 2024; specifically, two California areas have adopted plans 

for post-2020 attainment as noted above. To perform an analysis beyond 2020 involves the use 

of highly speculative assumptions that introduce a much higher level of uncertainty to the results. 

Thus, in these locations, we provide estimates of the costs and benefits of fully attaining the 

alternate primary standards at a later date (2030) in Appendix 7b. It is important to note that, as a 

result, the 2020 results presented here do not represent a complete “full attainment” scenario for 

the entire nation. Due to the differences in attainment year and other assumptions underlying 

2020 analysis presented here and the 2030 analysis in the appendix, it is not appropriate to add 

the results together to get a national “full attainment” scenario. Finally, Appendix 6b contains a 

health-based cost effectiveness analysis that complements the results found below. 

The following two tables summarize the costs and benefits of attaining the alternate primary 

standards in 2020 for all places except South Coast and San Joaquin. For purposes of this 

analysis, we assume attainment by 2020 for all areas except San Joaquin Valley and South Coast 

air basins in California. The state has submitted plans to EPA for implementing the current ozone 

standard which propose that these two areas of California meet that standard by 2024. We have 

assumed for analytical purposes that the San Joaquin Valley and South Coast air basin would 
7-2

attain a new standard in 2030. There are many uncertainties associated with the year 2030 

analysis. Between 2020 and 2030 several federal air quality rules are likely to further reduce 

emissions of NOx and VOC, such as, but not limited to National rules for Diesel Locomotives, 

Diesel Marine Vessels, and Small Nonroad Gasoline Engines. These emission reductions should 

lower ambient levels of ozone in California between 2020 and 2030. Complete emissions 

inventories as well as air quality modeling were not available for this year 2030 analysis. Due to 

these limitations, it is not possible to adequately model 2030 air quality changes that are required 

to develop robust controls strategies with associated costs and benefits. In order to provide a 

rough approximation of the costs and benefits of attaining 0.075 ppm and the alternate standards 

in San Joaquin and South Coast air basins, we have relied on the available data. Available data 

includes emission inventories, which do not include any changes in stationary source emissions 

beyond 2020, and 2020 supplemental air quality modeling. This data was used to develop 

extrapolated costs and benefits of 2030 attainment. To view the complete analysis for the San 

Joaquin Valley and South Coast air basins see Appendix 7b. 

The costs presented here are based on reducing emissions primarily within 200 km of counties 

projected to fail to attain a particular standard. Changes in emissions translate into changes in 

ozone within and beyond the 200 km control areas. Air quality modeling is used to estimate 

where the changes in ozone resulting from emission changes takes place. Benefits are then 

estimated based on the modeled changes in ozone. 

Tables 7.1a-d present benefits and costs. Table 7.2 provides the estimated reductions in 

premature mortality and morbidity. 
7-3

Table 7.1a: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5

Co-Benefits: 0.075 ppm Standard in 2020 in Billions of 2006$* 

Ozone 

Mortality

Function or 

Assumption Reference 

Total Benefits** 

3% 7% 

Total

Costs***

7% 

Net Benefits 

3% 7% 

NMMAPS Bell et al. 2004 2.6 – 17 2.4 – 16 7.6 – 8.8 –6.3 – 9.5 –6.4 – 7.9 

Bell et al. 2005 3.8 – 18 3.6 – 17 7.6 – 8.8 –5.0 – 11 –5.2 – 9.1 

Meta-analysis Ito et al. 2005 4.4 – 19 4.3 – 17 7.6 – 8.8 –4.4 – 11 –4.5 – 9.8 

Levy et al. 2005 4.5 – 19 4.4 – 17 7.6 – 8.8 –4.3 – 11 –4.5 – 9.9 

Assumption that association is 

not causal**** 

2.0 – 17 1.8 – 15 7.6 – 8.8 –6.8 – 9 –7.0 – 7.4 

Table 7.1b: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5

Co-Benefits: 0.079 ppm Standard in 2020 in Billions of 2006$* 

Ozone 

Mortality

Function or 

Assumption Reference 

Total Benefits** 

3% 7% 

Total

Costs***

7% 

Net Benefits 

3% 7% 

NMMAPS Bell et al. 2004 1.4 – 11 1.3 – 9.9 2.4 – 2.9 –1.5 – 8.5 –1.6 – 7.5 

Bell et al. 2005 1.9 – 11 1.8 – 10 2.4 – 2.9 –1.1 – 8.9 –1.2 – 7.9 

Meta-analysis Ito et al. 2005 2.1 – 12 2.0 – 11 2.4 – 2.9 –0.83 – 9.2 –0.9 – 8.1 

Levy et al. 2005 2.1 – 12 2.0 – 11 2.4 – 2.9 –0.80 – 9.2 –0.9 – 8.2 

Assumption that association is 

not causal**** 

1.2 – 11 1.1 – 9.7 2.4 – 2.9 –1.7 – 8.3 –1.8 – 7.3 

Table 7.1c: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5

Co-Benefits: 0.070 ppm Standard in 2020 in Billions of 2006$* 

Ozone 

Mortality

Function or 

Assumption Reference 

Total Benefits** 

3% 7% 

Total

Costs***

7% 

Net Benefits 

3% 7% 

NMMAPS Bell et al. 2004 5.4 – 29 5.1 – 27 19 – 25 –20 – 10 –20 – 7.6 

Bell et al. 2005 9.7 – 34 9.5 – 31 19 – 25 –15 – 15 –16 – 12 

Meta-analysis Ito et al. 2005 12 – 36 12 – 33 19 – 25 –13 – 17 –13 – 14 

Levy et al. 2005 12 – 36 12 – 33 19 – 25 –13 – 17 –13 – 14 

Assumption that association is 

not causal**** 

3.5 – 27 3.2 – 25 19 – 25 –22 – 8 –22 – 5.7 
7-4

Table 7.1d: Estimated Range of Annual Monetized Costs and Ozone Benefits and PM2.5

Co-Benefits: 0.065 ppm Standard in 2020 in Billions of 2006$* 

Ozone 

Mortality

Function or 

Assumption Reference 

Total Benefits** 

3% 7% 

Total

Costs***

7% 

Net Benefits 

3% 7% 

NMMAPS Bell et al. 2004 9.0 – 46 8.6 – 42 32 – 44 –35 – 14 –35 – 9.7 

Bell et al. 2005 17 – 54 16 – 50 32 – 44 –27 – 22 –28 – 18 

Meta-analysis Ito et al. 2005 21 – 58 21 – 54 32 – 44 –23 – 26 –23 – 22 

Levy et al. 2005 21 – 58 21 – 54 32 – 44 –23 – 26 –23 – 22 

Assumption that association is 

not causal**** 

5.5 – 42 5.1 – 38 32 – 44 –39 – 10 –39 – 6.2 

*All estimates rounded to two significant figures. As such, they may not sum across columns. These estimates do 

not include visibility benefits. Only includes areas required to meet the current standard by 2020, does not include 

San Joaquin and South Coast areas in California. Appendix 7b shows the costs and benefits of attaining alternate 

standards in San Joaquin and South Coast California. 

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

premature mortality function to both the lower and upper ends of the range of the PM2.5 premature mortality 

functions characterized in the expert elicitation. Tables exclude unquantified and nonmonetized benefits. 

***Range reflects lower and upper bound cost estimates. Data for calculating costs at a 3% discount rate was not 

available for all sectors, and therefore total annualized costs at 3% are not presented here. Additionally, these

estimates assume a particular trajectory of aggressive technological change. An alternative storyline 

might hypothesize a much less optimistic technological trajectory, with increased costs, or with 

decreased benefits in 2020 due to a later attainment date. 

****Total includes ozone morbidity benefits and total PM co-benefits only. 

The individual row estimates for benefits reflect the variability in the functions available for 

estimating a major source of benefits—avoided ozone premature mortality. Ranges within the 

total benefits column reflect variability in the estimates of PM premature mortality co-benefits 

across the available effect estimates. Ranges in the total costs column reflect different 

assumptions about the extrapolation of costs. The low end of the range of net benefits is 

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

range is constructed by subtracting the lowest cost from the highest benefit. Following these 

tables is a discussion of the implications of these estimates, as well as the uncertainties and 

limitations that should be considered in interpreting the estimates. These tables do not include 

visibility benefits, which are estimated at $160 million/yr. 

Below are three graphs illustrating the net benefits of the selected and alternative standards. 

Figures 7.1 and 7.2 provide visual depictions of all available net benefit estimates. Figure 7.3 

contains a subset of estimates from the graphic above, displaying four combinations of ozone 

and PM benefits estimates with the two primary cost estimates for each alternative. These figures 

depict the richness and variability in the estimates of costs and benefits that may not be captured 

by the truncated summary tables above. 

Figure 7.1 displays all possible combinations of net benefits, utilizing the five different ozone 

functions, the fourteen different PM functions, and the two cost methods. Each of the 140 bars in 
7-5

each graph represents an independent and equally probability point estimate of net benefits under 

a certain combination of cost and benefit estimation methods. Thus it is not possible to infer the 

likelihood of any single net benefit estimate. The blue bars indicate combinations where the net 

benefits are negative, whereas the green bars indicate combinations where net benefits are 

positive. 

Figure 7.2 displays a close-up view of the range of net benefits for the selected standard. For the 

selected standard of 0.075 ppm, the median value of all of the independent point estimates is 

$0.8 billion, and the majority (64%) of the combinations indicate positive net benefits for this 

standard.

Figure 7.3 illustrates a subset of the net benefit estimates shown in Figure 7.1. While we treat 

each combination of costs and benefit estimates as being equally probable in our model, here we 

select a series of combinations of an ozone benefits estimate, a PM2.5 co-benefit estimate, and a 

cost estimate. Consistent with the distribution shown in Figure 7.1 above, the net benefits 

estimate is very sensitive to the choice of ozone mortality function, PM2.5 mortality function, and 

cost estimation approach. These intermediate combinations (which are discussed more 

completely in the benefits chapter) represent reference points: 

x Bell 2004 is the epidemiological study that underlies the ozone NAAQS risk assessment and Pope 

is the PM mortality function that was in several EPA RIAs, and 

x Bell 2005 is one of three ozone meta-analyses and Laden is a more recent PM epidemiological 

study that was used as an alternative in the PM NAAQS RIA

These figures show that for the intermediate points on the distribution the costs and benefits of 

the selected standard are slightly positive or slightly negative. The tails of the distribution, 

depending on the specific combination of assumptions, show that benefits are either significantly 

higher than costs (over $10 billion in net benefits) or that the benefits are significantly lower than

costs (roughly negative $6 billion in net benefits).
7-6

Figure 7.1: Range of Net Benefits (2006$) for All Standard Alternatives (7% discount) 

Range of Net Benefits Across Standard Alternatives*

* This graph shows all 140 combinations of the 5 different ozone mortality functions and assumptions, the 14 different PM mortality functions, and the 2 cost methods. All 

combinations are treated as independent and equally probable. These estimates do not include visibility benefits, which are estimated at $160 million/yr. Only includes areas 

required to meet the current standard by 2020, does not include San Joaquin and South Coast areas in California.

Costs exceed Benefits 

Costs exceed Benefits 

Costs exceedBenefits

Benefits exceed Costs Benefits exceed Costs 

Benefits exceed 

Costs

Benefits exceed 

Costs

Costs exceedBenefits
7-7

Figure 7.2: Range of Net Benefits (2006$) for Selected Standard 

* This graph shows all 140 combinations of the 5 different ozone mortality functions and assumptions, the 14 different PM mortality functions, and the 2 cost methods. All 

combinations are treated as independent and equally probable. For the selected standard of 0.075 ppm, the median value of all of the independent point estimates is $0.8 billion, and the majority (64%) of the combinations indicate positive net 

benefits for this standard. These estimates do not include visibility benefits, which are estimated at $160 million/yr. Only includes areas required to meet the current standard by 2020, does not include 

San Joaquin and South Coast areas in California.

*

Benefits exceed Costs 

Costs exceed Benefits 
7-8

 Figure 7.3: Range of Net Benefits for Select Combinations at 3% and 7%* 

*See Section 7.3 for discussion of the ozone and PM premature mortality estimates. See 

Section 5.2 for discussion of the hybrid and fixed cost estimates. 
7-9

Table 7.2: Summary of Total Number of Annual Ozone and PM2.5-Related Premature 

Mortalities and Premature Morbidity Avoided: 2020 National Benefits* 

Combined Estimate of Mortality

Standard Alternative and 

Model or Assumption 

Combined Range of Ozone Benefits and 

 PM2.5 Co-Benefits** 

 0.079 ppm 0.075 ppm 0.070 ppm 0.065 ppm 

NMMAPS Bell (2004) 140 – 1,300 260 – 2,000 560 – 3,500 940 – 5,500 

Bell (2005) 200 – 1,300 420 – 2,200 560 – 4,100 2,000 – 6,500 

Meta-Analysis Ito (2005) 230 – 1,300 500 – 2,300 1,100 – 4,300 2,500 – 7,000 

Levy (2005) 230 – 1,400 510 – 2,300 1,400 – 4,400 2,500 – 7,100 

Assumption that association is not 

causal 120 – 1,200 190 – 2,000 310 – 3,200 490 – 5,000 

Combined Estimate of Morbidity

Acute Myocardial Infarction 570 890 1,500 2,300 

Upper Respiratory Symptoms 3,100 4,900 8,100 13,000

Lower Respiratory Symptoms 4,200 6,700 11,000 17,000 

Chronic Bronchitis 240 380 630 970 

Acute Bronchitis 640 1,000 1,700 2,600 

Asthma Exacerbation 3,900 6,100 10,000 16,000 

Work Loss Days 28,000 43,000 72,000 110,000 

School Loss Days 72,000 200,000 640,000 1,100,000 

Hospital and ER Visits 890 1,900 5,100 9,400 

Minor Restricted Activity Days 340,000 750,000 2,100,000 3,500,000 

*Only includes areas required to meet the current standard by 2020, does not include San Joaquin Valley 

and South Coast air basins in California. Appendix 7b shows the costs and benefits of attaining 

alternate standards in San Joaquin and South Coast California. 

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

ozone premature mortality function to both the lower and upper ends of the range of the PM2.5 

premature mortality functions characterized in the expert elicitation described in Chapter 6. 

7.3 Discussion of Results 

7.3.1 Sensitivity of Changes to Costs and Benefits Under an Alternate Baseline Scenario 

Circular A-4 of the Office of Management and Budget’s (OMB) guidance under Executive Order 

12866 defines a no-action baseline as “what the world will be like if the proposed rule is not 

adopted”. The illustrative analysis in this RIA assesses the costs and benefits of moving from this 

“no-action” baseline to a suite of possible new standards. Circular A-4 states that the choice of 

an appropriate baseline may require consideration of a wide range of potential factors, including: 

x evolution of the market, 

x changes in external factors affecting expected benefits and costs, 

x changes in regulations promulgated by the agency or other government entities, and 

x the degree of compliance by regulated entities with other regulations. (OMB 2003) 
7-10

Circular A-4 also recommends that... 

When more than one baseline is reasonable and the choice of baseline will significantly 

affect estimated benefits and costs, you should consider measuring benefits and costs 

against alternative baselines. In doing so you can analyze the effects on benefits and costs 

of making different assumptions about other agencies‘ regulations, or the degree of 

compliance with your own existing rules. (OMB 2003)

This sensitivity analysis is intended to provide information about how the no-action baseline

would differ under different assumptions about mobile technologies. It also assesses nationally 

what the change would be to costs and benefits of all standards. Cost for all standards would 

increase by $1.81 billion and benefits for all standards would increase by $360 million to $3.1 

billion using 2006$ and a 3% discount rate, and $330 million to $2.8 billion when using a 7% 

discount rate.

The primary analysis baseline included some mobile controls characterized as additional 

technology changes in the onroad transportation sector. The application of these controls to the 

baseline assumes an optimistic future where reductions in emissions are achieved through the 

implementation nationally of cutting-edge mobile technologies. This sensitivity analysis 

estimates nationally how the costs and benefits of attaining 0.075 and the alternate primary 

standards would change if these technology changes were not implemented to meet the current 

standard, but were instead implemented as part of the strategy for attaining a new tighter 

standard.

In this sensitivity analysis scenario, 169,000 tons of NOx would not be reduced prior to the 

benefit/cost analysis. The alternate baseline or starting point for assessing the costs and benefits 

of the standard of 0.075 and the alternate primary standards would be higher across the board. 

Benefits from improved ozone and co-controlled PM2.5 air quality would increase. The costs of 

control would increase, as well The air quality improvements would be accomplished by 

including additional onroad transportation control measures in the control scenario, equivalent to 

the reductions ‘removed’ from the alternate baseline. The value in benefits of those 

improvements is estimated on a $/ton emissions reduced basis derived from the Locomotive 

Marine Diesel Rule.

It should be noted that these benefits are only a partial accounting of the total benefits associated 

with the mobile controls included in this sensitivity analysis. The sensitivity analysis does not 

estimate the benefits of other co-controlled emission reductions achieved by the mobile controls, 

such as VOCs (a precursor to ozone formation) and direct PM. The benefits presented here are 

therefore an underestimate of total benefits. Furthermore, these estimates are highly uncertain 

and are purely illustrative estimates of the potential costs and benefits of these mobile source 

1

 This cost could be offset in states that choose to replace existing periodic physical inspection of 

vehicles with remote onboard diagnostic device inspection in I/M programs. As explained in 

Appendix 9a, Remote OBD eliminates the need for periodic inspections of OBD-equipped 

vehicles by car owners. EPA estimates that the nationwide installation of Remote OBD would 

save the nation’s motorists about $16 to $22 billion in inspection and convenience costs over a 

10 year period. 
7-11

control strategies. We present them only as screening-level estimates to provide a bounding 

estimate of the costs and benefits of including these emissions controls in the ozone NAAQS 

baseline. As such, it would be inappropriate to apply these benefit per-ton estimates to other 

policy contexts, including other regulatory impact analyses. For more details on the baseline 

sensitivity analysis, please reference Appendix 7a. 

7.3.2 Relative Contribution of PM Benefits to Total Benefits 

Because of the relatively strong relationship between PM2.5 concentrations and premature 

mortality, PM co-benefits resulting from reductions in NOx emissions can make up a large 

fraction of total montetized benefits, depending on the specific PM mortality impact function 

used, and on the relative magnitude of ozone benefits, which is dependent on the specific ozone 

mortality function assumed. PM co-benefits based on daily average concentrations are calculated 

over the entire year, while ozone related benefits are calculated only during the summer ozone 

season. Because the control strategies evaluated in this RIA are assumed to operate year round 

rather than only during the ozone season, this means that PM benefits will accumulate during 

both the ozone season and the rest of the year. 

For the 0.075ppm alternative, PM2.5 co-benefits account for between 42 and 99 percent of total 

benefits. The lower end of the range assumes a combination of Levy et al. (2005) & Expert K. 

The upper end of the range assumes a combination of the assumption of no causality & Expert E. 

7.3.3 Challenges to Modeling Full Attainment in All Areas 

Because of relatively higher ozone levels in several large urban areas (Southern California, 

Chicago, Houston, and the Northeastern urban corridor) and because of limitations on the 

available database of currently known emissions control technologies, EPA recognized from the 

outset that known and reasonably anticipated emissions controls would likely be insufficient to 

bring some areas into attainment with either the current or alternative, more stringent ozone 

standards. Therefore, we designed this analysis in two stages: the first stage focused on analyzing 

the air quality improvements that could be achieved through application of documented, wellcharacterized emissions controls, and the costs and benefits associated with those controls. The 

second stage utilized extrapolation methods to estimate the costs and benefits of additional 

emissions reductions needed to bring all areas into full attainment with the standards. Clearly, the 

second stage analysis is a highly speculative exercise, because it is based on estimating emission 

reductions and air quality improvements without any information about the specific controls that 

would be available to do so.

The structure of the RIA reflects this 2-stage analytical approach. Separate chapters are provided 

for the cost, emissions and air quality impacts of modeled controls and for extrapolated costs and 

air quality impacts. We have used the information currently available to develop reasonable 

approximations of the costs and benefits of the extrapolated portion of the emissions reductions 

necessary to reach attainment. However, due to the high level of uncertainty in all aspects of the 

extrapolation, we judged it appropriate to provide separate estimates of the costs and benefits for 

the modeled stage and the extrapolated stage, as well as an overall estimate for reaching full 

attainment. There is a single chapter on benefits, because the methodology for estimating 

benefits does not change between stages. However, in that chapter, we again provide separate 
7-12

estimates of the benefits associated with the modeled control scenario which provides the 

foundation upon which benefits for full attainment are extrapolated for all four alternate primary 

standards (0.079, 0.075, 0.070, and 0.065 ppm). 

In both stages of the analysis, it should be recognized that all estimates of future costs and 

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

standards. Ultimately, states and urban areas will be responsible for developing and 

implementing emissions control programs to reach attainment of the ozone NAAQS, with the 

timing of attainment being determined by future decisions by states and EPA. Our estimates are 

intended to provide information on the general magnitude of the costs and benefits of alternative 

standards, rather than precise predictions of control measures, costs, or benefits. With these 

caveats, we expect that this analysis can provide a reasonable picture of the types of emissions 

controls that are currently available, the direct costs of those controls, the levels of emissions 

reductions that may be achieved with these controls, the air quality impact that can be expected 

to result from reducing emissions, and the public health benefits of reductions in ambient ozone 

levels. This analysis identifies those areas of the U.S. where our existing knowledge of control 

strategies is not sufficient to allow us to model attainment, and where additional data or research 

may be needed to develop strategies for attainment. 

The ozone NAAQS RIA provided great challenges when compared to previous RIAs. Why was 

this so? Primarily because as we tighten standards across multiple pollutants with overlapping 

precursors (e.g., the recent tightening of the PM2.5 standards), we move further down the list of 

cost-effective known and available controls. As we deplete our database of available choices of 

known controls, we are left with background emissions and remaining anthropogenic emissions 

for which we do not have enough knowledge to determine how and at what cost reductions can 

be achieved in the future when attainment would be required. With the more stringent NAAQS, 

more areas will need to find ways of reducing emissions, and as existing technologies are either 

inadequate to achieve desired reductions, or as the stock of low-cost existing technologies is 

depleted (causing the cost per ton of pollution reduced to increase), there will be pressure to 

develop new technologies to fill these needs. While we can speculate on what some of these 

technologies might look like based on current research and development and model programs 

being evaluated by states and localities, the actual technological path is highly uncertain.

Because of the lack of knowledge regarding the development of future emissions control 

technologies, a significant portion of our analysis is based on extrapolated tons generated from 

air quality sensitivity modeling necessary to reach full attainment of an alternative ozone 

NAAQS and the resulting costs and benefits. Studies indicate that it is not uncommon for preregulatory cost estimates to be higher than later estimates, in part because of inability to predict 

technological advances. Over longer time horizons, such as the time allowed for areas with high 

levels of ozone pollution to meet the ozone NAAQS, the opportunity for technical advances is 

greater (see Chapter 5 for details). 

Our estimates of costs of attainment in 2020 assume a particular trajectory of aggressive 

technological change. This trajectory leads to a particular level of emissions reductions and 

costs which we have estimated based on two different approaches, the fixed cost and hybrid 

approaches. An alternative storyline might hypothesize a much less optimistic technological 

change path, such that emissions reductions technologies for industrial sources would be more 
7-13

expensive or would be unavailable, so that emissions reductions from many smaller sources 

might be required for 2020 attainment, at a potentially greater cost per ton. Under this 

alternative storyline, two outcomes are hypothetically possible: Under one scenario, total costs 

associated with full attainment might be substantially higher. Under the second scenario, states 

may choose to take advantage of flexibility in the Clean Air Act to adopt plan with later 

attainment dates to allow for additional technologies to be developed and for existing programs 

like EPA’s Onroad Diesel, CAIR, Nonroad Diesel, and Locomotive and Marine rules to be fully 

implemented. If states were to submit plans with attainment dates beyond our 2020 analysis 

year, benefits would clearly be lower than we have estimated under our analytical storyline.

However, in this case, state decision makers, seeking to maximize economic efficiency, would 

not impose costs, including potential opportunity costs of not meeting their attainment date, 

when they exceed the expected health benefits that states would realize from meeting their 

modeled 2020 attainment date. In this case, upper bound costs are difficult to estimate because 

we do not have an estimate of the point where marginal costs are equal to marginal benefits plus 

the costs of nonattainment. 

Due to the nature of the extrapolation method for benefits (which focuses on reductions in ozone 

only at monitors that exceed the NAAQS), we generally understate the total benefits that would 

result from implementing additional emissions controls to fully attain the ozone NAAQS (i.e., 

assuming that the application of control strategies would result in ozone reductions both at 

nonattaining and attaining monitors). On the other hand, the possibility also exists that benefits 

are overestimated, both because it is possible that new technologies might not meet the 

specifications, development time lines, or cost estimates provided in this analysis and because 

the analysis assumes there are quantifiable benefits to reducing ambient ozone below each of the 

alternative standards. 

Estimated benefits and costs may reflect both bias and uncertainty. While we strive to avoid bias 

and characterize uncertainty to the extent possible, we note that in some cases, biased estimates 

were used due to data and/or methodological limitations. In these cases we have tried to identify

the direction and potential magnitude of the bias. These extrapolated benefits are uncertain, but 

the relative uncertainty compared to the modeled benefits is similar, once the underestimation 

bias has been taken into account. The emissions and cost extrapolations do not have a clear 

directional bias, however, they are much more uncertain relative to the modeled emissions and 

cost estimates, because of the lack of refined information about the relationship between 

emissions reductions and ozone changes in specific locations, and because of the difficulties in 

extrapolating costs well beyond the observed data. Of course, these benefits and costs will only 

be realized if the emission reductions projected in this extrapolated approach actually occur in 

the future. 

7.4 What Did We Learn through this Analysis? 

1. As in our analysis for the PM NAAQS RIA, in selecting controls, we focused more on 

the ozone cost-effectiveness (measured as $/ppb) than on the NOx or VOC costeffectiveness (measured as $/ton). When compared on a $/ton basis, many VOC controls 

appear cost-effective relative to NOx reductions (see Figures 5.1 and 5.2). However, the 

air quality sensitivity analysis showed that NOx reductions were more effective than 
7-14

VOC reductions in reducing Ozone concentrations except in urban areas which are VOC 

limited. In those locations, NOx reductions can actually result in increases in ozone, and 

as such, VOC reductions can be cost-effective relative to NOx on a $/ppb basis. 

2. Our knowledge of technologies that might achieve NOx and VOC reductions to attain 

alternative ozone NAAQS is insufficient. In some areas of the U.S., our existing controls 

database was insufficient to meet even the current ozone standard. After applying 

existing rules and the hypothetical controls applied in the PM NAAQS RIA across the 

nation we were able to identify controls that reduced overall NOx emissions nationwide 

by 6 percent and VOC by 2 percent. After these reductions, remaining emissions were 

still substantial, with over 9 million tons of NOx and 12 million tons of VOCs remaining 

nationwide. The large remaining inventories of NOx and VOC emissions suggests that 

additional control measures need to be developed, with appropriate consideration of the 

relative effectiveness of NOx and VOC in achieving ozone reductions.

3. Most of the overall reductions in NOx achieved in our illustrative control strategy were 

from nonEGU point sources. This was due to the fact that: 1) EGUs have been heavily 

controlled under the recent NOx SIP call and Clean Air Interstate Rules. The EGU 

program we included in our strategy for meeting the alternative ozone standards was not 

intended to achieve overall reductions in NOx beyond the CAIR caps, but instead to 

obtain NOx emission reductions in areas where they would more effectively reduce ozone 

concentrations in downwind nonattainment areas; and 2) mobile sources are already 

subject to ongoing emission reduction programs through the Tier 2 highway, onroad 

diesel and nonroad diesel rules. Thus, the opportunities for controlling NOx emissions 

were much greater in the nonEGU point sector than in the mobile or EGU sectors. 

However, the remaining uncontrolled NOx emissions from EGU and mobile sectors are 

still greater than nonEGU point sources2

, and additional reductions from these sectors 

may need to be considered in developing strategies to achieve full attainment. 

Exploratory analyses indicate that there are opportunities to achieve emission reductions 

from EGU peaking units on High Energy Demand Days (HEDD) with targeted strategies. 

Another area under analysis is the energy efficiency/clean distributed generation based 

emission reductions. 

4. Tightening the ozone standards can provide significant, but not uniform, health benefits.

The magnitude of the benefits is highly uncertain, and is not expected to be uniform 

throughout the nation. While our illustrative analyses showed that the benefits of 

implementing a tighter standard will likely result in reduced health impacts for the nation 

as a whole, the particular scenarios that we modeled show that some areas of the U.S. 

will see ozone (and PM2.5) levels increase. This is due to two reasons. The first reason is 

that the complexities involved in the atmospheric processes which govern the 

transformation of emissions into ozone result in some locations and times when reducing 

NOx emissions can actually increase ozone levels on some days (see Chapter 2 for more 

discussion). For most locations, these days are few relative to the days when ozone levels 

are decreased. However, in some urban areas the net effect of implementing NOx controls 

2

 NonEGU point source emission projections currently do not include estimated activity or 

economic growth. 
7-15

is to increase overall ozone levels and increase the health effects associated with ozone. 

This same phenomenon results in some areas also seeing increases in PM2.5 formation. 

The second reason is that the particular control strategy that we modeled for EGU sources 

is a modification to controls on sources within the overall cap and trade program in the 

Eastern U.S., established under the CAIR. As with any cap and trade program, changes in 

requirements at particular sources will result in shifts in power generation and emissions 

at other sources. Because under our chosen EGU control scenario the overall emissions 

cap for the CAIR region remains the same, some areas of the country will see a decrease 

in emissions, while others will see an increase. This is not unexpected, and is an essential 

element of the cap and trade program. Our goal in selecting the EGU control strategy was 

to focus the emissions reductions in areas likely to benefit the most from EGU NOx

emissions reductions, with emissions increases largely occurring in areas in attainment 

with the ozone NAAQS. However, this necessarily means that in those areas where 

emissions increases occurred, ozone levels would also be expected to increase, with 

commensurate increases in health impacts. On a national level, however, we expected 

overall health benefits of the modeled EGU strategy to be positive. In addition, our air 

quality modeling analysis showed that while ozone levels did increase in some areas, 

none of these increases resulted in an attaining area moving into nonattainment. 

Adjustments to our control scenario might achieve a pattern of reductions that achieves 

further air quality improvement. 

5. The 0.079 ppm and 0.075 ppm benefits estimates reflect special uncertainties. EPA

interpolated the benefits of the 0.070 ppm alternative to estimate the full attainment 

benefits of the less stringent 0.075 ppm and 0.079 ppm alternatives. These two 

interpolated benefits estimates are subject to two sources of uncertainty: (1) the 

uncertainties inherent in the original 0.070 ppm benefits analysis that was the basis for 

the interpolation; (2) the incremental uncertainty added through the interpolation 

approach. A chief source of uncertainty in the 0.070 ppm analysis was the use of the 

monitor rollback technique to estimate full attainment benefits. This approach likely 

understates the benefits that would result from state implementation of emissions controls 

because controls implemented to reduce ozone concentrations at the highest monitor 

would likely result in some reductions in ozone concentrations at nearby attaining 

monitors. Therefore, air quality improvements and resulting health benefits from full 

attainment would be more widespread than we estimated in our rollback analysis for the 

0.070 ppm alternative. The interpolation approach adds its own uncertainties. We made a 

reasonable judgment regarding the geographic area within which to interpolate benefits. 

However, this area may not match the ultimate geographic distribution of air quality 

improvements under a state-implemented control strategy to attain either the 0.075 ppm 

or 0.079 ppm alternative; this could result in an under- or over-estimate of benefits. The 

complexity of the various uncertainties makes it challenging to draw conclusions about 

their combined directional influence on the benefits estimates. 

6. Tightening the ozone standards can incur significant, but uncertain, costs. An 

engineering cost comparison demonstrates that the cost of the 0.070 ppm Ozone NAAQS 
7-16

known control costs ($3.3 billion per year3

 (2006$)) is only slightly lower than the Clean 

Air Interstate Rule (approximately $4 billion per year (2006$)) and roughly one and half 

to just over four times higher than the PM NAAQS 15/35 control strategy with annual 

engineering costs of $1.0 billion (2006$). It should be noted that for the Ozone NAAQS 

$3.3 billion represent the engineering cost of partial attainment. Full attainment using 

extrapolation methods are expected to increase total costs significantly. For example, 

total costs for the 0.070 ppm standard are significant at $19 to $25 billion (2006$). Yet, 

the magnitude and distribution of costs across sectors and areas is highly uncertain. Our 

estimates of costs for a set of modeled NOx and VOC controls comprise only a small part 

of the estimated costs of full attainment. These estimated costs for the modeled set of 

controls are still uncertain, but they are based on the best available information on control 

technologies, and have their basis in real, tested technologies. Estimating costs of full 

attainment required several techniques for extrapolation of the costs based upon the 

degree of difficulty to reach attainment Based on air quality supplemental modeling, 

there is clearly significant spatial variability in the relationship between local and 

regional NOx emission reductions and ozone levels across urban areas. For some 

locations, the extrapolation requires only a modest reduction beyond known controls. In 

these cases, the extrapolation is likely reasonable and not as prone to uncertainties. 

However, for areas where the bulk of air quality improvements were derived from 

extrapolated emissions reductions that go well beyond the area of the known controls, the 

uncertainty associated with costs increases. 

7. NonEGU point source controls dominate the estimated costs. These costs account for 

about 54 percent of modeled control costs. The average cost per ton for these reductions 

is approximately $3,800 (2006$) and the highest marginal cost for the last known control 

applied is $22,000 (2006$). Mobile source controls were also significant contributors to 

overall costs, accounting for over 23 percent of total modeled control costs. 

8. Costs and benefits will depend on implementation timeframes. States will ultimately 

select the specific timelines for implementation as part of their State Implementation 

Plans. To the extent that states seek classification as extreme nonattainment areas, the 

timeline for implementation may be extended beyond 2020, meaning that the amount of 

emissions reductions that will be required in 2020 will be less, and costs and benefits in 

2020 will also be lowered. 

7.5 References 

U.S. Office of Management and Budget. September 2003. Circular A-4, Regulatory Analysis 

Guidance sent to the Heads of Executive Agencies and Establishments. Washington, DC. 

http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf.

3

 Known controls include the modeled control strategy ($2.8 billion dollars per year (2006$)) as 

well as any supplemental and giveback controls applied (Appendix 5a.4).
7a-1

Appendix 7a: National Baseline Sensitivity Analysis 

7a.1 Synopsis 

Circular A-4 of the Office of Management and Budget’s (OMB) guidance under 

Executive Order 12866 defines a no-action baseline as “what the world will be like if the 

proposed rule is not adopted.” The illustrative analysis in this RIA assesses the costs and 

benefits of moving from this “no-action” baseline to a suite of possible new standards. 

Circular A-4 states that the choice of an appropriate baseline may require consideration 

of a wide range of potential factors, including: 

x evolution of the market, 

x changes in external factors affecting expected benefits and costs, 

x changes in regulations promulgated by the agency or other government entities, 

and

x the degree of compliance by regulated entities with other regulations. (OMB 

2003)

Circular A-4 also recommends that... 

When more than one baseline is reasonable and the choice of baseline will 

significantly affect estimated benefits and costs, you should consider measuring 

benefits and costs against alternative baselines. In doing so you can analyze the 

effects on benefits and costs of making different assumptions about other 

agencies’ regulations, or the degree of compliance with your own existing rules. 

(OMB, 2003) 

This sensitivity analysis is intended to provide information about how the no-action 

baseline would differ under different assumptions about mobile technologies. It also 

assesses nationally what the change would be to costs and benefits of a new standard of 

0.075 ppm and alternate primary standards of 0.079, 0.070, and 0.065 ppm. Cost for all 

standards would increase by $1.8 billion1 and benefits for all standards would increase by 

1

 This cost could be offset in states that choose to replace existing periodic physical 

inspection of vehicles with remote onboard diagnostic device inspection in Inspection 

and Maintenance programs. As explained in the Appendix to Chapter 3, Remote On 

Board Diagnostics (OBD) eliminates the need for periodic inspections of OBD-equipped 

vehicles by car owners. EPA estimates that the nationwide installation of Remote OBD 

would save the nation’s motorists about $16 to $22 billion in inspection and convenience 

costs over a 10 year period. Refer to the Appendix 5a for more details on the cost 

savings of remote OBD. 
7a-2

$360 million to $3.1 billion using 2006$ and a 3% discount rate, and $330 million to $2.8 

billion when using a 7% discount rate.2

The process of analysis of costs and benefits of attaining 0.075 and the alternate primary 

standard is, in some ways, an incremental building exercise. EPA begins with a Base 

Case (that includes promulgated rules, consent decrees, existing promulgated programs) 

and layers onto that illustrative control strategies from previous NAAQS RIA analyses, 

and finally, a simulated control strategy for attaining the current NAAQS in question (O3 

at 0.084 ppm). This is the point at which the “no-action baseline” is established.

Once the no-action baseline is established, EPA begins assessing the costs and benefits of 

moving to a tighter standard. EPA does not assess the costs and benefits of reaching the 

no-action baseline. Decisions about what is in the baseline affect the starting point of the 

assessment of costs and benefits, and thus affect the total incremental cost and benefit 

estimates. 

The primary analysis baseline included some mobile controls characterized as additional 

technology changes in the onroad transportation sector. The application of these controls 

to the baseline assumes an optimistic future where reductions in emissions are achieved 

through the implementation nationally of cutting-edge mobile technologies. This 

sensitivity analysis estimates nationally how the costs and benefits of attaining 0.075 and 

the alternate primary standards would change if these technology changes were not 

implemented to meet the current standard, but were instead implemented as part of the 

strategy for attaining a new tighter standard.

In this sensitivity analysis scenario, 169,000 tons of NOx would not be reduced prior to 

the benefit/cost analysis. The alternate baseline or starting point for assessing the costs 

and benefits of the standard of 0.075 and the alternate primary standards would be higher 

across the board. Benefits from improved ozone and co-controlled PM2.5 air quality 

would increase. The costs of control would increase, as well. The air quality 

improvements would be accomplished by including additional onroad transportation 

control measures in the control scenario, equivalent to the reductions ‘removed’ from the 

alternate baseline. The value in benefits of those improvements is estimated on a $/ton 

emissions reduced basis derived from the Locomotive Marine Diesel Rule. 

A description of the control measures added to the alternate control scenario for this 

sensitivity analysis follows. 

2

 These estimates are highly uncertain and are purely illustrative estimates of the potential 

costs and benefits of these mobile control strategies. We present them only as screeninglevel estimates to provide a bounding estimate of the costs and benefits of including these 

emissions controls in the ozone NAAQS control case for all standards. As such, it would 

be inappropriate to apply these benefit per-ton estimates to other policy contexts, 

including other regulatory impact analyses. Furthermore, the benefits only reflect a 

partial accounting of the total benefits associated with emission reductions related to the 

mobile controls included in this sensitivity analysis. 
7a-3

7a.2 Control Details 

7a.2.1 Improved Catalyst Design 

Improved Catalyst Design is a nationwide strategy that results in tailpipe emission 

reductions for new vehicles. The principle technologies used to achieve the Improved 

Catalyst Design standards are improved catalysts and increased use of electronically 

controlled air injection, reducing NOx and VOC emissions for new light duty gasoline 

vehicles.

We modeled a program that would achieve Bin 23

 emission levels (see Table 7a.1) for a 

program starting in 2013 and fully phased in by 2015. 

Phase-in Scenario Cars: 50% in 2013 Trucks: 100% in 2015 

 100% in 2014 

Table 7a.1: Emission Standards 

 NOx NMOG 

Bin 2 0.02 0.01 

Bin 5 (reference) 0.07 0.09 

Table 7a.2: Nationwide 2020 Tailpipe Emission Reductions (tons(%)) 

 2020 NOx 2020 HC 

Bin 2 87,705 (7%) 93,676 (6%) 

In comparison, Tier 2 reduced NOx by about 2.2 million tons in 2020 and nearly 3 

million tons in 2030, a 74% reduction. 

The above results are modeled relative to a Bin 5 baseline 

x Modeled difference in level of the standards: Bin 5 vs. Bin 2. 

x In reality, new standards would likely provide fewer benefits because many Bin 5 

vehicles are certified well below the standard, and many are in fact 50-state 

vehicles certified in California as ULEVs. 

x The costs are also modeled relative to a Bin 5 baseline, so the fact that many 

vehicles are actually cleaner today will also result in lower total costs. 

3

 For information on Bin emission levels, see: 

http://www.epa.gov/greenvehicles/summarychart.pdf 
7a-4

The technology is described as follows: 

x The technology relies on catalyst improvements—adding Rhodium, improved 

substrate/washcoat, and 900 cpsi density (all vehicles are assumed to need these 

changes)

x All vehicles are assumed to have close-coupled catalysts (1 or 2) 

x Increased use of electronically controlled air injection—100% implementation on 

everything except 4-cylinder engines 

Engineering costs for this program are estimated to be approximately $90-250 per vehicle 

for LDVs to LDT4s. 

x Based on an analysis similar to that done for Tier 2 and LEV-II, estimating 

penetration rates of emission control technologies, coupled with estimated costs 

for each technology.

x A significant driver of costs is the market price of Rhodium, which has varied in 

the last 5 years from below $1000 to above $6000 per Troy ounce. We used the 5-

year average of $2200. 

x These costs are the result of a preliminary analysis intended to achieve rough 

estimates. An in-depth bottom-up detailed cost analysis would need to be done to 

support an actual Improved Catalyst Design regulatory program.

x Most of the costs are for catalyst improvements—adding Rhodium, improved 

substrate/washcoat, and 900 cpsi density (all vehicles are assumed to need these 

changes)

Cost-effectiveness is $8,400 per ton for HC+NOx, and $17,500 per ton for NOx alone. 

Based on assumptions and variables in the analysis, these numbers can vary +/- 30%.

7a.2.2 Plug-In Hybrid Electric Vehicles 

Plug-In Hybrid Electric Vehicles (PHEVs) are very similar to Hybrid Electric Vehicles, 

but with three significant functional differences. The first is the addition of a means to 

charge the battery pack from an outside source of electricity (usually the electric grid). 

Second, a PHEV would have a larger battery pack with more energy storage, and a 

greater capability to be discharged. Finally, a PHEV would have a control system that 

allows the battery pack to be significantly depleted during normal operation. 

PHEVs offer a significant opportunity to replace petroleum used for transportation 

energy with domestically-produced electricity. The reduction in petroleum usage does, of 

course, depend on the amount of electric drive the vehicle is capable of under its duty 

cycle. PHEVs can lower localized emissions of criteria pollutants and air toxics 

especially in urban areas by operating on electric power. The emissions with this 

technology occur more from power generation outside the urban area at the power 
7a-5

generation plant rather than from the vehicle tailpipe, which may provide health benefits 

for residents of the more densely populated urban areas. Unlike most other oil-saving 

technologies, PHEVs also use existing infrastructure for fueling with gasoline and 

electricity so large investments in fueling infrastructure are not required. Since emissions 

from utilities are capped by existing programs, increases in power generation are 

generally not expected to impact attainment of air quality standards. 

For this analysis, we assumed that PHEVs would be available as passenger cars and as 

light trucks in all light truck weight classes by 2012. We assumed the following phase-in 

schedule for PHEVs (Table 7a.3) as a fraction of new vehicle sales for the period from 

2012 to 2020. This is an illustrative example of what could be feasible for the market 

penetration of PHEVs based on reductions that are needed for attainment of the revised 

ozone NAAQS and EPA’s internal expertise and judgment. Recent announcements by 

Toyota and General Motors that they plan to introduce PHEVs by 2010 provide 

additional support for these assumptions. 

Table 7a.3: Plug-In Hybrid Percentage of Total Sales of New Vehicles by Year 

Year Percentage of New Vehicles 

2012 1% 

2013 3% 

2014 7% 

2015 12% 

2016 18% 

2017 25% 

2018 30% 

2019 30% 

2020 30% 

We believe that the first consumers of PHEVs are likely to be the ones who can take best 

advantage of the PHEV while still operating on an overnight charge, i.e., urban and 

suburban residents with shorter commutes. We also assume continuing improvements in 

the range of PEHVs while operating on the overnight charge. For this analysis, we 

assumed that 70% of the VMT of PHEVs would be powered by the overnight charge 

rather than the vehicle engine and would have no direct exhaust emissions.4 We used that 

estimate, and the assumptions of vehicle sales given above, to adjust the travel fractions 

in EPA’s MOBILE6.2 emission model to account for the impact of reduced emissions for 

each model year of PHEVs. 

All light-duty gasoline vehicles and trucks: Affected SCC:

x 2201001000 Light Duty Gasoline Vehicles (LDGV), Total: All Road Types 

4

 Note that this assumption is different than the assumption used in the payback analysis 

used to determine costs of PHEVs in: Interim Report: New Powertrain Technologies and 

Their Projected Costs. U.S. E.P.A, October 2005. 

http://epa.gov/otaq/technology/420r05012.pdf. That study assumes that only 30% of 

PHEV VMT is powered by overnight charge, but still shows a positive payback potential. 
7a-6

x 2201020000 Light Duty Gasoline Trucks 1 (LDGT1), Total: All Road Types 

x 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Total: All Road Types 

Using the assumptions and methods described above, we estimated that HC emissions 

would be reduced by a range of 2.4% to 3.9% for passenger cars and light trucks 

(reductions vary by vehicle class). For NOx, we estimate reductions in the range of 1.6% 

to 2.5% for passenger cars and light trucks. 

For purposes of this RIA, we identified this measure as a no cost strategy i.e., $0/ton 

NOx. Plug-in hybrids have upfront capital costs, but these costs can be fully recovered by 

the fuel savings during the life of the vehicle. According to research conducted by the 

EPA, the potential consumer payback for the hypothetical PHEV midsize car and large 

SUV can be calculated from the modeled fuel economy and projected cost of the vehicle 

package5. Using a retail price markup factor of 1.26 from the projected cost, the 

additional cost of a PHEV midsize car over the base vehicle is $6,072. The large SUV is 

projected to cost $7,884 more than the comparable base vehicle. 

Appling these costs, the modeled fuel economy, and the standard economic assumptions 

used in this analysis of $2.50 per gallon gasoline price, 7% discount rate, and a 14 year 

life with annual VMT taken from the MOBILE6 model, results in consumer payback 

shown below. The payback period for the midsize car is 10.7 years, and 7.5 years for the 

large SUV.

Table 7a.4: Cost Effectiveness of PHEV Midsize Car and SUV 

 Midsize Car Large SUV 

Incremental Vehicle Price $5,646 $8,577 

Fuel Economy Gain 126% 92% 

Tailpipe CO2 decrease 56% 48% 

Discounted Fuel Savings $6,493 $11,751 

Discounted Electricity Cost $929 $1,346 

Discounted Brake Savings $376 $533 

Reduced Fueling Time Savings $395 $428 

Lifetime Savings $688 $2,789 

Payback Period 10.7 years 7.5 years 

Improved After-Market Catalysts 

Both EPA and CARB have standards in place for aftermarket catalysts. CARB now 

requires higher quality replacement catalysts for OBDII vehicles and is considering 

expanding that requirement to pre-OBDII vehicles as well. (Even though higher quality, 

these replacement catalysts do not constitute a new standard for the vehicle—they just 

bring it closer to its original as-new performance level.) CARB has done testing and has 

5

 Draft Revision to: Interim Report: New Powertrain Technologies and Their Projected 

Costs. U.S. E.P.A., October 2005. http://epa.gov/otaq/technology/420r05012.pdf 
7a-7

found that substantial emission reductions can be had by upgrading the quality of 

aftermarket catalysts. 

Applying the proposed aftermarket catalyst requirements to the national fleet would bring 

about nationwide reductions. According to the Manufacturers of Emission Controls 

Association (MECA), approximately 3 million aftermarket catalysts are sold each year. 

Estimated benefits are derived by comparing performance of existing replacement 

catalysts to that of the proposed catalysts. The difference is applied to the 3 million 

vehicles in the fleet that get aftermarket replacement catalysts. 

All light-duty gasoline vehicles and trucks: Affected SCC:

x 2201001000 Light Duty Gasoline Vehicles (LDGV), Total: All Road Types 

x 2201020000 Light Duty Gasoline Trucks 1 (LDGT1), Total: All Road Types 

x 2201040000 Light Duty Gasoline Trucks 2 (LDGT2), Total: All Road Types 

The table below (Table 7a.5) shows the emissions of the current aftermarket catalysts at 

25,000 miles and the performance of the OBDII-type aftermarket catalysts at the same 

mileage. The emission reductions from improved aftermarket catalysts are substantial, 

even for Tier 0 vehicles. 

Table 7a.5: Emissions of Aftermarket Catalysts 

Current Aftermarket 

Catalysts 

Proposed Aftermarket 

Catalysts Percent Reduction 

Category HC NOx HC NOx HC NOx 

Tier 0 0.600 2.4 0.1750 0.20 71% 92% 

Tier 1 0.600 2.4 0.1350 0.15 78% 94% 

TLEV 0.600 1.6 0.0580 0.20 90% 88% 

LEV 0.600 1.6 0.0250 0.05 96% 97% 

ULEV 0.450 1.2 0.0125 0.07 97% 94% 

LEV II LEV 0.450 1.2 0.0300 0.07 93% 94% 

LEV II ULEV 0.450 0.8 0.0125 0.07 97% 91% 

LEV II SULEV 0.375 0.8 0.0100 0.02 97% 98% 

Based on this information, if starting in 2010 we required the 3 million replacement 

catalysts installed each year to meet these standards, by 2020 there would be 15 million 

vehicles with such catalysts left in the fleet (the other 15 million are assumed to be 

scrapped during this time period). In 2020, the emission reductions we calculate are as 

follows: 
7a-8

Table 7a.6: Emission Reductions from Replacement Catalysts 

 HC NOx 

LDGV 3.5% 7.1% 

LDGT1 3.4% 7.0% 

LDGT2 3.6% 7.1% 

LDGT3 3.7% 7.2% 

LDGT4 3.9% 7.3% 

Both EPA and CARB have standards in place for aftermarket catalysts. CARB now 

requires higher quality replacement catalysts for OBDII vehicles and is considering 

expanding that requirement to pre-OBDII vehicles as well. (Even though higher quality, 

these replacement catalysts do not constitute a new standard for the vehicle—they just 

bring it closer to its original as-new performance level.) CARB has done testing and has 

found that substantial emission reductions can be had by upgrading the quality of 

aftermarket catalysts. 

Estimated engineering cost of the proposed replacement catalyst is $275, compared to 

approximately $100 for current replacement catalysts. These cost numbers are based on a 

review of prices published on the internet for OBDII and pre-OBDII replacement 

catalysts.6

Table 7a.7: CARB Cost Effectiveness for Improved After Market Catalysts 

Category NOx + HC NOx only HC only 

Tier 0 $1,423 $1,722 $8,187 

Tier 1 $1,353 $1,665 $7,238 

TLEV $1,889 $2,774 $5,917 

LEV $1,659 $2,378 $5,488 

ULEV $2,275 $3,329 $7,186 

LEV II LEV $2,090 $2,887 $7,567 

LEV II ULEV $2,736 $4,419 $7,186 

LEV II SULEV $2,782 $4,232 $8,120 

For the O3 RIA, we used an average cost of $3,700/ton NOx reduced. 

7a.2.3 Summary of Emission Reductions and Costs 

Total emission reductions and costs for the 3 control measures included in the alternative 

baseline analysis are presented in Table 7a.8: 

Table 7a.8: NOx Emission Reductions and Costs for Alternative Baseline Analysis 

Sector Control Measure Annual Emission Reductions (Tons) Total Cost (M$) 

Improved Catalyst Design 77,000 $1,600 Onroad 

Plug-In Hybrid 22,000 $--- 

6

 See: www.discountconverters.com and autopartswharehouse.com 
7a-9

Improved After-market Catalyst 70,000 $260 

TOTAL 169,000 $1,900 

7a.3 Methods for Estimation of Benefits ($/ton NOx reduced)

We estimated the monetary value of the 169,000 tons of mobile source NOx emission 

reductions in our baseline through a benefit per ton approach. Because NOx is both an 

ozone and PM2.5 precursor, these reductions will yield both reductions in the ambient 

levels of these pollutants as well as monetized benefits. Because these reductions occur in 

the mobile source sector, we decided to estimate total ozone benefits by imputing an 

ozone benefit per-ton estimate from the soon-to-be-promulgated Locomotive and Marine 

Diesel Rule. While this rule does not affect an identical set of sources, it is a reasonable 

representation of the benefits of emission reductions in mobile source emissions, which is 

the sector of interest. We have included these benefit per-ton calculations in a separate 

Technical Support Document (TSD). To estimate the PM2.5 co-benefits we used a set of 

benefit per-ton estimates consistent with the main analysis. The process for deriving these 

estimates can be found in the same TSD. 

The range of total combined ozone and PM2.5-related 2020 benefits associated with the 

emission reductions are between $360 million to $3.1 billion in 2006$ using a 3% 

discount rate. The lower-end of this range represents the combination of the assumption 

of no causality for ozone benefits and the Expert K PM mortality function for PM2.5 cobenefits (US EPA, 2006; US EPA, 2005). Using these same two combinations of 

studies, the range changes to between $330 million to $2.8 billion when using a 7% 

discount rate. It should be noted that these benefits are only a partial accounting of the 

total benefits associated with the mobile controls included in this sensitivity analysis. The 

sensitivity analysis does not estimate the benefits of other co-controlled emission 

reductions achieved by the mobile controls, such as VOCs (a precursor to ozone 

formation) and direct PM. The benefits presented here are therefore an underestimate of 

total benefits. Furthermore, these estimates are highly uncertain and are purely illustrative 

estimates of the potential costs and benefits of these mobile control strategies. We present 

them only as screening-level estimates to provide a bounding estimate of the costs and 

benefits of including these emissions controls in the ozone NAAQS control case for all 

standards. As such, it would be inappropriate to apply these benefit per-ton estimates to 

other policy contexts, including other regulatory impact analyses.

7a.4 References 

U.S. Office of Management and Budget. September 2003. Circular A-4, Regulatory 

Analysis Guidance sent to the Heads of Executive Agencies and Establishments. 

Washington, DC. http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf.

Draft Revision to: Interim Report: New Powertrain Technologies and Their Projected 

Costs. U.S. E.P.A. October 2005. http://epa.gov/otaq/technology/420r05012.pdf
7a-10

U.S. Environmental Protection Agency Science Advisory Board. 2005. EPA’s Review of 

the National Ambient Air Quality Standards for Particulate Matter (Second Draft PM 

Staff Paper, January 2005). EPA-SAB-CASAC-05-007. June.

U.S. Environmental Protection Agency, 2006. Air Quality Criteria for Ozone and 

Related Photochemical Oxidants Volume I of III. National Center for Environmental 

Assessment, Office of Research and Development, U.S. Environmental Protection 

Agency, Research Triangle Park, NC EPA 600/R-05/004aF 

Industrial Economics, Incorporated (IEc). April 2004. “Expert Judgment Assessment of 

the Relationship Between PM2.5 Exposure and Mortality.” Available at 

<www.epa.gov/ttn/ecas/benefits.html>. 
7b-1

Appendix 7b: Post 2020 Attainment Analysis 

7b.1 Uncertainties of Post 2020 Attainment Analysis 

Attainment dates will be determined in the future through the SIP process based on criteria in the 

CAA, future air quality data, and future rulemakings and are not knowable at this time. For 

analytical simplicity, and in keeping with the proposal analysis, we have chosen to use an 

analysis year of 2020 and generally assume attainment in that year. The exception is the San 

Joaquin and South Coast California areas where SIP submittals for the current standard show that 

they would have current standard attainment dates later than 2020. For these two areas in 

California, we are assuming a new standard attainment date of 2030. Estimates of the benefits 

and costs of attaining .075 and the alternate air quality standards for these two areas in 2030 are 

included below. 

There are many uncertainties associated with the year 2030 analysis. Between 2020 and 2030 

several onroad mobile and nonroad mobile source federal air quality rules are expected to further 

reduce emissions of NOx and VOC. Because mobile source rules affect new vehicles and 

equipment, they reduce inventories over a long period of time, as older vehicles and equipment 

are gradually scrapped and are replaced by new, regulated, lower-emitting vehicles and 

equipment. Among the onroad rules that contribute to the expected decline in mobile-source 

emissions between 2020 and 2030 are the Tier 2 Rule (light-duty cars and trucks) that went into 

effect in 2004, the 2007 Onroad Heavy-Duty Rule, and the Mobile Source Air Toxics Rule 

(“MSAT Final”, EPA, 2007a and EPA, 2007b) that goes into effect in 2011. Major nonroad rules 

also contribute to this decline, including the Locomotive Emissions Final Rulemaking (EPA, 

1998), the Locomotive-Marine Final Rule (EPA, 2007c), the Clean Air Nonroad Diesel Final 

Rule—Tier 4 (EPA, 2004), and Control of Air Pollution from Aircraft (EPA, 2005), among 

others. California has also regulated most of these same categories, often more stringently than 

the Federal government, resulting in substantial expected inventory decreases between 2020 and 

2030. The emission reductions from these programs should lower ambient levels of ozone 

between 2020 and 2030 across the state of California; this would facilitate the process of 

reaching attainment with a revised ozone standard in San Joaquin and South Coast by 2030. In 

addition, activity data beyond 2025 does not exist for aircraft data; therefore, 2030 aircraft 

emissions are held at year 2025 levels. 

However, the onroad mobile and nonroad mobile sectors are the only sectors projected to 2030 in 

our emission inventories; we do not have 2030 inventories for any stationary sources and 

therefore do not have a comprehensive estimate of Ozone precursor emissions around which to 

craft control strategies to determine costs. All stationary source emissions are held at year 2020 

levels because of uncertainties in how to project stationary emissions beyond 2020, and the lack 

of consistent projection methodologies beyond 2020 (e.g., the model used to create future year 

EGU emissions does not project to year 2030). Without a complete set of future 2030 emission 

inventories and control strategies, it is not possible to adequately model either baseline air 

quality or changes from control strategies. Without modeled changes in Ozone ambient 

concentrations, it is not possible to perform a sophisticated benefits analysis. In order to provide 

some idea of costs and benefits of attaining 0.075 and the alternate standards in San Joaquin and 
7b-2

South Coast air basins, we’ve relied on the available data. Due to the previously mentioned 

limitations, these analysis results do not capture potential economic growth, or changes in 

emissions beyond 2020. 

7b.2 Post 2020 Attainment Analysis 

7b.2.1 Air Quality and Emissions Targets 

We have used the 2020-based supplemental air quality modeling as a rough indicator of the 

percent control needed to meet the four alternate standards by 2030. Table 7b.1 shows the NOx 

targets estimated to get the Los Angeles and San Joaquin Valley areas into attainment by 2030. 

The supplemental air quality modeling showed (see Fig4-2d) that there was a sharp dropoff in 

ozone between the 60% and 90% additional NOx control cases. This may be due to the South 

Coast region transitioning from VOC-limited to NOx-limited conditions at this level of NOx 

emissions reductions. 

Table 7b.1: Estimated Percentage Reductions of NOx beyond the RIA Control Scenario 

Necessary to Meet the Various Ozone Standards in Los Angeles and the San Joaquin 

Valley in 2030

0.065 0.070 0.075 0.079 0.084

Los Angeles South Coast Air Basin, CA 0.122 > 90% 88% 83% 79% 75%

San Joaquin Valley, CA 0.096 76% 67% 59% 49% 37%

All 2030 Extrapolated Cost Areas

(NOx only)

2020 Design Value after RIA 

Control Scenario (ppm)

Additional local control needed to meet various 

standards

Table 7b.2 shows the NOx reductions needed to get the Los Angeles and San Joaquin Valley 

areas, into attainment by 2030. These reductions are based on the NOx targets for Los Angeles 

South Coast Air Basin in Table 7b.1. The higher reductions for Los Angeles compared to the San 

Joaquin Valley should enable all of California to attain, even after transport effects. Inventory 

reductions in 2030 from the onroad mobile, nonroad mobile, and aircraft/locomotive/commercial 

marine sources were credited to the estimates prior to creating the estimated extrapolated 

reductions needed in Table 7b.2. This table reveals that the majority of emission reductions are 

needed for these areas to reach the current ozone standard The reductions also include the Final 

Loco-Marine controls for 2030 (EPA, 2008). Overall, the loco-marine 2030 inventory contains 

about 120,000 fewer tons of NOx than the 2020 loco-marines inventory for the geographic area 

in California being analyzed.

Table 7b.2: Estimated Extrapolated Emissions Reductions of NOx Beyond the RIA Control 

Scenario Necessary to Meet the Various Ozone Standards in Los Angeles and the San 

Joaquin Valley in 2030 

0.065 0.070 0.075 0.079 0.084

Los Angeles-San Joaquin Valley, CAA

390,000 380,000 350,000 330,000 300,000

All 2030 Extrapolated Cost Areas

(NOx only)

Additional local emissions reductions [annual tons/year] needed to meet 

various standards (ppm)

a

 The Los Angeles South Coast Air Basin and San Joaquin Valley are included in the Sacramento Metro 

buffer.
7b-3

To calculate the incremental costs of attainment for the Los Angeles and San Joaquin Valley 

areas the reductions to meet the current standard are removed from the reductions needed for the 

various standards.1 Table 7b.3 contains the remaining 2030 emissions reductions needed for Los 

Angeles and the San Joaquin Valley.

Table 7b.3: Additional Local Emissions Reductions [annual tons/year] Needed to Meet 

Various Standards (ppm) Incremental to the Current Standard 

0.065A

0.070 0.075 0.079

Los Angeles-San Joaquin Valley, CAB

78,000 73,000 45,000 23,000

All 2030 Extrapolated Cost Areas

(NOx only)

Additional local emissions reductions [annual tons/year] 

needed to meet various standards (ppm) incremental to 

the current standard

a

 The 0.065 ppm emission reductions required are incremental to the reductions achieved by Sacramento 

in 2020 (see Table 4.6a).

b

 The Los Angeles South Coast Air Basin and San Joaquin Valley are included in the Sacramento Metro 

buffer.

The additional tons of reductions needed to attain the various standards may appear relatively 

low at first glance. It is important to note that these are incremental to progress made in San 

Joaquin and South Coast air basins toward attainment of the various standards in Sacramento. 

Additionally, between 2020 and 2030 other rules are expected to reduce emissions. Among 

these are the Tier 2 Rule (light-duty cars and trucks) that went into effect in 2004, the 2007 

Onroad Heavy-Duty Rule, and the Mobile Source Air Toxics Rule (“MSAT Final”, EPA, 2007a 

and EPA, 2007b) that goes into effect in 2011. Major nonroad rules also contribute to this 

decline, including the Locomotive Emissions Final Rulemaking (EPA, 1998), the LocomotiveMarine Final Rule (EPA, 2007c), the Clean Air Nonroad Diesel Final Rule—Tier 4 (EPA, 2004), 

and Control of Air Pollution from Aircraft (EPA, 2005), among others. California has also 

regulated most of these same categories, often more stringently than the Federal government, 

resulting in substantial expected inventory decreases between 2020 and 2030. A final factor that 

influences the total number of tons needed to attain in 2030 is the relatively greater effectiveness 

in California of NOx reductions that happen in the higher range of percentage reduced from the 

total NOx inventory. For example, a ton reduced when 80% of the total NOx inventory has 

already been controlled and reduced has a greater effect on ozone concentrations than a ton 

reduced when only 30% of the total NOx inventory has been thus far reduced.

7b.2.2 Extrapolated Costs 

The same two methodologies (fixed and hybrid) were used to estimate the costs of the additional 

local emission reductions for this 2030 analysis as were used in the national 2020 analysis. There 

is even more uncertainty associated with this analysis because there is more time for all types of 

change. Technological change, change in energy policy, changes in the sources of emissions are 

all expected to be more important for 2030 than for 2020. Because the South Coast and San 

1

 In one case, the 0.065 ppm alternate standard, the reductions for the Sacramento Metro area in 

2020 (again, includes Los Angeles and the San Joaquin Valley areas that do not require 

attainment in 2020) are greater than the reductions required to meet the current standard, and 

these reductions are the subtracted from the increment needed for California to meet the 0.065 

ppm standard.. 
7b-4

Joaquin Valley cost area has historically had a difficult time attaining air quality standards, it 

might be expected that the 2020 cost methodologies might underestimate the costs of the 

additional local emission reductions. However, the additional time for technological change 

between 2020 and 2030 might be expected to lower costs and result in an overestimate of costs 

from using the 2020 methodologies. The net bias of using the methodology employed for 2020 in 

the 2030 analysis is unknown. Additionally it is important to note, most of the air quality 

improvement needed for these areas is to reach the 0.08 ozone standard. The cost analysis below 

represents the incremental costs of attaining alternate ozone standards.

7b.2.2.1 Fixed Cost Approach Results

Table 7b.4 shows the estimated costs using the fixed cost methodology with a $15,000 a ton cost 

applied to the local emission reductions from Table 7b.3 

Table 7b.4: Extrapolated Cost to Meet Various Alternate Standards 

Using Fixed Cost Approach ($15,000/ton) a

All 2030 Extrapolated Cost Areas Fixed Cost Approach Extrapolated Cost (M 2006$). 

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

Los Angeles-San Joaquin Valley, CA $1,200 $1,100 $680 $340 

a

 All estimates rounded to two significant figures. 

7b.2.2.2 Hybrid Approach Results

Table 7b.6 shows the estimated costs using the fixed cost methodology with the hybrid approach 

using the average costs shown in Table 7b.5 applied to the local emission reductions from Table 

7b.3. The calculations for average cost used for Los Angeles and San Joaquin Valley use the 

same formulas presented in the Appendix 5a. There are large uncertainties when extrapolating to 

2030, therefore keeping the approach consistent yielded the average cost numbers seen in Table 

7b.5.

Table 7b.5: Hybrid Approach (Mid) Parameter Values for Various Standards a, b

All 2030 Extrapolated Cost Areas 

(NOx only) 

0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

R

 c

Average 

Cost/Ton

(2006$) 

R

 d

Average 

Cost/Ton 

(2006$) 

R

 d

Average 

Cost/Ton 

(2006$) 

R

 d

Average 

Cost/Ton 

(2006$) Los Angeles-San Joaquin Valley, CA 

1.42 $24,000 1.37 $24,000 1.27 $23,000 1.19 $ 23,000 

a

 All estimates rounded to two significant figures. 

b These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 

c Los Angeles-San Joaquin Valley, CA did not meet the baseline and therefore has an addition R to reach 

the current standard of 1.11. 

d Los Angeles-San Joaquin Valley, CA have an R of 1.13 for the 0.65 ppm standard only, due to the 

emission reductions from Sacramento being limiting.
7b-5

Table 7b.6: Extrapolated Cost to Meet Various Standards Using

Hybrid Approach (Mid) a, b

All 2030 Extrapolated Cost Areas Hybrid Approach Extrapolated Cost (M 2006$). 

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

Los Angeles-San Joaquin Valley, CA $1,900 $1,700 $1,000 $520 

a

 All estimates rounded to two significant figures. 

b

 These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 

7b.2.2.3 Sensitivity Analysis Results

Extrapolated cost ensitivity results for the fixed cost approach using a lower ($10,000/ton) and a 

higher ($20,000/ton) are presented in Table 7b.7 and Table 7b.8. Tables 7b.9 and 7b.11 present 

the average cost/ton for a higher and lower value of M (0.47 for the high and 0.12 for the low in 

place of the 0.24 used in the mid estimate). The total extrapolated costs for the Hybrid (Low) 

and Hybrid (High) are presented in Tables 7b.10 and 7b.12. 

Table 7b.7: Extrapolated Cost to Meet Various Alternate Standards 

Using Fixed Cost Approach ($10,000/ton) a

All 2030 Extrapolated Cost Areas Fixed Cost Approach Extrapolated Cost (M 2006$). 

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

Los Angeles-San Joaquin Valley, CA $780 $730 $450 $230 

a

 All estimates rounded to two significant figures. 

Table 7b.8: Extrapolated Cost to Meet Various Alternate Standards 

Using Fixed Cost Approach ($20,000/ton) a

All 2030 Extrapolated Cost Areas Fixed Cost Approach Extrapolated Cost (M 2006$). 

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

Los Angeles-San Joaquin Valley, CA $1,600 $1,500 $900 $450 

a

 All estimates rounded to two significant figures. 

Table 7b.9: Hybrid Approach (Low) Average Cost/Ton for Various Standards a, b

All 2030 Extrapolated Cost Areas Hybrid Approach Average Cost/Ton (2006$) 

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

Los Angeles-San Joaquin Valley, CA $19,000 $19,000 $19,000 $19,000 

a

 All estimates rounded to two significant figures. 

b These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date.
7b-6

Table 7b.10: Extrapolated Cost to Meet Various Standards Using 

Hybrid Approach (Low)a, b

All 2030 Extrapolated Cost Areas Hybrid Approach Extrapolated Cost (M 2006$) 

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

Los Angeles-San Joaquin Valley, CA $1,500 $1,400 $860 $430 

a

 All estimates rounded to two significant figures. 

b These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date.

Table 7b.11: Hybrid Approach (High) Average Cost/Ton for Various Standards a, b

All 2030 Extrapolated Cost Areas Hybrid Approach Average Cost/Ton (2006$) 

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

Los Angeles-San Joaquin Valley, CAA $33,000 $33,000 $32,000 $31,000 

a

 All estimates rounded to two significant figures. 

b These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 

Table 7b.12: Extrapolated Cost to Meet Various Standards Using 

Hybrid Approach (High)a, b

All 2030 Extrapolated Cost Areas Hybrid Approach Extrapolated Cost (M 2006$) 

(NOx only) 0.065 ppm 0.070 ppm 0.075 ppm 0.079 ppm 

Los Angeles-San Joaquin Valley, CAA $2,600 $2,400 $1,400 $700 

a

 All estimates rounded to two significant figures. 

b These estimates assume a particular trajectory of aggressive technological change. An alternative 

storyline might hypothesize a much less optimistic technological trajectory, with increased costs, or 

with decreased benefits in 2020 due to a later attainment date. 

7b.2.3 Benefits 

The Estimated Benefits of 2030 Attainment with Alternate Ozone Standards

The ozone analysis for San Joaquin and South Coast applies the same methods described 

elsewhere in the benefits chapter with the exception of: (1) the population year and (2) the year 

for the income growth adjustment. We updated both to 2030 to be consistent with the attainment 

year. Table 7b.13 below summarizes the updated benefits estimates. 
7b-7

Table 7b.13: Total Estimated Ozone Benefits of Attaining Alternate Ozone Standards in 

2030 in San Joaquin and South Coast (2006$) 

Mortality Function or Assumption Valuation Estimate

0.079 ppm

No Causality $13,000,000

Bell et al. (2004) $130,000,000

Bell et al. (2005) $380,000,000

Ito et al. (2005) $520,000,000

Levy et al. (2005) $530,000,000

0.075 ppm

No Causality $25,000,000

Bell et al. (2004) $250,000,000

Bell et al. (2005) $770,000,000

Ito et al. (2005) $1,000,000,000

Levy et al. (2005) $1,100,000,000

0.070 ppm

No Causality $64,000,000

Bell et al. (2004) $530,000,000

Bell et al. (2005) $1,600,000,000

Ito et al. (2005) $2,100,000,000

Levy et al. (2005) $2,200,000,000

0.065 ppm

No Causality $97,000,000

Bell et al. (2004) $800,000,000

Bell et al. (2005) $2,400,000,000

Ito et al. (2005) $3,100,000,000

Levy et al. (2005) $3,300,000,000

Estimating the Monetized Benefit per ton of PM2.5 Precursor Reduced

The NOx emission reductions necessary to reach attainment with an alternate revised standard 

would also reduce levels of PM2.5. The process for estimating the PM2.5 co-benefit for these two 

airsheds is very similar to the national co-benefit analysis described in the body of the RIA, with 

a single exception noted further below. The steps are as follows: 

1. Estimate the number of tons of NOx necessary to attain a baseline of 0.08 ppm. As noted 

above, Table 7b.2 includes the estimate of extrapolated NOx tons necessary to attain each 

standard alternative.

2. Calculate the benefits of attaining 0.08 ppm incremental to partial attainment of 0.08 

ppm. To estimate the benefits of fully attaining 0.08 ppm incremental to partial 

attainment of 0.08 ppm, the relevant benefit per ton is simply multiplied by the total 

number of extrapolated NOx tons abated. Note that this calculation step allows us to net 
7b-8

out the benefits of attaining the current standard, so that all subsequent benefits are 

incremental to the full attainment of 0.080 ppm. 

3. Calculate the benefits of partially attaining 0.070 ppm incremental to full attainment of 

0.08 ppm. Subtract the benefits of fully attaining 0.080 ppm incremental to the partial 

attainment of 0.08 ppm to create a new estimate of incremental 0.070 ppm partial 

attainment.

4. Calculate the PM2.5 benefits of fully attaining 0.070 ppm. Multiplying the estimate of the 

extrapolated NOx tons necessary to attain 0.070 ppm fully (Table 7b.3) produces an 

estimate of the incremental benefits of fully attaining 0.070 ppm incremental to partial 

attainment of 0.070 ppm. By adding this incremental benefit estimate to the benefits 

generated in step 3, we derived a total benefit estimate of attaining 0.070 ppm 

incremental to 0.08 ppm. 

5. Repeat step 4 to estimate the benefits of 0.075 ppm, 0.079 ppm and 0.065 ppm. Step 4 

may be repeated by substituting the NOx tons necessary to attain the selected alternative 

of 0.075 ppm and the remaining alternatives of 0.079 ppm and 0.065 ppm to produce an 

estimate of total PM2.5 co-benefits. 

Because this analysis estimates the PM2.5 co-benefits of full attainment for these two airsheds in 

2030, it was necessary to apply a PM2.5 benefit per ton estimate that incorporates this population 

year. The Technical Support Document for this RIA describes the technique for calculating a 

benefit per ton estimate that reflected population growth to 2030 (EPA, 2008). Table 7b.14 

below summarizes the total monetized PM2.5 co-benefits associated with attainment of each 

standard alternative. 

Total Estimate of Combined Ozone Benefits and PM2.5 Co-Benefits

Table 7b.15 summarizes the total combined benefits for each standard alternative. 

The following tables summarize the costs, benefits, and net benefits of attaining the alternate 

primary standards for South Coast and San Joaquin. 
7b-9

Table 7b.14: Total Estimated PM2.5 Co-Benefits of Attaining Alternate Ozone Standards 

in 2030 in San Joaquin and South Coast (2006$) 

ȱ ȱ Valuation Estimate

Mortality Function 3% Discount Rate 7% Discount Rate

0.079 ppm ȱ ȱ

ACS Study $120,000,000 $110,000,000

Harvard SixȬCity Study $260,000,000 $240,000,000

Expert K $54,000,000 $50,000,000

Expert E $450,000,000 $410,000,000

ȱ ȱ ȱ ȱ

0.075 ppm ȱ ȱ

ACS Study $240,000,000 $220,000,000

Harvard SixȬCity Study $530,000,000 $480,000,000

Expert K $110,000,000 $100,000,000

Expert E $900,000,000 $820,000,000

ȱ ȱ ȱ ȱ

0.070 ppm ȱ ȱ

ACS Study $400,000,000 $360,000,000

Harvard SixȬCity Study $860,000,000 $780,000,000

Expert K $180,000,000 $160,000,000

Expert E $1,500,000,000 $1,300,000,000

ȱ ȱ ȱ ȱ

0.065 ppm ȱ ȱ

ACS Study $420,000,000 $380,000,000

Harvard SixȬCity Study $910,000,000 $820,000,000

Expert K $190,000,000 $170,000,000

Expert E $1,600,000,000 $1,400,000,000
7b-10

Table 7b.15: Total Combined Ozone Benefits and PM2.5 Co-Benefits of Attaining Alternate Ozone Standards in 2030 in San 

Joaquin and South Coast (2006$, 3% Discount Rate) 

Alternative Standard and Model or Assumption

Bell et al. (2004) Bell et al. (2005) Ito et al. (2005) Levy et al. (2005)

Assumption of No

Causality

0.079 ppm Alternative

ACS Study $250,000,000 $510,000,000 $640,000,000 $650,000,000 $130,000,000

Harvard SixȬCity Study $390,000,000 $650,000,000 $780,000,000 $800,000,000 $280,000,000

Expert K $180,000,000 $440,000,000 $570,000,000 $590,000,000 $67,000,000

Expert E $580,000,000 $840,000,000 $970,000,000 $990,000,000 $460,000,000

0.075 ppm Selected Alternative

ACS Study $500,000,000 $1,000,000,000 $1,300,000,000 $1,300,000,000 $270,000,000

Harvard SixȬCity Study $780,000,000 $1,300,000,000 $1,600,000,000 $1,600,000,000 $550,000,000

Expert K $360,000,000 $870,000,000 $1,100,000,000 $1,200,000,000 $130,000,000

Expert E $1,200,000,000 $1,700,000,000 $1,900,000,000 $2,000,000,000 $930,000,000

0.070 ppm Alternative

ACS Study $930,000,000 $2,000,000,000 $2,500,000,000 $2,600,000,000 $460,000,000

Harvard SixȬCity Study $1,400,000,000 $2,400,000,000 $3,000,000,000 $3,100,000,000 $920,000,000

Expert K $710,000,000 $1,800,000,000 $2,300,000,000 $2,400,000,000 $240,000,000

Expert E $2,000,000,000 $3,100,000,000 $3,600,000,000 $3,700,000,000 $1,500,000,000

0.065 ppm Alternative

ACS Study $1,200,000,000 $2,800,000,000 $3,500,000,000 $3,700,000,000 $520,000,000

Harvard SixȬCity Study $1,700,000,000 $3,300,000,000 $4,000,000,000 $4,200,000,000 $1,000,000,000

Expert K $990,000,000 $2,600,000,000 $3,300,000,000 $3,500,000,000 $280,000,000

Expert E $2,400,000,000 $3,900,000,000 $4,700,000,000 $4,900,000,000 $1,700,000,000
7b-11

Table 7b.16: Total Combined Ozone Benefits and PM2.5 Co-Benefits of Attaining Alternate Ozone Standards in 2030 in San 

Joaquin and South Coast (2006$, 7% Discount Rate) 

Alternative Standard and Model or Assumption

Bell et al. (2004) Bell et al. (2005) Ito et al. (2005) Levy et al. (2005)

Assumption of No

Causality

0.079 ppm Alternative

ACS Study $240,000,000 $490,000,000 $630,000,000 $640,000,000 $120,000,000

Harvard SixȬCity Study $370,000,000 $620,000,000 $760,000,000 $770,000,000 $250,000,000

Expert K $180,000,000 $430,000,000 $570,000,000 $580,000,000 $63,000,000

Expert E $540,000,000 $790,000,000 $930,000,000 $940,000,000 $420,000,000

0.075 ppm Selected Alternative

ACS Study $480,000,000 $990,000,000 $1,300,000,000 $1,300,000,000 $250,000,000

Harvard SixȬCity Study $730,000,000 $1,200,000,000 $1,500,000,000 $1,500,000,000 $500,000,000

Expert K $350,000,000 $860,000,000 $1,100,000,000 $1,200,000,000 $130,000,000

Expert E $1,100,000,000 $1,600,000,000 $1,900,000,000 $1,900,000,000 $840,000,000

0.070 ppm Alternative

ACS Study

Harvard SixȬCity Study $1,300,000,000 $2,400,000,000 $2,900,000,000 $3,000,000,000 $840,000,000

Expert K $700,000,000 $1,700,000,000 $2,300,000,000 $2,400,000,000 $230,000,000

Expert E $1,900,000,000 $2,900,000,000 $3,500,000,000 $3,500,000,000 $1,400,000,000

0.065 ppm Alternative

ACS Study $1,200,000,000 $2,800,000,000 $3,500,000,000 $3,700,000,000 $480,000,000

Harvard SixȬCity Study $1,600,000,000 $3,200,000,000 $3,900,000,000 $4,100,000,000 $920,000,000

Expert K $970,000,000 $2,600,000,000 $3,300,000,000 $3,500,000,000 $270,000,000

Expert E $2,200,000,000 $3,800,000,000 $4,500,000,000 $4,700,000,000 $1,500,000,000
7b-12

Table 7b.17: Annual Monetized Costs and Benefits in 2030 in San Joaquin and South 

Coast: 0.075 ppm Standard in Billions of 2006$*

Total Benefits** 

Total

Costs** Net Benefits 

Mortality

Function or 

Assumption Reference 3% 7% 7% 3% 7% 

NMMAPS Bell et al. 2004 0.36 - 1.2 0.35 - 1.1 0.68 - 1.0 -0.64 - 0.48 -0.65 - 0.39 

 Bell et al. 2005 0.87 - 1.7 0.86 - 1.6 0.68 - 1.0 -0.13 - 0.99 -0.14 - 0.90 

Meta-analysis Ito et al. 2005 1.1 - 1.9 1.1 - 1.9 0.68 - 1.0 0.14 - 1.26 0.13 - 1.2 

 Levy et al. 2005 1.2 - 2.0 1.2 - 1.9 0.68 - 1.0 0.17 - 1.29 0.16 - 1.20 

Assumption that association is not 

causal*** 0.13 - 0.93 0.13 - 0.84 0.68 - 1.0 -0.87 - 0.25 -0.87 - 0.16 

Table7b.18: Annual Monetized Costs and Benefits in 2030 in San Joaquin and South 

Coast: 0.079 ppm Standard in Billions of 2006$* 

Total Benefits** 

Total

Costs** Net Benefits 

Mortality

Function or 

Assumption Reference 3% 7% 7% 3% 7% 

NMMAPS Bell et al. 2004 0.18 - 0.58 0.18 - 0.54 0.34 - 0.52 -0.34 - 0.24 -0.34 - 0.20 

 Bell et al. 2005 0.44 - 0.84 0.43 - 0.79 0.34 - 0.52 -0.08 - 0.50 -0.09 - 0.45 

Meta-analysis Ito et al. 2005 0.57 - 0.97 0.57 - 0.93 0.34 - 0.52 0.05 - 0.63 0.05 - 0.59 

 Levy et al. 2005 0.59 - 0.99 0.58 - 0.94 0.34 - 0.52 0.07 - 0.65 0.06 - 0.60 

Assumption that association is not 

causal*** 0.07 - 0.46 0.06 - 0.42 0.34 - 0.52 -0.45 - 0.12 -0.46 - 0.08 

Table 7b.19: Annual Monetized Costs and Benefits in 2030 in San Joaquin and South 

Coast: 0.070 ppm Standard in Billions of 2006$* 

Total Benefits** 

Total

Costs** Net Benefits 

Mortality

Function or 

Assumption Reference 3% 7% 7% 3% 7% 

NMMAPS Bell et al. 2004 0.71 - 2.0 0.70 - 1.9 1.1 - 1.7 -0.99 - 0.90 -1.0 - 0.76 

 Bell et al. 2005 1.8 - 3.1 1.7 - 2.9 1.1 - 1.7 0.06 - 2.0 0.05 - 1.8 

Meta-analysis Ito et al. 2005 2.3 - 3.6 2.3 - 3.5 1.1 - 1.7 0.62 - 2.5 0.60 - 2.4 

 Levy et al. 2005 2.4 - 3.7 2.4 - 3.5 1.1 - 1.7 0.67 - 2.6 0.66 - 2.4 

Assumption that association is not 

causal*** 0.24 - 1.5 0.23 - 1.4 1.1 - 1.7 -1.5 - 0.43 -1.5 - 0.29 
7b-13

Table 7b.20: Annual Monetized Costs and Benefits in 2030 in San Joaquin and South 

Coast: 0.065 ppm Standard in Billions of 2006$* 

Total Benefits** 

Total

Costs** Net Benefits 

Mortality

Function or 

Assumption Reference 3% 7% 7% 3% 7% 

NMMAPS Bell et al. 2004 0.99 - 2.4 0.97 - 2.2 1.2 - 1.9 -0.91 - 1.2 -0.93 - 1.0 

 Bell et al. 2005 2.6 - 3.9 2.6 - 3.8 1.2 - 1.9 0.67 - 2.7 0.65 - 2.6 

Meta-analysis Ito et al. 2005 3.3 - 4.7 3.3 - 4.5 1.2 - 1.9 1.4 - 3.5 1.4 - 3.3 

 Levy et al. 2005 3.5 - 4.9 3.5 - 4.7 1.2 - 1.9 1.6 - 3.7 1.6 - 3.5 

Assumption that association is not 

causal*** 0.28 - 1.7 0.27 - 1.5 1.2 - 1.9 -1.6 - 0.46 -1.63 - 0.31 

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

ozone premature mortality function to both the lower and upper ends of the range of the PM2.5 

premature mortality functions characterized in the expert elicitation. Tables exclude unquantified and 

nonmonetized benefits. All estimates rounded to two significant figures, so totals may not sum across 

columns. 

**Range reflects lower and upper bound cost estimates. Data for calculating costs at a 3% discount rate 

was not available for all sectors, and therefore total annualized costs at 3% are not presented here. 

***Total includes ozone morbidity benefits only. 

7b.3 References 

U.S. Environmental Protection Agency (EPA). 1998. Locomotive Emission Standards: 

Regulatory Support Document, U.S. Environmental Protection Agency, Office of Transportation 

and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105. Available at 

http://www.epa.gov/otaq/regs/nonroad/locomotv/frm/locorsd.pdf 

U.S. Environmental Protection Agency (EPA). 2004. Final Regulatory Analysis: Control of 

Emissions from Nonroad Diesel Engines, U.S. Environmental Protection Agency, Office of 

Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105, 

EPA420-R-04-007, May 2004. Available at http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf.

U.S. Environmental Protection Agency (EPA). 2005. Emission Standards and Test Procedures 

for Aircraft and Aircraft Engines: Summary and Analysis of Comments, U.S. Environmental 

Protection Agency, Office of Transportation and Air Quality, Assessment and Standards Division, 

Ann Arbor, MI 48105, EPA420-R-05-004, November, 2005. Available at 

http://www.epa.gov/otaq/regs/nonroad/aviation/420r05004.pdf.

U.S. Environmental Protection Agency (EPA). 2007a. Regulatory Impact Analysis for Final 

Rule: Control of Hazardous Air Pollutants from Mobile Sources, U.S. Environmental Protection 

Agency, Office of Transportation and Air Quality, Assessment and Standards Division, Ann

Arbor, MI 48105, EPA420-R-07-002, February 2007. Available at 

http://www.epa.gov/otaq/regs/toxics/420r07002.pdf.
7b-14

U.S. Environmental Protection Agency (EPA). 2007b. National Scale Modeling for the Final 

Mobile Source Air Toxics Rule, Office of Air Quality Planning and Standards, Emissions 

Analysis and Monitoring Division, Research Triangle Park, NC 27711, EPA 454/R-07-002, 

February 2007. Available at http://www.epa.gov/otaq/regs/toxics/454r07002.pdf.

U.S. Environmental Protection Agency (EPA). 2007c. Draft Regulatory Impact Analysis: 

Control of Emissions of Air Pollution from Locomotive Engines and Marine CompressionIgnition Engines Less than 30 Liters per Cylinder, Chapter 3: Emission Inventory, U.S. 

Environmental Protection Agency, Office of Transportation and Air Quality, Assessment and 

Standards Division, Ann Arbor, MI 48105. EPA420-D-07-001, March 2007. Available at 

http://www.epa.gov/otaq/regs/nonroad/420d07001chp3.pdf.

U.S. Environmental Projections Agency (EPA). 2008. Technical Support Document: Preparation 

of Emissions Inventories For the 2002-based Platform, Version 3, Criteria Air Pollutants, 

USEPA, January, 2008. 
8-1

Chapter 8: Statutory and Executive Order Impact Analyses 

Synopsis

This chapter summarizes the Statutory and Executive Order (EO) impact analyses relevant for 

the ozone NAAQS RIA. In general, because this RIA analyzes an illustrative attainment strategy 

to meet the revised NAAQS, and because States will ultimately implement the new NAAQS, the 

Statutory and Executive Orders below did not require additional analysis. For each EO and 

Statutory requirement we describe both the requirements and the way in which the RIA 

addresses these requirements. Further analyses of the NAAQS proposal and its impact on these 

statutory and executive orders are found in section VII of the NAAQS preamble. 

8.1 Executive Order 12866: Regulatory Planning and Review 

Under section 3(f)(1) of Executive Order (EO) 12866 (58 FR 51735, October 4, 1993), the ozone 

NAAQS action is an “economically significant regulatory action” because it is likely to have an 

annual effect on the economy of $100 million or more. Accordingly, EPA prepared this 

regulatory impact analysis (RIA) of the potential costs and benefits associated with this action. 

The RIA estimates the costs and monetized human health benefits of attaining three alternative 

ozone NAAQS nationwide. Specifically, the RIA examines the alternatives of 0.079 0.075 ppm, 

0.070 ppm, and 0.065 ppm. The RIA contains illustrative analyses that consider a limited number 

of emissions control scenarios that States and Regional Planning Organizations might implement 

to achieve these alternative ozone NAAQS. However, the Clean Air Act (CAA) and judicial 

decisions make clear that the economic and technical feasibility of attaining ambient standards 

are not to be considered in setting or revising NAAQS, although such factors may be considered 

in the development of State plans to implement the standards. Accordingly, although an RIA has 

been prepared, the results of the RIA have not been considered in issuing this rule. 

8.2 Paperwork Reduction Act 

This RIA does not impose an information collection burden under the provisions of the 

Paperwork Reduction Act, 44 U.S.C. 3501 et seq. There are no information collection 

requirements directly associated with revisions to a NAAQS under section 109 of the CAA. 

Burden is defined as the total time, effort, or financial resources expended by persons to 

generate, maintain, retain, or disclose or provide information to or for a Federal agency. This 

includes the time needed to review instructions; develop, acquire, install, and utilize technology 

and systems for the purposes of collecting, validating, and verifying information, processing and 

maintaining information, and disclosing and providing information; adjust the existing ways to 

comply with any previously applicable instructions and requirements; train personnel to be able 

to respond to a collection of information; search data sources; complete and review the collection 

of information; and transmit or otherwise disclose the information. 
8-2

An agency may not conduct or sponsor information collection, and a person is not required to 

respond to a collection of information unless it displays a currently valid OMB control number. 

The OMB control numbers for EPA’s regulations in 40 CFR are listed in 40 CFR part 9. 

8.3 Regulatory Flexibility Act 

The EPA has determined that it is not necessary to prepare a regulatory flexibility analysis in 

connection with this RIA. For purposes of assessing the impacts of today’s rule on small entities, 

small entity is defined as: (1) a small business that is a small industrial entity as defined by the 

Small Business Administration’s (SBA) regulations at 13 CFR 121.201; (2) a small 

governmental jurisdiction that is a government of a city, county, town, school district or special 

district with a population of less than 50,000; and (3) a small organization that is any not-forprofit enterprise which is independently owned and operated and is not dominant in its field. 

After considering the economic impacts of today’s rule on small entities, EPA has concluded that 

this action will not have a significant economic impact on a substantial number of small entities. 

This rule will not impose any requirements on small entities. This rule establishes national 

standards for allowable concentrations of ozone in ambient air, as required by section 109 of the 

CAA. See also ATA I at 1044-45 (NAAQS do not have significant impacts upon small entities 

because NAAQS themselves impose no regulations upon small entities). 

8.4 Unfunded Mandates Reform Act 

Title II of the Unfunded Mandates Reform Act of 1995 (UMRA), Public Law 104-4, establishes 

requirements for Federal agencies to assess the effects of their regulatory actions on State, local, 

and Tribal governments and the private sector. Under section 202 of the UMRA, EPA generally 

must prepare a written statement, including a cost-benefit analysis, for proposed and final rules 

with “Federal mandates” that may result in expenditures to State, local, and Tribal governments, 

in the aggregate, or to the private sector, of $100 million or more in any 1 year. Before 

promulgating an EPA rule for which a written statement is needed, section 205 of the UMRA 

generally requires EPA to identify and consider a reasonable number of regulatory alternatives 

and adopt the least costly, most cost-effective or least burdensome alternative that achieves the 

objectives of the rule. The provisions of section 205 do not apply when they are inconsistent with 

applicable law. Moreover, section 205 allows EPA to adopt an alternative other than the least 

costly, most cost-effective or least burdensome alternative if the Administrator publishes with 

the final rule an explanation why that alternative was not adopted. Before EPA establishes any 

regulatory requirements that may significantly or uniquely affect small governments, including 

Tribal governments, it must have developed under section 203 of the UMRA a small government 

agency plan. The plan must provide for notifying potentially affected small governments, 

enabling officials of affected small governments to have meaningful and timely input in the 

development of EPA regulatory proposals with significant Federal intergovernmental mandates, 

and informing, educating, and advising small governments on compliance with the regulatory 

requirements. 

This proposal contains no Federal mandates (under the regulatory provisions of Title II of the 

UMRA) for State, local, or Tribal governments or the private sector. The rule imposes no new 
8-3

expenditure or enforceable duty on any State, local or Tribal governments or the private sector, 

and EPA has determined that this rule contains no regulatory requirements that might 

significantly or uniquely affect small governments. Furthermore, as indicated previously, in 

setting a NAAQS, EPA cannot consider the economic or technological feasibility of attaining 

ambient air quality standards, although such factors may be considered to a degree in the 

development of State plans to implement the standards. See also ATA I at 1043 (noting that 

because EPA is precluded from considering costs of implementation in establishing NAAQS, 

preparation of a Regulatory Impact Analysis pursuant to the Unfunded Mandates Reform Act 

would not furnish any information which the court could consider in reviewing the NAAQS). 

Accordingly, EPA has determined that the provisions of sections 202, 203, and 205 of the 

UMRA do not apply to this final decision. The EPA acknowledges, however, that any 

corresponding revisions to associated SIP requirements and air quality surveillance requirements, 

40 CFR part 51 and 40 CFR part 58, respectively, might result in such effects. Accordingly, EPA 

has addressed unfunded mandates in the notice that announces the revisions to 40 CFR part 58, 

and will, as appropriate, address unfunded mandates when it proposes any revisions to 40 CFR 

part 51. 

8.5 Executive Order 13132: Federalism 

Executive Order 13132, entitled “Federalism” (64 FR 43255, August 10, 1999), requires EPA to 

develop an accountable process to ensure “meaningful and timely input by State and local 

officials in the development of regulatory policies that have federalism implications.” “Policies 

that have federalism implications” is defined in the Executive Order to include regulations that 

have “substantial direct effects on the States, on the relationship between the national 

government and the States, or on the distribution of power and responsibilities among the various 

levels of government.” 

At the time of the proposal, EPA concluded that the proposed rule would not have substantial 

direct effects on the States, on the relationship between the national government and the States, 

or on the distribution of power and responsibilities among the various levels of government, as 

specified in Executive Order 13132. 

8.6 Executive Order 13175: Consultation and Coordination with Indian Tribal 

Governments 

Executive Order 13175, entitled “Consultation and Coordination with Indian Tribal 

Governments” (65 FR 67249, November 9, 2000), requires EPA to develop an accountable 

process to ensure “meaningful and timely input by tribal officials in the development of 

regulatory policies that have tribal implications.” This rule concerns the establishment of ozone 

NAAQS. The Tribal Authority Rule gives Tribes the opportunity to develop and implement 

CAA programs such as the ozone NAAQS, but it leaves to the discretion of the Tribe whether to 

develop these programs and which programs, or appropriate elements of a program, they will 

adopt.
8-4

This rule does not have Tribal implications, as specified in Executive Order 13175. It does not 

have a substantial direct effect on one or more Indian Tribes, since Tribes are not obligated to 

adopt or implement any NAAQS. Thus, Executive Order 13175 does not apply to this rule.. 

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

Safety Risks 

Executive Order 13045, “Protection of Children from Environmental Health Risks and Safety 

Risks” (62 FR 19885, April 23, 1997) applies to any rule that: (1) is determined to be 

“economically significant” as defined under Executive Order 12866, and (2) concerns an 

environmental health or safety risk that EPA has reason to believe may have a disproportionate 

effect on children. If the regulatory action meets both criteria, the Agency must evaluate the 

environmental health or safety effects of the rule on children, and explain why the regulation is 

preferable to other potentially effective and reasonably feasible alternatives considered by the 

Agency. This rule is subject to Executive Order 13045 because it is an economically significant 

regulatory action as defined by Executive Order 12866, and we believe that the environmental 

health risk addressed by this action may have a disproportionate effect on children.

The NAAQS constitute uniform, national standards for ozone pollution; these standards are 

designed to protect public health with an adequate margin of safety, as required by CAA section 

109. However, the protection offered by these standards may be especially important for children 

because children, along with other sensitive population subgroups such as the elderly and people 

with existing heart or lung disease, are potentially susceptible to health effects resulting from 

ozone exposure. Because children are considered a potentially susceptible population, we have 

carefully evaluated the environmental health effects of exposure to ozone pollution to this subpopulation. These effects and the size of the population affected are summarized in section 8.7 of 

the Criteria Document and section 3.6 of the Staff Paper, and the results of our evaluation of the 

effects of ozone pollution on children are discussed in sections II.A-C of the NAAQS proposal 

preamble. 

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

Distribution or Use 

Executive Order 13211, “Actions Concerning Regulations That Significantly Affect Energy 

Supply, Distribution, or Use” (66 FR 28355 (May 22, 2001), requires EPA to prepare and submit 

a Statement of Energy Effects to the Administrator of the Office of Information and Regulatory 

Affairs, Office of Management and Budget, for certain actions identified as “significant energy 

actions.” Section 4(b) of Executive Order 13211 defines “significant energy actions” as “any 

action by an agency (normally published in the Federal Register) that promulgates or is expected 

to lead to the promulgation of a final rule or regulation, including notices of inquiry, advance 

notices of proposed rulemaking, and notices of proposed rulemaking: (1)(i) that is a significant 

regulatory action under Executive Order 12866 or any successor order, and (ii) is likely to have a 

significant adverse effect on the supply, distribution, or use of energy; or (2) that is designated by 

the Administrator of the Office of Information and Regulatory Affairs as a significant energy 
8-5

action.” OMB has designated this rulemaking as a significant energy action. We have prepared a 

Statement of Energy Effects for this action as follows. 

Application of the modeled illustrative control strategy containing only known controls shown 

Chapter 5 of this RIA leads to an estimated decrease nationwide in 2020 in coal production of 

less than 0.2 percent, an estimated decrease in crude oil production of about 0.1 percent, an 

estimated decrease in natural gas production of less than 0.1 percent, and an estimated increase 

in electricity production of less than 0.1 percent. Estimates of price changes for these energy 

products are of the same magnitude nationwide in 2020 as the estimates of output changes. For 

more details on how energy impacts are modeled in this analysis and the caveats and limitations 

that should be understood in interpreting these impacts, please refer to Appendix 5B of this RIA. 

For the electricity generating sector, installation of approximately 9.4 gigawatts (GWs) of SCR 

and 2.4 GWs of SNCR are projected in 2020 as a result of applying the illustrative EGU control 

strategy mentioned earlier in this RIA. There are very small changes expected in the mix of 

electricity generation (i.e., the number of coal-fired EGUs compared to the number of natural 

gas-fired and oil-fired EGUs) as a response to the illustrative EGU control strategy. Hydro, 

nuclear, other, and renewable based generation are projected to remain the same. Projected 

retirements of both coal and oil/gas units remained the same after applying the illustrative EGU 

control strategy. For more details on the energy impacts estimated for EGUs, please refer to 

Chapter 5 of this RIA and its appendix.

We provide the energy impact results reflecting only the modeled illustrative control strategy 

because these results have a greater degree of certainly associated with them when compared to 

results associated with the other alternate primary ozone standards analyzed. This greater degree 

of certainty is due to the application of photochemical air quality modeling (i.e., CMAQ) to 

assess where precursor emission reductions are most needed to attain a particular alternate 

primary ozone standard. Since such CMAQ modeling was not applied for these other alternate 

primary ozone standards, we thus have a differing degree of certainty with regards to impacts 

associated with the modeled illustrative control strategy as opposed to other strategies applied for 

the other alternate primary ozone standards. Other caveats associated with our illustrative control 

strategies and results from applying them are explained in Chapter 3 of this RIA. Finally, the 

energy impacts reported in this RIA do not incorporate the extrapolated costs estimated for 

Chapter 5 of this RIA. The proportion of the engineering costs that are extrapolated can also be 

found in that RIA chapter.

8.9 National Technology Transfer Advancement Act 

Section 12(d) of the National Technology Transfer Advancement Act of 1995 (NTTAA), Public 

Law No. 104-113, §12(d) (15 U.S.C. 272 note), directs EPA to use voluntary consensus 

standards in its regulatory activities unless to do so would be inconsistent with applicable law or 

otherwise impractical. Voluntary consensus standards are technical standards (e.g., materials 

specifications, test methods, sampling procedures, and business practices) that are developed or 

adopted by voluntary consensus standards bodies. The NTTAA directs EPA to provide Congress, 

through OMB, explanations when the Agency decides not to use available and applicable 

voluntary consensus standards. Since EPA is not changing any of the monitoring requirements as 

part of this proposal, there are no impacts associated with the NTTAA. 
8-6

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

Minority Populations and Low-Income Populations 

Executive Order 12898, “Federal Actions to Address Environmental Justice in Minority 

Populations and Low-Income Populations,” requires Federal agencies to consider the impact of 

programs, policies, and activities on minority populations and low-income populations. 

According to EPA guidance, agencies are to assess whether minority or low-income populations 

face a risk or a rate of exposure to hazards that are significant and that “appreciably exceeds or is 

likely to appreciably exceed the risk or rate to the general population or to the appropriate 

comparison group” (EPA, 1998). 

In accordance with Executive Order 12898, the Agency has considered whether these decisions 

may have disproportionate negative impacts on minority or low-income populations. This rule 

establishes uniform, national ambient air quality standards for ozone, and is not expected to have 

disproportionate negative impacts on minority or low income populations. In this NAAQS 

proposal, the Administrator considered the available information regarding health effects among 

vulnerable and susceptible populations, such as those with preexisting conditions. Thus it 

remains EPA’s conclusion that this rule is not expected to have disproportionate negative 

impacts on minority or low income populations.